DEFINITION

WHOLE

_WHOLE:
* sympan#cptCore92#

OTHER-VIEW#cptCore505#

DEFINITION

sysMngOrgm'GENERIC

_GENERIC:
* system.managing.bio#cptCore659.3#
* entity.body.material.nodeTwp.nodeOrgm.nodeRtn1#cptCore482.9#

sysMngOrgm'WHOLE

_WHOLE:
* ORGANISM#cptCore482#

sysMngOrgm'WholeNo-relation#cptCore546.15#

name::
* McsEngl.sysMngOrgm'WholeNo-relation,

stimulus#cptCore1069.1#

sysMngOrgm'Biological-clock

name::
* McsEngl.sysMngOrgm'Biological-clock,
* McsEngl.conceptCore84.2,
* McsEngl.biological'clock@cptCore84.2,

Biological Clocks, physiological systems that enable organisms to live in harmony with the rhythms of nature, such as the cycles of day and night and of the seasons. Such biological "timers" exist for almost every kind of periodicity throughout the plant and animal world, but most of what is known about them comes from the study of circadian, or daily, rhythms. Circadian rhythms cue typical daily behaviour patterns even in the absence of external cues such as sunrise, demonstrating that such patterns depend entirely on internal timers for their periodicity.
No clock is perfect, however. When organisms are deprived of the cues the world normally provides, they display a characteristic "free-running" period of not quite 24 hours. As a result, free-running animals drift slowly out of phase with the natural world. In experiments in which people are isolated for long periods of time, they continue to eat and sleep on regular but increasingly out-of-phase schedules. Such drift does not take place under normal circumstances, because external cues reset the clocks each day. Light is usually the most important cue, but many organisms can make use of rhythmic variations in temperature or other sensory inputs to readjust their internal timers. When a clock's error becomes large, however, complete resetting sometimes requires days. This phenomenon is well known to long-distance air travellers as jet lag.
The physiological circuitry underlying the use of external cues is quite simple; for example, a single flash of light can act as a trigger, signifying dawn. Research in the 1980s has suggested that such simple light-triggering extends even to the behaviour of human beings. Research also suggests that, at least in some organisms, a single gene may be responsible for the mechanism of biological clocks. In the fruit fly, for example, a gene known as per (short for period) is required by the insect to maintain its biological rhythms. This gene has been found to code for a chemical called a proteoglycan, a long-chain molecule containing sugar units attached to a protein. Proteoglycans are also found in mammals.
Apparently, biological clocks can exist in every cell and even in different parts of a cell. Hence, an isolated piece of tissue-for example, the eye of a sea slug-will maintain its own daily rhythm but will quickly adopt that of the whole organism when restored to it. In the brains of most animals a master clock appears to exist that communicates its timing signals chemically to the rest of the organism. For example, a brain removed from a moth pupa and exposed to an artificial sunrise of one time zone, then implanted into the abdomen of a headless pupa on a different time-zone schedule, will cause the second pupa to emerge at the time of day appropriate to the disconnected brain floating in its abdomen. The clock in the brain triggers the release of a hormone that switches on all the complex behaviour involved in pupal emergence. In hamsters, experiments have shown a master biological clock to be located in the hypothalamus. The brain-cell groups are called suprachiasmatic nuclei. The hormone melatonin, secreted by the pineal gland (See Pineal Body), is also known to be involved in long-term biological rhythms. Besides being of scientific interest, a fuller understanding of biological clocks could be important in many ways. One promising theory of ageing, for example, is based on an observation that, in old age, the many separate, subordinate clocks in the body seem somehow to become less tightly coupled to the master clock in the brain. This lack of synchronization may contribute to many of the problems associated with ageing.
"Biological Clocks," Microsoft(R) Encarta(R) 97 Encyclopedia. (c) 1993-1996 Microsoft Corporation. All rights reserved.

sysMngOrgm'Functing#cptCore475.2#

name::
* McsEngl.sysMngOrgm'Functing,

sysMngOrgm'Hormone

name::
* McsEngl.sysMngOrgm'Hormone,
* McsEngl.conceptCore84.4,
* McsEngl.hormone@cptCore84.4,

_DEFINITION:
A hormone (from Greek όρμή - "to set in motion") is a chemical messenger that carries a signal from one cell (or group of cells) to another via the blood. All multicellular organisms produce hormones (including plants - see phytohormone).
[http://en.wikipedia.org/wiki/Hormone]

hormone'ATRIBEINO:
Hormone, substance in animals and plants that regulates bodily processes such as growth, metabolism, reproduction, and the functioning of various organs. In animals, hormones are secreted directly into the bloodstream by ductless or endocrine glands (See Endocrine System). A state of dynamic equilibrium is maintained among the hormones, and they are able to produce their effects in surprisingly minute concentrations. Their distribution through the bloodstream results in a response that, although slower than that of a nervous reaction, is frequently maintained over a longer period of time.

Hormones in Animals The principal organs involved in the production of hormones are the pituitary gland; the thyroid gland; the parathyroid gland; the adrenal, or suprarenal, gland; the pancreas; the gonads, or reproductive glands; the placenta (See Reproductive System) and, under certain conditions, the mucous membrane of the small intestine.
The pituitary gland has three parts; the anterior lobe; the intermediate lobe, which is generally thought to be nonfunctional or virtually absent in humans; and the posterior lobe. The anterior lobe is considered the master gland of the endocrine system. It controls the growth of the skeleton; regulates the function of the thyroid; affects the action of the gonads and the adrenals; produces substances that interact with those excreted by the pancreas; and may influence the parathyroids. It also secretes the hormone prolactin, except when inhibited by the progesterone secreted by the placenta (see below); prolactin stimulates the formation of milk in mature mammary glands. The anterior lobe also secretes the melanocyte-stimulating hormone intermedin, which stimulates the functioning of pigment cells. Hormones produced or stored in the posterior lobe increase blood pressure, prevent excessive secretion of urine (pressor-antidiuretic factor), and stimulate contraction in uterine muscle (oxytocic factor). Several of the pituitary hormones are opposed in effect to other hormones, as, for example, the diabetogenic effect that inhibits the performance of insulin. See ACTH.
The hormone of the thyroid gland stimulates general metabolism; it also increases the sensitivity of various organs, especially the central nervous system (See Brain), and has a pronounced effect on the rate of metamorphosis, that is, the change from infantile to adult form. The secretion of the thyroid hormone is controlled primarily by the anterior lobe of the pituitary but is also affected by the hormones of the ovaries and, in turn, affects the development and function of the ovaries.
The hormone of the parathyroid glands controls the concentration of calcium and phosphate in the blood.
The pancreas secretes at least two hormones, insulin and glucagon, which regulate metabolism of carbohydrates in the body. Insulin, which is a protein, was synthesized by American scientists in 1965, and glucagon was synthesized by German researchers in 1968.
The adrenal glands are divided into two parts, an outer cortex and an inner medulla. Extracts of the cortex contain hormones that control the concentration of salts and water in the body fluids and are essential for the maintenance of life in an individual (See Hydrocortisone). The cortical hormones are also necessary for the formation of sugar from proteins and its storage in the liver and for maintenance of resistance to physical, emotional, and toxic stresses. The cortex also secretes hormones that affect secondary sexual characteristics. The medulla, which is functionally and embryologically independent of the cortex, produces adrenaline, which increases blood sugar and acts to stimulate the circulatory system and the sympathetic nervous system (See Autonomic Nervous System), and the related hormone, noradrenaline.
The gonads, under the influence of the anterior lobe of the pituitary, produce hormones controlling sexual development and the various processes of reproduction. The hormones of the testes control the development of sperm in the testes and the appearance of secondary sexual characteristics of the male (See Androgen; Testosterone). The hormones of the ovaries are produced primarily in the ovarian follicles. These hormones, called oestrogens, are produced by the granulosa cells, and include oestradiol, the most important, and oestrone, which is related chemically to oestradiol and is similar in action but much less potent. Oestrogenic hormones interact with those of the anterior lobe of the pituitary to control the cycle of ovulation. During this cycle the corpus luteum is produced, which in turn secretes mainly progesterone, and thus controls the cycle of menstruation. Progesterone is also formed in large amounts by the placenta during gestation; together with the oestrogens, it causes development of the mammary glands and, at the same time, instructs the hypothalamus to inhibit the secretion of prolactin by the pituitary. Various progesterone-like hormones are now used as oral contraceptives to inhibit ovulation and conception. The placenta also secretes a hormone, similar to one produced by the pituitary and called chorionic gonadotrophin, which inhibits ovulation. This hormone is present in the blood in substantial quantities and is excreted readily by the kidneys; it is the basis of some tests for pregnancy. See Gonadotrophin; Hormone Replacement Therapy.
A special group of hormones is secreted by the mucous membrane of the small intestines at a certain stage of digestion. They act to coordinate digestive activities, controlling the motility of the pylorus, duodenum, gallbladder, and bile duct. They also stimulate formation of the digestive juices of the small intestines, of liver bile, and of the internal and external secretion of the pancreas. The hormone gastrin is produced by one part of the lining of the stomach and is released into the blood by nerve impulses that are initiated by tasting food or by the presence of food in the stomach. In the stomach, gastrin stimulates the secretion of pepsin-a protein-splitting enzyme-and hydrochloric acid, and stimulates contractions of the stomach wall. Gastrin stimulates secretion of digestive enzymes and insulin by the pancreas, and secretion of bile by the liver. See Digestive System.
Deficiency or excess of any one of the hormones upsets the chemical equilibrium that is essential to health, normal growth, and, in extreme cases, life. The method of treating diseases arising from endocrine disturbances is called organotherapy; it involves the use of preparations of animal organs and synthetic products and has achieved marked, and at times spectacular, success. See Addison's Disease; Cretinism; Diabetes Mellitus; Gigantism; Goitre; Myxedoema.
Hormonal Mechanisms When secreted into the bloodstream, hormones bond with specific plasma or carrier proteins that prevent them from degenerating prematurely and keep them from becoming immediately absorbed by the tissues they affect, called target tissues. Target tissues usually have receptor sites or cells that selectively trap and concentrate their respective hormone molecules, which are then held until the moment they are to react with target tissues.
Hormones are believed to affect target tissues in three basic ways. First, they regulate the permeability of the outer cell membrane and intracellular membranes. The hormone insulin is thus believed to relax the membranes of skeletal muscle cells, enabling them to transport glucose rapidly. Second, hormones modify intracellular enzymes. Adrenaline, for example, coming from the adrenal medulla, enables the breakdown of glycogen into six-carbon sugars in liver and muscle cells by activating the membrane-bound enzyme adenyl cyclase. This process is mediated by "second messenger" molecules-nonhormone chemicals located in the target cells. When cell receptors bond with hormones from the bloodstream, they alter the activity level of the second messengers, which either stimulate or inhibit the target tissue.
The third way that hormones may affect target tissues is by changing the gene activity of the target cells. Either by directly entering the target cells, or more likely by acting indirectly through second messengers, hormones have been found to cause "puffs" in particular chromosomes, indicating that genes are actively involved in the synthesis of messenger ribonucleic acid (mRNA) molecules (See Genetics; Nucleic Acids). The mRNA molecules, in turn, are translated into specific proteins that are necessary for such diverse hormonally produced processes as moulting in insects, or maintaining secondary sex characteristics in vertebrates.
Producing Hormones from Bacteria Through recombinant DNA technology, or gene splicing (See Genetic Engineering), researchers have developed techniques for using genetically modified bacteria to produce insulin in quantity for diabetes patients. Similar methods are used to produce growth hormone, a substance in great demand especially for treating growth-deficient children. (By conventional methods, the growth hormone from 50 donated human pituitary glands is required for only one year's treatment.) Medical researchers have high hopes of using a plentiful bacterial synthesis to treat severe bleeding peptic ulcers and to reunite difficult bone fractures.
"Hormone," Microsoft(R) Encarta(R) 97 Encyclopedia. (c) 1993-1996 Microsoft Corporation. All rights reserved.

_GENERIC:
* entity.body.material.pure.chem_compound#cptCore942#
* entity.body.material.pure#cptCore742.3#
* entity.body.material#cptCore742#
* entity.body#cptCore538#
* entity#cptCore387#

hormone'SPECIFEFINO:
* ANIMAL-HORMONE
   * HUMAN-HORMONE#cptHBody077: attSpe#
* PLANT-HORMONE

sysMngOrgm'Pheromone

name::
* McsEngl.sysMngOrgm'Pheromone,
* McsEngl.conceptCore84.1,
* McsEngl.pheromone@cptCore84.1,

_DESCRIPTION:
Pheromone, odour produced by an animal that affects the behaviour of other animals. The way pheromones work is analogous to the way hormones in the body send specific chemical signals from one set of cells to another, causing them to perform a certain action.
Pheromones are found throughout the living world and are probably the most ancient form of animal communication. The complex but primitive single-celled amoeba Dictyostelium, for example, uses a pheromone to attract others of its kind for reproduction. Insects regularly use pheromones for the same purpose; thus, female gypsy moths and Japanese beetles each emit a species-specific sexual pheromone to attract males. The males simply fly upwind when they encounter the appropriate odour. Traps baited with synthetic pheromones are now used to capture many such pest species. The sexual pheromones of other undesirable insects are sometimes sprayed over an infested area so as to disorient males seeking females of their species.
Insects also use pheromones in more complex ways. Female Douglas fir beetles locate a host tree by its odour (a piny scent peculiar to Douglas firs), bore a hole, and then broadcast their sexual pheromone. Males fly upwind, find the females, shut off their production of pheromone with an acoustic signal, and then themselves produce an odour that blocks the receptors for Douglas fir odour in other beetles. When a tree has collected a critical number of mated females, this inhibition odour makes the tree "invisible" to the beetles' sense of smell and prevents the tree from being overly parasitized.
Social insects-insects that live together in groups-usually have a repertoire of pheromonal messages. Ants, for example, usually have a pheromone for marking trails to food, another for eliciting attacks on enemies they have discovered, a third that signals the need to flee, and yet others that identify their larvae in the darkness of the nest. Several species of invertebrates have broken the elaborate code of ants; for example, assassin bugs lay false odour trails and eat the ants that follow them, and a variety of parasitic or symbiotic beetles, millipedes, and arachnids produce the pheromones typical of larval ants and manage thereby to live undisturbed inside ant nests, often feasting on eggs and larvae.
Such deception is by no means limited to animals. A few species of tropical orchids, for example, produce the sexual pheromones of certain wasps. The male wasps' unsuccessful attempts to mate with a succession of the wasplike flowers serve to carry the orchid pollen from one flower to another.
Pheromones are also common in vertebrates. Mammals regularly mark their territorial boundaries with pheromones from specialized glands. These odours can be detected by males at enormous distances and can alter male behaviour dramatically. Owners of unspayed female dogs, for example, regularly find their pets attracting unaltered males from more than a kilometre away. Vertebrates also have additional odours of variable chemistry that serve to identify animals individually. Neighbouring mammals of many species come to recognize one another by the odours they each leave along mutual boundaries or at traditional "scenting posts", and intruders are detected almost immediately. Even mates and offspring often recognize one another by odour. Pheromones have recently been discovered to play a major part in the lives of primates as well, and the observation that human sweat takes on an odour only at puberty suggests that pheromones may also have once affected the behaviour of humans.
"Pheromone," Microsoft(R) Encarta(R) 97 Encyclopedia. (c) 1993-1996 Microsoft Corporation. All rights reserved.
===
What are pheromones? Do humans have pheromones?
Written by Christian Nordqvist Knowledge center
Last updated: Friday 26 September 2014
A pheromone is a chemical an animal produces which changes the behavior of another animal of the same species (animals include insects). Some describe pheromones as behavior-altering agents. Many people do not know that pheromones trigger other behaviors in the animal of the same species, apart from sexual behavior.

Pheromones, unlike most other hormones are ectohormones - they act outside the body of the individual that is secreting them - they impact a behavior on another individual. Hormones usually only affect the individual that is secreting them.

Pheromones can be secreted to trigger many types of behaviors, including:
Alarm
To follow a food trail
Sexual arousal
To tell other female insects to lay their eggs elsewhere. Called epideictic pheromones
To respect a territory
To bond (mother-baby)
To back off.
It is believed that the first pheromone was identified in 1953. Bombykol is secreted by female moths and is designed to attract males. The pheromone signal can travel enormous distances, even at low concentrations.

Experts say that the pheromone system of insects is much easier to understand than that of mammals, which do not have simple stereotyped insect behavior. It is believed that mammals detect pheromones through an organ in the nose called the VNO (Vomeronasal Organ), and connects to the hypothalamus in the brain. The VNO in humans consists of just pits that probably do not do anything. If humans do respond to hormones, most likely they use their normal olfactory system.

Pheromones are commonly used in insect control. They can be used as bait to attract males into a trap, prevent them from mating, or to disorient them. Do humans have pheromones? According to thousands of web sites which promise sexual conquests if you buy their pills, human pheromones exist - bear in mind that their aim is to get you to buy their products. However, most proper well-controlled scientific studies have failed to show any compelling evidence.

Gustav Jδger (1832-1917), a German doctor and hygienist is thought to be the first scientist to put forward the idea of human pheromones. He called them anthropines. He said they were lipophilic compounds associated with skin and follicles that mark the individual signature of human odors. Lipophilic compounds are those that tend to combine with, or are capable of dissolving in lipids.

Researchers in the University of Chicago claimed that they managed to link the synchronization of women's menstrual cycles to unconscious odor cues. The head researcher was called Martha McClintock, hence the coined term the McClintock effect. When exposing a group of women to a whiff of sweat from other women, their menstrual cycles either accelerated or slowed down, depending on when during the menstrual cycle the sweat was collected - before, during or after ovulation. The scientists said that the pheromone collected before ovulation shortened the ovarian cycle, while the pheromone collected during ovulation lengthened it. Even so, recent analyses of McClintock's study and methodology have questioned its validity.

A Swedish study found that lesbians react differently to AND (progesterone derivative 4,16-androstadien-3-one) than heterosexual women do. AND is ten times more abundant in human male sweat than female sweat. (Link to article)

There are four principal kinds of pheromones:
Releaser pheromones - they elicit an immediate response, the response is rapid and reliable. They are usually linked to sexual attraction.
Primer pheromones - these take longer to get a response. They can, for example, influence the development or reproduction physiology, including menstrual cycles in females, puberty, and the success or failure of pregnancy. They can alter hormone levels. In some mammals, scientists found that females who had become pregnant and were exposed to primer pheromones from another male, could spontaneously abort the fetus.
Signaler pheromones - these provide information. They may help the mother to recognize her newborn by scent (fathers cannot usually do this). Signaler pheromones give out our genetic odor print.
Modulator pheromones - they can either alter or synchronize bodily functions. Usually found in sweat. In animal experiments, scientists found that when placed on the upper lip of females, they became less tense and more relaxed. Modulator hormones may also affect a female's monthly cycle.
[http://www.medicalnewstoday.com/articles/232635.php]

sysMngOrgm'measure#cptCore88#

name::
* McsEngl.sysMngOrgm'measure,

Κάθε ανθρώπινος οργανισμός έχει ένα "information system" για να μπορεί να επιβιώσει. Ετσι και κάθε corporation.
[hmnSngo.1990.02_nikos]

sysMngOrgm'Receptor

_CREATED: {2012-12-13}

name::
* McsEngl.sysMngOrgm'Receptor,
* McsEngl.receptor, {2012-12-13}

sysMngOrgm'Response

name::
* McsEngl.sysMngOrgm'Response,
* McsEngl.conceptCore84.5,
* McsEngl.response@cptCore84.5,

Response, Biological, specific and usually repeatable result of an external or internal stimulus on a part or the whole of the organism which can be interpreted as an adaptation enhancing survival. When an organism responds to a stimulus it can be considered to be displaying a fundamental feature of all living systems: irritability. The initial detection of some change, or signal, in its internal or external environment, such as light, touch, or chemicals, requires specialized receptors ranging in complexity from molecules to sense organs. The receptor converts this signal into a different form, of which there is a large variety. This forms part of the response which typically includes the release of growth substances in plants and the release of hormones or the initiation of nerve impulses in animals (See nervous system). All plants and single-celled organisms respond in the absence of nervous systems. Even the apparently simple bacterial responses, such as those of Escherichia coli to dissolved chemicals and of Thiospirillum jenense to light, are very complex at molecular and cellular levels and have much in common with responses of all higher organisms, including humans.
Growth responses-most common in plants-result from such stimuli as light, heat, and water, and include tropisms (responses sensitive to stimulus direction) and nastic movements (responses independent of stimulus direction). Tropisms may be towards the stimulus (a positive tropism) or away from it (a negative tropism). Geotropism is a response to gravity, phototropism to light, and hydrotropism to water. Orientations and movements of whole organisms in response to the direction of a stimulus are called taxes (singular, taxis) and include the negative phototaxis (avoidance of light) of house fly larvae and many other insect larvae which seek dark places in order to pupate.
Periodic responses may involve an internal "clock" of some kind, producing rhythmic behaviour. They differ in the extent to which they are entrained, or modified, by environmental cues and include circadian rhythms (with a cycle of about 24 hours), seasonal, and yearly (circennial) responses. Changes in day length are the most reliable environmental cues predicting seasonal change and may initiate the beginning of hibernation in mammals. The suspension of development in insects (diapause) and the onsets of migration and reproductive behaviour are other seasonal responses often triggered by change in day length.
Contributed By: Michael Thain
"Response, Biological," Microsoft(R) Encarta(R) 97 Encyclopedia. (c) 1993-1996 Microsoft Corporation. All rights reserved.

sysMngOrgm'Sensor

_CREATED: {2012-12-13}

name::
* McsEngl.sysMngOrgm'Sensor,
* McsEngl.biological-sensor, {2012-12-15}
* McsEngl.sensor.biological, {2012-12-13}

_DESCRIPTION:
All living organisms contain biological sensors with functions similar to those of the mechanical devices described. Most of these are specialized cells that are sensitive to:
Light, motion, temperature, magnetic fields, gravity, humidity, moisture, vibration, pressure, electrical fields, sound, and other physical aspects of the external environment
Physical aspects of the internal environment, such as stretch, motion of the organism, and position of appendages (proprioception)
Environmental molecules, including toxins, nutrients, and pheromones
Estimation of biomolecules interaction and some kinetics parameters
Internal metabolic milieu, such as glucose level, oxygen level, or osmolality
Internal signal molecules, such as hormones, neurotransmitters, and cytokines
Differences between proteins of the organism itself and of the environment or alien creatures.
[http://en.wikipedia.org/wiki/Sensor]

sysMngOrgm'Signal

name::
* McsEngl.sysMngOrgm'Signal,
* McsEngl.signal.organism, {2012-12-16}

_GENERIC:
* signal#cptCore659.7#

_DESCRIPTION:

sysMngOrgm'Signalling-pathway

name::
* McsEngl.sysMngOrgm'Signalling-pathway,
* McsElln.σήματος-μεταγωγή, {2012-12-15}

Signal transduction occurs when an extracellular signaling[1] molecule activates a cell surface receptor. In turn, this receptor alters intracellular molecules creating a response.[2] There are two stages in this process:
A signaling molecule activates a specific receptor protein on the cell membrane.
A second messenger transmits the signal into the cell, eliciting a physiological response.
In either step, the signal can be amplified. Thus, one signalling molecule can cause many responses.[3]
[http://en.wikipedia.org/wiki/Signalling_pathway]

sysMngOrgm'Stimulus

_CREATED: {2012-12-15}

name::
* McsEngl.sysMngOrgm'Stimulus,
* McsEngl.conceptCore84.8,
* McsEngl.conceptCore1069.1,
* McsEngl.stimulus-of-organism@cptCore84.8, {2012-12-15}
* McsEngl.living-organism-stimulus,
* McsEngl.living'organism's'stimulus@cptCore334,
* McsEngl.objective@cptCore334,
* McsEngl.stimulus@cptCore1069.1,
* McsEngl.stimuli@cptCore1069.1,
====== lagoSINAGO:
* McsEngl.stimulento@lagoSngo,
====== lagoGreek:
* McsElln.ΑΝΤΙΚΕΙΜΕΝΙΚΟ@cptCore334,
* McsElln.ΖΩΝΤΑΝΟΥ-ΟΡΓΑΝΙΣΜΟΥ-ΕΡΕΘΙΣΜΑ,
* McsElln.ΖΩΝΤΑΝΟΥ'ΟΡΓΑΝΙΣΜΟΥ'ΕΡΕΘΙΣΜΑ@cptCore334,
* McsElln.ΕΡΕΘΙΣΜΑ@cptCore334,
====== lagoEsperanto:
* McsEngl.stimulo@lagoEspo,
* McsEspo.stimulo,

_GENERIC:
* stimulus#cptCore659.6#
* entity#cptCore387#

_DESCRIPTION:
Entities internal or external DETECTING by organism's-managing-system.
[hmnSngo.2012-12-15]
===
* Stimulento is the REFERENTO a sense-organ perceives.
[hmnSngo.2007-11-07_KasNik]
===
* STIMULUS is the ENTITY that the "information-system" of another entity perceives.
[hmnSngo.2002-08-16_nikkas]

* ΖΩΝΤΑΝΟΥ ΟΡΓΑΝΙΣΜΟΥ ΕΡΕΘΙΣΜΑ είναι η ΟΝΤΟΤΗΤΑ που προσλαμβάνει το 'πληροφοριακό συστημα' ενός 'ζωντανου οργανισμού' και δημιουργεί την 'πληροφορια'.
[hmnSngo.1995.04_nikos]

* 1. stimulation, stimulus, stimulant, input -- (any stimulating information or event; acts to arouse action) [WordNet 1.6]

* In physiology, a stimulus is a detectable change in the internal or external environment. When a stimulus is applied to a sensory receptor, it elicits or influences a reflex via stimulus transduction. A stimulus is often the first component of a homeostatic control system. When a sensory nerve and a motor nerve communicate with each other, it is called a nerve stimulus.
In psychology, a stimulus is part of the stimulus-response relationship of behavioural learning theory.
[http://en.wikipedia.org/wiki/Stimulus_%28physiology%29]

_SPECIFIC:
* stimulusOrgm.animal.
* stimulusOrgm.animal.referent#cptCore181.68#
* stimulusOrgm.plant

sysMngOrgm'structure#cptCore515#

name::
* McsEngl.sysMngOrgm'structure,

NOURVOUS-SYSTEM:
ΑΠΟΘΗΚΗ ΠΛΗΡΟΦΟΡΙΑΣ (ΜΝΗΜΗ)
ΕΠΕΞΕΡΓΑΣΤΗΣ ΕΡΕΘΙΣΜΑΤΩΝ (ΕΓΚΕΦΑΛΟΣ)
ΥΠΟΔΟΧΕΙΣ ΕΡΕΘΙΣΜΑΤΩΝ

MEASAGE = INFORMATION + MEDIUM
[hmnSngo.2002-12-10_nikkas]

sysMngOrgm'Tropism

name::
* McsEngl.sysMngOrgm'Tropism,
* McsEngl.conceptCore84.3,
* McsEngl.tropism@cptCore84.3,

Tropism (Grk., tropos, "a turning"), fixed, automatic, inherited movements in response to particular stimuli. Movement towards the source of stimulation is known as positive tropism; movement away from the source is known as negative tropism. An individual organism may exhibit a positive or negative tropism to the same stimulus at different times, depending on the strength of the stimulation and the internal physiological condition of the organism. Among the more complex animals, learned rather than stereotyped responses become increasingly prominent.

