907 resultados para Model selection criteria
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Federal Highway Administration, Office of Research and Development, Washington, D.C.
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Federal Highway Administration, Office of Research and Development, Washington, D.C.
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Federal Highway Administration, Washington, D.C.
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"Issued April 1980."
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"NUREG-0767."
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"15 July 1975."
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A phytotoxicity assay based on the ToxY-PAM dual-channel yield analyser has been developed and successfully incorporated into field assessments for the detection of phytotoxicants in water. As a means of further exploring the scope of the assay application and of selecting a model biomaterial to complement the instrument design, nine algal species were exposed to four chemical substances deemed of priority for water quality monitoring purposes (chlorpyrifos, copper, diuron and nonylphenol ethoxylate). Inter-species differences in sensitivity to the four toxicants varied by a factor of 1.9-100. Measurements of photosystem-II quantum yield using these nine single-celled microalgae as biomaterial corroborated previous studies which have shown that the ToxY-PAM dual-channel yield analyser is a highly sensitive method for the detection of PS-II impacting herbicides. Besides Phaeodactylum tricornutum, the previously applied biomaterial, three other species consistently performed well (Nitzschia closterium, Chlorella vulgaris and Dunaliella tertiolecta) and will be used in further test optimisation experiments. In addition to sensitivity, response time was evaluated and revealed a high degree of variation between species and toxicants. While most species displayed relatively weak and slow responses to copper, C. vulgaris demonstrated an IC10 of 51 μ g L-1, with maximum response measured within 25 minutes and inhibition being accompanied by a large decrease in fluorescence yield. The potential for this C vulgaris-based bioassay to be used for the detection of copper is discussed. There was no evidence that the standard ToxY-PAM protocol, using these unicellular algae species, could be used for the detection of chlorpyrifos or nonylphenol ethoxylate at environmentally relevant levels. © 2005 Elsevier B.V. All rights reserved.
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Selection of a power market structure from the available alternatives is an important activity within an overall power sector reform programme. The evaluation criteria for selection are both subjective as well as objective in nature and the selection of alternatives is characterised by their conflicting nature. This study demonstrates a methodology for power market structure selection using the analytic hierarchy process (AHP), a multiple attribute decision-making technique, to model the selection methodology with the active participation of relevant stakeholders in a workshop environment. The methodology is applied to a hypothetical case of a State Electricity Board reform in India.
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This paper introduces a compact form for the maximum value of the non-Archimedean in Data Envelopment Analysis (DEA) models applied for the technology selection, without the need to solve a linear programming (LP). Using this method the computational performance the common weight multi-criteria decision-making (MCDM) DEA model proposed by Karsak and Ahiska (International Journal of Production Research, 2005, 43(8), 1537-1554) is improved. This improvement is significant when computational issues and complexity analysis are a concern.
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Supplier evaluation and selection problem has been studied extensively. Various decision making approaches have been proposed to tackle the problem. In contemporary supply chain management, the performance of potential suppliers is evaluated against multiple criteria rather than considering a single factor-cost. This paper reviews the literature of the multi-criteria decision making approaches for supplier evaluation and selection. Related articles appearing in the international journals from 2000 to 2008 are gathered and analyzed so that the following three questions can be answered: (i) Which approaches were prevalently applied? (ii) Which evaluating criteria were paid more attention to? (iii) Is there any inadequacy of the approaches? Based on the inadequacy, if any, some improvements and possible future work are recommended. This research not only provides evidence that the multi-criteria decision making approaches are better than the traditional cost-based approach, but also aids the researchers and decision makers in applying the approaches effectively.
