Search Results (457)

This research aims to identify and prioritize key planning elements for the redevelopment of such housing complexes by incorporating perspectives from both experts (supply-side) and residents (demand-side). To achieve this, a hybrid multi-criteria decision-making framework was developed by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). A total of 25 planning elements were identified through Focus Group Interviews and organized into five domains: legal and institutional reforms, project feasibility, residential conditions, social integration, and complex design. The AHP was used to assess the relative importance of each element based on responses from 30 experts and 130 residents. The analysis revealed a clear divergence in priorities: experts emphasized feasibility and regulatory considerations, while residents prioritized livability and spatial quality. Subsequently, the TOPSIS method was applied to evaluate four real-world redevelopment cases. From the supply-side perspective, Seoul A District received the highest score (0.58), whereas from the demand-side perspective, Gyeonggi D District ranked highest (0.69), illustrating the differing priorities of stakeholders. Overall, Gyeonggi D District emerged as the most favorable option in the combined evaluation. This research contributes a structured and inclusive decision-making framework for the regeneration of public housing. By explicitly comparing and quantifying the contrasting preferences of key stakeholders, it underscores the critical need to balance technical feasibility with resident-centered values in future redevelopment initiatives. Full article

The justice system has increasingly applied AI techniques for legal judgment to enhance efficiency. However, most AI techniques focus on decision-making outcomes, failing to capture the deliberative nature of the real-world judicial process. To address these challenges, we propose a large language model-based multi-agent framework named AgentsBench. Our approach leverages multiple LLM-driven agents that simulate the discussion process of the Chinese judicial bench, which is often composed of professional and lay judge agents. We conducted experiments on a legal judgment prediction task, and the results show that our framework outperforms existing LLM-based methods in terms of performance and decision quality. By incorporating these elements, our framework reflects real-world judicial processes more closely, enhancing accuracy, fairness, and societal consideration. While the simulation is based on China’s lay judge system, our framework is generalizable and can be adapted to various legal scenarios and other legal systems involving collective decision-making processes. Full article

This study explores the challenges and theoretical transformations that the widespread application of AI technology in social governance brings to the protection of citizens’ fundamental rights. By examining typical cases in judicial assistance, technology-enabled law enforcement, and welfare supervision, it explains how AI characteristics such as algorithmic opacity, data bias, and automated decision-making affect fundamental rights including due process, equal protection, and privacy. The article traces the historical evolution of privacy theory from physical space protection to informational self-determination and further to modern data rights, pointing out the inadequacy of traditional rights-protection paradigms in addressing the characteristics of AI technology. Through analyzing AI-governance models in the European Union, the United States, Northeast Asia, and international organizations, it demonstrates diverse governance approaches ranging from systematic risk regulation to decentralized industry regulation. With a special focus on China, the article analyzes the special challenges faced in AI governance and proposes specific recommendations for improving AI-governance paths. The article argues that only within the track of the rule of law, through continuous theoretical innovation, institutional construction, and international cooperation, can AI technology development be ensured to serve human dignity, freedom, and fair justice. Full article

Background/Objectives: Respectful maternity care involves privacy, dignity, and informed choice within the process of delivery as stipulated by the World Health Organization (WHO). Informed consent is a cornerstone of patient-centered care, representing not just a formal document, but an ongoing ethical and clinical process through which women are offered objective, understandable information to support autonomous, informed decision-making. Methods: This narrative review critically examines the literature on informed consent in maternity care, with particular attention to both obstetric-led and midwifery-led models of care. In addition to identifying institutional, cultural, and systemic obstacles to its successful implementation, the review examines the definition and application of informed consent in perinatal settings and evaluates its effects on women’s autonomy and satisfaction with care. Results: Important conclusions emphasize that improving women’s experiences and minimizing needless interventions require active decision-making participation, a positive provider–patient relationship, and ongoing support from medical professionals. However, significant gaps persist between legal mandates and actual practice due to provider attitudes, systemic constraints, and sociocultural influences. Women’s experiences of consent can be more effectively understood through the use of instruments such as the Mothers’ Respect (MOR) Index and the Mothers’ Autonomy in Decision Making (MADM) Scale. Conclusions: To promote genuinely informed and considerate maternity care, this review emphasizes the necessity of legislative reform and improved provider education in order to close the gap between policy and practice. Full article

