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Currently, the literature that examines relationships between learning and memory tasks to intelligence and academic achievement are contradictory. Early findings give no support for these relationships, however more recent research does provide support for them. The present study aimed to add to this modern body of work supporting the relationship between learning and memory tasks with academic achievement and intelligence. Through the comparison of participants’ scores on a specific set of discrimination tasks to GPA and SAT scores, the findings from this study provide evidence for these relationships.
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Residents in coastal communities face multiple challenges when protecting their homes. Hurricane winds and storm surges have caused widespread structural damage throughout eastern and southern communities in the United States and internationally. This reality, coupled with existing research indicating rising sea levels and increased hurricane intensity has forced coastal communities to address the issue. One strategy being implemented and continuously refined is adaptive coastal structural design. This thesis explores adaptive coastal design techniques for residential structures, focusing on floating housing. A literature review is conducted on existing design concepts of coastal housing that explored the advantages and disadvantages of various concepts as well the challenges associated with them. The floating home structural design concept presented in this thesis includes a lightweight concrete hollow slab base and steel guideposts to resist lateral loads and prevent lateral movements of the house under an extreme flood event. The presented design concept discusses the critical factors that influence the design of the floating home components and other related factors. The design loads and load combinations applied to the floating home structure were based on a 100- year flood event with hurricane level wind forces and high storm surges following FEMA recommendations. Results of the analysis and design of the floating home structure showed that the design is feasible and sustainable in a 100-year flood event with minimum to minor structural damage.Additionally, a life-cycle cost analysis was conducted for a 50-year period. Using estimates of construction, maintenance and insurance costs, the analysis compared the costs of floating homes built in a New Jersey coastal community to the repair and restoration costs of existing homes damaged following 100-year flood event. The results showed that the costs of floating homes were about 12% lower than the repair and restoration costs.
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High performance concrete (HPC) is being used on daily basis by the construction industry, due to its suburb properties. HPC attains high strength and durability that made it out preform other concrete mixes. With the extensive use of HPC in all type of infrastructures, challenges tended to arise, and shrinkage issues became obvious, and accelerated curing was needed by the construction industry. Shrinkage in HPC resulted into cracking, which made the steel reinforcement in all the structures vulnerable to corrosion. Fiber reinforcement in concrete became one of the practices to minimize shrinkage cracks, and avoid corrosion concerns. Moreover, accelerator admixtures started gaining popularity because it allowed faster construction, which resulted in shorter traffic closures, as well as lowering construction costs.Effects on creep and shrinkage behaviors with the use of fibers and accelerator liquid admixtures in HPC remains in question. Although fiber reinforced HPC and high early strength (HES) concrete is being frequently used, there is not enough research preformed to understand their creep and shrinkage phenomena. The objective of this research study is to investigate the effects of polypropylene fibers and accelerated admixtures to creep and shrinkage deformations in HPC. Several theoretical models will also be modeled, analyzed and evaluated with comparison to the experimental results. Finally, adjustments to the models will be suggested to improve creep predictions.
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Ecosystem Health, Conservation Medicine, EcoHealth, One Health, Planetary Health and GeoHealth are inter-related disciplines that underpin a shared understanding of the functional prerequisites of health, sustainable vitality and wellbeing. All of these are based on recognition that health interconnects species across the planet, and they offer ways to more effectively tackle complex real-world challenges. Herein we present a bibliometric analysis to document usage of a subset of such terms by journals over time. We also provide examples of parasitic and vector-borne diseases, including malaria, toxoplasmosis, baylisascariasis, and Lyme disease. These and many other diseases have persisted, emerged or re-emerged, and caused great harm to human and animal populations in developed and low income, biodiverse nations around the world, largely because of societal drivers that undermined natural processes of disease prevention and control, which had developed through co-evolution over millennia. Shortcomings in addressing drivers has arisen from a lack or coordinated efforts among researchers, health stewards, societies at large, and governments. Fortunately, specialists collaborating under transdisciplinary and socio-ecological health umbrellas are increasingly integrating established and new techniques for disease modeling, prediction, diagnosis, treatment, control, and prevention. Such approaches often emphasize conservation of biodiversity for health protection, and they provide novel opportunities to increase the efficiency and probability of success.
