Search Results (3,473)

Structural Health Monitoring (SHM) of steel bridges is vital for ensuring the longevity, safety, and reliability of critical transportation infrastructure. This review synthesizes recent advancements in SHM technologies and methodologies for steel bridges, highlighting the shift from traditional vibration-based monitoring to data-driven, intelligent systems. It covers core technological themes, including various sensing systems such as wireless sensor networks, fiber optics, and piezoelectric transducers, along with the impact of machine learning, artificial intelligence, and statistical pattern recognition. The paper explores applications for damage detection, such as fatigue life assessment and monitoring of components like expansion joints. Persistent challenges, including deployment costs, data management complexities, and the need for real-world validation, are addressed. The future of SHM lies in integrating diverse sensing technologies with computational analytics, advancing from periodic inspections to continuous, predictive infrastructure management, which enhances bridge safety, resilience, and economic sustainability. Full article

This paper deals with the optimal design-for-control of water distribution networks (WDNs) with the objectives of minimizing pressure-induced background leakage and maximizing resilience. This problem entails defining locations for installing valves and/or pipes and for simultaneously determining valve settings and belongs to the class of non-convex mixed-integer nonlinear problems. Solving highly complex infrastructure problems, such as WDNs, raises a fundamental question about the accuracy of the solutions to be implemented for sound water management. Therefore, two kinds of optimization methods are applied and assessed on two case studies. While the first is an exact global optimization method, the second is the metaheuristic based on the concept of simulated annealing. This paper proposes an innovative methodological analysis to interpret and discuss the results provided by both methods, as well as to identify their impact on the performance of the WDN. This type of analysis may help in highlight how the integration of the best features of both solution methods can promote a step forward in solving WDN problems. Full article

This study explores the role of sustainability-oriented education in supporting post-disaster recovery and resilience in Derna, Libya, following the catastrophic floods of September 2023. Using a qualitative descriptive design, twenty semi-structured interviews were conducted with academic experts, public health professionals, policymakers, and community leaders. The findings reveal that Education for Sustainable Development (ESD) is perceived as both a critical resilience tool and a moral imperative in fragile, disaster-affected contexts. However, institutional fragility, limited resources, and weak policy integration hinder its implementation. The study highlights the need to embed ESD within both formal education systems and informal community networks, aligning recovery strategies with local environmental realities. It offers practical recommendations for leveraging schools, faith-based institutions, and grassroots initiatives to foster adaptive capacity. These insights contribute to global debates on localising sustainable development in post-conflict settings and underscore the potential of ESD to bridge immediate recovery and long-term sustainability. The study explicitly aligns with the objectives of Sustainable Development Goal 4 (Quality Education) and Sustainable Development Goal 11 (Sustainable Cities and Communities). It demonstrates how sustainability-oriented learning can strengthen community resilience by connecting education with local recovery systems, environmental adaptation, and social rebuilding. Through this alignment, the research underscores the role of education as a mechanism for both immediate recovery and long-term sustainability within fragile and disaster-affected societies. Full article

Underground mine emergencies destroy communication infrastructure when situational awareness is most critical. Current systems rely on centralized network infrastructure, which fails during emergencies when miners are trapped and require rescue coordination. This paper proposes an energy-harvesting LoRa mesh network that addresses self-powered operation, interference management, and adaptive physical layer optimization under severe underground propagation conditions. A dual-antenna architecture separates RF energy harvesting (860 MHz) from LoRa communication (915 MHz), enabling continuous operation with supercapacitor storage. The core contribution is a decentralized scheduler that derives optimal timing offsets by modeling concurrent transmissions as a Poisson collision process, exploiting LoRa’s capture effect while maintaining network coherence. A SINR-aware physical layer adapts spreading factor, bandwidth, and coding rate with hysteresis, controls recomputing timing parameters after each change. Experimental validation in Missouri S&T’s operational mine demonstrates far-field wireless power transfer (WPT) reaching 35 m. Simulations across 2000 independent trials show a 2.2× throughput improvement over ALOHA (49% vs. 22% delivery ratio at 10 nodes/hop), 64% collision reduction, and 67% energy efficiency gains, demonstrating resilient emergency communications for underground environments. Full article

Mesozooplankton are pivotal for Black Sea food webs, yet they are highly vulnerable to hydrographic variability, eutrophication, and human pressures. This study analysed mesozooplankton dynamics along the Romanian coast (2013–2020) across three sectors (north, central, and south) and two distinct periods (cold and warm seasons), integrating Abundance–Biomass Comparison (ABC) curves with Fuzzy Cognitive Mapping (FCM). Results revealed a clear disturbance gradient: the Danube-influenced north supported high abundances of small-bodied taxa; the central sector maintained the most resilient and functionally diverse assemblages; and the southern sector showed chronic degradation with Noctiluca scintillans dominance. ABC curves quantified disturbance, with curve convergence in the north and near overlap in the south during summer, while FCM highlighted network simplification and reduced functional redundancy. Climate scenario simulations projected further declines in cladocerans and meroplankton under warming and freshening, whereas copepods showed relative resilience. Collectively, the findings demonstrate progressive simplification of mesozooplankton and declining energy transfer efficiency, underscoring the need to integrate zooplankton-based indicators into Black Sea monitoring and management frameworks. Full article

