947 resultados para Investment evaluation methods


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Mode of access: Internet.

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"Report no. ITRC FR 95/96-1".

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Includes bibliographical references.

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"April 1980."

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Bibliography: p. 20-23.

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"Annotated bibliography": p. 33-39.

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This economic evaluation was part of the Australian National Evaluation of Pharmacotherapies for Opioid Dependence (NEPOD) project. Data from four trials of heroin detoxification methods, involving 365 participants, were pooled to enable a comprehensive comparison of the cost-effectiveness of five inpatient and outpatient detoxification methods. This study took the perspective of the treatment provider in assessing resource use and costs. Two short-term outcome measures were used-achievement of an initial 7-day period of abstinence, and entry into ongoing post-detoxification treatment. The mean costs of the various detoxification methods ranged widely, from AUD $491 (buprenorphine-based outpatient); to AUD $605 for conventional outpatient; AUD $1404 for conventional inpatient; AUD $1990 for rapid detoxification under sedation; and to AUD $2689 for anaesthesia per episode. An incremental cost-effectiveness analysis was carried out using conventional outpatient detoxification as the base comparator. The buprenorphine-based outpatient detoxification method was found to be the most cost-effective method overall, and rapid opioid detoxification under sedation was the most costeffective inpatient method.

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Background: Determination of the subcellular location of a protein is essential to understanding its biochemical function. This information can provide insight into the function of hypothetical or novel proteins. These data are difficult to obtain experimentally but have become especially important since many whole genome sequencing projects have been finished and many resulting protein sequences are still lacking detailed functional information. In order to address this paucity of data, many computational prediction methods have been developed. However, these methods have varying levels of accuracy and perform differently based on the sequences that are presented to the underlying algorithm. It is therefore useful to compare these methods and monitor their performance. Results: In order to perform a comprehensive survey of prediction methods, we selected only methods that accepted large batches of protein sequences, were publicly available, and were able to predict localization to at least nine of the major subcellular locations (nucleus, cytosol, mitochondrion, extracellular region, plasma membrane, Golgi apparatus, endoplasmic reticulum (ER), peroxisome, and lysosome). The selected methods were CELLO, MultiLoc, Proteome Analyst, pTarget and WoLF PSORT. These methods were evaluated using 3763 mouse proteins from SwissProt that represent the source of the training sets used in development of the individual methods. In addition, an independent evaluation set of 2145 mouse proteins from LOCATE with a bias towards the subcellular localization underrepresented in SwissProt was used. The sensitivity and specificity were calculated for each method and compared to a theoretical value based on what might be observed by random chance. Conclusion: No individual method had a sufficient level of sensitivity across both evaluation sets that would enable reliable application to hypothetical proteins. All methods showed lower performance on the LOCATE dataset and variable performance on individual subcellular localizations was observed. Proteins localized to the secretory pathway were the most difficult to predict, while nuclear and extracellular proteins were predicted with the highest sensitivity.

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The evaluation and selection of industrial projects before investment decision is customarily done using marketing, technical and financial information. Subsequently, environmental impact assessment and social impact assessment are carried out mainly to satisfy the statutory agencies. Because of stricter environment regulations in developed and developing countries, quite often impact assessment suggests alternate sites, technologies, designs, and implementation methods as mitigating measures. This causes considerable delay to complete project feasibility analysis and selection as complete analysis requires to be taken up again and again till the statutory regulatory authority approves the project. Moreover, project analysis through above process often results sub-optimal project as financial analysis may eliminate better options, as more environment friendly alternative will always be cost intensive. In this circumstance, this study proposes a decision support system, which analyses projects with respect to market, technicalities, and social and environmental impact in an integrated framework using analytic hierarchy process, a multiple-attribute decision-making technique. This not only reduces duration of project evaluation and selection, but also helps select optimal project for the organization for sustainable development. The entire methodology has been applied to a cross-country oil pipeline project in India and its effectiveness has been demonstrated. © 2005 Elsevier B.V. All rights reserved.