To make sense

The fundamental equilibrium relationships we have discussed in the last sections are undoubtedly satisfied to the extent possible in polymer crystallization, but this possibility is limited by kinetic considerations. To make sense of the latter, both the mechanisms for crystallization and experimental rates of crystallization need to be examined. 

Yet when Max von Laue, in 1943, commemorated the centenary of Groth .s birth, he praised him for keeping alive the hypothesis of the space lattice which was languishing everywhere else in Germany, and added that without this hypothesis it w ould have been unlikely that X-ray diffraction would have been discovered and even if it had been, it would have been quite impossible to make sense of it. 

The contiguity factor, Cj is actually a so-called fudge factor used to make sense out of the comparison of experimental dataTwitlirtheore-tical predictions. This correlation factor is useful only when the data fall between the theoretical bounds. The concept of a contiguity factor, i.e.. 

It takes a membrane to make sense out of disorder in biology. Yon have to be able to catch energy and hold it, storing precisely the needed amount and releasing it in measured shares. A cell does this, and so do the organelles inside.. .. To stay alive, yon have to be able to hold out against equilibrium, maintain imbalance, bank against entropy, and yon can only transact this business with membranes in our kind of world. 

Even so, artificial neural networks exhibit many brainlike characteristics. For example, during training, neural networks may construct an internal mapping/ model of an external system. Thus, they are assumed to make sense of the problems that they are presented. As with any construction of a robust internal model, the external system presented to the network must contain meaningful information. In general the following anthropomorphic perspectives can be maintained while preparing the data ... 

We issue a mild caution. One must always be mindful of the temptation to see things not as they really are but only how they appear to be at the moment using whatever happens to be the most convenient or accepted way of looking at things. This is the dreaded solution in search of a problem phenomenon, disguised, in the case of physicists trying to make sense of the world, as the temptation to use whatever is the latest fad or theory simultaneously as a lens and litmus test for reality. 

The use of Polya s Theorem in a specialized context such as the above, has led to the extension of the theorem along certain useful lines. One such derivation pertains to the situation where the boxes are not all filled from the same store of figures. More specifically, the boxes are partitioned into a number of subsets, and there is a store of figures peculiar to each subset. To make sense of this we must assume that no two boxes in different subsets are in the same orbit of the group in question. A simple extension of Polya s Theorem enables us to tackle problems of this type. Instead of the cycle index being a function of a single family of variables, the 5j, we have other families of variables, one for each subset. An example from chemical enumeration will make this clear. 

Nitmerotts examples of chmbing the ladder can be fotmd in textbooks for secondary edncation. For example, textbooks start the stndy of the snbject of salts with the (strb-) microscopic particles of atoms and molectrles, followed by how atoms theoretically ate converted into iotts, and how ionic srrbstances ate brrilt from charged ions. Textbooks continne with the macroscopic properly of the soln-bility of ionic snbstances in water. Snbseqnently mote complex ions, snch as strl-phates and nitrates, ate addressed to become part of the stndents repertoire ns-ing the sub-microscopic world of chemistry and the symbolic representations. For other subjects, such as organic chemistiy, the pathway for stndy from the basic sub-microscopic particles and related chemical principles to making sense of a relevant macro-world of applications (e.g. production of medicines) is very long. Moreover, the sub-microscopic world of state-of-the-art chemistry has become very complex. 

Even a simple representation such as this assumes a good deal of background knowledge in those expected to make sense of it. The representation combines graphical features meant to model the shape of the molecule, but includes symbols... 

Human perception is. .. inferred from fragmentary and often hardly relevant data signalled by the eyes, so requiring inferences from knowledge of the world to make sense of the sensory signal. 

Different fields within chemistry have developed their own specialist forms of symbolism. Organic chemistry uses a range of symbols in representations that learners need to make sense of For example, minimal structural representation in organic chemistry (where stractiues may be extensive) uses a formalism that a fine represents two carbon atomic centres joined by a single covalent bond, and saturated with hydrogen except where shown otherwise. 

For students to make sense of the basic grammar of chemical equations they need to appreciate the concept of the chemical reaction. This, in turn, requires an understanding of the notion of chemical snbstance. Although these are basic concepts in chemistry, they are known to present difficulties to many learners. 

