Search Results (490)

Uncovering the interdependencies among barrier factors and pinpointing the most critical obstacles are essential to overcoming the resistance encountered by photovoltaic (PV) integration into electricity markets. This study first employs grounded theory to identify and categorize the key barriers impeding PV participation, thereby constructing a comprehensive barrier factor model. Subsequently, Interpretive Structural Modeling (ISM) is applied to systematically analyze the interrelations and hierarchical structure among these barriers. The results reveal that: (1) The complex system of PV participation comprises 15 distinct barriers, which can be grouped into 4 overarching categories: economic and cost-related challenges, policy and regulatory uncertainties, technological and infrastructure constraints, and environmental and resource limitations. (2) These barriers form a six-tier hierarchical structure, reflecting their layered influence. (3) Root-level barriers—such as inadequate government fiscal support and the absence of a comprehensive coordination mechanism—play a foundational role in hindering progress. In response, this study proposes policy recommendations, including establishing a unified and effective coordination framework to align renewable energy policies and formulating standardized guidelines for PV panel recycling. Full article

The ATHENA (Advanced Tool for Holistic Evaluation and Nurturing of Abilities) competency framework proposes a multidimensional approach to human performance structured around five interdependent dimensions (cognition, conation, knowledge, emotion, and sensori-motion), operationalized through 60 fine-grained facets. Although ATHENA is grounded in contemporary psychological theory and supported conceptually by multivariate research in intelligence, creativity, and skill acquisition, empirical evidence regarding the clarity and practical comprehensibility of its facets remains limited. This study investigates the extent to which instructional designers and human resource development (HRD) professionals—two groups who routinely operationalize competencies for learning, assessment, and workforce development—understand and evaluate the semantic clarity and usability of the 60 facets. Seventy-five practitioners completed a structured evaluation of the ATHENA framework facets, which are designed to be used in a hybrid intelligence system for competency management. This article presents the theoretical background, methodological design, and results concerning users’ comprehension of the framework’s components. The findings support, in general, the compatibility of ATHENA’s facets and practitioners’ conceptions. Full article

Global health faces unprecedented challenges driven by communicable diseases, which are increasingly amplified by persistent health inequities, the impact of climate change, and the speed of emerging crises. Prevention is not merely a component but the foundational strategy for an effective, sustainable, and fiscally responsible public health response. This paper delves into the pivotal role of core prevention levers: robust vaccination programs, stringent hygiene standards, advanced epidemiological surveillance, and targeted health education. We detail how contemporary technological advancements, including Artificial Intelligence (AI), big data analytics, and genomics, are fundamentally reshaping infectious disease management, enabling superior predictive capabilities, faster early warning systems, and personalized prevention models. Furthermore, we thoroughly examine the imperative of integrating the One Health approach, which formally recognizes the close, interdependent links between human, animal, and environmental health as critical for combating complex threats like zoonoses and Antimicrobial Resistance (AMR). Despite significant scientific progress, persistent socio-economic disparities, the pervasive influence of health-related misinformation (infodemics), and structural weaknesses in global preparedness underscore the urgent need for decisive international cooperation and equitable financing models. We conclude that only through integrated, multidisciplinary, and resource-equitable strategies can the global community ensure effective prevention, mitigate severe socio-economic disruption, and successfully build resilient healthcare systems capable of withstanding future global health threats. Full article

The COVID-19 pandemic profoundly disrupted the lives of older adults, yet their experiences have remained underexplored. This paper draws on empirical evidence from a two-wave (W1 April–July 2020, W2 January–April 2021) qualitative study in the UK (n = 62) and a companion study in Colombia (n = 32), focusing on participants aged 60 and above. Data was analysed using constructivist grounded theory principles, leading to the development of an ecological theory of older adults’ well-being within the context of a health crisis at three interconnected levels: individual, community, and societal. Individual resources, such as adaptability and support systems, contributed to enhancing and maintaining their well-being. Community support and a sense of belonging were essential to meet the needs of people in later life, whilst necessary social health protection measures during the pandemic restricted social activities, further impacting well-being, mostly perceived as challenging. Cultural differences and societal support systems shaped participants’ experiences. The study emphasises the interdependence of the different levels in impacting older adults’ well-being, offers strategies for policy and practice, and advocates and contributes for the development of gerontological theories in the context of health crises. Full article

