This paper presents numerical solutions to integration problems with bivariate integrands. Using equally spaced nodes in Adaptive Simpson's Rule as a base case, two ways of sampling the domain over which the integration will take place are examined. Drawing from Ouellette and Fiume, Voronoi sampling is used along both axes of integration and the corresponding points are used as nodes in an unequally spaced degree two Newton-Cotes method. Then the domain of integration is triangulated and used in the Triangular Prism Rules discussed by Limaye. Finally, both of these techniques are tested by running simulations over heavily oscillatory and monomial (up to degree five) functions over polygonal regions.

The purpose of this dissertation is to model biosocial determinants of group-directed altruistic behavior – exploring the nomological net around it. To do this a study will be presented to determine existing associations among various biological and social predictors and test a life-history-derived causal cascade using a partially exploratory and partially confirmatory statistical technique called Sequential Canonical Analysis to ultimately predict living-donor, non-directed kidney donations (NDKD). Toward that end, some important methodological considerations first need to be discussed. The first consideration revolves around the level of analysis and how this frames the cascade model and its interpretation. Following a general discussion, an exercise in some of the general principles is provided – investigating the higher-order factor structure of the Big-5 personality constructs across two levels of analysis. The second consideration is the use of unit-weighted factor scores and their appropriateness. Following the theoretical discussion, a demonstration is provided – deriving an estimate of genetic relatedness from a set of heterogeneous data sets. Once the methodological considerations have been discussed, the primary cascade model is presented in two parts: 1) the measurement model – operationalizing the measures incorporated into 2) the structural model – testing the proposed causal cascade using Sequential Canonical Analysis. A discussion follows in which the results are summarized, limitations are articulated, and further research directions are explored.

Online communities provide a fertile ground for analyzing people\'s behavior and improving our understanding of social processes. For instance, when modeling social interaction online, it is important to understand when people are reacting to each other. Also, since both people and communities change over time, we argue that analyses of online communities that take time into account will lead to deeper and more accurate results. In many cases, however, users behavior can be easily missed: users react to content in many more ways than observed by explicit indicators (such as likes on Facebook or replies on Twitter) and poorly aggregated temporal data might hide, misrepresent and even lead to wrong conclusions about how users are evolving. In order to address the problem of detecting non-explicit responses, we present a new approach that uses tf-idf similarity between a user\'s own tweets and recent tweets by people they follow. Based on a month\'s worth of posting data from 449 ego networks in Twitter, this method demonstrates that it is likely that at least 11% of reactions are not captured by the explicit reply and retweet mechanisms. Further, these uncaptured reactions are not evenly distributed between users: some users, who create replies and retweets without using the official interface mechanisms, are much more responsive to followees than they appear. This suggests that detecting non-explicit responses is an important consideration in mitigating biases and building more accurate models when using these markers to study social interaction and information diffusion. We also address the problem of users evolution in Reddit based on comment and submission data from 2007 to 2014. Even using one of the simplest temporal differences between usersyearly cohortswe find wide differences in people\'s behavior, including comment activity, effort, and survival. Furthermore, not accounting for time can lead us to misinterpret important phenomena. For instance, we observe that average comment length decreases over any fixed period of time, but comment length in each cohort of users steadily increases during the same period after an abrupt initial drop, an example of Simpson\'s Paradox. Dividing cohorts into sub-cohorts based on the survival time in the community provides further insights; in particular, longer-lived users start at a higher activity level and make more and shorter comments than those who leave earlier. These findings both give more insight into user evolution in Reddit in particular, and raise a number of interesting questions around studying online behavior going forward. / Comunidades online proporcionam um ambiente fértil para análise do comportamento de indivíduos e processos sociais. Por exemplo, ao modelarmos interações sociais online, é importante compreendemos quando indivíduos estão reagindo a outros indivíduos. Além disso, pessoas e comunidades mudam com o passar do tempo, e levar em consideração sua evolução temporal nos leva a resultados mais precisos. Entretanto, em muitos casos, o comportamento dos usuários pode ser perdido: suas reações ao conteúdo ao qual são expostos não são capturadas por indicadores explícitos (likes no Facebook, replies no Twitter). Agregações temporais de dados pouco criteriosas podem ocultar, enviesar ou até levar a conclusões equivocadas sobre como usuários evoluem. Apresentamos uma nova abordagem para o problema de detectar respostas não-explicitas que utiliza similaridade tf-idf entre tweets de um usuário e tweets recentes que este usuário recebeu de quem segue. Com base em dados de postagens de um mês para 449 redes egocêntricas do Twitter, este método evidencia que temos um volume de ao menos 11% de reações não capturadas pelos mecanismos explicitos de reply e retweet. Além disso, essas reações não capturadas não estão uniformemente distribuídas entre os usuários: alguns usuários que criam replies e retweets sem utilizar os mecanismos formais da interface são muito mais responsivos a quem eles seguem do que aparentam. Isso sugere que detectar respostas não-explicitas é importante para mitigar viéses e construir modelos mais precisos a fim de estudar interações sociais e difusão de informação. Abordamos o problema de evolução de usuários no Reddit com base em dados entre o período de 2007 a 2014. Utilizando métodos simples de diferenciação temporal dos usuários -- cohorts anuais -- encontramos amplas diferenças entre o comportamento, que incluem criação de comentários, métricas de esforço e sobrevivência. Desconsiderar a evolução temporal pode levar a equívocos a respeito de fenômenos importantes. Por exemplo, o tamanho médio dos comentários na rede decresce ao longo de qualquer intervalo de tempo, mas este tamanho é crescente em cada uma das cohorts de usuários no mesmo período, salvo de uma queda inicial. Esta é uma observação do Paradoxo de Simpson. Dividir as cohorts de usuários em sub-cohorts baseadas em anos de sobrevivência na rede nos fornece uma perspectiva melhor; usuários que sobrevivem por mais tempo apresentam um maior nível de atividade inicial, com comentários mais curtos do que aqueles que sobrevivem menos. Com isto, compreendemos melhor como usuários evoluem no Reddit e levantamos uma série de questões a respeito de futuros desdobramentos do estudo de comportamento online.