This study investigated the combined effects of lactic acid bacteria (LAB) fermentation and different milling methods (wet, semi-dry, and dry) on the physicochemical properties of glutinous rice flour (GRF) and the texture of the final product. A systematic analysis of rice samples treated with three LAB strains (Lactiplantibacillus plantarum CGMCC 1.12974, Limosilactobacillus fermentum CICC 22704, and Lactobacillus acidophilus CICC 22162) revealed that fermentation pretreatment created favorable conditions for subsequent physical milling by degrading the protein network and modifying the starch structure. The results demonstrated that fermentation combined with dry or semi-dry milling significantly improved the whiteness of GRF and the contents of γ-aminobutyric acid (GABA), total phenols, and total flavonoids, while reducing the contents of damaged starch (except in samples fermented with Lb. acidophilus) and protein by 2.91–12.43% and 17.80–32.09%, respectively. The functional properties of the GRF were also optimized: fermented flour exhibited higher peak viscosity, lower gelatinization temperature, and higher gelatinization enthalpy. Texture profile analysis revealed that glutinous dumplings prepared from fermented dry/semi-dry milled GRF, particularly those fermented with Lp. plantarum, showed significantly reduced hardness and chewiness, along with significantly improved cohesiveness and resilience. Consequently, their texture approximated that of high-standard wet-milled products. Correlation analysis based on the top ten discriminative features selected by random forest identified peak viscosity and breakdown viscosity as the most important positive factors associated with superior texture (high resilience, high cohesiveness, and low hardness), whereas damaged starch content and protein content were key negative correlates. In summary, this study confirms that the combination of fermentation and milling exerts a beneficial influence on the functional quality of GRF. Full article

Plant disease recognition is a critical technology for ensuring food security and advancing precision agriculture. However, challenges such as class imbalance, heterogeneous image quality, and limited model interpretability remain unresolved. In this study, we propose a Synergistic Dual-Augmentation and Class-Aware Hybrid (SDA-CAH) model designed to achieve robust and interpretable recognition of plant diseases. Our approach introduces two innovative augmentation strategies: (1) an optimized MixUp method that dynamically integrates class-specific features to enhance the representation of minority classes; and (2) a customized augmentation pipeline that combines geometric transformations with photometric perturbations to strengthen the model’s resilience against image variability. To address class imbalance, we further design a class-aware hybrid sampling mechanism that incorporates weighted random sampling, effectively improving the learning of underrepresented categories and optimizing feature distribution. Additionally, a Grad-CAM–based visualization module is integrated to explicitly localize lesion regions, thereby enhancing the transparency and trustworthiness of the predictions. We evaluate SDA-CAH on the PlantVillage dataset using a pretrained EfficientNet-B0 as the backbone network. Systematic experiments demonstrate that our model achieves 99.95% accuracy, 99.89% F1-score, and 99.89% recall, outperforming several strong baselines, including an optimized Xception (99.42% accuracy, 99.39% F1-score, 99.41% recall), standard EfficientNet-B0 (99.35%, 99.32%, 99.33%), and MobileNetV2 (95.77%, 94.52%, 94.77%). For practical deployment, we developed a web-based diagnostic system that integrates automated recognition with treatment recommendations, offering user-friendly access for farmers. Experimental evaluations indicate that SDA-CAH outperforms existing approaches in predictive accuracy and simultaneously defines a new paradigm for interpretable and scalable plant disease recognition, paving the way for next-generation precision agriculture. Full article

Salinity is a long-standing global environmental stressor of terrestrial agroecosystems, with important implications for viticulture sustainability, especially in arid and semi-arid environments. Salt-induced physiological and biochemical disruptions to grapevines undermine yield and long-term vineyard sustainability. This review aims to integrate physiological, molecular, and omics-based insights to elucidate how grapevine rootstocks confer salinity tolerance and to identify future breeding directions for sustainable viticulture. This review critically assesses the ecological and molecular processes underlying salt stress adaptation in grapevine (Vitis spp.) rootstocks, with an emphasis on their contribution to modulating scion performance under saline conditions. Core adaptive mechanisms include morphological plasticity, ion compartmentalization, hormonal regulation, antioxidant defense, and activation of responsive genes to stress. Particular emphasis is given to recent integrative biotechnological developments—including transcriptomics, proteomics, metabolomics, and genomics—that reveal the intricate signaling and regulatory networks enabling rootstock-mediated tolerance. By integrating advances across eco-physiological, agronomic, and molecular realms, this review identifies rootstock selection as a promising strategy for bolstering resilience in grapevine production systems confronted by salinization, a phenomenon increasingly exacerbated by anthropogenic land use and climate change. The research highlights the value of stress ecology and adaptive root system strategies for alleviating the environmental consequences of soil salinity for perennial crop systems. Full article

