The rapid diffusion of artificial intelligence (AI) in higher education presents significant governance challenges, particularly in institutions operating within distinct ethical and cultural contexts. However, existing AI governance models remain largely technocratic and insufficiently responsive to local institutional realities. This study aims to develop a contextually grounded and policy-relevant Artificial Intelligence Ecosystem Framework tailored to Islamic higher education institutions. Employing a qualitative multisite comparative design, the research was conducted at STIT Bustanul Ulum (Indonesia), the Zawia University (Libya), and Badakhshan University (Afghanistan). Data were collected through semi-structured interviews, institutional observations, and document analysis, and were analyzed using cross-case thematic comparison. The findings reveal that effective AI adoption is not determined solely by technological capacity but by coordinated institutional readiness across five interdependent dimensions: digital infrastructure, human resource competence, adaptive institutional policy, ethical orientation, and collaborative sustainability networks. The study further demonstrates that within Islamic higher education, ethical and religious values function as internal regulatory mechanisms that promote responsible AI governance rather than inhibit innovation. The proposed AI Ecosystem Framework offers a holistic governance model that integrates technological advancement with contextual, ethical, and institutional considerations, contributing both theoretically to AI governance discourse and practically to policy development in faith-based higher education settings.

Do environmental regulations affect corporate investment preferences? Institutional considerations based on fiscal decentralization

Neuromuscular blocking agents (NMBAs) show potential benefits on mortality and other complications of acute respiratory distress syndrome (ARDS) in adult patients. Evidence-based decisions and processes ensure appropriate use of neuromuscular blockade in adult patients with ARDS. The objective of these guidelines was to develop evidence-based recommendations for the administration of NMBAs in critically ill adult patients with ARDS. The American College of Critical Care Medicine Board convened a 21-member multidisciplinary panel of experts in critical care medicine, nursing, respiratory therapy, pharmacology, surgery, neurology, and anesthesiology. The panel included two expert methodologists specialized in developing evidence-based recommendations in alignment with the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. Conflict-of-interest policies were strictly followed during all phases of guidelines development including task force selection and voting. The panel members identified and formulated five Population, Intervention, Comparison, and Outcome questions. We conducted a systematic review for each question to identify the best available evidence, statistically analyzed the evidence, and assessed the certainty of the evidence using the GRADE methodology. We used the GRADE evidence-to-decision framework to formulate the recommendations. The panel generated two conditional recommendations. One recommendation is to use NMBAs in adults with ARDS with Pao2/Fio2 less than 150. For the other recommendations, there was equipoise in the recommendation for and against using titratable vs. fixed-dose NMBA dosing, a monitoring-based strategy for assessing depth of sedation and analgesia in adults with ARDS before initiating or while receiving neuromuscular blockade, and administration of NMBAs for patients who are proned, due to overall lack of evidence in critically ill patients and due to considerations of patient safety and experience concerns. These guidelines provide additional perspectives on the use of NMBA in patients with ARDS, recognizing that institutional and patient-specific considerations must help to guide the decision-making process.

Abstract - The growing complexity of sustainable development challenges requires policy systems capable of integrating diverse data sources, anticipating long-term impacts, and supporting evidence-based decision-making across sectors. Artificial Intelligence based Decision Support Systems offer a transformative approach to sustainable development policy and planning by combining predictive analytics, optimization modeling, and real-time data integration within adaptive governance frameworks. This paper conceptualizes the architecture and functional dimensions of AI-enabled decision support systems tailored to sustainable development objectives, examining their capacity to enhance policy design, resource allocation, risk forecasting, and cross-sectoral coordination. Drawing on interdisciplinary scholarship in artificial intelligence, public policy and sustainability studies, the study proposes an integrated framework that aligns AI capabilities with Sustainable Development Goal monitoring and implementation processes. It further evaluates governance, ethical and institutional considerations that shape responsible deployment, including transparency, fairness, data privacy, and digital capacity constraints. The analysis argues that AI-based decision support systems function not merely as technical tools but as enabling infrastructures that strengthen institutional intelligence and policy responsiveness. When implemented within accountable and inclusive governance structures, such systems hold significant potential to improve the effectiveness, efficiency, and equity of sustainable development planning. Key Words: Artificial Intelligence; Decision Support Systems, Sustainable Development, Policy Planning, SDGs, Digital Governance.

