Sustainable Development Goal 9: Industry, Innovation and Infrastructure (47108)

Climate action has been deemed as fundamental to counteract the impacts of rising global temperatures on health which will disproportionately affect low-income populations, racial and ethnic minorities, women, and other historically marginalized groups. Along with poverty reduction, inequality mitigation, gender equality promotion, and public health protection, climate action has been recognized as a fundamental goal for achieving Sustainable Development Goals (SDGs). However, despite growing recognition of the need to align climate action with development goals, there is a knowledge gap regarding how the implementation of climate change mitigation and adaptation policies impacts social inequalities. To address this knowledge gap, this document proposes a scoping review protocol aimed at identifying and synthesizing research that examines the impacts of climate policies on inequalities at the subnational scales, within the transport, infrastructure and health. The objective of this review is to map existing evidence, identify conceptual and empirical gaps and inform policy strategies that promote climate action in line with values of social justice and equality. Full article

Digital Transformation (DT) is a strategic challenge that reshapes the way companies operate. Nevertheless, its adoption in the construction industry remains slow. This paper analyzes the DT process in Brazilian construction companies through two phases. Initially, an exploratory study was conducted with 17 firms using semi-structured interviews with their Technical Directors. Second, three companies were selected for case studies involving 14 in-depth interviews, observation, and document analysis. Data underwent content analysis. In the exploratory phase, DT was found to be mainly pursued to improve construction efficiency. Barriers were strongly associated with individual aspects, especially limited knowledge about technologies and resistance to change, reinforced by difficulties in implementing organizational changes. Most problems that DT seeks to address are concentrated in the technical department and construction site. Companies adopted approaches such as technology investments, open innovation, organizational restructuring, and training, but the success of these strategies depends on top management engagement and employee acceptance. Besides cultural barriers, technological obstacles, system integration and digital delay were identified, along with process difficulties such as the complexity and costs of the DT journey. Indirect sustainability objectives also emerged, indicating that DT is perceived as both technological advancement and a means to transform the sector. Finally, based on the empirical findings, a multi-level framework comprising 12 strategies for DT in the construction industry was proposed. Overall, the empirical field investigated remains in the early stages of DT, with experimentation with technologies and a focus on efficiency, characteristics of digitization, a step prior to total transformation. The study provides a valuable diagnosis of DT to support the digital transition and informs policymakers in designing initiatives that foster DT, contributing to sector sustainability and SDG9. Full article

In the context of the shipping industry’s transition towards low-carbon solutions, hydrogen energy exhibits substantial application potential in marine propulsion systems. Fuel reforming for hydrogen production represents one of the key technologies for efficient hydrogen production in maritime applications. Nevertheless, this process involves complex multi-scale reaction mechanisms, challenges in catalyst design, and difficulties in system optimization. This paper conducts a comprehensive review of the recent progress in the application of machine learning in fuel reforming hydrogen production technology. In the realm of catalysts, machine learning has expedited the design of efficient catalysts via high-throughput screening, performance prediction, and active site regulation. In reaction modeling, machine learning has facilitated the development of multi-scale kinetic models, enhancing the interpretability and predictive accuracy of reaction pathways. Regarding equipment and system optimization, machine learning has enabled innovations in reactor design, collaborative optimization of process parameters, and intelligent system control. This review aims to provide theoretical foundations and practical guidance for the technological development of ship propulsion systems. Moreover, it explores the future directions for the deep integration of machine learning and hydrogen energy technologies, thereby promoting the low-carbon and intelligent transformation of the shipping industry. Full article

The Russo–Ukrainian conflict (RUC) escalated on 24 February 2022 with Russia’s large-scale military operation in Ukraine. Our review aims to present the impact of the RUC on Ukrainian water resources and infrastructure. Its primary objective was to analyze 61 relevant papers, selected and screened according to the PRISMA methodology, concerning changes in inland and marine water quality, employing diverse scientific and analytical methods, and technical tools. Key recurring themes included “Ukraine”, “Russian-Ukrainian War”, and “Ecocide”. Beyond assessing the environmental consequences of destroyed treatment plants, supply systems, and sewerage units, as the secondary objective, the review introduces the concept of “aquacide”—the deliberate or incidental destruction and contamination of water infrastructures and resources during military operations. The most severe cases were documented in southern and eastern Ukraine, with the destruction of the Kakhovka Dam standing out as the most widely reported “aquacide”. Finally, the review highlights the critical role of satellite imagery and remote sensing as the most effective tools in monitoring water quality and infrastructures under wartime conditions, when in situ observations and measurements are often impossible. Full article

