The rapid spread of the coronavirus disease (COVID-19) has impacted the lives of millions around the globe. The COVID-19 pandemic has caused increasing concern among treatment professionals about mental health and risky substance use, especially among those who are struggling with a substance use disorder (SUD). The pandemic's impact on those with an SUD may be heightened in vulnerable communities, such as those living in under-resourced and rural areas. Despite policies loosening restrictions on treatment requirements, unintended mental health consequences may arise among this population. We discuss challenges that under-resourced areas face and propose strategies that may improve outcomes for those seeking treatment for SUDs in these areas.

Background Vancomycin dosing strategies targeting trough concentrations of 15–20 mg/L are no longer supported due to lack of efficacy evidence and increased risk of nephrotoxicity. Area-under-the-curve (AUC24) nomograms have demonstrated adequate attainment of AUC24 goals ≥ 400 mg h/L with more conservative troughs (10–15 mg/L). Objective The purpose of this study is to clinically validate a vancomycin AUC24 dosing nomogram compared to conventional dosing methods with regards to therapeutic failure and rates of acute kidney injury. Setting This study was conducted at a tertiary, community, teaching hospital in the United States. Method This retrospective, cohort study compared the rates of therapeutic failures between AUC24-extrapolated dosing and conventional dosing methods. Main outcome measure Primary outcome was treatment failure, defined as all-cause mortality within 30 days, persistent positive methicillin-resistant Staphylococcus aureus blood culture, or clinical failure. Rates of acute kidney injury in non-dialysis patients was a secondary endpoint. Results There were 96 participants in the extrapolated-AUC24 cohort and 60 participants in the conventional cohort. Baseline characteristics were similar between cohorts. Failure rates were 11.5% (11/96) in the extrapolated-AUC24 group compared to 18.3% (11/60) in the conventional group (p = 0.245). Reasons for failure were 6 deaths and 5 clinical failures in the extrapolated-AUC24 cohort and 10 deaths and 1 clinical failure in the conventional group. Acute kidney injury rates were 2.7% (2/73) and 16.4% (9/55) in the extrapolated-AUC24 and conventional cohorts, respectively (p = 0.009). Conclusion Extrapolated-AUC24 dosing was associated with less nephrotoxicity without an increase in treatment failures for bloodstream infections compared to conventional dosing. Further investigation is warranted to determine the relationship between extrapolated-AUC24 dosing and clinical failures.

Målet med det här projektet var att undersöka huruvida en XRF signalkan förbättras genom att filtrera denna genom en tunn skiva avantingen aluminium, bly eller molybden. För att undersöka dettaanvändes programmet MCNP6.2 för att simulera strålningsspektrat fråntvå olika strålkällor; ett röntgenrör av Wolfram som kan genererafotoner med energier så höga som 120keV, och ett radioaktivt Co-57preparat. Projektet visade på att detta är möjligt, och även attaluminium är att föredra i de flesta fall då det förbättrar XRFsignalenavsevärt, utan att behöva vara varken väldigt tjockt, elleroapplicerbart tunt. Utöver detta är aluminium även billigare samtlättare än de båda andra materialen. Även molybden kunde filtrerastrålningen på ett användbart vis när en Co-57 källa används. Dockbehövde filtret vara extremt tunt, därför rekommenderas ej molybden.Projektets slutsats var att ett 0,3 mm tjockt aluminiumfilterförbättrar signalen som mest när röntgenröret användes som källa,medans ett 0,8 mm tjockt aluminiumfilter fungerar bäst för Co-57 källan.

The issue of natural resource scarcity has so far been addressed in the literature on the basis of various measures such as the unit cost of production, the relative market price, and the shadow price of a resource. Although it has been recognized that there exists some kind of jointness (sometimes inseparable) between an extractible resource and its surrounding environment, none of the measures, either theoretically or empirically, have included this concern. In order to extract and use a natural resource (e.g., coal) the environment (air, water, etc.) must also be used as a repository of the discharged wastes (e.g., sulphur oxides, nitrous oxides, particulates, etc.) . Moreover, if there is a mandated level of the environmental resource (e. g., clean air) that has to be maintained, then certain additional costs must be borne by society (firms utilizing the resource). Thus, in evaluating the scarcity of an extractible resource, the relative position of the environmental resource also must be evaluated. The present study has incorporated such jointness in the evaluation of the measure of resource scarcity. The theoretical model has been developed in an optimal control framework. It has been analytically shown that this new measure of resource scarcity would indicate a different trend compared to earlier ones. The measure of resource scarcity developed in this study captures previous measures as special cases. In an uncertain world, when the impacts of use of an extractible resource on the environment is not known the stock size of the environmental resource becomes uncertain. It has been analytically shown that in a situation of uncertain environmental stock the scarcity indicator would indicate a relatively slower extraction compared to that of a deterministic world. Empirical investigations in this study suggest that coal in use might be becoming relatively scarce if one considers the use of it in the electricity industry as the major use, compared to a situation where no environmental concerns are in effect.

