Internationella studier har visat att poliser drivs av såväl inre som yttre motivation och att de vidtar varierande copingstrategier för att hantera yrkets baksidor. Syftet med undersökningen var att utforska vad som påverkar arbetsmotivationen för svenska poliser i yttre tjänst samt vilka copingstrategier de använder sig av för att hantera yrkets psykologiska påfrestningar. Kvalitativa djupintervjuer genomfördes med 10 poliser i yttre tjänst. En tematisk analysmetod resulterade i sex teman under samtliga frågeställningar. Resultatet visade att polisers motivation är beroende av faktorer från individ till samhälle. Faktumet att polisyrket i hög grad tillfredsställer psykologiska behov tycktes delvis kompensera för yrkets psykologiska påfrestningar. Copingstrategier förekom i form av individuell mental självhjälp såväl som att söka hjälp utifrån. Organisatoriska faktorer och pågående attityd- och normförskjutningar i samhället visade sig ha en kraftig påverkan på polisernas motivation. Studien synliggjorde hur dessa pågående processer färgar polisers vardagliga arbete, vilket utgjorde studiens främsta bidrag.

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Intrinsic connectivity networks (ICNs) refer to brain functional networks that are consistently found under various conditions, during tasks or at rest. Some studies demonstrated that while some stimuli do not impact intrinsic connectivity, other stimuli actually activate intrinsic connectivity through suppression, excitation, moderation or modi cation. Most analyses of functional magnetic resonance imaging (fMRI) data use ad-hoc methods to estimate the latent structure of ICNs. Modeling the effects on ICNs has also not been fully investigated. Bayesian Intrinsic Connectivity Network (BICNet) captures the ICN structure with We propose a BICNet model, an extended Bayesian dynamic sparse latent factor model, to identify the ICNs and quantify task-related effects on the ICNs. BICNet has the following advantages: (1) It simultaneously identifies the individual and group-level ICNs; (2) It robustly identifies ICNs by jointly modeling resting-state fMRI (rfMRI) and task-related fMRI (tfMRI); (3) Compared to independent component analysis (ICA)-based methods, it can quantify the difference of ICNs amplitudes across different states; (4) The sparsity of ICNs automatically performs feature selection, instead of ad-hoc thresholding. We apply BICNet to the rfMRI and language tfMRI data from the Human Connectome Project (HCP) and identify several ICNs related to distinct language processing functions.

This thesis describes a robust and efficient design of Student-t based Robust Spatio-Temporal Prediction, namely, St-RSTP, to provide estimation based on observations over spatio-temporal neighbors. It is crucial to many applications in geographical information systems, medical imaging, urban planning, economy study, and climate forecasting. The proposed St-RSTP is more resilient to outliers or other small departures from model assumptions than its ancestor, the Spatio-Temporal Random Effects (STRE) model. STRE is a statistical model with linear order complexity for processing large scale spatiotemporal data. However, STRE has been shown sensitive to outliers or anomaly observations. In our design, the St-RSTP model assumes that the measurement error follows Student's t-distribution, instead of a traditional Gaussian distribution. To handle the analytical intractable inference of Student's t model, we propose an approximate inference algorithm in the framework of Expectation Propagation (EP). Extensive experimental evaluations, based on both simulation and real-life data sets, demonstrated the robustness and the efficiency of our Student-t prediction model compared with the STRE model. / Master of Science

京都大学 / 0048 / 新制・課程博士 / 博士(社会健康医学) / 甲第20289号 / 社医博第78号 / 社新制||医||9(附属図書館) / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 山田 亮, 教授 中山 健夫, 教授 古川 壽亮 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM

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Woodpeckers are a group of avian species that cause damage to wooden power utility structures. In the southeastern United States, Tennessee Valley Authority (TVA), has accrued an estimated $5 million USD annually from woodpecker damage. Previous work has focused on effectiveness of reactive mitigation and restoration efforts with little investigation of preventative methods. To address this knowledge gap, this study will i) use species distribution model techniques to predict damage suitability across the TVA service area, ii) use Bayesian hierarchical community model techniques to estimate species richness of the woodpecker community in the service area, and iii) recommend target areas for increased preventative measures in the service area. The suitability map indicated that damage was most likely to occur in the southwestern portions of the TVA service area. Woodpecker species richness was stable across the environmental covariate values estimated with 2-3 species found throughout the service area.

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