Search Results (13)

By examining the significant flaws in multivariate risk analysis and integrated risk analysis, this article introduces a new approach to evaluating the total risk within complex risk systems: the principle of multi-dimensional risk (MDR) analysis. Under this framework, the scope of each individual risk is first defined, and the risk-bearing entity is identified. Each risk is then analyzed independently, and the results are subsequently integrated to provide a comprehensive view of MDR. Multivariate risk analysis becomes increasingly impractical as the number of factors grows, due to the correspondingly large sample size required—often unattainable in real-world conditions. Integrated risk analysis methods, such as weighted combinations and Copula techniques, are heavily influenced by subjective factors, which compromise the reliability of their results. In contrast, MDR analysis involves fewer variables per individual risk, reducing the sample size requirement and making data collection more feasible. Individual risks can be quantified using objective physical indicators such as economic loss or physical injury, enabling more accurate calculations of the total risk across the system. A case study involving two-dimensional risks—flood and earthquake—demonstrated that these events often have vastly different occurrence cycles. When these risks are entangled in conventional analysis, the resulting annual total risk value can be severely distorted. By analyzing individual risks separately, maintaining the focus on overall system risk, and treating the total risk as an MDR problem, a more reliable foundation for policy-making and risk management can be established. There are at least three types of MDR relationships: independent, compounding, and negatively correlated. As a result, no universal MDR analysis model exists. Full article

Using the volatility spillover index method based on the quantile vector autoregression (QVAR) model, this paper systematically examines structural changes and corresponding spillover effects within 20 major stock markets under both extreme and normal market conditions, using data spanning from January 2005 to January 2023. The results show that, compared to the traditional volatility spillover index method, which focuses mainly on average spillover effects, the QVAR model-based spillover index better captures spillover effects under extreme and various market conditions among global stock markets. The connections between stock markets are closer in extreme market conditions. The total spillover index of major global stock markets significantly increases in extreme conditions compared to normal conditions. In extreme market conditions, inflow indices show varying degrees of increase, with emerging economy stock markets displaying more significant increases. The outflow indices exhibit heterogeneity; emerging economies show consistent increases, while developed economies show mixed changes. Full article

Supplier selection is a multi-attribute decision-making (MADM) problem that is affected by often-conflicting factors (e.g., price, quality, and delivery performance). If a supplier selection problem (SSP) is solved by different MADM methods, different solutions are likely to be obtained. This can be advantageous for decision makers because they have a good choice of alternative solutions. However, it brings about the need for a comparison approach for choosing the solution that best fits the decision maker’s purchasing strategy. So, decision makers may have two needs: (1) a good choice of alternative solutions and (2) a comparison approach. To help decision makers with the first need, we make two contributions to the literature on SSPs. For one, we formulate an integer nonlinear optimization model that evaluates and sorts the suppliers based on similarity to the ideal solution. For another, we make enhancements to the existing Factor Rating (FR) method. For the second need, we propose a comparison procedure to rank different solutions by measuring their relative closeness, both Rectilinear and Euclidean, to the ideal solution. The first two proposed methods along with the existing FR and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methods are applied to a set of test SSPs, and then, the comparison procedure is used to identify the ‘superior’ method for each test problem. Full article

Predicting nurse turnover is a growing challenge within the healthcare sector, profoundly impacting healthcare quality and the nursing profession. This study employs the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance issues in the 2018 National Sample Survey of Registered Nurses dataset and predict nurse turnover using machine learning algorithms. Four machine learning algorithms, namely logistic regression, random forests, decision tree, and extreme gradient boosting, were applied to the SMOTE-enhanced dataset. The data were split into 80% training and 20% validation sets. Eighteen carefully selected variables from the database served as predictive features, and the machine learning model identified age, working hours, electric health record/electronic medical record, individual income, and job type as important features concerning nurse turnover. The study includes a performance comparison based on accuracy, precision, recall (sensitivity), F1-score, and AUC. In summary, the results demonstrate that SMOTE-enhanced random forests exhibit the most robust predictive power in the classical approach (with all 18 predictive variables) and an optimized approach (utilizing eight key predictive variables). Extreme gradient boosting, decision tree, and logistic regression follow in performance. Notably, age emerges as the most influential factor in nurse turnover, with working hours, electric health record/electronic medical record usability, individual income, and region also playing significant roles. This research offers valuable insights for healthcare researchers and stakeholders, aiding in selecting suitable machine learning algorithms for nurse turnover prediction. Full article