Plant Tropism In his pioneering work on plant tropisms, Charles Darwin demonstrated in 1880 that growing tips of plants bend towards a light source. This phenomenon is known as phototropism. (Darwin also observed that some plants that flourish in shady conditions turn away from bright light by a negative form of phototropism.) The turning is due to the action of the plant hormone auxin, which causes elongation. On the side of a plant facing the light the auxin is inactivated, and only the side away from the light elongates; hence the plant tends to bend towards the light. As a result of phototropism, plants avoid excessive shading by other plants. Phototropism stimulated by sunlight is called heliotropism.
Other responses are observed in plant growth. When a seed germinates, the young root turns downwards regardless of the way in which the seed is planted. This bending, known as positive geotropism, enables a plant to anchor itself in the soil. The young stem, which turns upwards away from the earth, is said to be negatively geotropic. The positive geotropism of roots may be modified if more water is present near the surface of the soil than at greater depths. In this case, roots tend to grow towards the source of water, a response known as hydrotropism.
Climbing plants or creepers have to depend for their support on other plants or surfaces, and the tendency of the creeper to respond to touch or contact with such supports is known as thigmotropism. Creepers may climb and support themselves by twining their stems around plants or other objects, as is the case with the nasturtium and the mistletoe; by attaching specialized tips of leaves, known as tendrils (sweet pea and Boston ivy); or by forming aerial roots during growth (common ivy and philodendron). In 1975 scientists observed that the tips of these plants creep along the ground towards vertical objects by responding to the stimulus of the darkest sector of the nearby horizon. This response was termed skototropism (growing towards darkness).

Animal Tropism The response to chemical stimuli is called chemotropism. Flies and other insects are attracted to certain odours emanating from the chemical decomposition of meat or other suitable mediums and are stimulated to lay their eggs in that medium. The same insects react negatively to certain smokes and fumes, which are often used to repel them. In some organisms, chemical attraction often causes isolated cells and cell masses to move towards or away from each other. This characteristic of cells is known as cytotropism. Other commonly observed tropisms include galvanotropism or electrotropism, movement in response to an electric current; rheotropism, or orientation in response to the direction of a current of water; anemotropism, or movement with respect to the wind; and thermotropism, or movement in relation to unequal conditions of temperature. Thigmotropism in many less complex animals enables them to respond to specific crevices and to differentiate between rough and smooth areas. Neurotropism is the attraction or repulsion that certain substances exercise on regenerating nerve fibres. A substance is positively neurotropic when regenerating nerve fibres tend to grow towards and into it; and negatively neurotropic when the nerve fibres avoid it.
The term tropism was formerly applied only to the responsive movements of fixed organisms such as rooted plants and attached animals, orientative movements among free-swimming or freely locomoted animals being known by the term taxis. Although today the terms are sometimes used interchangeably, taxis is preferred in describing movements of such free-swimming bodies as zoospores and sperm cells. The attraction of a sperm to an egg is the result of chemotaxis, as the sperm locates the egg by swimming towards increasing concentrations of chemicals secreted by the egg.
See Also Biological Clocks; Pheromone.
"Tropism," Microsoft(R) Encarta(R) 97 Encyclopedia. (c) 1993-1996 Microsoft Corporation. All rights reserved.

SPECIFIC

name::
* McsEngl.sysMngOrgm.specific,

_SPECIFIC:
* sysMngOrgm.animal#cptCore84.6#
* sysMngOrgm.plant#cptCore84.7#

sysMngOrgm.ANIMAL

_CREATED: {2002-12-28}

sysMngOrgm.PLANT

_CREATED: {2012-12-12} {2002-12-28}

name::
* McsEngl.sysMngOrgm.PLANT,
* McsEngl.conceptCore84.7,
* McsEngl.conceptCore64,
* McsEngl.governance'system.plant@cptCore64,
* McsEngl.plant-governance-system,
* McsEngl.plant'governance'system@cptCore64,
* McsEngl.plant'GOVERNING-SYSTEM,
* McsEngl.sysMngPlnt@cptCore84.7, {2012-12-15}
====== lagoGreek:
* McsElln.ΣΥΣΤΗΜΑ-ΔΙΑΚΥΒΕΡΝΗΣΗΣ-ΦΥΤΟΥ,

_GENERIC:
* entity.whole.system.governing.organism#cptCore84#

_WHOLE:
* PLANT#cptCore502#

DEFINITION

Current is a flow of electrical charge carriers, usually electrons or electron-deficient atoms. The common symbol for current is the uppercase letter I. The standard unit is the ampere, symbolized by A. One ampere of current represents one coulomb of electrical charge (6.24 x 1018 charge carriers) moving past a specific point in one second. Physicists consider current to flow from relatively positive points to relatively negative points; this is called conventional current or Franklin current. Electrons, the most common charge carriers, are negatively charged. They flow from relatively negative points to relatively positive points.

Electric current can be either direct or alternating. Direct current (DC) flows in the same direction at all points in time, although the instantaneous magnitude of the current might vary. In an alternating current (AC), the flow of charge carriers reverses direction periodically. The number of complete AC cycles per second is the frequency, which is measured in hertz. An example of pure DC is the current produced by an electrochemical cell. The output of a power-supply rectifier, prior to filtering, is an example of pulsating DC. The output of common utility outlets is AC.

Current per unit cross-sectional area is known as current density. It is expressed in amperes per square meter, amperes per square centimeter, or amperes per square millimeter. Current density can also be expressed in amperes per circular mil. In general, the greater the current in a conductor, the higher the current density. However, in some situations, current density varies in different parts of an electrical conductor. A classic example is the so-called skin effect, in which current density is high near the outer surface of a conductor, and low near the center. This effect occurs with alternating currents at high frequencies. Another example is the current inside an active electronic component such as a field-effect transistor (FET).

An electric current always produces a magnetic field. The stronger the current, the more intense the magnetic field. A pulsating DC, or an AC, characteristically produces an electromagnetic field. This is the principle by which wireless signal propagation occurs.
[http://whatis.techtarget.com/definition/current]

Electric current is a flow of electric charge through a medium.[1] This charge is typically carried by moving electrons in a conductor such as wire. It can also be carried by ions in an electrolyte, or by both ions and electrons in a plasma.[2]
[http://en.wikipedia.org/wiki/Electric_current]

Ονομάζουμε ηλεκτρικό ρεύμα την προσανατολισμένη κίνηση των ηλεκτρονίων ή γενικότερα των φορτισμένων σωματιδίων.
[http://digitalschool.minedu.gov.gr/modules/ebook/show.php/DSGYM-C201/368/2458,9394/]

The flow of charge in a wire is called current.
"Electricity," Microsoft(R) Encarta(R) 97 Encyclopedia. (c) 1993-1996 Microsoft Corporation. All rights reserved.

ΗΛΕΚΤΡΙΚΟ ΡΕΥΜΑ είναι ...'εννοια' της 'ΦΥΣΙΚΗΣ'.
[hmnSngo.1995.04_nikos]

elcr'WHOLE

_WHOLE:
physics#cptCore57#

elcr'Battery

name::
* McsEngl.elcr'Battery,
* McsEngl.conceptCore90.12,
* McsEngl.battery@cptCore90.12, {2012-06-17}
* McsEngl.electrical-battery@cptCore90.12, {2012-06-17}
====== lagoGreek:
* McsElln.μπαταρια@cptCore90.12#,

An electrical battery is one or more electrochemical cells that convert stored chemical energy into electrical energy.[1] Since the invention of the first battery (or "voltaic pile") in 1800 by Alessandro Volta and especially since the technically improved Daniell cell in 1836, batteries have become a common power source for many household and industrial applications. According to a 2005 estimate, the worldwide battery industry generates US$48 billion in sales each year,[2] with 6% annual growth.[3]
There are two types of batteries: primary batteries (disposable batteries), which are designed to be used once and discarded, and secondary batteries (rechargeable batteries), which are designed to be recharged and used multiple times. Batteries come in many sizes, from miniature cells used to power hearing aids and wristwatches to battery banks the size of rooms that provide standby power for telephone exchanges and computer data centers.
[http://en.wikipedia.org/wiki/Battery_(electricity)]

elcr'Capacitance

name::
* McsEngl.elcr'Capacitance,
* McsEngl.conceptCore90.10,
* McsEngl.capacitance@cptCore90.10, {2012-06-17}
====== lagoGreek:
* McsElln.χωρητικοτητα@cptCore90.10, {2012-06-17}

Capacitance is the ability of a body to store an electrical charge. Any body or structure that is capable of being charged, either with static electricity or by an electric current exhibits capacitance. A common form of energy storage device is a parallel-plate capacitor. In a parallel plate capacitor, capacitance is directly proportional to the surface area of the conductor plates and inversely proportional to the separation distance between the plates. If the charges on the plates are +q and -q, and V gives the voltage between the plates, then the capacitance is given by

The SI unit of capacitance is the farad; a 1 farad capacitor when charged with 1 coulomb of electrical charge will have a potential difference of 1 volt between its plates.[1] Historically, a farad was regarded as an inconveniently large unit, both electrically and physically. Its subdivisions were invariably used, namely the microfarad, nanofarad and picofarad. More recently, technology has advanced such that capacitors of 1 farad and larger can be constructed in a structure little larger than a coin battery (so called 'super capacitors'). Such capacitors are principally used for energy storage replacing more traditional batteries.
The energy (measured in joules) stored in a capacitor is equal to the work done to charge it. Consider a capacitor of capacitance C, holding a charge +q on one plate and -q on the other. Moving a small element of charge dq from one plate to the other against the potential difference V = q/C requires the work dW:

where W is the work measured in joules, q is the charge measured in coulombs and C is the capacitance, measured in farads.
The energy stored in a capacitor is found by integrating this equation. Starting with an uncharged capacitance (q = 0) and moving charge from one plate to the other until the plates have charge +Q and -Q requires the work W:
[http://en.wikipedia.org/wiki/Capacitance]

elcr'conductor

name::
* McsEngl.elcr'conductor,
* McsEngl.conceptCore90.8,
* McsEngl.conductor-of-electric-current@cptCore90.8, {2012-06-17}
* McsEngl.wire, [wikipedia]
====== lagoGreek:
* McsElln.αγωγος@cptCore90.8, {2012-06-08}

Is Copper the Best Electrical Conductor?
Silver is the best conductor of electricity among metals, but copper is much cheaper, so it is used more often.

Copper is not technically the best electrical conductor, or a physical
object that allows the transference of heat or electricity through it.
Silver is actually considered to be the most effective metal for
transferring heat and electricity; however, it tends to be more costly than
other metals.

Read More: http://www.wisegeek.com/is-copper-the-best-electrical-conductor.htm?m, {2015-06-16}

elcr'density

_CREATED: {2014-01-30}

name::
* McsEngl.elcr'density,
* McsEngl.current-density,
====== lagoGreek:
* McsElln.ένταση-ηλεκτρικού-ρεύματος,

_DESCRIPTION:
In electromagnetism, and related fields in solid state physics, condensed matter physics etc. current density is the electric current per unit area of cross section. It is defined as a vector whose magnitude is the electric current per cross-sectional area at a given point in space (i.e. it's a vector field). In SI units, the electric current density is measured in amperes per square metre.[1]
[http://en.wikipedia.org/wiki/Current_density]

elcr'Electric-charge

name::
* McsEngl.elcr'Electric-charge,
* McsEngl.conceptCore90.7,
* McsEngl.electric-charge@cptCore90i,
====== lagoGreek:
* McsElln.ηλεκτρικο-φορτίο,

_DESCRIPTION:
Με τον όρο ηλεκτρικό φορτίο εννοούμε μια ιδιότητα ορισμένων υποατομικών σωματιδίων, η οποία καθορίζει τις μεταξύ τους ηλεκτρομαγνητικές αλληλεπιδράσεις. Ένα υλικό σώμα που έχει ηλεκτρικό φορτίο, επηρεάζεται και δημιουργεί ηλεκτρομαγνητικό πεδίο.
[http://el.wikipedia.org/wiki/Ηλεκτρικό_φορτίο]
===
Electric charge is a physical property of matter that causes it to experience a force when near other electrically charged matter. Electric charge comes in two types, called positive and negative. Two positively charged substances, or objects, experience a mutual repulsive force, as do two negatively charged objects. Positively charged objects and negatively charged objects experience an attractive force. The SI unit of electric charge is the coulomb (C), although in electrical engineering it is also common to use the ampere-hour (Ah). The study of how charged substances interact is classical electrodynamics, which is accurate insofar as quantum effects can be ignored.
The electric charge is a fundamental conserved property of some subatomic particles, which determines their electromagnetic interaction. Electrically charged matter is influenced by, and produces, electromagnetic fields. The interaction between a moving charge and an electromagnetic field is the source of the electromagnetic force, which is one of the four fundamental forces (See also: magnetic field).
Twentieth-century experiments demonstrated that electric charge is quantized; that is, it comes in multiples of individual small units called the elementary charge, e, approximately equal to 1.602Χ10-19 coulombs (except for particles called quarks, which have charges that are multiples of ?e). The proton has a charge of e, and the electron has a charge of -e. The study of charged particles, and how their interactions are mediated by photons, is quantum electrodynamics.
[http://en.wikipedia.org/wiki/Electric_charge]

elcr'Electric-energy#ql:erg.electrical#

name::
* McsEngl.elcr'Electric-energy,
* McsEngl.conceptCore90.16,

elcr'Electric-Network

name::
* McsEngl.elcr'Electric-Network,
* McsEngl.conceptCore90.14,
* McsEngl.electric-network@cptCore90.14, {2012-06-18}

An electrical network is an interconnection of electrical elements such as resistors, inductors, capacitors, transmission lines, voltage sources, current sources and switches.
[http://en.wikipedia.org/wiki/Electric_circuit]

elcr'Electric-power

_CREATED: {2012-06-18} {2012-05-26}

name::
* McsEngl.elcr'Electric-power,
* McsEngl.conceptCore90.17,
* McsEngl.conceptCore655.17,
* McsEngl.electric-power@cptCore90.17, {2012-05-26}
* McsEngl.wattage@cptCore90.17, {2012-07-21}
====== lagoGreek:
* McsElln.ηλεκτρικη-ισχυ@cptCore90.17, {2012-07-21}

_DESCRIPTION:
Electric power is the rate at which electric energy is transferred by an electric circuit. The SI unit of power is the watt, one joule per second.
[http://en.wikipedia.org/wiki/Electric_power]
===
Η ηλεκτρική ισχύς είναι ο ρυθμός με το οποίον ηλεκτρική ενέργεια μεταφέρεται από ένα ηλεκτρικό κύκλωμα. Η μονάδα SI της ισχύος είναι το watt.
[http://el.wikipedia.org/wiki/Ηλεκτρική_ισχύς]

elcr'Electrical-circuit

_CREATED: {2012-07-15}

name::
* McsEngl.elcr'Electrical-circuit,
* McsEngl.conceptCore90.21,
* McsEngl.electrical-circuit@cptCore90.21, {2012-07-15}
* McsEngl.electrical-network@cptCore90.21, {2012-07-15}

_DESCRIPTION:
An electrical network is an interconnection of electrical elements such as resistors, inductors, capacitors, transmission lines, voltage sources, current sources and switches. An electrical circuit is a special type of network, one that has a closed loop giving a return path for the current. Electrical networks that consist only of sources (voltage or current), linear lumped elements (resistors, capacitors, inductors), and linear distributed elements (transmission lines) can be analyzed by algebraic and transform methods to determine DC response, AC response, and transient response.
A resistive circuit is a circuit containing only resistors and ideal current and voltage sources. Analysis of resistive circuits is less complicated than analysis of circuits containing capacitors and inductors. If the sources are constant (DC) sources, the result is a DC circuit.
A network that contains active electronic components is known as an electronic circuit. Such networks are generally nonlinear and require more complex design and analysis tools.
[http://en.wikipedia.org/wiki/Electrical_circuit]

elcr'Electrical-element

_CREATED: {2012-07-15}

name::
* McsEngl.elcr'Electrical-element,
* McsEngl.conceptCore90.23,
* McsEngl.electrical-element@cptCore90.23, {2012-07-15}

Electrical elements are conceptual abstractions representing idealized electrical components, such as resistors, capacitors, and inductors, used in the analysis of electrical networks. Any electrical network can be analysed as multiple, interconnected electrical elements in a schematic diagram or circuit diagram, each of which affects the voltage in the network or current through the network. These ideal electrical elements represent real, physical electrical or electronic components but they do not exist physically and they are assumed to have ideal properties according to a lumped element model, while components are objects with less than ideal properties, a degree of uncertainty in their values and some degree of nonlinearity, each of which may require a combination of multiple electrical elements in order to approximate its function.
Circuit analysis using electric elements is useful for understanding many practical electrical networks using components. By analyzing the way a network is affected by its individual elements it is possible to estimate how a real network will behave.
[http://en.wikipedia.org/wiki/Electrical_element]

elcr'Electrical-generator

_CREATED: {2012-07-15}

name::
* McsEngl.elcr'Electrical-generator,
* McsEngl.conceptCore90.24,
* McsEngl.electrical-generato@cptCore90.24, {2012-07-15}

In electricity generation, an electric generator is a device that converts mechanical energy to electrical energy. A generator forces electric charge (usually carried by electrons) to flow through an external electrical circuit. It is analogous to a water pump, which causes water to flow (but does not create water). The source of mechanical energy may be a reciprocating or turbine steam engine, water falling through a turbine or waterwheel, an internal combustion engine, a wind turbine, a hand crank, compressed air or any other source of mechanical energy.
The reverse conversion of electrical energy into mechanical energy is done by an electric motor, and motors and generators have many similarities. Many motors can be mechanically driven to generate electricity, and frequently make acceptable generators.
[http://en.wikipedia.org/wiki/Electrical_generator]

elcr'Electrical-law

_CREATED: {2012-07-15}

name::
* McsEngl.elcr'Electrical-law,
* McsEngl.conceptCore90.22,
* McsEngl.electrical-law@cptCore90.22, {2012-07-15}

Electrical laws

A number of electrical laws apply to all electrical networks. These include:
Kirchhoff's current law: The sum of all currents entering a node is equal to the sum of all currents leaving the node.
Kirchhoff's voltage law: The directed sum of the electrical potential differences around a loop must be zero.
Ohm's law: The voltage across a resistor is equal to the product of the resistance and the current flowing through it.
Norton's theorem: Any network of voltage or current sources and resistors is electrically equivalent to an ideal current source in parallel with a single resistor.
Thιvenin's theorem: Any network of voltage or current sources and resistors is electrically equivalent to a single voltage source in series with a single resistor.
[http://en.wikipedia.org/wiki/Electrical_circuit]

elcr'electrocute

name::
* McsEngl.elcr'electrocute,
* McsEngl.electrocute,
====== lagoGreek:
* McsElln.ηλεκτροπληξία,

Did President Benjamin Harrison Enjoy Having Electricity in the White House?
When the White House first got electricity, Benjamin Harrison wouldn't use the switches for fear of being shocked.

In 1880, Thomas Edison figured out how to make a longer-lasting light bulb,
using carbonized bamboo as a filament. The discovery jump-started the
world’s first incandescent lighting systems, which earned much acclaim at
the Paris Lighting Exhibition of 1881 and London's Crystal Palace in 1882.
Soon, gas lights began to fade away, slowly replaced by electrical systems
using alternating current. But the technology was still unfamiliar -- and
somewhat unreliable -- when electricity was first installed in the White
House in 1891, during the administration of President Benjamin Harrison.
The Edison Company installed a generator in the basement of the State, War
& Navy building next door, and strung wires across the White House lawn.
When the work was completed, though, President Harrison and his wife,
Caroline, remained skeptical -- and never touched the wall switches, for
fear of being electrocuted.

