Search Results (265)

Wicked problems spanning systemic educational inequities, economic disparities, and environmental sustainability resist most traditional change efforts. This theory-building article advances a systems explanation that introduces complex conceptual systems theory which models collective conceptualizations as complex adaptive systems composed of densely interconnected ideas. These systems stabilize around attractor states that generate emergent potentials for what becomes sayable, seeable, doable, and valuable, thereby constraining the very practices needed for transformation. The article defines core constructs and articulates operational principles for diagnosis and intervention in complex social and socio-technical systems. It then specifies a first-generation analytical workflow, complex conceptual systems analysis (CCSA), that integrates qualitative coding with network-based modeling to map conceptual architectures, identify attractor states, and locate leverage points where sustained pressure can catalyze system reorganization. Empirical grounding is provided through a synthesis of a decade-long research program reported in prior publications across multiple domains, rather than through a single new empirical dataset. Accordingly, the manuscript is organized as a theory-development and methodology contribution, moving from conceptual architecture to operational principles, analytic workflow, and cross-domain exemplars. The theory offers systems science a pragmatic, justice-attentive approach for anticipatory, intervention-oriented change in entrenched wicked problems. Full article

Renewable energy sources, and wind energy in particular, constitute a central pillar of energy policy at both national and European levels. Nevertheless, the deployment of onshore wind farms is frequently associated with spatial, environmental, and social conflicts, making the evaluation of existing projects imperative. The present study aimed to assess the sustainability of existing onshore wind farms in the Region of Thessaly, with particular emphasis on their spatial planning, technical characteristics, and environmental impacts. The methodological framework consists of four distinct stages: (i) identification and spatial mapping of existing wind farms in the study area, (ii) assessment of the compliance of existing wind installations with the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD–RES), (iii) application of the Rapid Impact Assessment Matrix (RIAM) to enable a systematic and comparable evaluation of the impacts of wind installations on specific environmental and anthropogenic parameters, and (iv) estimation of project hazard and operational vulnerability through the application of Operational Risk Management (ORM). Geographic Information Systems (GISs) were employed for data processing and spatial analysis. The assessment showed that 40% of the evaluated wind farms fully comply with all eleven exclusion criteria of the SFSPSD-RES, whereas the remaining 60% show partial compliance, failing to meet between one and three criteria. RIAM results indicate that the most significant adverse impacts (−D and −C) during construction are associated with morphology/soils and the natural environment, mainly due to loss/fragmentation of vegetation and disturbance of fauna, and, in some cases, in areas of increased sensitivity. During operation, the main negative effects (−D and −C) relate to landscape and visual quality, as well as continued disturbance to the natural environment. At the same time, the operation generates important positive effects (+E) on the atmospheric environment through reduced CO₂ emissions. The ORM analysis further shows that the most important risks for most wind farms arise during construction (ORM = 2 and 3), particularly from serious worker accidents during lifting, roadworks, and foundation activities. The study demonstrates that the sustainability of existing wind installations depends on a complex set of spatial, environmental, and technical factors. The proposed framework integrates spatial compliance screening, RIAM-based environmental impact assessment, and ORM-based risk and opportunity evaluation. This connection links the importance of impacts with their operational manageability during construction and operation phases, as well as across sustainability dimensions. Consequently, the study provides a more decision-focused approach for assessing existing wind farms and supporting policy development. Full article

