This study investigates how artificial intelligence strategic capabilities, transformational leadership, and policy environments collectively influence organizational agility in small and medium-sized enterprises through dynamic capability mechanisms. Employing a mixed-methods design, the research analyzes survey data from 300 SMEs across manufacturing, service, and technology sectors, complemented by qualitative case studies. Structural equation modeling reveals that AI strategic capabilities constitute the strongest predictor of organizational agility (β=0.42, p<0.001), with digital dynamic capabilities mediating 67% of this total effect. Technology-management fit emerges as a critical boundary condition, amplifying AI effectiveness by 123% under high alignment scenarios (β=0.58 versus β=0.26 in low alignment contexts). Transformational leadership exhibits dual mechanisms through direct positive effects on agility (β=0.28, p<0.001) and moderating influences on AI-agility relationships (β=0.21, p<0.01). Notably, AI capabilities demonstrate buffering properties against policy environment uncertainty (β=0.12, p<0.05), transforming institutional constraints into manageable strategic variables. Machine learning analyses reveal nonlinear effects with diminishing returns beyond the 75th percentile of AI adoption. The structural model explains substantial variance in organizational agility (R²=0.64) and firm performance (R²=0.52). These findings extend dynamic capability theory to digital contexts, reconceptualize AI as a strategic capability rather than an operational tool, and illuminate digital leadership dimensions, offering evidence-based guidance for SME managers, technology vendors, and policymakers navigating digital transformation challenges.

Abstract During the 1950s, a major element in Paul Samuelson’s work was done for RAND, a think tank, initially funded by the Air Force and based in California, where he was wanted as someone able to bridge the gap between economists and mathematicians. His main concern at RAND was activity analysis: the use of linear models to simplify and thereby solve optimization problems that would otherwise have been too difficult to solve in the absence of modern electronic computers. The chapter covers his work on game theory, a subject that later became central to much economics, and the story of his lengthy collaboration with Robert Dorfman and Robert Solow on a book on linear programming, published in 1958.

Expressway congestion and traffic accidents often hinder the passage of emergency vehicle, while traditional fixed emergency lanes suffer from issues such as low resource utilization and poor adaptability. To address mixed traffic flows comprising connected and automated vehicle, human-driven vehicle, and truck, this paper proposes a dynamic virtual emergency lane control strategy. This strategy creates continuous passage space for emergency vehicle through segmented dynamic allocation of right-of-way and stepwise lane-changing guidance. Based on cellular automata theory, a simulation model for mixed traffic flows is established by integrating the NS model, the IDM following model, and an improved STCA lane-changing rule.Using a 2,000-meter, four-lane expressway as the simulation scenario, the results indicate that, compared to a fixed emergency lane, this strategy can increase peak traffic volume by 30.2% and reduce the congestion rate by 10%–27% under moderate traffic density. It performs best in scenarios with four lanes and a low to moderate proportion of truck. This strategy effectively improves the efficiency of emergency vehicle and the utilization of road resources, making it suitable for emergency management scenarios on smart expressways.

A proactive framework for cybercrime risk assessment, incorporated using machine learning algorithms and game theory, analyzes platform content moderation effectiveness. With machine learning algorithms, including K-means clustering, ridge regression, interaction analysis, and optimization of Nash equilibrium in 27 quarterly platform analyses, this research proposes four categories of content risks differentiated by systematic differences in levels of threat. The strongest predictor in this sample (β = 0.63) for effectiveness is AI capabilities, accounting for 56.2% of explained variations, although effectiveness is substantially diminished by complexity in moderated content. Optimal automation rates vary from 78% for low complexity to sophisticated approaches at only 29%, offering 55% cost savings and a 54.5% decrease in breaches for low complexity, but risking higher degrees of threat for complex moderated contents. This study suggests that technological development is imperative to supplement resource development for platforms in content moderation approaches.