In the recent Central American migrations spurred by violence, political instability, and economic insecurity, women grapple with whether and when to bring their children with them in pursuit of safety in another country, and with fulfilling their roles as mothers from afar. Drawing from the transnational motherhood literature and critical feminist theories, this interpretive qualitative study examined transnational motherhood grounded in the lived experiences of Central American women (n = 19) over the course of their migrations to the US. Informed by the principles of grounded theory, the inductive analysis identified five processes in which migration and violence shaped meanings of motherhood: risking everything, embodying separation, braving reunification, mothering others, and experiencing motherhood due to sexual violence. The findings contribute knowledge of how violence shapes and informs women’s migrations and decision-making, and the consequences women endure in taking action to mitigate threats of violence in their own and their children’s lives. The analysis furthermore highlights the specific and profound effects of family separation on mothers. The voices, perspectives, and experiences of migrating mothers and the ways in which migration and violence shapes notions and lived experiences of motherhood are imperative to research, practice, and advocacy to change oppressive immigration policies. Full article

Introduction: Brain plasticity is not limited to childhood or adolescence, as originally assumed, but continues into adulthood. Understanding this conceptual evolution about the nervous system, neuroscience and neurorehabilitation, researchers have left different proverbs and aphorisms derived of their investigations that are still used in university and postgraduate training. A proverb is defined as a phrase of popular origin traditionally repeated invariably, in which a moral thought, advice or teaching is expressed. On the other hand, an aphorism is understood as a brief and doctrinal phrase or sentence that is proposed as a rule in some science or art. The aim of this paper is to present a compilation of proverbs and aphorisms related to neuroscience and neurorehabilitation, classified chronologically, to illustrate the conceptual evolution about the brain and to improve our understanding about the management of neurological patients through the methods and techniques developed during the 19th, 20th and 21st centuries, as many therapies are based on them. Methods: A literature review was conducted based on the recommendations for Systematic Reviews guidelines for scoping reviews. A computerized search was conducted in the following electronic databases: CINAHL Medical Science, Medline through EBSCO, PubMed, Physiotherapy Evidence Database (PEDro) and Scopus, limiting the search to papers published until April 2021 in English and Spanish. Inverse searches were also carried out based on papers found in the databases. The following data were extracted: technique or approach; author; date of birth and death; proverbs and aphorisms; clinical interpretation. Results: Proverbs and aphorisms linked to authors such as Charles Edward Beevor (1854–1908), Heinrich Sebastian Frenkel (1860–1931), Rudolf Magnus (1873–1927), Nikolai Bernstein (1896–1966), Donald O. Hebb (1904–1985), Elwood Henneman (1915–1996), Wilder Graves Penfield (1891–1976), Humberto Augusto Maturana Romesín (1928), Edward Taub (1931), Janet Howard Carr (1933–2014), Roberta Barkworth Shepherd (1934), Brown & Hardman (1987), Jeffrey A. Kleim and Theresa A. Jones (2008) were compiled. Conclusion: Different authors have developed throughout history a series of proverbs and aphorisms related to neurosciences and neurorehabilitation that have helped to better our understanding of the nervous system and, therefore, in the management of the neurological patient through the methods and techniques developed throughout the 19th, 20th and 21st centuries. Full article

Manufacturing activities of China and the U.S. account for a substantial portion of the global manufacturing output and environmental sustainability impacts. The two countries’ economies account for one third of the global economic output. Their supply chains are critically linked with and serve most of the production and service industries across the globe. Recent global trends in manufacturing necessitate a study that comparatively analyzes the two countries’ manufacturing industries from an economic and environmental perspective. In this paper, U.S. and China manufacturing industries were investigated to analyze the economic and mid and endpoint environmental impacts over a 20-year study period. The literature is abundant with single period and single country focused works, and this study contributes to the state-of-art by extending the temporal dimension to 20 years and spatial focus to the global economy (40 countries and rest of the world). In terms of the methodology, Multi-region input-output (MRIO) models were built using the World Input-Output Database (WIOD) as the primary database, global input-output tables, environmental impact and economic output multipliers, and manufacturing industries’ final demand. Twenty MRIO models, each comprised of 40 major economies and the rest of the world (ROW), were built to cover the global trade linkages, which yielded the global supply chain linked cradle-to-gate life cycle inventory (LCI) of economic outputs and environmental impacts. The environmental LCI was extended to midpoint (Global Warming Potential (GWP) and Ozone Depletion Potential (ODP)) and endpoint (human health and ecosystem) impact dimensions by ReCipe framework. Lastly, the relative impact of a unit change in Leontief inverse, final demand and Green House Gas (GHG) emission multipliers on the total economic output and environmental impacts were explored with structural decomposition analysis (SDA). Results indicated that both countries’ manufacturing industries experienced positive economic output growth, in which China was more dominant in recent years. Both countries’ manufacturing industries’ midpoint and endpoint impacts were found to be steeply rising despite the negative growth observed in emissions intensities. The amount of GHG emissions and related midpoint (global warming and ozone depletion) and endpoint (damage to ecosystems and human life) impacts seemed to be quickly worsening in China compared to the USA. Full article

