6,694 research outputs found

    A Multi-level Analysis on Implementation of Low-Cost IVF in Sub-Saharan Africa: A Case Study of Uganda.

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    Introduction: Globally, infertility is a major reproductive disease that affects an estimated 186 million people worldwide. In Sub-Saharan Africa, the burden of infertility is considerably high, affecting one in every four couples of reproductive age. Furthermore, infertility in this context has severe psychosocial, emotional, economic and health consequences. Absence of affordable fertility services in Sub-Saharan Africa has been justified by overpopulation and limited resources, resulting in inequitable access to infertility treatment compared to developed countries. Therefore, low-cost IVF (LCIVF) initiatives have been developed to simplify IVF-related treatment, reduce costs, and improve access to treatment for individuals in low-resource contexts. However, there is a gap between the development of LCIVF initiatives and their implementation in Sub-Saharan Africa. Uganda is the first country in East and Central Africa to undergo implementation of LCIVF initiatives within its public health system at Mulago Women’s Hospital. Methods: This was an exploratory, qualitative, single, case study conducted at Mulago Women’s Hospital in Kampala, Uganda. The objective of this study was to explore how LCIVF initiatives have been implemented within the public health system of Uganda at the macro-, meso- and micro-level. Primary qualitative data was collected using semi-structured interviews, hospital observations informal conversations, and document review. Using purposive and snowball sampling, a total of twenty-three key informants were interviewed including government officials, clinicians (doctors, nurses, technicians), hospital management, implementers, patient advocacy representatives, private sector practitioners, international organizational representatives, educational institution, and professional medical associations. Sources of secondary data included government and non-government reports, hospital records, organizational briefs, and press outputs. Using a multi-level data analysis approach, this study undertook a hybrid inductive/deductive thematic analysis, with the deductive analysis guided by the Consolidated Framework for Implementation Research (CFIR). Findings: Factors facilitating implementation included international recognition of infertility as a reproductive disease, strong political advocacy and oversight, patient needs & advocacy, government funding, inter-organizational collaboration, tension to change, competition in the private sector, intervention adaptability & trialability, relative priority, motivation &advocacy of fertility providers and specialist training. While barriers included scarcity of embryologists, intervention complexity, insufficient knowledge, evidence strength & quality of intervention, inadequate leadership engagement & hospital autonomy, poor public knowledge, limited engagement with traditional, cultural, and religious leaders, lack of salary incentives and concerns of revenue loss associated with low-cost options. Research contributions: This study contributes to knowledge of factors salient to implementation of LCIVF initiatives in a Sub-Saharan context. Effective implementation of these initiatives requires (1) sustained political support and favourable policy & legislation, (2) public sensitization and engagement of traditional, cultural, and religious leaders (3) strengthening local innovation and capacity building of fertility health workers, in particular embryologists (4) sustained implementor leadership engagement and inter-organizational collaboration and (5) proven clinical evidence and utilization of LCIVF initiatives in innovator countries. It also adds to the literature on the applicability of the CFIR framework in explaining factors that influence successful implementation in developing countries and offer opportunities for comparisons across studies

    A Simulation of the Impacts of Climate Change on Civil Aircraft Takeoff Performance

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    Climate change affects the near-surface environmental conditions that prevail at airports worldwide. Among these, air density and headwind speed are major determinants of takeoff performance, and their sensitivity to global warming carries potential operational and economic implications for the commercial air transport industry. Previous archival and prospective research observed a weakening in headwind strength and predicted an increase in near-surface temperatures, respectively, resulting in an increase in takeoff distances and weight restrictions. The main purpose of the present study was to update and generalize the extant prospective research using a more representative sample of worldwide airports, a wider range of climate scenarios, and next-generation climate models. The research questions included how much additional thrust and payload removal will be required to offset the centurial changes in takeoff conditions. This study relied on a quantitative method using the simulation instrument. Forecast climate data corresponding to four shared socioeconomic pathways (SSP1‒2.6, SSP2‒4.5, SSP3‒7.0, and SSP5‒8.5) over the available 2015‒2100 period were sourced from a high-resolution CMIP6 global circulation model. These data were used to characterize the six-hourly near-surface environmental conditions prevailing at all 881 airports worldwide having at least one million passengers in pre-COVID‒19 traffic. The missing air density was iii numerically derived from the air temperature, pressure, and humidity variables, while the headwind speed for each airport’s active runway configuration was triangulated from the wind vector components. Separately, a direct takeoff-dynamics simulation model was developed from first principles and calibrated against published performance data under international standard atmospheric conditions for two narrowbody and two widebody aircraft. The model was used to simulate 1.8 billion unique takeoffs, each initiated at 75% of maximum takeoff thrust and 100% of maximum takeoff mass. When the resulting takeoff distance required exceeded that available, the takeoff thrust was gradually increased to 100%, after which the takeoff mass was gradually decreased to an estimated breakeven load factor. In total, 65 billion takeoff iterations were simulated. Longitudinal changes to takeoff thrust, distance, and payload were recorded and examined by aircraft type, climate scenario, and climate zone. The results show that despite a marked centurial increase in the global mean air temperature of 9.4%‒18.0% relative to the year 2015 under SSP2‒4.5 and SSP3‒7.0, air density will only decrease by 0.6%‒1.1% due to its weak sensitivity to temperature. Likewise, mean headwinds were observed to remain almost unchanged relative to the 2015 baseline. As a result, the global mean takeoff thrust was found to increase by no more than 0.3 percentage point while payload removals did not exceed 1.1 passenger. Significant deviations from the mean were observed at climatic outlier airports, including those located around the Siberian plateau, where takeoff operations may become more difficult. This study contributes to the air transport climate adaption body of knowledge by providing contrasting results relative to earlier research that reported strong impacts of global warming on takeoff performance

