939 resultados para Assessment Systems
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Exposure to combination antiretroviral therapy (cART) can lead to important metabolic changes and increased risk of coronary heart disease (CHD). Computerized clinical decision support systems have been advocated to improve the management of patients at risk for CHD but it is unclear whether such systems reduce patients' risk for CHD.
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The translation from psychiatric core symptoms to brain functions and vice versa is a largely unresolved issue. In particular, the search for disorders of single brain regions explaining classical symptoms has not yielded the expected results. Based on the assumption that the psychopathology of psychosis is related to a functional imbalance of higher-order brain systems, the authors focused on three specific candidate brain circuitries, namely the language, and limbic and motor systems. These domains are of particular interest for understanding the disastrous communication breakdown during psychotic disorders. Core symptoms of psychosis were mapped on these domains by shaping their definitions in order to match the related brain functions. The resulting psychopathological assessment scale was tested for interrater reliability and internal consistency in a group of 168 psychotic patients. The items of the scale were reliable and a principal component analysis (PCA) was best explained by a solution resembling the three candidate systems. Based on the results, the scale was optimized as an instrument to identify patient subgroups characterized by a prevailing dysfunction of one or more of these systems. In conclusion, the scale is apt to distinguish symptom domains related to the activity of defined brain systems. PCA showed a certain degree of independence of the system-specific symptom clusters within the patient group, indicating relative subgroups of psychosis. The scale is understood as a research instrument to investigate psychoses based on a system-oriented approach. Possible immediate advantages in the clinical application of the understanding of psychoses related to system-specific symptom domains are also discussed.
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Java Enterprise Applications (JEAs) are large systems that integrate multiple technologies and programming languages. With the purpose to support the analysis of JEAs we have developed MooseJEE an extension of the \emphMoose environment capable to model the typical elements of JEAs.
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STEPanizer is an easy-to-use computer-based software tool for the stereological assessment of digitally captured images from all kinds of microscopical (LM, TEM, LSM) and macroscopical (radiology, tomography) imaging modalities. The program design focuses on providing the user a defined workflow adapted to most basic stereological tasks. The software is compact, that is user friendly without being bulky. STEPanizer comprises the creation of test systems, the appropriate display of digital images with superimposed test systems, a scaling facility, a counting module and an export function for the transfer of results to spreadsheet programs. Here we describe the major workflow of the tool illustrating the application on two examples from transmission electron microscopy and light microscopy, respectively.
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In many animals, sexual selection on male traits results from female mate choice decisions made during a sequence of courtship behaviors. We use a bower-building cichlid fish, Nyassachromis cf. microcephalus, to show how applying standard selection analysis to data on sequential female assessment provides new insights into sexual selection by mate choice. We first show that the cumulative selection differentials confirm previous results suggesting female choice favors males holding large volcano-shaped sand bowers. The sequential assessment analysis reveals these cumulative differentials are the result of selection acting on different bower dimensions during the courtship sequence; females choose to follow males courting from tall bowers, but choose to engage in premating circling with males holding bowers with large diameter platforms. The approach we present extends standard selection analysis by partitioning the variances of increasingly accurate estimates of male reproductive fitness and is applicable to systems in which sequential female assessment drives sexual selection on male traits.