Inadequate hygiene knowledge and poor sanitation practices remain key challenges to safe learning environments in South Africa, with 462 million learners attending schools without basic handwashing facilities and many schools failing to meet sanitation standards. Although national policies and infrastructure investments have improved water, sanitation, and hygiene (WASH) services in some schools, access and behaviours remain uneven across socio-economic contexts. This study evaluated knowledge, attitudes, and practices (KAP) related to WASH among 1200 learners from 40 high schools in Durban using a cross-sectional design and interviewer-administered questionnaires. Data were analysed using descriptive statistics, Pearson correlations, ANOVA, and multiple regression. The study addressed the research question: To what extent do learners’ knowledge and attitudes predict hygiene practices across socio-economic contexts? It was hypothesised that higher knowledge and more positive attitudes would significantly predict improved hygiene practices. Results showed that while 74.6% reported handwashing after toilet use, only 39.3% consistently used soap. Knowledge of disease transmission through unsafe water was 35.4%, although overall attitudes were positive. Learners from higher-quintile schools had significantly better KAP scores than those from lower quintiles (p < 0.001). Both knowledge (β = 0.232, p < 0.001) and attitudes (β = 0.266, p < 0.001) were significant predictors of learners’ hygiene practices. Significant group differences were also observed by gender (t = 18.032, p = 0.001) and district (t = −3.895, p = 0.001). These findings highlight persistent WASH gaps and inequities across schools, underscoring the need for integrated interventions that strengthen both hygiene education and school infrastructure to achieve Sustainable Development Goal 6. Full article

Substance-based medical devices (SBMDs) are increasingly used in wound care due to their favorable safety profile, physicochemical mechanisms of action, and therapeutic effectiveness. These products often incorporate biopolymers such as hyaluronic acid or chitosan, alone or in combination with antimicrobial agents like silver nanoparticles (AgNPs) or silver sulfadiazine (SSD), offering hydration, tissue protection, and control of microbial burden in both acute and chronic wounds. Despite their widespread clinical use, the regulatory classification of SBMDs under Regulation (EU) 2017/745 (MDR) remains one of the most challenging and debated areas within the current European framework. This review analyzes the scientific and regulatory context of topical SBMDs, with particular emphasis on borderline products that share similarities with medicinal products in terms of formulation, composition, or claimed effects. The discussion focuses on the application of MDR Annex VIII, specifically Rule 21 for substance-based devices and Rule 14 for devices incorporating medicinal substances with ancillary action, together with interpretative guidance provided by MDCG 2022-5 Rev.1 and the Association of the European Self-Care Industry (AESGP) Position Paper. Particular attention is given to the identification of the critical role of the primary mode of action (MoA) as the determining criterion for regulatory qualification, especially for products containing antimicrobial substances. Through selected examples and case analyses, the review highlights inconsistencies in classification across Member States and underscores the need for a more harmonized, evidence-based, and proportionate regulatory approach. Overall, SBMDs challenge traditional regulatory boundaries and call for a framework capable of accommodating complex, multifunctional products while ensuring patient safety and regulatory coherence. Full article

Urban flooding has become a critical environmental challenge under global climate change and rapid urbanization. This study develops a multi-indicator hazard mechanism framework for flood hazard assessment in Rizhao, a coastal city in China, by integrating three fundamental hydrological processes: runoff generation, flow convergence, and drainage. Based on geospatial data—including DEM, road networks, land cover, and soil characteristics—six key indicators were evaluated using the TOPSIS method: runoff curve number, impervious surface percentage, topographic wetness index, time of concentration, pipeline density, and distance to rivers. The results show that extreme-hazard zones, covering 6.41% of the central urban area, are primarily clustered in northern sectors, where flood susceptibility is driven by the synergistic effects of high imperviousness, short concentration time, and inadequate drainage infrastructure. Independent validation using historical flood records confirmed the model’s reliability, with 83.72% of documented waterlogging points located in predicted high-hazard zones and an AUC value of 0.737 indicating good discriminatory performance. Based on spatial hazard patterns and causal mechanisms, an integrated mitigation strategy system of “source reduction, process regulation, and terminal enhancement” is proposed. This strategy provides practical guidance for pipeline rehabilitation and sponge city implementation in Rizhao’s resilience planning, while the developed hazard mechanism framework of “runoff–convergence–drainage” provides a transferable methodology for flood hazard assessment in large-scale urban environments. Full article

The intangible accessibility of real estate markets via platforms like Airbnb profoundly influences the urban development industry, propelled by the dynamics of short- to medium-term rentals for tourists. The suggested study aims to examine the association between the prices of listed properties and the influence of proximity to tourist attractions on location-driven pricing. The city of Rome acts as a case study from which to derive pertinent conclusions and proof on the phenomena intended for exploration. The methodological approach relies on a comprehensive classification of locations recognized as tourist attractions, drawn from public resources, travel guides, search engines, and online trends. The identified attractionswere subsequently classified to analyze how spatial proximity influences price formation. Data on short-term rental listings were obtained from the Inside Airbnb platform. The results enable the characterization of Rome as a polycentric urban system, composed of multiple tourism hubs whose spatial interactions are closely associated with prevailing hotel pricing patterns. This study emphasizes the influence of tourist demand on land values, a phenomenon intricately connected to urban gentrification and the capitalization of the real estate market. These findings enhance comprehension of tourism’s impact on the geographical and economic structure of cities. Full article

