This paper focuses on the nature of the assisted dying (AD) decision including the object of the mental capacity test. Specifically, we inquire into whether AD is a treatment decision. In Part I, we analyse how the AD decision is characterised in all international AD statutes also analysing what government guidance says when the primary legislation is ambiguous. In Part II, we address the question normatively: firstly, from the perspective of clinical ethics and secondly from the perspective of legal rules (the doctrine of informed consent and the duties of governments in states with socialised healthcare laws). We found that the nature of AD is variably characterised across international laws with a significant number (10/32) framing AD as a healthcare or treatment decision. In laws where the characterisation is ambiguous (14/32) government guidance tends toward a treatment characterisation. We argue in Part II that AD should not be classified as a treatment for reasons of clinical intelligibility, legal coherence and unintended policy consequences. We conclude with some recommendations, notwithstanding complexities, on a better characterisation of the decision to inform future AD policy research.

• A dual taxonomy is introduced linking generative AI tools with reinforcement learning. • First review to analyze RL training and fine-tuning of generative policies for robotics control tasks. • Covers 245 papers integrating Transformer and Diffusion-based architectures into RL pipelines. • Highlights key roles of LLMs, VLMs, diffusion models, world and video prediction models in robotics policy learning. • Identifies open challenges in grounding, scalability, and safety of robotics generative policies. Recently, generative AI and reinforcement learning (RL) have been redefining what is possible for AI agents that take information flows as input and produce intelligent behavior. As a result, we are seeing similar advancements in embodied AI and robotics for control policy generation. Our review paper examines the integration of generative AI models with RL to advance robotics. Our primary focus is on the duality between generative AI and RL for robotics downstream tasks. Specifically, we investigate: (1) The role of prominent generative AI tools as modular priors for multi-modal input fusion in RL tasks. (2) How RL can train, fine-tune and distill generative models for policy generation, such as VLA models, similarly to RL applications in large language models. We then propose a new taxonomy based on a considerable amount of selected papers. Lastly, we identify open challenges accounting for model scalability, adaptation and grounding, giving recommendations and insights on future research directions. We reflect on which generative AI models best fit the RL tasks and why. On the other side, we reflect on important issues inherent to RL-enhanced generative policies, such as safety concerns and failure modes, and what are the limitations of current methods. A curated collection of relevant research papers is maintained on our GitHub repository , serving as a resource for ongoing research and development in this field.

Collective intelligence as collective information processing.

Purpose This study aims to investigate how leaders’ communication styles, such as expressiveness and verbal aggressiveness, influence employee engagement at both individual and team levels. Moreover, this study examines how affective responses of employees mediate these relationships and how team affective climate moderates them. Anchored in both Leader–Member Exchange (LMX) theory and affective events theory (AET), this research uses a multilevel design. Design/methodology/approach Survey data collected from 363 employees across 64 teams in information technology (IT)/information technology-enabled service organisations operating in India, were analysed using multilevel analysis to examine direct and indirect relationships through mediation and moderation. Findings The results show that leader expressiveness was positively related to employee engagement via enhanced positive affect and that verbal aggression has a negative association with engagement via higher negative affect. At the team level, collective perceptions of expressiveness nurture engagement while aggressiveness reduces it. In addition, team affective climate moderates these relationships: positive climates lessen the negative influence of aggressiveness while negative climates ignite the salience of expressive communication. Practical implications This study underscores the necessity for businesses to focus on leader communication training that fosters expressive, helpful and emotionally intelligent behaviours while mitigating vocal aggression. Organisations may create psychologically safe spaces that increase participation by fostering positive emotions and improving emotional climates of teams. Interventions like coaching for leaders, feedback systems and team-building programs can help leaders communicate more positively, reduce negative emotional triggers and keep employees motivated, healthy and doing well across teams. Originality/value This research provides novel multilevel findings illustrating how leaders’ expressive and verbally aggressive communication styles influence engagement via employees’ affective responses and collective team affective climates. By combining LMX with AET, this study extends understanding of communication as both a relational and emotional event. Furthermore, the research notably differentiates between individual- and team-level mechanisms, uncovering cross-level effects that remained unexplored in previous research. It offers novel theoretical perspectives and establishes a comprehensive framework for understanding the role of leadership communication in fostering engagement in team-oriented environments.