Read More:
http://www.wisegeek.com/did-president-benjamin-harrison-enjoy-having-electricity-in-the-white-house.htm?m {2019-03-19}

elcr'Electromagnetic-field

name::
* McsEngl.elcr'Electromagnetic-field,

An electromagnetic field (also EMF or EM field) is a physical field produced by moving electrically charged objects. It affects the behavior of charged objects in the vicinity of the field. The electromagnetic field extends indefinitely throughout space and describes the electromagnetic interaction. It is one of the four fundamental forces of nature (the others are gravitation, the weak interaction, and the strong interaction).
The field can be viewed as the combination of an electric field and a magnetic field. The electric field is produced by stationary charges, and the magnetic field by moving charges (currents); these two are often described as the sources of the field. The way in which charges and currents interact with the electromagnetic field is described by Maxwell's equations and the Lorentz force law.
From a classical perspective, the electromagnetic field can be regarded as a smooth, continuous field, propagated in a wavelike manner; whereas from the perspective of quantum field theory, the field is seen as quantized, being composed of individual particles.[citation needed]
[http://en.wikipedia.org/wiki/Electromagnetic_field]

elcr'Inductor

name::
* McsEngl.elcr'Inductor,
* McsEngl.conceptCore90.18,
* McsEngl.inductor@cptCore90.18, {2012-06-18}
====== lagoGreek:
* McsElln.πηνιο@cptCore90.18, {2012-06-18}

An inductor (also choke, coil or reactor) is a passive two-terminal electrical component that stores energy in its magnetic field. For comparison, a capacitor stores energy in an electric field, and a resistor does not store energy but rather dissipates energy as heat.
Any conductor has inductance. An inductor is typically made of a wire or other conductor wound into a coil, to increase the magnetic field.
When the current flowing through an inductor changes, creating a time-varying magnetic field inside the coil, a voltage is induced, according to Faraday's law of electromagnetic induction, which by Lenz's law opposes the change in current that created it. Inductors are one of the basic components used in electronics where current and voltage change with time, due to the ability of inductors to delay and reshape alternating currents.
[http://en.wikipedia.org/wiki/Inductor]

elcr'Multimeter

name::
* McsEngl.elcr'Multimeter,
* McsEngl.conceptCore90.20,
* McsEngl.multimeter@cptCore90.20,
====== lagoGreek:
* McsElln.πολυμετρο@cptCore90ε, {2012-06-18}

Μπορούμε να μετρήσουμε την αντίσταση ενός διπόλου με όργανα που κυκλοφορούν στο εμπόριο και ονομάζονται «ωμόμετρα». Τα ωμόμετρα συνήθως είναι τμήματα οργάνων με πολλές δυνατότητες μέτρησης έντασης, τάσης, αντίστασης κ.λπ., που είναι γνωστά ως «πολύμετρα».
[http://digitalschool.minedu.gov.gr/modules/ebook/show.php/DSGYM-C201/368/2458,9394/]
===
A multimeter or a multitester, also known as a VOM (Volt-Ohm meter), is an electronic measuring instrument that combines several measurement functions in one unit. A typical multimeter may include features such as the ability to measure voltage, current and resistance. Multimeters may use analog or digital circuits—analog multimeters (AMM) and digital multimeters (often abbreviated DMM or DVOM.) Analog instruments are usually based on a microammeter whose pointer moves over a scale calibrated for all the different measurements that can be made; digital instruments usually display digits, but may display a bar of a length proportional to the quantity being measured.
A multimeter can be a hand-held device useful for basic fault finding and field service work or a bench instrument which can measure to a very high degree of accuracy. They can be used to troubleshoot electrical problems in a wide array of industrial and household devices such as electronic equipment, motor controls, domestic appliances, power supplies, and wiring systems.
Multimeters are available in a wide range of features and prices. Cheap multimeters can cost less than US$10, while the top of the line multimeters can cost more than US$5,000.
[http://en.wikipedia.org/wiki/Multimeter]

Εύκολα μπορούμε να συμπεράνουμε ότι η τάση στα άκρα:
α. ενός καταναλωτή είναι μηδέν όταν από αυτόν δεν διέρχεται ηλεκτρικό ρεύμα και
β. μιας μπαταρίας είναι διαφορετική από το μηδέν είτε διέρχεται από αυτή ηλεκτρικό ρεύμα είτε όχι.
[http://digitalschool.minedu.gov.gr/modules/ebook/show.php/DSGYM-C201/368/2458,9394/]

elcr'Resistance

name::
* McsEngl.elcr'Resistance,
* McsEngl.conceptCore90.5,
* McsEngl.resistance-of-electricity@cptCore90.5, {2012-06-17}
====== lagoGreek:
* McsElln.ηλεκτρικη-αντισταση@cptCore90.5, {2012-06-18}

_DESCRIPTION:
Ηλεκτρική αντίσταση ενός ηλεκτρικού διπόλου ονομάζεται το πηλίκο της ηλεκτρικής τάσης (V) που εφαρμόζεται στους πόλους του διπόλου προς την ένταση (Ι) του ηλεκτρικού ρεύματος που το διαρρέει:
Resistance = Volt / Intensity
===
RESISTANCE/ΑΝΤΙΣΤΑΣΗ (OHM=ΜΟΝΑΔΑ ΜΕΤΡΗΣΗΣΗΣ)

elcr'ResourceInfHmnn#cptResource843#

name::
* McsEngl.elcr'ResourceInfHmnn,

_ADDRESS.WPG:
* http://digitalschool.minedu.gov.gr/modules/ebook/show.php/DSGYM-C201/368/2458,9394//
* ηλεκτρολογικά_σύμβολα: http://electricallab.gr/index2.php?option=com_docman&task=doc_view&gid=75&Itemid=34,

elcr'Price

name::
* McsEngl.elcr'Price,

Μειώστε τον λογαριασμό της ΔΕΗ Με λίγες απλές και έξυπνες κινήσεις κάντε το σπίτι σας λιγότερο ενεργοβόρο και κερδίστε
ΤΗΝ ΕΠΟΧΗ αυτή, που ο καιρός κρυώνει, η ημέρα μικραίνει και όλο και περισσότερες ώρες μένουμε στο σπίτι, η κατανάλωση ηλεκτρικής ενέργειας αυξάνεται και μαζί της «φουσκώνουν» και οι λογαριασμοί της ΔΕΗ. Αν δεν θέλετε κάθε δίμηνο να βάζετε βαθιά το χέρι στην τσέπη, μπορείτε να ακολουθήσετε μερικούς βασικούς κανόνες και να εξοικονομήσετε αρκετά χρήματα από τους λογαριασμούς του ηλεκτρικού κάνοντας σωστή χρήση των ηλεκτρικών σας συσκευών. Τις κιλοβατώρες που θα κερδίσετε, π.χ., ρυθμίζοντας σωστά τον θερμοστάτη του θερμοσίφωνα ή του κλιματιστικού μπορείτε να τις «κάψετε» τις κρύες νύχτες του χειμώνα και παράλληλα να παραμείνετε στις χαμηλότερες κλίμακες του τιμολογίου της ΔΕΗ.
Η οικονομία ξεκινά από τις πιο «ενεργοβόρες» οικιακές ηλεκτρικές συσκευές. Ποιες είναι αυτές; Από πλευράς ισχύος ο θερμοσίφωνας βρίσκεται στην πρώτη θέση, καθώς η ισχύς ενός κοινού θερμοσίφωνα είναι 4 κιλοβατώρες (ΚΒΩ). Αν λοιπόν ο θερμοσίφωνας μένει ανοικτός μισή ώρα θα καταναλώσει το ίδιο ή και περισσότερο ρεύμα από ένα κοινό κλιματιστικό που λειτουργεί μία ώρα. Η ισχύς των κλιματιστικών που αγοράζονται για οικιακή χρήση είναι 1,5 ΚΒΩ ως 2 ΚΒΩ. Η κατανάλωση του θερμοσίφωνα μπορεί να περιοριστεί ρυθμίζοντας τον θερμοστάτη του σε μια σχετικά χαμηλή θερμοκρασία, περίπου στους 55 με 60 βαθμούς. Ενας άλλος καλός τρόπος είναι η μόνωσή του. Να καλυφθεί δηλαδή με ένα τεχνικά ελεγμένο μονωτικό, η εγκατάσταση του οποίου είναι απλή υπόθεση και μπορεί να γίνει ακόμη και από τον ίδιο τον καταναλωτή.
Αλλά και στα κλιματιστικά που χρησιμοποιούνται για θέρμανση μπορεί να γίνει οικονομία από τη χαμηλή ρύθμιση του θερμοστάτη τους. Μη ζητάτε περισσότερους από 18 βαθμούς, που θεωρείται η ιδανική θερμοκρασία για τους εσωτερικούς χώρους. Από εκεί και πέρα θα πρέπει να γνωρίζετε ότι η κατανάλωση των κλιματιστικών μεγαλώνει όσο πιο κρύα είναι η ατμόσφαιρα, καθώς η λειτουργία τους σχετίζεται με τη θερμοκρασία του περιβάλλοντος. Πάντως, είναι οικονομικότερο να καίει περισσότερες ώρες η κεντρική θέρμανση από το να χρησιμοποιείται κάποιο κλιματιστικό.
Υψηλή κατανάλωση έχουν επίσης και τα πλυντήρια ρούχων και πιάτων. Σε μία ώρα ­ και συνήθως το πρόγραμμα πλύσης διαρκεί τόσο περίπου ­ καταναλώνουν περί τις 3 ΚΒΩ. Οικονομία από τη λειτουργία των συσκευών αυτών μπορεί να γίνει με δύο τρόπους: επιλέγοντας όσο συχνότερα μπορείτε προγράμματα με χαμηλή θερμοκρασία και επιλέγοντας να πλύνετε τις ώρες που ισχύει το μειωμένο οικιακό τιμολόγιο ρεύματος.
Για να εκμεταλλευθεί κανείς το μειωμένο οικιακό τιμολόγιο της ΔΕΗ θα πρέπει να έχει τοποθετηθεί από ηλεκτρολόγο ο πρόσθετος αγωγός και μετρητής που απαιτείται. Το χαμηλό τιμολόγιο, το οποίο σύμφωνα με τα στοιχεία της ΔΕΗ χρησιμοποιεί μόνο 1 στα 7 νοικοκυριά, ισχύει από τις 11 το βράδυ ως τις 7 το πρωί για όλο το έτος, ενώ τον χειμώνα, δηλαδή από την 1η Νοεμβρίου ως την 1η Μαΐου, οι καταναλωτές μπορούν να επιλέξουν το τμηματικό ωράριο, δηλαδή από τις 2 το βράδυ ως τις 8 το πρωί και από τις 3.30 μ.μ. ως τις 5.30 μ.μ. Τις ώρες του χαμηλού τιμολογίου η μονάδα της ΚΒΩ είναι ως 40% φθηνότερη.
Οσον αφορά το μαγείρεμα, αν δεν προτιμάτε να χρησιμοποιείτε χύτρα ταχύτητας, μπορείτε να κάνετε οικονομία κλείνοντας το μάτι της κουζίνας ή τον φούρνο ένα τέταρτο προτού γίνει το φαγητό. Η «φωτιά» που υπάρχει αρκεί για να ολοκληρωθεί το μαγείρεμα.
Μεγάλη οικονομία στο ηλεκτρικό μπορεί επίσης να επιτευχθεί και από την αντικατάσταση των κοινών λαμπτήρων πυρακτώσεως με λαμπτήρες φθορισμού. Οι νέας τεχνολογίας συμπαγείς λαμπτήρες, όπως ονομάζονται, αποδίδουν τον ίδιο φωτισμό καταναλώνοντας μόλις το 20% της ενέργειας που χρειάζονται οι κοινοί λαμπτήρες. Κοστίζουν ωστόσο πολύ περισσότερο από τους συμβατικούς. Για παράδειγμα, ένας κοινός λαμπτήρας 100 W στοιχίζει περίπου 300 δρχ. ενώ ένας αντίστοιχης απόδοσης φθορισμού περί τις 5.500 δραχμές. Ωστόσο η διάρκεια ζωής των λαμπτήρων φθορισμού είναι 8 με 10 φορές μεγαλύτερη από αυτήν των συμβατικών. Αντικαθιστώντας λοιπόν τους παλιούς με λαμπτήρες νέας τεχνολογίας μπορεί να επιτευχθεί οικονομία στον λογαριασμό της ΔΕΗ περίπου 10%, αφού σύμφωνα με τα στοιχεία της Επιχείρησης σε ένα πλήρως εξοπλισμένο νοικοκυριό η κατανάλωση ρεύματος από τον φωτισμό συμμετέχει στον λογαριασμό κατά μέσον όρο με 15%.
Οποια μέτρα και αν πάρετε για να μειώσετε τον λογαριασμό της ΔΕΗ θα πρέπει να γνωρίζετε ότι όταν η κατανάλωση περάσει το όριο των 800 ΚΒΩ, οι επιπλέον μονάδες υπολογίζονται με την αυξημένη τιμή της επόμενης κλίμακας. Το ίδιο και πολύ χειρότερο συμβαίνει αν ξεπεραστεί το επόμενο όριο, που είναι οι 1.601 μονάδες.
Γ. ΠΑΠΑΪΩΑΝΝΟΥ
[ΒΗΜΑ 1997-10-26]

elcr'Source of Electromotive Force

name::
* McsEngl.elcr'Source of Electromotive Force,
====== lagoGreek:
* McsElln.πηγη-ηλεκτρικης-ενεργειας,

To produce a flow of current in any electrical circuit, a source of electromotive force or potential difference is necessary. The available sources are:
(1) electrostatic machines such as the Van de Graaff generator, which operate on the principle of inducing electric charges by mechanical means ;
(2) electromagnetic machines, which generate current by mechanically moving conductors through a magnetic field or a number of fields (See Electric Motors and Generators);
(3) batteries, which produce an electromotive force through electrochemical action;
(4) devices that produce electromotive force through the action of heat (See Crystal: Other Crystal Properties; Thermoelectricity);
(5) devices that produce electromotive force by the photoelectric effect, the action of light; and
(6) devices that produce electromotive force by means of physical pressure-the piezoelectric effect.
"Electricity," Microsoft(R) Encarta(R) 97 Encyclopedia. (c) 1993-1996 Microsoft Corporation. All rights reserved.
===
Κάθε συσκευή στην οποία μια μορφή ενέργειας μετατρέπεται σε ηλεκτρική ονομάζεται πηγή ηλεκτρικής ενέργειας ή απλώς ηλεκτρική πηγή. Βεβαίως σε μια ηλεκτρική πηγή δεν παράγεται ενέργεια από το μηδέν. Απλώς μια μορφή ενέργειας μετατρέπεται σε ηλεκτρική. Η μορφή της ενέργειας που μετατρέπεται σε ηλεκτρική εξαρτάται από το είδος της ηλεκτρικής πηγής. Έτσι
ΚΕΦΑΛΑΙΟ 2 ΗΛΕΚΤΡΙΚO ΡΕΥΜΑ
σ’ ένα ηλεκτρικό στοιχείο (κοινή μπαταρία) ή σ’ ένα συσσωρευτή (μπαταρία αυτοκινήτου) χημική ενέργεια μετατρέπεται σε ηλεκτρική, ενώ σε μια γεννήτρια κινητική ενέργεια μετατρέπεται σε ηλεκτρική (εικόνες 2.15, 2.16). Άλλες ηλεκτρικές πηγές είναι το φωτοστοιχείο και το θερμοστοιχείο. Σ’ ένα φωτοστοιχείο (εικόνα 2.17) ενέργεια της ακτινοβολίας μετατρέπεται σε ηλεκτρική, ενώ σ’ ένα θερμοστοιχείο θερμική ενέργεια μετατρέπεται σε ηλεκτρική.
[http://digitalschool.minedu.gov.gr/modules/ebook/show.php/DSGYM-C201/368/2458,9394/]

Η χώρα που ζει από τα σκουπίδια της
Σε εναλλακτικό καύσιμο έχει μετατρέψει η Δανία τα απορρίμματά της, μειώνοντας την εξάρτησή της από το πετρέλαιο
Πέμπτη 15 Απριλίου 2010

ΚΟΠΕΓΧΑΓΗ Oταν άλλοι θεωρούν τα σκουπίδια βρώμικο πρόβλημα, οι Δανοί τα βλέπουν ως καθαρό και εναλλακτικό καύσιμο, ή ακόμη και ως πηγή παραγωγής ηλεκτρικής ενέργειας. Η Δανία διαθέτει πλέον δεκάδες μη συμβατικές μονάδες αποτέφρωσης, επεξεργασίας και ανακύκλωσης σκουπιδιών, δηλαδή υπερσύγχρονα εργοστάσια τα οποία μετατρέπουν τα απορρίμματα σε ενέργεια και θέρμανση για τις κοινότητες που τα φιλοξενούν, με τεράστια οφέλη για το περιβάλλον. Η χρήση αυτών των μονάδων έχει μειώσει όχι μόνο τις ενεργειακές δαπάνες και την εξάρτηση της Δανίας από το πετρέλαιο και το φυσικό αέριο, αλλά και τη χρήση των χωματερών και των εκπομπών διοξειδίου του άνθρακα. Στην πραγματικότητα, οι μονάδες αυτές λειτουργούν τόσο καθαρά και χωρίς οσμές που πλέον τα τζάκια και οι ψησταριές των σπιτιών απελευθερώνουν περισσότερες διοξίνες από τους αποτεφρωτήρες των σκουπιδιών.

Αυτή τη στιγμή λειτουργούν 29 εργοστάσια καύσης σκουπιδιών για την παραγωγή ενέργειας, τα οποία εξυπηρετούν 98 δήμους, σε μια χώρα 5,5 εκατομμυρίων κατοίκων. Ηδη κατασκευάζονται άλλα 10. Δεν είναι να απορεί κάποιος που τέτοιες μονάδες λειτουργούν χωρίς αντιδράσεις ακόμη και στο κέντρο της Κοπεγχάγης ή σε πλούσια οικιστικά προάστια, καθώς οι ιθύνοντες των μονάδων φροντίζουν η μεταφορά των σκουπιδιών σε αυτές να γίνεται με άκρα διακριτικότητα και προσοχή. Εδώ μετράει ακόμη και η αισθητική: τα πιο πρόσφατα κατασκευασμένα εργοστάσια ξεγελούν το μάτι, καθώς είναι «ντυμένα» με περίτεχνα «κελύφη» που θυμίζουν... γλυπτά! Το σημαντικότερο όμως είναι ότι αυτές οι μονάδες «νέας γενιάς» διαθέτουν δεκάδες φίλτρα που συλλέγουν ρυπογόνους παράγοντες, από βαριά μέταλλα ως διοξίνες, που μόλις πριν από δέκα χρόνια θα ξέφευγαν στο περιβάλλον, και χρησιμοποιούν ως καύσιμο μόνον οικιακά και βιομηχανικά απορρίμματα που δεν μπορούν να ανακυκλωθούν, μειώνοντας έτσι σημαντικά το ενεργειακό αποτύπωμα της χώρας και προωθώντας την ανακύκλωση. Και επειδή τίποτα δεν πάει χαμένο, στο τέλος της διαδικασίας καύσης, τα οξέα, τα βαριά μέταλλα και ο γύψος πωλούνται στον κατασκευαστικό κλάδο, ενώ οι μικρές ποσότητες τοξικών υλικών υψηλής συγκέντρωσης δημιουργούν μια πάστα η οποία συσκευάζεται με ασφάλεια και αποστέλλεται σε ειδικό μέρος υγειονομικής ταφής τους.

Η Ευρώπη πρωτοστατεί στην κατασκευή και τη λειτουργία τέτοιων πρωτοποριακών μονάδων, καθώς διαθέτει περίπου 400 από αυτές. Οι χώρες που ηγούνται αυτού του τρόπου παραγωγής ενέργειας και ταυτοχρόνως αντιμετώπισης του ζητήματος των απορριμμάτων είναι η Δανία, η Γερμανία και η Ολλανδία. Αντιθέτως, οι Ηνωμένες Πολιτείες των 300 εκατομμυρίων πολιτών διαθέτουν μόνο 87 μονάδες καύσης απορριμμάτων και μάλιστα παλαιάς τεχνολογίας, καθώς παρά τα πολυάριθμα προτερήματά της η μέθοδος αυτή έχει συναντήσει σημαντικές αντιστάσεις στην Αμερική από πανίσχυρα «λόμπι» που εκπροσωπούν συμφέροντα βιομηχανιών παλαιάς τεχνολογίας.

Το πρότυπο του Χόρσχολμ
Υπόδειγμα κοινότητας η οποία χρησιμοποιεί ενέργεια και θέρμανση από σκουπίδια είναι το πεντακάθαρο οικιστικό προάστιο Χόρσχολμ, έξω από την Στοκχόλμη. Το τοπικό εργοστάσιο κρύβεται πίσω από έναν καλόγουστο φράχτη, σε απόσταση μόλις 360 μέτρων από τις αυλές των τελευταίων σπιτιών. Εδώ το κόστος για τη θέρμανση κάθε σπιτιού είναι ιδιαιτέρως χαμηλό, καθώς το 80% της θερμότητας και το 20% του ηλεκτρικού ρεύματος παράγονται από την καύση των απορριμμάτων. Μόνο το 4% των σκουπιδιών του Χόρσχολμ καταλήγει σε χωματερές, το 64% ανακυκλώνεται και το 34% καίγεται, αφήνοντας μόνο 1% επικίνδυνα απόβλητα.
[http://www.tovima.gr/world/article/?aid=325781]

elcr'Switch

name::
* McsEngl.elcr'Switch,
* McsEngl.conceptCore90.19,

In electrical engineering, a switch is an electrical component that can break an electrical circuit, interrupting the current or diverting it from one conductor to another.[1][2]
The most familiar form of switch is a manually operated electromechanical device with one or more sets of electrical contacts, which are connected to external circuits. Each set of contacts can be in one of two states: either "closed" meaning the contacts are touching and electricity can flow between them, or "open", meaning the contacts are separated and the switch is nonconducting. The mechanism actuating the transition between these two states (open or closed) can be either a "toggle" (flip switch for continuous "on" or "off") or "momentary" (push-for "on" or push-for "off") type.
A switch may be directly manipulated by a human as a control signal to a system, such as a computer keyboard button, or to control power flow in a circuit, such as a light switch. Automatically operated switches can be used to control the motions of machines, for example, to indicate that a garage door has reached its full open position or that a machine tool is in a position to accept another workpiece. Switches may be operated by process variables such as pressure, temperature, flow, current, voltage, and force, acting as sensors in a process and used to automatically control a system. For example, a thermostat is a temperature-operated switch used to control a heating process. A switch that is operated by another electrical circuit is called a relay. Large switches may be remotely operated by a motor drive mechanism. Some switches are used to isolate electric power from a system, providing a visible point of isolation that can be pad-locked if necessary to prevent accidental operation of a machine during maintenance, or to prevent electric shock.
[http://en.wikipedia.org/wiki/Switch]

elcr'Voltage | Ταση

name::
* McsEngl.elcr'Voltage | Ταση,
* McsEngl.conceptCore90.3,
* McsEngl.conceptCore990,
* McsEngl.electric-tention@cptCore90.3, {2012-06-17}
* McsEngl.electrical-potential-difference@cptCore90.3, {2012-06-17}
* McsEngl.electro-motive-force,
* McsEngl.EMF,
* McsEngl.potential-difference,
* McsEngl.voltage,

* McsElln.διαφορα-ηλεκτρικου-δυναμικου@cptCore90.3, {2012-06-17}
* McsElln.ηλεκτρικη-ταση@cptCore90.3, {2012-06-17}
* McsElln.ΤΑΣΗ,

_DESCRIPTION:
Voltage, otherwise known as electrical potential difference or electric tension (denoted ΔV and measured in volts, or joules per coulomb) is the potential difference between two points — or the difference in electric potential energy per unit charge between two points.[1] Voltage is equal to the work which would have to be done, per unit charge, against a static electric field to move the charge between two points. A voltage may represent either a source of energy (electromotive force), or it may represent lost or stored energy (potential drop). A voltmeter can be used to measure the voltage (or potential difference) between two points in a system; usually a common reference potential such as the ground of the system is used as one of the points. Voltage can be caused by static electric fields, by electric current through a magnetic field, by time-varying magnetic fields, or a combination of all three.[2][3]
[http://en.wikipedia.org/wiki/Voltage]
===
(Or "potential difference", "electro-motive force" (EMF)) A quantity measured as a signed difference between two points in an electrical circuit which, when divided by the {resistance} in {Ohms} between those points, gives the current flowing between those points in {Amperes}, according to {Ohm's Law}. Voltage is expressed as a signed number of Volts (V). The voltage gradient in Volts per metre is proportional to the force on a charge.

Voltages are often given relative to "earth" or "ground" which is taken to be at zero Volts. A circuit's earth may or may not be electrically connected to the actual earth.

The voltage between two points is also given by the charge present between those points in {Coulombs} divided by the {capacitance} in {Farads}. The capacitance in turn depends on the {dielectric constant} of the insulators present.

Yet another law gives the voltage across a piece of circuit as its {inductance} in {Henries} multiplied by the rate of change of current flow through it in Amperes per second.

A simple analogy likens voltage to the pressure of water in a pipe. Current is likened to the amount of water (charge) flowing per unit time.
(04 Dec 1995)
[FOLDOC, 1998.02]

This relationship is known as Ohm's law and is named after the German physicist Georg Simon Ohm, who discovered the law in 1827. Ohm's law may be stated in the form of the algebraic equation E = I Χ R, in which E is the electromotive force in volts, I is the current in amperes, and R is the resistance in ohms. From this equation any of the three quantities for a given circuit can be calculated if the other two quantities are known.
"Electricity," Microsoft(R) Encarta(R) 97 Encyclopedia. (c) 1993-1996 Microsoft Corporation. All rights reserved.

