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This article presents measurements of inclusive J / ψ production at midrapidity (| y | < 1.0) in Au+Au collisions at sNN=54.4 GeV with the STAR detector at the Relativistic Heavy Ion Collider. A suppression of the J / ψ yield, quantified using the nuclear modification factors ( R AA, R CP), is observed with respect to the scaled production in p+p collisions. The dependence of R AA on collision centrality and J / ψ transverse momentum is measured with improved precision compared to previous measurements at 39 and 62.4 GeV, while the centrality dependence of R CP is measured and compared to the same results at 39, 62.4, and 200 GeV. In central collisions, no significant collision energy dependence of R AA is found within uncertainties for collision energies between 17.3 and 200 GeV. Two transport model calculations that include dissociation and regeneration contributions are consistent with the experimental results within uncertainties. Although no significant collision energy dependence of the J / ψ suppression in high energy heavy-ion collisions up to sNN=200 GeV is observed within uncertainties, the newly measured results at 54.4 GeV Au+Au collisions provide additional constraints on theoretical calculations of the hot medium evolution and cold nuclear matter effects. © 2026 The Authors.

The COVID-19 vaccine supply chain faced unprecedented disruption from information disorder (collectively, misinformation, disinformation, and malinformation (MDM)). While prior research has examined the societal and health consequences of information disorder, little is known about how it disrupts supply chain management (SCM) or how supply chain actors respond. Guided by organizational information processing theory (OIPT) and stakeholder theory (ST), this study employs an embedded qualitative case study of the U.S. COVID-19 vaccine supply chain. We find that crisis-induced MDM functioned as an exogenous shock that heightened uncertainty and equivocality, overwhelming information-processing capacity, and fragmenting stakeholder responsibility. Downstream actors (i.e., pharmacies and clinicians) were disproportionately burdened in their efforts to combat MDM, relying largely on reactive tactics. Additionally, SC actors prioritized correcting false claims while deferring attribution of intent. This led to the conflation of misinformation and disinformation and the underdiagnosis of malinformation. By integrating OIPT and ST, we develop a framework that explains how information-processing misfit and shifting stakeholder salience jointly produce operational disruption and uneven accountability. Our findings extend SCM theory by conceptualizing MDM as a systemic supply chain risk and offer actionable guidance to improve resilience and trust. © 2026 The Author(s). Journal of Business Logistics published by Wiley Periodicals LLC.

Croesus famously consulted oracles to determine whether to make war on Persia in 547/46 BCE. But Herodotus tells us he misunderstood the oracular response, due to a mix of hubris and fate. And yet, was this the oracle that brought doom to the Lydian Empire? The defeat of Croesus is not just the affirmation of the divine knowledge of the Delphic oracle because the king does not interpret the oracle correctly. Rather, his defeat is the affirmation of the divine knowledge of the oracle over the course of the history of his dynasty. From the opposite perspective, we can ask not only why Croesus lost, but why Cyrus won. His victory is based, according to other sources, on a different divine oracle that has nothing to do with Croesus or the dynasty of the Mermnadai. The goal of all these oracles and prophecies will be the return and rise of the legitimate rulers of the world; these oracles not only predict the future over long spans of time but are the overarching cause for the history that unfolds.

Here, we report the first evidence of spin interference in exclusive 𝐽/𝜓 → 𝑒+⁢𝑒− photoproduction in ultraperipheral heavy-ion collisions at STAR at $\sqrt{𝑠_{𝑁⁢𝑁}}$ = 200 GeV. In Au + Au collisions, a negative cos⁡(2⁢𝜙) modulation is found for 𝑝𝑇 &lt; 120 MeV/𝑐 with a significance of 3.2⁢𝜎, while the isobar data (Ru + Ru, Zr + Zr) show a consistent negative modulation with a significance of 1.9⁢𝜎, opposite in sign to that in 𝜌0 → 𝜋+⁢𝜋− photoproduction. This establishes for the first time that the interference sign is controlled by the spin structure of the final-state daughters, resolving the ambiguity present in the all-boson 𝜌0 channel. The compact 𝐽/𝜓 probes gluon distributions at perturbative scales, resulting in a weaker modulation and providing stringent constraints on color glass condensate calculations. These findings demonstrate that spin-dependent interference in heavy vector mesons provides a new, experimentally accessible handle on gluon structure beyond traditional cross-section measurements.

