逆物流回收的複雜度遠比正向物流高，企業為專注核心價值多半將逆物流活動委外專業物流服務供應商。對第三方逆物流業者而言，選擇適當的回收處理中心位址為其重要核心能力之一，而現今研究對於選址模式中之回收不確定性，大多以歷史資料作為參數，無根據區域特性不同而有所分別。故本研究希望探討在零售商提供資訊的情境下，結合消費者問卷建構廢棄產品的使用年限機率、並以二元迴歸邏輯分析建構回收機率以此預測區域回收數量，透過資訊分享以建立更好的回收處理中心選址設置模式，使第三方逆物流業者可按照此模式選擇最適當的回收點位置與回收處理量安排用以求得利潤最大化。 / Since reverse logistics is much more complex than forward logistics, third-party logistics providers are often the prior choice for firms to obtain their core value when a reverse logistic activity is needed. For third-party logistics providers, the location is one of their crucial core values; while most of them can only rely on historical data to assume the best location, due to the uncertainty of recycling in present studies.Therefore, this paper tries to construct the probability of products’ used-years by combining the retailers’ information with consumer-oriented questionnaires. Binary logistic regression is the methodology used to analyze and predict recycling probability. By information-sharing the third-party logistics providers will be able to construct a better selecting model for the best facility location, which will reach the most suitable recycling quantity to maximize their profits.

永續發展的概念現今已被運用於都市，其中，都市綠地在環境、生態、景觀、社會各層面之機能皆可提升都市永續性，在快速的都市化下，都市內綠地減少，土地利用變遷帶來之環境衝擊影響已自個體單元累積到全球。然而，過去研究中未有關注在綠地的變化趨勢與其他土地利用間的互動關係，以及在不同區域下的變化差異。因此，本研究採用GIS和景觀指數看在1995年至2006年間台北都會區綠地變遷趨勢，並分區探討土地利用間的互動關係，最後藉由二元羅吉斯迴歸分析綠地變化可能原因。 研究結果顯示，在1995年至2006年間，台北都會區整體發展是建地增加，林地也呈上升趨勢，而草地是土地利用轉移下被犧牲掉最多的土地，綠地轉移成其他土地利用情形以都會邊緣地區最嚴重。不同綠地型態在1995年至2006年間的變遷仍有差異，林地在整個台北都會區屬於景觀中的基質，主導性未受動搖，僅在都會中心減少並受破壞；而農地面積略微下降，呈破碎化發展，尤其以都會中心外圍區農地被破壞情形最明顯；草地面積亦下降，破碎化情形較農地更嚴重，在都會郊區、次中心之草地被破壞嚴重，草地各方面機能降低。透過二元羅吉斯迴歸分析發現自然環境、社會經濟與計畫環境皆影響台北都會區的綠地變遷。根據研究結果，建議未來政府於都市計畫上應將綠地空間納入考量，對於不同綠地型態應有不同管制措施，考量各區域綠地型態之差異性，以及自然環境、社會經濟和計畫環境對於綠地變遷的影響，以促進都市朝向永續發展。 / The concept of sustainable development has been applied in cities. Urban green space plays an important role in enhancing the sustainability of the city in regards to the environment, ecology, landscape and society aspects. Under rapid urbanization, green space has greatly declined in cities. Environmental impact resulting from land use change has grown from local to global proportions. However, researches did not pay attention to interactions between green spaces and other land-use change trends or different types of change in different areas. This research used GIS and landscape metrics to investigate the green space change trend and interactions among different land use types in the Taipei metropolitan area from 1995 to 2006. Furthermore, this research analyzed possible reasons that may have caused green space change through logistic regression. The results showed that, from 1995 to 2006, the built up area and the forest increased in Taipei Metropolitan Area; however, the grass decreased because of land use change. Urban fringe was the place that green space changed to other land-use most. There were differences of land use change for different types of green space. Forest was the matrix in the landscape of Taipei metropolitan area. It still kept the predominant role, only decreased and was destroyed in the center of metropolitan area. Farmland slightly decreased and became fragmented, especially in the periphery of the urban center. Grassland area decreased and became fragmented much more than farmland. In suburb and sub-center, grassland was destroyed seriously and became less functional. Through binary logistic regression, the study found that natural environment, socio-economic and government planning do have influence on green space changes in the Taipei metropolitan area. According to the result of the study, the recommendation was that government should take green space into consideration when doing urban planning. For different types of green space and different areas, the government needs to have different measures and needs to consider the impact factors of green space change in order to accelerate sustainable development in cities.

