Search Results (375)

The implementation of water reuse strategies requires robust Decision-Support Tools (DSTs) capable of integrating legal, environmental, site-specific, and technological aspects. However, existing approaches are often limited by fragmented methodologies or high data and modelling requirements, restricting their applicability in early-stage planning. This study presents the development and validation of LëtzREUSE, a DST designed to support the selection of treatment technologies for the reuse of urban water sources, including rainwater, stormwater, light greywater, and wastewater treatment plant (WWTP) effluents. The tool is based on a parameter-driven framework that combines (i) regulatory compliance as a first filtering step, (ii) technology applicability defined through operational thresholds linked to water quality parameters, and (iii) prediction of treatment performance along treatment trains. In contrast to multi-criteria approaches, the methodology avoids subjective weighting by directly linking input water quality to process feasibility and expected effluent characteristics. Additionally, ecotoxicological risk is quantified through risk quotient (RQ) reduction, enabling the evaluation of environmental relevance alongside technical performance. The DST was validated using experimental data from the Bleesbruck WWTP, where measured influent characteristics were used to assess the performance of the tool. The results demonstrate that the tool successfully identifies feasible technologies, with Granular Activated Carbon (GAC) + UV (Ultraviolet light) and UV/H₂O₂ + GAC emerging as the most suitable options. By providing the expected quality parameters, both treatment trains can be compared. The final decision should be based on other parameters such as operational complexity. Full article

Background: Legal capacity assessment requires multidisciplinary evaluation integrating cognitive, functional, neurological, psychiatric, and medico-legal information. Although large language models (LLMs) have shown promise in structured clinical reasoning, their role in supporting legal capacity assessment remains unclear. This study evaluated the performance of LLMs as AI-assisted decision-support tools using standardized medico-legal case vignettes, with interdisciplinary medical board (IMBD) recommendations under the Turkish Civil Code (TCC) as the expert medical reference standard; IMBD recommendations constitute an expert medical reference standard rather than final judicial determinations. Methods: We retrospectively analyzed 234 court-referred adult cases (2018–2024). Standardized, anonymized medico-legal case vignettes were independently evaluated by ChatGPT-5.2, Gemini 3 Pro, and Claude 4.5 Sonnet. Model outputs were compared with IMBD recommendations. The models were evaluated as AI-assisted decision-support tools and did not replace or influence clinical or judicial decision-making. Performance was assessed using accuracy, macro-F1, Cohen’s κ, AUC, calibration, decision-curve analysis, test–retest reliability, and human-factor outcomes; probability-based metrics were derived from secondary logistic models fitted to the categorical model outputs. Results: Article 405 was the most frequent outcome (65.8%). Dementia increased the likelihood of Article 405 recommendations (OR 6.5, 95% CI 1.2–35.2; p = 0.029), whereas higher Activities of Daily Living scores were protective (OR 0.96; p = 0.004). Gemini 3 Pro achieved the highest accuracy (86.3%), while ChatGPT-5.2 achieved the highest macro-F1 score (0.79) and AUC (0.91). Agreement with IMBD recommendations ranged from κ = 0.60 to 0.73, with high temporal stability (κ = 0.87–0.95); pairwise differences between the three models were not statistically significant after Holm correction. Performance was highest for cases in which Article 405 was recommended and for cases in which no guardianship was recommended, but remained limited for Article 408 (29.6% accuracy). The mean System Usability Scale score was 72.4, and safety flags were identified in 4 of 234 cases (1.7% of cases, corresponding to 4 of 702 individual model outputs). Conclusions: LLMs demonstrated agreement with IMBD recommendations on standardized medico-legal case vignettes, supporting further investigation of their potential role as AI-assisted decision-support tools under expert supervision. Because errors in this domain can directly affect fundamental rights, the use of AI in the legal system and in sensitive medical fields carries substantial risks and must remain strictly limited to expert-supervised decision support. Further prospective studies are needed to evaluate their safe integration into medico-legal practice. Full article