Settlements and settlement systems are key arenas of human–environment interaction, and reconstructing their spatial patterns is essential for understanding historical socio-environmental dynamics. Using the Complete Map of the Great Qing Empire (1905), this study employs digital extraction and spatial-statistical analysis to examine the nationwide settlement system of late Qing China. The results reveal that: (1) The system features dispersed high-level settlements and highly clustered low-level ones; provincial and prefectural cities follow administrative divisions, while counties, towns, and villages display strong spatial self-organization. (2) Mid-to high-level systems exhibit hierarchical fractures, whereas low-level settlements conform to Zipf’s law, highlighting the regularity and universality of grassroots networks. (3) Road accessibility, slope, and elevation significantly influence settlement hierarchy, whereas river proximity plays a limited role—indicating greater dependence on transportation and terrain adaptability. Overall, the study elucidates the spatial structure and formative mechanisms of the Qing settlement system and provides empirical insights into the evolution of surface patterns and regional resilience since the modern era. Full article

In an era of intensifying global technological competition and systemic disruptions, the resilience of metropolitan innovation networks has emerged as a cornerstone of sustainable regional development. Based on joint invention patents, this study employs a multi-method analytical framework integrating social network analysis, network motif analysis, a random walk algorithm, and the Exponential Random Graph Model (ERGM) to trace the evolution of resilience across node, structural, and community levels in the Shanghai Metropolitan Area (2011–2020). Our findings reveal a significant trajectory of strengthening resilience, marked not only by a shift from a monocentric to a polycentric structure at the node level but also by a qualitative change in collaborative patterns at the structural level, and enhanced integration at the community level. ERGM analysis identifies policy coordination and industrial upgrading as the most potent drivers of this evolution, with a pivotal finding being that digital connectivity, measured by information proximity, has superseded geographic proximity in facilitating collaboration. This study develops and applies a multi-scale resilience framework, while also extending proximity theory by highlighting the growing importance of policy and information dimensions over geographic distance. It offers actionable insights for building resilient innovation ecosystems in policy-driven metropolitan regions. Full article

Carnauba (Copernicia spp.) is a palm tree native to the Brazilian semi-arid region, valued for its significant economic, social, and environmental importance. This resilient species possesses adaptive mechanisms that enable it to endure prolonged periods of soil water scarcity and conditions of flooding and salinity. However, despite its relevance, there is a notable lack of scientometric data on this species in the literature, representing a significant research gap. This study aimed to analyze the state of research on carnauba palm from 2007 to 2022. Datasets were collected from the Web of Science central database, totaling 658 publications related to the terms “carnauba” or “copernicia”. The bibliometric software VOSviewer was used to create visual maps of keyword co-occurrence networks, providing deeper insights into the progress and research trends on the topic. Since 2014, the number of publications on carnauba has steadily increased, peaking between 2019 and 2021. The most prominent focus in these articles is on carnauba wax, with extensive research on its properties and applications in the food production chain. This significance is also reflected in the keyword co-occurrence networks. However, studies combining carnauba with soil sciences remain underexplored. Given carnauba’s importance in environmental and soil conservation, future research linking these areas could become a key avenue for advancing knowledge on the subject. Full article

Current power systems are facing noticeable power quality (PQ) performance deterioration, which has been attributed to nonlinear loads, distributed generation, and extensive renewable energy infiltration (REI). These conditions cause voltage sags, harmonic distortion, flicker, and disadvantageous power factors. The traditional PI/PID-based scheme of control, when applied to Flexible AC Transmission Systems (FACTSs), demonstrates low adaptability and low anticipatory functions, which are required to operate a grid in real-time and dynamic conditions. Artificial Intelligence (AI) opens proactive, reactive, or adaptive and self-optimizing control schemes, which reformulate FACTS to thoughtful, data-intensive power-system objects. This literature review systematically studies the convergence of AI and FACTS technology, with an emphasis on how AI can improve voltage stability, harmonic control, flicker control, and reactive power control in the grid formation of various types of grids. A new classification is proposed for the identification of AI methodologies, including deep learning, reinforcement learning, fuzzy logic, and graph neural networks, according to specific FQ goals and FACTS device categories. This study quantitatively compares AI-enhanced and traditional controllers and uses key performance indicators such as response time, total harmonic distortion (THD), precision of voltage regulation, and reactive power compensation effectiveness. In addition, the analysis discusses the main implementation obstacles, such as data shortages, computational time, readability, and regulatory limitations, and suggests mitigation measures for these issues. The conclusion outlines a clear future research direction towards physics-informed neural networks, federated learning, which facilitates decentralized control, digital twins, which facilitate real-time validation, and multi-agent reinforcement learning, which facilitates coordinated operation. Through the current research synthesis, this study provides researchers, engineers, and system planners with actionable information to create a next-generation AI-FACTS framework that can support resilient and high-quality power delivery. Full article