Cultural competence is widely recognized as an essential strategy for reducing health disparities. As the United States' population becomes increasingly ethno-culturally diverse, these disparities are becoming even more pronounced. One particular challenge in this regard concerns overweight/obesity prevalence among American adults, as a disproportionately high number of racial and ethnic minority adults are classified as overweight or obese. Dietary behavior assessments are often utilized by health and human services professionals to obtain the data necessary to promote goals such as the reduction and elimination of overweight/obesity across all ethno-cultural groups. The primary objective of this research study was to develop, test, and evaluate a culturally-competent dietary behavior assessment instrument by effectively synthesizing qualitative methods from Cognitive Anthropology with appropriate survey research and quantitative statistical methods. Specifically, a quantitative methods triangle of hierarchical cluster analysis, binary logistic regression, and Poisson regression in conjunction with the free listing qualitative research technique from Cognitive Anthropology was explored as a possible combined methodological approach for researchers and public health professionals wishing to develop a comprehensive understanding of dietary behaviors at the local community level. Binary logistic regression and Poisson regression enabled the relationship between selected food categories and certain demographic/cultural indicators to be modeled, while hierarchical cluster analyses enabled modeling of the distinct patterns of food category groupings that comprise individuals' regular diet. Additionally, initial qualitative analyses of the raw data promoted an understanding of the influence that the local fast food and dine-in restaurant environment has on the dietary behaviors of the target population. The results of this study suggest that a quantitative methods triangle of hierarchical cluster analysis, binary logistic regression analysis, and Poisson regression analysis founded upon qualitative research principles has potential for use as a combined methodological approach for researchers and public health professionals wishing to develop a comprehensive understanding of dietary behaviors at the local community level. By employing these techniques, researchers can analyze individual dietary behaviors and eating patterns from a multifaceted perspective. In turn, public health professionals can develop community-based, cross-culturally relevant programs and interventions that are equally effective across all ethno-cultural groups in their target population.

This thesis investigates the factors that are associated with annual changes in the share price of Johannesburg Stock Exchange (JSE) listed companies. In this study, an increase in value of a share is when the share price of a company goes up by the end of the financial year as compared to the previous year. Secondary data that was sourced from McGregor BFA website was used. The data was from 2004 up to 2011. Deciding which share to buy is the biggest challenge faced by both investment companies and individuals when investing on the stock exchange. This thesis uses binary logistic regression to identify the variables that are associated with share price increase. The dependent variable was annual change in share price (ACSP) and the independent variables were assets per capital employed ratio, debt per assets ratio, debt per equity ratio, dividend yield, earnings per share, earnings yield, operating profit margin, price earnings ratio, return on assets, return on equity and return on capital employed. Different variable selection methods were used and it was established that the backward elimination method produced the best model. It was established that the probability of success of a share is higher if the shareholders are anticipating a higher return on capital employed, and high earnings/ share. It was however, noted that the share price is negatively impacted by dividend yield and earnings yield. Since the odds of an increase in share price is higher if there is a higher return on capital employed and high earning per share, investors and investment companies are encouraged to choose companies with high earnings per share and the best returns on capital employed. The final model had a classification rate of 68.3% and the validation sample produced a classification rate of 65.2% / Mathematical Sciences / M.Sc. (Statistics)

The diploma thesis concerns itself with the analysis of connection between the media exposure to refugeedom topics and the perceived salience of the issue. Combined qualitative and quantitative research designs ruled by QUAN → qual scheme are used in the thesis. In the quantitative part the effect of exposure to refugeedom-related news on the perceived salience of the subject is constructed using TV and newspaper viewing figures while controlling socio- demographic characteristics of respondents. Binary logistic regression was used to find the influence. The analysis shows that the exposure to content concerning immigration and refugees has influenced the rated importance of the issue. However, there are other variables not included in the model which are affecting the salience. The qualitative approach offers insights into the relationship discovered with quantitative methods. In-depth interviews showed people are aware of the media influence mainly regarding topics they are thinking about and discussing with their social surroundings. According to some interviewees this influence is stronger observed in topics where we lack personal experience, like the refugees. Apart from the media other issues like value orientation, life experience, social surroundings or the extent of criticism may have...