Urban and population densification have resulted in deteriorating living conditions for populations and the loss of UGSs. The COVID-19 pandemic has exposed the social, economic and environmental vulnerabilities of our societies, but it has also demonstrated the importance of UGSs as intrinsic elements for maintaining the quality of life of the population and making urban spaces sustainable. Due to its considerable area of UGS, the district of Benfica in Lisbon, Portugal, is the object of study. The study focuses on understanding how the proximity of UGS influences the practice of leisure activities for different publics, and how they are reflected in the populations’ lives, exploring the context during the COVID-19 pandemic. It develops a methodology with a mixed-methods approach: (1) literature review, policies, and urban planning; (2) observation methods, mapping and spatial analysis of UGS types; and (3) surveys. The empirical results indicate the importance of proximity to improve the frequency, namely for the elderly and children. The results also demonstrate that the quality (infrastructure and equipment) of UGS, despite having less walking proximity, is an important element to attract people to use the UGS. A general conclusion is that the proximity and accessibility (walking or public transport) are interlinked in both profiles of UGS, demonstrating a relationship between the place of residence, easy access and frequency of UGS in the practice of activities and the self-assessed physical and mental health benefits. Full article

This study examines the relationship between industrial agglomeration and carbon emissions in China’s forestry industry, using panel data from 30 provincial-level regions between 2009 and 2020. The industrial agglomeration level is measured by the Location Quotient (LQ), which is calculated based on regional employment shares to reflect the concentration of the forest products industry. This study finds that LQ exhibits a multiplicative effect—meaning that its influence on carbon emissions amplifies through interactive mechanisms of scale, technology diffusion, and spatial concentration. Four carbon indicators—carbon emissions from export products, carbon emission intensity, energy intensity, and energy structure cleanliness—are analyzed. Employing a threshold regression model, the study identifies nonlinear effects of agglomeration on carbon outcomes. The estimated threshold value (LQ = 0.7122) divides the process into three stages: (1) an embryonic stage (LQ < 0.7122) with rising emissions and declining efficiency; (2) a growth stage (around LQ ≈ 0.7122) with simultaneous increases in emissions and efficiency; and (3) a mature stage (LQ > 0.7122) where emissions decline as efficiency improves. These results reveal that the environmental effects of forestry industrial agglomeration evolve nonlinearly across development stages. Full article

This study identifies key determinants shaping countries’ Sustainable Development Goals performance and develops classification models for predicting country group membership based on the SDG Index. The research addresses the urgent need to optimize development policies amid limited resources and the approaching 2030 Agenda deadline. Using data from 154 countries (2024), the analysis reveals that key SDG determinants are fundamentally method-dependent: discriminant analysis identified Goals 10, 6, 15, and 5 as most influential for differentiating countries by SDGI level, while Random Forest identified Goals 4, 9, and 2 as the most important predictors. This divergence reflects fundamentally different analytical perspectives—linear contributions to group separation versus complex nonlinear interactions and synergies between goals—with critical policy implications for prioritization strategies. Correlation analysis demonstrates that sustainable development dynamics operate differently across development stages: high-development countries show strongest associations with technological advancement and institutional capacity, while low-development countries exhibit compensation effects where basic infrastructure provision occurs alongside lagging human capital development. The discriminant model achieved 94.08% overall accuracy with perfect classification for extreme SDGI categories, while the Random Forest model provides complementary insights into interactive pathways. The scientific contribution lies in demonstrating that perceived variable importance depends on analytical framework rather than representing objective reality, and in providing validated classification tools for rapid assessment in data-limited contexts. These findings offer actionable guidance for evidence-based resource allocation and policy prioritization in the critical final years of SDG implementation. Full article

The coordinated development of the agroforestry economy and the ecological environment is crucial for promoting the sustainable development and high-quality transformation of the agroforestry economy. Based on TOE theory and utilizing provincial-level panel data from China covering 2012–2023, this study comprehensively employs dynamic QCA and NCA methods to explore the multi-faceted driving pathways and supporting factors for the coordinated development of the agroforestry economy and ecological environment across temporal and spatial dimensions. Key findings include: (1) Coordinated development requires synergistic contributions from multiple factors—technological, organizational, and environmental—rather than isolated effects of any single element. While no single factor alone constitutes a necessary condition for coordination, the importance of technological innovation, market demand, and industrial support is progressively increasing; (2) The coordinated development of the agroforestry economy and ecological environment involves multiple pathways and complex mechanisms. Specifically, it encompasses four distinct approaches: enterprise-driven and industry-supported model, technology-innovation-led model, market-driven factor integration model, and government-led multi-stakeholder collaboration model; (3) No significant temporal effects emerged across all pathways, but pronounced spatial heterogeneity was evident. The enterprise-driven and industry-supported model suits Northeast and Central China; the technology-innovation-led model is suitable for South China and Northeast China; the market-driven factor integration model is suitable for East China, Central China, and Southwest China; the government-led multi-stakeholder collaboration model is suitable for Southwest China and Central China. Therefore, to enhance the coordinated development of the agroforestry economy and ecological environment, each region should adopt a holistic perspective, leverage its unique resource and factor endowments, strengthen the integrated matching of technological, organizational, and environmental factors, and explore development pathways tailored to local conditions. Full article