Across societies, judges are entrusted with extraordinary authority to shape legal norms, public policy, and individual rights, with decisions that can influence communities and future generations. This social contract rests on expectations of competence, impartiality, and ethical self-regulation. Popular representations often portray judges as detached, stoic, and immune to typical human emotional reactivity and outside influence. However, judges remain subject to the same biological, psychological, and social realities that affect all humans, including stress physiology, neurocognitive load, emotional strain, and the cumulative effects of allostatic burden. Emerging research suggests that these factors may have important implications for judicial well-being, decision-making, and the effective functioning of justice systems. The recent Nauru Declaration, recognizing that the well-being of judges is essential for a properly functioning judiciary, has led to the 2025 United Nations General Assembly proclamation that the 25th July be recognized as an annual International Day for Judicial Well-being. Drawing on literature identified through scholarly databases, this concept article examines evidence relating to stress, burnout, resilience, and well-being within justice systems, with particular emphasis on judges and judicial well-being. We explore the relevance of lifestyle medicine and whole-person approaches to health in supporting judicial functioning and vitality. Framed through the lens of the judiciary’s societal responsibilities and the broader social contract, we contend that judicial well-being is not merely an individual concern, but one with institutional and societal significance. The article argues that well-being within systems of justice matters to society; it examines evidence relating to judicial stress and self-control fatigue, and concludes with solutions-oriented approaches and future directions for promoting judicial well-being and sustainable justice systems. Full article

An Environmental Impact Assessment (EIA) is a legal obligation in Bangladesh for projects with substantial environmental and socio-economic impacts. Over time, Bangladesh has established sound policies, rules, and a growing workforce to support the EIA process. However, two key issues persist: the effectiveness of EIA reports and weak implementation of Environmental Management Plans (EMPs). In practice, implementing agencies often prioritize physical progress over environmental safeguards, treating the EIA as merely a clearance formality for Planning Commission approval. Regulatory bodies, such as the Department of Environment and the Planning Commission, frequently neglect the substantive review of EIA reports, focusing on checklist requirements rather than rigorous environmental scrutiny. This procedural mindset undermines the role of EIA as a strategic planning and decision-making tool. This study examines each step of the EIA process, from report preparation to EMP implementation, highlighting gaps that require sustainable improvements. A mixed-method approach was employed to evaluate the quality of the EIA process, with particular attention to its methodological rigor, accuracy of impact assessment, challenges in public participation, and the effectiveness of the proposed EMPs and their implementation. The recommendations presented in this study aim to enhance the efficiency of the EIA process and promote its application in similar contexts across developing countries. Full article

Artificial intelligence is increasingly used to support clinical decision making, yet concerns remain regarding algorithmic aversion, automation bias and the preservation of meaningful human oversight; while explainable AI aims to improve transparency, less attention has been devoted to the design of human–AI interaction protocols. This study investigates Frictional AI, an interaction paradigm that introduces cognitive friction to encourage critical engagement with AI recommendations. First, semi-structured interviews were conducted with a legal expert and a psychologist and analyzed through thematic analysis to identify legal, ethical, and cognitive requirements for AI-assisted decision support. Second, a user study involving 96 medical residents compared three interaction protocols: a conventional explainable AI-first design (XAI) and two friction-based protocols, namely a judicial protocol based on juxtaposed explanations (Judicial AI, JAI) and an adjunct protocol requiring an initial unsupported decision before AI exposure (AAI). Diagnostic accuracy and confidence, perceived usefulness, completion time, and reliance patterns were evaluated. The interviews highlighted the importance of human-centered explanations, contrastive reasoning, preservation of professional responsibility, and the role of user studies in evaluating human–AI interaction. The quantitative results showed that none of the AI-assisted conditions improved diagnostic accuracy relative to the no-support baseline. However, JAI achieved performance comparable to the baseline, outperforming XAI and AAI, and exhibited the lowest level of over-reliance. Overall findings suggest that the effectiveness of decision-support systems depends not only on model performance and explanation quality but also on interaction design. In conclusion, while preserving diagnostic performance, judicial protocols showed promise in mitigating automation bias and promoting active cognitive engagement in clinical decision support. Full article