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Although sustainable land management (SLM) is widely promoted to prevent and mitigate land degradation and desertification, its monitoring and assessment (M&A) has received much less attention. This paper compiles methodological approaches which to date have been little reported in the literature. It draws lessons from these experiences and identifies common elements and future pathways as a basis for a global approach. The paper starts with local level methods where the World Overview of Conservation Approaches and Technologies (WOCAT) framework catalogues SLM case studies. This tool has been included in the local level assessment of Land Degradation Assessment in Drylands (LADA) and in the EU-DESIRE project. Complementary site-based approaches can enhance an ecological process-based understanding of SLM variation. At national and sub-national levels, a joint WOCAT/LADA/DESIRE spatial assessment based on land use systems identifies the status and trends of degradation and SLM, including causes, drivers and impacts on ecosystem services. Expert consultation is combined with scientific evidence and enhanced where necessary with secondary data and indicator databases. At the global level, the Global Environment Facility (GEF) knowledge from the land (KM:Land) initiative uses indicators to demonstrate impacts of SLM investments. Key lessons learnt include the need for a multi-scale approach, making use of common indicators and a variety of information sources, including scientific data and local knowledge through participatory methods. Methodological consistencies allow cross-scale analyses, and findings are analysed and documented for use by decision-makers at various levels. Effective M&A of SLM [e.g. for United Nations Convention to Combat Desertification (UNCCD)] requires a comprehensive methodological framework agreed by the major players.
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Purpose The accuracy, efficiency, and efficacy of four commonly recommended medication safety assessment methodologies were systematically reviewed. Methods Medical literature databases were systematically searched for any comparative study conducted between January 2000 and October 2009 in which at least two of the four methodologies—incident report review, direct observation, chart review, and trigger tool—were compared with one another. Any study that compared two or more methodologies for quantitative accuracy (adequacy of the assessment of medication errors and adverse drug events) efficiency (effort and cost), and efficacy and that provided numerical data was included in the analysis. Results Twenty-eight studies were included in this review. Of these, 22 compared two of the methodologies, and 6 compared three methods. Direct observation identified the greatest number of reports of drug-related problems (DRPs), while incident report review identified the fewest. However, incident report review generally showed a higher specificity compared to the other methods and most effectively captured severe DRPs. In contrast, the sensitivity of incident report review was lower when compared with trigger tool. While trigger tool was the least labor-intensive of the four methodologies, incident report review appeared to be the least expensive, but only when linked with concomitant automated reporting systems and targeted follow-up. Conclusion All four medication safety assessment techniques—incident report review, chart review, direct observation, and trigger tool—have different strengths and weaknesses. Overlap between different methods in identifying DRPs is minimal. While trigger tool appeared to be the most effective and labor-efficient method, incident report review best identified high-severity DRPs.
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We evaluated the suitability of single and multiple cell type cultures as model systems to characterise cellular kinetics of highly lipophilic compounds with potential ecotoxicological impact. Confluent mono-layers of human skin fibroblasts, rat astrocytoma C6 cells, non-differentiated and differentiated mouse 3T3 cells were kept in culture medium supplemented with 10% foetal calf serum. For competitive uptake experiments up to four different cell types, grown on glass sectors, were exposed for 3h to (14)C-labelled model compounds, dissolved either in organic solvents or incorporated into unilamellar lecithin liposomes. Bromo-, or chloro-benzenes, decabromodiphenylether (DBP), and dichlorodiphenyl ethylene (DDE) were tested in rather high concentration of 20 microM. Cellular toxicity was low. Compound levels were related to protein, DNA, and triglyceride contents. Cellular uptake was fast and dependent on physico-chemical properties of the compounds (lipophilicity, molecular size), formulation, and cell type. Mono-halogenated benzenes showed low and similar uptake levels (=low accumulation compounds). DBP and DDE showed much higher cellular accumulations (=high accumulation compounds) except for DBP in 3T3 cells. Uptake from liposomal formulations was mostly higher than if compounds were dissolved in organic solvents. The extent of uptake correlated with the cellular content of triglycerides, except for DBP. Uptake competition between different cell types was studied in a sectorial multi-cell culture model. For low accumulation compounds negligible differences were found among C6 cells and fibroblasts. Uptake of DDE was slightly and that of DBP highly increased in fibroblasts. Well-defined cell culture systems, especially the sectorial model, are appropriate to screen for bioaccumulation and cytotoxicity of (unknown) chemical entities in vitro.
