Rank-based selection

A data table of a case-base can be divided into input and output sections. Input parameters are retrieval parameters and output parameters are design specification parameters. The problem is characterized as input data to the system. In the retrieval phase a set of retrieval parameter values of all cases in the case-base are compared to the input data. The most similar cases are then selected and ranked based on the comparison. 

Indices are needed that can be used to rank or select alternative distributions based on how well they agree with a sample of data. Such indices may be particularly useful for comparative analyses designed to select default distribution types. There are various possibilities for useful indices ... 

Most of the analyses of Antarctic coal were based on blocks of transportable size taken from surface outcrops. In earlier work, sections of purer coal were cut from the blocks and submitted for analysis. In later work, selected blocks were crushed, and a float fraction of purer coal was submitted to provide analytic data from each deposit. These samples, while not useful for determining coal grade, may be adequate for indicating rank. Practically all of the Antarctic coal, with the exception of that at Amery (which may represent high volatile bituminous coal), corresponds to medium volatile bituminous or higher rank. The high apparent rank Antarctic coal may be classified according to conventional ASTM standards of rank based on proximate analysis, but it seems clear that these results serve only as a first approximation. 

The whole set of possible thermochemical cycles has been considered and ranked based on a list of predefined criteria such as levels of temperatures required, rarity or toxicity of the reactants, number of electrochemical steps,. This led to the selection of a few cycles of interest (Mg-I, Ce-Cl and Cu-Cl). After further evaluation, Cu-Cl (shown in Figure 8) was retained. This cycle presents the advantage of dealing with only moderate temperature reactions (< 530°C), which offers the possibility of coupling it with other Gen-IV systems such as the Sodium Fast Reactor and the Supercritical Water Reactor. 

The shape-based virtual screening protocols performed better than the docking protocols overall. Finally, a consensus rank-byvote selection technique was used and the five best compounds were selected from the hits for synthesis and for determining antiviral activity. Remarkably, two of the five compounds (Fig. 13) showed activity at low micromolar ranges (0.022 pg/mL and 0.058 pg/mL, respectively). 

A ranking model is a relationship between a set of dependent attributes, experimentally investigated, and a set of independent attributes, i.e. model variables. As in regression and classification models the variable selection is one of the main step to find predictive models. In the present work, the Genetic Algorithm (GA-VSS) approach is proposed as the variable selection method to search for the best ranking models within a wide set of predictor variables. The ranking based on the selected subsets of variables is... 

For instance, one inbound and outbound optimization planning software package has the ability to manage shipments from multiple origins to multiple destinations and builds and consolidates loads as orders are imported into the system, using a library of transportation algorithms. Combined with a costing module, it rates, ranks, and selects carriers based on customer needs. 

Once the improvement ideas are selected for assessment, the first thing to do is to determine the sequence of potential implementation because different sequences of project implementation will have different effects on OSBL. As a general guideline, quick hits of improvement with no and low costs should be considered for implementation first at the early stage. Capital projects are ranked based on benefits and costs and then prioritized. Other criteria such as easy implementation and turnaround schedule are also considered in determining the implementation sequence. 

Maximum Dissimilarity-Based Selection The original algorithm for dissimilarity ranking in the chemical structure context seems to have been proposed by Bawden, although the basic algorithm may be due to Kennard and Stone. The basic operation of a dissimilarity selection algorithm is to start with a compound selected at random and make this the first selected compound. Subsequent compounds are selected so that they are maximally dissimilar to all those in the currendy selected set. Dissimilarity may be measured by... 

Virtual spectra eire then calculated for the structure candidates using spectrum simulation. These eire then compared to experimental data. Based on such comparisons, good structure candidates are ranked and selected (Section 8.4). We summarize simulation, comparison, ranking, and selection under the term structure verification. 

One is rank-based fitness function the other is to set a fitness function through appropriate adjustments (called fitness scaling) of the actual target value. For the second method, in initial phase there are often chromosomes with a hypemormal fitness which constitute a larger proportion of the population if we reproduce them in proportion. Because of their outstanding competitiveness which may lead to their control of the selection process, they may further influence the global optimization... 

Fonseca and Fleming (1993) proposed a modification of the simple genetic algorithm (SGA) at the selection level. The basic concepts of the proposed MOGA are the ranking based on the Pareto dominance and sharing function. The Pareto dominance-based rank is the same as one plus the number that certain individual dominates... 

The second approach was based on best practices in the chemical industry, which are documented in public records. The committee ranked the selected technologies qualitatively according to these criteria. Unlike the Mitretek and Stone and Webster studies mentioned above, the committee made no attempt to weight the criteria quantitatively. 

The aim of this section is to describe our integration of two existing approaches, one for the model-based security assessment, and the other for quantitative security controlling strategies, in such a way to provide a novel approach for the evaluation, ranking, and selection of the best security countermeasure, if any. 

Price-Based Selection (PBS) A client, owner, or customer selects a professional service firm solely or mostly on the basis of price (fees plus expenses). QuaMcations-based selection (QBS) A prospect, after evaluating and shortlisting firms based on their qualifications, selects the top-ranked firm for price negotiation based on a detailed project scope. If an agreement cannot be reached, which is uncommon, negotiations begin with the second most qualified firm and so on (Chinowsky and Kingsley 2009). 

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