The management of patients suspected, but not confirmed, as being poisoned is challenging. The Royal College of Emergency Medicine and National Poisons Information Service have produced this guidance to provide a generalised clinical approach to any poisoned patient in the emergency department. This guideline provides a clinical approach to support the initial assessment of a patient, identification of potential toxic agents and emergency management. A toxidromic approach is used, with emphasis on consideration of the toxicokinetics of potential poisons and how the patient's clinical condition may change. It does not replace poison-specific guidance available from TOXBASE and the NPIS or a locally appropriate poisons centre.

This study investigates the function of middle managers in subsidiaries of foreign multinational firms within the Middle East and North Africa (MENA) region. Utilizing agency theory, the resource-based approach, and institutional theory, we investigate the impact of strategic involvement, decision-making autonomy, and cultural intelligence on subsidiary performance, as well as the moderating effects of geographic and national cultural distance on these relationships. Data were gathered through a standardized survey administered to 404 middle managers in several businesses. Hierarchical multiple regression analysis reveals robust evidence for all proposed relationships: each managerial capability significantly enhances internationalization success, with these impacts intensified in geographically and culturally distant environments. The results show how important middle managers are to the company’s strategy by connecting headquarters and subsidiaries, adapting strategies to local conditions, and using their cross-cultural skills. This study aids to the body of knowledge on international business by combining managerial skills with contextual distance variables and focusing on the MENA region, which hasn’t been studied enough cultural intelligence is important for making multinational companies work better in difficult and faraway places, as shown by their real-world effects.

Mass casualty incidents (MCIs) continue to pose significant operational challenges for health care facilities, particularly when compounded by electronic health record (EHR) downtime or cyberattacks. Despite advancements in technology, providers may consider using simple, paper-based patient triage and tracking methods during an MCI. This study describes the implementation of a paper-based triage and patient tracking tool, integrated into a broader MCI Toolkit, to support operational continuity. Developed by NYU Langone Hospital-Brooklyn Emergency Department in collaboration with Emergency Management, the tool was deployed in 6 full-scale exercises (2021-2025) and 2 real-world MCIs across trauma and non-trauma ED settings. The tool follows a 3-step process: rapid triage using Simple Triage and Rapid Treatment (START), documentation of acuity and location, and post-triage identity reconciliation. The MCI Toolkit includes operational resources such as contact lists, patient placement maps, and job action sheets. After each event, feedback was gathered from clinical staff and senior leadership. In the feedback sessions, the tool was noted to be intuitive and required minimal training. It enabled rapid triage, patient placement, and real-time situational awareness for Incident Command. During a downtime simulation, it supported a seamless transition from electronic to manual processes. Across incidents, it improved patient throughput, ensured appropriate team assignment, and supported role flexibility when leadership was unavailable. Our experience using the paper-based Triage Tracker showed it reliably maintained patient tracking without electronic systems. Its ease of use and integrated resources supported coordination, patient flow, and operational continuity during MCIs and EHR disruptions.

To examine how the WHO and the World Medical Association (WMA) frame bioethical principles and address implementation barriers in their pain management policies, providing insights for global health policy and ethical analysis. Qualitative content analysis of international policy documents using the Standards for Reporting Qualitative Research to ensure methodological transparency and analytical rigour. Analysis of publicly available policy documents produced by the WHO and WMA between January 2000 and December 2024. Documents addressing pain management with ethical content, current and not superseded (n=18 from 314 screened). 18 policy documents were retrieved through relevance screening and analysed with reference to ethical values and systemic constraints using MAXQDA Analytics Pro 2022. Thematic coding identified ethical principles, structural barriers and strategic policy directions shaping global pain management frameworks. Nine ethical principles underpin global pain management policies, including human rights-based access, professional duty to relieve suffering and equitable care. Seven major barriers, such as regulatory restrictions, educational deficiencies and systemic inequities, hinder implementation. Five policy directions were identified to bridge principles and practice. WHO and WMA frameworks articulate a shared normative commitment to equitable, safe and person-centred pain management but differ in emphasis between public health and clinical ethics perspectives. Addressing identified structural barriers, integrating biopsychosocial approaches, and promoting culturally sensitive ethical guidance are critical for improving global pain management policies. While international guidelines provide the ethical foundations, achieving equitable global pain care requires coordinated transformation across regulatory, educational and health system domains. The persistent gap between ethical commitments and real-world implementation underscores the urgent need for binding accountability mechanisms, stronger international coordination and systematic approaches to address structural determinants of inequity.