As the hospitality industry adapts to meet customers’ increasing demand for wellness, incorporating innovative wellness services (WSs) has become a crucial strategy to retain customer engagement. This study explored how the psychological trait of grit may serve as a significant determinant of customer engagement with WS in the hospitality context. Grounded in the transtheoretical model (TTM) stages of change, the study examined the relation between grit and customers’ perceptions of wellness hospitality services. By integrating TTM as a theoretical framework, this research attempted to understand the way that individuals interact with and perceive wellness-oriented amenities; also, it offers actionable insights into ways to enhance customers’ engagement. The study employed a quantitative method. By using an online survey (N = 337) and structural equation modeling (SEM), the study explored the relation between grit, WS, and customer engagement in the hospitality industry. Grit was found to be an important antecedent of using and engaging with various WSs. This study also demonstrated that WSs have a significant positive effect on customers’ engagements with WS. These study findings can help hospitality professionals to identify gritty customer segments to retain possible customer retention. Full article

The construction industry significantly contributes to global sustainability challenges, producing 30–40 percent of global carbon dioxide emissions and consuming large amounts of natural resources. Pervious concrete has emerged as a sustainable alternative to conventional pavements due to its ability to promote stormwater infiltration and groundwater recharge. However, the absence of fine aggregates creates a highly porous structure that results in reduced compressive strength, limiting its broader structural use. Determining compressive strength traditionally requires destructive laboratory testing of concrete specimens, which demands considerable material, energy, and curing time, often up to 28 days—before results can be obtained. This makes iterative mix design and optimization both slow and resource intensive. To address this practical limitation, this study applies Machine Learning (ML) as a rapid, preliminary estimation tool capable of providing early predictions of compressive strength based on mix composition and curing parameters. Rather than replacing laboratory testing, the developed ML models serve as supportive decision-making tools, enabling engineers to assess potential strength outcomes before casting and curing physical specimens. This can reduce the number of trial batches produced, lower material consumption, and minimize the environmental footprint associated with repeated destructive testing. Multiple ML algorithms were trained and evaluated using data from existing literature and validated through laboratory testing. The results indicate that ML can provide reliable preliminary strength estimates, offering a faster and more resource-efficient approach to guiding mix design adjustments. By reducing the reliance on repeated 28-day test cycles, the integration of ML into previous concrete research supports more sustainable, cost-effective, and time-efficient material development practices. Full article

Industrial heat exchangers are widely used in industries such as petrochemicals, energy and power, and food processing, making them one of the most important pieces of heat and mass transfer equipment in industry. During operation, a layer of fouling often adheres to the heat transfer surfaces, which reduces the heat transfer coefficient of the equipment and increases the thermal resistance of the surfaces. Additionally, fouling can corrode the material of the heat transfer surfaces, compromise their integrity, and even lead to perforations and leaks, severely impacting equipment operation and safety while increasing energy consumption and costs for enterprises. The application of anti-fouling coatings on surfaces is a key technology to address fouling on heat transfer surfaces. This paper focuses on introducing major types of anti-fouling coatings, including polymer-based coatings, “metal material + X”-type coatings, “inorganic material + X”-type coatings, carbon-based material coatings, and other varieties. It analyzes and discusses the current research status and hotspots for these coatings, elaborates on their future development directions, and proposes ideas for developing new coating systems. On the other hand, this paper summarizes the current research on the main factors—surface roughness, surface free energy, surface wettability, and coating corrosion resistance—that affect the anti-fouling performance of coatings. It outlines the research hotspots and challenges in understanding the influence of these three factors and suggests that future research should consider the synergistic effects of multiple factors, providing valuable insights for further studies in the field of anti-fouling coatings. Full article