The goal of learning analytics is to optimize learning and the environments in which it occurs. Since 2011, when learning analytics was defined as a separate and distinct area of academic inquiry, the literature has identified a need for research that presents evidence of effective learning analytics, as well as, learning analytics research that is conducted in conjunction with learning theory. This study uses Efklides' metacognitive and affective model of self-regulated learning (MASRL) to define cognitive, metacognitive, and affective variables that can explain students' learning outcomes in hybrid/online sections of Calculus I in the 2020-21 academic year. Cognitive variables were measured according to the cognitive operational framework for analytics (COPA). Metacognitive variables were defined according to the ways in which students interacted with the course content in the learning management system (LMS) and supplemental instruction, and affective variables were measured by ways students gave evidence of their affective states, such as in discussion board posts. All variables were compared across the course learning design, activities, and outcomes. Binary logistic regression revealed five significant variables: two cognitive, one metacognitive, and two affective. Thus, this study provided a learning analytics, evidence-based link between self-regulated learning theory and learning design, activities, and outcomes. In addition, implications for students, instructors, and learning theory were explored, as well as, the qualifications of this study as evidence of effective learning analytics.

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The General Data Protection Regulation enforced on the 25th of may in 2018 is a response to the growing importance of IT in today’s society, accompanied by public demand for control over personal data. In contrast to the previous directive, the new regulation applies to personal data stored in an unstructured format, such as email, rather than solely structured data. Companies are now forced to accommodate to this change, among others, in order to be compliant. This study aims to provide a code of conduct for the processing of personal data in email as a measure for reaching compliance. Furthermore, this study investigates whether Named Entity Recognition (NER) can aid this process as a means of finding personal data in the form of names. A literature review of current research and recommendations was conducted for the code of conduct proposal. A NER system was constructed using a hybrid approach with Binary Logistic Regression, hand-crafted rules and gazetteers. The model was applied to a selection of emails, including attachments, obtained from a small consultancy company in the automotive industry. The proposed code of conduct consists of six items, applied to the consultancy firm. The NER-model demonstrated low ability to identify names and was therefore deemed insufficient for this task. / Dataskyddsförordningen började gälla den 25e maj 2018, och uppstod som ett svar på den okände betydelsen av IT i dagens samhälle samt allmänhetens krav på ökad kontroll över personuppgifter för den enskilde individen. Till skillnad från det tidigare direktivet, omfattar den nya förordningen även personuppgifter som är lagrad i ostrukturerad form, som till exempel e-post, snarare än endast i strukturerad form. Många företag tvingas därmed att anpassa sig efter detta, tillsammans med ett flertal andra nya krav, i syfte att efterfölja förordningen. Den här studien syftar till att lägga fram ett förslag på en uppförandekod för behandling av personuppgifter i e-post som ett verktyg för att nå medgörlighet. Utöver detta undersöks det om Named Entity Recognition (NER) kan användas som ett hjälpmedel vid identifiering av personuppgifter, mer specifikt namn. En litteraturstudie kring tidigare forskning och aktuella rekommendationer utfördes inför utformningen av uppförandekoden. Ett NER-system konstruerades med hjälp av Binär Logistisk Regression, handgjorda regler och ordlistor. Modellen applicerades på ett urval av e-postmeddelanden, med eventuella bilagor, som tillhandahölls från ett litet konsultbolag aktivt inom bilindustrin. Den rekommenderade uppförandekoden består av sex punkter, applicerade på konsultbolaget. NER-modellen påvisade en låg förmåga att identifiera namn och ansågs därför inte vara lämplig för den utsatta uppgiften.