Ηλεκτρικο-διπολο

name::
* McsElln.Ηλεκτρικο-διπολο,

Είδαμε ότι όλες οι ηλεκτρικές συσκευές που χρησιμοποιούμε (μπαταρίες, λαμπτήρες, οικιακές ηλεκτρικές συσκευές κ.λπ.) διαθέτουν δύο άκρα (πόλους) με τα οποία συνδέονται στο ηλεκτρικό κύκλωμα. Οι ίδιες οι συσκευές ονομάζονται ηλεκτρικά δίπολα (εικόνα 2.24).
[http://digitalschool.minedu.gov.gr/modules/ebook/show.php/DSGYM-C201/368/2458,9394/]

Καταναλωτης

Καθώς τα ηλεκτρόνια περνούν μέσα από ένα λαμπτήρα, ηλεκτρική ενέργεια μετατρέπεται σε θερμική και φωτεινή. Ο λαμπτήρας, όπως και κάθε συσκευή που μετατρέπει την ηλεκτρική ενέργεια σε ενέργεια άλλης μορφής, ονομάζεται μετατροπέας ή καταναλωτής.
[http://digitalschool.minedu.gov.gr/modules/ebook/show.php/DSGYM-C201/368/2458,9394/]

SPECIFIC

name::
* McsEngl.elcr.specific,

elcr.Dangerous

name::
* McsEngl.elcr.Dangerous,

καθόσον 30mA θεωρείται το όριο της επικίνδυνης έντασης για τον άνθρωπο
[http://digitalschool.minedu.gov.gr/modules/ebook/show.php/DSGL-A103/389/2566,10032/]

elcr.Intensity-of-electricity

name::
* McsEngl.elcr.Intensity-of-electricity,
* McsEngl.conceptCore90.4,
* McsEngl.current-intensity@cptCore90.4, {2012-07-21}
* McsEngl.intensity-of-electricity@cptCore90.4, {2012-06-17}
====== lagoGreek:
* McsElln.ενταση-ηλεκτρικου-ρευματος@cptCore90.4, {2012-06-17}

_DESCRIPTION:
Η 'ενταση' είναι μια ΠΟΣΟΤΗΤΑ ηλεκτρικου-ρεύματος!!!
Η "ενταση" ΔΕΝ είναι διαφορετικο μεγεθος απο το "ηλεκτρικο-ρευμα".
[hmnSngo.2012-06-18]
===
Το μέγεθος που μετρά το ηλεκτρικό ρεύμα είναι η ένταση του ηλεκτρικού ρεύματος,
[http://el.wikipedia.org/wiki/Ένταση_ηλεκτρικού_ρεύματος]
===
Ορίζουμε την ένταση (I) του ηλεκτρικού ρεύματος που διαρρέει έναν αγωγό ως το φορτίο (q) που διέρχεται από μια διατομή του αγωγού σε χρονικό διάστημα (t) προς το χρονικό διάστημα.
Στη γλώσσα των μαθηματικών η παραπάνω σχέση γράφεται ως εξής:
I=q/t
[http://digitalschool.minedu.gov.gr/modules/ebook/show.php/DSGYM-C201/368/2458,9394/]

DEFINITION

_DESCRIPTION:
In the most general sense, a model is anything used in any way to represent anything else.
[http://en.wikipedia.org/wiki/Conceptual_model]
===
* MAPPING-ENTITY is the entity which is mapped with an original one, through a mapping-corelation.
[hmnSngo.2003-10-26_nikkas]
===
Scientific modelling is the process of generating abstract, conceptual, graphical or mathematical models. Science offers a growing collection of methods, techniques and theory about all kinds of specialized scientific modelling. A scientific model can provide a way to read elements easily which have been broken down to a simpler form.
Modelling is an essential and inseparable part of all scientific activity, and many scientific disciplines have their own ideas about specific types of modelling. There is an increasing attention for scientific modelling[1] in fields such as of philosophy of science, systems theory, and knowledge visualization.
[http://en.wikipedia.org/wiki/Scientific_modeling]
==
A domain model in problem solving and software engineering can be thought of as a conceptual model of a domain of interest (often referred to as a problem domain) which describes the various entities, their attributes, roles and relationships, plus the constraints that govern the integrity of the model elements comprising that problem domain.
[http://en.wikipedia.org/wiki/Domain_model]

model'archetype (original)

_CREATED: {2014-01-01} {2003-11-01}

name::
* McsEngl.model'archetype (original),
* McsEngl.conceptCore437.19,
* McsEngl.archetype@cptCore437.19,
* McsEngl.domain-of-model@cptCore437.19, {2012-04-29}
* McsEngl.problem-domain@cptCore437.19-in-proble-solving, {2012-04-29}
* McsEngl.model'archetype, {2014-01-01}
* McsEngl.model'original-entity, {2014-01-01}
* McsEngl.model'referent,
* McsEngl.model'ReferentModel,
* McsEngl.model'referent-of-model,
* McsEngl.modeled-entity@cptCore437.19, {2012-04-29}
* McsEngl.modeled-system@cptCore437.19, {2012-04-29}
* McsEngl.input-of-model,
* McsEngl.origin-of-model,
* McsEngl.referent-of-model@cptCore437.19, {2012-08-24}
* McsEngl.target-of-model,
* McsEngl.target-system, {2012-11-24}

* McsEngl.mapeino'archetype,
* McsEngl.mapeino'MAPEOLO,
* McsEngl.mapeino'primary-entity,
* McsEngl.mapeino'original,
* McsEngl.mapeolo@cptCore546.54,
* McsEngl.primary-entity-in-relationMapping, {2012-11-26}
* McsEngl.original-entity-in-relationMapping, {2012-11-26}
* McsEngl.original'entito@cptCore387.3,

* McsEngl.archetype, {2014-01-31}
* McsEngl.archetype.mapping-method,
* McsEngl.entityIn,
* McsEngl.input-entity,
* McsEngl.mtdMap'entityIn,
* McsEngl.mtdMap'UNFORMATED-ENTITY,
* McsEngl.uncode, {2014-01-25}
* McsEngl.uncoded-entity@cptCore320i,
* McsEngl.unformated-entity@cptCore320i,
* McsEngl.artp, {2014-03-03}
====== lagoSINAGO:
* McsSngo.ono, {2016-03-06}
* McsEngl.ono@lagoSngo,

_GENERIC:
* entity.whole.system#cptCore765#

_WHOLE:
* domainIn#linkL#

_DESCRIPTION:
Archetype I call the ENTITY a model represents.
[hmnSngo.2014-03-03]
===
I call 'referent' the entity a model represents. Every concept, because it is a model of reality, has a refernt. Then every 'model' will have a referent, the entity that represents, and as a concept (because anything we talk about are concepts) will have a reference, the models we talk about.
[hmnSngo.2012-08-24]
===
Every model REPRESENTS an entity. This entity is its target.
[hmnSngo.2012-05-20]
===
* ORIGINAL-ENTITY is the first entity we map with another one, the "mapping-entity", through a mapping-corelation.
[hmnSngo.2003-11-01_nikkas]

_ENVIRONMENT:
* MAPEELO
* relation-to-referent#ql:model'referent_and_target###

model'archetype'SET (DOMAIN)

name::
* McsEngl.model'archetype'SET (DOMAIN),
* McsEngl.conceptCore320.1,
* McsEngl.archetype-domain,
* McsEngl.archetype-set, {2014-02-27}
* McsEngl.domain.archetype, {2014-03-03}
* McsEngl.domain-of-mapping-method@cptCore320.1, {2012-08-05}
* McsEngl.mtdMap'archetype'domainIN,
* McsEngl.mtdMap'domain,
* McsEngl.mthMap'domainIn,
* McsEngl.mtdMap'Set.original,
* McsEngl.original-set-of-mapping-method@cptCore320.1, {2012-08-05}

_GENERIC:
* MATH'SET#cptCore503.2#

_DESCRIPTION:
The set of all entities we want to map.
[hmnSngo.2014-02-09]

model'archetype'structure.UNIT

_CREATED: {2014-02-06}

name::
* McsEngl.model'archetype'structure.UNIT,
* McsEngl.mtdMap'archetype.UNITm,
* McsEngl.unit.archetype,

_DESCRIPTION:
Archetype-unit is the elementary entities by which archetype-structures are created.
[hmnSngo.2014-02-06]

model'CODOMAIN

_CREATED: {2014-03-02}

name::
* McsEngl.model'CODOMAIN,
* McsEngl.conceptCore320.5,
* McsEngl.model'codomain,
* McsEngl.model-domain, {2014-03-03}
* McsEngl.model'domainOUT,
* McsEngl.mtdMap'code'domainOUT,
* McsEngl.code-set-of-mapping-method@cptCore320.5, {2012-08-20}
* McsEngl.codomain-of-mapping-method@cptCore320.5, {2012-08-20}
* McsEngl.mtdMap'codomain,
* McsEngl.mtdMap'domainOut,
* McsEngl.mtdMap'Set.Code,

_DESCRIPTION:
It is the set, which is mapped with the original-entity (the domain).
[hmnSngo.2012-08-20]
===
noun: codomain; plural noun: codomains
1. a set that includes all the possible values of a given function.
[Google dict]

model'mapping-method

_CREATED: {2007-12-21}

model'mapping-relation#cptCore546.54#

name::
* McsEngl.model'mapping-relation,

model'medium

_CREATED: {2014-01-01} {2012-11-23}

name::
* McsEngl.model'medium,
* McsEngl.model'modality,

* McsEngl.mtdMap'code'medium,
* McsEngl.medium.mthMap,
* McsEngl.mtdMap'medium,

_DESCRIPTION:
The entity we use to construct a model: information, matter, both.
[hmnSngo.2012-11-23]
===
The entity used to create the entitiesOut#ql:mtdmap'entityout#.
[hmnSngo.2014-01-01]

_SPECIFIC:
* image##
* sound##
* text##
* visual##

model'agent

_CREATED: {2012-11-14}

name::
* McsEngl.model'agent,
* McsEngl.model'actor,

_DESCRIPTION:
In economics, an agent is an actor and decision maker in a model. Typically, every agent makes decisions by solving a well- or ill-defined optimization/choice problem.

For example, buyers and sellers are two common types of agents in partial equilibrium models of a single market. Macroeconomic models, especially dynamic stochastic general equilibrium models that are explicitly based on microfoundations, often distinguish households, firms, and governments or central banks as the main types of agents in the economy. Each of these agents may play multiple roles in the economy; households, for example, might act as consumers, as workers, and as voters in the model. Some macroeconomic models distinguish even more types of agents, such as workers and shoppers[1] or commercial banks.[2]

The term agent is also used in relation to principal–agent models; in this case it refers specifically to someone delegated to act on behalf of a principal.[3]

In Agent-based computational economics, the concept of an agent has been more broadly interpreted to be any persistent individual, social, biological, or physical entity interacting with other such entities within the context of a dynamic multi-agent economic system.

[edit]Representative vs. heterogenous agents

An economic model in which all agents of a given type (such as all consumers, or all firms) are assumed to be exactly identical is called a representative agent model. A model which recognizes differences among agents is called a heterogeneous agent model. Economists often use representative agent models when they want to describe the economy in the simplest terms possible. In contrast, they may be obliged to use heterogeneous agent models when differences among agents are directly relevant for the question at hand.[4] For example, considering heterogeneity in age is likely to be necessary in a model used to study the economic effects of pensions;[5] considering heterogeneity in wealth is likely to be necessary in a model used to study precautionary saving[6] or redistributive taxation.[7]
[http://en.wikipedia.org/wiki/Agent_(economics)] {2012-11-14}

model'human#cptCore401#

name::
* McsEngl.model'human,

_Human:

model'referent-of-concept-model#cptCore546.79#

name::
* McsEngl.model'referent_of_concept-model,
* McsEngl.model'ReferentConcept,

_GENERIC:
* referentConcept#cptCore606.4#

_DESCRIPTION:
Every concept, because it is a model of reality, has a referent. Then every 'model' will have a referent, the entity that represents, and as a concept (because anything we talk about are concepts) will have a reference, the models we talk about.
[hmnSngo.2012-08-24]
===
model'Referent_and_target:
The referent-of-model is the set of all existing models.
The target-of-model is the entity that the model represents.
[hmnSngo.2012-04-29]

_Referent:
A scientific model seeks to represent empirical objects, phenomena, and physical processes in a logical and objective way.
[http://en.wikipedia.org/wiki/Scientific_modeling]

model'ResourceInfHmnn#cptResource843#

name::
* McsEngl.model'ResourceInfHmnn,

_ADDRESS.WPG:
* http://en.wikipedia.org/wiki/Model,
* http://en.wikipedia.org/wiki/Conceptual_model,
* The Model Thinker: http://vserver1.cscs.lsa.umich.edu/~spage/ONLINECOURSE/R1Page.pdf,

Callender, Craig and Jonathan Cohen (2006), “There Is No Special Problem About Scientific Representation,” Theoria, forthcoming.

Frigg, Roman (2006), “Scientific Representation and the Semantic View of Theories”, Theoria 55: 37-53.

Humphreys, Paul (2004), Extending Ourselves: Computational Science, Empiricism, and Scientific Method. Oxford: Oxford University Press.

Bokulich, Alisa (2003), “Horizontal Models: From Bakers to Cats”, Philosophy of Science 70: 609-627.

Winsberg, Eric (2003), “Simulated Experiments: Methodology for a Virtual World”, Philosophy of Science 70: 105–125.

Woodward, James (2003), Making Things Happen: A Theory of Causal Explanation. New York: Oxford University Press.

===

Achinstein, Peter (1968), Concepts of Science. A Philosophical Analysis. Baltimore: Johns Hopkins Press.

Ackerlof, George A (1970), “The Market for ‘Lemons’: Quality Uncertainty and the Market Mechanism”, Quarterly Journal of Economics 84: 488-500.

Apostel, Leo (1961), “Towards the Formal Study of Models in the Non-Formal Sciences”, in Freudenthal 1961, 1-37.

Bailer-Jones, Daniela M. (1999), “Tracing the Development of Models in the Philosophy of Science”, in Magnani, Nersessian and Thagard 1999, 23-40.
––– (2003) “When Scientific Models Represent”, International Studies in the Philosophy of Science 17: 59-74.
––– and Bailer-Jones C. A. L. (2002), “Modelling Data: Analogies in Neural Networks, Simulated Annealing and Genetic Algorithms”, in Magnani and Nersessian 2002: 147-165.

Batterman, Robert (2004), “Intertheory Relations in Physics”, The Stanford Encyclopedia of Philosophy (Spring 2004 Edition), Edward N. Zalta (ed.), URL = http://plato.stanford.edu/archives/spr2004/entries/physics-interrelate/.

Bell, John and Moshι Machover (1977), A Course in Mathematical Logic. Amsterdam: North-Holland.

Black, Max (1962), Models and Metaphors. Studies in Language and Philosophy. Ithaca, New York: Cornell University Press.

Bogen, James and James Woodward (1988), “Saving the Phenomena,” Philosophical Review 97: 303-352.

Braithwaite, Richard (1953), Scientific Explanation. Cambridge: Cambridge University Press.

Brown, James (1991), The Laboratory of the Mind: Thought Experiments in the Natural Sciences. London: Routledge.

Brzezinski, Jerzy and Leszek Nowak (eds.) (1992), Idealization III: Approximation and Truth. Amsterdam: Rodopi.

Campbell, Norman (1920), Physics: The Elements. Cambridge: Cambridge University Press. Reprinted as Foundations of Science. New York: Dover, 1957.

Carnap, Rudolf (1938), “Foundations of Logic and Mathematics”, in Otto Neurath, Charles Morris and Rudolf Carnap (eds.), International Encyclopaedia of Unified Science. Vol. 1. Chicago: University of Chicago Press, 139-213.

Cartwright, Nancy (1983), How the Laws of Physics Lie. Oxford: Oxford University Press.
––– (1989), Nature's Capacities and their Measurement. Oxford: Oxford University Press.
––– (1999), The Dappled World. A Study of the Boundaries of Science. Cambridge: Cambridge University Press.
––– Towfic Shomar and Mauricio Suαrez (1995), “The Tool-box of Science”, in Herfel 1995, 137-150.

Brewer, W. F. and C. A. Chinn (1994), “Scientists’ Responses to Anomalous Data: Evidence from Psychology, History, and Philosophy of Science,” in: Proceedings of the 1994 Biennial Meeting of the Philosophy of Science Association, Volume 1: Symposia and Invited Papers, 304-313.

Da Costa, Newton, and Steven French (2000) “Models, Theories, and Structures: Thirty Years On”, Philosophy of Science 67, Supplement, S116-127.
––– (2003), Science and Partial Truth: A Unitary Approach to Models and Scientific Reasoning. Oxford: Oxford University Press.

Downes, Stephen (1992), “The Importance of Models in Theorizing: A Deflationary Semantic View”. Proceedings of the Philosophy of Science Association, Vol.1, edited by David Hull et al., 142-153. East Lansing: Philosophy of Science Association.

Elgin, Mehmet and Elliott Sober (2002), “Cartwright on Explanation and Idealization”, Erkenntnis 57: 441-50.
Falkenburg, Brigitte, and Wolfgang Muschik (eds.) (1998), Models, Theories and Disunity in Physics, Philosophia Naturalis 35.
Fine, Arthur (1993), “Fictionalism”, Midwest Studies in Philosophy 18: 1-18.
Forster, Malcolm, and Elliott Sober (1994), “How to Tell when Simple, More Unified, or Less Ad Hoc Theories will Provide More Accurate Predictions”, British Journal for the Philosophy of Science 45: 1-35.
Freudenthal, Hans (ed.) (1961), The Concept and the Role of the Model in Mathematics and Natural and Social Sciences. Dordrecht: Reidel.

Gδhde, Ulrich (1997), “Anomalies and the Revision of Theory-Nets. Notes on the Advance of Mercury's Perihelion”, in Marisa Dalla Chiara et al. (eds.), Structures and Norms in Science. Dordrecht: Kluwer.

Galison, Peter (1997) Image and Logic. A Material Culture of Microphysics. Chicago: Chicago: University of Chicago Press.
Gendler, Tamar (2000) Thought Experiment: On the Powers and Limits of Imaginary Cases. New York and London: Garland.
Gibbard, Allan and Hal Varian (1978), “Economic Models”, Journal of Philosophy 75: 664-677.
Giere, Ronald (1988), Explaining Science: A Cognitive Approach. Chicago: University of Chicago Press.
––– (1999), Science Without Laws. Chicago: University of Chicago Press.
––– (2004), “How Models Are Used to Represent Reality”, Philosophy of Science 71, Supplement, S742-752.

Groenewold, H. J. (1961), “The Model in Physics” in Freudenthal 1961, 98-103.

Hacking, Ian (1983), Representing and Intervening. Cambridge: Cambridge University Press.

Harris, Todd (2003), “Data Models and the Acquisition and Manipulation of Data”, Philosophy of Science 70: 1508-1517.

Hartmann, Stephan (1995), “Models as a Tool for Theory Construction: Some Strategies of Preliminary Physics”, in Herfel et al. 1995, 49-67.
––– (1996), “The World as a Process. Simulations in the Natural and Social Sciences”, in Hegselmann et al. 1996, 77-100.
––– (1998), “Idealization in Quantum Field Theory”, in Shanks 1998, 99-122.
––– (1999), “Models and Stories in Hadron Physics”, in Morgan and Morrison 1999, 326-346.
––– (2001), ‘Effective Field Theories, Reduction and Scientific Explanation’, Studies in History and Philosophy of Modern Physics 32, 267-304.

Hegselmann, Rainer, Ulrich Mόller and Klaus Troitzsch (eds.) (1996), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View. Theory and Decision Library. Dordrecht: Kluwer.
Hellman, D. H. (ed.) (1988), Analogical Reasoning. Kluwer: Dordrecht.
Hempel, Carl G. (1965), Aspects of Scientific Explanation and other Essays in the Philosophy of Science. New York: Free Press.
Herfel, William, Wladiyslaw Krajewski, Ilkka Niiniluoto and Ryszard Wojcicki (eds.) (1995), Theories and Models in Scientific Process. (Poznan Studies in the Philosophy of Science and the Humanities 44.) Amsterdam: Rodopi.
Hesse, Mary (1963), Models and Analogies in Science. London: Sheed and Ward.
––– (1974), The Structure of Scientific Inference. London: Macmillan.
Hodges, Wilfrid (1997), A Shorter Model Theory. Cambridge: Cambridge University Press.
Holyoak, Keith and Paul Thagard (1995), Mental Leaps. Analogy in Creative Thought. Cambridge, Mass.: Bradford.
Horowitz, Tamara and Gerald Massey (eds.) (1991), Thought Experiments in Science and Philosophy. Lanham: Rowman and Littlefield.
Hughes, R. I. G. (1997), “Models and Representation”, Philosophy of Science 64: S325-336.

Kroes, Peter (1989), “Structural Analogies between Physical Systems”, British Journal for the Philosophy of Science 40: 145-154.
Laymon, Ronald (1985), “Idealizations and the Testing of Theories by Experimentation”, in Peter Achinstein and Owen Hannaway (eds.), Observation Experiment and Hypothesis in Modern Physical Science. Cambridge, Mass.: M.I.T. Press, 147-173.
––– (1991), “Thought Experiments by Stevin, Mach and Gouy: Thought Experiments as Ideal Limits and Semantic Domains”, in Horowitz and Massey 1991, 167-91.
Leplin, Jarrett (1980), “The Role of Models in Theory Construction”, in: T. Nickles (ed.), Scientific Discovery, Logic, and Rationality. Reidel: Dordrecht: 267-284.
Lloyd, Elisabeth (1984), “A Semantic Approach to the Structure of Population Genetics”, Philosophy of Science 51: 242-264.
Lloyd, Elisabeth (1994), The Structure and Confirmation of Evolutionary Theory. Princeton: Princeton University Press.
Magnani, Lorenzo, and Nancy Nersessian (eds.) (2002), Model-Based Reasoning: Science, Technology, Values. Dordrecht: Kluwer.
––– and Paul Thagard (eds.) (1999), Model-Based Reasoning In Scientific Discovery. Dordrecht: Kluwer.
Mδki, Uskali (1994), “Isolation, Idealization and Truth in Economics”, in Bert Hamminga and Neil B. De Marchi (eds.), Idealization VI: Idealization in Economics. Poznan Studies in the Philosophy of the Sciences and the Humanities, Vol. 38: 147-168. Amsterdam: Rodopi.
Mayo, Deborah (1996), Error and the Growth of Experimental Knowledge. Chicago: University of Chicago Press.
McMullin, Ernan (1968), “What Do Physical Models Tell Us?”, in B. van Rootselaar and J. F. Staal (eds.), Logic, Methodology and Science III. Amsterdam: North Holland, 385-396.
––– (1985), “Galilean Idealization”, Studies in the History and Philosophy of Science 16: 247-73.
Morgan, Mary (1999), “Learning from Models”, in Morgan and Morrison 1999, 347-88.
––– (2001) “Models, Stories and the Economic World”, Journal of Economic Methodology 8:3, 361-84. Reprinted in Fact and Fiction in Economics, edited by Uskali Mδki, 178-201. Cambridge: Cambridge University Press, 2002.
––– and Margaret Morrison (1999), Models as Mediators. Perspectives on Natural and Social Science. Cambridge: Cambridge University Press.
––– and Margaret Morrison (1999), “Models as Mediating Instruments”, in Morgan and Morrison 1999, 10-37.

Morrison, Margaret (1998), “Modelling Nature: Between Physics and the Physical World”, Philosophia Naturalis 35: 65-85.
––– (1999) “Models as Autonomous Agents”, in Morgan and Morrison 1999, 38-65.
––– (2000), Unifying Scientific Theories. Cambridge: Cambridge University Press.

Mundy, Brent (1986), “On the General Theory of Meaningful Representation”, Synthese 67: 391-437.

Musgrave, Alan (1981), “‘Unreal Assumptions’ in Economic Theory: The F-Twist Untwisted”, Kyklos 34: 377-387.