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From the Deepwater Horizon disaster to the opioid crisis, multidistrict litigation—or simply MDL—has become the preeminent forum for devising solutions to the most difficult problems in the federal courts. MDL works by refusing to follow a regular procedural playbook. Its solutions are case-specific, evolving, and ad hoc. This very flexibility, however, provokes charges that MDL violates basic requirements of the rule of law. At the heart of these charges is the assumption that MDL is simply a larger version of the litigation that takes place every day in federal district courts. But MDL is not just different in scale than ordinary litigation; it is different in kind. In structure and operation, MDL parallels programs like Social Security where an administrative agency continuously develops new procedures to handle a high volume of changing claims. Accordingly, MDL is appropriately judged against the “administrative” rule of law that emerged in the decades after World War II, and which underpins the legitimacy of the modern administrative state. When one views MDL as an administrative program instead of a larger version of ordinary civil litigation, the real threats to the legitimacy of its model of aggregate litigation come into focus. The problem is not that MDL is ad hoc. Rather, it is that MDL lacks guarantees of transparency, public participation, and judicial review that administrative agencies have operated under since the middle of the twentieth century. The history of the administrative state suggests that MDL’s continued success as a forum for resolving staggeringly complex problems depends on how it addresses these governance deficits.
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Recently, in 2007, a new electronic phase was discovered – termed as Topological Insulator (TI) – a bulk insulator that has conducting states bound to its surface. An apt analogy would be a ceramic block coated with a nanometer thick layer of metallic paint, so that electrical conduction occurs only on the surface, except in this case, the material is the same throughout. The understanding of topological insulators is based on the previously thought to be complete band theory, but taking into account the topological effects. The term topological implies the presence of certain bulk invariants that help differentiate between different systems having the same symmetry. The work presented here concerns the understanding of the transport properties of these electronic states that emerge only on the surfaces of this very special class of materials.The spin of electrons in the heavy metals of the TIs is linked to their intrinsic angular momentum; this spin-orbit coupling (SOC) leads to twisting of the electronic states in certain regions of momentum space, establishing a topological order. The term topology comes from mathematics, and deals with quantities remaining invariant under continuous modifications – it is the topology of the electronic band structures originating from SOC that protects these metallic surface states against disorders. The SOC also coerces the motion of spin-up electrons in one direction and spin-down electrons in the other – a distinguishing feature that forbids complete backscattering and localization i.e. the electrons can move freely with little or no resistance in their preferred direction. This ‘spin-momentum locking’ makes these materials interesting for future spintronic devices that require generation, control and detection of spins as information carriers. The dissertation begins by reviewing the developing field of TIs which inspired this work, followed by an introduction to the many aspects involved in the growth of atomically precise thin TI films, mainly Bismuth Selenide (Bi2Se3). The details of electrical transport are mentioned next; the experimental techniques thus introduced are used to examine the interplay of bulk and surface contributions to the transport in thin grown Bi2Se3 films. Growth of thin films using molecular beam epitaxy (MBE) with atomic precision requires precise control of each flux, thus, we first discuss the flux stability in harsh oxidation conditions and derive the optimal configuration that helps grow stoichiometric thin-films. Following this, we discuss the growth of Bi2Se3 films on various substrates and study how this affects the electronic transport. The final work in the dissertation involves the transfer of these grown thin films to other substrates, including plastic, so as to provide a platform for future device applications.