To improve the intelligence and scientific rigor of sports behavior assessment in digital sports scenarios, this study proposed a deep learning–based motion behavior recognition and intelligent evaluation framework that integrated “recognition + evaluation” into a unified system. First, a pose estimation model was employed to extract skeletal keypoints from sports videos with high precision. Long Short-Term Memory (LSTM) and Transformer architectures were then combined to model temporal pose sequences and enable multi-class sports behavior recognition. Existing research primarily focused on isolated recognition tasks and lacked finegrained quantitative evaluation of movement quality. Moreover, many systems depended on standardized experimental environments, resulting in limited generalization ability and weak support for personalized feedback. To address these limitations, a prototype system integrating data acquisition, behavior recognition, performance evaluation, and visualization modules was developed. Comparative experiments were conducted to assess system performance and evaluation capability. In terms of computational performance, the optimized model achieved a Top-1 Accuracy of 94.117% under image-based input conditions. The average inference time was 12.487 ms, and the model size was 71.298 MB, demonstrating strong real-time capability and deployment feasibility. Regarding behavioral evaluation, the mean score deviation was 0.66, the key action point recognition rate reached 98.62%, and overall system acceptance was 95.38%. Users rated the actionability of feedback suggestions at 4.88, indicating high practical value. Experimental results demonstrated that the proposed system outperformed existing methods in recognition accuracy, responsiveness, and user experience. The framework therefore provided an effective paradigm for the design and deployment of intelligent sports behavior evaluation systems in digital sports contexts. This study offered meaningful contributions to the fields of intelligent sports analytics, human behavior recognition, and interactive training systems.

Abstract: The thinking ability and mental capacity of employees are significantly affected by work pressure in deadline-driven environments. This study investigates the relationship between cognitive load and mental bandwidth among employees at Larsen & Toubro (L&T) Constructions, a leading infrastructure conglomerate in India. Using a structured questionnaire-based quantitative approach, primary data was collected from 179 employees across various departments. Statistical tools including Chi-Square, Mann-Whitney U Test, Kruskal-Wallis H Test, and Pearson Correlation were employed for analysis. Key findings reveal that 49% of employees experience high cognitive workload, 86% face high work pressure, and 78% report mental fatigue. Notably, 90% believe AI tools can reduce mental workload, though 51% do not currently use them. Mental bandwidth was found to be a significant mediator between workload and stress, with 75% of respondents affirming its role in coping with work-related pressure. The study concludes with recommendations for workload management strategies, AI tool integration, and well-being programs. Keywords: Cognitive Load, Mental Bandwidth, Work Pressure, AI Tools, Employee Performance, Construction Industry, L&T Constructions

The immediate management of poisoned patients is a major activity in emergency departments and acute medical wards. This can mean the neglect of more detailed enquiry that would make clear the underlying cause of the poisoning and suggest ways to reduce the risk of repeated poisoning for the individual and more generally. The history and circumstances of poisoning may make the precipitants evident, but it is still important to consider what factors contributed. Here, we divide poisoning into unintentional and intentional, and suggest how these major groups may be subdivided. There is a need in unintentional poisoning to consider whether the patient has been poisoned because of their vulnerability, for example, resulting from dementia. Where a carer such as a parent or partner has committed an error, they too may be vulnerable. If the poisoning is a result of error by a clinical professional, then the question of clinical competence can arise. If the poisoning is deliberate, mental ill-health may have contributed, and the patient may not have capacity to make decisions. There may also be a conflict between what the patient wants and what the clinician deems to be in the patient's best interest, in which case it may be necessary to consider mental capacity. A systematic approach to these important questions can offer the best prospect for prevention of further poisoning for the individual. Broader attempts to prevent poisoning require good data on the causes and circumstances in which it occurs, but these are often lacking.

Intrinsic capacity (IC) refers to the sum of the physical and mental capacities of an individual. Conventional IC assessment requires substantial temporal and human resources. Digital twin (DT) technology emerges as a promising solution for efficiently mapping ICs. This study aims to explore older adults' perspectives on the DT technology and their perceptions of how it could effectively represent their ICs. A qualitative study was used. Face-to-face semistructured interviews with 23 older adults were conducted. The interviews were transcribed verbatim and analyzed via content analysis approach. The analysis identified five themes and 16 subthemes: (1) "opt for or not my digital twin," revealing the older adults' decisions regarding whether to use DT technology for mapping ICs; (2) "my ideal digital avatar," describing the older adults' preferences for personalized digital avatar appearances; (3) "my digital twin maps my intrinsic capacity," highlighting how multimodal reminders and synchronized avatar changes enhanced their comprehension of ICs; (4) "the benefits my digital twin can deliver," emphasizing the potential of the DT system to provide feedback services to older adults; (5) "some expectations for my digital twin," outlining their expectations for DT technology. Based on the above insights, a conceptual model, "windmill" model, was further developed to better understand how to build DTs of older adults and map their ICs. DT technology was a promising tool for mapping ICs of older adults. Furthermore, the "windmill" model provided a framework to build tailored DTs. The findings of this study could provide references to develop DT model to support IC management.