Air quality monitoring networks are essential for characterising spatial and temporal patterns in air pollution concentrations that inform the management of population exposure, but are often spatially sparse, limiting their ability to capture hyperlocal variation in air pollution. Lower-cost air quality sensors are increasingly used to address this challenge by complementing regulatory networks where they exist, extending monitoring into under-represented locations where monitoring is sparse, and establishing networks where monitoring is non-existent. However, deploying such sensors in practice frequently requires bespoke, context-responsive system configurations, particularly where access to power and infrastructure is limited. By deploying a lower-cost particulate matter sensor in a high-latitude context characterised by strong seasonal variability, using a customised solar-powered configuration, the paper consolidates practical considerations for the design and deployment of an off-grid air quality monitoring system, highlighting how design decisions, system trade-offs, and deployment processes could be adapted depending on context. While solar-powered systems can support hyperlocal monitoring, performance and scalability are shaped by seasonal variability, energy storage and charge-control limitations, connectivity-related power demand, siting constraints, and resilience to power interruptions. Deployment also highlighted institutional and governance considerations, including permissions, public space management, and ongoing maintenance burden. Synthesised practical considerations and recommendations are presented to inform the design and deployment of autonomous sensor networks capable of supporting hyperlocal air quality assessment, targeted mitigation, and more actionable decision-making.

Critical infrastructure (CI)-the essential systems and facilities that support various societal functions and economic activities-is increasingly at risk from climate change. In Canada, evidence on these risks remains fragmented and uneven. This study presents a systematic review of peer-reviewed research on climate change impacts on CI in Canada, following PRISMA guidelines and a PICO-informed search strategy. Existing research is concentrated geographically in Ontario and British Columbia and focused primarily on transportation, water, wastewater, and energy systems. Flooding, extreme precipitation, temperature variability, and permafrost thaw dominate the hazards examined, while wildfires and compound climate risks receive comparatively little attention. Across sectors, studies consistently document physical damage, service disruptions, economic losses, and cascading failures arising from infrastructure interdependencies. Non-climatic factors, including asset age, geographic location, governance arrangements, and investment levels, emerge as critical determinants of vulnerability and recovery. Methodologically, the literature is dominated by engineering and hydrological modeling, with limited integration of social, institutional, and equity considerations. This review synthesizes current knowledge, identifies persistent gaps, and outlines priorities for advancing climate-resilient CI research and policy in Canada.

This article examines the role of context and underlying assumptions in the formulation of econometric models for economic policy analysis. While econometric models are widely employed to support policy decisions, their construction is often treated as a purely technical process, overlooking the contextual factors that shape variable selection, model specification, and interpretation of results. This study explores how institutional settings, policy objectives, and behavioral considerations influence the assumptions embedded in econometric modeling. By drawing on policy documents, academic literature, and illustrative cases from applied economic research, the article highlights how implicit assumptions may affect the validity and relevance of econometric outcomes. The analysis emphasizes that econometric models are not value-neutral tools but are shaped by theoretical choices and contextual judgments made during their formulation. Understanding these dimensions is crucial to avoid misinterpretation of empirical findings and to enhance the usefulness of econometric analysis in policymaking. The study contributes to the methodological discussion in applied econometrics by encouraging greater transparency and reflexivity in model construction, particularly in the context of economic policy evaluation.

