Search Results (6,644)

Flood risk is a wicked problem, characterized by non-linear dynamics, cross-scale interdependencies, and contested responsibilities. Disentangling who or what has the capacity to drive change in flood risk management (FRM) systems is a critical step for designing inclusive and sustainable risk-reducing interventions. Given the diverse range of actors often involved in FRM decision-making, it is necessary to consider different interpretations of what system features are important. Using participatory visualization methods this study applied a systems-thinking lens to examine how experts visualize the FRM system and its drivers of change in British Columbia (BC), Canada. Workshop one participants undertook group timeline mapping to visualize their understanding of the events and processes that have driven BC FRM system development. Workshop two participants completed open-ended concept maps, producing personal mental models of the features and relationships that make up the present-day FRM system. Data from both workshops were synthesized into a ‘master’ timeline and concept map, and the experts’ perceived drivers of system change were identified. Our results revealed decentralizing governance shifts and past flood events as drivers of historic system development in the minds of our experts. Our results also indicated leverage points that experts had included as drivers of system improvement in their concept maps, with frequently named and connected features, such as the most interconnected feature: ‘flood mapping’, offering potential opportunities for cross-sector collaboration and cascading risk-reduction action. Possible gaps in the FRM system were also revealed by system features that were acknowledged as important by participants but were represented as disconnected, including themes of ‘climate change impact’ and ‘reconciliation’. Participatory visualization methods, especially when used in combination, offer a practical approach for representing experts’ mental models of FRM systems, revealing expert-identified leverage points for practical FRM improvement that can contribute to sustainable flood risk reduction goals. Full article

This study analyzes the relationship between functional digital access and knowledge-economy practices among young Ecuadorian adults, with emphasis on their implications for sustainable digital inclusion and knowledge-based development. The study is based on the premise that access to the Internet, devices, and technological tools does not necessarily ensure critical, productive, collaborative, or knowledge-generating uses of information. A quantitative, non-experimental, cross-sectional, and descriptive-correlational design was applied to a non-probabilistic sample of 441 young Ecuadorian adults aged 18 to 30. Data were collected through an online self-reported questionnaire and analyzed using descriptive statistics, reliability analysis, KMO and Bartlett indicators, confirmatory factor analysis, the Mann–Whitney U test, effect sizes, and Spearman’s rho with 95% confidence intervals. The instrument showed very high internal consistency for knowledge-economy practices ($\alpha =0.978$; $\omega =0.978$) and functional digital access and technological resources ($\alpha =0.963$; $\omega =0.964$). The CFA supported the proposed three-factor structure of knowledge-economy practices more strongly than a one-factor model, although the high reliability coefficients also suggest possible item homogeneity. The results showed that functional digital access was perceived more favorably than knowledge-economy practices. A very strong, positive, and statistically significant association was found between functional digital access and knowledge-economy practices ($\rho =0.822$, 95% CI [0.765, 0.870], $p<0.001$). Information management and collaboration was the dimension most strongly associated with functional digital access ($\rho =0.820$, 95% CI [0.764, 0.868], $p<0.001$). Women reported higher scores than men in knowledge-economy practices, although the effect size was small ($r=0.158$; $r_{rb}=0.184$). These findings suggest that functional digital access is a necessary but insufficient condition for sustainable digital inclusion. The study contributes empirical evidence from a developing-country context and highlights the need for educational and public-policy strategies that transform connectivity into critical learning, collaboration, innovation, employability, and knowledge creation. Full article

Marine envenomations are common non-infectious hazards for travelers. Lionfish, venomous fish native to Indo-Pacific waters, have become an invasive species in the Atlantic Ocean and threat to native marine ecosystems. Various control measures have been implemented in response to rapidly expanding lionfish populations, including licensed culling by recreational divers. We herein review lionfish envenomation through framing with a case that occurred during a diving trip to Curacao for the purpose of lionfish spearfishing. Following initial management in Curacao with hot water immersion, wound care, and antibiotic prophylaxis, the patient continued to have persistent swelling, bruising, and pain to the puncture site and was referred to our outpatient clinic for further evaluation. In addition to reviewing clinical syndromes and approach to management for common marine envenomations that may be encountered in the post-travel setting, we situate this case within the broader ecological context of expanding invasive species ranges with climate change and rising sea temperatures. Pre-travel providers should counsel patients at high risk for marine envenomations on preventative measures, along with how and when to seek care following exposure. Post-travel providers should be familiar with the immediate and long-term sequelae of non-infectious envenomations and intoxications, including marine exposures. Larger national and multinational collaborations are required to mitigate the effects of climate change and international marine movement on invasive species, especially those that incur risk to marine and human health alike. Full article