Online communities provide a fertile ground for analyzing people\'s behavior and improving our understanding of social processes. For instance, when modeling social interaction online, it is important to understand when people are reacting to each other. Also, since both people and communities change over time, we argue that analyses of online communities that take time into account will lead to deeper and more accurate results. In many cases, however, users behavior can be easily missed: users react to content in many more ways than observed by explicit indicators (such as likes on Facebook or replies on Twitter) and poorly aggregated temporal data might hide, misrepresent and even lead to wrong conclusions about how users are evolving. In order to address the problem of detecting non-explicit responses, we present a new approach that uses tf-idf similarity between a user\'s own tweets and recent tweets by people they follow. Based on a month\'s worth of posting data from 449 ego networks in Twitter, this method demonstrates that it is likely that at least 11% of reactions are not captured by the explicit reply and retweet mechanisms. Further, these uncaptured reactions are not evenly distributed between users: some users, who create replies and retweets without using the official interface mechanisms, are much more responsive to followees than they appear. This suggests that detecting non-explicit responses is an important consideration in mitigating biases and building more accurate models when using these markers to study social interaction and information diffusion. We also address the problem of users evolution in Reddit based on comment and submission data from 2007 to 2014. Even using one of the simplest temporal differences between usersyearly cohortswe find wide differences in people\'s behavior, including comment activity, effort, and survival. Furthermore, not accounting for time can lead us to misinterpret important phenomena. For instance, we observe that average comment length decreases over any fixed period of time, but comment length in each cohort of users steadily increases during the same period after an abrupt initial drop, an example of Simpson\'s Paradox. Dividing cohorts into sub-cohorts based on the survival time in the community provides further insights; in particular, longer-lived users start at a higher activity level and make more and shorter comments than those who leave earlier. These findings both give more insight into user evolution in Reddit in particular, and raise a number of interesting questions around studying online behavior going forward. / Comunidades online proporcionam um ambiente fértil para análise do comportamento de indivíduos e processos sociais. Por exemplo, ao modelarmos interações sociais online, é importante compreendemos quando indivíduos estão reagindo a outros indivíduos. Além disso, pessoas e comunidades mudam com o passar do tempo, e levar em consideração sua evolução temporal nos leva a resultados mais precisos. Entretanto, em muitos casos, o comportamento dos usuários pode ser perdido: suas reações ao conteúdo ao qual são expostos não são capturadas por indicadores explícitos (likes no Facebook, replies no Twitter). Agregações temporais de dados pouco criteriosas podem ocultar, enviesar ou até levar a conclusões equivocadas sobre como usuários evoluem. Apresentamos uma nova abordagem para o problema de detectar respostas não-explicitas que utiliza similaridade tf-idf entre tweets de um usuário e tweets recentes que este usuário recebeu de quem segue. Com base em dados de postagens de um mês para 449 redes egocêntricas do Twitter, este método evidencia que temos um volume de ao menos 11% de reações não capturadas pelos mecanismos explicitos de reply e retweet. Além disso, essas reações não capturadas não estão uniformemente distribuídas entre os usuários: alguns usuários que criam replies e retweets sem utilizar os mecanismos formais da interface são muito mais responsivos a quem eles seguem do que aparentam. Isso sugere que detectar respostas não-explicitas é importante para mitigar viéses e construir modelos mais precisos a fim de estudar interações sociais e difusão de informação. Abordamos o problema de evolução de usuários no Reddit com base em dados entre o período de 2007 a 2014. Utilizando métodos simples de diferenciação temporal dos usuários -- cohorts anuais -- encontramos amplas diferenças entre o comportamento, que incluem criação de comentários, métricas de esforço e sobrevivência. Desconsiderar a evolução temporal pode levar a equívocos a respeito de fenômenos importantes. Por exemplo, o tamanho médio dos comentários na rede decresce ao longo de qualquer intervalo de tempo, mas este tamanho é crescente em cada uma das cohorts de usuários no mesmo período, salvo de uma queda inicial. Esta é uma observação do Paradoxo de Simpson. Dividir as cohorts de usuários em sub-cohorts baseadas em anos de sobrevivência na rede nos fornece uma perspectiva melhor; usuários que sobrevivem por mais tempo apresentam um maior nível de atividade inicial, com comentários mais curtos do que aqueles que sobrevivem menos. Com isto, compreendemos melhor como usuários evoluem no Reddit e levantamos uma série de questões a respeito de futuros desdobramentos do estudo de comportamento online.