This article examines the construction of criminal responsibility for minors under the Syariah Penal Code (Cap. 275) within Brunei Darussalam’s dual legal system, where civil and Syariah laws operate concurrently for Muslim citizens. The study aims to analyze how the Syariah framework conceptualizes juvenile accountability and to assess its compatibility with international child justice standards. Employing a doctrinal and comparative legal approach, the research systematically reviews statutory provisions in the Syariah Penal Code (Cap. 275), contrasts them with the Penal Code (Cap. 22), and evaluates their alignment with Article 40 of the United Nations Convention on the Rights of the Child (UNCRC). The findings reveal that, unlike the civil legal system, which primarily relies on chronological age thresholds, the Syariah Penal Code adopts a capacity-based model grounded in the concept of taklif. Criminal responsibility is determined by indicators such as discernment (tamyiz), puberty (bulugh), and ʿaql (mental capacity), enabling a more individualized assessment of culpability. This framework effectively excludes minors from the full application of hudud and qisas punishments, instead emphasizing mitigated or discretionary sanctions. However, the absence of a clearly defined minimum age of criminal responsibility poses significant challenges, including potential legal uncertainty, inconsistent judicial interpretation, and tension with international norms that prioritize clear age limits, diversion mechanisms, and detention as a last resort. Academically, this research contributes to the development of comparative Islamic criminal law by offering a nuanced analytical framework that bridges classical doctrines of taklif with contemporary human rights discourse. It enriches the scholarship on dual legal systems by demonstrating how normative tensions between religious and international legal standards can be constructively reconciled.

A digital transformation of organizations has spawned a pressing necessity to incorporate Artificial Intelligence, Sustainable Human Resource Management, and organizational sustainability to deal with the challenges pertaining to resilience of the workforce, environmental responsibility, and ethical decision-making. Although there is an increased literature concerning AI-based HRM, current literature is still very scattered which has limited synthesis of technologies, applications and future research within the framework of Green HRM, HR analytics and ESG-oriented human resource practices. The purpose of the study is to examine the literature on Artificial Intelligence and Sustainable Human Resource Management. A thorough review of the literature was carried out concerning digital HRM, predictive analytics, machine learning in HR, ethical AI, and sustainable workforce management. The review shows that intelligent recruitment systems, people analytics, HR automation, and AI-based performance management are changing the role of HR functions that allow the use of data in decision-making and assist in achieving Sustainable Development Goals by making better use of resources and promoting employee welfare. Nevertheless, the issue of algorithmic bias, AI governance, privacy, and responsible AI can still be seen as a major obstacle to the sustainable implementation. The recent developments like Industry 5.0, collaboration between humans, and AI, and AI-assisted ESG practices demonstrate that the human-centric and sustainable HR model is turning more human-based and sustainable. The results offer a synthesized model of association between Artificial Intelligence, Sustainable HRM, and organizational sustainability, and gives insights into future studies on ethical AI, talent analytics, and workforce sustainability in the changing digital workplace.

Cognitive impairment in the elderly is a gradual process that affects a significant segment of the population, interfering with their mental faculties and functional capacity in the physical, mental, and social domains. The objective of this study was to determine the relationship between cognitive impairment and functional capacity in older adults. A quantitative, descriptive, and cross-sectional research design was employed, and studies were conducted in Ecuador on 659 older adults aged 60 and over who were part of a project aimed at fostering social connectivity. The study was conducted with the informed consent of each participant. Cognitive impairment variables were measured, and functional capacity was assessed with the Barthel Index (IB) Original Version (Basic Activities of Daily Living, ABVD) with the Mini-Mental State Examination (Mini-Mental). To this end, a descriptive analysis was performed. Results: With respect to functional capacity, the study found that 57.8% of the older adult population was classified as independent, 21.9% exhibited moderate dependence, and 17.3% demonstrated low dependence. With respect to cognitive impairment, it is evident that 48.6% of the sample is within the normal range, while 26.3% exhibit moderate cognitive impairment, and 24.3% demonstrate mild cognitive impairment. In summary, the study demonstrates that a significant proportion of older adults maintain independence in their daily activities and do not exhibit cognitive impairment. However, findings also indicate the presence of dependence and moderate cognitive impairment. It is imperative to ascertain whether the degree of dependence corresponds to that of the cognitive impairment.