This essay examines hierarchical structures in two versions of Jules Vernes’ Twenty Thousand Leagues Under the Sea (1870). A full translation by Erik Carlquist is compared with a retelling of the story by Peter Gotthardt (2010), and the comparison is intended to show similarities and differences in the hierarchical structures that can be found in the two versions. The essay uses an ecofeminist perspective to locate and analyse the hierarchies, and a postcolonial view is also present to deepen the analysis of the imperialistic aspects that can be found in the novel. The essay examines the relationship between human and nature, civilisation and the primitive and also between four characters in the novels. The essay shows that the hierarchical structures that can be found in the full translation can still be observed in the retelling, even though the retelling is around 300 pages shorter. In both versions, humans are dominant in its relation to nature, and the imperialistic tendencies maintain a hierarchical relationship between civilisation and the primitive, with the nationalistic features and the Western identity further establishing the civilisation as dominant. The hierarchical structures between the characters mainly stay the same between the two versions, although attempts to flatten out the hierarchical relationships can be seen in the retelling.

京都大学 / 0048 / 新制・課程博士 / 博士(社会健康医学) / 甲第20623号 / 社医博第81号 / 社新制||医||9(附属図書館) / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 木原 正博, 教授 中川 一路, 教授 平家 俊男 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM

Spreadsheets are often used for data input and resource planning, but studies in health care indicate various barriers causing under-reporting. Other studies suggest mobile reporting systems could prevent under-reporting. Nevertheless, the importance of smartphone-based reporting systems within industries is scarcely researched. This thesis studies the importance of such systems and their usability within an industrial context, and the connection to sustainable resource planning. A literature study was conducted and a smartphone prototype was developed. Using a qualitative and mixed-methods approach, eight industrial employees currently using a spreadsheet-based reporting system were asked about barriers to data reporting and the importance of smartphone-based reporting systems. After testing the prototype, they were asked about the importance of different usability-related aspects. The recordings were transcribed and the collected data was sorted and analyzed. This was done to understand the reasoning behind the aspects perceived as the most significant among the respondents. The case study showed that not remembering was regarded as the most important barrier to data reporting among end users of a spreadsheet-based reporting system. Smartphone-based reporting systems that are easy to use with daily reporting and reminders sent to the phone were considered by all respondents to have a positive impact on reporting frequency. However, privacy concerns and phone-related distractions could be issues if no work-related smartphones are provided. This thesis is of interest to developers of smartphone applications with usability in mind, and to companies striving for more efficient and cost-effective resource planning. On a larger scale, it provides insight into how smartphone-based reporting based on usability could lead to improved sustainability through more effective resource usage.

This work includes an attempt to enhance the autonomy of intelligent agents via real-time learning.In nature, the ability to learn at runtime gives species which can do so key advantages over others. While most AI systems do not need to have this ability but can be trained before deployment, it allows agents to adapt, at runtime, to changing and generally unknown circumstances, and then to exploit their environment for their own purposes. To reach this goal, in this thesis a pragmatic design (ONA) for a general-purpose reasoner incorporating Non-Axiomatic Reasoning System (NARS) theory is explored. The design and implementation is presented in detail, in addition to the theoretical foundation. Then, experiments related to various system capabilities are carried out and summarized, together with application projects where ONA is utilized: a traffic surveillance application in the Smart City domain to identify traffic anomalies through real-time reasoning and learning, and a system to help first responders by providing driving assistance and presenting of mission-critical information. Also it is shown how reliable real-time learning can help to increase autonomy of intelligent agents beyond the current state-of-the-art. Here, theoretical and practical comparisons with established frameworks and specific techniques such as Q-Learning are made, and it is shown that ONA does also work in non-Markovian environments where Q-Learning cannot be applied. Some of the reasoner's capabilities are also demonstrated on real robotic hardware. The experiments there show combining learning knowledge at runtime with the utilization of only partly complete mission-related background knowledge given by the designer, allowing the agent to perform a complex task from an only minimal mission specification which does not include learnable details. Overall, ONA is suitable for autonomous agents as it combines, in a single technique, the strengths of behavior learning, which is usually captured by Reinforcement Learning, and means-end reasoning (such as Belief-Desire-Intention models with planner) to effectively utilize knowledge expressed by a designer. / Computer and Information Science