Precise experimental information on hyperon-nucleon interactions is scarce but of paramount importance to our understanding of the inner structure of compact stars. In this Letter, we report the first experimental results of correlation functions between deuterons (𝑑) and Λ hyperons in Au+Au collisions at √𝑠NN=3.0 GeV measured by the STAR experiment at the Relativistic Heavy Ion Collider. A clear enhancement at small relative momenta has been observed in the correlation function. Through a Bayesian inference analysis, the source size parameters as a function of collision centrality and the spin-dependent strong interaction parameters (scattering length 𝑓0 and effective range 𝑑0) are extracted using the Lednický-Lyuboshitz formalism. The derived doublet spin state parameters (𝑓0, 𝑑0) lead to a novel method to precisely determine Λ separation energy for the weakly bounded hypertriton 3ΛH.

Arabic extractive text summarization remains challenging due to rich morphology and limited lexical consistency, which weaken traditional surface-based sentence ranking methods. Existing approaches typically rely either on graph centrality to capture document structure or on transformer-based embeddings to estimate semantic relevance, yet the relationship between these two ranking signals has not been systematically examined. This paper investigates the complementary roles of semantic similarity and structural centrality in Arabic summarization. A controlled hybrid ranking formulation is implemented that combines contextual sentence embeddings with PageRank-based graph scoring and evaluate it using multi-reference ROUGE on the Extended Arabic Summaries Corpus (EASC). The study analyzes how each component influences sentence salience, coverage, and redundancy. Experimental results show that semantic embeddings improve the identification of informative sentences, while graph centrality enhances coverage and reduces repetition; their integration consistently yields stronger summaries, achieving ROUGE-1 = 0.605, ROUGE-2 = 0.497, and ROUGE-L = 0.514. These findings provide empirical evidence that semantic and structural centrality capture complementary aspects of importance in morphologically rich languages, offering guidance for designing more robust extractive summarization systems.

Background/Objectives: Obstructive sleep apnea (OSA) is a common and serious sleep-related disorder that causes repeated interruptions in breathing during sleep. Traditional diagnostic methods, such as polysomnography, are accurate but costly, time-consuming, and unsuitable for large-scale screening. This study proposes and evaluates a lightweight diagnostic framework based on an Extreme Learning Machine (ELM) optimized by a set of basic and advanced metaheuristic optimizers. The model aims to evaluate whether metaheuristic optimization can improve ELM-based classification performance using structured demographic, clinical, and sleep-related predictors. Methods: Two real datasets were employed to train and evaluate the proposed framework: (i) a clinical OSA dataset with 274 subjects and 31 demographic/anthropometric and sleep-related predictors, and (ii) a public strongly imbalanced Sleep-Disordered Breathing (SDB) dataset with 500 subjects and 10 structured predictors. Metaheuristic algorithms are used to optimize ELM weights and biases, addressing the instability of random initialization and improving model generalization. The optimized models are evaluated against eight baseline classifiers, including logistic regression (LR), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), XGBoost (XGB), and a standard ELM classifier. Results: Results show that metaheuristic optimization moderately improves ELM on the OSA dataset, increasing ROC-AUC from 0.6527 to about 0.73 and accuracy from 0.6573 to about 0.69–0.70, while on the highly imbalanced SDB dataset, it yields modest ROC-AUC gains (from 0.5132 to about 0.544–0.548) with small decreases in accuracy and F1-score. We additionally assess class-imbalance handling on the SDB dataset and analyze feature importance with permutation importance and SHAP, which shows the models rely heavily on diagnosis-derived predictors. Conclusions: The proposed framework provides a lightweight ELM-based decision-support approach with low inference cost after offline optimization. The results suggest potential value for screening-oriented OSA/SDB classification, but further validation with larger cohorts and a screening-only feature set is needed before clinical implementation.

