京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第22029号 / 理博第4533号 / 新制||理||1651(附属図書館) / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)教授 橋本 学, 教授 福田 洋一, 准教授 深畑 幸俊 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

Approximately 80% of reproductive age women experience physical or emotional symptoms prior to onset of menses. Of these women, approximately 20% experience symptoms severe enough to interfere with social functioning and life activities and meet criteria for premenstrual syndrome (PMS). More than 100 different symptoms are associated with PMS, the most common of which include breast tenderness, headache, anger, and depression. Symptom groupings tend to be stable within an individual but can vary distinctly between women. Potential differences in the etiology of symptoms suggest that PMS should not be considered a single condition in research or clinical studies, but rather may represent distinct entities that group by symptom patterns. The primary goal of this study was to identify symptom patterns using factor and cluster analysis. Analysis included: 1) a cohort of healthy women aged 18-30 (n =414); and 2) the subgroup of women meeting established criteria for PMS (n=80). All participants provided information on the occurrence and severity of 26 menstrual symptoms by validated questionnaire. Four distinct symptom patterns emerged: Emotional, Psychological, Physical, and Consumption. Cronbach’s alpha levels demonstrating reliability were high in both the total population (0.71 – 0.90) and in the PMS subset (0.69-0.80). Additionally, cluster analysis identified 4 clusters in both the total population and PMS subset. These symptom patterns were consistent with those identified in prior studies in diverse populations. These observations suggest that distinct subtypes of PMS may exist, and should be considered when recommending treatments and evaluating risk factors.

>Magister Scientiae - MSc / The thesis aims to determine the depositional environments, rock types and petrophysical characteristics of the reservoirs in Wells E-S3, E-S5 and F-AH4 of Area X in the Bredasdorp Basin, offshore South Africa. The three wells were studied using methods including core description, petrophysical analysis, seismic facies and multivariate statistics in order to evaluate their reservoir potential. The thesis includes digital wireline log signatures, 2D seismic data, well data and core analysis from selected depths. Based on core description, five lithofacies were identified as claystone (HM1), fine to coarse grained sandstone (HM2), very fine to medium grained sandstone (HM3), fine to medium grained sandstone (HM4) and conglomerate (HM5). Deltaic and shallow marine depositional environments were also interpreted from the core description based on the sedimentary structures and ichnofossils. The results obtained from the petrophysical analysis indicate that the sandstone reservoirs show a relatively fair to good porosity (range 13-20 %), water saturation (range 17-45 %) and a predicted permeability (range 4- 108 mD) for Wells E-S3, E-S5 andF-AH4. The seismic facies model of the study area shows five seismic facies described as parallel, variable amplitude variable continuity, semi-continuous high amplitude, divergent variable amplitude and chaotic seismic facies as well as a probable shallow marine, deltaic and submarine fan depositional system. Linking lithofacies to seismic facies maps helped to understand and predict the distribution and quality of reservoir packages in the studied wells. Multivariate statistical methods of factor, discriminant and cluster analysis were used. For Wells E-S3, E-S5 and F-AH4, two factors were derived from the wireline log data reflecting oil and non- oil bearing depths. Cluster analysis delineated oil and non-oil bearing groups with similar wireline properties. This thesis demonstrates that the approach taken is useful because petrophysical analysis, seismic facies and multivariate statistics has provided useful information on reservoir quality such as net to gross, depths of hydrocarbon saturation and depositional environment.

My thesis is focused on the presentation of a scoring model for companies in chemical industry with use of cluster analysis methods. There is a description of financial risks, financial analysis indicators and models which are used to evaluate financial risks of a company. There is also a mathematical description of hierarchical cluster methods.

Orientador: Thiago de Melo / Banca: Sergio Tsuyoshi Ura / Banca: Marcio Fuzeto Gameiro / Resumo: Neste trabalho estudamos networks e métodos de Clustering Hierárquico de forma axiomática. Apresentamos alguns programas na linguagem Python aplicados à análise de dados de migração populacional, com o intuito de ilustrar os métodos estudados / Abstract: In this work we study networks and an axiomatic construction of Hierarchical Clustering. We present some Python programs used to analyze human migration data and illustrate the studied methods / Mestre

<p>Purpose - The goal of this study is to identify the current distinct market segments within the US agricultural credit lending market, predict segment membership based on readily available characteristics, and better understand farmer financing preferences. </p> <p><br></p> <p>Design/methodology/approach - A two stage clustering analysis was used to identify five distinct market segments. A multinomial logit regression was used to predict segment membership based on demographic and psychographic characteristics. </p> <p><br></p> <p>Findings - The segmentation analysis produced five distinct market segments. The identified segments are service, convenience, balance, price, and performance. </p> <p><br></p> <p>Practical implications - This information can aid credit lenders in segmenting the market and tailoring their sales approach to the different farmer segments. </p> <p><br></p> <p>Originality/value - This paper contributes to the literature in several ways. First, previous studies of farmer selection of lending institutions rely on supply side data (Brewer et al., 2019; Dodson & Koenig, 2004; Ifft and Fiechter, 2020). While these studies are useful in knowing how farmers may be segmented according to their choice set of particular lending institutions, what we cannot examine is why the farmer is choosing that choice set. Our study incorporates psychographic and buying preferences. Prior work has highlighted the trend away from demographics and socioeconomic characteristics towards psychographic characteristics as categories for customer segmentation (Sherrick et al., 1994). Secondly, as described above, much has changed in the agricultural lending markets concerning the lending institutions available to farmers and the technology that changes how farmers and lending institutions interact. Thus, this study updates the literature as farmers preferences may have changed due to the new market structure </p>