Background/Objectives: Shared Care Planning (SCP) represents a collaborative decision-making process integrating advance care planning and shared decision-making to support ethically complex clinical pathways. Since 2016, the Clinical Ethics Consultation (CEC) service at the Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS (FPUG) in Rome has implemented SCP through the use of the Shared Document (SD) for healthcare ethics planning. This study aimed to describe the SD as an operational tool supporting SCP, focusing on its procedural characteristics, multidisciplinary dimension, and the ethical and contextual issues emerging during its implementation. Methods: This single-center, retrospective observational study analyzed digitized medical records of patients who underwent SCP through the drafting of one or more SDs between 2016 and 2024 at FPUG. Data were extracted from SDs and clinical records in accordance with the RECORD guidelines. Variables included processing time, number of meetings with the clinical ethics consultant (CEc), number of healthcare professionals and family members involved, ethical–clinical issues, contextual challenges, and care orientations proposed within the SDs. Descriptive statistics were used to characterize the cohort and operational aspects of the service. Results: Among 454 patients referred to the CEC service, 154 patients underwent SCP, resulting in 160 SDs. The most frequent ethical issues concerned the proportionality of treatments to initiate (70%) and ongoing treatments (31%). Contextual issues emerged in 74% of cases, particularly pregnancy-related situations (44%) and absence of a legal guardian (19%). Drafting of SD required multiple interdisciplinary meetings (mean: 2.4), with an average processing time of 7 days. The number of healthcare professionals involved increased over time, reflecting growing multidisciplinary participation. The most frequent care orientations included palliative care (58%), withholding invasive/intensive maneuvers (34%), and indications for surgical or diagnostic treatments. Conclusions: The SD emerged as a structured clinical–ethical tool supporting complex shared care planning processes beyond issues of informed consent alone. Its use facilitated multidisciplinary deliberation, the integration of ethical and contextual factors, and continuity in care planning across different clinical trajectories. Full article

The digital transformation of Morocco’s construction sector requires new approaches to address increasing project complexity and evolving regulatory demands. This exploratory qualitative study investigates the integration of building information modeling (BIM) and artificial intelligence (AI) through eight semi-structured interviews with field engineers, design engineers, sector experts, and researchers. The interview data were analyzed using thematic analysis in NVivo, supported by systematic coding, lexical similarity analysis, and cross-matrix synthesis. The findings reveal that BIM implementation in Morocco remains predominantly at Level 1, despite unanimous recognition of its high perceived usefulness. This gap between perceived usefulness and actual adoption is primarily explained by economic, institutional, legal, and capacity-related barriers rather than technological limitations. Participants also reported successful BIM-based clash detection experiences, highlighting measurable returns on investment through reduced design conflicts, rework, and project coordination efforts. Furthermore, AI was consistently perceived as an augmentation technology that enhances professional expertise and decision-making rather than replacing human roles. Based on these findings, the study recommends establishing a national BIM mandate, developing a national BIM competence center, strengthening regulatory and legal frameworks, and promoting context-specific research and development to accelerate Morocco’s digital transition while adapting BIM and AI implementation to local technical, institutional, and climatic conditions. Future research should validate these findings through pilot implementation projects. Full article