Background: Testicular dysgenesis syndrome (TDS) is a complex disorder of the male reproductive system related to disfunction of the fetal testis. The clinical features of TDS may be evident at birth or infancy (cryptorchidism, hypospadias and/or reduced anogenital distance) or occur later in adulthood (testis cancer, infertility). Genetic background seems to be important for genetic predisposition, with new genes being associated with components of the syndrome in last years. Interestingly, the incidence of clinical manifestations of TDS has been increasing in many countries in recent decades, suggesting that genetic predisposition alone cannot explain this trend. Consequently, the hypothesis of multifactorial etiopathogenesis is becoming increasingly accepted nowadays, with environmental factors probably acting during early developmental stages in genetically predisposed individuals. Methods: In this narrative review, we aim to critically evaluate genetic and non-genetic factors involved in the pathogenesis of TDs. Results: Important associations with intrauterine growth disorders and maternal diseases (overweight/obesity and diabetes) as well as lifestyle factors (e.g., smoking and alcohol abuse) were found. In such context, endocrine disruptors probably play a major role. These substances are widely used in industry and can exert estrogenic and antiandrogenic effects, potentially interfering with the development of the fetal gonad. Conclusions: Considering their possible impact on male sexual health, more attention should be focused on maternal modifiable factors to confirm with prospective studies the mixed results of available evidence. Full article

The Citrus limon (L.) Burm. f. industry suffers significant losses due to fungal diseases. Therefore, this study aimed to evaluate the effectiveness of sodium benzoate (SB) and potassium sorbate (PS) on the incidence of fungal decay and fruit quality when used as preharvest treatments on Fino lemon trees over two consecutive seasons (2021–2023). Lower concentrations of SB and PS (0.1% and 0.5%) applied in one or two treatments successfully controlled fungal decay. On average, SB achieved a greater reduction in decay, ranging from 45% to 60%, compared to PS’s reduction of 25% to 50%. This approach minimised the negative impact on lemon fruit quality, in contrast to the highest doses (more than 1%) and the greatest number of applications (more than three times), which increased lemon susceptibility to decay. Furthermore, lemons treated with 0.5% SB twice enhanced antioxidant systems, showing a 35% increase in total phenolic content in the flavedo at harvest compared to the control. Consequently, the application of 0.5% SB twice at preharvest emerges as a promising and potential alternative to conventional fungicides for effective fungal decay control and maintenance of acceptable lemon quality traits during cold storage. Full article

Biodiversity knowledge is fundamental to conservation planning and sustainable environmental decision-making; however, general-purpose Large Language Models (LLMs) frequently produce hallucinations when responding to biodiversity-related queries. To address this challenge, we propose BioChat, a domain-specific question-answering system that integrates a Retrieval-Augmented Generation (RAG) framework with a Re-Ranker–based retrieval and routing mechanism. The system is built upon a verified biodiversity dataset curated by the National Institute of Biological Resources (NIBR), comprising 25,593 species and approximately 970,000 structured data points. We systematically evaluate the effects of embedding selection, routing strategy, and generative model choice on factual accuracy and hallucination mitigation. Experimental results show that the proposed Re-Ranker-based routing strategy significantly improves system reliability, increasing factual accuracy from 47.9% to 71.3% and reducing hallucination rate from 34.0% to 24.4% compared with Naive RAG baseline. Among the evaluated LLMs, Qwen2-7B-Instruct achieves the highest factual accuracy, while Gemma-2-9B-Instruct demonstrates superior hallucination control. By delivering transparent, verifiable, and context-grounded biodiversity information, BioChat supports environmental education, citizen science, and evidence-based conservation policy development. This work demonstrates how trustworthy AI systems can serve as sustainability-enabling infrastructure, facilitating reliable access to biodiversity knowledge for long-term ecological conservation and informed public decision-making. Full article

The safe disposal of high-level radioactive waste (HLW) is a significant challenge in the nuclear industry. As the buffer backfill material for deep geological disposal engineering barriers, the shrinkage characteristics of bentonite–sand mixtures are critical to the long-term stability of repositories. This study systematically conducted drying shrinkage tests using an improved thin-film technique under varying sand contents R_s (0–50%), salt solution concentrations (0–1.5 mol/L), and ion types (Na⁺, Mg²⁺, Ca²⁺, Cl⁻, SO₄²⁻). The mechanisms of the effects of sand content and salt solutions on the shrinkage behavior of bentonite were revealed based on the results. In addition, the rationality of the MCG-B model in simulating the shrinkage characteristics of mixtures was also discussed. The results show that a sand content of 30% is the minimum sand content for inhibiting the shrinkage behavior of bentonite–sand mixtures observed in this work: below this ratio, bentonite dominates the shrinkage process, and samples are prone to cracking due to uneven matrix suction; above this ratio, quartz sand forms a rigid skeleton that significantly inhibits volume shrinkage and accelerates water evaporation. Salt solutions suppress shrinkage by compressing the thickness of the diffuse double layer and inducing ion crystallization. Higher cation concentrations and valences (Mg²⁺ > Na⁺ > Ca²⁺) enhance the inhibitory effect. Crystalline salts such as Na₂SO₄ cause measurement deviations in water content due to hydration and delay the shrinkage process. However, NaCl solutions effectively inhibit shrinkage with minimal impact on shrinkage time. Fitting results with the MCG-B model (Coefficient of determination > 0.97) demonstrate that the MCG-B model can empirically describe the results of thin-film technique experiment, though the model’s prediction accuracy decreases for the residual shrinkage stage at high sand contents (>40%). This study provides a theoretical basis for optimizing buffer material proportions and curing processes, with significant implications for the long-term safety of HLW repositories. Full article