As a critical raw material in the semiconductor and new energy sectors, antimony is a strategic mineral resource for nations to safeguard industrial chain security. However, the scarcity of its resources and the complexity of its trade pattern underscore the urgency of antimony-related research. This study aims to reveal the structural characteristics of the global antimony trade network and explore the external factors influencing trade. Based on global antimony trade data from 2007 to 2022, the characteristics of the antimony trade network were analyzed using the complex network analysis method, and the influencing factors of antimony trade were examined via the fixed effects model. The results show that the global antimony trade network maintains a density of 0.05–0.06, with an average path length of 2.4–2.7 and a network diameter that mainly fluctuates between 5 and 6. The average clustering coefficient fluctuates within the range of 0.35–0.45. Overall, the network exhibits the characteristics of stable transmission efficiency, loose overall connectivity, and local agglomeration without a consistent upward or downward trend. Countries such as Germany, China, and the United States occupy core positions in the network. The fixed effects model indicates that GDP and LOGISTICS development are key factors promoting trade, while TARIFFS and REGULATORY policies have a significant inhibitory effect on trade. Therefore, ① Focus on the High-End Development of the Antimony Industry Chain and Promote the In-Depth Integration of Antimony Trade with the Semiconductor and New Energy Industries; ② Improve the Cross-Border Logistics and Warehousing System for Antimony Trade to Ensure the Efficient Circulation of Strategic Resources; ③ Promote; Promote Tariff Liberalization in Antimony Trade and Eliminate Market Access Barriers; ④ Strengthen the Government’s Strategic Support for the Antimony Industry to Enhance Global Discourse Power in Antimony Trade; Trade; ⑤ Maintain Macroeconomic Stability and Flexibly Manage Exchange Rates to Safeguard the Resilience of Antimony Trade. Full article

Accelerating urbanization and the intensifying pace of climate change have heightened the occurrence of urban pluvial flooding, threatening urban sustainability. As the preferred approach to urban stormwater management, coupled gray and green infrastructure (GI–GREI) integrates GREI’s rapid runoff conveyance with GI’s infiltration and storage capacities, and their siting and scale can affect life-cycle cost (LCC) and urban drainage system (UDS) resilience. Focusing on Fengxi New City, China, this study develops a multi-objective optimization framework for the GI–GREI system that integrates GI suitability and pipe-network importance assessments and evaluates the Pareto set through entropy-weighted TOPSIS. Across multiple rainfall return periods, the study explores optimal trade-offs between UDS resilience and LCC. Compared with the scenario where all suitable areas are implemented with GI (maximum), the TOPSIS-optimal schemes reduce total life-cycle cost (LCC) by CNY 3.762–4.298 billion (53.36% on average), rebalance cost shares between GI (42.8–47.2%) and GREI (52.8–57.2%), and enhance UDS resilience during periods of higher rainfall return (P = 20 and 50). This study provides an integrated optimization framework and practical guidance for designing cost-effective and resilient GI–GREI systems, supporting infrastructure investment decisions and climate-adaptive urban development. Full article

To mitigate the impacts of urbanisation and the attendant surface sealing, appropriate measures are required when adapting urban spaces and drainage infrastructure. In this context, the deployment of Sustainable Drainage Systems (SuDSs) has emerged as a viable alternative, delivering highly positive outcomes by enhancing hydrological, hydraulic and landscape performance while restoring ecosystem services to the community. This study evaluates the relative performance of five SuDS typologies, green roofs, bioretention cells, infiltration trenches, permeable pavements, and rain barrels, implemented in a 64 ha subbasin of the metropolitan area of Barcelona, Spain. Using Giswater integrated with the SWMM, the stormwater drainage network was modelled under multiple rainfall scenarios. Performance was assessed using two qualitative indicators, the junction index (I_j) and the conduit index (I_c), which measure surcharge levels in manholes and pipes, respectively. The results show that SuDS implementation affecting 42.8% of the drained area can enhance network performance by 35.6% and reduce flooded junctions by 67%. Among the typologies, rain barrels and bioretention cells were the most effective. The study concludes that SuDS construction, supported by open-source tools and performance-based indicators, constitutes a replicable and technically robust strategy for mitigating the effects of surface sealing and increasing urban resilience. Full article