Digital finance is reshaping financial intermediation through platform-based credit, stablecoin-based payment and settlement arrangements, and a rapidly deepening reliance on critical third-party technology providers. Existing international frameworks identify many of these vulnerabilities, but macroprudential authorities still lack a traceable operational bridge from FinTech activities to systemic-risk channels, measurable indicators, stress-test assumptions and staged policy escalation. This article proposes the FinTech Macroprudential Integration Framework (FMIF) as an evidence-mapped operationalisation layer for existing macroprudential regimes. The FMIF does not claim to replace the frameworks issued by the FSB, BIS, IMF, BCBS, the EU or cyber-resilience authorities; instead, it translates their risk taxonomies and policy insights into a structured workflow linking activity perimeter, dependency registry, indicator formulas, modular stress tests and governance interfaces. The framework is developed for three activity clusters: platform credit and bank–FinTech partnerships; stablecoin-based payment and settlement; and critical third-party technology dependencies. These clusters are mapped to three channels: liquidity/run dynamics, interconnectedness and market spillovers, and operational concentration/correlated disruption. The article contributes by clarifying the research gap, disclosing the evidence-mapping procedure and source-level coding, classifying evidence quality, specifying indicator formulas and calibration principles, adding a stablecoin-run illustrative application, and discussing legal and institutional feasibility. The FMIF should be understood as a decision-support and decision-preparedness framework rather than as a fully validated decision-ready model. Empirical validation, jurisdiction-specific thresholds and legal triggers remain necessary next steps. Full article

Morocco has emerged as a prominent renewable energy leader in North Africa and a significant contributor to the Middle East and North Africa (MENA) region through the extensive implementation of solar, wind, and hydropower technologies, supported by a sophisticated legal framework established since 2010. This study presents an integrated sustainability assessment of Morocco’s renewable energy transition through a structured comparative analysis of these three technologies across technical, economic, environmental, and socioeconomic dimensions. The methodology is built on a structured literature search and screening process covering 71 peer-reviewed articles and institutional reports published between 2010 and 2025. In order to facilitate the comparative sustainability assessment, sources were identified through Scopus, ScienceDirect, Web of Science, Google Scholar, and institutional repositories such as MASEN, IRENA, IEA, and the World Bank. Each technology was relatively evaluated using six sustainability criteria: energy security contribution, economic competitiveness, environmental sustainability, grid stability contribution, scalability potential, and socioeconomic impact. The assessment utilized quantitative indicators extracted from institutional databases and peer-reviewed literature with a standardized scoring framework and sensitivity analysis to determine the robustness of the technology rankings under alternative policy-priority scenarios. Technologies were comparatively scored on a standardized 1–5 scale based on quantitative proxies including installed capacity (MW), levelized cost of energy (USD/MWh), capacity factor (%), CO₂ avoided (tCO₂/GWh), jobs per MW, and dispatchability characteristics. A sensitivity analysis using five alternative weighting scenarios representing different policy priorities confirmed the robustness and consistency of the technology rankings. The comparative assessment indicates that solar energy achieved the highest scalability score (5/5), wind energy achieved the highest economic competitiveness score (5/5), and hydropower achieved the highest grid stability score (5/5), owing to its dispatchability and pumped-storage capability. The findings confirm that Morocco’s transition rests on technological complementarity rather than dominance by a single source. Structural challenges persist, including intermittency, storage insufficiency, grid modernization requirements, external financing dependency, and territorial disparities in socioeconomic benefits. The study proposes a replicable comparative sustainability assessment framework that can support renewable energy planning and policy decisions in Morocco and other emerging economies pursuing large-scale energy transitions. Full article

AI-driven digitalisation transforms how organisations process personal data and introduces risks that traditional Data Protection Impact Assessment (DPIA) frameworks cannot adequately address. Automated decision-making and large-scale processing in AI, IoT, big data analytics, and blockchain environments create privacy concerns beyond the scope of existing DPIA methodologies. The EU AI Act extends this scope through the Fundamental Rights Impact Assessment (FRIA) under Article 27, which links data protection obligations to broader fundamental rights governance. This study addresses these gaps through a two-phase research design. Phase 1 conducts a systematic literature review of 25 studies and applies framework analysis to identify DPIA implementation challenges across four categories: legal and regulatory, risk assessment, scope, and complexity. AI-specific challenges appear across all four categories. Phase 2 develops a governance framework built on the Plan-Do-Check-Act (PDCA) cycle and organised through a four-level hierarchy of Lifecycle Phase, Risk Management Domain, Control Objective, and Operational Activity. The framework translates relevant requirements of ISO 31000:2018, ISO/IEC 27701:2025, and ISO/IEC 29134:2023 into traceable activities and encompasses algorithmic fairness and socio-ethical impacts. The actionable DPIA framework supports compliance with the GDPR, the EU AI Act and the three ISO standards and will be of interest to company practitioners and other researchers investigating the theoretical and practice-based aspects of digitalisation and data privacy. Full article