The U.S. Department of Labor reports that only 5% of U.S. workers are employed in fields related to science and engineering, yet they are responsible for more than 50% of our sustained economic expansion (U.S Department of Labor, 2007). Furthermore, minorities makeup 0.0025 % (1/400) of that Science, Technology, Engineering, and Mathematics (STEM) workforce. Methods are currently underway to understand and address the attrition of minority students in the STEM workforce. The problem of underrepresentation of minorities (URMs) in STEM careers continues to result in a “leaky pipeline” where URMs have cited institutional factors such “chilly campus” climates as barriers to persistence and success (Tinto, 1993 Astin, 1993, Seymour & Hewitt, 1997). Despite the “separate but equal” challenge surrounding the early establishment of US educational institutions, the US remains a model for accessible and affordable education. Social equity concerning URM student outcomes in STEM has become a prominent topic of discussion. Researchers and practitioners seek to understand why the growing disparity exists for minority students as this underserved population represents those that higher education has been least successful in educating (Bensimon, 2007). This daunting assertion of disparate educational attainment by race and ethnicity is alarming. In this study the researcher used archived data and web content analysis to conduct a quantitative study to understand the effect of institutional constructs on the graduation rate of African American students pursing engineering degrees. The research model included hypotheses resulting from independent institutional variables of African American engineering students, institutional size and type, institutional endowment and social equity initiatives. The dependent variable of African American engineering student graduation rates was considered in relation to each independent variable. To answer research questions 1 and 2, descriptive statistics were used to analyze data that provided a comprehensive description of the institutions’ resources and social equity initiatives. Spearman’s Rho with ordinal variables and a small number of cases were computed to analyze the data. This analysis revealed a positive correlation between the numbers of social equity initiatives and engineering graduation rates of African American students at PWI southeast land-grant colleges and universities located in the southeastern portion of the U.S. The outcomes of this study help to expand the literature on underrepresented minority (URM) STEM retention in higher education. Understanding the effects of institutional constructs on the success of African American engineering students allows for the implementation of effective intervention strategies that will help to increase the pipeline of well-prepared African American engineers for the global STEM workforce.

A large class of practical planning problems that require reasoning about uncertain outcomes, as well as tradeoffs among competing goals, can be modeled as Markov decision processes (MDPs). This model has been studied for over 60 years, and has many applications that range from stochastic inventory control and supply-chain planning, to probabilistic model checking and robotic control. Standard dynamic programming algorithms solve these problems for the entire state space. A more efficient heuristic search approach focuses computation on solving these problems for the relevant part of the state space only, given a start state, and using heuristics to identify irrelevant parts of the state space that can be safely ignored. This dissertation considers the heuristic search approach to this class of problems, and makes three contributions that advance this approach. The first contribution is a novel algorithm for solving MDPs that integrates the standard value iteration algorithm with branch-and-bound search. Called branch-and-bound value iteration, the new algorithm has several advantages over existing algorithms. The second contribution is the integration of recently-developed suboptimality bounds in heuristic search algorithm for MDPs, making it possible for iterative algorithms for solving these planning problems to detect convergence to a bounded-suboptimal solution. The third contribution is the evaluation and analysis of some techniques that are widely-used by state-of-the-art planning algorithms, the identification of some weaknesses of these techniques, and the development of a more efficient implementation of one of these techniques -- a solved-labeling procedure that speeds converge by leveraging a decomposition of the state-space graph of a planning problem into strongly-connected components. The new algorithms and techniques introduced in this dissertation are experimentally evaluated on a range of widely-used planning benchmarks.