This study systematically investigated the impact of head-borne load weight moment (WM), shooter experience, and exercise-induced fatigue on dismounted Soldier marksmanship performance. Thirty-four participants consisting of experienced (n = 18) and novice (n = 16) active-duty U.S. Army Soldiers completed a modified Individual Shooting Scenario evaluating fundamental (unsupported prone) and operational (dynamic, friend-foe discrimination) marksmanship. Participants were tested across five days wearing unique head-borne configurations (1.5–3.0 kg; varying WMs up to 164 N-cm) before and after a standardized, high-intensity exercise protocol. Multilevel modeling revealed that increased head-borne load WM significantly degraded fundamental accuracy, precision, and vertical stability. Experienced Soldiers demonstrated higher vertical rifle sway (+ 5 mm per 100 N-cm) under load but maintained superior decision-making, target acquisition speed, and overall resilience to post-exercise fatigue. Conversely, novices experienced a 17
The United States Air Force seeks to develop “Collaborative Combat Aircraft” to provide an affordable force multiplier. Current Artificial Intelligence (AI) research efforts often focus on developing the AI necessary to support task work within target domains without sufficiently considering integration in existing systems, including the capabilities necessary to enable the teamwork required to enable human-AI teams. The current research employs a recently published team-oriented analysis and design method, referred to as Agent-Oriented Systems Modeling (AOSM), to provide an architecture-level analysis of the human-AI team to establish requirements for the AI systems in both manned and unmanned aircraft. The approach is applied to an F-16 formation performing defensive counter air, to evaluate task and teamwork requirements within the analogous system. These requirements, namely the goals, responsibilities, and capabilities of the agents in the existing system are then applied to envision the “To-Be” system to ensure equivalent or greater capabilities exist in the proposed architecture. This analysis produces numerous traceable findings that can be applied to establish system level requirements for future development to ensure effective human-AI teaming, including 45 capabilities required of the system and five agent roles to be fulfilled in the “To-Be” system. The project discusses and supports the importance of efficient communication and coordination mechanisms for all AI agents when teaming with the human pilot.
Self-regulating emotions, thoughts, and behaviours is central to psychological well-being but often difficult to sustain without structured support, particularly under stress or cognitive overload. This study introduces a human-centred digital system designed to support everyday emotional self-regulation through personalised, explainable self-care guidance grounded in established models of emotion regulation and self-regulated behaviour. The system adapts to users’ reported emotional state, cognitive load, time and energy constraints, and personal preferences to generate actionable self-care plans accompanied by natural-language explanations that clarify the system’s reasoning and contrast alternative strategies, supporting user understanding and autonomy. The system is intended for non-clinical, self-care contexts and does not provide diagnosis or treatment. A formative scenario-based evaluation was conducted using three representative non-clinical user archetypes. The generated outputs were independently assessed using a structured rubric, producing mean scenario scores of 4.2–4.8 out of 5. Findings suggest that explainable digital systems can function as cognitive and emotional scaffolding, enhancing reflective engagement and supporting behaviour change in everyday contexts. This work contributes to research at the intersection of behavioural science and technology by demonstrating how explainability and personalisation can be operationalised in supportive digital systems, offering design insights for transparent, adaptive technologies that promote trust, autonomy, and sustained engagement in psychological well-being support.
Prescriptive artificial intelligence is shifting small and medium-sized enterprises (SMEs) from data-informed judgement to algorithmically recommended action in energy management, sourcing, logistics, and regulatory compliance. Existing responsible-AI frameworks predominantly emphasise general ethical principles, model-level risks or organisations with substantial governance capacity. Consequently, they provide limited guidance on how resource-constrained SMEs should allocate human and algorithmic authority when prescriptive systems optimise competing economic, environmental and social objectives. This article addresses this gap through a theory-informed narrative review and conceptual synthesis of literature on prescriptive analytics, human-centred and responsible AI, SME digital transformation and sustainability governance. The synthesis identifies four interrelated governance failures: accountability diffusion, opaque optimisation, value drift and context-blind deployment. These failures inform the Adaptive Human–AI Sustainability Governance framework, comprising human role calibration, explainable optimisation, value-sensitive objective design and contextual accountability. The framework also links decision criticality, model uncertainty and reversibility to differentiated levels of human oversight. The study contributes an integrated governance model, operationally defined propositions and a staged implementation pathway for SMEs seeking sustainability improvements while retaining identifiable human responsibility.
This study aims to examine the scientific landscape, intellectual structure, thematic evolution, and emerging research directions at the intersection of artificial intelligence (AI) and brand research. Given the rapid integration of AI into marketing, consumer behavior analysis, customer engagement, and brand management, mapping the development of this interdisciplinary field can provide a comprehensive understanding of its current structure and future directions. A bibliometric and scientometric analysis was conducted using data retrieved from the Scopus database. The search was performed on November 3, 2025, using the TITLE-ABS-KEY field and a comprehensive combination of AI-related and brand-related keywords. From 3,172 retrieved documents, duplicates, retracted records, publications from 2026 onward, and irrelevant documents were excluded. Following screening and data cleaning, 2,006 documents published between 1980 and 2025 were retained for analysis. Bibliometrix/Biblioshiny and VOSviewer were employed to analyze publication trends, scientific collaboration, keyword co-occurrence, thematic evolution, and the intellectual structure of the field. The results show an annual scientific production growth rate of 16.63