Open banking reshapes the financial sector by enabling regulated third-party providers to access bank data through APIs, fostering innovation but amplifying operational and financial-crime risks due to increased ecosystem interdependence. To address these challenges, this study proposes an integrated risk-management framework combining System Dynamics, Agent-Based Modelling, and Monte Carlo simulation. This hybrid approach captures feedback effects, heterogeneous agent behaviour, and loss uncertainty within a simulated PSD2-style environment. Simulation experiments, particularly those modelling credential-stuffing waves, demonstrate that stricter onboarding thresholds, tighter API rate limits, and enhanced anomaly detection reduce operational tail losses by approximately 20–30% relative to baseline scenarios. Beyond these specific findings, the proposed framework exhibits significant universality; its modular design facilitates adaptation to broader contexts, including cross-border regulatory variations or emerging BigTech interactions. Ultimately, this multi-method approach translates complex open-banking dynamics into actionable risk metrics, providing a robust basis for targeted resource allocation and supervisory stress testing in evolving financial ecosystems. Full article

This paper examines bank failures during the subprime mortgage crisis, emphasizing sibling dynamics within multi-bank holding companies (MBHCs). While traditional risk indicators effectively predict failures for one bank holding companies (OBHCs), they exhibit limited explanatory power for MBHCs, where internal capital markets and interdependencies across affiliates shape risk outcomes. We extend the standard failure framework by incorporating group-level characteristics that capture sibling network structure and the distribution of risk across affiliates. Using pre-crisis data from 2006 to 2007, we show that group structure significantly influences failure risk. Larger sibling networks reduce individual bank failure risk through diversification, while greater size dispersion across affiliates increases vulnerability by constraining internal resource allocation. Beyond these aggregate effects, we introduce a weakest link approach that identifies the most distressed affiliate based on extreme tail risk in capitalization, asset quality, liquidity, earnings, and income volatility, capturing organizational fragility that aggregate measures miss. Concentrated vulnerabilities at a single affiliate significantly amplify failure risk throughout the holding company, even after controlling for traditional bank-level fundamentals and parent-level characteristics. These findings, derived from the 2007–2010 crisis, a severe stress test of holding company structures, identify organizational dynamics: resource competition among siblings and concentrated vulnerabilities at the weakest affiliate. Supervisory frameworks should explicitly account for within-group interdependencies rather than relying solely on individual bank metrics or aggregate indicators when monitoring bank holding company structures. Full article

Mission-oriented supply chains involve multi-phase tasks, strong resource interdependencies, and stringent reliability requirements, which make demand planning complex and uncertain. This study develops a structured demand modeling framework to support multi-phase mission-oriented supply chains under budget and reliability constraints by integrating digital twin technology with an adaptive inertia weight particle swarm optimization (AIW-PSO) algorithm. The supply support process is decomposed into four sequential phases—storage, transportation, preparation, and execution—and phase-specific demand models are constructed based on system reliability theory, explicitly incorporating redundancy, maintainability, and repairability. In this work, digital twin technology functions as a data acquisition and virtual experimentation layer that supports parameter calibration, state-aware scenario simulation, and event-triggered re-optimization rather than continuous real-time control. Physical-state updates are mapped to model parameters such as phase durations, failure rates, repair rates, and instantaneous availability, after which the integrated optimization model is re-solved using a warm-start strategy to generate updated demand plans. The resulting multi-phase demand optimization problem is solved using AIW-PSO to enhance global search performance and mitigate premature convergence. The proposed method is validated using a representative mission-oriented supply support scenario with operational and simulated data. Simulation results demonstrate that, under identical budget constraints, the proposed approach achieves higher mission completion capability than conventional PSO-based methods, providing effective and practical decision support for multi-phase mission-oriented supply chain planning. Full article

Geothermal energy is a crucial component of climate adaptation and sustainability transitions, as it provides a dependable, low-carbon source of baseload power that can accelerate sustainable energy transitions and enhance climate resilience. Yet, in East Africa—one of the world’s most promising geothermal regions, with the East African Rift—a unique climate-energy opportunity zone—the harnessing of geothermal power remains slow and uneven. This study examines the contextual conditions that facilitate the successful and sustainable development of geothermal power in the region. Drawing on semi-structured interviews with 17 experienced professionals who have worked extensively on geothermal projects across East Africa, the analysis identifies how technical, institutional, managerial, and relational circumstances interact to shape outcomes. The findings indicate an interdependent configuration of success conditions, with structural, institutional, managerial, and meta-conditions jointly influencing project trajectories rather than operating in isolation. The most frequently emphasised enablers were resource confirmation and technical design, leadership and team competence, long-term stakeholder commitment, professional project management and control, and collaboration across institutions and communities. A co-occurrence analysis reinforces these insights by showing strong patterns of overlap between core domains—particularly between structural and managerial factors and between managerial and meta-conditions, highlighting the mediating role of managerial capability in translating contextual conditions into operational performance. Together, these interrelated circumstances form a system in which structural and institutional foundations create the enabling context, managerial capabilities operationalise this context under uncertainty, and meta-conditions sustain cooperation, learning, and adaptation over time. The study contributes to sustainability research by providing a context-sensitive interpretation of how project success conditions manifest in geothermal development under climate transition pressures, and it offers practical guidance for policymakers and partners working to advance SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), and SDG 13 (Climate Action) in Africa. Full article