With the continuous development of the social economy and the increasing service life of bridges, in-service bridges generally face multiple challenges such as safety decline, durability deterioration, and insufficient traffic capacity. Demolition and reconstruction have become an important way for some old bridges to achieve functional renewal and ensure traffic safety. This paper takes the first concrete self-anchored suspension bridge in China that has undergone demolition—the Zicai Bridge in Qinzhou—as the specific engineering basis. In response to the safety requirements and smooth progress of its demolition construction, after a comprehensive comparison and optimization of multiple demolition schemes, the core technical solution of reverse sequence removal of the hangers was finally determined. To fully verify the technical applicability, structural safety, and feasibility of this demolition scheme, this study adopts a core research method combining theoretical calculation and numerical simulation, and systematically and deeply analyzes the entire process of bridge system transformation, the evolution law of structural force, and the mechanical responses of key parts during the hanger removal process. The study found that the maximum stress of the hangers in the system during the hanger removal process was much lower than the material breaking stress. The tilt of the bridge tower and the deformation of the main cables were all within the controllable range. Only the local tensile stress at the lower edge of the main beam had a cracking risk exceeding the material’s tensile limit. Based on this, specific construction optimization suggestions and control measures were proposed. This research not only solved the core technical problems of this type of special bridge demolition, but its research ideas and quantitative analysis results can also provide important theoretical references and technical support for the subsequent demolition construction of similar cable-bearing system bridges, and has positive significance for promoting the scientific and standardized development of complex bridge demolition construction. Full article

Artificial intelligence (AI) has become a significant paradigm of methodology and epistemology in the social sciences. Machine learning (ML), natural language processing (NLP), and generative models enable researchers to work with big, multimodal datasets, identify complex patterns, and recreate events in the social world in ways that previously were not feasible. At the same time, these innovations also lead to ethical challenges related to algorithmic bias, black boxes, data extractivism, and reinforced structural inequalities in welfare, government services, education, and criminal justice. The article critically questions the social sciences in the light of AI on three dimensions that are inextricably linked, namely: (1) the opportunities that AI provides to social-scientific inquiry; (2) the biases and constraints generated through data, models, and institutional application; and (3) ethical pathways that are necessary for the responsible governance of AI-facilitated research and decision support. The article is based on a scoping, critical thematic review of the recent literature, and its conceptualization of AI as a socio-technical infrastructure is that it produces knowledge and, at the same time, offers power. It explains the impact AI practices have on restructuring disciplines like sociology, psychology, political science, and policy analysis, and how it blindly predicts how data practices, design choices, and governance arrangements can either preserve or destroy existing hierarchies. The paper suggests an analytical framework synthesizing AI practices, social research practices, and governance structures in ethical frameworks. It argues that the emancipatory promise of AI in the social sciences is dependent on the attainment of something beyond principle-based claims of so-called ethical AI by operational governance mechanisms that make systems visible, debatable, and responsible in their respective situations. Full article

Designing long-range aircraft for 2050 is a complex, multi-disciplinary challenge requiring integration of technical performance with sustainability objectives, including environmental responsibility, economic viability, circularity, and social acceptance. Existing studies on stakeholder needs in aviation are limited, focusing on specific groups, technical requirements, or individual aircraft concepts, resulting in a fragmented understanding of sustainability-driven needs. This study addresses this gap by systematically identifying stakeholders who influence long-range aircraft development and deriving 191 stakeholder needs, organized into coherent categories spanning manufacturers, operators, passengers, regulators, communities, and energy suppliers. Needs were classified across technical, environmental, economic, circular, and social dimensions, based on a comprehensive review of academic and grey literature, regulatory documents, and industry sources. The resulting framework provides a structured, reproducible approach to support conceptual aircraft design and requirement definition within the European EXAELIA project. By integrating multi-dimensional stakeholder expectations early in the design process, this approach facilitates aircraft development that is technically robust, environmentally sustainable, economically viable, circular, and socially inclusive, demonstrating the value of a stakeholder-driven method for sustainable systems engineering. Full article

The deployment of distributed renewable energy systems at the neighborhood scale is a key lever for urban decarbonization. In Europe, the regulatory framework now enables collective self-consumption, allowing multiple end-users to share locally produced energy. However, the complexity and early-stage uncertainties of such projects, especially in new district development, pose challenges for feasibility assessment and investor confidence. This study proposes a method to identify the impact of numerous technical, economic, and social parameters that may affect the feasibility of a project and that are uncertain at the early design stage, across multiple key performance indicators, thus addressing the concerns of various stakeholders. A key objective is to provide an integrated method applicable during the early stages of district development, when the integration of a collective self-consumption scheme is under consideration. The developed tools and methods are compatible with the available data at this stage and provide a basis for multi-criteria analysis. The simulation workflow was built around URBANopt and enhanced with probabilistic occupancy modeling, energy sharing mechanisms, and financial analysis modules. It was further complemented by sensitivity and risk analysis layers. The method was applied to a pre-design case study, illustrating how key design and operational uncertainties influence project viability. The results showed that despite the uncertainties on a wide array of parameters, reliable risk assessment per KPI could be performed on only a handful of parameters, which were identified through a sensitivity analysis using the Morris screening method. Full article