Whose welfare and interests matter from a moral perspective? This question is at the center of many polarizing debates, for example, on the ethicality of abortion or meat consumption. A widely cited hypothesis holds that attributions of moral standing are guided by which mental capacities an entity is perceived to have. Specifically, perceived sentience (the capacity to feel pleasure and pain) is thought to be the primary determinant, rather than perceived agency (the capacity to navigate the world and social relationships) or other abilities. This has been described as a general feature of moral cognition, but the evidence for this is mixed and overwhelmingly based on Western participants. Here, we examined the link between attributions of mind and moral standing across six culturally diverse countries-Brazil, Nigeria, Italy, Saudi Arabia, India, and the Philippines-using a sample of 1,255 participants (aged 18-74 years old) who were recruited via the online platform Toloka. In every country, entities' moral standing was most strongly related to their perceived sentience.

This paper presents a conceptual complexity theory-based framework that reveals the choice of green accommodation of Generation Z individuals as a complex phenomenon of interacting actors and relationships among them. This paper also describes a portrait of Generation Z through the complex concept of smartness, which includes the traits of knowledgeability, digitality, sustainability, intelligence, innovativeness, agility, network based and learning. It is also emphasised that the changes to influence the positive behaviour regarding sustainable choices should be directed to these traits, using them as leverage points.

Cerebrovascular accidents (strokes) represent a critical medical emergency re quiring rapid and accurate diagnosis. Automated segmentation of stroke lesions from computed tomography (CT) images remains challenging due to low con trast, image noise, and high anatomical variability between ischemic and hem orrhagic subtypes. This paper introduces a novel hybrid approach combining the trophallactic optimization algorithm (TOA), inspired by cooperative nectar exchange in bee colonies, with markov random fields (MRF) for spatial coher ence modeling. The proposed TOA-MRF method operates semi-automatically from a single user-defined seed point, leveraging bio-inspired collective intel ligence to progressively explore and refine regions of interest. The algorithm simulates the enzymatic transformation of nectar into honey through iterative information exchange between virtual bees, followed by MRF-based regulariza tion to ensure anatomical consistency. Evaluated on a clinical CT dataset from [Hospital Name], the method achieves a Dice similarity coefficient of 87.3% for ischemic strokes and 91.2% for hemorrhagic strokes, with an overall detection accuracy exceeding 89%. Comparative analysis demonstrates the complemen tary strengths of TOA exploration and MRF refinement, offering a robust and efficient solution for clinical stroke assessment with minimal user intervention.

This extensive review of large language models (LLMs) aims to highlight the importance of scaling the current generation of large language models toward artificial general intelligence, which is a dead end, while also considering the risks of unregulated use of such models. Through this, it is aimed to explicitly highlight the intelligence factor of current large language models and their malicious manipulative ability. While many large language model organisations compete to achieve better results by scaling up their models, this ultimately leads to the models collapse. It is too early to understand the development and benefits of large language models; many have cited LLMs as the primary means of achieving general intelligence agents. To counter this, this paper gathers and evaluates resources from multiple research articles and tests several frequently used LLMs, highlighting their importance in different scenarios. As these models are trained on a wide variety of data, they exhibit domain-independent intelligent behaviour but fail to exhibit causal intelligent behaviour.

This study investigated the impact of artificial intelligence on student intelligence and learning behaviors in the context of modern education. A quantitative research design was adopted using a cross-sectional survey method, and data were collected from a sample of 240 students selected through stratified random sampling from secondary schools, colleges, and universities. A structured questionnaire was used to gather responses regarding AI usage, cognitive abilities, independent learning, critical thinking, problem-solving skills, and academic performance. Data were compiled and analyzed through SPSS where correlation, multiple regression, and chi-square statistics were used to analyze the data. The results found out that there was a significant positive association between the use of AI and cognitive abilities and independent learning skills. The regression analysis revealed that the application of AI, critical thinking, and problem-solving skills were significant predictors of the academic performance, the strongest of which was the application of AI. Additionally, the chi-square test revealed that there was a significant relationship between the perceptions of students in regard to the AI-assisted learning and the learning behaviors. The paper has found that, although artificial intelligence can improve academic performance and cognitive development, overuse can contribute to dependency and decrease in independent thinking, which is why the researchers identified an AI Learning Trap in schools. Thus, the key to achieving the maximum benefit and minimum harm of AI use is its responsible and balanced use to reduce its adverse impact on the intelligence of students.