Measuring the U.S.’s COVID-19 response performance is an extremely important challenge for health care policymakers. This study integrates Data Envelopment Analysis (DEA) with four different machine learning (ML) techniques to assess the efficiency and evaluate the U.S.’s COVID-19 response performance. First, DEA is applied to measure the efficiency of fifty U.S. states considering four inputs: number of tested, public funding, number of health care employees, number of hospital beds. Then, number of recovered from COVID-19 as a desirable output and number of confirmed COVID-19 cases as a undesirable output are considered. In the second stage, Classification and Regression Tree (CART), Boosted Tree (BT), Random Forest (RF), and Logistic Regression (LR) were applied to predict the COVID-19 response performance based on fifteen environmental factors, which were classified into social distancing, health policy, and socioeconomic measures. The results showed that 23 states were efficient with an average efficiency score of 0.97. Furthermore, BT and RF models produced the best prediction results and CART performed better than LR. Lastly, urban, physical inactivity, number of tested per population, population density, and total hospital beds per population were the most influential factors on efficiency. Full article

In 1891, Edward A. Pace, a Catholic priest and first-generation psychologist, commenced a career at the Catholic University of America in Washington, D.C. Amidst the daunting challenges in being a professor and researcher, particularly at a newly established university, he thrust himself into a third role, apologist. Habits related to the Monsignor’s three roles have contemporary relevance for psychologically-trained Protestants; in this case study, we examine four notable practices. Dr. Pace modeled an appetence for wisdom in multiple disciplines, a keen awareness of rival worldviews, intentional ripostes to Catholic critics of scientific psychology, and last, unrelenting steadfastness to the Christian faith. To characterize the priest-psychologist, we present a brief biographical sketch and an overview of influential historical movements in the zeitgeist of the late 19th and early 20th centuries affecting his life. In addition, the aforementioned habits of Pace and applications for Protestants engaging in psychology in the 21st century are delineated. Full article

This article uses two examples of postwar German Jewish literature to explore the way in which these literary reflections on fictionality can also serve to subvert and complicate the national narratives that were developed in East and West Germany. The novels explored here, Jurek Becker’s Jakob the Liar (1969) and Edgar Hilsenrath’s The Nazi and the Barber (1977), directly thematize storytelling and specifically, storytelling in the context of the Holocaust and its aftermath. Both also share an interest in the intersections between German and Yiddish narrative traditions and reflect on the ways in which the latter was coopted by the former in the decades following the Second World War. Ultimately, this article argues that these two novels of lying create spaces in which the foundational myths of both German states are called into question. Full article

In recent decades, a growing body of literature focused on the effects of scandals on congressional elections. The studies concluded that scandals decrease candidates’ vote totals, and that certain types of scandals have a more deleterious effect than others. Virtually all of these studies focus on House elections. The obvious differences between the two chambers calls into question the applicability of these findings for Senate elections. This study examines the impact that incumbent scandals had on senatorial elections from 1972 to 2016. Scandals are categorized based on the nature of the transgression in order to determine if the type of scandal made a difference. The results reveal that senators seeking reelection while confronting a scandal suffered a 4% decrease in the popular vote. Scandals involving political misdeeds, financial improprieties, and controversial statements hurt incumbents the most. Scandals also attracted challengers who spent more money against the incumbents. Full article

This piece recommends the implicit as a resource for examining normativity within the study of religion. Attention to the implicit serves at least two purposes toward this end. First, it gives the scholar of religion a clearer sense of the norms of the communities she seeks to understand, norms that, depending partly on one’s methodological commitments, may be evaluated as well as described. Second, it deepens the scholar’s reflections on the implicit norms that guide her own work. These claims—which extend the work of Tyler Roberts, Kevin Schilbrack, and Thomas A. Lewis—are embedded within specific understandings of language and mind as drawn from Robert Brandom and Peter Ochs. Brandom and Ochs help speak to the questions of whether the academic study of a religious tradition can or should evaluate that tradition, answering “yes” and “it depends”, respectively. This presents scholars of religion with both a challenge and an opportunity. The challenge is that religionists no longer have recourse to a strict distinction between fact and value. The opportunity is that, by linking implicit facts and values to explicit analysis and evaluation, scholarly investigations can be expanded in both descriptive and prescriptive contexts. Full article

This project serves as a study comparing greenhouse gas (GHG) emissions between universities in Texas. Over 90 percent of climate scientists believe that increased climate change is due to anthropogenic causes. These anthropogenic causes result in the GHG that we emit in our day-to-day activities. Our study quantifies the GHG data from our university, St. Edward’s University in Austin, and compares it to data obtained from other Texas universities. This report will serve as a reference to the universities involved to improve sustainability initiatives in place by comparing practices and metrics. These findings may also serve as a catalyst for action for other universities to begin implementing their own sustainability practices. Our hypotheses are exploratory in nature; schools with sustainability offices will have lower emissions than those without, and St. Edward’s emissions will have decreased since the institution of a sustainability program. The results show that there does seem to be a correlation between the schools with the lowest GHG emissions and their creation of a sustainability office. Full article