Nagel, Ernest (1961), The Structure of Science. Problems in the Logic of Scientific Explanation. New York: Harcourt, Brace and World.
Norton, John (1991), “Thought Experiments in Einstein's Work”, in Horowitz and Massey 1991, 129-148.
Nowak, Leszek (1979), The Structure of Idealization: Towards a Systematic Interpretation of the Marxian Idea of Science. Reidel: Dordrecht.
Oppenheim, Paul, and Hilary Putnam (1958), “Unity of Science as a Working Hypothesis”, in Herbert Feigl, Grover Maxwell, and Michael Scriven (eds.), Minnesota Studies in the Philosophy of Science. Minneapolis: University of Minnesota Press, 3-36. Reprinted in The Philosophy of Science, edited by Richard Boyd et al., Ch. 22. Cambridge, Mass.: M.I.T. Press, 1991.
Peirce, Charles Sanders (1931-1958), Collected Papers of Charles Sanders Peirce. Vol 3. Edited by Charles Hartshorne, Paul Weiss, and Arthur Burks. Harvard University Press, Cambridge, Mass.
Psillos, Stathis (1995), “The Cognitive Interplay between Theories and Models: The Case of 19th Century Physics”, in Herfel et al. 1995, 105-133.
Quine, Willard Van Orman (1953), “On What There Is”, in From a Logical Point of View. Cambridge, Mass.: Harvard University Press.
Redhead, Michael (1980), “Models in Physics”, British Journal for the Philosophy of Science 31: 145-163.
Reiss, Julian (2003), “Causal Inference in the Abstract or Seven Myths about Thought Experiments”, in Causality: Metaphysics and Methods Research Project. Technical Report 03/02. LSE.
––– (2006), “Beyond Capacities”, in Luc Bovens and Stephan Hartmann (eds.), Nancy Cartwright's Philosophy of Science. London: Routledge.
Rohrlich, Fritz (1991) “Computer Simulations in the Physical Sciences”, in Proceedings of the Philosophy of Science Association, Vol. 2, edited by Arthur Fine et al., 507-518. East Lansing: The Philosophy of Science Association.
Rueger, Alexander (2005), “Perspectival Models and Theory Unification”, British Journal for the Philosophy of Science 56.
Schnell, Rainer (1990), “Computersimulation und Theoriebildung in den Sozialwissenschaften”, Kφlner Zeitschrift fόr Soziologie und Sozialpsychologie 1, 109-128.
Shanks, Niall (ed.). (1998), Idealization in Contemporary Physics. Amsterdam: Rodopi.
Sismondo, Sergio and Snait Gissis (eds.) (1999), Modeling and Simulation. Special Issue of Science in Context 12.
Skyrms, Brian (1996), Evolution of the Social Contract. Cambridge: Cambridge University Press.
Sorensen, Roy (1992), Thought Experiments. New York: Oxford University Press.
Spector, Marshall (1965), “Models and Theories”, British Journal for the Philosophy of Science 16: 121-142.
Staley, Kent W. (2004), The Evidence for the Top Quark: Objectivity and Bias in Collaborative Experimentation. Cambridge: Cambridge University Press.
Suαrez, Mauricio (2003), “Scientific Representation: Against Similarity and Isomorphism.” International Studies in the Philosophy of Science 17: 225-244.
––– (2004), “An Inferential Conception of Scientific Representation”, Philosophy of Science 71, Supplement, S767-779.
Suppe, Frederick. (1989), The Semantic View of Theories and Scientific Realism. Urbana and Chicago: University of Illinois Press.
Suppes, Patrick. (1960), “A Comparison of the Meaning and Uses of Models in Mathematics and the Empirical Sciences”, Synthθse 12: 287-301. Reprinted in Freudenthal (1961), 163-177, and in Patrick Suppes: Studies in the Methodology and Foundations of Science. Selected Papers from 1951 to 1969. Dordrecht: Reidel 1969, 10-23.
––– (1962), “Models of Data”, in Ernest Nagel, Patrick Suppes and Alfred Tarski (eds.), Logic, Methodology and Philosophy of Science: Proceedings of the 1960 International Congress. Stanford: Stanford University Press, 252-261. Reprinted in Patrick Suppes: Studies in the Methodology and Foundations of Science. Selected Papers from 1951 to 1969. Dordrecht: Reidel 1969, 24-35.
––– (2002), Representation and Invariance of Scientific Structures. Stanford: CSLI Publications.
Swoyer, Chris (1991), “Structural Representation and Surrogative Reasoning”, Synthese 87: 449-508.

Teller, Paul (2001), “Twilight of the Perfect Model”, Erkenntnis 55, 393-415.
––– (2004), “How We Dapple the World”, Philosophy of Science 71: 425-447.

Vaihinger, Hans (1911), The Philosophy of ‘As If’. German original. English translation: London: Kegan Paul 1924.

van Fraassen, Bas C. (1980), The Scientific Image. Oxford: Oxford University Press.
––– (1989), Laws and Symmetry. Oxford: Oxford University Press.
––– (2004), “Science as Representation: Flouting the Criteria”, Philosophy of Science 71, Supplement, S794-804.

Wimsatt, William. (1987), “False Models as Means to Truer Theories”, in N. Nitecki and A. Hoffman(eds.), Neutral Models in Biology. Oxford: Oxford University Press, 23-55.

Winsberg, Eric (2001), “Simulations, Models and Theories: Complex Physical Systems and their Representations”, Philosophy of Science 68 (Proceedings): 442-454.

model'style

name::
* McsEngl.model'style,

The second problem is concerned with representational styles.
It is a commonplace that one can represent the same subject matter in different ways.
This pluralism does not seem to be a prerogative of the fine arts as the representations used in the sciences are not all of one kind either.
Weizsδcker's liquid drop model represents the nucleus of an atom in a manner very different from the shell model, and a scale model of the wing of an air plane represents the wing in a way that is different from how a mathematical model of its shape does.
What representational styles are there in the sciences?
[http://plato.stanford.edu/entries/models-science/]

model'course

name::
* McsEngl.model'course,

model'structure#cptCore515#

name::
* McsEngl.model'structure,

_STRUCTURE:
* mapping-unit##
* unit##

model'structure.mapping-unit

_CREATED: {2014-02-27} {2014-02-06}

name::
* McsEngl.model'structure.mapping-unit,
* McsEngl.mapping-unit-of-model, {2014-02-27}
* McsEngl.meaning-unit, {2014-11-10}
* McsEngl.mtdMap'code.ARCHETYPE-UNIT,
* McsEngl.atom.methodMapping,
* McsEngl.mtdMap'atom,

_DESCRIPTION:
Mapping-unit-of-model is a STRUCTURE of units-of-the-model that map an archetype-structure.
[hmnSngo.2014-02-27]
===
Atom is any INDIVISIBLE code that MAPS to an archetype construct.
[hmnSngo.2014-02-06]
===
Atom is any ELEMENTARY code construct that MAPS to an archetype construct.
[hmnSngo.2014-02-06]

model'structure.unit

_CREATED: {2014-02-27} {2014-02-06}

name::
* McsEngl.model'structure.unit,
* McsEngl.mtdMap'code.UNIT,
* McsEngl.unit-of-model, {2014-02-27}
* McsEngl.code-unit,
* McsEngl.unit.code,

_DESCRIPTION:
Unit-of-model is an INDIVISIBLE entity of the medium we use to construct a model, from which we start to build the model.
The units may map an archetype-structure or NOT.
[hmnSngo.2014-02-27]
===
Unit-of-code is any INDIVISIBLE code.
[hmnSngo.2014-02-09]
===
Code-unit is the elementary-entities from which the atoms-of-code are comprised.
They may map to archetypes or NOT.
[hmnSngo.2014-02-06]

model'doing.CREATING

name::
* McsEngl.model'doing.CREATING,
* McsEngl.conceptCore437.1,
* McsEngl.implementing.model,
* McsEngl.model'doing.implementing,
* McsEngl.model-implementation,

model'doing.Designing

_CREATED: {2012-04-29}

name::
* McsEngl.model'doing.Designing,
* McsEngl.conceptCore437.18,
* McsEngl.architecture-of-model@cptCore437.18,

_DESCRIPTION:
Architecture is the art and science of designing and constructing buildings and other structure for human use and shelter.
[http://en.wikipedia.org/wiki/Architecture_(disambiguation)]

model'doing.Evaluating#cptCore475.176#

name::
* McsEngl.model'doing.Evaluating,

_Evaluating:

model'usage

name::
* McsEngl.model'usage,
* McsEngl.model'application,
* McsEngl.model'use,

_DESCRIPTION:
Models are of central importance in many scientific contexts.
The centrality of models such as the billiard ball model of a gas, the Bohr model of the atom, the MIT bag model of the nucleon, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka-Volterra model of predator-prey interaction, the double helix model of DNA, agent-based and evolutionary models in the social sciences, or general equilibrium models of markets in their respective domains are cases in point.
Scientists spend a great deal of time building, testing, comparing and revising models, and much journal space is dedicated to introducing, applying and interpreting these valuable tools.
In short, models are one of the principal instruments of modern science.
[http://plato.stanford.edu/entries/models-science/]

_SPECIFIC:
* prediction##
* understand-data##

model'evoluting#cptCore546.171#

name::
* McsEngl.model'evoluting,

_SPECIFIC: _Evoluting: _model:
* creating##
* maintaining##
* evolving##

model'WHOLE

_WHOLE:
* system'society#cptCore331#

_WHOLE:
* domainOut,

model'wholeNo-relation#cptCore546.15#

name::
* McsEngl.model'wholeNo-relation,

_ENVIRONMENT:

model'GENERIC

_GENERIC:
* entity#cptCore387# 2012-08-07,
* entity.whole.system#cptCore765# 2012-08-07,
* knowledge-human#cptCore50.7# {2012-05-17}

SPECIFIC

name::
* McsEngl.model.specific,

_SPECIFIC: model.alphabetically:
* model.analogical#cptCore437.4#
* model.artificial_neural_network#cptCore588#
* model.computer#cptCore493#
* model.concept#cptCore606#
* model.data#cptCore437.#
* model.dynamic#cptCore437.12#
* model.economic#cptEconomy679#
* model.equation#cptCore437.#
* model.idealized#cptCore437.#
* model.information#cptCore181#
* model.material#cptCore437.8#
* model.materialNo#cptCore437.9#
* model.mathematical#cptCore437.#
* model.organization#cptCore92#
* model.phenomenological#cptCore437.#
* model.scale#cptCore437.2#
* model.system
* model.text
* model.theory#cptCore406.4#
* model.view#cptCore1100#
* model.visual
* model.worldview#cptCore1099#

model.SPECIFIC-DIVISION.doing

_CREATED: {2015-09-22}

name::
* McsEngl.model.SPECIFIC-DIVISION.doing,

_SPECIFIC:
* decision-making-model##
* predictive-model##

model.SPECIFIC-DIVISION.structure

_CREATED: {2014-02-28}

name::
* McsEngl.model.SPECIFIC-DIVISION.structure,

_SPECIFIC:
* same-structure##
* sameNo-structure##

model.SPECIFIC-DIVISION.archetype

name::
* McsEngl.model.SPECIFIC-DIVISION.archetype,

_SPECIFIC:
* model.societal_human#cptEconomy679#
* model.organization#cptCore925#
* model.people##
* model.worldmodel#cptCore1099#

_DESCRIPTION:
The centrality of models such as the billiard ball model of a gas, the Bohr model of the atom, the MIT bag model of the nucleon, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka-Volterra model of predator-prey interaction, the double helix model of DNA, agent-based and evolutionary models in the social sciences, or general equilibrium models of markets in their respective domains are cases in point.
...
Probing models, phenomenological models, computational models, developmental models, explanatory models, impoverished models, testing models, idealized models, theoretical models, scale models, heuristic models, caricature models, didactic models, fantasy models, toy models, imaginary models, mathematical models, substitute models, iconic models, formal models, analogue models and instrumental models are but some of the notions that are used to categorize models.
...
It is now common to construe models as non-linguistic entities rather than as descriptions.
[http://plato.stanford.edu/entries/models-science/]

model.SPECIFIC-DIVISION.medium

_CREATED: {2014-02-27}

name::
* McsEngl.model.SPECIFIC-DIVISION.medium,

_SPECIFIC:
* same-medium##
* sameNo-medium##
===
* material-model#cptCore437.8#
* materialNo-model#cptCore437.9#
===
* model.computer#cptCore493#
* model.information#cptCore181#
* model.text
* model.theory#cptCore406.4#

model.SPECIFIC-DIVISION.mapping-method

_CREATED: {2014-03-02}

name::
* McsEngl.model.SPECIFIC-DIVISION.mapping-method,

_SPECIFIC:
* abstract-model
* abstractNo-model
* analogical-model
* linear-model
* mathematical-model##
* one-way-model##
* two-way-model##

model.ABSTRACT

_CREATED: {2012-11-24}

name::
* McsEngl.model.ABSTRACT,
* McsEngl.abstract-model@cptCore437, {2012-11-24}

_DESCRIPTION:
Abstract-model is a model if it misses some attributes of the archetype.
Every generic-concept is an abstract-model of its referents.
[hmnSngo.2014-02-27]

model.ABSTRACT.NO

_CREATED: {2014-02-27}

name::
* McsEngl.model.ABSTRACT.NO,

model.ACCURATE (true)

_CREATED: {2012-11-23}

name::
* McsEngl.model.ACCURATE (true),
* McsEngl.accurate-model@cptCore437, {2012-11-23}
* McsEngl.true-model, {2014-02-27}

model.ACCURATE.NO (false)

_CREATED: {2012-11-24}

name::
* McsEngl.model.ACCURATE.NO (false),
* McsEngl.accurateNo-model@cptCore437, {2012-11-24}
* McsEngl.false-model, {2014-02-27}

model.ANALOGICAL

name::
* McsEngl.model.ANALOGICAL,
* McsEngl.conceptCore437.4,
* McsEngl.analogical-model@cptCore437.4,

_DESCRIPTION:
IF there is not an 'analogy', THEN we do NOT have a model.
[hmnSngo.2014-02-27]
===
Analogical models. Standard examples of analogical models include the hydraulic model of an economic system, the billiard ball model of a gas, the computer model of the mind or the liquid drop model of the nucleus. At the most basic level, two things are analogous if there are certain relevant similarities between them. Hesse (1963) distinguishes different types of analogies according to the kinds of similarity relations in which two objects enter. A simple type of analogy is one that is based on shared properties. There is an analogy between the earth and the moon based on the fact that both are large, solid, opaque, spherical bodies, receiving heat and light from the sun, revolving around their axes, and gravitating towards other bodies. But sameness of properties is not a necessary condition. An analogy between two objects can also be based on relevant similarities between their properties. In this more liberal sense we can say that there is an analogy between sound and light because echoes are similar to reflections, loudness to brightness, pitch to color, detectability by the ear to detectability by the eye, and so on.
Analogies can also be based on the sameness or resemblance of relations between parts of two systems rather than on their monadic properties. It is this sense that some politicians assert that the relation of a father to his children is analogous to the relation of the state to the citizens. The analogies mentioned so far have been what Hesse calls ‘material analogies’. We obtain a more formal notion of analogy when we abstract from the concrete features the systems possess and only focus on their formal set-up. What the analogue model then shares with its target is not a set of features, but the same pattern of abstract relationships (i.e. the same structure, where structure is understood in the formal sense). This notion of analogy is closely related to what Hesse calls ‘formal analogy’. Two items are related by formal analogy if they are both interpretations of the same formal calculus. For instance, there is a formal analogy between a swinging pendulum and an oscillating electric circuit because they are both described by the same mathematical equation.
A further distinction due to Hesse is the one between positive, negative and neutral analogies. The positive analogy between two items consists in the properties or relations they share (both gas molecules and billiard balls have mass), the negative analogy in the ones they do not share (billiard balls are colored, gas molecules are not). The neutral analogy comprises the properties of which it is not known yet whether they belong to the positive or the negative analogy (do gas molecules obeying Newton's laws of collision exhibit an approach to equilibrium?). Neutral analogies play an important role in scientific research because they give rise to questions and suggest new hypotheses. In this vein, various authors have emphasized the heuristic role that analogies play in theory construction and in creative thought (Bailer-Jones and Bailer-Jones 2002; Hesse 1974, Holyoak and Thagard 1995, Kroes 1989, Psillos 1995, and the essays collected in Hellman 1988).
[http://plato.stanford.edu/entries/models-science/]
===
Analogical models are a method of representing a phenomenon of the world, often called the ‘target system’ by another, more understandable or analysable system. They are also called dynamical analogies.
[http://en.wikipedia.org/wiki/Analogical_models]

model.archetype.BRAIN

_CREATED: {2012-12-01}

name::
* McsEngl.model.archetype.BRAIN,
* McsEngl.model.brain,
* McsEngl.model.of-brain,

_DESCRIPTION:
Model of a physical-brain-organ.
[hmnSngo.2012-12-01]

model.archetype.DOING

name::
* McsEngl.model.archetype.DOING,
* McsEngl.conceptCore437.17,
* McsEngl.doing-model@cptCore437.17, {2012-04-28}
* McsEngl.function-model@cptCore437.17, {2012-04-28}
* McsEngl.model.function@cptCore437.17, {2012-04-29}
* McsEngl.model.process@cptCore437.17, {2012-04-29}
* McsEngl.process-model@cptCore437.17, {2012-04-28}

_DESCRIPTION:
A function model or functional model in systems engineering and software engineering is a structured representation of the functions (activities, actions, processes, operations) within the modeled system or subject area.[1]
A function model, also called an activity model or process model, is a graphical representation of an enterprise's function within a defined scope. The purposes of the function model are to describe the functions and processes, assist with discovery of information needs, help identify opportunities, and establish a basis for determining product and service costs.[2]
[http://en.wikipedia.org/wiki/Function_model]
===
The term process model is used in various contexts. For example, in business process modeling the enterprise process model is often referred to as the business process model. Process models are core concepts in the discipline of process engineering.
[http://en.wikipedia.org/wiki/Process_modeling]

model.archetype.SYSTEM

name::
* McsEngl.model.archetype.SYSTEM,
* McsEngl.conceptCore437.21,
* McsEngl.model.system,
* McsEngl.system-model@cptCore437.21, {2012-04-29}

_GENERIC:
* information-composed-model#

_DESCRIPTION:
A system model is the conceptual model that describes and represents a system. A system comprises multiple views such as planning, requirement (analysis), design, implementation, deployment, structure, behavior, input data, and output data views. A system model is required to describe and represent all these multiple views.
The system model describes and represents the multiple views possibly using two different approaches. The first one is the non-architectural approach and the second one is the architectural approach.
The non-architectural approach respectively picks a model for each view. For example, Structured Systems Analysis and Design Method (SSADM), picking the Structure Chart (SC) for structure description and the Data Flow Diagram (DFD) for behavior description, is categorized into the non-architectural approach.
The architectural approach, instead of picking many heterogeneous and unrelated models, will use only one single coalescence model. For example, System architecture, using the Architecture Description Language (ADL) for both structure and behavior descriptions, is categorized into the architectural approach.
[http://en.wikipedia.org/wiki/System_model]
===
A system model is the conceptual model that describes and represents the structure, behavior, and more views of a system. A system model can represent multiple views of a system by using two different approaches. The first one is the non-architectural approach and the second one is the architectural approach. The non-architectural approach respectively picks a model for each view. The architectural approach, also known as system architecture, instead of picking many heterogeneous and unrelated models, will use only one integrated architectural model.
[http://en.wikipedia.org/wiki/System_model]
===
Using models
Models play important and diverse roles in systems engineering. A model can be defined in several ways, including:[36]
* An abstraction of reality designed to answer specific questions about the real world
* An imitation, analogue, or representation of a real world process or structure; or
* A conceptual, mathematical, or physical tool to assist a decision maker.
Together, these definitions are broad enough to encompass physical engineering models used in the verification of a system design, as well as schematic models like a functional flow block diagram and mathematical (i.e., quantitative) models used in the trade study process. This section focuses on the last.[36]
The main reason for using mathematical models and diagrams in trade studies is to provide estimates of system effectiveness, performance or technical attributes, and cost from a set of known or estimable quantities. Typically, a collection of separate models is needed to provide all of these outcome variables. The heart of any mathematical model is a set of meaningful quantitative relationships among its inputs and outputs. These relationships can be as simple as adding up constituent quantities to obtain a total, or as complex as a set of differential equations describing the trajectory of a spacecraft in a gravitational field. Ideally, the relationships express causality, not just correlation.[36]
[http://en.wikipedia.org/wiki/Systems_engineering]

_ENVIRONMENT:
* entity.whole.system#cptCore765#

model.CODE

_CREATED: {2014-02-28}

name::
* McsEngl.model.CODE,
* McsEngl.code.model, {2014-02-28}

_DESCRIPTION:
Code we call a model if comprised of information.
[hmnSngo.2014-03-11]
===
Code is a model with different structure from his archetype from which we can reconstruct the archetype using the coding-method.
[hmnSngo.2014-02-28]

model.DATA

name::
* McsEngl.model.DATA,
* McsEngl.conceptCore437.6,
* McsEngl.model-of-data@cptCore437.6,

_DESCRIPTION:
Another kind of representational models are so-called ‘models of data’ (Suppes 1962). A model of data is a corrected, rectified, regimented, and in many instances idealized version of the data we gain from immediate observation, the so-called raw data. Characteristically, one first eliminates errors (e.g. removes points from the record that are due to faulty observation) and then present the data in a ‘neat’ way, for instance by drawing a smooth curve through a set of points. These two steps are commonly referred to as ‘data reduction’ and ‘curve fitting’. When we investigate the trajectory of a certain planet, for instance, we first eliminate points that are fallacious from the observation records and then fit a smooth curve to the remaining ones. Models of data play a crucial role in confirming theories because it is the model of data and not the often messy and complex raw data that we compare to a theoretical prediction.
The construction of a data model can be extremely complicated. It requires sophisticated statistical techniques and raises serious methodological as well as philosophical questions. How do we decide which points on the record need to be removed? And given a clean set of data, what curve do we fit to it? The first question has been dealt with mainly within the context of the philosophy of experiment (see for instance Galison 1997 and Staley 2004). At the heart of the latter question lies the so-called curve fitting problem, which is that the data themselves do not indicate what form the fitted curve should take. Traditional discussions of theory choice suggest that this issue is settled by background theory, considerations of simplicity, prior probabilities, or a combination of these. Forster and Sober (1994) point out that this formulation of the curve fitting problem is a slight overstatement because there is a theorem in statistics due to Akaike which shows (given certain assumptions) that the data themselves underwrite (though not determine) an inference concerning the curve's shape if we assume that the fitted curve has to be chosen such that it strikes a balance between simplicity and goodness of fit in a way that maximizes predictive accuracy. (Further discussions of data models can be found in Chin and Brewer 1994, Harris 2003, and Mayo 1996).
[http://plato.stanford.edu/entries/models-science/]

model.DISCRETE-EVENT

_CREATED: {2012-11-22}

name::
* McsEngl.model.DISCRETE-EVENT,
* McsEngl.conceptCore437.23,
* McsEngl.discrete-event-simulation@cptCore437.23, {2012-11-22}

_GENERIC:
* dynamic_model#cptCore437.12#

_DESCRIPTION:
In discrete-event simulation (DES), the operation of a system is represented as a chronological sequence of events. Each event occurs at an instant in time and marks a change of state in the system.[1] For example, if an elevator is simulated, an event could be "level 6 button pressed", with the resulting system state of "lift moving" and eventually (unless one chooses to simulate the failure of the lift) "lift at level 6".
A number of mechanisms have been proposed for carrying out discrete-event simulation, among them are the event-based, activity-based, process-based and three-phase approaches (Pidd, 1998). The three-phase approach is used by a number of commercial simulation software packages, but from the user's point of view, the specifics of the underlying simulation method are generally hidden.
[http://en.wikipedia.org/wiki/Discrete_event_simulation]

model.DYNAMIC

name::
* McsEngl.model.DYNAMIC,
* McsEngl.conceptCore437.12,
* McsEngl.dynamic-model@cptCore437.12, {2012-04-24}
* McsEngl.dynamical-model@cptCore437.12, {2012-04-24}
* McsEngl.time-model@cptCore437.12, {2012-04-24}