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PURPOSE: Two noninvasive methods to measure dental implant stability are damping capacity assessment (Periotest) and resonance frequency analysis (Osstell). The objective of the present study was to assess the correlation of these 2 techniques in clinical use. MATERIALS AND METHODS: Implant stability of 213 clinically stable loaded and unloaded 1-stage implants in 65 patients was measured in triplicate by means of resonance frequency analysis and Periotest. Descriptive statistics as well as Pearson's, Spearman's, and intraclass correlation coefficients were calculated with SPSS 11.0.2. RESULTS: The mean values were 57.66 +/- 8.19 implant stability quotient for the resonance frequency analysis and -5.08 +/- 2.02 for the Periotest. The correlation of both measuring techniques was -0.64 (Pearson) and -0.65 (Spearman). The single-measure intraclass correlation coefficients for the ISQ and Periotest values were 0.99 and 0.88, respectively (95% CI). No significant correlation of implant length with either resonance frequency analysis or Periotest could be found. However, a significant correlation of implant diameter with both techniques was found (P < .005). The correlation of both measuring systems is moderate to good. It seems that the Periotest is more susceptible to clinical measurement variables than the Osstell device. The intraclass correlation indicated lower measurement precision for the Periotest technique. Additionally, the Periotest values differed more from the normal (Gaussian) curve of distribution than the ISQs. Both measurement techniques show a significant correlation to the implant diameter. CONCLUSION: Resonance frequency analysis appeared to be the more precise technique.
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The electric utility business is an inherently dangerous area to work in with employees exposed to many potential hazards daily. One such hazard is an arc flash. An arc flash is a rapid release of energy, referred to as incident energy, caused by an electric arc. Due to the random nature and occurrence of an arc flash, one can only prepare and minimize the extent of harm to themself, other employees and damage to equipment due to such a violent event. Effective January 1, 2009 the National Electric Safety Code (NESC) requires that an arc-flash assessment be performed by companies whose employees work on or near energized equipment to determine the potential exposure to an electric arc. To comply with the NESC requirement, Minnesota Power’s (MP’s) current short circuit and relay coordination software package, ASPEN OneLinerTM and one of the first software packages to implement an arc-flash module, is used to conduct an arc-flash hazard analysis. At the same time, the package is benchmarked against equations provided in the IEEE Std. 1584-2002 and ultimately used to determine the incident energy levels on the MP transmission system. This report goes into the depth of the history of arc-flash hazards, analysis methods, both software and empirical derived equations, issues of concern with calculation methods and the work conducted at MP. This work also produced two offline software products to conduct and verify an offline arc-flash hazard analysis.
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Biofuels are alternative fuels that have the promise of reducing reliance on imported fossil fuels and decreasing emission of greenhouse gases from energy consumption. This thesis analyses the environmental impacts focusing on the greenhouse gas (GHG) emissions associated with the production and delivery of biofuel using the new Integrated Hydropyrolysis and Hydroconversion (IH2) process. The IH2 process is an innovative process for the conversion of woody biomass into hydrocarbon liquid transportation fuels in the range of gasoline and diesel. A cradle-to-grave life cycle assessment (LCA) was used to calculate the greenhouse gas emissions associated with diverse feedstocks production systems and delivery to the IH2 facility plus producing and using these new renewable liquid fuels. The biomass feedstocks analyzed include algae (microalgae), bagasse from a sugar cane-producing locations such as Brazil or extreme southern US, corn stover from Midwest US locations, and forest feedstocks from a northern Wisconsin location. The life cycle greenhouse gas (GHG) emissions savings of 58%–98% were calculated for IH2 gasoline and diesel production and combustion use in vehicles compared to fossil fuels. The range of savings is due to different biomass feedstocks and transportation modes and distances. Different scenarios were conducted to understand the uncertainties in certain input data to the LCA model, particularly in the feedstock production section, the IH2 biofuel production section, and transportation sections.