Distributed energy systems are increasingly important for strengthening critical infrastructure resilience under conditions of technological, climatic, geopolitical, and cyber disruption. However, existing research on energy resilience is still dominated by technical approaches focused on reliability, renewable energy integration, microgrid control, and storage optimisation, while the role of distributed energy systems in the full crisis-management cycle remains insufficiently conceptualised. This article addresses this gap by combining a scoping review, lexicographic and semantic analysis using IRaMuTeQ version 0.7 alpha 2, and a conceptual-methodological framework for assessing distributed energy systems as instruments of crisis management. The main contribution of the study is the M_ZK-DES model, which integrates technological-infrastructural, decision-operational, legal-institutional, and socio-organisational dimensions with four crisis-management phases: prevention, preparedness, response, and recovery. The model distinguishes distributed energy systems, distributed energy resources, distributed generation, microgrids, prosumers, energy communities, and energy clusters and links them to measurable resilience indicators. These include SAIDI, SAIFI, energy not supplied, restoration time, share of critical load served, islanding capability, voltage and frequency stability, storage autonomy, procedural readiness, and local coordination capacity. The analysis shows that distributed energy systems may reduce vulnerability to cascading failures, support islanded operation, protect vulnerable consumers, improve emergency power continuity, and strengthen local energy autonomy. The proposed scoring and weighting logic enables future empirical validation, scenario testing, and comparative assessment across regions and crisis types, including extreme weather events, cyberattacks, and supply-chain disruptions. The article contributes to energy resilience and crisis-management studies by offering an integrated and operational framework for evaluating distributed energy systems as practical tools for critical infrastructure protection and continuity of essential public services. Full article

Since organ donation after citizen death became the sole legal source in 2015, lung transplantation (LTx) in China has undergone a systematic transformation, shifting from numerical scarcity to scale expansion and from technical exploration to sustained improvement in quality. From a management perspective, this article provides a comprehensive review of the development of LTx in China between 2015 and 2024, demonstrating that its central achievement has been the establishment of an integrated management system that combines national top-level policy design, standardized and homogeneous training, multidisciplinary collaboration, standardized clinical pathways, and rigorous quality assessment. Supported by a mechanism based on data-driven decision making, standard-guided implementation, and closed-loop management, this system has effectively ensured quality homogeneity and controllable risk during the dissemination of advanced transplantation techniques. Over the past decade, the number of hospitals performing LTx increased from 9 to 45, annual procedure volume rose from 118 to 925 cases, and the cumulative total of completed LTx was 5483. During the same period, 30-day survival improved from 78.5% to 85.3%. This system-oriented management model provides an important reference for the global community, particularly for developing countries facing challenges related to resource imbalance, by demonstrating how institutional innovation can facilitate the safe and efficient dissemination of highly complex medical technologies. Full article