Climate change implication and rural livelihood capitals remain the major inextricable dimensions of sustainability in this twenty first century globally. As a result, the impact and outcome of climate change on rural livelihood capitals, including economic development cannot be overemphasized in Ngaka Modiri Molema District Municipality of the North West Province of South Africa, where the study took place. It is one of the largest maize production regions in South Africa, where a preponderance of the people in the province obtain their livelihood from agriculture which contributes enormously to the promotion of household’s food security. The study, therefore, investigated the adaptation strategies, awareness of climate change, factors that influenced climate change adaptation in North West Province of South Africa, with the aim of ascertaining the effects of climate change on livelihood capitals among small and emerging maize farmers. Stratified random sampling technique was used to select three hundred and forty-six (346) farmers who were interviewed from the study area, while a pre-tested questionnaire was administered to the maize farmers, aiming at matters related to climate change impact on livelihood and adaptation. Data were analyzed using descriptive statistics while inferential statistical tools employed were Principal Component Analysis, Two-Stage Least Square regression model, Binary Logistic regression model, and Tobit regression model. The results of the study showed that climate change was linked to rural livelihood capitals as climate change awareness, low profit and co-operative finance were statistically significant (p<0.05). The study also established that majority of the rural farmers in the study area were aware of climate change, in which farm size, education, ownership of the farm, information received on climate change, source of climate change information, climate change information through extension services, channel of information received on climate change and support received on climate change were statistically significant (p<0.05). Factors such as farm size, household gender, type of farms, who owns the farm, land acquisition, source of climate change information, support received on climate change, and adaptation barrier were statistically significant (p<0.05) and influenced climate change adaptation in the study area. Conclusively, climate change is entwined with rural livelihood, and the variables that are significant to the study were identified. It was therefore recommended that government intervention, access to information, extension service and support, farmers’ networking, adoption of drought and heat stress tolerant seeds, indigenous knowledge should be improved, practiced and promoted among the rural farmers and the stakeholders involved in the study area. / Agriculture, Animal Health and Human Ecology / D. Phil. (Agriculture)

房屋貸款保證保險制度可移轉部分違約風險予保險公司。然而，保險公司與金融機構在共同承擔風險之際，因房貸保證保險制度之施行，於提高貸款成數後，產生違約風險提高之矛盾現象；而估計保險之預期損失時，以目前尚無此制度下之違約數據估計損失額，將有錯估之可能。 本研究以二元邏吉斯特迴歸模型(Binary Logistic Regression Model)與存活分析(Survival Analysis)估計違約行為，並比較各模型間資料適合度及預測能力，進而單獨分析變數-貸款成數對違約率之邊際機率影響。以探討房貸保證保險施行後，因其對借款者信用增強而提高之貸款成數，所增加之違約風險。並評估金融機構因提高貸款成數後可能之違約風險變動，據以推估違約率數據，並根據房貸保證保險費率結構模型，計算可能之預期損失額，估算變動的保險費率。 實證結果發現，貸款成數與違約風險呈現顯著正相關，貸款成數增加，邊際影響呈遞增情形，違約率隨之遞增，而違約預期損失額亦同時上升。保險公司因預期損失額增加，為維持保費收入得以支付預期損失，其保險費率將明顯提升。故實施房屋貸款保證保險，因借款者信用增強而提高之貸款成數，將增加違約機率並對保險費率產生直接變動。 / Mortgage insurance system may transfer part of the default risk to insurance companies. However, the implementation of mortgage insurance system, on increasing loan to value ratio, the resulting increase default risk. And literatures estimate the expected loss without the default data, there will be misjudge. Our study constructs the binary logistic regression model and survival analysis to estimate the mortgage default behavior, and compare the data between the model fit and the predictive power. Analyzes the effect of loan to value ratio on the marginal probability of default rate. Furthermore, assess the financial institutions in the risk of default due to loan to value ratio changes. According to the estimated default rate data, we employ the mortgage insurance rate structural model to calculate the expected amount of loss and the changes in premium rates. Empirical results found loan to value ratio have a significant positive effect on borrowers’ default. Loan to value ratio increase, the marginal effect progressively increase, along with increasing default rates and expected default losses. Due to the ascendant expected loss, insurance companies increase premiums to cover the expected loss, the premium rate will be significantly improved. Therefore, the implementation of mortgage insurance, credit enhancement for the borrower to improve loan to value ratio, will increase the probability of default and insurance rates.