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Business education is undergoing rapid transformations thanks to advancements in emerging technologies. Business school leaders are engaging in identifying approaches to revitalize business education, preparing students to succeed in a technologically advancing workplace. Primarily anchored in the concept of digital transformation, our article provides practical guidance to help business schools integrate artificial intelligence (AI) into their curricula and infrastructure, benefiting students, faculty, administration, and leadership. We offer a roadmap that lays out current industry trends and articulates pertinent action items, related cost efforts, and associated benefits for business education. This article positions business schools to instill a human-AI augmentation mindset in students, faculty, staff, and leadership that prepares them to work with ever-changing technologies. Regularly revising business course curricula to stay aligned with evolving industry needs is key to providing topical content and meaningful skill development opportunities for students. We argue that these efforts directly enhance student employability and learning, better preparing students to succeed in the increasingly technology-enabled workplace. © 2026 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

The purpose of this entry is to provide an orientation to new materialism, a form of inquiry or philosophy that brings renewed interest to materiality. New materialist philosophies offer an end to certainty, an end to the belief that meaning, knowledge, and agency are fixed and stable. New materialism, however, is not a cohesive body of work but rather a collection of theories, theorists, works, and assumptions, each with its own goals, methods, and desired end points. What constitutes new materialism, what contributes to new materialism, and what inspires new materialist scholarship are not necessarily the same. We trace some of the historical influences of new materialism, highlighting key theories and theorists, describing the intersecting philosophical traditions that mutually constitute new materialism, and exploring the implications of new materialism both for social justice and for qualitative research. © 2026 selection and editorial matter, Aaron M. Kuntz; individual chapters, the contributors.

IntroductionIncarceration in the United States disproportionately affects individuals with mental illness and substance use disorders and often exacerbates mental health conditions due to the trauma of arrest, separation, disrupted care, and stigma. Forensic Peer Support (FPS) can improve recovery outcomes by building unique trust and rapport that supports community reintegration and recovery outcomes.MethodThis paper describes findings from a qualitative study of 22 participants who are a sub-sample of the larger Recovery Finance study, a multi-level community based participatory research study designed to reduce financial hardships of individuals involved in criminal justice with mental health challenges through system level interventions as well as an individual intervention (randomized control trial testing one-on-one financial capability support (FCS) versus FCS plus forensic peer support). The focus of this paper is the mental health and peer support experiences of the qualitative sub-sample.ResultsThematic analysis of in-depth qualitative interviews revealed the following themes: 1. The Impact of Incarceration, Trauma, and Mental Health, 2. Systemic Failings in Mental Health Care in Prison and Post-Release, 3. The Cycle of Financial Strain and Mental Health, and 4. The Unique Value of Shared Lived Experience through Peer Support. Results highlight the trauma and psychological impact of incarceration, systemic failures in treatment, financial stress as a dominant source of anxiety, often perpetuating cycles of poverty and criminal activity, and the critical role of supportive relationships, in particular FPS.DiscussionThese findings point to the need for integrated models in which forensic peer support is embedded within financial wellness and reentry services.

Background: Maternal mortality remains a major global health challenge, disproportionately affecting black and Indigenous women. Hypertensive disorders of pregnancy and postpartum hemorrhage are the leading direct causes of maternal death. Artificial intelligence (AI) tools have emerged as potential strategies for predicting these complications, yet concerns persist about their equity and validation across racial groups. Methods: A rapid review was conducted in five databases, PubMed, EMBASE, Web of Science, Scopus and LILACS, to synthesize recent evidence on the use of AI for preventing maternal mortality due to hypertension and postpartum hemorrhage. Studies published in the last five years that included racial or ethnic data were selected and analyzed narratively. Results: Ten studies met the inclusion criteria, showing high predictive accuracy of AI models (AUROC often >0.95) for severe maternal outcomes. However, few models incorporated racial variables or underwent external validation in racially diverse or low-resource populations. Evidence suggests that unrepresentative datasets may perpetuate or exacerbate existing health inequities. Conclusions: AI demonstrates strong technical performance in predicting maternal complications but limited equity in application. Broader racial representation, external validation, and ethical governance are essential for ensuring that AI-based tools reduce rather than reinforce racial disparities in maternal mortality.