_DESCRIPTION:
What is a simulation? Simulations characteristically are used in connection with dynamic models, i.e. models that involve time. The aim of a simulation is to solve the equations of motion of such a model, which is designed to represent the time-evolution of its target system. So one can say that a simulation imitates a (usually real) process by another process (Hartmann 1996, Humphreys 2004).
It has been claimed that computer simulations constitute a genuinely new methodology of science or even a new scientific paradigm (Humphreys 2004, Rohrlich 1991, Winsberg 2001 and 2003, and various contributions to Sismondo and Gissis 1999). Although this contention may not meet with univocal consent, there is no doubt about the practical significance of computer simulations. When standard methods fail, computer simulations are often the only way to learn something about a dynamical model; they help us to ‘extend ourselves’ (Humphreys 2004), as it were. In situations in which the underlying model is well confirmed and understood, computer experiments may even replace real experiments, which has economic advantages and minimizes risk (as, for example, in the case of the simulation of atomic explosions). Computer simulations are also heuristically important. They may suggest new theories, models and hypotheses, for example based on a systematic exploration of a model's parameter space (Hartmann 1996).
But computer simulations also bear methodological perils. They may provide misleading results because due to the discrete nature of the calculations carried out on a digital computer they only allow for the exploration of a part of the full parameter space; and this subspace may not reveal certain important features of the model. The severity of this problem is somehow mitigated by the increasing power of modern computers. But the availability of more computational power also may have adverse effects. It may encourage scientists to swiftly come up with increasingly complex but conceptually premature models, involving poorly understood assumptions or mechanisms and too many additional adjustable parameters (for a discussion of a related problem in the context of individual actor models in the social sciences see Schnell 1990). This may lead to an increase in empirical adequacy—which may be welcome when it comes, for example, to forecasting the weather—but not necessarily to a better understanding of the underlying mechanisms. As a result, the use of computer simulations may change the weight we assign to the various goals of science. So it is important not to be carried away with the means that new powerful computers offer and to thereby place out of sight the actual goals of research.
[http://plato.stanford.edu/entries/models-science/]

_SPECIFIC:
* continuous_event##
* discrete_event#cptCore437.23#

model.INFORMATION

_CREATED: {2012-05-20}

name::
* McsEngl.model.INFORMATION,
* McsEngl.conceptCore437.22,
* McsEngl.code, {2014-02-27}
* McsEngl.information-model@cptCore437.22, {2012-05-20}
* McsEngl.mdlInf@cptCore437.22, {2012-11-24}

_DESCRIPTION:
A-model that represents info OR comprised of info.
[hmnSngo.2016-03-01]
===
A model represented OR comprised of information.
[hmnSngo.2012-11-24]
===
A model that is comprised of information#cptCore181#.
[hmnSngo.2012-05-20]

_SPECIFIC: mdlInf.alphabetically:
* mdlInf.brainual
* mdlInf.computer##
* mdlInf.data##
* mdlInf.graphics
* mdlInf.information_comprised#cptCore437.25#
* mdlInf.information_mapped#cptCore437.26#
* mdlInf.text

_SPECIFIC: mdlInf.SPECIFIC_DIVISION.MEDIUM_CONSTRUCTED:
* mdlInf.information_comprised#cptCore437.25#
* mdlInf.information_mapped#cptCore437.26#

model.information.COMPRISED (medium)

_CREATED: {2012-11-24}

name::
* McsEngl.model.information.COMPRISED (medium),
* McsEngl.conceptCore437.25,
* McsEngl.information-comprised-model@cptCore437.25,

_DESCRIPTION:
A model comprised of information#cptCore181# and DENOTED any entity.
[hmnSngo.2012-11-24]

_SPECIFIC:
* model.brainual#cptCore437.9#
* model.brainual.sensorial#cptCore437.20#
* model.computer_based#cptCore493#
* model.concept
* model.equation#cptCore437.11#
* model.information#cptCore181#
* model.mathematical#cptCore437.13#
* model.statistical#cptCore437.14#
* model.text#cptCore437.10#
* model.theory#cptCore437.7#
* model.view
* model.worldview

model.information.MAPPED (archetype)

_CREATED: {2012-11-24}

name::
* McsEngl.model.information.MAPPED (archetype),
* McsEngl.conceptCore437.26,
* McsEngl.information-denoted-model@cptCore437.25,
* McsEngl.information-mapped-model@cptCore437.25,

_DESCRIPTION:
A model comprised of anything and DENOTED information#cptCore181#.
[hmnSngo.2012-11-24]

_SPECIFIC:
* model.data#cptCore437.6#

model.MATHEMATICAL

name::
* McsEngl.model.MATHEMATICAL,
* McsEngl.conceptCore437.13,
* McsEngl.mathematical-model@cptCore437.13, {2012-04-24}
* McsEngl.quantitative-model@cptCore437.13,
* McsEngl.mdlMath-437.13, {2012-04-26}

_GENERIC:
* information_comprised_model#cptCore437.25#

_DESCRIPTION:
Not to be confused with the same term used in model theory, a branch of mathematical logic. An artifact that is used to illustrate a mathematical idea may also be called a mathematical model, the usage of which is the reverse of the sense explained in this article.

This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (May 2008)
A mathematical model is a description of a system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modelling. Mathematical models are used not only in the natural sciences (such as physics, biology, earth science, meteorology) and engineering disciplines (e.g. computer science, artificial intelligence), but also in the social sciences (such as economics, psychology, sociology and political science); physicists, engineers, statisticians, operations research analysts and economists use mathematical models most extensively. A model may help to explain a system and to study the effects of different components, and to make predictions about behaviour.
Mathematical models can take many forms, including but not limited to dynamical systems, statistical models, differential equations, or game theoretic models. These and other types of models can overlap, with a given model involving a variety of abstract structures. In general, mathematical models may include logical models, as far as logic is taken as a part of mathematics. In many cases, the quality of a scientific field depends on how well the mathematical models developed on the theoretical side agree with results of repeatable experiments. Lack of agreement between theoretical mathematical models and experimental measurements often leads to important advances as better theories are developed.
[http://en.wikipedia.org/wiki/Mathematical_model]

_DEFINITION:
A mathematical model is an abstract model that uses mathematical language to describe the behaviour of a system. Mathematical models are used particularly in the natural sciences and engineering disciplines (such as physics, biology, and electrical engineering) but also in the social sciences (such as economics, sociology and political science); physicists, engineers, computer scientists, and economists use mathematical models most extensively.
Eykhoff (1974) defined a mathematical model as 'a representation of the essential aspects of an existing system (or a system to be constructed) which presents knowledge of that system in usable form'.
[http://en.wikipedia.org/wiki/Mathematical_model]

model.medium.BRAININ

name::
* McsEngl.model.medium.BRAININ,
* McsEngl.conceptCore437.9,
* McsEngl.brainual-model@cptCore437.9, {2012-04-26}
* McsEngl.fictional-model@cptCore437.9, {2012-04-24}
* McsEngl.materialNo-model@cptCore437.9, {2012-04-24}
* McsEngl.mental-model@cptCore437.9,
* McsEngl.model.materialNo@cptCore437.9, {2012-04-26}

=== _NOTES: Conceptual model, a nonphysical model
[http://en.wikipedia.org/wiki/Model]

_GENERIC:
* information_comprised_model#cptCore437.25#
* model.information#cptCore437.22#

_DESCRIPTION:
Many models are not material models. The Bohr model of the atom, a frictionless pendulum, or isolated populations, for instance, are in the scientist's mind rather than in the laboratory and they do not have to be physically realized and experimented upon to perform their representational function. It seems natural to view them as fictional entities. This position can be traced back to the German neo-Kantian Vaihinger (1911), who emphasized the importance of fictions for scientific reasoning. Giere has recently advocated the view that models are abstract entities (1988, 81). It is not entirely clear what Giere means by ‘abstract entities’, but his discussion of mechanical models seems to suggest that he uses the term to designate fictional entities.

This view squares well with scientific practice, where scientists often talk about models as if they were objects, as well as with philosophical views that see the manipulation of models as an essential part of the process of scientific investigation (Morgan 1999). It is natural to assume that one can manipulate something only if it exists. Furthermore, models often have more properties than we explicitly attribute to them when we construct them, which is why they are interesting vehicles of research. A view that regards models as objects can easily explain this without further ado: when we introduce a model we use an identifying description, but the object itself is not exhaustively characterized by this description. Research then simply amounts to finding out more about the object thus identified.

The drawback of this suggestion is that fictional entities are notoriously beset with ontological riddles. This has led many philosophers to argue that there are no such things as fictional entities and that apparent ontological commitments to them must be renounced. The most influential of these deflationary accounts goes back to Quine (1953). Building on Russell's discussion of definite descriptions, Quine argues that it is an illusion that we refer to fictional entities when we talk about them. Instead, we can dispose of these alleged objects by turning the terms that refer to them into predicates and analyse sentences like ‘Pegasus does not exist’ as ‘nothing pegasizes’. By eliminating the troublesome term we eschew the ontological commitment they seem to carry. This has resulted in a glaring neglect of fictional entities, in particular among philosophers of science. Fine (1993), in a programmatic essay, draws attention to this neglect but does not offer a systematic account of how fictions are put to use in science.
[http://plato.stanford.edu/entries/models-science/]

model.medium.BraininNo

name::
* McsEngl.model.medium.BraininNo,
* McsEngl.conceptCore437.8,
* McsEngl.mentalNo-model@cptCore437.8, {2012-04-26}
* McsEngl.material-model@cptCore437.8, {2012-04-24}
* McsEngl.physical-model@cptCore437.8, {2012-04-24}
* McsEngl.sensorial-model@cptCore437.8, {2012-04-26} (our senses can 'view'them)

_DESCRIPTION:
Some models are straightforward physical objects. These are commonly referred to as ‘material models’. The class of material models comprises anything that is a physical entity and that serves as a scientific representation of something else. Among the members of this class we find stock examples like wooden models of bridges, planes, or ships, analogue models like electric circuit models of neural systems or pipe models of an economy, or Watson and Crick's model of DNA. But also more cutting edge cases, especially from the life sciences, where certain organisms are studied as stand-ins for others, belong to this category.
Material models do not give rise to any ontological difficulties over and above the well-known quibbles in connection with objects, which metaphysicians deal with (e.g. the nature of properties, the identity of objects, parts and wholes, and so on).
[http://plato.stanford.edu/entries/models-science/]

_SPECIFIC:
* brainual-sensorial-model#
* computer-model#cptCore493#
* flight-simulator
* physical-model#cptCore437.15#
* text-model

model.medium.Brainual.SENSORIAL

_CREATED: {2012-04-29}

name::
* McsEngl.model.medium.Brainual.SENSORIAL,
* McsEngl.conceptCore437.20,
* McsEngl.brainual-sensorial-model@cptCore437.20, {2012-04-29}

_GENERIC:
* model.brainualNo#cptCore437.8#

_SPECIFIC:
* concept-brain-sensorial#cptCore50.28#
* view-brainual-sensorial#cptCore1100.4#
* worldview-brainual-sensorial#cptCore1099.5#

model.medium.PHYSICAL

_CREATED: {2012-04-26}

name::
* McsEngl.model.medium.PHYSICAL,
* McsEngl.conceptCore437.15,
* McsEngl.physical-model@cptCore437.15, {2012-04-26}

_GENERIC:
* model.brainualNo#cptCore437.8#

_SPECIFIC_COMPLEMENT:
* model.information#

_DESCRIPTION:
Some models are straightforward physical objects. These are commonly referred to as ‘material models’. The class of material models comprises anything that is a physical entity and that serves as a scientific representation of something else. Among the members of this class we find stock examples like wooden models of bridges, planes, or ships, analogue models like electric circuit models of neural systems or pipe models of an economy, or Watson and Crick's model of DNA. But also more cutting edge cases, especially from the life sciences, where certain organisms are studied as stand-ins for others, belong to this category.
Material models do not give rise to any ontological difficulties over and above the well-known quibbles in connection with objects, which metaphysicians deal with (e.g. the nature of properties, the identity of objects, parts and wholes, and so on).
[http://plato.stanford.edu/entries/models-science/]

model.medium.SAME

_CREATED: {2014-02-28}

name::
* McsEngl.model.medium.SAME,

model.medium.SAME.NO

_CREATED: {2014-02-28}

name::
* McsEngl.model.medium.SAME.NO,

model.PHENOMENOLOGICAL

name::
* McsEngl.model.PHENOMENOLOGICAL,
* McsEngl.conceptCore437.5,
* McsEngl.phenomenological-model@cptCore437.5,

Phenomenological models have been defined in different, though related, ways. A traditional definition takes them to be models that only represent observable properties of their targets and refrain from postulating hidden mechanisms and the like. Another approach, due to McMullin (1968), defines phenomenological models as models that are independent of theories. This, however, seems to be too strong. Many phenomenological models, while failing to be derivable from a theory, incorporate principles and laws associated with theories. The liquid drop model of the atomic nucleus, for instance, portrays the nucleus as a liquid drop and describes it as having several properties (surface tension and charge, among others) originating in different theories (hydrodynamics and electrodynamics, respectively). Certain aspects of these theories—though usually not the complete theory—are then used to determine both the static and dynamical properties of the nucleus.
[http://plato.stanford.edu/entries/models-science/]

model.REAL-TIME

_CREATED: {2012-11-22}

name::
* McsEngl.model.REAL-TIME,
* McsEngl.conceptCore437.24,
* McsEngl.real-time-simulation@cptCore437.24, {2012-11-22}

_GENERIC:
* model.doing#cptCore437.17#

_DESCRIPTION:
Real-time simulation refers to a computer model of a physical system that can execute at the same rate as actual "wall clock" time. In other words, the computer model runs at the same rate as the actual physical system. For example if a tank takes 10 minutes to fill in the real-world, the simulation would take 10 minutes as well.

Real-time simulation occurs commonly in computer gaming, but also is important in the industrial market for operator training and off-line controller tuning[1]. Computer languages like VisSim and Simulink allow quick creation of such real-time simulations and have connections to industrial displays and Programmable Logic Controllers via OLE for process control or digital and analog I/O cards.

[edit]Real-time simulation in academic curricula

Real-time simulators are used extensively in many engineering fields. As a consequence, the inclusion of simulation applications in academic curricula can provide great value to the student. Statistical power grid protection tests, aircraft design and simulation, motor drive controller design methods and space robot integration are a few examples of real-time simulator technology applications. [2]
[http://en.wikipedia.org/wiki/Real-time_Simulation]

_ADDRESS.WPG:
* http://www.opal-rt.com/sites/default/files/technical_papers/PES-GM-Tutorial_04%20-%20Real%20Time%20Simulation.pdf,
Abstract-- Simulation tools have been widely used for the
design and improvement of electrical systems since the midtwentieth century. The evolution of simulation tools has
progressed in step with the evolution of computing technologies.
In recent years, computing technologies have improved
dramatically in performance and become widely available at a
steadily decreasing cost. Consequently, simulation tools have also
seen dramatic performance gains and steady cost decreases.
Researchers and engineers now have access to affordable, high
performance simulation tools that were previously too costprohibitive, except for the largest manufacturers and utilities.
This paper introduces the role and advantages of using real-time
simulation by answering three fundamental questions: what is
real-time simulation; why is it needed and where does it best fit.
The recent evolution of real-time simulators is summarized. The
importance of model validation, mixed use of real-time and
offline modes of simulation and test coverage in complex systems
is discussed.
Index Terms—accelerated simulation, hardware-in-the-loop
(HIL), model-based design (MBD), power system simulation,
rapid control prototyping (RCP), real-time simulation, softwarein-the-loop (SIL).

model.REPLICA

name::
* McsEngl.model.REPLICA,
* McsEngl.conceptCore437.16,
* McsEngl.copy-model@cptCore437.16, {2012-04-28}
* McsEngl.replica-model@cptCore437.16, {2012-04-28}

_DESCRIPTION:
Replica is a model of the same medium and structure with the archetype.
[hmnSngo.2014-02-28]
===
replica   replica; replicas
   
 1   A replica of something such as a statue, building, or weapon is an accurate copy of it.
   ...a human-sized replica of the Statue of Liberty...
   Royce Hall was an exact replica of the basilica of Sant Ambrogio in Milan.
   It was a replica gun, for display only.
  N-COUNT: usu N of n
  = model
 2   If you say that one person is a replica of another, you mean that the first person looks very like the second.
   Tina as a child was a replica of her mother.
  N-COUNT: usu sing, usu N of n
 
(c) HarperCollins Publishers.

model.PROTOTYPE

_CREATED: {2012-11-24}

name::
* McsEngl.model.PROTOTYPE,
* McsEngl.prototype.model,

_DESCRIPTION:
Prototypes are Mockups of an application, allowing users to visualize an application that has not yet been constructed. Prototypes help people get an idea of what the system will look like, and make it easier for projects to make design decisions without waiting for the system to be built. Major improvements in communication between users and developers were often seen with the introduction of prototypes. Early views of applications led to fewer changes later and hence reduced overall costs considerably.[citation needed]
Prototypes can be flat diagrams (often referred to as wireframes) or working applications using synthesized functionality. Wireframes are made in a variety of graphic design documents, and often remove all color from the design (i.e. use a greyscale color palette) in instances where the final software is expected to have graphic design applied to it. This helps to prevent confusion as to whether the prototype represents the final visual look and feel of the application.[citation needed]
[http://en.wikipedia.org/wiki/Requirement_analysis]

model.SCALE

name::
* McsEngl.model.SCALE,
* McsEngl.conceptCore437.2,
* McsEngl.scale-model@cptCore437.2, {2012-04-24}

_DESCRIPTION:
Scale models. Some models are basically down-sized or enlarged copies of their target systems (Black 1962). Typical examples are wooden cars or model bridges. The leading intuition is that a scale model is a naturalistic replica or a truthful mirror image of the target; for this reason scale models are sometimes also referred to as ‘true models’ (Achinstein 1968, Ch. 7). However, there is no such thing as a perfectly faithful scale model; faithfulness is always restricted to some respects. The wooden model of the car, for instance, provides a faithful portrayal of the car's shape but not its material. Scale models seem to be a special case of a broader category of representations that Peirce dubbed icons: representations that stand for something else because they closely resemble it (Peirce 1931-1958 Vol. 3, Para. 362). This raises the question of what criteria a model has to satisfy in order to qualify as an icon. Although we seem to have strong intuitions about how to answer this question in particular cases, no theory of iconicity for models has been formulated yet.
[http://plato.stanford.edu/entries/models-science/]

model.SCIENTIFIC

_CREATED: {2012-11-24}

name::
* McsEngl.model.SCIENTIFIC,
* McsEngl.scientific-model, {2012-11-24}

_DESCRIPTION:
Scientific modelling is the process of generating abstract, conceptual, graphical or mathematical models. Science offers a growing collection of methods, techniques and theory about all kinds of specialized scientific modelling. A scientific model can provide a way to read elements easily which have been broken down to a simpler form.
Modelling is an essential and inseparable part of all scientific activity, and many scientific disciplines have their own ideas about specific types of modelling. There is also an increasing attention to scientific modelling[1] in fields such as philosophy of science, systems theory, and knowledge visualization.
[http://en.wikipedia.org/wiki/Scientific_modelling]

model.structure.SAME

_CREATED: {2014-02-28}

name::
* McsEngl.model.structure.SAME,

model.structure.SAME.NO

_CREATED: {2014-02-28}

name::
* McsEngl.model.structure.SAME.NO,

model.Surrogate

name::
* McsEngl.model.Surrogate,
* McsEngl.surrogate-model@cptCore437i, {2012-04-28}

_DESCRIPTION:
A surrogate model is an engineering method used when an outcome of interest cannot be easily directly measured, so a model of the outcome is used instead. Most engineering design problems require experiments and/or simulations to evaluate design objective and constraint functions as function of design variables. For example, in order to find the optimal airfoil shape for an aircraft wing, an engineer simulates the air flow around the wing for different shape variables (length, curvature, material, ..). For many real world problems, however, a single simulation can take many minutes, hours, or even days to complete. As a result, routine tasks such as design optimization, design space exploration, sensitivity analysis and what-if analysis become impossible since they require thousands or even millions of simulation evaluations.
One way of alleviating this burden is by constructing approximation models, known as surrogate models, response surface models, metamodels or emulators, that mimic the behavior of the simulation model as closely as possible while being computationally cheap(er) to evaluate. Surrogate models are constructed using a data-driven, bottom-up approach. The exact, inner working of the simulation code is not assumed to be known (or even understood), solely the input-output behavior is important. A model is constructed based on modeling the response of the simulator to a limited number of intelligently chosen data points. This approach is also known as behavioral modeling or black-box modeling, though the terminology is not always consistent. When only a single design variable is involved, the process is known as curve fitting as illustrated in the Figure.
While this article is written around the subject of using surrogate models in lieu of experiments and simulations in engineering design, surrogate modelling may be used in many other areas of science where there are expensive experiments and/or function evaluations.
An important distinction can be made between two different applications of surrogate models: design optimization and design space approximation (also known as emulation).
In surrogate model based optimization an initial surrogate is constructed using some of the available budget of expensive experiments and/or simulations. The remaining experiments/simulations are run for designs which the surrogate model predicts may have promising performance. The process usually takes the form of the following search/update procedure.
1. Initial sample selection (the experiments and/or simulations to be run)
2. Construct surrogate model
3. Search surrogate model (the model can be searched extensively, e.g. using a genetic algorithm, as it is cheap to evaluate)
4. Run and update experiment/simulation at new location(s) found by search and add to sample
5. Iterate steps 2 to 4 until out of time or design 'good enough'
Depending on the type of surrogate used and the complexity of the problem, the process may converge on a local or global optimum, or perhaps none at all.[1]
In design space approximation, one is not interested in finding the optimal parameter vector but rather in the global behavior of the system. Here the surrogate is tuned to mimic the underlying model as closely as needed over the complete design space. Such surrogates are a useful, cheap way to gain insight into the global behavior of the system. Optimization can still occur as a post processing step, although with no update procedure (see above) the optimum found cannot be validated.
The scientific challenge of surrogate modeling is the generation of a surrogate that is as accurate as possible, using as few simulation evaluations as possible. The process comprises three major steps which may be interleaved iteratively:
Sample selection (also known as sequential design, optimal experimental design (OED) or active learning)
Construction of the surrogate model and optimizing the model parameters (Bias-Variance trade-off)
Appraisal of the accuracy of the surrogate.
The accuracy of the surrogate depends on the number and location of samples (expensive experiments or simulations) in the design space. Various design of experiments (DOE) techniques cater to different sources of errors, in particular errors due to noise in the data or errors due to an improper surrogate model.
The most popular surrogate models are polynomial response surfaces, Kriging, support vector machines and artificial neural networks. For most problems, the nature of true function is not known a priori so it is not clear which surrogate model will be most accurate. In addition, there is no consensus on how to obtain the most reliable estimates of the accuracy of a given surrogate.
[http://en.wikipedia.org/wiki/Surrogate_model]

model.TEXT

name::
* McsEngl.model.TEXT,
* McsEngl.conceptCore437.10,
* McsEngl.description-of-theory@cptCore437.10, {2012-04-24}
* McsEngl.text-of-theory@cptCore437.10, {2012-04-24}

_GENERIC:
* information_comprised_model#cptCore437.25#

_DESCRIPTION:
A time-honored position has it that what scientists display in scientific papers and textbooks when they present a model are more or less stylized descriptions of the relevant target systems (Achinstein 1968, Black 1962).

This view has not been subject to explicit criticism. However, some of the criticisms that have been marshaled against the syntactic view of theories equally threaten a linguistic understanding of models. First, it is a commonplace that we can describe the same thing in different ways. But if we identify a model with its description, then each new description yields a new model, which seems to be counterintuitive. One can translate a description into other languages (formal or natural), but one would not say that one hereby obtains a different model. Second, models have different properties than descriptions. On the one hand, we say that the model of the solar system consists of spheres orbiting around a big mass or that the population in the model is isolated from its environment, but it does not seem to make sense to say this about a description. On the other hand, descriptions have properties that models do not have. A description can be written in English, consist of 517 words, be printed in red ink, and so on. None of this makes any sense when said about a model. The descriptivist faces the challenge to either make a case that these arguments are mistaken or to show how to get around these difficulties.
[http://plato.stanford.edu/entries/models-science/]

model.THEORY

name::
* McsEngl.model.THEORY,
* McsEngl.conceptCore437.7,
* McsEngl.model-of-theory@cptCore437.7, {2012-04-24}

_GENERIC:
* information_comprised_model#cptCore437.25#

_DESCRIPTION:
In modern logic, a model is a structure that makes all sentences of a theory true, where a theory is taken to be a (usually deductively closed) set of sentences in a formal language (see Bell and Machover 1977 or Hodges 1997 for details). The structure is a ‘model’ in the sense that it is what the theory represents. As a simple example consider Euclidean geometry, which consists of axioms—e.g. ‘any two points can be joined by a straight line’—and the theorems that can be derived from these axioms. Any structure of which all these statements are true is a model of Euclidean geometry.