ABSTRACT Background Artificial intelligence (AI) has the potential to significantly transform health care, with substantial implications for pharmacy practice. The integration of AI technologies into formulary management and pharmacy and therapeutics (P&T) committee workflows offers opportunities to enhance evidence‐based, safe, and timely clinical decision‐making by processing large datasets and delivering actionable insights. While professional pharmacy organizations emphasize responsible AI integration, limited evidence exists on AI performance for formulary management tasks. Methods This pilot study qualitatively evaluated six freely available AI tools (Perplexity, Google Gemini, ChatGPT, Microsoft Copilot, Llama, and Claude) for drug monograph development using three previously completed monographs as reference standards. An independent reviewer assessed each AI tool's performance for content accuracy, completeness, source credibility, and clinical relevance. Results Initial broad prompts requesting complete monographs yielded responses that were vague, incomplete, or inaccurate across all tools. Refined prompt engineering strategies using targeted, section‐specific requests substantially improved output quality, with all AI tools producing more detailed, accurate, and scholarly responses. The tools demonstrated marked variability in source quality and transparency, with some consistently providing citations to peer‐reviewed literature while others offered minimal sourcing or relied on less credible references. Literature review capabilities improved substantially when full‐text articles were uploaded compared with abstract‐only inputs; however, human oversight remains essential for critical evaluation and synthesis of conclusions. Discussion The findings from this pilot evaluation provided the foundation for the development of institutional implementation considerations at West Virginia University (WVU) Medicine, establishing five core principles for AI use in formulary management, privacy and compliance requirements, and emphasis on human oversight, with clear delineation of permitted and prohibited applications. Conclusion This pilot evaluation provides health care organizations with qualitative performance insights for responsible AI implementation in formulary management workflows.

Purpose Abattoir waste remains a major environmental and public health challenge in many developing countries because of uncontrolled discharge, high biological oxygen demand/chemical oxygen demand loads and methane emissions. However, its resource potential for renewable energy remains largely untapped. The purpose of this study is to develop and validate a circular economy–renewable energy (CE–RE) framework that converts abattoir waste from an ecological liability into a sustainable resource, supporting Nigeria’s energy transition and broader climate and development goals. Design/methodology/approach A mixed-methods approach integrated field surveys of 192 respondents across three abattoirs in Ogun State with direct waste quantification, Buswell-based biogas modelling, financial feasibility analysis and a Delphi-based expert validation. The framework was evaluated across technical, economic, environmental, institutional and social dimensions to ensure practical applicability. Findings Waste loads of 1.2–3.2 tonnes/day produced methane yields of 192–512 m³/day (3,800–10,240 kWh/month), with co-digestion improving output by 15%–25%. Financial modelling showed strong viability (internal rates of return 17%–21%; payback 4–5 years). CE–RE integration lowered effluent pollution, reduced zoonotic risks and produced nutrient-rich digestate. Social analysis highlighted opportunities for gender-responsive cooperatives to enhance inclusion and economic participation. Originality/value This study offers one of the first empirically grounded CE–RE frameworks for abattoir systems in the Global South, integrating technical modelling with financial, institutional and social inclusion considerations. Its key contribution lies in providing a scalable and policy-ready framework that supports Nigeria’s Energy Transition Plan and demonstrates a replicable pathway for waste-to-energy adoption in resource-constrained settings.

Artificial intelligence (AI) has emerged as one of the most transformative technologies shaping contemporary organisations, economies and managerial practices. Although recent advances—particularly in machine learning, deep learning and generative AI—have intensified academic and managerial interest, the foundations of AI research in business and management extend well beyond the current wave of technological enthusiasm. This study aims to provide a comprehensive review of the evolution of artificial intelligence and to examine how AI-related technologies have been conceptualised, adopted and analysed within the business and management literature. Drawing on an extensive review of international studies, the article synthesises research across key managerial domains, including strategic decision-making, human resource management, innovation, governance, ethics and organisational performance. The findings indicate that AI has moved beyond its role as an operational efficiency tool and increasingly functions as a strategic and organisational capability, reshaping managerial decision processes, organisational structures and human–machine collaboration. At the same time, the literature highlights persistent challenges related to algorithmic bias, transparency, data governance, regulatory uncertainty and workforce adaptation. By integrating theoretical discussions with practical examples, this study contributes to the growing body of management research by clarifying dominant research streams, identifying conceptual gaps and outlining future research directions. The article concludes that while artificial intelligence offers significant opportunities for enhancing organisational performance and competitiveness, its effective and responsible integration requires careful managerial, ethical and institutional consideration.