    Making friends with failure in STS

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    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    Leveraging a machine learning based predictive framework to study brain-phenotype relationships

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    An immense collective effort has been put towards the development of methods forquantifying brain activity and structure. In parallel, a similar effort has focused on collecting experimental data, resulting in ever-growing data banks of complex human in vivo neuroimaging data. Machine learning, a broad set of powerful and effective tools for identifying multivariate relationships in high-dimensional problem spaces, has proven to be a promising approach toward better understanding the relationships between the brain and different phenotypes of interest. However, applied machine learning within a predictive framework for the study of neuroimaging data introduces several domain-specific problems and considerations, leaving the overarching question of how to best structure and run experiments ambiguous. In this work, I cover two explicit pieces of this larger question, the relationship between data representation and predictive performance and a case study on issues related to data collected from disparate sites and cohorts. I then present the Brain Predictability toolbox, a soft- ware package to explicitly codify and make more broadly accessible to researchers the recommended steps in performing a predictive experiment, everything from framing a question to reporting results. This unique perspective ultimately offers recommen- dations, explicit analytical strategies, and example applications for using machine learning to study the brain

    Intelligent computing : the latest advances, challenges and future

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    Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing

    Numerical Simulations of Dusty Colliding Wind Binaries

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    Colliding Wind Binary (CWB) systems are relatively rare phenomena, but have a significant influence on galactic evolution in terms of dust production -- especially in the early universe. The mechanisms behind this dust production, however, are poorly understood. The strong winds from both partners in the binary system drive shocks that heat the dust forming region to temperatures in excess of 100 million Kelvin; whilst this region does rapidly cool, the initial shock temperatures would destroy any dust grains that formed outside the collision region. Furthermore, this collision region is difficult to observe and simulate, limiting our understanding of how grains form and evolve in this region. This thesis attempts to improve our understanding of the evolution of dust grains within these systems, particularly growth of these grains from small dust grain cores to micron-scale grains. A co-moving dust grain model was implemented that simulates growth through accretion of gas onto the dust grains, as well as destruction through gas-grain sputtering. The model also simulates cooling through collisional excitation and subsequent emission for both dust grains and gas. Overall, the goal of this model was to determine how dust growth was influenced by the wind and orbital characteristics of the system, and which of these characteristics were most important for dust growth. First, a parameter space exploration of dust producing CWB systems (WCd systems) was conducted, varying the orbital separation, the wind terminal velocity and the mass loss rate of each star. It was found that dust production is strongly influenced by the ratio of wind terminal velocities between each star, as well as the orbital separation. Following up on this, a limited simulation of the episodic dust forming system WR140 was conducted, in order to understand how variance in orbital separation through eccentricity changed dust production rates over the course of a periastron passage. Furthermore, it was determined that dust production occurs over a very short period immediately prior to periastron passage and a small period after, with an ``active'' phase of approximately 1 year, or an eighth of the systems orbital period Whilst there is much to be done in the future, and many more systems to be simulated (in particular the recently discovered WR+WR CWB systems WR48a and WR70-16) this model is a good first step towards shedding light on these elusive and dust-shrouded systems

    Composing games into complex institutions

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    Game theory is used by all behavioral sciences, but its development has long centered around tools for relatively simple games and toy systems, such as the economic interpretation of equilibrium outcomes. Our contribution, compositional game theory, permits another approach of equally general appeal: the high-level design of large games for expressing complex architectures and representing real-world institutions faithfully. Compositional game theory, grounded in the mathematics underlying programming languages, and introduced here as a general computational framework, increases the parsimony of game representations with abstraction and modularity, accelerates search and design, and helps theorists across disciplines express real-world institutional complexity in well-defined ways. Relative to existing approaches in game theory, compositional game theory is especially promising for solving game systems with long-range dependencies, for comparing large numbers of structurally related games, and for nesting games into the larger logical or strategic flows typical of real world policy or institutional systems.Comment: ~4000 words, 6 figure

    Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

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    In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

    EXPLORING THE ROLE OF PERCEPTUAL REASONING IN THE ORGANIZATION OF WRITTEN TEXT

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    Written expression has long been viewed as a verbal task. Although there is evidence that written expression requires a fluid interaction of multiple cognitive skills, including verbal comprehension, long-term memory, and working memory, there has been little or no attention paid to the role of perceptual reasoning skills. This study aimed to fill that gap by examining the hypothesis that writing is a problem-solving task that requires perceptual thinking in actively synthesizing or transforming knowledge into written text. When students translate their ideas into written language, they construct sentences out of words, paragraphs out of sentences, and essays out of paragraphs, a multi-level constructive process that is hypothesized to require perceptual reasoning. A correlational study was conducted on retrospective data from seventh grade students on verbal comprehension, perceptual reasoning, and written expression. The results suggest that for some aspects of written expression, perceptual reasoning has a significant contribution even after controlling for verbal comprehension skills. The results are discussed with respect to the inclusion of perceptual reasoning skills in interventions aimed at improving students’ written expression
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