This paper showcases how electric scooter data can be used to visually explore and interpret urban dynamics, offering a perspective on city structure and mobility patterns. The goal of the study is to investigate how visual analysis of micromobility data can reveal spatial and temporal patterns that support urban planning and operational decision-making. Through a series of visual analyses, the article identifies high-demand areas and popular travel routes, with areas of particularly strong traffic—insights valuable for infrastructure planning and operational optimization. Temporal visualizations reveal distinct peaks in e-scooter activity during lunch hours and late evenings, highlighting behavior patterns that may inform service adjustments. Clustering techniques are used to delineate functional zones within the city, which are then visualized to reflect how users interact with urban space. These visuals help uncover mobility-based boundaries and support a deeper understanding of the city’s layout. Additionally, the approach highlights key locations that may be attractive for business development, such as new commercial spots, based on user behavior. By focusing on visual storytelling rather than predictive modeling, this work proposes analyses suitable for urban planners, mobility providers, and other stakeholders with actionable insights into urban movement and structure. Full article

The integration of IT services is a critical challenge for public organizations that seek to modernize their operational ecosystems and strengthen mission-oriented processes. In the field of fiscal oversight, supreme audit institutions (SAIs) increasingly require systematized and interoperable service architectures to ensure transparency, accountability, and effective public resource control. However, existing literature reveals persistent gaps concerning how service integration models can be deployed and validated within complex government environments. This study describes an enterprise architecture-driven service integration model designed and evaluated within the Office of the General Comptroller of the Republic of Colombia (Contraloría General de la República, CGR). The study tests the hypothesis that an Enterprise Architecture-driven integration model provides the necessary structural coupling to align technical IT performance with the legal requirements of fiscal oversight, which is an alignment that typically does not appear in generic governance frameworks. The methodological approach followed in this study combines an IT service management maturity assessment, process analysis, architecture repository review, and iterative validation sessions with institutional stakeholders. The model integrates ITILv4 (Information Technology Infrastructure Library), TOGAF (The Open Group Architecture Framework), COBIT (Control Objectives for Information and Related Technologies), and ISO20000 into a coherent framework tailored to the operational and regulatory requirements of an SAI. Results show that the proposed model reduces service fragmentation, improves process standardization, strengthens information governance, and enables a unified service catalog aligned with fiscal oversight functions. The empirical validation demonstrates measurable improvements in service delivery, transparency, and organizational responsiveness. The study contributes to the field of applied system innovation by: (i) providing an integration model, which is scientifically grounded and evidence-based, (ii) demonstrating how hybrid governance and architecture frameworks can be adapted to complex public-sector environments, and (iii) offering a replicable approach for SAIs that seek to modernize their technological service ecosystems through enterprise architecture principles. Future research directions are also discussed to provide guidelines to advance integrated governance and digital transformation in oversight institutions. Full article

The global shift towards sustainable energy and stringent climate policies has underscored the need for decarbonizing energy systems, electrifying transportation, and transforming supply chains. In this context, China’s new energy vehicle (NEV) industry, as the largest global producer and consumer of automobiles, is pivotal in advancing energy substitution and achieving carbon reduction goals. This study investigates the energy efficiency and supply chain transformation within China’s NEV sector, leveraging panel data from 12 representative provinces over the period 2017–2023. Employing a robust analytical framework that integrates the DEA-BCC model, Malmquist index, and Tobit regression, the study provides a dynamic and regionally differentiated assessment of NEV industry efficiency. The results reveal significant improvements in total factor energy efficiency, predominantly driven by technological progress. R&D intensity, infrastructure development, and environmental regulation are identified as key enablers of efficiency, while excessive government intervention tends to hinder performance. The findings offer valuable empirical insights and policy recommendations for optimizing China’s NEV industry in the context of energy system transformation and sustainable industrial development. Full article

The study explores the concept of the X-Minute City, an evolution of the 15-min city paradigm, as an operational tool for sustainable urban regeneration in Europe. Starting from the goal of ensuring daily accessibility to key services within 5–20 min on foot or by bicycle, the research analyses how this proximity model can respond to contemporary environmental, social, and infrastructural challenges. Through a comparative approach between Amsterdam and Milan, chosen for their regulatory and cultural differences, the study combines documentary analysis, urban policy evaluation, and the construction of a grid of multidimensional indicators relating to proximity, sustainable mobility, spatial reuse, and social inclusion. In conceptual terms, the X-Minute City is understood here as a flexible and governance-oriented extension of the 15-min city, in which proximity is treated as an adaptive temporal band (5–20 min) and as an infrastructure of multilevel urban governance rather than a fixed and universal design rule. The findings highlight that in the Netherlands, the model is supported by a coherent and integrated regulatory framework, while in Italy, innovative local experiments and bottom-up participatory practices prevail. The analysis demonstrates that integrating the X-Minute City with multilevel governance tools and inclusive policies can foster more equitable, resilient, and sustainable cities. Finally, the research proposes an adaptable and replicable framework, capable of transforming the X-Minute City from a theoretical vision to an operational infrastructure for 21st-century European urban planning. The limitations of this predominantly qualitative, document-based approach are discussed, together with future directions for integrating spatial accessibility modelling and participatory methods. Full article

