Maximal oxygen consumption (VO2max) is considered by many to be the best overall measure of an individual's cardiovascular health. Collecting the measurement, however, requires subjecting an individual to prolonged periods of intense exercise until their maximal level, the point at which their body uses no additional oxygen from the air despite increased exercise intensity, is reached. Collecting VO2max data also requires expensive equipment and great subject discomfort to get accurate results. Because of this inherent difficulty, it is often avoided despite its usefulness. In this research, we propose a set of Bayesian hierarchical models to predict VO2max levels in adolescents, ages 12 through 17, using less extreme measurements. Two models are developed separately, one that uses submaximal exercise data and one that uses physical fitness questionnaire data. The best submaximal model was found to include age, gender, BMI, heart rate, rate of perceived exertion, treadmill miles per hour, and an interaction between age and heart rate. The second model, designed for those with physical limitations, uses age, gender, BMI, and two separate questionnaire results measuring physical activity levels and functional ability levels, as well as an interaction between the physical activity level score and gender. Both models use separate model variances for males and females.

In recent years, fantasy baseball has seen an explosion in popularity. Major League Baseball, with its long, storied history and the enormous quantity of data available, naturally lends itself to the modern-day recreational activity known as fantasy baseball. Fantasy baseball is a game in which participants manage an imaginary roster of real players and compete against one another using those players' real-life statistics to score points. Early forms of fantasy baseball began in the early 1960s, but beginning in the 1990s, the sport was revolutionized due to the advent of powerful computers and the Internet. The data used in this project come from an actual fantasy baseball league which uses a head-to-head, points-based scoring system. The data consist of the weekly point totals that were accumulated over the first three-fourths of the 2011 regular season by the top 110 hitters and top 70 pitchers in Major League Baseball. The purpose of this project is analyze the relative value of pitchers versus hitters in this league using hierarchical Bayesian models. Three models will be compared, one which differentiates between hitters and pitchers, another which also differentiates between starting pitchers and relief pitchers, and a third which makes no distinction whatsoever between hitters and pitchers. The models will be compared using the deviance information criterion (DIC). The best model will then be used to predict weekly point totals for the last fourth of the 2011 season. Posterior predictive densities will be compared to actual weekly scores.

This thesis article analyzes Rodolfo Teófilo's novel "A fome" through the lens of ecofeminism, exploring the representations of gender and nature in Brazilian literature. The novel, set during a devastating drought in the Northeast, follows Manuel de Freitas and his family as they seek relief in Ceará. While existing criticism has focused on the novel's use of nature and masculinity, the portrayal of female characters and the connection between gender and nature representations have been overlooked. Ecofeminist theory offers a critical perspective to understand these dynamics, revealing the devaluation of women and nature in favor of men and human beings in the narrative. The novel inadvertently perpetuates this oppressive view, objectifying women and land as submissive entities open to domination, despite acknowledging their abundance and creative power. While "A fome" effectively depicts the horrors of drought and societal injustices, it misses the opportunity to explore the imbalanced, hierarchical binaries of men/women and human/nature. By embracing an ecofeminist approach, the novel could have delved into the intertwined discourses of environmental and gender justice, presenting potential for empowerment and positive change. This article emphasizes the need to interweave these discourses in Brazilian literature to create a more inclusive and equitable societal narrative.

Growth plays a key role in regulating ecological and population dynamics. Life history characteristics such as age at maturity, fecundity and age and size at migration are tightly linked to growth rate. In addition, size can often determine survival and individual breeding success. To fully understand the process of growth it is important to understand the mechanisms that drive growth rates. In Atlantic salmon, growth is critical in determining life history pathways. Models to estimate growth could be useful in the broader context of predicting population dynamics. In this dissertation I investigate the growth process in juvenile Atlantic salmon (Salmo salar). I first used basic modeling approaches and data on individually tagged salmon to investigate the assumptions of different growth metrics. I demonstrate the size-dependency in certain growth metrics when assumptions are violated. Next, I assessed the efficacy of linear mixed effects models in modeling length-weight relationships from longitudinal data. I show that combining a random effects approach with third order polynomials can be an effective way to model length-weight relationships with mark-recapture data. I extend this hierarchical modeling approach to develop a Bayesian growth model. With limited assumptions, I derive a relatively simple discrete time model from von Bertalanffy growth that includes a nonparametric seasonal growth function. The linear dynamics of this model allow for efficient estimation of parameters in a Bayesian framework. Finally, I investigated the role of life history in driving compensatory growth patterns in immature Atlantic salmon. This analysis demonstrates the importance of considering life history as a mechanism in compensatory growth. Information provided in this dissertation will help provide ecologists with statistical tools to estimate growth rates, estimate length-weight relationships, and forecast growth from mark-recapture data. In addition, comparisons of seasonal growth within and among life history groups and within and among tributaries should make a valuable contribution to the important literature on growth in Atlantic salmon.