This study introduces a novel risk detection and control system to enhance social stability in major construction projects. Utilizing a heterogeneous cellular automaton model, the system simulates complex interactions among project stakeholders to identify and mitigate Social Stability Risks (SSR). Integrating the Ignorant–Latent–Malcontent–Recovered (ILMR) framework, the model applies principles from epidemiology to predict and manage the spread of social stability risks. Simulation results demonstrate the model’s effectiveness in reducing the number of malcontent and ignorant individuals while increasing the recovered category, stabilizing the social environment around large projects. This approach helps manage immediate risks and improves long-term social acceptance and sustainability of engineering projects. By bridging risk management with advanced simulation techniques, this research contributes to major construction projects by providing a robust framework for managing complex social dynamics, thereby enhancing project success and stakeholder satisfaction. The findings underscore the potential of integrating innovative technological tools with traditional risk management strategies to address the socio-technical challenges of large-scale engineering projects. Full article

Engineering education often emphasizes technical competencies while underemphasizing and devaluing the social, ethical, and political contexts of engineering systems. This gap is particularly pronounced in middle-year courses, where students develop technical fluency but rarely confront the sociotechnical complexity of real-world problems. We propose sociotechnical judgment as a framework to help students see the intimately intertwining nature of technical knowledge and social, ethical, and contextual reasoning, using energy systems—particularly offshore wind—as an illustrative domain. We designed three course-integrated case studies in thermodynamics, circuits, and statics/dynamics to embed sociotechnical judgment in middle-year engineering courses. These cases include pedagogical strategies, such as project-based learning, problem-based learning, and role-play exercises connecting technical analysis with social, environmental, and policy considerations. The design of these case studies is rooted in real-world problems surrounding U.S. offshore wind, engineering science learning outcomes, and ABET student outcomes. In these pedagogies, we have created opportunities for students to analyze technical systems while engaging with social, ecological, and political factors. Offshore wind projects, including turbine siting, transmission system design, and efficiency trade-offs, provide opportunities to operationalize sociotechnical reasoning in authentic, regionally relevant contexts. Sociotechnical judgment provides a practical framework for bridging technical competency and contextual reasoning in engineering education. Integrating sociotechnical cases into core courses will prepare students to navigate complex, real-world systems through engagement with ethical, social, and environmental considerations inherent in engineering practice. Full article

Hydropower refurbishment is increasingly recognized as a key strategy for maintaining renewable electricity generation and minimizing the environmental and social impacts of developing new infrastructure. With much of the global hydropower fleet approaching or exceeding its original design life, refurbishment decisions must strike complex trade-offs between technical performance, environmental impacts, economic viability, and social acceptability. This review provides a comprehensive summary of the scientific and policy literature on sustainable hydropower refurbishment, with a particular focus on the integration of life cycle assessment (LCA) and multi-criteria decision analysis (MCDA) from a circular economy perspective. The study systematically reviews the latest results in the fields of environmental LCA, life cycle costing (LCC), social LCA (S-LCA), and integrated life cycle sustainability assessment (LCSA), highlighting their application in the refurbishment and modernization of hydropower plants. The results show that construction-related impacts, particularly those associated with concrete and steel, dominate the environmental load over the life cycle, making refurbishment and component recycling highly effective strategies for reducing embodied emissions. The integration of LCA and MCDA allows for the transparent prioritization of refurbishment alternatives by explicitly considering stakeholder preferences and trade-offs between environmental, economic, social, and technical criteria. Overall, the results support the use of integrated, multi-criteria life cycle frameworks as reliable decision-making tools for managing sustainable hydropower refurbishment and long-term energy system resilience. Full article

