Load balancing has been a topic of interest in both academia and industry, mainly because of the scope for performance enhancement that is available to be exploited in many parallel and distributed processing environments. Among the many approaches that have been used to solve the load balancing problem, we find that only very few use prediction of code execution times. Our reasoning for this is that the field of code prediction is in its infancy. As of this writing, we are not aware of any prediction-based load balancing approach that uses prediction8 of code-execution times, and uses neither the information provided by the user, nor an off-line step that does the prediction, the results of which are then used at run-time. In this context, it is important to note that prior studies have indicated the feasibility of predicting the CPU requirements of general application programs. Our motivation in using prediction-based load balancing is to determine the feasibility of the approach. The reasoning behind that is the following: if prediction-based load balancing does yield good performance, then it may be worthwhile to develop a predictor that can give a rough estimate of the length of the next CPU burst of each process. While high accuracy of the predictor is not essential, the computation overhead of the predictor must be sufficiently' small, so as not to offset the gain of load balancing. As for the system, we assume a set of autonomous computers, that are connected by a fast, shared medium. The individual nodes can vary in the additional hardware and software that may be available in them. Further, we assume that the processes in the workload are sequential. The first step is to fix the parameters for our assumed predictor. Then, an algorithm that takes into account the characteristics of the predictor is proposed. There are many trade-off decisions in the design of the algorithm, including certain steps in which we have relied on trial and error method to find suitable values. The next logical step is to verify the efficiency of the algorithm. To assess its performance, we carry out event driven simulation. We also evaluate the robustness of the algorithm with respect to the characteristics of the predictor. The contribution of the thesis is as follows: It proposes a load-balancing algorithm for a heterogeneous cluster of workstations connected by a fast network. The simulation assumes that the heterogeneity is limited to variability in processor clock rates; but the algorithm can be applied when the nodes have other types of heterogeneity as well. The algorithm uses prediction of CPU burst lengths as its basic input unit. The performance of the algorithm is evaluated through event driven simulation using assumed workload distributions. The results of the simulation show that the algorithm yields a good improvement in response times over the scenario in which no load redistribution is done.

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 178-195).

The purpose of my study is to study Social Democrats immigration policy over the period 1990 and 2013. My material consists of party programs and motions that social democracy has raised in Parliament. As a research method I used McGee´s ideograph theory. I use a cluster analysis inspired by Kenneth Burke to analyze the meaning of those found ideographs. I also present and use Bitzers rhetorical situation in my analyze With help of Burke´s rhetorical situation I could see how the Social Democrats' rhetorical approach to immigration policy in the different periods in society. My investigation of the Social Democratic Party program and motions / propositions revealed the following ideographs: Democracy, solidarity, equality and freedom. keywords that went to associate to the ideographs relating to immigration policy was school, adults, racism, equal rights, democratic freedom. Rhetorical could be interpreted as a way to show that in year 2013 the party stands behind a more solidarity, equality of human beings in society. Groups such as migrants and refugees should not stick out like a social group, but the group should be included in the collective Sweden.

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In the United States (U.S.), extreme temperature-related deaths account for far more deaths than hurricanes, lightning, tornadoes, floods, and earthquakes combined. An extreme heat event (EHE) or a heat wave is a sustained period of substantially hotter and/or more humid weather. EHEs cause a wide range of health problems such as rashes, cramps, heat exhaustion, heat stroke, and, in some instances, death. Further, meteorology plays a dominant role in the formation of air pollutants. In particular, extremely high temperatures are conducive to the formation of certain air pollutants. In order to understand the adverse health impacts of extreme heat and air pollution levels prevailing during EHEs, it is necessary to define what constitutes a heat episode; however, there is a lack of scientific consensus on definitions and procedures to accurately identify EHEs. This work employs a hierarchical clustering technique to group 92 different EHE definitions into homogeneous sets and uses negative binomial rate regression approach to identify those definitions that are most strongly associated with mortality. Our findings suggest that definitions with thresholds that are either too extreme or too moderate are poorly associated with heat-related mortality for most climate regions. Additionally, the association between air pollution and health, especially mortality, is well understood. However, the role of air pollutants in modifying the relationship between EHEs and mortality is not well characterized in the U.S., yet is critical to generating accurate estimates of health burden. Our results indicate that air pollution confounds the relationship between EHE and mortality, and the extent of confounding varies with climate regions. Further, through this work, the sensitivities associated with selecting an EHE definition is taken into consideration when providing region-specific health and economic burden associated with EHEs. Ideally, the excess deaths and costs presented in this work could be useful to study and quantify the public health risk associated with EHEs, either in a prospective or a retrospective setting.