Abstract Endometrial cancer is the most frequent gynecologic malignancy in developed countries, with increasing incidence in Latin America. Minimally invasive approaches have become the standard for early-stage disease, with robotic surgery offering enhanced ergonomics and precision. Sentinel lymph node (SLN) mapping using radioguided techniques is a validated strategy to reduce the morbidity associated with systematic lymphadenectomy while maintaining accurate staging. We report the case of a 60-year-old woman diagnosed with endometrioid adenocarcinoma, FIGO grade 1, who underwent robotic radical hysterectomy with radioguided SLN mapping. This is the first reported case in Ecuador combining robotic surgery with nuclear medicine-guided sentinel node detection. Introduction Robotic surgery and sentinel lymph node (SLN) mapping are established approaches for staging early-stage endometrial cancer. However, their combined implementation in low- and middle-income countries remains limited. This case highlights the first documented robotic staging with radioguided SLN mapping performed in Ecuador, illustrating practical considerations for institutions initiating robotic and nuclear-guided programs. Case description A 60-year-old woman with well-differentiated endometrioid adenocarcinoma underwent robotic radical hysterectomy with bilateral salpingo-oophorectomy and SLN biopsy. Preoperative SPECT/CT identified unilateral left-sided drainage, including a sentinel node at the common iliac level. Radioguided detection with Tc-99m nanocolloid and Patent Blue V enabled precise nodal localization. Surgery proceeded without complications, and the patient achieved rapid postoperative recovery with discharge on Day 3. The final pathology confirmed FIGO IA disease with negative SLNs. She remains disease-free at 12 months. Discussion This case illustrates key educational aspects for centers developing robotic staging pathways: managing unilateral SLN mapping with high-iliac drainage, integrating hybrid radioguided techniques during early learning-curve phases, and demonstrating safe, efficient postoperative recovery.

Purpose: This editorial reflects on the outgoing Editor-in-Chief's (Collins G. Ntim) tenure (September 2023 to August 2025) at the African Accounting and Finance Journal (AAFJ), highlights key institutional and scholarly achievements, introduces the contributions published in Volume 7, Issue 1, and offers forward-looking reflections on the journal's future direction within African and global accounting and finance scholarship.Design/methodology/approach: The editorial adopts a reflective and narrative approach, drawing on editorial experience, institutional developments, and synthesis of the papers included in the issue. It combines reflective commentary with descriptive analysis to situate progress within broader debates on academic publishing, governance, sustainability, and accounting in emerging markets. Findings: The editorial documents substantial progress in strengthening AAFJ's editorial governance, publication consistency, digital infrastructure, and international visibility. The four papers in this issue demonstrate the journal's commitment to context-sensitivecorporate governance, nuanced sustainability performance relationships, and critical reflections on accounting developments in emerging markets. Together, they underscore the importance of institutional context, stability, and ethical considerations in shaping accounting and finance outcomes in Africa and beyond.Originality/value: This editorial provides rare insider reflections on the development of an Africa-focused academic journal and contributes to the literature on scholarly publishing in emerging economies. It adds value by integrating editorial leadership insights with a substantive synthesis of contemporary research themes relevant to African accounting and finance.

Chinas 1994 housing privatization decoupled employer-provided housing from state-sector employment, transforming a key component of the danwei welfare system into private property. This reform plausibly reduced job lock, by allowing workers to change jobs without losing housing, and relaxed liquidity institutional considerations through a one-time wealth transfer. Using panel data from the China Health and Nutrition Survey (CHNS) between 1989 to 2004, this paper applies a difference-in-differences-in-differences (DDD) design comparing treated households that received employer-tied housing with untreated households and contrasting women with men. The results show that the reform substantially increased labor mobility among treated men, who were 18 percentage points more likely to enter non-state wage employment after 1994 (s.e. 0.017, p < 0.01). However, the gender differential in this transition is small and statistically insignificant (3.4 percentage points, s.e. 0.021), suggesting that women did not experience a comparable expansion in private-sector opportunities. Event-study estimates confirm that no systematic differences existed between treated and control groups before the reform, supporting the validity of the identification strategy. Overall, the findings indicate that unbundling housing from employment successfully reduced job lock and promoted market-based mobility but did not narrow gender differences. These results highlight the importance of complementary policies, such as childcare support and equal-opportunity measures, to ensure inclusive labor-market benefits from structural reforms. Overall, the analysis highlights the 1994 reform as a landmark achievement in Chinas urban welfare modernization, strengthening household property rights and supporting orderly, high-quality labor-market development.