This study examines how village spatial institutional capacity (knowledge resources, relational resources, and mobilization capacity) and intra-organizational collaboration jointly shape the governance of agricultural irrigation systems. Using survey data from 840 households in six provinces of China’s Yellow River Basin, we employ OLS regression, bootstrapped quantile regression and moderation analysis. The empirical results indicate that both village spatial institutional capacity and internal collaboration significantly and positively affect comprehensive irrigation governance performance. Specifically, OLS results reveal that knowledge resources ($\mathsf{\beta }$ = 0.0029, p < 0.1), relational resources ($\mathsf{\beta }$ = 0.0711, p < 0.01), and mobilization capacity ($\mathsf{\beta }$ = 0.0236, p < 0.05) significantly enhance comprehensive performance, and internal collaboration exerts a significant positive moderating effect between institutional capacity and governance performance. Furthermore, quantile regression analysis reveals a non-linear distribution of this effect: the safeguarding role of institutional capacity is more prominent in the early stages of governance, whereas the driving role of internal collaboration shows an increasing trend in the middle and later stages. This study theoretically addresses the limitations of past reliance solely on the concept of “social capital” and provides robust empirical evidence for transitioning irrigation management from traditional top-down administrative dominance to a multi-centered, participatory self-governance model. Full article

In the current Architecture, Engineering, Construction, and Operations (AECO) sector, the demand for a skilled workforce capable of responding to rapidly changing needs is increasing. However, academic programs are struggling to keep up with this transformation. The integration of Building Information Modeling (BIM) tools into design studios and the objective evaluation of the pedagogical outcomes of this process are not yet fully clear. This study develops a pedagogical evaluation framework to integrate professional BIM competencies into architectural design studio curricula. This framework aims to measure student competency development and guide the restructuring of academic programs for BIM-supported education. A mixed methodology was adopted in the research; utilizing a combination of purposive and convenience sampling techniques, the studio performances, submission processes, and survey data of 409 students studying in architecture and interior architecture departments over a four-year period were analyzed longitudinally using the developed measurement-evaluation model. The proposed framework serves to pedagogically grade students’ in-studio performance and to measure acquired competencies with structured criteria. The qualitative data obtained from the surveys were analyzed through thematic and content analysis. The research revealed that students possessed limited technical skills in BIM projects and experienced deficiencies in collaboration and data management. Furthermore, it determined that instructors’ lack of knowledge regarding integrating BIM into the curriculum negatively impacted students’ learning processes. This study recommended enhancing teacher training for BIM-supported education, improving collaboration and coordination skills, and aligning the curriculum with professional requirements. The findings provide a framework that not only better prepares students for professional life but also helps bridge the gap between education and industry. Through this framework, students’ competencies can be measured at the pedagogical level. Full article

This study focuses on the multi-dimensional uncertainties in synergistic pollution reduction and carbon mitigation pathways for the coal chemical industry in the Yellow River Basin, a region facing extreme water scarcity (only 2% of national water) and fragile ecology, and constructs a Multi-Level Uncertainty Conduction (MLUC) Model integrating data, modeling, and validation. Using 2011–2025 data, Monte Carlo (10,000) simulations quantify the impacts of policy, technology, market, and ecological uncertainties on synergistic benefits. Sobol’ global sensitivity (Saltelli) and Shapley decomposition (14 technologies) identify key drivers and technology contributions. A system dynamics model simulates 2023–2050 pathways under baseline, policy-enhanced, technology breakthrough, and composite uncertainty scenarios. Logarithmic Mean Divisia Index (LMDI-I) decomposition reveals a six-factor driving mechanism for carbon emission changes. Results show that policy uncertainty exerts the largest influence, with a variance contribution of approximately 35%, followed by technology (28%), market (22%), and ecological factors (15%)—the latter primarily reflecting water availability and regional ecological carrying capacity. Critical thresholds are 80 CNY/t CO₂ for carbon capture, utilization and storage (CCUS) viability, and 6.8 CNY/t for green hydrogen substitution. Comprehensive resource utilization is optimal near term, while green hydrogen substitution and CCUS–green hydrogen coupling dominate medium- to- long term. The proposed dynamic threshold response mechanism and technology portfolio strategy could boost synergistic benefits by 22% to 35%. These findings underscore the need for watershed-scale collaborative governance and integrated water–carbon–energy management to ensure robust mitigation under the basin’s constraints. Full article