Background: The integration of unmanned aerial vehicles (UAVs) into warehouse management is shaped by a broad spectrum of influencing factors, yet practical adoption lagged behind its potential due to scarce quantitative models of factor interdependencies. Methods: This study systematically reviewed academic literature to identify key factors affecting UAV adoption and explored their interrelationships using complex network and social network analysis. Results: Sixty-six distinct factors were identified and mapped into a weighted network with 527 connections, highlighting the multifaceted nature of UAV integration. Notably, two factors, i.e., Disturbance Prediction and System Resilience, were found to be isolated, suggesting they have received little research attention. The overall network is characterized by low density but includes a set of 25 core factors that strongly influence the system. Significant interconnections were uncovered among factors such as drone design, societal factors, rack characteristics, environmental influences, and simulation software. Conclusions: These findings provide a comprehensive understanding of the dynamics shaping UAV adoption in warehouse management. Furthermore, the open-access dataset and network model developed in this research offer valuable resources to support future studies and practical decision-making in the field. Full article

Background: The COVID-19 pandemic exposed major deficiencies in global health governance, including fragmented authority, inequitable resource distribution, and weak compliance mechanisms. Although the WHO Pandemic Agreement (2025) addresses several of these gaps, significant operational and institutional challenges remain. This study aims to develop and empirically validate a Global Governance for Health (GGFH) Framework that strengthens leadership, financing, equity, and legal accountability across global, regional, and national levels. Methods: A three-round Delphi study was conducted. Thirty-one experts from diverse sectors, including public health, international law, economics, environment, and diplomacy, evaluated 32 structured governance statements across seven domains. Experts rated all statements using a 7-point Likert scale. Consensus was determined using a strict threshold median ≥ 6; SD ≤ 1.35; ≥75% agreement. Open-text comments were systematically reviewed through thematic analysis. All statements were systematically mapped to the WHO Pandemic Agreement articles to identify areas lacking operational clarity or enforceability. Results: All seven governance domains achieved consensus by Round 3. High agreement emerged on strengthening WHO leadership, implementing sustainable and equitable financing mechanisms, embedding LMIC representation, establishing legal preparedness and capacity-building, and integrating independent accountability tools. Correlation and interdependence analyses demonstrated that governance goals form an integrated, mutually reinforcing system, with financing, equity, and legal frameworks identified as core enablers of effective treaty implementation. Conclusions: The Delphi process validated a comprehensive and operational Global Governance for Health Framework. The GGFH complements the WHO Pandemic Agreement by addressing its unresolved governance, financing, and equity limitations and offers a structured roadmap to guide global pandemic preparedness and treaty implementation. Full article

This study provides an integrated assessment of progress toward Sustainable Development Goal 12 (Responsible Consumption and Production) by applying a multivariate, indicator-based framework to a comprehensive set of EU-27 performance metrics. Rather than proposing new indicators, the analysis advances SDG 12 monitoring by systematically integrating official indicators of material efficiency, circularity, waste generation, consumption-based environmental pressure, and environmental economic activity with key cross-sectoral drivers. Using harmonized statistical data, the study examines raw material consumption, circular material use rates, hazardous chemical consumption, consumption footprints, hazardous waste generation, and the economic value added of the environmental goods and services sector, complemented by energy productivity and average CO₂ emissions from new passenger cars. Through z-score normalization, correlation analysis, and exploratory factor analysis, the research identifies structural interdependencies and latent systemic regimes that characterize responsible consumption and production dynamics in the EU. The results reveal a persistent divergence between efficiency- and circularity-oriented improvements and ongoing material and waste pressures, highlighting structural constraints within current sustainability pathways. By offering a replicable and integrative analytical framework, the study contributes to the literature by supporting evidence-based policymaking and identifying priority areas for advancing resource efficiency and circular economy transitions. Full article