In recent years, there has been a growing interest in understanding how and why cities grow and how to make this growth more economically and environmentally sustainable. This study is interested in two questions. The first question is how to trace the growth pattern among U.S metropolitan areas after 2000 based on the two types of growth strategies. The second research question is how different growth patterns affect environmental outcomes and income inequality. A quantitative study is used to measure the two faces of urban growth processes in U.S metropolitan areas. After cluster analysis, five groups are developed. Then the study moves to how these groups might impact the performances of sustainability. This thesis closes by summarizing the empirical finding and gives recommendations for future researches. / text

The study aims to estimate the ability of different grouping techniques on categorical response. We try to find out how well do they work? Do they really find clusters when clusters exist? We use Cancer Problems in Living Scales from the ACS as our categorical data variables and lung cancer survivors as our studying group. Five methods of cluster analysis are examined for their accuracy in clustering on both real CPILS dataset and simulated data. The methods include hierarchical cluster analysis (Ward's method), model-based clustering of raw data, model-based clustering of the factors scores from a maximum likelihood factor analysis, model-based clustering of the predicted scores from independent factor analysis, and the method of latent class clustering. The results from each of the five methods are then compared to actual classifications. The performance of model-based clustering on raw data is poorer than that of the other methods and the latent class clustering method is most appropriate for the specific categorical data examined. These results are discussed and recommendations are made regarding future directions for cluster analysis research.

Nuotolinių aplinkų duomenų bazėse kaupiamas didelis kiekis informacijos apie studentus ir jų veiksmus nuotolinėje aplinkoje. Kad būtų paprasčiau analizuoti šiuos duomenis į pagalbą pasitelkiama duomenų gavyba. Darbo tikslas - sudaryti sistemą, skirtą duomenų, gautų iš virtualios nuotolinio mokymo aplinkos Moodle, pirminiam apdorojimui ir duomenų gavybai. Gautus duomenis ištirti pritaikant programinės įrangos paketo StatSoft STATISTICA 7 duomenų gavybos algoritmus besimokančiųjų mokymosi intensyvumo duomenų analizei. Įvertinus gautus rezultatus parengtos rekomendacijos tolimesnei duomenų analizei. Duomenų analizei pritaikyta klasterinė k-vidurkių analizė. / The databases of virtual learning environments store large quantity of information about students and theirs activity. The data mining is usable to easer analysis of these information. The object of work is to make a system for preprocessing and data mining of data, obtained from virtual learning environment Moodle. The historical learning data can be analysed after preprocessing to study learners learning intensity with data mining algorithms by StatSoft STATISTICA 7 software. k-means cluster analysis was applied as example of data mining of learning data. Recommendations to further application of data mining of learning activities are given, too.

The following thesis outlines a study assessing the levels of perfectionism, self-injurious behaviour, and functions of anorexia nervosa (AN) through use of a cross-sectional online survey, among English speaking participants 15 years or older, self-reporting a current, previous, or suspected diagnosis of AN. Three distinct clusters were found using self-report measures from individuals with a current or suspected diagnosis, with each cluster corresponding to a unique theoretical understanding of AN. The three clusters can be distinguished by high asceticism, appearance, and avoidance of fertility/sexuality functions for AN respectively. Two distinct clusters were found for participants with a previous diagnosis of AN. These clusters can be differentiated by lingering sentiments held for the condition, as the first cluster viewed AN negatively, and the second cluster continued to see some benefits of the condition. Possible implications for understanding etiology, mechanisms, and treatment of AN are discussed. / Canadian Institute of Health Research

This thesis is an investigation of external biosecurity and its application to risk-based surveillance approaches in the southern Ontario swine industry. In each of two datasets, the best number of groups to describe biosecurity practices was identified, resulting in two groups with high biosecurity standards and one group with low biosecurity standards. Multinomial logistic regression models identified herd density, herd size, and herd type among significant predictors of biosecurity group membership. A map of southern Ontario that can be used as a tool in the risk-based surveillance of contagious swine diseases was developed using geographic information about swine density, and the distribution of herds belonging to the high biosecurity groups. Finally, multiple correspondence analysis examined how individual biosecurity practices form strategies on sow farms. Some practices that are generally considered high-risk were closely associated with other practices that mitigate the risk, suggesting that evaluation of the overall strategy is essential for complete assessment of biosecurity. / The Ontario Ministry of Agriculture, Food and Rural Affairs (under the Emergency Management research theme); Ontario Pork; the Ontario Pork Industry Council's Swine Health Advisory Board; the Natural Sciences and Engineering Research Council of Canada;