Surface congestion and taxiing delays at high-density airports increasingly constrain aviation sustainability, as ground-phase fuel consumption and emissions constitute a significant share of total airport emissions. Existing studies typically decouple air traffic flow management from ground resource scheduling, hindering coordinated optimization of punctuality, environmental benefits, and resource utilization. This paper proposes a multi-objective optimization method for runway-taxiway systems oriented toward air–ground collaborative decision-making, integrating Calculated Take-Off Time (CTOT) compliance constraints. A tri-objective mixed-integer programming model is formulated to minimize CTOT deviation, total taxiing time, and runway workload imbalance. A hybrid intelligent algorithm, SSA-SCA-NSGA-II, is designed with a bidirectional elite feedback mechanism to address this NP-hard problem. Validation uses real operational data of 58 departure flights during a peak period at Beijing Daxing International Airport. The results demonstrate that the proposed method achieves effective trade-offs on the Pareto front: CTOT compliance rate increased from 77.6% to 89.7–96.6%; total taxiing time decreased from 692 min to 551–635 min; and dual-runway utilization imbalance declined from 5.2% to 1.7–3.8%. These improvements translate into quantifiable sustainability gains: fuel consumption is reduced by 1425–3525 kg and CO₂ emissions by 4503–11,139 kg per peak hour, alongside a 19-percentage point improvement in punctuality that lowers passenger delay costs and reduces controller coordination workload. By simultaneously advancing environmental sustainability (carbon footprint reduction), economic sustainability (fuel and operational cost savings), and social sustainability (service punctuality and labor efficiency), the framework provides a measurable, monitorable, and policy-relevant decision-support tool for green airport surface operations aligned with sustainable development goals (SDGs). Full article

The rapid advancement of laboratory automation, robotics, and digital technologies has significantly transformed laboratory medicine worldwide, improving efficiency, diagnostic accuracy, and quality management. However, the adoption of these technologies in developing countries such as Indonesia remains uneven and is influenced by infrastructural, financial, regulatory, and workforce-related challenges. This structured narrative review aimed to critically examine the current landscape of laboratory automation and robotics in Indonesia, with particular emphasis on implementation challenges, workforce transformation among medical laboratory scientists (Ahli Teknologi Laboratorium Medik, ATLM), and pathways toward equitable integration. Studies published between 2015 and 2025 were identified through PubMed, Scopus, and Google Scholar, complemented by Indonesian regulatory documents, professional guidelines, and relevant grey literature. The review was informed by PRISMA principles and synthesized narratively to explore technological developments, operational impacts, policy contexts, and implementation barriers relevant to Indonesian laboratory systems. The findings indicate that automation and robotics offer substantial benefits, including improved turnaround time, enhanced quality assurance, reduced laboratory errors, and greater operational efficiency. Nevertheless, significant barriers persist, particularly disparities in digital infrastructure, financial constraints, limited workforce readiness, and the absence of comprehensive implementation frameworks. The review further highlights that automation is reshaping rather than replacing the role of ATLM, shifting professional responsibilities toward digital competency, automation oversight, data interpretation, and quality management. Achieving sustainable laboratory automation in Indonesia therefore requires an equity-centered and systems-oriented approach involving regulatory strengthening, workforce development, infrastructure investment, and multi-stakeholder collaboration. With strategic planning and policy alignment, laboratory automation and robotics hold considerable potential to modernize laboratory services and support Indonesia’s broader healthcare transformation agenda. Full article