Electrically conductive fabrics are one of the major components of smart textile that attracts a lot of attention by the energy, medical, sports and military industry. The principal contributors to the conductivity of the smart textiles are the intrinsic properties of the fiber, functionalization by the addition of conductive particles and the architecture of fibers. In this study, intrinsic properties of non-woven carbon fabric derived from a novel linear lignin, poly-(caffeyl alcohol) (PCFA) discovered in the seeds of the vanilla orchid (Vanilla planifolia) was investigated. In contrast to all known lignins which comprise of polyaromatic networks, the PCFA lignin is a linear polymer. The non-woven fabric was prepared using electrospinning technique, which follows by stabilization and carbonization steps. Results from Raman spectroscopy indicate higher graphitic structure for PCFA carbon as compared to the Kraft lignin, as seen from G/D ratios of 1.92 vs 1.15 which was supported by a high percentage of graphitic (C-C) bond observed from X-ray photoelectron spectroscopy (XPS). Moreover, from the XRD and TEM a larger crystal size (Lc=12.2 nm) for the PCFA fiber was obtained which correlates to the higher modulus and conductivity of the fiber. These plant-sourced carbon fabrics have a valuable impact on zero carbon footprint materials. In order to improve the strength and flexibility of the non-woven carbon fabric, lignin was blended with the synthetic polymer Poly acrylonitrile (PAN) in different concertation, resulting in electrical conductivity up to (7.7 S/cm) on blend composition which is enough for sensing and EMI shielding applications. Next, the design of experiments approach was used to identify the contribution of the carbonization parameters on the conductivity of the fabrics and architecture of the fibers, results show carbonization temperature as the major contributing factor to the conductivity of non-woven fabric. Finally, a manufacturing procedure was develop inspired by the architecture of plant fibers to induce controlled porosity either on the skin or core of fibers which results in stiffness and flexibility in the fibers. Coaxial Electrospinning and Physical foaming (CO2 foaming) techniques were utilized to create the hierarchical fiber architecture. Finite Element model was developed to design for mechanical properties of the bioinspired fiber mesh. Results show the polymers contributes less in a coaxial design as compared to the individual fibers for mechanical properties. This manufacturing method can use for hierarchical functionalization of fibers by adding conductive nanoparticles at different levels of fiber cross-section utilized for sensing applications in sports and medical industry.

This work focuses on building frameworks to strengthen the relation between human and machine learning. This is achieved by proposing a new category of algorithms and a new theory to formalize the perception and categorizationof objects. For what concerns the algorithmic part, we developed a series of procedures to perform Interactive Continuous Open World learning from the point of view of a single user. As for humans, the input of the algorithms are continuous streams of visual information (sequences of frames), that enable the extraction of richer representations by exploiting the persistence of the same object in the input data. Our approaches are able to incrementally learn and recognize collections of objects, starting from emph{zero} knowledge, and organizing them in a hierarchy that follows the will of the user. We then present a novel Knowledge Representation theory that formalizes the property of our setting and enables the learning over it. The theory is based on the notion of separating the visual representation of objects from the semantic meaning associated with them. This distinction enables to treat both instances and classes of objects as being elements of the same kind, as well as allowing for dynamically rearranging objects according to the needs of the user. The whole framework is gradually introduced through the entire thesis and is coupled with an extensive series of experiments to demonstrate its working principles. The experiments focus also on demonstrating the role of a developmental learning policy, in which new objects are regularly introduced, enabling both an increase in recognition performance while reducing the amount of supervision provided by the user.

The optimal implementation of matrix multiplication on modern computer architectures is of great importance for scientific and engineering applications. However, achieving the optimal performance for matrix multiplication has been continuously challenged both by the ever-widening performance gap between the processor and memory hierarchy and the introduction of new architectural features in modern architectures. The conventional way of dealing with these challenges benefits significantly from the blocking algorithm, which improves the data locality in the cache memory, and from the highly tuned inner kernel routines, which in turn exploit the architectural aspects on the specific processor to deliver near peak performance. A state-of-art improvement of the blocking algorithm is the self-tuning approach that utilizes "heroic" combinatorial optimization of parameters spaces. Other recent research approaches include the approach that explicitly blocks for the TLB (Translation Lookaside Buffer) and the hierarchical formulation that employs memoryriendly Morton Ordering (a spaceilling curve methodology). This thesis compares and contrasts the TLB-blocking-based and Morton-Order-based methods for dense matrix multiplication, and offers a qualitative model to explain the performance behavior. Comparisons to the performance of self-tuning library and the "vendor" library are also offered for the Alpha architecture. The practical benchmark experiments demonstrate that neither conventional blocking-based implementations nor the self-tuning libraries are optimal to achieve consistent high performance in dense matrix multiplication of relatively large square matrix size. Instead, architectural constraints and issues evidently restrict the critical path and options available for optimal performance, so that the relatively simple strategy and framework presented in this study offers higher and flatter overall performance. Interestingly, maximal inner kernel efficiency is not a guarantee of global minimal multiplication time. Also, efficient and flat performance is possible at all problem sizes that fit in main memory, rather than "jagged" performance curves often observed in blocking and self-tuned blocking libraries.

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