Microplastics are contaminants of increasing environmental concern, particularly in marine ecosystems where they can be easily ingested by marine organisms, causing adverse health problems in animals and, through trophic transfer, in humans. While numerous studies have examined microplastic pollution in marine environments, most focus on water, sediment, or biota, thereby only measuring cumulative effects from multiple pollution sources in one area. This review aims to assess existing research on microplastic pollution originating from shipyards and maritime transport activities, with the goal of identifying current knowledge, methodological approaches, and existing research gaps. A review of the scientific literature was conducted, focusing on studies that investigated microplastic pollution associated with shipyards and maritime transport. Priority was given to peer-reviewed publications that included quantitative or qualitative measurements of microplastics. The reviewed literature reveals a limited number of studies explicitly addressing microplastic emissions from shipyards and maritime transport. Available studies employ diverse sampling strategies and analytical methods, making direct comparisons challenging. This review highlights significant gaps in current knowledge regarding microplastic sources and pathways linked to maritime industries. By synthesizing existing data, the paper provides a foundation for future targeted research and supports the development of more effective pollution reduction strategies. Full article

Background/Objectives: The poultry industry faces severe heat-stress challenges that threaten both economic sustainability and animal welfare. Embryonic thermal manipulation (ETM) has been proposed as a thermal programming strategy to enhance chick heat tolerance, yet its efficacy in layers requires verification, and its effects on growth performance and neurodevelopment remain unclear. Methods: White Leghorn embryos at embryonic days 13 to 18 (ED 13–18) were exposed to 39.5 °C (ETM). Hatch traits and thermotolerance were recorded, and morphometric and histopathological analyses were performed on brain sections. Transcriptome profiling of the whole brains and hypothalami was conducted to identify differentially expressed genes (DEGs). Representative pathway genes responsive to ETM were validated by RT-qPCR. Results: ETM reduced hatchability, increased deformity rate, and decreased hatch weight and daily weight gain. During a 37.5 °C challenge, ETM chicks exhibited delayed panting and lower cloacal temperature. Histopathology revealed impaired neuronal development and myelination. Transcriptomic analysis of ED18 whole brains showed DEGs enriched in neurodevelopment, stimulus response, and homeostasis pathways. RT-qPCR confirmed hypothalamic sensitivity to ETM: up-regulation of heat-shock gene HSP70, antioxidant gene GPX1, the inflammatory marker IL-6, and apoptotic genes CASP3, CASP6, CASP9; elevated neurodevelopmental marker DCX, indicative of a stress-responsive neuronal state; and reduced orexigenic neuropeptide AGRP. Conclusions: ETM improves heat tolerance in layers but compromises hatching performance and brain development, with widespread perturbation of hypothalamic stress responses and neurodevelopmental gene networks. These findings elucidate the mechanisms underlying ETM and provide a reference for enhancing thermotolerance in poultry. Full article

This study examines the impact of sociodemographic factors on the attitudes of EU citizens towards animal welfare and their implications for trade policy. Variations in animal welfare legislation across major commercial blocks create ethical and economic challenges, with the EU implementing stringent standards compared with other countries. Data were drawn from the Special Eurobarometer 533 survey, conducted between the 3rd and 26th March 2023, which employed a multistage, clustered sampling method across all 27 EU Member States, yielding a representative sample of 26,368 respondents. The survey collected detailed information on attitudes toward animal welfare alongside sociodemographic characteristics. The results revealed substantial public support for stricter regulations and informative labelling, with attitudes differing with age (p < 0.001), education (p < 0.001), income (p < 0.001), and political orientation (p < 0.001), according to the multinomial regression applied to each of the independent variables. These results emphasise the importance of these factors in shaping consumer expectations. The findings highlight the need for policymakers to integrate ethical and societal values into trade negotiations, ensuring that policies reflect public concerns, promote fair competition, and encourage higher animal welfare standards internationally. Additionally, understanding the perspectives and motivations of livestock industry stakeholders remains critical for implementing effective welfare strategies. By aligning EU trade policy with citizen values and stakeholder practices, it is possible to advance animal welfare globally while balancing economic and ethical considerations and promoting a fair trade. Full article