A structure S = <U, O, R> is a composite entity consisting of (i) a non-empty set U of individuals called the domain (or universe) of S, (ii) an indexed set O (i.e. an ordered list) of operations on U (which may be empty), and (iii) a non-empty indexed set R of relations on U. It is important to note that nothing about what the objects are matters for the definition of a structure—they are mere dummies. Similarly, operations and functions are specified purely extensionally; that is, n-place relations are defined as classes of n-tuples, and functions taking n arguments are defined as classes of (n+1)-tuples. If all sentences of a theory are true when its symbols are interpreted as referring to either objects, relations, or functions of a structure S, then S is a model of this theory.

Many models in science carry over from logic the idea of being the interpretation of an abstract calculus. This is particularly pertinent in physics, where general laws—such as Newton's equation of motion—lie at the heart of a theory. These laws are applied to a particular system—e.g. a pendulum—by choosing a special force function, making assumptions about the mass distribution of the pendulum etc. The resulting model then is an interpretation (or realization) of the general law.
[http://plato.stanford.edu/entries/models-science/]

model.VISUAL

_CREATED: {2012-11-24}

name::
* McsEngl.visual-model, {2012-11-24}

_DESCRIPTION:
Visual modeling is the graphic representation of objects and systems of interest using graphical languages. Visual modeling languages may be General-Purpose Modeling (GPM) languages (e.g., UML, Southbeach Notation, IDEF) or Domain-Specific Modeling (DSM) languages (e.g., SysML). They include industry open standards (e.g., UML, SysML), as well as proprietary standards, such as the visual languages associated with VisSim, MATLAB and Simulink, OPNET, and NI Multisim. VisSim is unique in that it provides a royalty-free, downloadable Viewer that lets anyone open and interactively simulate VisSim models. Visual modeling languages are an area of active research that continues to evolve, as evidenced by increasing interest in DSM languages, visual requirements, and visual OWL (Web Ontology Language).[1]
[http://en.wikipedia.org/wiki/Visual_modeling]

mdlSocHmn'DEFINITION

_DESCRIPTION:
Social simulation is a research field that applies computational methods to study issues in the social sciences. The issues explored include problems in psychology[1], organizational behavior [2], sociology, political science, economics, anthropology, geography, engineering[2], archaeology and linguistics (Takahashi, Sallach & Rouchier 2007).

Social simulation aims to cross the gap between the descriptive approach used in the social sciences and the formal approach used in the hard sciences, by moving the focus on the processes/mechanisms/behaviors that build the social reality.

In social simulation, computers supports human reasoning activities by executing these mechanisms. This field explores the simulation of societies as complex non-linear systems, which are difficult to study with classical mathematical equation-based models. Robert Axelrod regards social simulation as a third way of doing science, differing from both the deductive and inductive approach; generating data that can be analysed inductively, but coming from a rigorously specified set of rules rather than from direct measurement of the real world. Thus, simulating a phenomenon is akin to generating it - constructing artificial societies. These ambitious aims have encountered several criticisms.

The social simulation approach to the social sciences is promoted and coordinated by three regional associations, ESSA for Europe, North America (reorganizing under the new CSSS name), and PAAA Pacific Asia.
[http://en.wikipedia.org/wiki/Social_simulation]

mdlSocHmn'GENERIC

_GENERIC:
* entity.model#cptCore347#

mdlSocHmn'Agent

name::
* McsEngl.mdlSocHmn'Agent,

mdlSocHmn'Creating

name::
* McsEngl.mdlSocHmn'Creating,

The details of model construction vary with type of model and its application, but a generic process can be identified. Generally any modelling process has two steps: generating a model, then checking the model for accuracy (sometimes called diagnostics). The diagnostic step is important because a model is only useful to the extent that it accurately mirrors the relationships that it purports to describe. Creating and diagnosing a model is frequently an iterative process in which the model is modified (and hopefully improved) with each iteration of diagnosis and respecification. Once a satisfactory model is found, it should be double checked by applying it to a different data set.
[http://en.wikipedia.org/wiki/Economic_model]

mdlSocHmn'Evaluation#cptCore50.30#

name::
* McsEngl.mdlSocHmn'Evaluation,

_Evaluation:

Criticisms of Social Simulation
Since its creation, computerized social simulation has been the target of some criticism in regard to its practicality and accuracy. Social simulation's simplification of the complex to form models from which we can better understand the latter is sometimes seen as a draw back, as using fairly simple models to simulate real life with computers is not always the best way to predict behavior.

Most of the criticism seems to be aimed at agent-based models and simulation and how they work:

Simulations, being man-made from mathematical interfaces, predict human behavior in a far too simple manner in regard to the complexities of humanity and our actions.
Simulations cannot enlighten researchers as to how people interact or behave in ways not programmed into their models. For this reason, the scope of simulations are limited in that the researchers must already know what they are going to find (to a degree, for they cannot find anything they themselves did not place in the model) at least vaguely, possibly skewing the results.
Due to the complexities of what is being measured, simulations must be analyzed in unbiased ways; however, with the model running on a pre-made set of instructions coded into it by a modeler, biases exist almost universally.
It is highly difficult and often impractical to attempt to link the findings from the abstract world the simulation creates and our complex society and all of its variation.
Researchers working in social simulation might respond that the competing theories from the social sciences are far simpler than those achieved through simulation and therefore suffer the aforementioned drawbacks much more strongly. Theories in some social science tend to be linear models that are not dynamic, and are generally inferred from small laboratory experiments (laboratory tests are most common in psychology but rare in sociology, political science, economics and geography). The behavior of populations of agents under these models is rarely tested or verified against empirical observation.
[http://en.wikipedia.org/wiki/Social_simulation]

mdlSocHmn'EVOLUTION#cptCore546.171#

name::
* McsEngl.mdlSocHmn'EVOLUTION,

{time.2012-11-22}:
I EXTENDED this concept from 'model-economic' tot 'model-societal'.

mdlSocHmn'Failness

name::
* McsEngl.mdlSocHmn'Failness,

Complex systems specialist and mathematician David Orrell wrote on this issue and explained that the weather, human health and economics use similar methods of prediction (mathematical models). Their systems - the atmosphere, the human body and the economy - also have similar levels of complexity. He found that forecasts fail because the models suffer from two problems :
i- they cannot capture the full detail of the underlying system, so rely on approximate equations;
ii- they are sensitive to small changes in the exact form of these equations.
This is because complex systems like the economy or the climate consist of a delicate balance of opposing forces, so a slight imbalance in their representation has big effects. Thus, predictions of things like economic recessions are still highly inaccurate, despite the use of enormous models running on fast computers.
[http://en.wikipedia.org/wiki/Economic_model]

mdlSocHmn'Doing

name::
* McsEngl.mdlSocHmn'Doing,

In addition to their professional academic interest, the use of models include:

Forecasting economic activity in a way in which conclusions are logically related to assumptions;
Proposing economic policy to modify future economic activity;
Presenting reasoned arguments to politically justify economic policy at the national level, to explain and influence company strategy at the level of the firm, or to provide intelligent advice for household economic decisions at the level of households.
Planning and allocation, in the case of centrally planned economies, and on a smaller scale in logistics and management of businesses.
In finance predictive models have been used since the 1980s for trading (investment, and speculation), for example emerging market bonds were often traded based on economic models predicting the growth of the developing nation issuing them. Since the 1990s many long-term risk management models have incorporated economic relationships between simulated variables in an attempt to detect high-exposure future scenarios (often through a Monte Carlo method).
[http://en.wikipedia.org/wiki/Economic_model]

mdlSocHmn'Simplification

name::
* McsEngl.mdlSocHmn'Simplification,

Simplification is particularly important for economics given the enormous complexity of economic processes. This complexity can be attributed to the diversity of factors that determine economic activity; these factors include: individual and cooperative decision processes, resource limitations, environmental and geographical constraints, institutional and legal requirements and purely random fluctuations. Economists therefore must make a reasoned choice of which variables and which relationships between these variables are relevant and which ways of analyzing and presenting this information are useful.
[http://en.wikipedia.org/wiki/Economic_model]

SPECIFIC

name::
* McsEngl.mdlSocHmn.specific,

_SPECIFIC: mdlSocHmn.Alphabetically:
*
* conceptual-model##
* macroeconomic model#cptEconomy679.6#
* stochastic model##

mdlSocHmn.ACEGES

name::
* McsEngl.mdlSocHmn.ACEGES,
* McsEngl.ACEGES@cptEconomy679i,

The ACEGES (Agent-based Computational Economics of the Global Energy System) is a decision support tool for energy policy by means of controlled computational experiments.[1] The ACEGES tool is designed to be the foundation for large custom-purpose simulations of the global energy system. The ACEGES methodological framework, developed by Voudouris (2011)[2] by extending Voudouris (2010),[3] is based on the agent-based computational economics (ACE) paradigm. ACE is the computational study of economies modeled as evolving systems of autonomous interacting agents.[4][5]

The ACEGES tool is written in Java and runs on Windows, Mac OS and Linux platforms. The ACEGES tool is based on:

The MASON library - A discrete-event multiagent simulation library
The ECJ - An evolutionary computation toolkit
The R Project for statistical computing
The GAMLSS framework
GAMLSS, developed by Rigby and Stasinopoulos (2005),[6] is the back-end statistical model for the regression-based rules of the agents in the ACEGES model and R is the engine for the statistical computing used by the ACEGES decision-support tool. In specific cases, the ACEGES tool also uses the Mathematica kernel. The ACEGES model is based upon the work of Hallock et. al. (2004),[7] Wood et. al. (2004)[8] and Campbell (1997).[9]
[http://en.wikipedia.org/wiki/ACEGES]

mdlSocHmn.COMPUTER

name::
* McsEngl.mdlSocHmn.COMPUTER,
* McsEngl.conceptEconomy679.10,
* McsEngl.computer-social-simulation@cptEconomy679.10, {2012-11-22}

mdlSocHmn'ECONOMIC

_CREATED: {2012-11-22} {2012-04-24}

name::
* McsEngl.mdlSocHmn'ECONOMIC,
* McsEngl.conceptEconomy679.9,
* McsEngl.modelEconomic@cptEconomy679, {2012-04-24}
* McsEngl.model.economic@cptEconomy679,
* McsEngl.economic-model@cptEconomy679,
* McsEngl.mdlEcn@cptEconomy679, {2012-04-25}
* McsEngl.modelEcn@cptEconomy679, {2012-04-24}

_DESCRIPTION:
In economics, a model is a theoretical construct that represents economic processes by a set of variables and a set of logical and/or quantitative relationships between them. The economic model is a simplified framework designed to illustrate complex processes, often but not always using mathematical techniques. Frequently, economic models use structural parameters. Structural parameters are underlying parameters in a model or class of models.[1] A model may have various parameters and those parameters may change to create various properties.
[http://en.wikipedia.org/wiki/Economic_model]

mdlSocHmn.ECONOMY

_CREATED: {2011-08-07}

name::
* McsEngl.mdlSocHmn.ECONOMY,
* McsEngl.conceptEconomy679.2,
* McsEngl.mdlEcn,
* McsEngl.economic-information-system@cptSna2008v,
* McsEngl.economy-model@cptEconomy697.2,

=== _NOTES: 14.3 Supply and use tables are a powerful tool with which to compare and contrast data from various sources and improve the coherence of the economic information system.
[http://gym-eleous.ioa.sch.gr/textid/SNA2008.html#idP14.2]

mdlSocHmn.ECONOMETRIC

name::
* McsEngl.mdlSocHmn.ECONOMETRIC,
* McsEngl.conceptEconomy679.3,

In econometrics, as in statistics in general, it is presupposed that the quantities being analyzed can be treated as random variables. An econometric model then is a set of joint probability distributions to which the true joint probability distribution of the variables under study is supposed to belong. In the case in which the elements of this set can be indexed by a finite number of real-valued parameters, the model is called a parametric model; otherwise it is a nonparametric or semiparametric model. A large part of econometrics is the study of methods for selecting models, estimating them, and carrying out inference on them.

The most common econometric models are structural, in that they convey causal and counterfactual information,[2] and are used for policy evaluation. For example, an equation modeling consumption spending based on income could be used to see what consumption would be contingent on any of various hypothetical levels of income, only one of which (depending on the choice of a fiscal policy) will end up actually occurring.
[http://en.wikipedia.org/wiki/Econometric_model]

mdlSocHmn.EXOGENOUS-GROWTH

name::
* McsEngl.mdlSocHmn.EXOGENOUS-GROWTH,
* McsEngl.conceptEconomy679.5,
* McsEngl.exogenous-growth-model@cptEconomy679i,
* McsEngl.neoclassical-growth-model@cptEconomy679i,
* McsEngl.solow-swan-growth-model@cptEconomy679i,

The exogenous growth model, also known as the neo-classical growth model or Solow–Swan growth model is a term used to sum up the contributions of various authors to a model of long-run economic growth within the framework of neoclassical economics.
[http://en.wikipedia.org/wiki/Neoclassical_growth_model]

mdlSocHmn.HECKSCHER-OHLIN

name::
* McsEngl.mdlSocHmn.HECKSCHER-OHLIN,

The Heckscher–Ohlin model (H–O model) is a general equilibrium mathematical model of international trade, developed by Eli Heckscher and Bertil Ohlin at the Stockholm School of Economics. It builds on David Ricardo's theory of comparative advantage by predicting patterns of commerce and production based on the factor endowments of a trading region. The model essentially says that countries will export products that use their abundant and cheap factor(s) of production and import products that use the countries' scarce factor(s).[1]
[http://en.wikipedia.org/wiki/Heckscher-Ohlin_model]

mdlSocHmn.INPUT-OUTPUT

name::
* McsEngl.mdlSocHmn.INPUT-OUTPUT,
* McsEngl.conceptEconomy679.1,
* McsEngl.input-output-model@cptEconomy679.1, {2011-08-11}

_DESCRIPTION:
In economics, an input-output model is a quantitative economic technique that represents the interdependencies between different branches of national economy or between branches of different, even competing economies. [1] Wassily Leontief (1905-1999) developed this type of analysis and took the Nobel Memorial Prize in Economic Sciences for his development of this model. [1] Earlier Francois Quesnay developed a cruder version of this technique called Tableau ιconomique. And, in essence, Lιon Walras's work Elements of Pure Economics on general equilibrium theory is both a forerunner and generalization of Leontief's seminal concept. Leontief's main contribution was that he was able to simplify Walras's piece so that it could be implemented empirically.

The International Input-Output Association is dedicated to advancing knowledge in the field of input-output study, which includes "improvements in basic data, theoretical insights and modelling, and applications, both traditional and novel, of input-output techniques."
[http://en.wikipedia.org/wiki/Input-Output_model]

mdlSocHmn.MACROECONOMIC

name::
* McsEngl.mdlSocHmn.MACROECONOMIC,
* McsEngl.conceptEconomy679.6, (old 677)
* McsEngl.mdlEcn.macro@cptEconomy677,
* McsEngl.macroeconomic-model@cptEconomy677,
* McsEngl.macromodel@cptEconomy677,

_DESCRIPTION:
A macroeconomic model is an analytical tool designed to describe the operation of the economy of a country or a region. These models are usually designed to examine the dynamics of aggregate quantities such as the total amount of goods and services produced, total income earned, the level of employment of productive resources, and the level of prices.
Macroeconomic models may be logical, mathematical, and/or computational; the different types of macroeconomic models serve different purposes and have different advantages and disadvantages. Macroeconomics models may be used to clarify and illustrate basic theoretical principles; they may be used to test, compare, and quantify different macroeconomic theories; they may be used to produce "what if" scenarios (usually to predict the effects of changes in monetary, fiscal, or other macroeconomic policies); and they may be used to generate economic forecasts. Thus, macroeconomic models are widely used in academia, teaching and research, and are also widely used by international organizations, national governments and larger corporations, as well as by economics consultants and think tanks.
[http://en.wikipedia.org/wiki/Macroeconomic_model]

_SPECIFIC: modelMacro.Alphabetically:
* dynamic model##
* Empirical forecasting model
* IS-LM model of Keynesian macroeconomics
* Large-scale macroeconometric model
* Mundell-Fleming model of Keynesian macroeconomics
* Simple theoretical model
* Solow model of neoclassical growth theory
* static model##
* Wharton model

mdlSocHmn.STOCHASTIC

name::
* McsEngl.mdlSocHmn.STOCHASTIC,
* McsEngl.conceptEconomy679.4,
* McsEngl.economic-stochastic-model@cptEconomy679.4,

Stochastic models are formulated using stochastic processes. They model economically observable values over time. Most of econometrics is based on statistics to formulate and test hypotheses about these processes or estimate parameters for them. A widely used class of econometric models popularized by Tinbergen and later Wold are autoregressive models, in which the stochastic process satisfies some relation between current and past values. Examples of these are autoregressive moving average models and related ones such as autoregressive conditional heteroskedasticity (ARCH) and GARCH models for the modelling of heteroskedasticity.
[http://en.wikipedia.org/wiki/Economic_model]

mdlSocHmn.VIRTUAL-ECONOMY

_CREATED: {2012-07-01}

name::
* McsEngl.mdlSocHmn.VIRTUAL-ECONOMY,
* McsEngl.conceptEconomy679.7,
* McsEngl.game-economy-eptEconomy679.7, {2012-07-01}
* McsEngl.virtual-economy-eptEconomy679.7, {2012-07-01}
====== lagoGreek:
* McsElln.εικονικη-οικονομια@cptEconomy679.7, {2012-07-01}
* McsElln.παιγνιδο-οικονομια@cptEconomy679.7, {2012-07-01}

_DESCRIPTION:
A virtual economy (or sometimes synthetic economy) is an emergent economy existing in a virtual persistent world, usually exchanging virtual goods in the context of an Internet game. People enter these virtual economies for recreation and entertainment rather than necessity, which means that virtual economies lack the aspects of a real economy that are not considered to be "fun" (for instance, players in a virtual economy often do not need to buy food in order to survive, and usually do not have any biological needs at all). However, some people do interact with virtual economies for "real" economic benefit.
[http://en.wikipedia.org/wiki/Virtual_economy]
===
Some online gaming companies employ economists to manage their in-game
economies.

Some online gaming companies hire economists to help manage the in-game
economies of multiplayer games. Economists tend to be hired by gaming
companies that make their profits on in-game purchases and are responsible
for developing rules and regulations. For example, in 2012, renowned Greek
economist Yanis Varoufakis was hired by video game company Valve, the maker
of Half-Life games, to help establish a virtual trading system within
different games. Economists might also use video games to run economic
experiments, which can be difficult to do in real life, and some have
developed their own games for this purpose.

Read More: http://www.wisegeek.com/do-online-gaming-companies-employ-economists.htm?m, {2013-10-29}

mdlSocHmn.WONDERLAND

_CREATED: {2012-11-15}

name::
* McsEngl.mdlSocHmn.WONDERLAND,
* McsEngl.wonderland-model@cptEconomy679,

Wonderland is an integrated mathematical model used for studying phenomena in sustainable development. First introduced by (Sanderson 1994), there are now several related versions of the model in use. Wonderland allows economists, policy analysts and environmentalist to study the interactions between the economic, demographic and anthropogenic sectors of an idealized world, thereby enabling them to obtain insights transferable to the real world.
[http://en.wikipedia.org/wiki/Wonderland_Model]

mdlSocHmn.WORLD3

name::
* McsEngl.mdlSocHmn.WORLD3,
* McsEngl.conceptCore437.23,
* McsEngl.world3-model@cptCore437, {2012-04-25}

_GENERIC:
* model.computer#cptCore493#

The World3 model was a computer simulation of interactions between population, industrial growth, food production and limits in the ecosystems of the Earth. It was originally produced and used by a Club of Rome study that produced the model and the book The Limits to Growth. The principal creators of the model were Donella Meadows, Dennis Meadows, and Jorgen Randers.
The model was documented in the book Dynamics of Growth in a Finite World. It added new features to Jay W. Forrester's World2 model. Since World3 was originally created it has had minor tweaks to get to the World3/91 model used in the book Beyond the Limits, later improved to get the World3/2000 model distributed by the Institute for Policy and Social Science Research and finally the World3/2004 model used in the book Limits to growth: the 30 year update.
[http://en.wikipedia.org/wiki/World3]

DEFINITION

ann'GENERIC

_GENERIC:
* model#cptCore437#

ann.SPECIFIC#cptCore546.23#

name::
* McsEngl.ann.SPECIFIC,

ann'WholeNo-relation#cptCore546.15#

name::
* McsEngl.ann'WholeNo-relation,

* ANN_PROGRAM#cptIt340#

DEFINITION

_DESCRIPTION:
A computer simulation, a computer model or a computational model is a computer program, or network of computers, that attempts to simulate an abstract model of a particular system. Computer simulations have become a useful part of mathematical modeling of many natural systems in physics (computational physics), chemistry and biology, human systems in economics, psychology, and social science and in the process of engineering new technology, to gain insight into the operation of those systems, or to observe their behavior.[1]
Computer simulations vary from computer programs that run a few minutes, to network-based groups of computers running for hours, to ongoing simulations that run for days. The scale of events being simulated by computer simulations has far exceeded anything possible (or perhaps even imaginable) using the traditional paper-and-pencil mathematical modeling: over 10 years ago, a desert-battle simulation, of one force invading another, involved the modeling of 66,239 tanks, trucks and other vehicles on simulated terrain around Kuwait, using multiple supercomputers in the DoD High Performance Computer Modernization Program; [2] a 1-billion-atom model of material deformation (2002); a 2.64-million-atom model of the complex maker of protein in all organisms, a ribosome, in 2005;[3] and the Blue Brain project at EPFL (Switzerland), began in May 2005, to create the first computer simulation of the entire human brain, right down to the molecular level. [4]
[http://en.wikipedia.org/wiki/Computer_model]

mdlCmp'archetype

name::
* McsEngl.mdlCmp'archetype,

_GENERIC:
* model'target##

_SPECIFIC:
Computer simulations are used in many fields, including science, technology, entertainment, health care, and business planning and scheduling.
[http://en.wikipedia.org/wiki/Computer_simulation]

mdlCmp'ATTRIBUTE

name::
* McsEngl.mdlCmp'ATTRIBUTE,
* McsEngl.mdlCmp'parameter,

mdlCmp'Hardware-system

name::
* McsEngl.mdlCmp'Hardware-system,

mdlCmp'Input-data

name::
* McsEngl.mdlCmp'Input-data,

The external data requirements of simulations and models vary widely. For some, the input might be just a few numbers (for example, simulation of a waveform of AC electricity on a wire), while others might require terabytes of information (such as weather and climate models).
...
Systems that accept data from external sources must be very careful in knowing what they are receiving. While it is easy for computers to read in values from text or binary files, what is much harder is knowing what the accuracy (compared to measurement resolution and precision) of the values is. Often it is expressed as "error bars", a minimum and maximum deviation from the value seen within which the true value (is expected to) lie. Because digital computer mathematics is not perfect, rounding and truncation errors will multiply this error up, and it is therefore useful to perform an "error analysis"[5] to check that values output by the simulation are still usefully accurate.
Even small errors in the original data can accumulate into substantial error later in the simulation. While all computer analysis is subject to the "GIGO" (garbage in, garbage out) restriction, this is especially true of digital simulation. Indeed, it was the observation of this inherent, cumulative error, for digital systems that is the origin of chaos theory.
[http://en.wikipedia.org/wiki/Computer_simulation]

mdlCmp'Input-source

name::
* McsEngl.mdlCmp'Input-source,

Input sources also vary widely:
Sensors and other physical devices connected to the model;
Control surfaces used to direct the progress of the simulation in some way;
Current or Historical data entered by hand;
Values extracted as by-product from other processes;
Values output for the purpose by other simulations, models, or processes.
[http://en.wikipedia.org/wiki/Computer_simulation]

mdlCmp'Input-time

name::
* McsEngl.mdlCmp'Input-time,

Lastly, the time at which data is available varies:
"invariant" data is often built into the model code, either because the value is truly invariant (e.g. the value of p) or because the designers consider the value to be invariant for all cases of interest;
data can be entered into the simulation when it starts up, for example by reading one or more files, or by reading data from a preprocessor;
data can be provided during the simulation run, for example by a sensor network;
[http://en.wikipedia.org/wiki/Computer_simulation]

mdlCmp'program#Itsoft1221#

name::
* McsEngl.mdlCmp'program,
* McsEngl.conceptCore493.6,

mdlCmp'simulation-language

_CREATED: {2012-11-21}

name::
* McsEngl.mdlCmp'simulation-language,
* McsEngl.conceptCore493.5,
* McsEngl.computer-simulation-language@cptCore493.5, {2012-11-22}
* McsEngl.mdlCmp'language@cptCore493.5, {2012-11-24}
* McsEngl.modeling-language@cptCore493.5, {2012-11-24}
* McsEngl.simulation-language@cptCore493.5, {2012-11-22}
* McsEngl.mdllng@cptIt493.5, {2012-11-24}
* McsEngl.cslng@cptIt493.5, {2012-11-22}