Background and Objectives: The transition from pediatric to adult-oriented healthcare is challenging and data on parental involvement and perception regarding the transition of children with chronic digestive diseases are scarce. Materials and Methods: Legal guardians of adolescents with chronic digestive diseases receiving care at a North-Eastern Romanian tertiary center and private offices were administered a 30-item survey. Results: There were 124 responders; 73.4% lived in rural areas; 81.5% were patients’ mothers. Positive correlations were found between parents’ perception of the child’s readiness for health-related decisions and appreciation of the children’s preparedness for transition (0.544; p = 0.000), between parents encouraging their children to maintain healthcare records and their perception of the children’s knowledge about their disease (0.67; p = 0.000), between parents’ fear of therapeutic breaks during transition and their perception of the need for transition training (0.704; p = 0.000), between fears for children’s impropriate health-related choices, fears of therapeutic breaks (0.573; p = 0.00) and parental perception that the adult physicians would be more patient-oriented and less family-centered (0.453; p < 0.000) and between parents’ trust in their children’s self-management skills and encouraging them to make decisions on their own (0.673; p < 0.000). Conclusions: The results of our study highlight the importance of addressing parental fears during special parent–children counseling sessions and promoting a child’s independence, chronic disease knowledge, records and alone consultations. Full article

In the context of wastewater treatment plant optimization, this study presents a novel approach based on a virtual sensor architecture designed to estimate total nitrogen levels in effluent and assess plant performance using an operational indicator. The core of the system is an intelligent agent that integrates real-time sensor data with machine learning models to infer nitrogen dynamics and anticipate deviations from optimal operating conditions. Central to this strategy is the operational nitrogen indicator (ONI), a weighted aggregation of four sub-indicators: legal compliance (Nactual%), the nitrogen dynamic trend (Tnitr%), removal efficiency (Enitr%), and microbial balance (NP%), each of which captures a critical dimension of the nitrogen removal process. The ONI enables the early detection of stress conditions and facilitates adaptive decision-making by quantifying operational status in terms of regulatory thresholds, biological requirements, and dynamic stability. This approach contributes to a shift toward smart wastewater treatment plants, where virtual sensing, autonomous control, and throttling-aware diagnostics converge to improve process efficiency, reduce operational risk, and promote environmental compliance. Full article

This study discusses the role of organizational pro-environmental behavior in driving sustainable development. Studies of green practices highlight their capacity to achieve ecological goals while delivering economic sustainability with business strategies for sustainable businesses and advancing environmental sustainability law. It also considers how the development of artificial intelligence, resource management, big data analysis, blockchain, and the Internet of Things enables companies to maximize supply efficiency and address evolving environmental regulations and sustainable decision-making. Through digital technology, businesses can facilitate supply chain transparency, adopt circular economy practices, and produce in an equitable and environmentally friendly manner. Additionally, intelligent business management practices, such as effective decision-making and sustainability reporting, enhance compliance with authorities while ensuring long-term profitability from a legal perspective. Integrating business innovation and digital technology within legal entities enhances economic efficiency, reduces operational costs, improves environmental sustainability, reduces paper usage, and lowers the carbon footprint, creating a double-benefit model of long-term resilience. The policymakers’ role in formulating policy structures that lead to green digital innovation is also to ensure that economic development worldwide is harmonized with environmental protection and international governance. Using example studies and empirical research raises awareness about best practices in technology-based sustainability initiatives across industries and nations, aligning with the United Nations Sustainable Development Goals. Full article

The use of artificial intelligence (AI) in dermatology is skyrocketing, but a comprehensive overview integrating regulatory, ethical, validation, and clinical issues is lacking. This work aims to review current research, map applicable legal regulations, identify ethical challenges and methods of verifying AI models in dermatology, assess publication trends, compare the most popular neural network architectures and datasets, and identify good practices in creating AI-based applications for dermatological use. A systematic literature review is conducted in accordance with the PRISMA guidelines, utilising Google Scholar, PubMed, Scopus, and Web of Science and employing bibliometric analysis. Since 2016, there has been exponential growth in deep learning research in dermatology, revealing gaps in EU and US regulations and significant differences in model performance across different datasets. The decision-making process in clinical dermatology is analysed, focusing on how AI is augmenting skin imaging techniques such as dermatoscopy and histology. Further demonstration is provided regarding how AI is a valuable tool that supports dermatologists by automatically analysing skin images, enabling faster diagnosis and the more accurate identification of skin lesions. These advances enhance the precision and efficiency of dermatological care, showcasing the potential of AI to revolutionise the speed of diagnosis in modern dermatology, sparking excitement and curiosity. Then, we discuss the regulatory framework for AI in medicine, as well as the ethical issues that may arise. Additionally, this article addresses the critical challenge of ensuring the safety and trustworthiness of AI in dermatology, presenting classic examples of safety issues that can arise during its implementation. The review provides recommendations for regulatory harmonisation, the standardisation of validation metrics, and further research on data explainability and representativeness, which can accelerate the safe implementation of AI in dermatological practice. Full article