Cardiovascular diseases (CVDs) are still one of the foremost causes of death globally. An urgent need for an accurate premature diagnosis is essential for efficient treatment and avoidance. The goal of this research is to introduce our initial idea for developing a hybrid intelligent diagnosis model that integrates the Extreme Learning Machine (ELM) with Particle Swarm Optimization (PSO) to enhance diagnosis accuracy for heart disease. To address the problems of random initialization, the PSO algorithm is used to optimize the ELM’s input biases and weights. Experiments using the Cleveland Heart Disease dataset evaluated the performance of traditional ELM and the proposed PSO-ELM model. Results indicate that integrating PSO for parameter tuning yields significant improvements across evaluation metrics. The traditional ELM recorded an accuracy of 0.8595 on the training set and 0.8525 on the test set, while the PSO-ELM model achieved up to 0.9174 in training and 0.8852 in testing. Enhancements were also noted in precision, recall, and F1-score, with the F1-score increasing from 0.8768 for traditional ELM to over 0.92 for several PSO configurations.

The Diabetes Prevention Program (DPP) is an evidence-based approach to prevent Type 2 Diabetes (T2D), the leading cause of morbidity and mortality in the United States. Despite demonstrated benefits, acceptability and uptake of DPPs remain low, especially among communities with low incomes at increased risk for diabetes. The Yale-Griffin Prevention Research Center led the design of a community-engaged virtual DPP (vDPP) intervention and assessed its feasibility of implementation in two contrasting settings.

This chapter provides commentary on the four chapters in the section on the cultural, social, and linguistic aspects of school consultation. In each case, the authors pose penetrating insights and highlight a need for more extensive literature utilizing the full spectrum of research methodologies. In this spirit, this chapter suggests that strong consultation in these topical domains might be guided by the questions: (a) What is our mindset as researchers?, (b) Is the researcher culturally humble?, and (c) What might be most helpful? Building off the “what might be helpful” question, the commentary closes by framing two relevant areas where consultation research is lacking-consultation research in support of multiracial students and consultation research to address antisemitism and support Jewish students-as ways to expand the cultural, social, and linguistic aspects of school consultation literature. © 2026 selection and editorial matter, S. Andrew Garbacz, Daniel S. Newman, William P. Erchul, and Susan M. Sheridan; individual chapters, the contributors.

Gold serves as a safe-haven asset, an inflation hedge, and a store of value, among other economic purposes. History has a wealth of information about its connections to important macroeconomic and financial variables. We compare three supervised methods for one-day GLD (SPDR Gold Shares) prediction using cross-Asset drivers: SPX (S&P 500 Index), USO (United States Oil Fund), SLV (iShares Silver Trust), and EUR/USD (Euro-U.S. Dollar exchange rate). To fill this gap: (i) a linear regression baseline, (ii) a feedforward neural network (FFNN) trained by a Genetic Algorithm (GA), and (iii) an FFNN optimized through Particle Swarm Optimization (PSO). We use standard scaling, a held-out train/test split, and R2, Variance Accounted For (VAF), MSE, RMSE, MAE, and GA/PSO convergence curves to check how the optimizer works with daily data from 2008 to 2018. Empirically, PSO-FFNN does better than both the linear baseline and GA-FFNN. It has the best generalization (for example, Test MSE 38.99, R 0.929). This implies that (1) GLD possesses exploitable nonlinear structure concerning these predictors, and (2) PSO traverses the FFNN search space more effectively than GA in our context. The findings validate the application of evolutionary training for reliable and accurate gold price forecasts, with implications for risk management and strategic asset allocation. © 2025 IEEE.

Health education in preschool through 12th grade is an essential component of a well-rounded education and can positively impact student health and well-being. All school-age students deserve access to high-quality, effective health education. However, students with disabilities may not be receiving the health education they are entitled to for many reasons, including that there is not a clearly defined understanding of health education for students with disabilities. Working from the perspective that health education is both a social justice imperative and an important component of students’ “well-rounded education” under the Every Student Succeeds Act of 2015, this article is a call to action and brings attention to this area of need. It is framed around three critical questions: (1) Why do students with disabilities deserve access to health education? (2) How should health education for students with disabilities be conceptualized? (3) Where does accessible health education go from here? By articulating these questions and providing initial thoughts to spur further discussion and action, this article encourages invested partners and collaborators to address this critical need so that students with disabilities can meaningfully access the same high-quality health education opportunities as their peers. © 2026 SHAPE America.

A surprisingly large transverse polarization of Λ hyperons in unpolarized hadron-nucleon/nucleus collisions has been observed for 50 years, and the origin of this polarization remains an important open question. Recently, theoretical frameworks have advanced in describing this puzzle with the polarizing fragmentation function (PFF). We report the first measurement of Λ and transverse polarization inside jets in unpolarized proton-proton collisions, which is directly attributed to the PFF. The polarization is measured as a function of the jet transverse momentum, the fraction of the jet momentum carried by hyperons, and the transverse momentum of hyperons relative to the jet axis. Covering a wide jet-energy range, these data provide the first constraints on the gluon PFF and allow tests of TMD evolution and its universality. © The Author(s) 2026.