Abstract Planet Earth and the biodiversity it supports are in crisis. The human impact on terrestrial, marine, and freshwater ecosystems and the hundreds of thousands of organisms that inhabit them is global. To what extent can we push ecosystems before they collapse? Will species adapt to these changes and at what rate? What are the consequences, for the environment and humankind? These are some of the most pressing issues to date. Clear answers can only be addressed through long-term research programs that are extremely complex in their deployment and by the analyses of the unique data they produce on species and ecosystem responses to change. However, too little institutional support and consideration have been given to long-term ecological and evolutionary research. We describe the action recently taken by the French National Center for Scientific Research to recognize and support long-term ecological and evolutionary research. We provide some salient examples of critical knowledge attainable only through long-term studies in ecology and evolution, before highlighting how global institutional schemes can not only support long-term research, but lead to informed conservation efforts and societal change. Now more than ever, as manipulated facts and societal distrust in science are increasingly guiding mis- and disinformed politics, governmental programs are urgently needed to support data collection, establish data-grounded facts, inform political spheres, and refuel trust with society at large.

The sustainability of social housing policies in Buenos Aires, Argentina: The importance of social, economic, and institutional considerations

<p>Revised Abstract <b>(Version 1.2 – Disclosure & Structural Standardization Update)</b></p> <p>This paper examines structural divestiture remedies within contemporary antitrust and health policy frameworks as applied to vertically integrated healthcare conglomerates. It evaluates legal foundations, economic implications, and institutional design considerations associated with structural separation mechanisms recognized under existing competition law.</p> <p>The analysis is descriptive rather than normative. It assesses doctrinal trends, enforcement architecture, and structural remedy design without advocating specific enforcement actions against any individual company or corporate entity. Any discussion of integrated healthcare organizations, including publicly traded entities operating pharmacy, insurance, and provider platforms, reflects publicly available market structures and is not intended as an allegation of legal violation or regulatory noncompliance.</p> <p>This manuscript is written in the author’s personal capacity. The views expressed are solely those of the author and do not reflect the views of Liberty University or any affiliated institution. Portions of this manuscript were developed with the assistance of artificial intelligence tools for research organization, structural refinement, and citation formatting. All substantive analysis, legal reasoning, and final editorial judgment are solely those of the author.</p> <p>The author has no financial, advisory, or employment relationship with any healthcare corporation discussed in this analysis, including CVS Health or its subsidiaries.</p> <p>This Version 1.2 update integrates standardized disclosure language and formal version-control architecture. No substantive analytical revisions have been made.</p> <br> <br>

Objective: The purpose of this study is to analyze specific measures related to the construction of living labs for experts for the “AI-Robot-Based Digital Twin Platform” project in the hospital and present practical grounds for the construction of living labs through this. Methods: This study selected seven experts with experience related to living labs and care robots at home and abroad and conducted focus group interviews. The research was conducted in six stages: expert selection, preliminary preparation, interview, transcription, analysis, and result derivation, and the core subjects were derived through open coding, axial coding, and selective coding using Kryger (1998)'s four-stage analysis process. Results: As a result of the analysis, five key elements were identified to build a living lab in the hospital. First, space and environmental design that reflects the specificity of medical institutions such as infection management, patient safety, hospital room structure, and mobility of equipment are needed. Second, it is important to actively participate not only in management but also in working-level officials such as nurses and caregivers and to cooperate with stakeholders through feedback sharing. Third, data management and security are essential, such as pseudonymizing sensitive medical data, limiting access rights, and establishing a storage system in the hospital. Fourth, ethical and institutional considerations such as IRB approval, parental consent, and explanation and consent when installing CCTV and sensors are required. Finally, it was confirmed that repeated education, customized evaluation, and verification of user acceptability-based technology through parallel wearable sensors and questionnaires were effective. Conclusion: This study provided a substantial basis for operating the living lab in a special environment called a medical institution by deriving five key factors to consider when building a living lab in a hospital. It is necessary to present an integrated approach that increases the applicability of actual medical sites beyond simple technical demonstration, and to embody the direction of development of the hospital living lab through quantitative effect analysis and international comparative research in the future.