A study of load support and other criteria appropriate to the selection of industrial conveyor belts
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A study of conveying practice demonstrates that belt conveyors provide a versatile and. much-used method of transporting bulk materials, but a review of belting manufacturers' design procedures shows that belt design and selection rules are often based on experience with all-cotton belts no longer in common use, and are net completely relevant to modern synthetic constructions. In particular, provision of the property "load support", which was not critical with cotton belts, is shown to determine the outcome of most belt selection exercises and lead to gross over specification of other design properties in many cases. The results of an original experimental investigation into this property, carried out to determine the belt and conveyor parameters that affect it, how the major role that belt stiffness plays in its provision; the basis for a belt stiffness test relevant to service conditions is given. A proposal for a more rational method of specifying load support data results from the work, but correlation of the test results with service performance is necessary before the absolute toad support capability required from a belt for given working conditions can be quantified. A study to attain this correlation is the major proposal for future work resulting from the present investigation, but a full review of the literature on conveyor design and a study of present practice within the belting industry demonstrate other, less critical, factors that could profitably be investigated. It is suggested that the most suitable method of studying these would be a rational data collection system to provide information on various facets of belt service behaviour; a basis for such a system is proposed. In addition to the work above, proposals for simplifying the present belt selection methods are made and a strain transducer suitable for use in future experimental investigations is developed.
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This study proposes an integrated analytical framework for effective management of project risks using combined multiple criteria decision-making technique and decision tree analysis. First, a conceptual risk management model was developed through thorough literature review. The model was then applied through action research on a petroleum oil refinery construction project in the Central part of India in order to demonstrate its effectiveness. Oil refinery construction projects are risky because of technical complexity, resource unavailability, involvement of many stakeholders and strict environmental requirements. Although project risk management has been researched extensively, practical and easily adoptable framework is missing. In the proposed framework, risks are identified using cause and effect diagram, analysed using the analytic hierarchy process and responses are developed using the risk map. Additionally, decision tree analysis allows modelling various options for risk response development and optimises selection of risk mitigating strategy. The proposed risk management framework could be easily adopted and applied in any project and integrated with other project management knowledge areas.
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This paper suggests a data envelopment analysis (DEA) model for selecting the most efficient alternative in advanced manufacturing technology in the presence of both cardinal and ordinal data. The paper explains the problem of using an iterative method for finding the most efficient alternative and proposes a new DEA model without the need of solving a series of LPs. A numerical example illustrates the model, and an application in technology selection with multi-inputs/multi-outputs shows the usefulness of the proposed approach. © 2012 Springer-Verlag London Limited.
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Suboptimal maternal nutrition during gestation results in the establishment of long-term phenotypic changes and an increased disease risk in the offspring. To elucidate how such environmental sensitivity results in physiological outcomes, the molecular characterisation of these offspring has become the focus of many studies. However, the likely modification of key cellular processes such as metabolism in response to maternal undernutrition raises the question of whether the genes typically used as reference constants in gene expression studies are suitable controls. Using a mouse model of maternal protein undernutrition, we have investigated the stability of seven commonly used reference genes (18s, Hprt1, Pgk1, Ppib, Sdha, Tbp and Tuba1) in a variety of offspring tissues including liver, kidney, heart, retro-peritoneal and inter-scapular fat, extra-embryonic placenta and yolk sac, as well as in the preimplantation blastocyst and blastocyst-derived embryonic stem cells. We find that although the selected reference genes are all highly stable within this system, they show tissue, treatment and sex-specific variation. Furthermore, software-based selection approaches rank reference genes differently and do not always identify genes which differ between conditions. Therefore, we recommend that reference gene selection for gene expression studies should be thoroughly validated for each tissue of interest. © 2011 Elsevier Inc.
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The existing method of pipeline monitoring, which requires an entire pipeline to be inspected periodically, wastes time and is expensive. A risk-based model that reduces the amount of time spent on inspection has been developed. This model not only reduces the cost of maintaining petroleum pipelines, but also suggests an efficient design and operation philosophy, construction method and logical insurance plans.The risk-based model uses analytic hierarchy process, a multiple attribute decision-making technique, to identify factors that influence failure on specific segments and analyze their effects by determining the probabilities of risk factors. The severity of failure is determined through consequence analysis, which establishes the effect of a failure in terms of cost caused by each risk factor and determines the cumulative effect of failure through probability analysis.