Objective: Marital infidelity significantly impacts both the community and the institution of marriage. This study aims to develop a theoretical framework for analyzing marital infidelity through a game-theoretic lens. Methodology/Design/Approach: This research employs a game-theoretic model to predict the decision-making processes of unfaithful partners. Static game models are utilized to explore the interactions between spouses, focusing on identifying Nash equilibria that encapsulate the complexities and uncertainties inherent in infidelity-related decisions, whether through pure or mixed strategies. Results: The analysis reveals strategic dynamics in marital infidelity, where Nash equilibria indicate scenarios where one or both partners may engage in extramarital affairs. A Nash equilibrium is established when both partners perceive the benefits of infidelity as outweighing the costs, leading to diminished trust and communication. The Mixed-Strategy Nash Equilibrium (MSNE) hypothesis suggests that spouses may oscillate between fidelity and infidelity based on probabilistic strategies. Research Implications: This study provides a game-theoretic perspective on marital infidelity, whose findings may be used to inform legal frameworks and social policies addressing the consequences of infidelity, potentially impacting family counseling and legal services. Value/Originality: This research introduces a game-theoretic approach to understanding trust and transgression in marriages, identifying two primary categories of Nash equilibria. It fills a theoretical gap while providing practical insights into marital behavior. Full article

Artificial Intelligence (AI) is transforming smart healthcare by enhancing diagnostic precision, automating clinical workflows, and enabling personalized treatment strategies. This review explores the current landscape of AI in healthcare from two key perspectives: capability types (e.g., Narrow AI and AGI) and functional architectures (e.g., Limited Memory and Theory of Mind). Based on capabilities, most AI systems today are categorized as Narrow AI, performing specific tasks such as medical image analysis and risk prediction with high accuracy. More advanced forms like General Artificial Intelligence (AGI) and Superintelligent AI remain theoretical but hold transformative potential. From a functional standpoint, Limited Memory AI dominates clinical applications by learning from historical patient data to inform decision-making. Reactive systems are used in rule-based alerts, while Theory of Mind (ToM) and Self-Aware AI remain conceptual stages for future development. This dual perspective provides a comprehensive framework to assess the maturity, impact, and future direction of AI in healthcare. It also highlights the need for ethical design, transparency, and regulation as AI systems grow more complex and autonomous, by incorporating cross-domain AI insights. Moreover, we evaluate the viability of developing AGI in regionally specific legal and regulatory frameworks, using South Korea as a case study to emphasize the limitations imposed by infrastructural preparedness and medical data governance regulations. Full article

Brain metastases (BMs) are the most common intracranial tumors in adults. Their heterogeneity, potential multifocality, and complex biomolecular behavior pose significant diagnostic and therapeutic challenges. Artificial intelligence (AI) has the potential to revolutionize BM diagnosis by facilitating early lesion detection, precise imaging segmentation, and non-invasive molecular characterization. Machine learning (ML) and deep learning (DL) models have shown promising results in differentiating BMs from other intracranial tumors with similar imaging characteristics—such as gliomas and primary central nervous system lymphomas (PCNSLs)—and predicting tumor features (e.g., genetic mutations) that can guide individualized and targeted therapies. Intraoperatively, AI-driven systems can enable optimal tumor resection by integrating functional brain maps into preoperative imaging, thus facilitating the identification and safeguarding of eloquent brain regions through augmented reality (AR)-assisted neuronavigation. Even postoperatively, AI can be instrumental for radiotherapy planning personalization through the optimization of dose distribution, maximizing disease control while minimizing adjacent healthy tissue damage. Applications in systemic chemo- and immunotherapy include predictive insights into treatment responses; AI can analyze genomic and radiomic features to facilitate the selection of the most suitable, patient-specific treatment regimen, especially for those whose disease demonstrates specific genetic profiles such as epidermal growth factor receptor mutations (e.g., EGFR, HER2). Moreover, AI-based prognostic models can significantly ameliorate survival and recurrence risk prediction, further contributing to follow-up strategy personalization. Despite these advancements and the promising landscape, multiple challenges—including data availability and variability, decision-making interpretability, and ethical, legal, and regulatory concerns—limit the broader implementation of AI into the everyday clinical management of BMs. Future endeavors should thus prioritize the development of generalized AI models, the combination of large and diverse datasets, and the integration of clinical and molecular data into imaging, in an effort to maximally enhance the clinical application of AI in BM care and optimize patient outcomes. Full article