As virtual reality technologies evolve toward widespread adoption in education, industry, and social communication, their increasing complexity exposes new and often overlooked security challenges. Immersive environments collect continuous multimodal data, including motion tracking, gaze, voice, and biometric indicators that extend far beyond traditional computing attack surfaces. This paper synthesizes recent research (2023–2025) on cybersecurity, privacy, and behavioral safety in virtual reality (VR) systems, identifies the main vulnerabilities, and proposes a unified defense architecture: the three-layer VR Security Framework (TVR-Sec). Through comparative review and conceptual integration of 31 peer-reviewed studies, three interdependent protection domains emerged: (1) System Integrity, securing hardware, firmware, and network communications against spoofing and malware; (2) User Privacy, ensuring the ethical management of biometric and behavioral data through federated learning and consent-based control; and (3) Socio-Behavioral Safety, addressing harassment, manipulation, and psychological exploitation in shared virtual spaces. The framework situates VR security as a multidimensional adaptive process that combines technical hardening with human-centered defense and ethical design. By aligning cyber–human protections through an AI-driven monitoring and policy engine, TVR-Sec advances a holistic paradigm for securing future immersive ecosystems. Full article

Due to the significant challenges faced by healthcare systems, medical establishments strive to set the tone by integrating new concepts to bridge this gap. Here, Lean Healthcare (LH) has been inspired by Lean Management (LM). Utilizing LM to optimize industrial processes and reduce waste presented a real opportunity to enhance the quality of medical services. For more improvement, healthcare systems pushed themselves to keep up with progress by implementing Industry 4.0 (I4.0) tools, such as IoT, Big Data analytics, and AI with LH and sustainability practices. The results promised better quality of care. Although this concept offers significant potential for more efficient workflows and optimizing medical processes, studies examining their combined implementation are still scarce. This research fills the gap via a literature review (LR) of peer-reviewed articles published between 2015 and 2025. The review investigates the impact of integrating smart technologies into LH frameworks and highlights how LH contributes to sustainability across multiple dimensions: economic, social, technological and environmental. Key findings show the impact of combining advanced tools with lean principles by reducing waiting times (25%) and length of stay while also improving satisfaction. Sustainability-centered adaptations of LH incorporate social and environmental comparative parameters such as resource consumption, for instance, reducing operational costs by up to 30–40%. Many challenges were faced with this implementation, such as cultural, technical challenges (e.g., complexity of integration with digital systems), and sustainability barriers. However, to overcome these barriers, this paper proposes a holistic implementation that aligns lean processes with organizational change and sustainability goals. Full article

Storm surges are catastrophic marine disasters that pose severe threats to coastal populations, making the rapid extraction of key information from multi-source texts critical for effective emergency response. However, existing extraction methods often struggle with complex linguistic challenges, such as identifying nested entities (e.g., overlapping geographic names), capturing relationships across long texts, and handling the disparity between formal official reports and unstructured social media data. To address these limitations, this study proposes a Storm Surge Knowledge Extraction Model (SSKEM) based on Global Pointer Networks. By constructing a domain-specific dataset of 4000 records from government bulletins, news reports, and social media, the proposed model utilizes a unified matrix decoding mechanism to treat entity and relation extraction as a holistic task. Experimental results demonstrate that the model achieves an F1-score of 88.4%, outperforming robust baseline models by 5.5%. Notably, it improves the recognition accuracy of complex nested entities by 13.7% and enhances the recall rate for cross-sentence relations by 18.2%. Furthermore, the model exhibits high computational efficiency, processing speed suitable for real-time applications, and effectively bridges the performance gap between standardized and fragmented data sources. This research provides a robust technical solution for transforming heterogeneous disaster big data into actionable knowledge for decision-support systems. Full article