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The purpose of this study was to determine the predictors of health-related quality of life (HRQOL) in younger persons aged 18 to 40 years living with inflammatory bowel disease (IBD) and an intestinal stoma. Predictors under investigation included the symptoms of pain, fatigue, leakage of stomal appliance, peristomal skin problem, body image/sexual disturbance, and psychological distress, as well as the construct of self-rated health (SRH).The sample was made-up of 98 individuals who completed an online survey from October 2018 through December 2018. Data was obtained from survey responses by participants. Correlational and hierarchical multiple linear regression analyses were conducted for hypotheses testing.Significant correlations were found between the following symptoms: pain, fatigue, peristomal skin problem, psychological distress and HRQOL. SRH was also found to be significantly correlated with HRQOL. In hierarchical multiple linear regression, the variables of pain, fatigue, psychological distress, and SRH explained a significant portion of the variance in HRQOL in this sample study.This study contributed to the body of knowledge concerning predictors of HRQOL in younger persons living with IBD and an intestinal stoma as findings from this study suggest global disease symptoms and the manner in which individual’s perceive health are more important than transient stoma symptoms in this population. However, more empirical evidence is necessary gain further insight into these predictors. Additionally, it is reasonable that individuals in this population regularly make use of online resources. In order to understand how this impacts this population, it is also essential to gain further insight into the online management of stoma and disease.
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Process mining techniques attempt to extract non-trivial knowledge and insights from activity logs and use them for further analyses. The traditional process mining focuses on addressing three different problems: workflow discovery, conformance checking and model enhancement. Although many theoretical studies have been done in the process mining domain, studies that applying process mining on solving real-world problems are limited. This dissertation explores how process mining can be used in real-world process analysis to reveal process insights and help human decision making. Novel algorithms and frameworks were proposed to better model and address the real-world problems. In addition, we introduced the recommender system into the process mining domain to help build a data-driven decision support system. Specifically, this dissertation includes three main contributions: (1) application of process mining techniques in real-world medical process analysis; (2) two different process recommender systems; and (3) a process visual analytic tool. First, we applied process mining techniques to real-world medical process analysis. To enhance the existing workflow discovery algorithm, we developed a splitting-based workflow discovery method. Our method is able to tackle the duplicate-activity problem by allowing the activity nodes in the model to further split. By comparing our discovered model to hand-made expert workflow model of the same process, we were able to find the discrepancies between work-as-done and work-as-imaged. To further quantify and analyze the discrepancies between work-as-done and work-as-imaged, we invented a framework for automatic process deviation detection. Our framework first compares the observed process traces with knowledge-driven workflow models using a phase-based conformance checking algorithm. The discrepancies (process deviations) were analyzed and false alarms were identified. The false alarms were categorized into three types of causes: (1) model gaps or discrepancies between the model (“work as imagined”) and actual practice (“work as done”), (2) errors in activity trace coding, and (3) algorithm limitations. The deviation detection system was then repaired according to the false alarms. With our framework, the deviation detection accuracy was improved from 66.6% to 98.5%. The output system was then applied on unseen datasets to automatically detect the deviations. We applied our framework to two different medical processes and discovered meaningful medical findings. In addition, to analyze the differences between the medical treatment procedures of different patients, we introduced a framework for analyzing the association between treatment procedures and patient cohorts. The framework works by learning weights of context attributes by best-first search, deciding patient cohorts using clustering algorithms, discovering treatment procedures (or patterns) with process mining techniques, and analyzing the cohort-vs.-procedure through statistical analysis.Second, existing recommender systems have not been developed based on process mining. Our work presents such a bridge. We designed a data-driven process analysis and recommender system that can provide contemporaneous recommendations of process steps and help with retrospective analyses of the process. We first designed a prototype-based recommender system. This approach relies on mining historic data to uncover the potential association between the way of enacting a process and contextual attributes. If association tests are significant, we train a recommender system to output a prototypical enactment for the given context attributes. The system recommends all steps at once. Although it may not be feasible for the performers to study and follow a long list of steps, this recommendation can be used at runtime to automatically verify the process compliance and detect omitted steps and other process errors. Later, we proposed another recommender system that is able to provide step-by-step recommendations. The system was built on recurrent neural networks. The networks took both environmental and behavioral contextual information as input and output next-step suggestions. Last, we implemented our methods into a visual analytic tool. The tool was named as VIT-PLA, which is short for Visual Interactive Tool for Process Log Analysis. In this tool, we proposed a prototype-based process data visualization strategy. The strategy works by first clustering process data into clusters and then discovering the prototypical procedure from each cluster. Only such cluster prototypes were visualized and presented to the users. Our strategy can greatly reduce the data amount to visualize but preserve the characteristics of each cluster. Statistical analyses were followed and visualized to help analysts better understand their process data.
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