783 resultados para Reliability allocation
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This paper presents the German version of the Short Understanding of Substance Abuse Scale (SUSS) [Humphreys et al.: Psychol Addict Behav 1996;10:38-44], the Verstandnis von Storungen durch Substanzkonsum (VSS), and evaluates its psychometric properties. The VSS assesses clinicians' beliefs about the nature and treatment of substance use disorders, particularly their endorsement of psychosocial and disease orientation. The VSS was administered to 160 treatment staff members at 12 substance use disorder treatment programs in the German-speaking part of Switzerland. Because the confirmatory factor analysis of the VSS did not completely replicate the factorial structure of the SUSS, an exploratory factor analysis was undertaken. This analysis identified two factors: the Psychosocial model factor and a slightly different Disease model factor. The VSS Disease and Psychosocial subscales showed convergent and discriminant validity, as well as sufficient reliability.
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BACKGROUND: High intercoder reliability (ICR) is required in qualitative content analysis for assuring quality when more than one coder is involved in data analysis. The literature is short of standardized procedures for ICR procedures in qualitative content analysis. OBJECTIVE: To illustrate how ICR assessment can be used to improve codings in qualitative content analysis. METHODS: Key steps of the procedure are presented, drawing on data from a qualitative study on patients' perspectives on low back pain. RESULTS: First, a coding scheme was developed using a comprehensive inductive and deductive approach. Second, 10 transcripts were coded independently by two researchers, and ICR was calculated. A resulting kappa value of .67 can be regarded as satisfactory to solid. Moreover, varying agreement rates helped to identify problems in the coding scheme. Low agreement rates, for instance, indicated that respective codes were defined too broadly and would need clarification. In a third step, the results of the analysis were used to improve the coding scheme, leading to consistent and high-quality results. DISCUSSION: The quantitative approach of ICR assessment is a viable instrument for quality assurance in qualitative content analysis. Kappa values and close inspection of agreement rates help to estimate and increase quality of codings. This approach facilitates good practice in coding and enhances credibility of analysis, especially when large samples are interviewed, different coders are involved, and quantitative results are presented.
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The past decade has seen the energy consumption in servers and Internet Data Centers (IDCs) skyrocket. A recent survey estimated that the worldwide spending on servers and cooling have risen to above $30 billion and is likely to exceed spending on the new server hardware . The rapid rise in energy consumption has posted a serious threat to both energy resources and the environment, which makes green computing not only worthwhile but also necessary. This dissertation intends to tackle the challenges of both reducing the energy consumption of server systems and by reducing the cost for Online Service Providers (OSPs). Two distinct subsystems account for most of IDC’s power: the server system, which accounts for 56% of the total power consumption of an IDC, and the cooling and humidifcation systems, which accounts for about 30% of the total power consumption. The server system dominates the energy consumption of an IDC, and its power draw can vary drastically with data center utilization. In this dissertation, we propose three models to achieve energy effciency in web server clusters: an energy proportional model, an optimal server allocation and frequency adjustment strategy, and a constrained Markov model. The proposed models have combined Dynamic Voltage/Frequency Scaling (DV/FS) and Vary-On, Vary-off (VOVF) mechanisms that work together for more energy savings. Meanwhile, corresponding strategies are proposed to deal with the transition overheads. We further extend server energy management to the IDC’s costs management, helping the OSPs to conserve, manage their own electricity cost, and lower the carbon emissions. We have developed an optimal energy-aware load dispatching strategy that periodically maps more requests to the locations with lower electricity prices. A carbon emission limit is placed, and the volatility of the carbon offset market is also considered. Two energy effcient strategies are applied to the server system and the cooling system respectively. With the rapid development of cloud services, we also carry out research to reduce the server energy in cloud computing environments. In this work, we propose a new live virtual machine (VM) placement scheme that can effectively map VMs to Physical Machines (PMs) with substantial energy savings in a heterogeneous server cluster. A VM/PM mapping probability matrix is constructed, in which each VM request is assigned with a probability running on PMs. The VM/PM mapping probability matrix takes into account resource limitations, VM operation overheads, server reliability as well as energy effciency. The evolution of Internet Data Centers and the increasing demands of web services raise great challenges to improve the energy effciency of IDCs. We also express several potential areas for future research in each chapter.