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Highway infrastructure plays a significant role in society. The building and upkeep of America’s highways provide society the necessary means of transportation for goods and services needed to develop as a nation. However, as a result of economic and social development, vast amounts of greenhouse gas emissions (GHG) are emitted into the atmosphere contributing to global climate change. In recognizing this, future policies may mandate the monitoring of GHG emissions from public agencies and private industries in order to reduce the effects of global climate change. To effectively reduce these emissions, there must be methods that agencies can use to quantify the GHG emissions associated with constructing and maintaining the nation’s highway infrastructure. Current methods for assessing the impacts of highway infrastructure include methodologies that look at the economic impacts (costs) of constructing and maintaining highway infrastructure over its life cycle. This is known as Life Cycle Cost Analysis (LCCA). With the recognition of global climate change, transportation agencies and contractors are also investigating the environmental impacts that are associated with highway infrastructure construction and rehabilitation. A common tool in doing so is the use of Life Cycle Assessment (LCA). Traditionally, LCA is used to assess the environmental impacts of products or processes. LCA is an emerging concept in highway infrastructure assessment and is now being implemented and applied to transportation systems. This research focuses on life cycle GHG emissions associated with the construction and rehabilitation of highway infrastructure using a LCA approach. Life cycle phases of the highway section include; the material acquisition and extraction, construction and rehabilitation, and service phases. Departing from traditional approaches that tend to use LCA as a way to compare alternative pavement materials or designs based on estimated inventories, this research proposes a shift to a context sensitive process-based approach that uses actual observed construction and performance data to calculate greenhouse gas emissions associated with highway construction and rehabilitation. The goal is to support strategies that reduce long-term environmental impacts. Ultimately, this thesis outlines techniques that can be used to assess GHG emissions associated with construction and rehabilitation operations to support the overall pavement LCA.
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Civil infrastructure provides essential services for the development of both society and economy. It is very important to manage systems efficiently to ensure sound performance. However, there are challenges in information extraction from available data, which also necessitates the establishment of methodologies and frameworks to assist stakeholders in the decision making process. This research proposes methodologies to evaluate systems performance by maximizing the use of available information, in an effort to build and maintain sustainable systems. Under the guidance of problem formulation from a holistic view proposed by Mukherjee and Muga, this research specifically investigates problem solving methods that measure and analyze metrics to support decision making. Failures are inevitable in system management. A methodology is developed to describe arrival pattern of failures in order to assist engineers in failure rescues and budget prioritization especially when funding is limited. It reveals that blockage arrivals are not totally random. Smaller meaningful subsets show good random behavior. Additional overtime failure rate is analyzed by applying existing reliability models and non-parametric approaches. A scheme is further proposed to depict rates over the lifetime of a given facility system. Further analysis of sub-data sets is also performed with the discussion of context reduction. Infrastructure condition is another important indicator of systems performance. The challenges in predicting facility condition are the transition probability estimates and model sensitivity analysis. Methods are proposed to estimate transition probabilities by investigating long term behavior of the model and the relationship between transition rates and probabilities. To integrate heterogeneities, model sensitivity is performed for the application of non-homogeneous Markov chains model. Scenarios are investigated by assuming transition probabilities follow a Weibull regressed function and fall within an interval estimate. For each scenario, multiple cases are simulated using a Monte Carlo simulation. Results show that variations on the outputs are sensitive to the probability regression. While for the interval estimate, outputs have similar variations to the inputs. Life cycle cost analysis and life cycle assessment of a sewer system are performed comparing three different pipe types, which are reinforced concrete pipe (RCP) and non-reinforced concrete pipe (NRCP), and vitrified clay pipe (VCP). Life cycle cost analysis is performed for material extraction, construction and rehabilitation phases. In the rehabilitation phase, Markov chains model is applied in the support of rehabilitation strategy. In the life cycle assessment, the Economic Input-Output Life Cycle Assessment (EIO-LCA) tools are used in estimating environmental emissions for all three phases. Emissions are then compared quantitatively among alternatives to support decision making.