Best interests under the Mental Capacity Act 2005 has been cast as an empowering, person-centred process that protects a person’s rights and freedom of action. In practice this laudable goal is constrained by monetary and temporal resources. Drawing on a qualitative study which encompassed the views of patients, carers, healthcare professionals, and lawyers, we observe that, where resources are inadequate, the quality of decision-making declines and the options on offer are restricted. While austerity has disproportionately disadvantaged people with disabilities and additional needs in numerous ways, in mental capacity law, the impact of this is evident in the gap between the protection of procedural and substantive rights offered by the law. While the courts deal robustly with challenges to ‘faulty’ procedure, challenging substantive issues is difficult and has limited prospects of improving outcomes, even if the decision is clearly inadequate in any sensible interpretation of the court’s aspiration to person-centredness. Tracing these differences back to the different logics of the European Convention on Human Rights and the United Nations Convention on the Rights of Persons with Disabilities, we argue that, as things currently stand, the law cannot resolve these issues, dooming the aspiration to person-centredness to remain constrained and provisional.

Intrinsic capacity (IC), reflecting an individual’s composite physical and mental capacities, is a key indicator of healthy ageing. Early identification of IC impairment among community-dwelling older adults may help support timely assessment and management in community settings. This study aimed to develop and internally validate a nomogram for identifying IC impairment among community-dwelling older adults. This cross-sectional study included community-dwelling adults aged ≥ 60 years. IC impairment was operationally defined as impairment in at least one domain of the World Health Organization Integrated Care for Older People (WHO-ICOPE) framework. Candidate variables included demographic characteristics, lifestyle factors, fall history, chronic disease burden, medication use, handgrip strength, and appendicular skeletal muscle mass index (ASMI). Least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was used for variable selection, and retained variables were entered into a multivariable logistic regression model to construct a nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, Brier score, and decision curve analysis (DCA). The prevalence of IC impairment was 72.7%. At the more parsimonious λ.1se, age, history of falls, and appendicular skeletal muscle mass index (ASMI) were retained and used to develop the final model. The nomogram showed good discrimination, with AUCs of 0.8209 (95% CI: 0.7916–0.8502) in the training set and 0.7902 (95% CI: 0.7430–0.8373) in the validation set. Calibration was acceptable, and DCA suggested potential net benefit across a relatively wide range of threshold probabilities. A nomogram incorporating age, history of falls, and ASMI was developed and internally validated for identifying IC impairment among community-dwelling older adults. This model may serve as a useful reference for early community screening and stratified assessment. Further external validation is needed.

The existing polymer dielectrics as insulating packaging media can no longer meet the insulation demands in highly integrated power electronic devices. Self-adaptive dielectrics with nonlinear dielectric response have been explored to eliminate electric field distortion caused by charge accumulation, but traditional strategies based on Schottky barriers result in interface defects. Here, we report polymer dielectric composites with customizable potential wells in recycled melamine foam-derived graphitic carbon nitride frameworks that overcome concerns about interface defects. We demonstrate that potential wells can efficiently capture low-energy charge carriers and release them for rapid transport under high electric fields. Notably, by doping donor or acceptor states into the frameworks, precise control over potential well depth and distribution was achieved, allowing customization of both nonlinear conductivity and threshold electric field strength. This work establishes a generalizable strategy for engineering next-generation self-adaptive dielectrics, enabling intelligent insulation behavior and enhanced reliability in high-field, high-temperature electronic packaging environments.

Complex products and production processes are intertwined and demand expressive, lifecycle-wide digital representations. The Asset Administration Shell emerged as a standard for Digital Twins (DTs), structuring heterogeneous data across cloud-based Industrial Internet of Things (IIoT) infrastructures. However, today’s deployments predominantly realize passive or reactive DTs, while intelligent behavior remains underexploited. This paper addresses this gap, proposing an end-to-end architecture operationalizing the DT Reference Model through the integration of machine-interpretable granulated industrial skills, which are semantically accumulated into a knowledge graph enabling discovery and reasoning, while a multi-agent system provides autonomous, utility-based negotiation via machine-to-machine interactions within a federated marketplace. The approach is applied in a real smart manufacturing demonstrator, combining order processes, production orchestration, and lifecycle documentation into a unified execution pipeline spanning IIoT-connected shopfloor assets and cloud-based services. Quantitative experiments evaluating negotiation latency, renegotiation robustness, and utility variation demonstrate stable, predictable behavior even under concurrent demand and failure scenarios. The architecture lays a foundation for interoperable, sovereign collaboration across value chains to realize shared production. The results underline the effectiveness of the tightly coupled enabler technologies realizing proactive, reconfigurable, and semantically enriched intelligent DTs.