Expert chemists constitute a community of practice which uses representations to make sense of the activities of the disciphne. Thus the conventions used in interpreting these representations are essential to the functioning of this community. 

In contrast to common usage, the distinction between photosynthetic and respiratory Rieske proteins does not seem to make sense. The mitochondrial Rieske protein is closely related to that of photosynthetic purple bacteria, which represent the endosymbiotic ancestors of mitochondria (for a review, see also (99)). Moreover, during its evolution Rieske s protein appears to have existed prior to photosynthesis (100, 101), and the photosynthetic chain was probably built around a preexisting cytochrome be complex (99). The evolution of Rieske proteins from photosynthetic electron transport chains is therefore intricately intertwined with that of respiration, and a discussion of the photosynthetic representatives necessarily has to include excursions into nonphotosynthetic systems. 

The second perspective might be that of the leader of some large project where chemical analyses are just a side issue, where sample numbers are large and chemical niceties might be completely swamped by, say, biological variability here a statistician will be necessary to make sense of the results in the context of a very complex model. Chemistry is a bit harder to relate to than many other industries in that the measured quantities are often abstract, invisible, and only indirectly linked to what one wants to control. 

Bioinformatics requires people. It always has, and probably always will. To expect informatics to behave differently from experimental science is, at best, hopeful and overly optimistic and, at worse, naive or disingenuous. Experimental science is becoming ever more reliant on instrumental analysis and robotics, yet people are still required to troubleshoot and to make sense of the results. Much the same holds for bioinformatics We can devolve work that is routine to automation—scanning genomes, etc.— but people are still needed to ensure such automation works and to assess the results. New methods need to be developed and their results used and applied. There is... 

To make sense of large, complex, and conflicting data sets, humans have used mental models. Mental models for drug discovery and development have made manifest contributions. However, humans, even the brightest, can... 

The diversity of chemical reactions is immense. To make sense of this vast expanse of chemistry, we need a system for grouping chemical reactions into categories. The reactions within each category should share some characteristics or follow a common theme. One relatively simple category is precipitation reactions, in which cations and anions in aqueous solution combine to form neutral insoluble solids. 

The prevention plan, and in particular symbols and warnings on labels and packaging of a substance, will depend on this risk level. In order to make sense, the risk classification has to take into account the inflammability level of a substance, to be on a coherent risk scale and not the mere result of more or less unpredictable fluctuations, particularly those due to the choice of apparatus or working method. The aim of estimation is to be able to identify substances on a scale where their position directly indicates their level of inflammability risk. 

Several theories were offered to make sense of Rutherfords new structure. Rutherford himself speculated that yet another particle, one that weighed the same as a proton but carried no charge, might lurk in the atomic nucleus. Over a decade would pass before the new particle was found. 

For heat pumping to be economic on a stand-alone basis, it must operate across a small temperature difference, which for distillation means close boiling mixtures. In addition, the use of the scheme is only going to make sense if the column is constrained to operate either on a stand-alone basis or at a pressure that would mean it would be across the pinch. Otherwise, heat integration with the process might be a much better option. Vapor recompression schemes for distillation therefore only make sense for the distillation of close boiling mixtures in constrained situations3. 

As mentioned earlier, we acquire data in the time domain but to make sense of it, we need to view it in the frequency domain. This is where the Fourier transformation comes in. There is not too much to do here - there are no parameters to change, ft is a necessary step but the automatic routines will perform this for you with no input. 

To make sense out of this chaos, a method is needed to pick out the rules that will be useful in controlling the system from among the large number that are worthless. The process of identifying productive classifiers relies on a mechanism that provides rewards to those rules that are helpful with a penalty for those that are not. The better rules then gradually emerge from the background noise. 

Analytical methods provide such a wealth of detail and information that it is often difficult to find a framework to make sense of the data. It is becoming increasingly common for modelling methods to be used alongside experiment to help interpret the data and provide a model for their understanding. 

Ken Schaffner. I m not sure what the next stages are going to be to get to that point. You can see a sort of feedback - I m not sure I should use that word - but an interpolation of the known pathways and such into this in order to make sense of even as much as they ve got, which are just mNRA expressions lining up. 

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