With the intensification of climate change, deepening energy transition, and increasing social vulnerability, extreme power outage events pose escalating challenges to the governance capacity of modern power systems. Existing evaluation frameworks primarily focus on engineering reliability and economic loss estimation, lacking systematic quantification of the governance complexity arising from multidimensional interacting pressures behind outage events. This creates a blind spot in both theoretical research and governance practice, hindering differentiated resilience decision-making. To address this gap, this study develops a four-dimensional evaluation framework of power outage governance complexity encompassing event attributes, external environment, internal system, and social impacts. Based on county-level outage data and multi-source auxiliary data in the United States from 2015 to 2024 and employing the XGBoost–SHAP interpretable machine learning approach, we construct the Power Outage Complexity Index (POCI) for all U.S. counties and systematically analyze its spatiotemporal evolution and core driving factors. The results show that outage governance complexity in the U.S. power grid exhibits a significant upward trend during 2015–2024, with an average annual growth rate of 1.84%. Spatially, significant positive autocorrelation is observed, and 146 high-complexity hotspot counties are identified, mainly clustered along the East and West Coasts, the Gulf Coast, and the Southwest. Driver analysis reveals that social impact and event attribute dimensions together account for nearly 90% of the variance in complexity, with cumulative outage exposure burden, outage frequency, and large-scale event ratio being the most critical drivers. Theoretically, this study extends power resilience research from an engineering-physical paradigm to a socio-technical governance paradigm and provides a reproducible methodological framework for assessing governance complexity in critical infrastructure systems. Practically, the POCI can serve as a governance diagnostic tool for the power industry and regulators, supporting resilience investment prioritization, emergency resource optimization, and differentiated governance strategy formulation. It also provides empirical evidence for safeguarding energy security in highly vulnerable communities and promoting energy resilience equity. Full article

As China’s Sponge City Program (SCP) shifts towards retrofitting old communities, enhancing flood resilience is critical for sustainable urban renewal. However, engineering practice often encounters a performance inversion—characterized by high design evaluation scores but low operational efficiency. This issue largely stems from relying on the static importance of indicators while neglecting dynamic driving forces within the socio-technical system. To address this, this study aims to construct a Robust Causal Diagnostic Framework integrating Improved AHP, DEMATEL, and K-means clustering. Through a quadrant positioning and cluster locking mechanism, it identifies Hidden Leverage Factors (HLFs)—critical indicators typically assigned low weights but exerting strong driving forces. To demonstrate the practical application of this framework, an empirical analysis of H City’s DG Community was conducted, identifying residents’ willingness and design pertinence as the project’s HLFs. Optimization strategies based on this diagnosis were simulated using SWMM. Results show that the Annual Runoff Volume Capture Ratio increased by 44.45%, with significant improvements in peak flow reduction and water purification. This study facilitates a shift from empirical evaluation to precision diagnosis, offering a quantitative reference for enhancing urban flood resilience under complex social constraints. Full article

Plastic pollution constitutes a critical planetary health challenge, undermining the integrity of Earth systems while generating cascading harms to human health, livelihoods, and social equity particularly in low- and middle-income countries. Conventional top-down regulatory and technological responses have proven insufficient to address the complexity of plastic pollution, often excluding those most affected from decision-making and solution design. This paper examines how democratizing plastic governance through grassroots leadership can advance planetary health by simultaneously protecting ecosystems, improving human well-being, and strengthening socio-ecological resilience. Drawing on empirical evidence from the #RestorationX10000 initiative led by Community Action Against Plastic Waste (CAPws), this paper documents implementation processes and outcomes achieved between 2021 and 2025 across 71 impacted communities in 21 countries spanning Africa, Asia-Pacific, and Latin America. The initiative was designed to empower 10,000 youths and women as community leaders, practitioners, and advocates by equipping them with leadership, technical, and policy engagement skills to drive systemic change in plastic governance and circular economy practice. Using a transdisciplinary, community-based action research approach aligned with planetary health principles, the initiative integrates capacity building, citizen science, circular economy interventions (collection, sorting, repair, reuse, repurposing, and recycling), and policy advocacy. Quantitative and qualitative evidence demonstrates that grassroots-led interventions can simultaneously reduce plastic leakage, create decent green livelihoods, and strengthen environmental governance. We argue that inclusive, community-centered plastic governance is not only an environmental intervention but a planetary health strategy, offering policy-relevant insights for national plastic action plans, extended producer responsibility frameworks, and global negotiations toward a legally binding instrument on plastic pollution. Full article