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This dissertation investigates high performance cooperative localization in wireless environments based on multi-node time-of-arrival (TOA) and direction-of-arrival (DOA) estimations in line-of-sight (LOS) and non-LOS (NLOS) scenarios. Here, two categories of nodes are assumed: base nodes (BNs) and target nodes (TNs). BNs are equipped with antenna arrays and capable of estimating TOA (range) and DOA (angle). TNs are equipped with Omni-directional antennas and communicate with BNs to allow BNs to localize TNs; thus, the proposed localization is maintained by BNs and TNs cooperation. First, a LOS localization method is proposed, which is based on semi-distributed multi-node TOA-DOA fusion. The proposed technique is applicable to mobile ad-hoc networks (MANETs). We assume LOS is available between BNs and TNs. One BN is selected as the reference BN, and other nodes are localized in the coordinates of the reference BN. Each BN can localize TNs located in its coverage area independently. In addition, a TN might be localized by multiple BNs. High performance localization is attainable via multi-node TOA-DOA fusion. The complexity of the semi-distributed multi-node TOA-DOA fusion is low because the total computational load is distributed across all BNs. To evaluate the localization accuracy of the proposed method, we compare the proposed method with global positioning system (GPS) aided TOA (DOA) fusion, which are applicable to MANETs. The comparison criterion is the localization circular error probability (CEP). The results confirm that the proposed method is suitable for moderate scale MANETs, while GPS-aided TOA fusion is suitable for large scale MANETs. Usually, TOA and DOA of TNs are periodically estimated by BNs. Thus, Kalman filter (KF) is integrated with multi-node TOA-DOA fusion to further improve its performance. The integration of KF and multi-node TOA-DOA fusion is compared with extended-KF (EKF) when it is applied to multiple TOA-DOA estimations made by multiple BNs. The comparison depicts that it is stable (no divergence takes place) and its accuracy is slightly lower than that of the EKF, if the EKF converges. However, the EKF may diverge while the integration of KF and multi-node TOA-DOA fusion does not; thus, the reliability of the proposed method is higher. In addition, the computational complexity of the integration of KF and multi-node TOA-DOA fusion is much lower than that of EKF. In wireless environments, LOS might be obstructed. This degrades the localization reliability. Antenna arrays installed at each BN is incorporated to allow each BN to identify NLOS scenarios independently. Here, a single BN measures the phase difference across two antenna elements using a synchronized bi-receiver system, and maps it into wireless channel’s K-factor. The larger K is, the more likely the channel would be a LOS one. Next, the K-factor is incorporated to identify NLOS scenarios. The performance of this system is characterized in terms of probability of LOS and NLOS identification. The latency of the method is small. Finally, a multi-node NLOS identification and localization method is proposed to improve localization reliability. In this case, multiple BNs engage in the process of NLOS identification, shared reflectors determination and localization, and NLOS TN localization. In NLOS scenarios, when there are three or more shared reflectors, those reflectors are localized via DOA fusion, and then a TN is localized via TOA fusion based on the localization of shared reflectors.
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Small-scale farmers in the Chipata District of Zambia rely on their farm fields to grow maize and groundnuts for food security. Cotton production and surplus food security crops are used to generate income to provide for their families. With increasing population pressure, available land has decreased and farmers struggle to provide the necessary food requirements and income to meet their family’s needs. The purpose of the study was to determine how a farmer can best allocate his land to produce maize, groundnuts and cotton when constrained by labor and capital resources to generate the highest potential for food security and financial gains. Data from the 2008-2009 growing season was compiled and analyzed using a linear programming model. The study determined that farmers make the most profit by allocating all additional land and resources to cotton after meeting their minimum food security requirements. The study suggests growing cotton is a beneficial practice for small-scale subsistence farmers to generate income when restricted by limited resources.
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The aim of this study was to assess the ability to extract surgically relevant information from plain radiographs in trimalleolar fractures and to compare this with the information gathered from computed tomography (CT).
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OBJECTIVE: To evaluate the association of adequate allocation concealment and patient blinding with estimates of treatment benefits in osteoarthritis trials. METHODS: We performed a meta-epidemiologic study of 16 meta-analyses with 175 trials that compared therapeutic interventions with placebo or nonintervention control in patients with hip or knee osteoarthritis. We calculated effect sizes from the differences in means of pain intensity between groups at the end of followup divided by the pooled SD and compared effect sizes between trials with and trials without adequate methodology. RESULTS: Effect sizes tended to be less beneficial in 46 trials with adequate allocation concealment compared with 112 trials with inadequate or unclear concealment of allocation (difference -0.15; 95% confidence interval [95% CI] -0.31, 0.02). Selection bias associated with inadequate or unclear concealment of allocation was most pronounced in meta-analyses with large estimated treatment benefits (P for interaction < 0.001), meta-analyses with high between-trial heterogeneity (P = 0.009), and meta-analyses of complementary medicine (P = 0.019). Effect sizes tended to be less beneficial in 64 trials with adequate blinding of patients compared with 58 trials without (difference -0.15; 95% CI -0.39, 0.09), but differences were less consistent and disappeared after accounting for allocation concealment. Detection bias associated with a lack of adequate patient blinding was most pronounced for nonpharmacologic interventions (P for interaction < 0.001). CONCLUSION: Results of osteoarthritis trials may be affected by selection and detection bias. Adequate concealment of allocation and attempts to blind patients will minimize these biases.