Water flow prediction and planning significantly help decision-makers determine the most suitable irrigation strategy and crop type and help avoid risks from flooding, among other benefits. Conventional statistical and physical models are often challenged by the highly dynamic and nonlinear nature of hydrological processes. Recent advances in machine learning (ML), including artificial neural networks (ANNs), provide powerful tools for modeling these complex relationships. However, the performance of these models depends on the optimal parameter tuning. By combining ANN with Crow Search Optimization, we aim to improve prediction accuracy and robustness while providing a clever, adaptable, and reliable solution to real-world water flow forecasting problems. The Crow Search Algorithm (CSA) is one of the most recent metaheuristic algorithms used as a training algorithm for neural network models to achieve higher performance. This research provides an evolutionary-based model to predict the flow of the Black River, a well-known river in the USA. The adopted ANN model was used to train and predict daily flows at the initial Black Water River station (No. 02047500) near Dendron, Virginia. Among the well-known metaheuristic algorithms employed in this study for comparison are the Salp Swarm Algorithm (SSA), Particle Swarm Optimization (PSO), and the Dandelion Optimizer (DO). Based on comparative research, the CSA algorithm outperforms other training algorithms in predicting river flow, achieving an average fitness value of 0.0048926, which is 41% better than SSA, 81% better than PSO, and 49% better than DO. Furthermore, CSA has achieved a superior convergence curve, and high variance accounts for VAFs of up to 99.06% on the training data and 98.45% on the test data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Addressing the issues of water insecurity and unequal access to water has been a priority of the Sustainable Development Goals. Poor sanitation and inadequate access to drinking water are the second leading causes of death among children worldwide. Access to safe drinking water is a fundamental human right. Climate change represents one of public health’s greatest challenges and significantly exacerbates health disparities. According to the World Health Organization, approximately 3.6 billion people reside in areas highly vulnerable to the effects of climate change. Furthermore, climate change is anticipated to result in roughly 250,000 additional deaths each year between 2030 and 2050. In the United States, climate impacts such as severe storms and floods, escalating wildfires, extreme heat, poor air quality, and diminishing access to food and water disproportionately endanger Black, Brown, and Indigenous communities, low-income groups, people with disabilities, women, children, older adults, and others, making them more susceptible to the harmful health effects of climate change. The formulation and implementation of climate policies are essential to address the negative impacts of climate change. This chapter aims to: (a) describe the effects of climate change on access to safe drinking water; (b) emphasize the implications of climate change on drinking water disparities; (c) highlight policies to mitigate the effects of climate change; and d) discuss recommendations to tackle climate change and inequities in access to water. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PURPOSE: Artificial intelligence (AI) chatbots based on large language models (LLMs) can deliver medical information, but their performance on specialized topics such as central auditory processing disorder (CAPD) remains unexplored. This study evaluated the accuracy and completeness of three AI chatbots (ChatGPT, Gemini, and Claude) in providing CAPD-related information across varying levels of question complexity. METHOD: Forty-four questions, categorized into four difficulty levels (patient level, easy, intermediate, and specialized; n = 11 each), were submitted to each chatbot, generating 132 responses. Seven clinical experts, blinded to chatbot identity, independently rated accuracy and completeness on a 1-5 Likert scale. Data were analyzed with analyses of variance, correlations, and interrater comparisons. RESULTS: Chatbot performance was similar, with mean accuracy below 4.0 and completeness about 3.5. Complex questions often scored below 3.0 across experts. Only three of the 44 questions, primarily patient level or relatively simple, received consistently high expert ratings (≥ 4 for both accuracy and completeness) across all three chatbots. Performance declined with question difficulty, although differences were not statistically significant. Accuracy and completeness were correlated across chatbots. CONCLUSIONS: Current AI chatbots provided generally accurate CAPD information but fell short of clinical standards, particularly on specialized questions. Their limited performance underscores the need for clinician oversight in CAPD assessment and management. Chatbots may serve as helpful adjuncts but should not replace expert evaluation and guidance in clinical settings. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.31975101.

Nick’s millionth cartwheel looks like his first: He plants one hand on the grass and then, before I can catch his legs and help, he tumbles into a round-off. For a moment I’m taller than him, and I…

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