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It is well established that national retail chains impact small, single location retail businesses in terms of revenue generation, retail structure, retail type diversity, and location. This study examines the retail structure and diversity of five retail districts in the City of Denton, Texas. The analysis focuses on one central business district (CBD), one traditional retail strip center (University Drive, also known as US HWY 380), one special retail district (Fry Street District), one traditional enclosed shopping mall and associated development (Golden Triangle Mall), and one power retail center (Denton Crossing). The empirical foundation for the investigation is a historical business database covering years 1997 to 2010, obtained from Info Group's Reference USA. This Reference USA database includes location, industry, and status (single versus chain location) information for each business. Retail diversity and evenness were measured for each of the five retail districts using the Simpson's Diversity Index and the Simpsons Measure of Evenness, leading to specification of the differences that exist in retail structure and diversity among the districts. Golden Triangle Mall and Denton Crossing were primarily chain location in composition while Fry Street District, the CBD, and University Drive were primarily single location in composition. Across all years, the single versus chain status of the local business communities did not substantially change within any of the districts. The Fry Street District exhibited the most change in diversity as well as the lowest overall diversity among the retail districts, followed by University Drive and Golden Triangle Mall. The CBD did not experience any major change in retail type diversity. However, all retail districts experienced major changes in retail evenness. Overall for the city, single location retail businesses accounted for the majority of all the retail businesses, however, chain locations employed more people. In total, these findings indicate that the development of retail districts composed primarily of chain location retailer's affects retail district diversity and evenness but not retail structure.

Like coral reefs worldwide, the Florida Reef Tract has dramatically declined within the past two decades. Monitoring of 40 sites throughout the Florida Keys National Marine Sanctuary has undertaken a multiple-parameter approach to assess spatial and temporal changes in the status of the ecosystem. The objectives of the present study consist of the following: In chapter one, we review past coral reef studies; emphasis is placed on recent studies on the stony corals of reefs in the lower Florida Keys. We also review the economic impact of coral reefs on the state of Florida. In chapter two, we identify the underlying probability distribution function of the stony coral cover proportions and we obtain better estimates of the statistical properties of stony coral cover proportions. Furthermore, we improve present procedures in constructing confidence intervals of the true median and mean for the underlying probability distribution. In chapter three, we investigate the applicability of the normal probability distribution assumption made on the pseudovalues obtained from the jackknife procedure for the Shannon-Wiener diversity index used in previous studies. We investigate a new and more effective approach to estimating the Shannon-Wiener and Simpson's diversity index. In chapter four, we develop the best possible estimate of the probability distribution function of the jackknifing pseudovalues, obtained from the jackknife procedure for the Shannon-Wiener diversity index used in previous studies, using the xi nonparametric kernel density estimate method. This nonparametric procedure gives very effective estimates of the statistical measures for the jackknifing pseudovalues. Lastly, the present study develops a predictive statistical model for stony coral cover. In addition to identifying the attributable variables that influence the stony coral cover data of the lower Florida Keys, we investigate the possible interactions present. The final form of the developed statistical model gives good estimates of the stony coral cover given some information of the attributable variables. Our nonparametric and parametric approach to analyzing coral reef data provides a sound basis for developing efficient ecosystem models that estimate future trends in coral reef diversity. This will give the scientists and managers another tool to help monitor and maintain a healthy ecosystem.