Three cluster analysis programs were used to group the same 64 individuals, generated so as to represent eight populations of eight individuals each. Each individual had quantitative values for seven attributes. All eight populations shared a common attribute variance-covariance matrix. The first program, from F. J. Rohlf's MINT package, implemented single linkage. Correlation was used as the basis for similarity. The results were not satisfactory, and the further use of correlation is in question. The second program, MDISP, bases similarity on Euclidean distance. It was found to give excellent results, in that it clustered individuals into the exact populations from which they were generated. It is the recommended program of the three used here. The last program, MINFO, uses similarity based on mutual information. It also gave very satisfactory results, but, due to visualization reasons, it was found to be less favorable than the MDISP program.

Few researchers have examined the contributing factors to racial identity development for White Americans. In order to better understand White racial identity development, the current study was designed to use Helms’s (1990) theory of White racial identity development to examine the associations between racial attitudes and status profiles of White racial identity, with particular interest in color-blind racial attitudes (i.e., the belief that race is a non-issue in modern society) and belief in a just world (i.e., the view that the world is fair and just). To gain further insight into profiles of White racial identity, additional social attitudes were included in the analyses, including social dominance orientation and internal and external motivation to avoid prejudice, as well as demographic variables. A sample of 350 White American adults recruited from Amazon’s MTurk completed measures of racial identity, racial attitudes, social desirability, and demographic information. K means cluster analyses were conducted to create five status profiles of White identity. Among all study variables, cluster group membership was primarily defined by color-blind racial attitudes, social dominance orientation, and age. Results revealed color-blind racial attitudes were the strongest variables across all five clusters, even those in which the primary racial identity status was autonomy. Belief in a just world, on the other hand, did not appear to be a prominent factor in determining cluster membership in the current study. These results pointed to implications for both research and theory on White racial identity statuses, given that participants who were autonomous were also high in color-blind racial attitudes, which is inconsistent with current conceptualizations of the autonomy ego status. The results indicated the possibility of an ego status prior to autonomy and hold implications for identifying additional statuses of White racial identity within Helms’s (1990) model. The study results hold further implications for future research in the exploration of connections between White racial identity and multicultural counseling competence.

No / Model interpretation is one of the key aspects of the model evaluation process. The explanation of the relationship between model variables and outputs is relatively easy for statistical models, such as linear regressions, thanks to the availability of model parameters and their statistical significance . For “black box” models, such as random forest, this information is hidden inside the model structure. This work presents an approach for computing feature contributions for random forest classification models. It allows for the determination of the influence of each variable on the model prediction for an individual instance. By analysing feature contributions for a training dataset, the most significant variables can be determined and their typical contribution towards predictions made for individual classes, i.e., class-specific feature contribution “patterns”, are discovered. These patterns represent a standard behaviour of the model and allow for an additional assessment of the model reliability for new data. Interpretation of feature contributions for two UCI benchmark datasets shows the potential of the proposed methodology. The robustness of results is demonstrated through an extensive analysis of feature contributions calculated for a large number of generated random forest models.

To effectively implement climate change mitigation and carbon sequestration activities in the southern US, nonindustrial private forest (NIPF) landowner participation is necessary because of the significant number of acres of forest land under their ownership. This study intended to develop a typology of NIPF landowners based on their reasons of owning forestland, assess their attitude toward climate change and carbon sequestration, and evaluate their participation behavior toward forest carbon sequestration in the southern US. A mail survey of NIPF landowners in the southern US was used to collect the data necessary for this study. Study results indicated that landowners in the southern US could be segmented into multi-objective, timber and amenity oriented landowners; and landowner groups differed in terms of their ownership characteristics, management behavior, and interest toward forest carbon sequestration. Additionally, the southern landowner attitudes toward climate change and carbon sequestration could be grouped into positive, negative, and undecided types; with the undecided group composing the largest proportion of landowners. However, few landowners indicated having a good understanding of forest carbon sequestration, indicating the need for more education and outreach activities in this region. In addition, landowner willingness to participate in carbon sequestration practices was different when such practices were more profitable, revenue neutral or less profitable than timber management only. Although many landowners would require a significant profit to participate in carbon sequestration programs, others would participate with little or no incentives. Those having recreational goals for their property were the most likely landowners to participate in carbon sequestration. Similarly, positive attitudes toward climate change (i.e., with a belief that climate change is scientifically proven) and a good understanding of forest carbon sequestration positively affected landowner participation in forest carbon sequestration. Economic implementation of climate change policy could be achieved by designing education, incentives, or assistance programs to connect with recreational goal landowners in the southern US.