The creation of collaborative knowledge bases for decision support systems (DSS) mitigates the subjectivity of individual experts and enhances overall system efficacy. However, traditional knowledge engineering approaches are highly labor-intensive when eliciting and integrating expert knowledge, as they require extensive, expert-level interaction between knowledge engineers and domain specialists. Modern large language models (LLMs) and retrieval-augmented generation (RAG) technologies present novel opportunities for overcoming these limitations. This study presents a pilot investigation to assess the potential of LLM-based knowledge engineering for developing collaborative knowledge bases within knowledge-based DSS, thereby assisting decision-making in complex operational scenarios involving technical systems. The article proposes an LLM-based approach for creating collaborative knowledge bases, including extraction, consolidation of expert knowledge and evaluation of their operability. To implement and evaluate the proposed approach, specialized prompts were engineered, and pilot experiments were conducted to generate consolidated knowledge cases through expert-LLM interactions. The resulting knowledge cases were subsequently applied in an experimental decision-making inference procedure for fault diagnosis in gas-fired heating boilers. During this inference process, an LLM agent, guided by tailored prompts and a RAG-enabled knowledge base, interactively queries the user to identify the specific issue and subsequently proposes a contextually appropriate solution. Throughout this study, the LLMs demonstrated capabilities in dialogue management, expert knowledge elicitation, and knowledge consolidation, successfully facilitating the creation of a collaborative knowledge base grounded in the “Event-Cause-Symptoms-Action” model. The findings highlight the viability of future research in LLM-based knowledge engineering and support the further advancement of the “LLM-as-knowledge-engineer” paradigm. Full article

Requirements management is fundamental to complex projects, especially in areas such as engineering, infrastructure, and defense. This article develops an integrative theoretical framework for requirements management in complex projects, grounded in a PRISMA-guided systematic literature review with a qualitative synthesis of the key dimensions of the field. In this review, 136 studies selected from an initial set of 519 records identified across multiple databases were reviewed. Five pillars were found to underpin the proposal: (i) the definition and traceability of requirements, (ii) the mitigation of uncertainties and risks, (iii) team maturity, (iv) digitalization and organizational transformation, and (v) the application of model-based systems engineering (MBSE). A literature review revealed that high-quality requirements reduce errors, improve predictability, and optimize resources, whereas digital approaches and collaborative practices strengthen the adaptive capacity of projects. Thus, in the proposed framework, these dimensions are organized into a hierarchical structure, with an emphasis on the integration of technical, organizational, and digital processes. One limitation is the lack of empirical validation, necessitating future studies on the practical application of the model in real projects, interviews with experts, and the development of operational metrics. This conceptual model is aimed at contributing to the literature and supporting more resilient, automated, and sustainability-oriented practices in complex environments. Full article

Biodiversity and landscape heterogeneity are key components of agroecosystem functioning because they support ecosystem services and strengthen the capacity of agricultural systems to undertake sustainable agroecological transitions. This study assesses the landscape structure of the municipality of Ruvo di Puglia, within the Bio-Distretto delle Lame, to evaluate its potential to support such a transition. Bio-districts are territories in which farmers, local authorities, citizens, and other stakeholders collaborate to manage natural and agricultural resources sustainably, often with a strong connection to organic farming. The research combines freely available Sentinel-2 imagery with UAV-based ground truthing to update land-use/land-cover information and to derive landscape indicators. A systematic sampling scheme was designed in QGIS, and UAV flights over 14 areas were used to generate training and validation vectors. Two classification strategies were tested on 2024 Sentinel-2 data: a supervised pixel-based approach and an unsupervised multi-temporal object-based approach (GEOBIA). The best-performing map was obtained from the supervised classification of July NDVI data, with an overall accuracy of 91.76%. In respect to the 2018 official land-cover dataset indicates a decrease in agricultural land (−490.91 ha), a reduction in arable crops (−1216.43 ha), and an increase in permanent crops (+725.52 ha), suggesting a shift toward specialization. At the same time, natural and semi-natural areas increased, improving the landscape potential for ecological functions. However, the high fragmentation detected by the landscape metrics (average patch size approximately 0.25 ha) may limit habitat continuity and species stability. The results should therefore be interpreted as an assessment of landscape structure and potential biodiversity support, rather than as a direct measurement of biological diversity. Strengthening ecotones, hedgerows and semi-natural linear elements with native species would further improve landscape resilience and support agroecological planning. Full article

