Structural Model Results

The results of the statistical test are presented in Fig. 15.3. Generally, the hypotheses are supported. First of all, empirical evidence provided support for the central hypothesis of this study (HI). The level of asymmetric information has a positive effect ( = 0.24, / 0.05) on opportunistic behaviour. Therefore, asymmetric information could serve as a mediating variable for indirect effects of the independent variables. 

A surprising result was found for partner selection. Significant direct and indirect effects were found (H3a/b). However, they were not negative effects as 

The model explains 57% of the variance of opportunistic behaviour thus showing the exceptionally good quality of the model. 

The positive correlation between partner selection and asymmetric information as well as opportunism can be explained by a specific procedure which can often be seen in practice. In mai supply networks the initial partner selection is undertaken with considerable care, attention and effort. However, once a potential partner passes this stage, further testing at later stages is rarely undertaken. Some partners could take advantage of this situation by behaving opportunistically having successfully passed the initial phase of partner selectioa The survey captures only the initial partner selection effort but not whether partners are tested continuously. Therefore, this unexpected result may possibly be due to the need to define the indicators more closely to include tins possibihty. 

Another explanation is the existence of a non-observed factor which influences the partner selection effort and the level of opportunistic behaviour both in the same way. The discussion in the focus team led to several possible factors, e.g., the length of time the partners know each other. This non-observed variable may have a negative effect on partner selection effort and on opportunistic behaviour, thus resulting in a positive correlation of those two constiucts. The discussants agreed that the longer they know a partner, (e.g., from previous partnerships), the lower is the partner selection effort Combined with the fact that the longer they know a partner the less likely is the occurrence of opportunistic behaviour. This may cause the occurrence of quasi-correlatioa 

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The development of the solid state theory has created strong theoretical basis for the description of interactions between atoms in a crystal. The electronic structure model, resulting from these interactions, allows identifying electrical, magnetic and optical properties of a compound. Today, the development of computational methods and the numerical capabilities of big computers allow calculating the interactions in quite big crystalline clusters, and thus to determine their properties. Nevertheless there is no data allowing detailed analysis of the properties of the oxides to be performed. 

Table 7.18 Structural modeling results— indirect effects 7 Large-Scale Analysis and Testing ... 

Figure 1 The basis of comparative protein structure modeling. Comparative modeling is possible because evolution resulted in families of proteins, such as the flavodoxin family, modeled here, which share both similar sequences and 3D structures. In this illustration, the 3D structure of the flavodoxin sequence from C. crispus (target) can be modeled using other structures in the same family (templates). The tree shows the sequence similarity (percent sequence identity) and structural similarity (the percentage of the atoms that superpose within 3.8 A of each other and the RMS difference between them) among the members of the family.

Although comparative modeling is the most accurate modeling approach, it is limited by its absolute need for a related template structure. For more than half of the proteins and two-thirds of domains, a suitable template structure cannot be detected or is not yet known [9,11]. In those cases where no useful template is available, the ab initio methods are the only alternative. These methods are currently limited to small proteins and at best result only in coarse models with an RMSD error for the atoms that is greater than 4 A. However, one of the most impressive recent improvements in the field of protein structure modeling has occurred in ab initio prediction [155-157]. 

Periodic oscillations have been observed as a morphological instability in several systems grown under various conditions [148]. The correspondence of the observed structures with results of theoretical modelling [139,149] is striking. 

At one extreme, one has the structural models of perfect crystals, which have long-range positional order for all the atoms (apart thermal motion). A diffraction experiment on a set of such crystals oriented in one direction (corresponding, in most real cases of polymeric materials, to an oriented fiber) would result in a pattern of sharp reflections organized in layer lines. 

We have considered the larger AI4-AI6 clusters using both ab initio calculations and the parameterized model (9). The results for AI4 and AI5, summarized in Table IV, show that the parameterized model and ab initio calculations agree well on the relative energetics if both the two- and three-body interactions are included. For Ale it is difficult to treat all the structures at the TZ2P-CPF level, but for the structures considered, there is reasonable agreement between the ab initio and model results. 

Final detennination of the structure was made by proposing a structural model with Cu sitting in threefold hollow sites and O atoms on atop sites with respect to the Cu atoms (Fig. 27.16). A program, FEFFIT, was used to analyze the data (Stem et al., 1995). This calculates the phase and amplitude parameters for the various backscatters. The EXAFS for the parallel polarization could be fitted six Cu-Cu interactions at a bond distance of 2.67 A and three Cu-Pt interactions at 2.6 A. For the perpendicular polarization, the data could be fitted one Cu-0 interaction at 1.96 A and three Cu-Pt interactions at 2.6 A. The Cu-Pt bond length is shorter than the sum of the metallic radii of Cu and Pt, which is 2.66 A. This indicates a Cu oxidation state different from zero, which agrees with the XANES results. 

As described in Section 9.4, the determination and refinement of molecular conformations comprehends three main methods DG, MD and SA. Other techniques like Monte Carlo calculations have only a limited applicability in the field of structure elucidation. In principle, it is possible to exclusively make use of DG, MD or SA, but normally it is strongly suggested to combine these methods in order to obtain robust and reliable structural models. Only when the results of different methods match a 3D structure should be presented. There are various ways of combining the described techniques and the procedural methods may differ depending on what kind of molecules are investigated. However, with the flowchart in Fig. 9.13 we give an instruction on how to obtain a reliable structural model. 

The structure of alumina on NiAl(l 1 0) was the subject of a surface X-ray diffraction study by Stierle et al. [46]. The model derived by Stierle et al. from the analysis of the X-ray diffraction data was based on a strongly distorted double layer of hexagonal oxygen ions, where the Al ions are hosted with equal probability on octahedral- and tetrahedral-coordinated sites the resulting film structure was closely related to bulk k-A1203. An attractive feature of Stierle s model was that it provided a natural explanation of the domain structure of the alumina overlayer, which is induced by a periodic row matching between film and substrate lattices. However, as pointed out recently by Kresse et al. [47], this structure model has two bonds with... 

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