Entity physical

The absence of an electron from a covalent bond leaves a hole and the neighboring valence electron can vacate its covalent bond to fill the hole, thereby creating a hole in a new location. The new hole can, in turn, be filled by a valence electron from another covalent bond, and so on. Hence, a mechanism is estabUshed for electrical conduction that involves the motion of valence electrons but not free electrons. Although a hole is a conceptual artifact, it can be described as a concrete physical entity to keep track of the motion of the valence electrons. Because holes and electrons move in opposite directions under the influence of an electric field, a hole has the same magnitude of charge as an electron but is opposite in sign. 

American engineers are probably more familiar with the magnitude of physical entities in U.S. customary units than in SI units. Consequently, errors made in the conversion from one set of units to the other may go undetected. The following six examples will show how to convert the elements in six dimensionless groups. Proper conversions will result in the same numerical value for the dimensionless number. The dimensionless numbers used as examples are the Reynolds, Prandtl, Nusselt, Grashof, Schmidt, and Archimedes numbers. 

In fact, in a precise sense, no molecular fragment is rigorously transferable, although approximate transferability is an exceptionally useful and, if used judiciously, a valid approach within the limitations of the approximation. In particular, it is possible to define non-physical entities, such as fuzzy fragment electron densities, which do not exist as separate objects, yet they show much better transferability properties than actual, physically identifiable subsystems of well-defined, separate identity. This aspect of specially designed, custom- made , artificial subsystems of nearly exact additivity has been used to generate ab initio quality electron densities for proteins and other macromolecules. 

Classifier systems are software tools that can learn to control or interpret complex environments without help from the user. This is the sort of task to which artificial neural networks are often applied, but both the internal structure of a classifier system and the way that it learns are very different from those of a neural network. The "environment" that the classifier system attempts to learn about might be a physical entity, such as a biochemical fer-mentor, or it might be something less palpable, such as a scientific database or a library of scientific papers. 

In this discussion, we assume that the environment is a physical entity, but other environments may be used. It could be a database, a stream of messages, or any object that can provide input to the classifier system, accept output from it, and pass judgment on the quality or value of that output. 

We know that a model in which the principal values of the g tensor are random variables, leading to Equation 9.1, falls short of describing experimental data in detail. Therefore, we now expand the model as follows the random variables are the principal values of a physical entity that is characterized by a tensor in 3-D space, but... 

Despite the success of phase-diagram calculations, there is still a considerable reluctance by sections of the scientific community to accept that TC lattice stabilities represent a real physical entity as distinct from an operational convenience. This inevitably creates doubts concerning the ultimate validity of the calculations. It is therefore important to verify that TC lattice stabilities, largely derived by extrapolation, can be verified by ab initio calculations and placed on a sound physical basis. 

See = 69.633 and 8ch = 106.806 kcal/mol. Charge normalization terms are inadvertently treated as part of bond energy Both 8 and 8 are metaphors, not physical entities. In the final count. A, which is usually interpreted as being due to steric effects, is simply a function of local charge variations. 

Hereafter, we will use orbital-fiee (OF) to deseribe any physical entity that does not rely on an orbital or wavefunetion pieture and use orbital-based (OB) for the opposite. 

The current paradigm in chemistry celebrates the existence of physical entities called chemical atoms (now known simply as atoms). John Dalton (1766-1844) looked at the material world in which he hved and visualized it in terms of a set of different material objects of small size and combining capacity (7). He called these particles atoms in his New System of Chemical Philosophy (1808). Others, such as Humphry Davy (1778-1829), were not yet willing to see the world in this way. Dalton combined both a partictrlar theory of nature and specific observatiorrs to arrive at his views. The present paper will examine some episodes in the history of chemistry that enabled other chemists to see atoms as appropriate chemical constituents of our world. The view of what constitutes a chemical atom has changed during the time period from 1808 to 2008, but the common theme requires a context in which actual measurements can be viewed as evidence for atoms. ... 

A dimension is a purely qualitative description of a perception of a physical entity or a natural appearance. A length can be experienced as a height, a depth, or a breadth. A mass presents itself as a light or heavy body and time as a short moment or a long period. The dimension of a length is Length (L), the dimension of a mass is Mass (M), etc. 

Software is not a physical entity and, unlike some hardware failures, software failures occur without advanced warning. One of the most common software failures is branching, that is, the ability to execute alternative series of commands based on differing inputs. The software branching capacity makes the commands extremely complex and difficult to validate once errors occur as an answer of a specific input, and until the introduction of that specific input error has not been detected. Software input can be almost any data and, and since it is impossible to introduce all data into a software, validation of data is extremely difficult. Thus, results are considered to be of high confidence level. The majority of software problems occur as a consequence of errors in the software design and development and are not directly related to the software manufacture. It is simple to manufacture several software copies that work perfectly and as the original one. 

Irreducible representations are the building blocks of all other representations. Just as each molecule is made up of particular atoms, each representation is made up of particular irreducible representations. Unlike a molecule, whose properties are determined not only by which atoms it is made of, but also by their configuration, a representation is merely the sum of its irreducible parts. Mathematically, irreducible representations are useful because one can often reduce an idea or a calculation involving representations to an easier one involving only irreducible representations. Physically, irreducible representations correspond to fundamental physical entities. 

The synthetic approach of inorganic materials via the Split Pool methodology is closely connected to the question of synthesizing materials on or within physical entities that will contain the library member throughout the synthetic process. If physical entities are employed as carriers that show sufficient chemical inertness towards the screening process, a separation of the final library compound may... 

In computational chemistry we take the view that we are simulating the behaviour of real physical entities, albeit with the aid of intellectual models and that as our models improve they reflect more accurately the behavior of atoms and molecules in the real world. 

HKL [1] make the point that calculations are not just alternatives to experiment, as Dewar thinks, but can also illuminate experiment. In effect, they say that calculations are not only another way to get numbers, but can provide insight into physical processes. Their contention that such insight comes from ab initio, not from semiempirical, methods (which obscure the physical bases of their success and failure) seems to be justified, because in SE methods the fundamental physical entities have been deliberately subsumed into parameters designed to give the right, or rather the best, answers. 

The relationship between E(t) and E(0) is found from the basis that both represent the same physical entity, the fraction of exit fluid with age 0. Thus, E(0)d0 = E(t)dt or (l/t)E(0)dt = E(t)dt. Therefore,... 

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