Ferrofluid sealing is an important non-contact sealing technology for ultra-high vacuum (UHV) equipment, but its reliability is affected by more than pressure-bearing capacity alone. This review shows that carrier-liquid evaporation, material outgassing, thermal degradation, magnetic-field distortion, and liquid-ring instability are the main factors limiting UHV ferrofluid seals. Multiphysics simulation and parametric optimization remain the most mature tools for analyzing magnetic-field distribution, pressure resistance, temperature rise, and structural deformation. Data-driven condition identification improves failure monitoring, whereas physics-informed neural networks, topology optimization, and multi-objective optimization are still emerging methods for low-sample prediction and collaborative design. Future studies should focus on low-vapor-pressure ferrofluids, bake-out compatibility, thermal management, lifetime prediction, and integrated model–data design frameworks. Full article

Against the backdrop of rising global economic uncertainty and growing risks in agricultural supply chains, strengthening supply chain resilience has become essential for safeguarding national food security and advancing high-quality agricultural development. As a governance-optimizing force, the role of patient capital (e.g., through long-term credit and institutional investor participation) in shaping agricultural supply chain resilience merits systematic investigation. Drawing on panel data from 895 Chinese listed agricultural enterprises (2010–2024), this paper examines the association between patient capital and agricultural supply chain resilience and explores variables consistent with potential mechanisms. The results show that patient capital is positively associated with agricultural supply chain resilience, with evidence consistent with three possible mechanisms: improving internal management efficiency, advancing digital-intelligent transformation, and strengthening collaboration capabilities. Heterogeneity analyses reveal that this positive association is more pronounced among non-chain-leader enterprises, firms with low supply chain transparency, and those exhibiting a strong bullwhip effect. Further analysis indicates that strong ESG performance and high green total factor productivity strengthen the positive association between patient capital and agricultural supply chain resilience. This study provides enterprise-level evidence regarding the potential role of patient capital in promoting more efficient, modern, and sustainable agricultural supply chains. Full article

This research investigates how CE initiatives, interpreted as manifestations of corporate social responsibility, shape green value co-creation, green collaborative culture, and green supply chain integration across both supplier and customer contexts. The proposed conceptual framework also explores the possibility that digital maturity can moderate these interrelations, based on signaling theory. Empirical results, obtained through PLS SEM and NCA on a data sample of 493 suppliers and customers, confirm that CE practices have a significant positive impact on green value co-creation, which, in turn, influences the green collaborative behavior of the actors and the integration of the supply chain. The moderating role of digital maturity is reinforced in the linkages between green value co-creation, collaboration, and integration. Thereby, their effectiveness is strengthened. This study adds to the literature by proposing an integrative relationship between CE practices and outcomes within the supply chain dyad, offering a rare empirical study of relationships between suppliers and organizations in an emerging economy, and shedding light on the boundary condition of digital maturity within these relationships. The results have significant implications for managers seeking to maximize their sustainability performance through circular practices and digital capabilities. Full article

Cloud computing and mobile edge computing address the growing demand for computing power driven by the rise in data-intensive applications, but they are prone to creating computing silos, resulting in unbalanced resource utilization. To address this issue, the computing power network (CPN) has been introduced to enable the centralized management and scheduling of resources across the entire network. However, task scheduling in the CPN requires joint selection of computation nodes and routing paths, which greatly increases the complexity of the scheduling problem. In existing studies, heuristic methods are difficult to satisfy real-time requirements, whereas deep reinforcement learning methods ignore the collaborative optimization of network resources, making them difficult to adapt to complex CPN scenarios. To this end, we propose a task scheduling method for the CPN, called TS-DQNF. First, the method uses the Deep Q-Network (DQN) to determine the computation node for the computation task. Then, it introduces a dynamic congestion-aware mechanism to determine a low-cost routing path. Finally, it gradually obtains an effective task scheduling scheme through multiple rounds of alternating iterations. Simulation results show that the TS-DQNF improves the task success rate by 2.47–60.71% and reduces the average processing delay by 1.92–16.94% compared with other methods, while demonstrating good convergence performance. Full article