Although there have been many changes to community and family structure, and advances in scientific understanding of children’s health and wellbeing, there has been little difference in how healthcare is delivered to children. The focus is often a deficit approach to acute situations. Halfon’s Life Course Health Development Theory (LCHDT) focuses on a holistic approach to health and well-being. In the LCHDT, one’s health is impacted by specific contexts during childhood that have a lasting impact. Key areas of family, community, schools, environment, and healthcare systems impart positive and negative experiences that affect children’s health. Using the LCHDT as a guide, this consensus paper explores these five areas in the context of nursing practice roles and policy implications. With this approach, nurses lead and partner to build positive ecosystems for children and inform key policy actions to ensure change is a reality.

Leisure, as the pursuit of activities we choose to engage in during our free time, holds immense potential for enriching our lives. However, individuals with disabilities often face barriers that hinder their ability to participate fully in leisure activities. The delivery of leisure education to students in Special Education is of paramount importance in promoting independence and quality of life. This exploratory study investigates the delivery of leisure education to students in transition programs, aiming to understand current practices. In addition, this paper makes an argument for the inclusion of recreational therapists in the transition setting and encourages guardians to request recreational therapy as a related service on their child’s Individual Education Plan (IEP).&nbsp;

Background/Objectives: Telemedicine offers significant potential to improve the quality and accessibility of geriatric care, particularly in resource-constrained settings. However, its effective implementation depends largely on healthcare professionals’ acceptance and willingness to use such systems. Drawing on an extended Technology Acceptance Model (TAM), this study examines the determinants of doctors’ and nurses’ intentions to adopt telemedicine for elderly care in Algeria, with particular emphasis on self-efficacy and institutional support. Methods: This cross-sectional study employed a structured questionnaire administered to 130 healthcare professionals, including physicians and nurses, in Algeria. Hierarchical multiple regression analysis was conducted to test the proposed hypotheses and assess the incremental explanatory power of the extended model. Results: The extended TAM accounted for 48.7% of the variance in intention to use telemedicine. Institutional support (β = 0.432, p < 0.001) and self-efficacy (β = 0.264, p = 0.001) emerged as the strongest predictors. Perceived ease of use (β = 0.178, p = 0.038) and perceived usefulness (β = 0.139, p = 0.021) also had significant positive effects. The inclusion of self-efficacy and institutional support increased the model’s explanatory power by 23.5%. Conclusions: The findings highlight the critical role of organizational support mechanisms, digital competencies, and system usability in fostering telemedicine adoption among healthcare professionals. The study provides practical implications for policymakers and healthcare institutions, emphasizing the need for targeted training programs, supportive infrastructure, and institutional policies that enhance confidence and facilitate the integration of telemedicine into clinical workflows.

Although there have been many changes to community and family structure, and advances in scientific understanding of children's health and wellbeing, there has been little difference in how healthcare is delivered to children. The focus is often a deficit approach to acute situations. Halfon's Life Course Health Development Theory (LCHDT) focuses on a holistic approach to health and well-being. In the LCHDT, one's health is impacted by specific contexts during childhood that have a lasting impact. Key areas of family, community, schools, environment, and healthcare systems impart positive and negative experiences that affect children's health. Using the LCHDT as a guide, this consensus paper explores these five areas in the context of nursing practice roles and policy implications. With this approach, nurses lead and partner to build positive ecosystems for children and inform key policy actions to ensure change is a reality. © 2026 Elsevier Inc.

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.