ABSTRACT Optimizing biomass yield and biochemical composition in sea-based cultivation of Ulva fenestrata requires balancing environmental conditions with cultivation timing. This study examined how outplant and harvest timing influence growth and biochemical traits in a temperate sea-based aquaculture system. Outplant timing had a pronounced effect on biomass yield, while biochemical composition was more strongly influenced by harvest timing and associated environmental factors. Seedlings deployed in autumn (September–November) achieved up to eight-fold higher biomass than those outplanted in late winter or spring (February–March). Despite low temperature and light during early winter, autumn outplants maintained slow but steady growth, indicating physiological resilience and effective nutrient utilization under suboptimal conditions. Biochemical composition – including crude protein, chlorophyll, carotenoids and total fatty acids – peaked during late winter to early spring harvests when ambient nitrate levels were highest. As temperature and light increased in spring, tissue nitrogen and polyunsaturated fatty acid contents declined, reflecting nitrogen limitation and temperature-driven lipid remodelling. This trade-off between maximizing biomass under warmer conditions and maintaining nutritional quality during cooler, nutrient-rich periods highlights the need for strategic cultivation management. We conclude that optimal production of U. fenestrata in temperate waters is achieved by autumn outplanting and harvest before the spring nutrient decline when aimed at food purposes. Continuous environmental monitoring, adaptive harvest timing and strain selection could further enhance both biomass yield and quality, supporting sustainable and high-value Ulva cultivation.

The intensifying global competition among cities necessitates accurate and efficient evaluations of urban business environments to drive economic growth, attract investment, and foster innovation. Traditional assessment methods, often reliant on expert opinions and manual analysis, are prone to subjectivity and inefficiency. To address these limitations, this study introduces an optimized Siamese Neural Network model designed to improve the accuracy and efficiency of urban business environment evaluations. The model leverages feature extraction and multidimensional learning to analyze key indicators, including economic development, infrastructure integrity, policy friendliness, and market entry difficulty, utilizing publicly available datasets. Additionally, the model incorporates emerging technologies, including the Internet of Behaviors and generative artificial intelligence (AI), to bolster capabilities in capturing and analyzing complex behavioral data. The Internet of Behaviors enables the collection of real-time dynamic behavioral data from various urban activities, providing a comprehensive and detailed understanding of the business environment. Generative AI, on the other hand, generates predictive models from existing data, simulating future trends and scenarios, thereby enhancing the accuracy and foresight of decision-making. Performance comparison experiments demonstrate the model's superiority over baseline models across all evaluation metrics. Specifically, the optimized model achieves F1 Scores of 0.874, 0.879, and 0.882 on the Doing Business Indicators, Urban Land Cover Classification, and Open Cities Artificial Intelligence Challenge datasets, respectively, significantly outperforming the Graph Neural Network for Business Environment and Transformer-based Business Environment Evaluation models. Furthermore, the model exhibits exceptional efficiency, with training times of 29.648s, 31.327s, and 32.843s on the respective datasets. In terms of scalability and adaptability, the model achieves Scalability Scores and Generalization Capabilities of 0.821 and 0.876 on the DBI dataset, demonstrating its effectiveness in handling large-scale, multidimensional data. A comprehensive evaluation of urban business environments revealed specific strengths and weaknesses in cities A, B, and C. City A excelled in economic development (8.5) and infrastructure integrity (9.0) but scored lower in market entry difficulty (5.5). City B showed balanced performance across all metrics, while City C demonstrated strengths in policy friendliness (8.5) and market entry difficulty (8.0) but lower scores in infrastructure integrity (6.5). These results highlight the model's utility in identifying areas for improvement and fostering targeted interventions. This study advances the theoretical and practical application of deep learning techniques in urban business environment evaluation, offering city administrators an efficient and objective decision-support tool. By enabling data-driven policy formulation and resource optimization, the proposed model provides a robust strategy for enhancing urban competitiveness.