Legal mediation is an important mechanism for resolving social conflicts and handling disputes. It involves complex interpersonal interactions and unstructured decision-making processes, and therefore holds significant research value as a domain. Leveraging the outstanding logical reasoning capabilities of large language models, multi-agent systems for simulating complex social interactions have become a cutting-edge research direction in artificial intelligence, providing a new supporting vehicle and research pathway for the intelligent study and practical application of legal mediation. However, directly applying general-purpose multi-agent techniques or general-purpose, opaque LLMs to long-horizon, multi-party, and high-conflict professional mediation tasks exposes several deep-seated structural cognitive deficiencies, including a lack of process awareness, insufficient domain-specific intervention capabilities, and limited theory-of-mind reasoning. To address these challenges, this study proposes CogMed, a cognitively enhanced multi-agent framework for legal mediation simulation, which aims to compensate for the limitations of general models in professional strategic interactions through an explicit cognitive architecture. Rather than introducing entirely new individual reasoning modules, the proposed framework focuses on cognitively coordinated integration of process control, strategic intervention, and belief modeling mechanisms under legal mediation settings. CogMed models the mediation process as a Finite State Machine (FSM) to capture macro-level decision logic and introduces a Strategic Toolkit (STK) that serves as a set of action primitives for micro-level interventions. Meanwhile, a Dynamic Belief Tracking (DBT) mechanism is incorporated into party agents to simulate psychological anticipation and strategic reasoning during negotiation. Experimental results demonstrate that CogMed effectively improves both mediation success rates and the quality of negotiated outcomes. Furthermore, the findings suggest a preliminary framework-level compensation pattern under the current experimental setting, where cognitively structured coordination mechanisms may partially enhance the mediation capability of medium-scale models. These preliminary experimental observations suggest that cognitively structured coordination mechanisms may partially compensate for certain limitations associated with model scale under the current controlled mediation setting, thereby offering a potential research direction for cognitively structured legal mediation simulation systems under controlled experimental settings. Full article

Environmental damage assessment has become an important institutional component of regional sustainability transitions, especially in resource-dependent northern territories where industrial risks, fragile ecosystems, sparse monitoring networks, and limited institutional capacity intersect. Existing oil-spill valuation approaches provide well-developed legal, economic, and ecosystem-service tools; however, their use as decision-support instruments for compensation governance, prevention investment, monitoring design, and regional resilience policy remains insufficiently operationalized. This article addresses this gap by developing a modular institutional framework for selecting and combining established environmental damage valuation methods under the ecological, legal, data-related, and socio-cultural constraints of the Russian North. The study applies a transparent narrative methodological review and framework-building design based on international and Russian literature, official regulatory sources, natural-resource damage assessment practice, ecosystem-service valuation studies, and publicly available evidence on the 2020 Norilsk diesel fuel spill. The analysis organizes six established valuation approaches into three complementary modules: direct harm, indirect and opportunity-related losses, and non-market ecosystem-service losses. The Norilsk case is used as an illustrative plausibility check rather than a full empirical recalculation. It shows that legally recognized compensation can provide a liability signal, but indirect losses and ecosystem-service components require separate evidence, sensitivity analysis, and double-counting control. The contribution of the article is to adapt established valuation methods into a transparent decision-support protocol that links environmental liability, compensation governance, prevention, monitoring, institutional resilience and regional sustainability transitions in Arctic and sub-Arctic resource regions. Full article

Artificial intelligence (AI) is increasingly recognized as a transformative technology in healthcare, with growing evidence supporting its applicability across time-critical clinical environments. This perspective aims to evaluate the integration of AI and machine learning (ML) into pediatric emergency departments (PEDs) across three core domains: clinical decision support, stakeholder engagement, and medical education. Within clinical decision support, ML architectures have demonstrated high predictive performance across several high-acuity clinical scenarios, including triage stratification, pediatric traumatic brain injury risk classification, early sepsis detection and clinical deterioration prediction, and dermatological assessment. Model interpretability and real-world implementability remain critical prerequisites for clinical adoption, with explainability methods representing fundamental instruments to enhance transparency and stakeholder trust. Regarding stakeholder engagement, the triadic dynamic among clinicians, caregivers, and patients defines a unique communication challenge in PEDs, with large language models (LLMs) showing preliminary utility; however, stakeholder-inclusive model validation and robust data privacy protections for minors remain key challenges, particularly regarding legal ambiguities of LLM deployment in clinical pipelines. In medical education, AI-driven simulation platforms and LLM-generated adaptive curricula represent promising tools for competency-based training across pediatric emergency scenarios. Future directions emphasize the imperative of prospective multicenter validation in pediatric-specific cohorts, rigorous data quality standards addressing conformance, completeness, and plausibility, and the development of pediatric-tailored governance frameworks. Real-world implementation will require the systematic involvement of all stakeholders—including children, caregivers, clinicians, developers, and institutions—as co-designers of equitable, transparent, and safe AI systems for this uniquely vulnerable population. Full article