Risk management in large-scale construction projects is a critical yet complex process influenced by financial, safety, environmental, scheduling, and regulatory uncertainties. Effective risk management contributes directly to project optimization by minimizing disruptions, controlling costs, and enhancing decision-making efficiency. Early identification and mitigation of risks allow resources to be allocated where they have the greatest effect, thereby optimizing overall project outcomes. However, conventional methods such as expert judgment and probabilistic modeling often struggle to process extensive datasets and complex interdependencies among risk factors. This study explores the potential of an AI-based framework for risk identification, utilizing artificial intelligence to analyze project documentation and generate a preliminary set of identified risks. The proposed methodology is implemented on the ‘Trg pravde’ judicial infrastructure project in Zagreb, Croatia, applying AI models (GPT-5, Gemini 2.5, Sonnet 4.5) to identify phase-specific risks throughout the project lifecycle. The approach aims to improve the efficiency of risk identification, reduce human bias, and align with established project management methodologies such as PM². Initial findings suggest that the use of AI may broaden the range of identified risks and support more structured risk analysis, indicating its potential value as a complementary tool in risk management processes. However, human expertise remains crucial for prioritization, contextual interpretation, and mitigation. The study demonstrates that AI augments, rather than replaces, traditional risk management practices, enabling more proactive and data-driven decision-making in construction projects. Full article

The study investigates the presence of emerging contaminants in a river within a watershed located in the Brazilian semiarid region, specifically within the Caatinga biome, emphasizing the importance of environmental monitoring in areas that have historically been underrepresented in scientific research. The analysis focused on the associations between microplastics and pharmaceutical compounds, demonstrating that the discharge of untreated domestic effluents and the low efficiency of sanitation systems increase water resource contamination and threaten water security. The interdependence between these variables underscores the need for integrated public policies for waste management, complemented by environmental education strategies and technological innovations. The work makes an unprecedented contribution to expanding knowledge about emerging pollutants in semiarid environments, highlighting the urgency of holistic approaches, continuous monitoring, and strengthening environmental governance to ensure the sustainability and resilience of ecosystems like the Caatinga in the face of the challenges posed by global environmental change, urban growth, and those outlined in the Sustainable Development Goals. Full article

Current research on automotive supply networks predominantly examines single-type entities connected through one relationship type, resulting in oversimplified, single-layer network structures. This conventional approach fails to capture the complex interdependencies that exist among mineral resources, intermediate components, and finished products throughout the automotive industry. To overcome these analytical limitations, this study implements a multilayer network framework for examining global automotive supply chains spanning 2017 to 2023. The research particularly emphasizes the identification of critical risk sources through both static and dynamic analytical perspectives. The static analysis employs multilayer degree and strength centralities to illuminate the pivotal roles that countries such as China, the United States, and Germany play within these multilayer automotive supply networks. Conversely, the dynamic risk propagation model uncovers significant cascade effects; a disruption in a major upstream supplier can propagate through intermediary layers, ultimately impacting over 85% of countries in the finished automotive layer within a short temporal threshold. Furthermore, this study investigates how individual nations’ anti-risk capabilities influence the overall resilience of multilayer automotive supply networks. These insights offer valuable guidance for policymakers, enabling strategic topological modifications during disruption events and enhanced protection of the most vulnerable risk sources. Full article

Climate change has exacerbated flood risks for urban infrastructure, rendering sewage treatment facilities (STFs) particularly vulnerable due to their typical low-lying topographic placement. However, conventional flood risk assessment methodologies often rely solely on physical hazard parameters such as inundation depth, neglecting the functional interdependencies and operational criticality of individual treatment units. To address this limitation, this study proposes the Integrated Hydro-Operational Risk Assessment (IHORA) framework. The IHORA framework synthesizes 2D hydrodynamic modeling with a modified Hazard and Operability Study(HAZOP) study to systematically identify unit-specific physical failure thresholds and employs the Analytic Hierarchy Process (AHP) to quantify the relative operational importance of each process based on expert elicitation. The framework was applied to an underground STF under both fluvial flooding and internal structural breach scenarios. The results revealed a significant risk misalignment in traditional assessments; vital assets like electrical facilities were identified as high-risk hotspots despite moderate physical exposure, due to their high operational weight. Furthermore, Cause–Consequence Analysis (CCA) was utilized to trace cascading failure modes, bridging the gap between static risk metrics and dynamic emergency response protocols. This study demonstrates that the IHORA framework provides a robust scientific basis for prioritizing mitigation resources and enhancing the operational resilience of environmental facilities. Full article