Constraint, network

The constraint transformation algorithm accepts a network of goals partially ordered by constraints, and generates a constraint network of primitive actions, such that, if there exists a directed path from goal A to goal B (i.e., A must be achieved before B) in the first network, and if OP-A is the primitive action that achieves goal A, and OP-B the action that accomplishes B, then OP-A and OP-B are labels on nodes in the generated network, and there exists a directed path from the node labeled with OP-A to the node labeled with OP-B. 

J. F. Brinkley. 1992. Hierarchical geometric constraint networks as a representation for spatial structural knowledge. In Proceedings, 16th Annual Symposium on Computer Applications in Medical Care. pp. 140-144. 

Zukin M, Young RE (2001) Applying fuzzy logic and constraint networks to a problem of manufacturing flexibility. Int J Prod Res 39(14) 3253-3273... 

Software Component 1 System Constraint Network software Component n ... 

The proposed approach in the workflow form is depicted in Figure 1. On the top, a model of the system is defined. This model consists of the two elements software components which implement certain application-level functions, and the platform, which is a model of an embedded system. Both software components and the platform implement certain contracts, in order to express relations to other dependent components or platform. These contracts are the fundamental elements of the system model that allow us to maintain the consistency of the system. They contain the information about system attributes discussed in the previous section, and provide means to build relationships to other contracts. Based on those relationships, impact of changes in one particular contract can be tracked throughout the complete system. We introduce contracts later in Section 4.2. In the next step of the workflow, the system in terms of contracts is translated into a so called constraint network, i.e., a set of inter-connected variables and constraints. This constraint network represent contracts and their relationships in another problem domain, which allows us to automatically analyze the consistency of the system by evaluating constraints. 

In the last step of the workflow, components can be dynamically loaded into the platform, depending on results of the analysis. If all constraints in the analysis step are satisfied, the system modelled in the first step is consistent, i.e., we say the system configuration is assured. Thus, any change in the modelling step can be captured and analyzed in the constraint network. 

In our approach, we translate the system modelled in form of contracts into such a constraint network. For this purpose, we have defined a model of a contract, its variables, assumptions and guarantees, and relations between contracts as network elements, i.e., variables and constraints. The systems consistency is therefore analyzed by evaluating constraints that are derived from contracts (for more details, please refer to [11]). 

Thus loops, utility paths, and stream splits offer the degrees of freedom for manipulating the network cost. The problem is one of multivariable nonlinear optimization. The constraints are only those of feasible heat transfer positive temperature difference and nonnegative heat duty for each exchanger. Furthermore, if stream splits exist, then positive bremch flow rates are additional constraints. 

For more complex network designs, especially those involving many constraints, mixed equipment specifications, etc., design methods based on the optimization of a reducible structure can be used. 

Miscibility or compatibility provided by the compatibilizer or TLCP itself can affect the dimensional stability of in situ composites. The feature of ultra-high modulus and low viscosity melt of a nematic liquid crystalline polymer is suitable to induce greater dimensional stability in the composites. For drawn amorphous polymers, if the formed articles are exposed to sufficiently high temperatures, the extended chains are retracted by the entropic driving force of the stretched backbone, similar to the contraction of the stretched rubber network [61,62]. The presence of filler in the extruded articles significantly reduces the total extent of recoil. This can be attributed to the orientation of the fibers in the direction of drawing, which may act as a constraint for a certain amount of polymeric material surrounding them. 

III. The Network of Technological Factors and Constraints Affecting Catalyst Choice... 

It is beyond our control how the cross-links are spaced along the polymer chains during the vulcanization process. This extraordinary important fact demands a generalization of the Gibbs formula in statistical mechanics for amorphous materials that have fixed constraints of which the exact topology is unknown. Details of a modified Gibbs formula of polymer networks can be found in the pioneering paper of Deam and Edwards [13]. 

Here, b denotes the Kuhn s statistical segment length. The network is represented by a huge chain internally cross-linked at cross-linking points where it touches and at the surfaces of Mf filler particles. The point-like local cross-hnk constraints are easy to handle and can be represented by the term... 

The 6-function makes sure that if two segments and 2 meet on the huge network chain they can form a permanent constraint R( i) = R( 2)- Hence, this process will produce a network junction of functionality/n = 4, usually realized as sulfur bridges in technical elastomers like, for example, tire treads. 

Equations 22.3-22.14 represent the simplest formulation of filled phantom polymer networks. Clearly, specific features of the fractal filler structures of carbon black, etc., are totally neglected. However, the model uses chain variables R(i) directly. It assumes the chains are Gaussian the cross-links and filler particles are placed in position randomly and instantaneously and are thereafter permanent. Additionally, constraints arising from entanglements and packing effects can be introduced using the mean field approach of harmonic tube constraints [15]. 

Famili I, Forster J, Nielsen J, Palsson BO. Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc Natl Acad Sci USA 2003 100 13134-9. 

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