This article analyzes the revitalization process of the Kraków Fortress in the context of the amendment to the Revitalization Act of 29 July 2024, focusing on the legal, financial, social, and environmental effects of these changes. The aim of the work is to assess how the new regulations have affected the effectiveness of the revitalization of historic military facilities and the financial and participatory mechanisms that have enabled their effective implementation. The authors adopted an interdisciplinary approach, combining legal, urban, conservation, and social analysis, and applied the case study method of five forts: 52 “Borek”, 52a “Jugowice”, 2 “Kościuszko”, 49 “Krzesławice”, and 31 “Św. Benedict”. The selection of cases was based on different stages of implementation, financing models, and social functions. The research showed that the amendment to the Act accelerated decision-making processes and enabled more flexible management of space and better acquisition of financial resources, including from the EU and SKOZK. The use of a mixed financing model (local, European, private funds) and strong social participation contributed to the durability and acceptance of the projects. The effects of revitalization include, among others, an increase in the number of visitors (from 20,000 to 75,000 per year), the creation of approx. 120 jobs, and a reduction of energy consumption by over 30%. Revitalized facilities today perform cultural, educational, and recreational functions, supporting social integration and the development of a local identity. The article indicates that the Kraków model can be a model for other cities with military heritage. It also draws attention to the need to develop nationwide standards for the adaptation of historic buildings and recommends further research on the socio-economic durability of revitalization projects. Full article

Given the increasing energy demand and the environmental consequences of fossil fuel consumption, the shift toward sustainable energy sources has become a global priority. Renewable Energy Communities (RECs)—comprising citizens, businesses, and legal entities—are emerging to democratise access to renewable energy. These communities allow members to produce their own energy, sharing or selling any surplus, thus promoting sustainability and generating economic value. However, scaling RECs while ensuring profitability is challenging due to renewable energy intermittency, price volatility, and heterogeneous consumption patterns. To address these issues, this paper presents a Machine Learning as a Service (MLaaS) framework, where each REC microgrid has a customised Reinforcement Learning (RL) agent and electricity price forecasts are included to support decision-making. All the conducted experiments, using the open-source simulator Pymgrid, demonstrate that the proposed agents reduced operational costs by up to 96.41% compared to a robust baseline heuristic. Moreover, this study also introduces two cost-saving features: Peer-to-Peer (P2P) energy trading between communities and internal energy pools, allowing microgrids to draw local energy before using the main grid. Combined with the best-performing agents, these features achieved trading cost reductions of up to 45.58%. Finally, in terms of deployment, the system relies on an MLOps-compliant infrastructure that enables parallel training pipelines and an autoscalable inference service. Overall, this work provides significant contributions to energy management, fostering the development of more sustainable, efficient, and cost-effective solutions. Full article

Background/Objectives: To avoid becoming mired in prolonged deep dementia, some people seek to hasten death by advance instructions rejecting life-sustaining medical intervention (LSMI) at a point of cognitive decline they define in advance as unacceptable. When the time comes to implement such advance instructions and to allow the person in advanced dementia to die, many clinicians experience moral and ethical qualms. The decision makers face a clash between people’s legally recognized self-determination prerogative to control their post-competence medical fate and the decision makers’ conviction that humane treatment dictates sustaining the well-being, i.e., the physical “best interests,” of the patient who no longer recalls prior instructions grounded in concerns about personal dignity. The authors’ objective here is to provide guidance in resolving this anguishing dilemma confronting medical decision makers. Methods: The authors construct and analyze a case scenario involving a patient in a state of advanced dementia with a clear advance instruction rejecting LSMI at the current point of debilitation, but who is not ostensibly suffering, is experiencing a modicum of life satisfaction, and is making life-affirming utterances. The two lead authors present contrasting views on whether legal and moral factors impel the implementation of the advance directive rejecting treatment or rather dictate life-sustaining medical intervention. Results: At this early stage of jurisprudence involving persons in advanced dementia, there can be no definitive resolution of the difficult legal/moral clash confronting decision makers. Some sources would conclude that persons are legally entitled to define precipitous mental decline and complete dependence on others as intolerably undignified and inconsistent with their self-defined life narrative. Other sources would be guided by humane respect for the contemporary well-being of a non-suffering patient, especially one making life-affirming utterances. Conclusion: Through the lens of this illuminating case and contrasting analyses, readers should better understand how clinicians should weigh advance directives against shifting care preferences subsequently articulated by persons with advanced dementia. Full article

This study offers a unique perspective on the role of ancestral knowledge in sustainable river management and community resilience. Specifically, this study draws on (1) a systematic literature review using the PRISMA methodology and (2) a qualitative analysis of community surveys conducted with 39 women in Zambrano, Colombia, to examine the impact of ancestral knowledge on sustainability, peace promotion, and community development. The findings highlight that women’s traditional water management practices significantly contribute to environmental sustainability, conflict resolution, and social cohesion. Women play a central role in transmitting and applying ancestral water knowledge, yet they remain marginalized in decision-making processes, often facing barriers to participation in governance structures. Finally, these findings proposed strategies for integrating ancestral knowledge into sustainable resource management policies. This study underscores the urgent need for legal recognition, intercultural dialogue, gender-inclusive governance, and educational programs to ensure the transmission and adaptation of these practices in contemporary contexts. Integrating ancestral knowledge into water management policies is essential for strengthening gender equity, community resilience, and fostering governance models that harmoniously combine traditional and scientific approaches. Full article