Experimental Study On Thermal Performance And Visualization Of Loop Heat Pipe

Thermal management is an important issue for applications that generate high heat flux. Choosing an efficient cooling technique depends on thermal performance, reliability, manufacturing cost, and prospects for minimization. Based on these grounds, loop heat pipe (LHP) is a highly efficient two-phase passive cooling system used for cooling of electronics and many critical components of spacecraft and satellite. Loop Heat Pipe (LHP) uses capillary action to circulate cooling fluid inside the loop. Pressure developed in the pores of a wick provides the driving force to circulate the fluid. LHP has superior heat transport capability and can operate at any orientation. In the present study, a loop heat pipe with flat evaporator has been designed and manufactured and an experimental study was performed to investigate the effects of various parameters on loop performance. LHP was instrumented with thermocouples to measure the loop temperature at various positions. Temperature oscillations have been observed at the startup of LHP. Performance of LHP has been evaluated at a wide range of evaporator heat loads. In order to comprehend the complex phenomenon inside LHP, the main structure of LHP has been made from transparent acrylic plastic to visualize the evaporation and the condensation process. A high-speed video camera has been utilized to visualize the operational process. Data collected from the experiment provides a significant insight into the physics of LHP operation. An analysis is presented to explain the startup process. Understanding of startup process is necessary for predicting the performance of LHP

Multivariate Outlier Mining Using Cluster Analysis: Case Study - National Health Interview Survey

Outlier mining is a fundamental issue in many statistical analyses, especially in multivariate cases. Outliers may exert undue influence on outcomes of the analysis. In most cases, it is a big challenge to reveal the pattern of the outliers and the outlyingness . There are several approaches and methods to detect anomalous data points in data. But no single method is perfect for every data set especially when the data dimension and volume is high. In this thesis, I review distance-based clustering methods for multivariate outlier mining and demonstrate the usefulness of it in a medical setting. Specifically, I discuss Hierarchical clustering and the multivariate methods of determining appropriate cluster(s). After mining the multivariate outliers, I examine and describe the characteristics of the variables for those outliers. Finally, I demonstrate the application of these methods using the National Health Interview Survey (NHIS) 2008 database for the purposes of studying adolescent obesity

Superiority of Islamic Banking in Comparison with Conventional Banking in Bangladesh - A Comparative Study

This paper investigates the financial performance of interest- based conventional commercial banks and interestfree Islamic banks in Bangladesh using descriptive statistics ttest and test of hypotheses Data has been processed through Statistical Package for Social Science SPSS software The data consist of accounting figures of 4 interests based conventional commercial banks and 4 interest free Islamic banks from 2009 to 2013 The study revealed mixed results The study found that conventional commercial banks are superior in terms of performance regarding in commitment to economy and community development productivity and efficiency where performance of Islamic banks in terms of business development profitability liquidity and solvency is superior to that of conventional bank

Using hospitalization for ambulatory care sensitive conditions to measure access to primary health care: an application of spatial structural equation modeling

BACKGROUND: In data commonly used for health services research, a number of relevant variables are unobservable. These include population lifestyle and socio-economic status, physician practice behaviors, population tendency to use health care resources, and disease prevalence. These variables may be considered latent constructs of many observed variables. Using health care data from South Carolina, we show an application of spatial structural equation modeling to identify how these latent constructs are associated with access to primary health care, as measured by hospitalizations for ambulatory care sensitive conditions. We applied the confirmatory factor analysis approach, using the Bayesian paradigm, to identify the spatial distribution of these latent factors. We then applied cluster detection tools to identify counties that have a higher probability of hospitalization for each of the twelve adult ambulatory care sensitive conditions, using a multivariate approach that incorporated the correlation structure among the ambulatory care sensitive conditions into the model. RESULTS: For the South Carolina population ages 18 and over, we found that counties with high rates of emergency department visits also had less access to primary health care. We also observed that in those counties there are no community health centers. CONCLUSION: Locating such clusters will be useful to health services researchers and health policy makers; doing so enables targeted policy interventions to efficiently improve access to primary care

Forecasting of lentil pulse production: An econometric analysis

The paper describes an empirical study of modeling and forecasting time series data of lentil pulse production in Bangladesh. The Box Jenkins ARIMA methodology has been used for forecasting.The diagnostic checking has shown that ARIMA (0, 1, 9) is appropriate.The forecasts from 2011-12 to 2015-16 are calculated based on the selected model.These forecasts would be helpful for the policy makers to foresee ahead of time the future requirements of grain storage import and/or export and adopt appropriate measures in this regard