Šio darbo tikslas – įvertinti Dubysos upės vidurupio vandens kokybę, taikant bioindikacinius vandens telkinių vertinimo metodus. Naudojant Saprobiškumo (S) ir Simpsono įvairovės indeksų (D) metodus buvo identifikuoti zooplanktoniniai organizmai bei įvertinti rūšių skaičiaus, gausumo ir įvairovės pasiskirstymo dėsningumai. Pagal J. Brown-Blanquet skalę atliktas atskirų augalų rūšių gausumo (projekcinis padengimo) barelyje vertinamas. Apie 50 km atkarpoje buvo išdėstytos 15 tyrimų vietų, kurias pasirenkant buvo atsižvelgiama į netoliese esančius žemės ūkio, pramonės, transporto objektus bei gamtinę aplinką. Tiriant zooplanktoniną paimti 270 vandens ir vandens – dumblo mėginiai. Šie tyrimai atlikti tris kartus metuose, bandymą pakartojant tris kartus. Naudojant J. Brown-Blanquet skalę augalų projekcinio padengimo vertinimas atliktas liepos mėn. Taigi vandens kokybės vertinimui buvo skaičiuoti tokie rodikliai: zooplanktoninių organizmų rūšių skaičius, gausumas, Simpsono įvairovės (D), Saprobiškumo (S) indeksai bei augalų projekcinis padengimas. Tirtoje teritorijoje buvo aptikta 33 zooplanktono rūšys: 8 infuzorijos, 8 dumbliai, 2 bakterijos, 2 vabzdžių lervos, 3 verpetės, 4 pirmuonys, 2 šakotaūsiai ir 3 irklakojai vėžiagyviai. Taikyta vienfaktorinė dispersinė analizė (One–way ANOVA) parodė, kad tyrimų vietos vandens kokybė (vandens užterštumas arba švarumas) turi įtakos vandens kokybę indikuojantiems rodikliams (rūšių skaičiui, gausumui, D bei S indeksams). Rūšių sudėtis ir... [toliau žr. visą tekstą] / The aim – to assess middle of the river Dubysa water quality, using valuation techniques by bioindicationous water mines. Using saprobic index (S) and Simpson's diversity index (D) methods have been identified zooplanktons organisms and the number of species, abundance and diversity of distribution patterns. According to J. Brown-Blanquet scale of an individual plant species richness (covering projection) rating in the barrel. About 50 km stretch was down 15 test sites, the choice was taken to the nearby agricultural, industrial, transport facilities and the environment. Studying zooplankton, it was taken of 270 water mines, and water - sludge ("hanging drop") samples. These studies were conducted three times a year, repeating the test three times. Using J. Brown-Blanquet scale projection covering the plant assessment was carried out in July. Thus, the assessment of water quality indicators have been calculated as follows: organisms zooplankton number of species, abundance, Simpson's diversity index (D), saprobic index (S) and the projection coating plant. There was found 33 species of investigated zooplankton area: 8 infusoria, 8 algae, 2 bacteria, 2 insect larvae, 3 vortex, 4 protozoa, crustaceans 2 and 3 copepods. Applied to one factor analysis of variance (one-way ANOVA) revealed that research on local water quality (water pollution or purity) affect water quality for indication indicators (number of species, abundance, D index). Species composition and quantitative... [to full text]

Malaise traps are a common tool for collecting insects used by many researchers. Although there have been variations in the models and materials used for Malaise traps, the potential for sampling bias due to mesh gague has been explored inadequately. This study compared coarse and fine mesh Townes model Malaise traps in two habitats on the Grand Staircase-Escalante National Monument. The two habitats next to the Lick Wash trailhead were defined by dominant vegetation type – sagebrush and grasses or Piñon-Juniper. We collected from three sites per habitat type, over three consecutive days in June in both 2006 and 2007. A pair of Malaise traps consisting of one coarse mesh and one fine mesh trap were used at each site in order to compare differences in the diversity and in the average size of individuals collected by each type of Malaise trap. We measured diversity using both presence-absence data such as richness scores and Jaccard's Index of Similarity, and abundance-based measures of comparison, including Simpson's Index of Diversity and non-metric multidimensional scaling. We identified all individuals according to Order, and because of our interest in flies and their abundance, we further identified the Dipter ta to the Family level. Average insect size was determined by categorizing individuals according to one of 14 distinct size-classes. In sum, 71 samples totaling approximately 62,500 insects were identified and sized. Because we sampled from two adjacent habitats, we also discuss beta diversity across the sample sites. Although mesh-size appears to have a significant effect on the diversity of the catch according to some tests, not all of our analysis agrees. In addition, the gain in the amount of diversity collected by incorporating both mesh-sizes may not be worth the costs of that kind of sampling. Other means of collection may adequately make up that difference. Habitat on the other hand was a clear marker for difference in diversity. Size was not found to be significant overall, but there still may be reasons to examine the effect of mesh-size with respect to the Hymenoptera.