Hypertensive disorders of pregnancy (HDPs) complicate 8% to 9% of all pregnancies. They are a leading cause of maternal and neonatal morbidity and mortality and contribute to over $2.2 billion of health care expenditures annually. In 2023, the FDA first approved a soluble fms-like tyrosine kinase 1 to placental growth factor ratio system for HDP risk stratification; however, little is known about the implementation of such biomarker testing outside of research contexts. HDP severity drives clinical management and adverse perinatal outcomes. Placental biomarker testing aims to determine which patients are at risk for developing or progressing to the most severe HDPs. Widespread implementation of biomarker testing may increase access though it may not be cost-efficient or practice-changing for individual institutions. Accordingly, further attention must be paid to restrictive testing situations (e.g., low-resource settings) or even off-label uses (e.g., multiple gestations) that may solidify the role of biomarker testing in routine practice. This review aims to outline clinical and institutional considerations for placental biomarker utilization in the context of their FDA-approved uses and to highlight the potential advantages and disadvantages of various testing strategies.

This study presents a detailed examination of contemporary migration trends in the Western Balkans, with particular focus on the Republic of Serbia and the Republic of North Macedonia. Its primary purpose is to analyze the demographic structure, age and gender distribution, and citizenship composition of migrants, while identifying the economic, social, institutional, and geographic factors influencing mobility in the region. The study aims to provide insights into the implications of labor migration for domestic labor markets, regional development, and the management of human capital. Employing a retrospective observational methodology, the research relies on official statistical data provided by national agencies, including the Statistical Office of the Republic of Serbia and the State Statistical Office of North Macedonia, covering the most recent years with available data. Descriptive statistical methods were applied to assess totals, age and gender distributions, and to compare trends between citizens returning from abroad and foreign nationals, with particular attention to working-age populations. The results reveal that young and middle-aged adults constitute the majority of migration flows in both countries, with male foreign immigrants predominating in Serbia, whereas returning citizens in North Macedonia show a more balanced gender distribution. Migration patterns are driven by limited professional opportunities, regional disparities, labor market segmentation, governance quality, social attitudes, and geographic factors, including rural depopulation and local economic structures. The study highlights the persistent risk of brain drain, particularly among skilled professionals, alongside potential benefits of human capital mobility, such as knowledge transfer and network development. Conclusions emphasize the need for holistic demographic and migration policies that integrate economic, social, and institutional considerations, ensuring both the quantity and quality of employment, promoting retention of skilled workers, and fostering equitable professional opportunities. Recommendations include the implementation of targeted strategies to mitigate adverse effects of emigration, enhancement of institutional and governance frameworks, and the strategic use of international mobility to support regional economic growth and social stability. Additional insights underline the importance of future research incorporating longitudinal and qualitative approaches to capture personal, cultural, and socio-economic motivations behind migration, which can inform evidence-based policy interventions and labor market planning. Overall, the study provides a solid foundation for proactive policymaking and long-term strategies designed to balance mobility with sustainable economic development and social cohesion across the Western Balkans.

Purpose Due to growing expectations for academia to address complex societal challenges in inclusive and transparent ways, the question arises of how public engagement can be embedded not only within research projects but also in the governance structures of research organizations. Drawing on case studies of ministerial research institutes (MRIs), this paper aims to examine the institutional and ethical conditions under which participatory practices can be implemented and sustained. MRIs are a distinct type of publicly funded research organization operating at the intersection of science and policy, directly affiliated with government ministries. Design/methodology/approach This study uses a qualitative multiple case study design focused on three German MRIs. It is based on 26 semistructured expert interviews, supported by document analysis and observations. The COM-B model (capability, opportunity, motivation – behavior) serves as an analytical framework to explore enabling and constraining factors. Findings This study reveals a gap between normative commitments to participation and their institutional realization. This is shaped by limitations in capability (e.g. methodological and dialogical skills); opportunity (e.g. strategic anchoring and support); and motivation (e.g. recognition and incentives), but also highlights existing capacities and initiatives as entry points for change. Originality/value While established in behavioral science, the COM-B model is applied here for the first time to participatory governance and organizational change. This paper develops a three-level intervention model – micro, meso and macro – to guide the implementation of participation in research institutions.