_GENERIC:
* programming-language#cptItsoft248#

_DESCRIPTION:
A modeling language is any artificial language that can be used to express information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure.
[http://en.wikipedia.org/wiki/Modelling_language]
===
A computer simulation language describes the operation of a simulation on a computer. There are two major types of simulation: continuous and discrete event though more modern languages can handle combinations. Most languages also have a graphical interface and at least simple statistical gathering capability for the analysis of the results. An important part of discrete-event languages is the ability to generate pseudo-random numbers and variates from different probability distributions.
[http://en.wikipedia.org/wiki/Simulation_language]
===
Because of this variety, and that many common elements exist between diverse simulation systems, there are a large number of specialized simulation languages. The best-known of these may be Simula (sometimes Simula-67, after the year 1967 when it was proposed). There are now many others.
[http://en.wikipedia.org/wiki/Computer_simulation]

mdlCmp'structure#cptCore515#

name::
* McsEngl.mdlCmp'structure,
* McsEngl.mdlCmp'state,

_DESCRIPTION:
State of a model is its structure in a timepoint.
[hmnSngo.2012-11-24]

_STRUCTURE:

mdlCmp'Validating

name::
* McsEngl.mdlCmp'Validating,

επικυρωση,
νομιμοποιηση,

The final step is to validate the model by comparing the results with what’s expected based on historical data from the study area. Ideally, the model should produce similar results to what has happened historically. This is typically verified by nothing more than quoting the R2 statistic from the fit. This statistic measures the fraction of variability that is accounted for by the model. A high R2 value does not necessarily mean the model fits the data well. Another tool used to validate models is graphical residual analysis. If model output values are drastically different than historical values, it probably means there’s an error in the model. This is an important step to verify before using the model as a base to produce additional models for different scenarios to ensure each one is accurate. If the outputs do not reasonably match historic values during the validation process, the model should be reviewed and updated to produce results more in line with expectations. It is an iterative process that helps to produce more realistic models.
Validating traffic simulation models requires comparing traffic estimated by the model to observed traffic on the roadway and transit systems. Initial comparisons are for trip interchanges between quadrants, sectors, or other large areas of interest. The next step is to compare traffic estimated by the models to traffic counts, including transit ridership, crossing contrived barriers in the study area. These are typically called screenlines, cutlines, and cordon lines and may be imaginary or actual physical barriers. Cordon lines surround particular areas such as the central business district or other major activity centers. Transit ridership estimates are commonly validated by comparing them to actual patronage crossing cordon lines around the central business district.
[http://en.wikipedia.org/wiki/Computer_simulation]

mdlCmp'Variable

name::
* McsEngl.mdlCmp'Variable,

_DESCRIPTION:
It is the attribute-of-the-model whose 'value' is changing.
[hmnSngo.2012-11-24]

mdlCmp'Verificating

name::
* McsEngl.mdlCmp'Verificating,

επαληθευση,
επιβεβαιωση,

Model verification is achieved by obtaining output data from the model and comparing it to what is expected from the input data. For example in traffic simulation, traffic volume can be verified to ensure that actual volume throughput in the model is reasonably close to traffic volumes input into the model. Ten percent is a typical threshold used in traffic simulation to determine if output volumes are reasonably close to input volumes. Simulation models handle model inputs in different ways so traffic that enters the network, for example, may or may not reach its desired destination. Additionally, traffic that wants to enter the network may not be able to, if any congestion exists. This is why model verification is a very important part of the modeling process.
[http://en.wikipedia.org/wiki/Computer_simulation]

mdlCmp'DOING

name::
* McsEngl.mdlCmp'DOING,

mdlCmp'evoluting#cptCore546.171#

name::
* McsEngl.mdlCmp'evoluting,
* McsEngl.mdlCmp'evoluting,

{time.2005}:
=== BLUE-BRAIN-PROJECT:
The Blue Brain Project is an attempt to create a synthetic brain by reverse-engineering the mammalian brain down to the molecular level.
The aim of the project, founded in May 2005 by the Brain and Mind Institute of the Ecole Polytechnique Federale de Lausanne (Switzerland) is to study the brain's architectural and functional principles. The project is headed by the Institute's director, Henry Markram. Using a Blue Gene supercomputer running Michael Hines's NEURON software, the simulation does not consist simply of an artificial neural network, but involves a biologically realistic model of neurons.[1][2][not in citation given] It is hoped that it will eventually shed light on the nature of consciousness.[citation needed]
[http://en.wikipedia.org/wiki/Blue_Brain]

{time.decade1990late}:
=== ANALOG-SIMULATIONS:
By the late 1980s, however, most "analog" simulations were run on conventional digital computers that emulate the behavior of an analog computer.

mdlCmp'GENERIC

_GENERIC:
* entity.whole.system.model.information#cptCore437.22#
* model.brainualNo#cptCore437.8#

SPECIFIC

name::
* McsEngl.mdlCmp.specific,

_SPECIFIC: mdlCmp.alphabetically:
* model.computer.agent_based#cptCore493.1#
* model.computer.blue_brain_project##
* model.computer.economy#ql:mdlecn.computer#
* model.computer.organism#cptCore72.6#
* model.computer.vehicle
* model.computer.world3#cptCore437#
* model.computer.web##

Types

Computer models can be classified according to several independent pairs of attributes, including:
Stochastic or deterministic (and as a special case of deterministic, chaotic) - see External links below for examples of stochastic vs. deterministic simulations
Steady-state or dynamic
Continuous or discrete (and as an important special case of discrete, discrete event or DE models)
Local or distributed.
Another way of categorizing models is to look at the underlying data structures. For time-stepped simulations, there are two main classes:
Simulations which store their data in regular grids and require only next-neighbor access are called stencil codes. Many CFD applications belong to this category.
If the underlying graph is not a regular grid, the model may belong to the meshfree method class.
Equations define the relationships between elements of the modeled system and attempt to find a state in which the system is in equilibrium. Such models are often used in simulating physical systems, as a simpler modeling case before dynamic simulation is attempted.
Dynamic simulations model changes in a system in response to (usually changing) input signals.
Stochastic models use random number generators to model chance or random events;
A discrete event simulation (DES) manages events in time. Most computer, logic-test and fault-tree simulations are of this type. In this type of simulation, the simulator maintains a queue of events sorted by the simulated time they should occur. The simulator reads the queue and triggers new events as each event is processed. It is not important to execute the simulation in real time. It's often more important to be able to access the data produced by the simulation, to discover logic defects in the design, or the sequence of events.
A continuous dynamic simulation performs numerical solution of differential-algebraic equations or differential equations (either partial or ordinary). Periodically, the simulation program solves all the equations, and uses the numbers to change the state and output of the simulation. Applications include flight simulators, construction and management simulation games, chemical process modeling, and simulations of electrical circuits. Originally, these kinds of simulations were actually implemented on analog computers, where the differential equations could be represented directly by various electrical components such as op-amps. By the late 1980s, however, most "analog" simulations were run on conventional digital computers that emulate the behavior of an analog computer.
A special type of discrete simulation that does not rely on a model with an underlying equation, but can nonetheless be represented formally, is agent-based simulation. In agent-based simulation, the individual entities (such as molecules, cells, trees or consumers) in the model are represented directly (rather than by their density or concentration) and possess an internal state and set of behaviors or rules that determine how the agent's state is updated from one time-step to the next.
Distributed models run on a network of interconnected computers, possibly through the Internet. Simulations dispersed across multiple host computers like this are often referred to as "distributed simulations". There are several standards for distributed simulation, including Aggregate Level Simulation Protocol (ALSP), Distributed Interactive Simulation (DIS), the High Level Architecture (simulation) (HLA) and the Test and Training Enabling Architecture (TENA).
[http://en.wikipedia.org/wiki/Computer_simulation]

mdlCmp.AGENT-BASED-MODEL

_CREATED: {2012-05-11}

name::
* McsEngl.mdlCmp.AGENT-BASED-MODEL,
* McsEngl.conceptCore493.1,
* McsEngl.agent-based-model@cptCore493.1, {2012-05-11}
* McsEngl.individual-based-model@cptCore493.1, {2012-05-11}
* McsEngl.mdlCmp.MULTI-AGENT-SIMULATION, {2012-11-22}
* McsEngl.model.agent-based@cptCore493.1, {2012-05-11}
* McsEngl.multi-agent-simulation@cptCore493.1, {2012-05-11}
* McsEngl.multi-agent-system@cptCore493.1, {2012-05-11}

_DESCRIPTION:
An agent-based model (ABM) (also sometimes related to the term multi-agent system or multi-agent simulation) is a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo Methods are used to introduce randomness. ABMs are also called individual-based models. A review of recent literature on individual-based models, agent-based models and multiagent systems shows that ABMs are used on non-computing related scientific domains including Life Sciences, Ecological Sciences and Social Sciences.[1]
The models simulate the simultaneous operations and interactions of multiple agents, in an attempt to re-create and predict the appearance of complex phenomena. The process is one of emergence from the lower (micro) level of systems to a higher (macro) level. As such, a key notion is that simple behavioral rules generate complex behavior. This principle, known as K.I.S.S. ("Keep it simple and short") is extensively adopted in the modeling community. Another central tenet is that the whole is greater than the sum of the parts. Individual agents are typically characterized as boundedly rational, presumed to be acting in what they perceive as their own interests, such as reproduction, economic benefit, or social status,[2] using heuristics or simple decision-making rules. ABM agents may experience "learning", adaptation, and reproduction.[3]
Most agent-based models are composed of: (1) numerous agents specified at various scales (typically referred to as agent-granularity); (2) decision-making heuristics; (3) learning rules or adaptive processes; (4) an interaction topology; and (5) a non-agent environment.
[http://en.wikipedia.org/wiki/Agent-based_model]

mdlCmp.BLUE-BRAIN-PROJECT {2005}

name::
* McsEngl.mdlCmp.BLUE-BRAIN-PROJECT {2005},
* McsEngl.conceptCore493.2,
* McsEngl.blue-brain-project@cptCore493.2, {2012-04-25}

The Blue Brain Project is an attempt to create a synthetic brain by reverse-engineering the mammalian brain down to the molecular level.

The aim of the project, founded in May 2005 by the Brain and Mind Institute of the Ecole Polytechnique Federale de Lausanne (Switzerland) is to study the brain's architectural and functional principles. The project is headed by the Institute's director, Henry Markram. Using a Blue Gene supercomputer running Michael Hines's NEURON software, the simulation does not consist simply of an artificial neural network, but involves a biologically realistic model of neurons.[1][2][not in citation given] It is hoped that it will eventually shed light on the nature of consciousness.[citation needed]

There are a number of sub-projects, including the Cajal Blue Brain, coordinated by the Supercomputing and Visualization Center of Madrid (CeSViMa), and others run by universities and independent laboratories in the UK, US, and Israel.
[http://en.wikipedia.org/wiki/Blue_Brain]

mdlCmp.CGI

_CREATED: {2012-05-20}

name::
* McsEngl.mdlCmp.CGI,
* McsEngl.conceptCore493.4,
* McsEngl.CGI-computer-simulation@cptCore493.4, {2012-05-20}

CGI computer simulation
Formerly, the output data from a computer simulation was sometimes presented in a table, or a matrix, showing how data was affected by numerous changes in the simulation parameters. The use of the matrix format was related to traditional use of the matrix concept in mathematical models; however, psychologists and others noted that humans could quickly perceive trends by looking at graphs or even moving-images or motion-pictures generated from the data, as displayed by computer-generated-imagery (CGI) animation. Although observers couldn't necessarily read out numbers, or spout math formulas, from observing a moving weather chart, they might be able to predict events (and "see that rain was headed their way"), much faster than scanning tables of rain-cloud coordinates. Such intense graphical displays, which transcended the World of numbers and formulae, sometimes also led to output that lacked a coordinate grid or omitted timestamps, as if straying too far from numeric data displays. Today, weather forecasting models tend to balance the view of moving rain/snow clouds against a map that uses numeric coordinates and numeric timestamps of events.
Similarly, CGI computer simulations of CAT scans can simulate how a tumor might shrink or change, during an extended period of medical treatment, presenting the passage of time as a spinning view of the visible human head, as the tumor changes.
Other applications of CGI computer simulations are being developed to graphically display large amounts of data, in motion, as changes occur during a simulation run.
[http://en.wikipedia.org/wiki/Computer_simulation]

mdlCmp.CONTINUOUS-EVENT

name::
* McsEngl.mdlCmp.CONTINUOUS-EVENT,
* McsEngl.continuous-computer-model,

_DESCRIPTION:
Continuous Simulation refers to a computer model of a physical system that continuously tracks system response over time according to a set of equations typically involving differential equations.[1][2]

History
It is notable as one of the first uses ever put to computers, dating back to the Eniac in 1946. Continuous simulation allows prediction of
* rocket trajectories
* hydrogen bomb dynamics (N.B. this is the first use ever put to the Eniac)
* electric circuit simulation[3]
* robotics[4]
Established in 1952, The Society for Modeling & Simulation (SCS) is a nonprofit, volunteer-driven corporation dedicated to advancing the use of modeling & simulation to solve real-world problems. Their first publication strongly suggested that the Navy was wasting a lot of money through the inconclusive flight-testing of missiles, but that the Simulation Council's analog computer could provide better information through the simulation of flights. Since that time continuous simulation has been proven invaluable in military and private endeavors with complex systems. No Apollo moon shot would have been possible without it.

Dissociation
Continuous simulation must be clearly differentiated from discrete event simulation. While a discrete event simulation is based on a system which changes its behaviour only at discrete points in time, the system of a continuous simulation changes its behaviour at countless points in time. Even though, the execution of the simulation itself is totally different in this matter, in some cases one can simulate the same issue with both types of simulation.
In this example the sales of a certain product over time is shown. Using a discrete event simulation makes it necessary to have an occurring event to change the number of sales. In contrast to this the continuous simulation has a smooth and steady development in its number of sales. [5]

Conceptual model
Continuous simulations are based on a set of differential equations. These equations define the peculiarity of the state variables, the environment factors so to speak, of a system. These parameters of a system change in a continuous way and thus change the state of the entire system.[6]
The set of differential equations can be formulated in a conceptual model representing the system on an abstract level. In order to develop the conceptual model 2 approaches are feasible:
The deductive approach: The behaviour of the system arises from physical laws that can be applied
The inductive approach: The behaviour of the system arises from observed behaviour of an actual example[7]
A widely known example for a continuous simulation conceptual model is the “predator/prey model”.
The predator/prey model
This model is typical for revealing the dynamics of populations. As long as the population of the prey is on the rise, the predators population also rises, since they have enough to eat. But very soon the population of the predators becomes to large so that the hunting exceeds the recreation of the prey. This leads to a decrease in the prey’s population and as a consequence of this also to a decrease of predators population as they do not have enough food to feed the entire population.[8]

Mathematical theory
In continuous simulation, the continuous time response of a physical system is modeled using ODEs, embedded in a conceptual model.
In very few cases these ODEs can be solved in a simple analytic way. More common are ODEs, which do not contain enough information for a direct solution. In these cases one has to use numerical approximation procedures. This problem of solving the ODEs is called the initial value problem.

The main solution methods for the intivial value problem can be categorized into two groups:

The Runge-Kutta family
The Linear Multistep family.[9]
Using these solution methods makes it necessary to take especially care of

the stability of the method
the method property of stiffness
the discontinuity of the method
Concluding remarks contained in the method and available to the user
These points are crucial to the success of the usage of one method.[10]

[edit]Mathematical examples
Newton's 2nd law, F = ma, is a good example of a single ODE continuous system. Numerical integration methods such as Runge Kutta, or Bulirsch-Stoer could be used to solve this partictular system of ODEs.

By coupling the ODE solver with other numerical operators and methods a continuous simulator can be used to model many different physical phenomena such as

flight dynamics
robotics
automotive suspensions
hydraulics
electric power
electric motors
human respiration
polar ice cap melting
steam power plants
etc.
There is virtually no limit to the kinds of physical phenomena that can be modeled by a system of ODE's. Some systems though can not have all derivative terms specified explicitly from known inputs and other ODE outputs. Those derivative terms are defined implicitly by other system constraints such as Kirchoff's law that the flow of charge into a junction must equal the flow out. To solve these implicit ODE systems a converging iterative scheme such as Newton-Raphson must be employed.

Simulation software
In order to execute the continuous simulation in an efficient and comfortable way, one has to use appropriate simulation software like Simcad Pro. Simcad Pro offers a GUI to model the continuous simulation gaphically. Thus, no coding is necessary. The software provides a bunch of parameters to add like "lead time", "resource wait" or "utilization". As the created models are compatible to other systems (RFID, ERP, etc.) it is easy to monitor ongoing processes and to reveal problems early. Additionally, Simcad Pro can be used as a training software for managers and operators.[11]

Modern applications
Continuous simulation is found
inside Wii stations
commercial flight simulators
jet plane auto pilots[12]
advanced engineering design tools[13]
Indeed, much of modern technology that we enjoy today would not be possible without continuous simulation.

Other types of simulation
Computer simulation
Process simulation
Discrete event simulation
Instructional Simulation
Social simulation
[http://en.wikipedia.org/wiki/Continuous_simulation]

mdlCmp.ENTITY-ATTRIBUTE-VALUE-MODEL

_CREATED: {2012-11-23}

name::
* McsEngl.mdlCmp.ENTITY-ATTRIBUTE-VALUE-MODEL,
* McsEngl.entity-attribute-value-model, {2012-11-23}

Entity–attribute–value model (EAV) is a data model to describe entities where the number of attributes (properties, parameters) that can be used to describe them is potentially vast, but the number that will actually apply to a given entity is relatively modest. In mathematics, this model is known as a sparse matrix. EAV is also known as object–attribute–value model, vertical database model and open schema.
[http://en.wikipedia.org/wiki/Entity%E2%80%93attribute%E2%80%93value_model]

mdlCmp.ENTITY-RELATIONSHIP

_CREATED: {2012-11-22}

name::
* McsEngl.mdlCmp.ENTITY-RELATIONSHIP,
* McsEngl.entity-relationship-model, {2012-11-22}

In software engineering, an Entity – Relationship model (ER model for short) is an abstract way to describe a database. It usually starts with a relational database, which stores data in tables. Some of the data in these tables point to data in other tables - for instance, your entry in the database could point to several entries for each of the phone numbers that are yours. The ER model would say that you are an entity, and each phone number is an entity, and the relationship between you and the phone numbers is 'has a phone number'. Diagrams created to design these entities and relationships are called entity–relationship diagrams or ER diagrams.

This article refers to the techniques proposed in Peter Chen's 1976 paper.[1] However, variants of the idea existed previously,[2] and have been devised subsequently such as supertype and subtype data entities [3] and commonality relationships (an example with additional concepts is the enhanced entity–relationship model).
[http://en.wikipedia.org/wiki/Entity-relationship_model]

mdlCmp.MATHEMATICAL

_CREATED: {2012-11-22}

name::
* McsEngl.mdlCmp.MATHEMATICAL,
* McsEngl.mathematical-model, {2012-11-22}

_DESCRIPTION:
A mathematical model is a description of a system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modelling. Mathematical models are used not only in the natural sciences (such as physics, biology, earth science, meteorology) and engineering disciplines (e.g. computer science, artificial intelligence), but also in the social sciences (such as economics, psychology, sociology and political science); physicists, engineers, statisticians, operations research analysts and economists use mathematical models most extensively. A model may help to explain a system and to study the effects of different components, and to make predictions about behaviour.
Mathematical models can take many forms, including but not limited to dynamical systems, statistical models, differential equations, or game theoretic models. These and other types of models can overlap, with a given model involving a variety of abstract structures. In general, mathematical models may include logical models, as far as logic is taken as a part of mathematics. In many cases, the quality of a scientific field depends on how well the mathematical models developed on the theoretical side agree with results of repeatable experiments. Lack of agreement between theoretical mathematical models and experimental measurements often leads to important advances as better theories are developed.
[http://en.wikipedia.org/wiki/Mathematical_modelling]

mdlCmp.NETWORK

_CREATED: {2012-11-22}

name::
* McsEngl.mdlCmp.NETWORK,
* McsEngl.network-simulation,

In communication and computer network research, network simulation is a technique where a program models the behavior of a network either by calculating the interaction between the different network entities (hosts/packets, etc.) using mathematical formulas, or actually capturing and playing back observations from a production network. The behavior of the network and the various applications and services it supports can then be observed in a test lab; various attributes of the environment can also be modified in a controlled manner to assess how the network would behave under different conditions. while a simulation program is used in conjunction with live applications and services in order to observe end-to-end performance to the user desktop, this technique is also referred to as network emulation.
[http://en.wikipedia.org/wiki/Network_simulation]

mdlCmp.SIMULATION

name::
* McsEngl.mdlCmp.SIMULATION,

_DESCRIPTION:
A simulation is an imitation of some real device, state of affairs or process. Simulation attempts to represent certain features of the behavior of a physical or abstract system by the behavior of another system ( Wikipedia:Simulation)
Most often, simulations are fully or partially implemented with a software program that allows the user to learn something about a given object of interest by "playing" with parameters of a model ("What happens if I do this" ? ... and later, "why did this happen ?").
According to Mergendoller et al. (2004): Randel, Morris, Wetzel, and Whitehill (1992) examined 68 studies on the effectiveness of simulations and found that students engaged in simulations and games show greater content retention over time compared to students engaged in conventional classroom instruction.
Simulation types:
Computer simulations
Computer games, e.g. serious games
Microworlds, e.g. systems like AgentSheets
Simulation and gaming (including role play simulation and computer supported simulation and gaming)
In some pedagogical scenarios, learners have to build their own simulation with modeling software. Of course, some microworlds also have students model.
[http://edutechwiki.unige.ch/en/Simulation]

mdlCmp.SOFT-BODY-DYNAMICS

_CREATED: {2012-11-15}

name::
* McsEngl.mdlCmp.SOFT-BODY-DYNAMICS,
* McsEngl.soft-body-dynamics, {2012-11-15}

Soft body dynamics is a field of computer graphics that focuses on visually realistic physical simulations of the motion and properties of deformable objects (or soft bodies).[1] The applications are mostly in video games and film. Unlike in simulation of rigid bodies, the shape of soft bodies can change, meaning that the relative distance of two points on the object is not fixed. While the relative distances of points are not fixed, the body is expected to retain its shape to some degree (unlike a fluid). The scope of soft body dynamics is quite broad, including simulation of soft organic materials such as muscle, fat, hair and vegetation, as well as other deformable materials such as clothing and fabric. Generally, these methods only provide visually plausible emulations rather than accurate scientific/engineering simulations, though there is some crossover with scientific methods, particularly in the case of finite element simulations. Several physics engines currently provide software for soft-body simulation.[2][3][4][5][6][7]
[http://en.wikipedia.org/wiki/Soft_body_dynamics]

mdlCmp.SOCIAL-SIMULATION#cptEconomy679#

name::
* McsEngl.mdlCmp.SOCIAL-SIMULATION,

mdlCmp.SYSTEM-DYNAMICS

name::
* McsEngl.mdlCmp.SYSTEM-DYNAMICS,

_DESCRIPTION:
Convenient GUI system dynamics software developed into user friendly versions by the 1990s and have been applied to diverse systems. SD models solve the problem of simultaneity (mutual causation) by updating all variables in small time increments with positive and negative feedbacks and time delays structuring the interactions and control. The best known SD model is probably the 1972 The Limits to Growth. This model forecast that exponential growth would lead to economic collapse during the 21st century under a wide variety of growth scenarios.
[http://en.wikipedia.org/wiki/System_dynamics]

mdlCmp.SUGARSCAPE