As the concept of innovative, coordinated, green, open and shared development gains popularity, small- and medium-sized enterprises (SMEs) have come to acknowledge green product innovation (GPI) as essential to sustaining competitive advantage, embedding it within their strategic frameworks. However, most SMEs heavily rely on the continued support of stakeholders, unaware that organisational learning, such as perceived relative advantage (PRA) and innovation capabilities, is the core competitive strategy for achieving green transformation. Drawing on institutional theory and organisational learning theory, this study examines how institutional pressures influence innovation capability and PRA, which in turn drive GPI and corporate reputation. This study analyses data from a survey of 330 Chinese construction SMEs using structural equation modelling. The results show that GPI significantly enhances corporate reputation. Innovation capability and PRA act as mediators in the relationship between institutional pressure and GPI. These findings highlight the importance of organisational learning and explain the critical role of the institutional environment in promoting GPI and thus enhancing corporate reputation. This research provides pathways for SMEs in the construction industry to enhance sustainability while gaining a long-term competitive advantage, contributing to the building of ecological civilisation and a community with a shared future for mankind. Full article

The growing volume of textile waste discarded in the general rest fraction presents a critical challenge to achieving a circular economy. This study provides a comprehensive material characterization of 382.7 kg of textile waste, comprising 1682 individual pieces collected from general waste containers in Catalonia, Spain, with the aim of assessing their potential for high-value recycling. The analysis confirmed this stream consists predominantly of post-consumer textiles (97.3%). Its relevance lies in its composition: mono-component items dominate (54.0% by weight), mainly composed of cotton (51.6%) and polyester (28.4%). This prevalence of mono-material items suggests a substantial, and currently underestimated, volume of recoverable resources and confirms a high recycling potential. However, the study also identifies major challenges for the recovery of this waste stream. On the one hand, it exhibits a high degree of contamination, both in terms of moisture, dirtiness and non-textile disruptors (48.0% by weight), which increases the cost and complexity to the recycling workflow and directly impacts its current viability. On the other hand, the quantitative composition determined by Near-Infrared (NIR) spectroscopy agreed with the ISO 1833 standard in only 37.9% of cases, critically exposing the technological limitations of current automated techniques for quantitative analysis in textiles made of fiber blends. Despite these limitations, the findings are highly relevant for guiding strategic investments in infrastructure, technology, and policy to unlock the full potential of this high-volume waste as a resource. Full article

Advancing rapidly in modern bridge engineering technology, through concrete-filled steel tubular (CFST) arch bridges have achieved widespread application in transportation infrastructure development. Nevertheless, vehicle fires occurring in complicated operational settings may rapidly escalate into major disasters. Fires in oil tankers are particularly dangerous for the safety of bridges. This study examines the fire resistance of through concrete-filled steel tubular (CFST) arch bridges exposed to tanker truck fires. The study formulates a detailed model utilizing Fire Dynamics Simulator (FDS) to simulate fire scenarios, elucidating the spatial temperature distribution characteristics within arch bridge structures. A three-dimensional finite element model established in ABAQUS (Abaqus 2024, Dassault Systèmes Simulia Corp, Providence, RI, USA) is employed to simulate structural responses by analyzing the mechanical behavior of key components under different fire conditions. Practical fire resistance design recommendations for extreme tanker truck fire scenarios are ultimately proposed. Numerical results demonstrate that structural components near the fire source (such as transverse bracings, hangers, and fire-exposed arch surfaces) experience significantly higher temperatures than other regions. Notable temperature gradients developing along hangers and arch ribs in fire-affected zones are observed, while substantial cross-sectional temperature gradients occurring in these components under tanker truck fires reveal their damage evolution mechanisms. The fire exposure scenario at the quarter-point of the midspan is identified as the most critical fire exposure scenario for through CFST arch bridges under tanker truck fires. Under this extreme scenario, the deflection on the fire-exposed side of the global structure exhibits a significant three-stage distribution characteristic: an initial ascending phase around 0–800 s, followed by a sharp descending phase during 800–1100 s, and then a stabilization trend. A fire resistance limit criterion based on component failure ($t_{f3}$ = 853.43 s) is established, and a global fire resistance limit assessment methodology for through CFST arch bridges under extreme tanker truck scenarios is proposed. Full article

The nexus of rapid demographic transition and underdeveloped geriatric infrastructure poses a critical, yet understudied challenge in Sub-Saharan Africa (SSA). As global life expectancies rise, SSA’s older population is projected to triple by 2050, intensifying the need for sustainable age-friendly environments (AFEs) and robust healthy ageing interventions. Informal or family caregiving structures, while vital, are under strain from rapid urbanisation and shifting social dynamics, creating a compelling gap between need and provision. This expert viewpoint draws on the authors’ professional and scholarly experience regarding population ageing, AFEs, and healthy ageing to provide a comprehensive outlook on these issues in SSA. Selective literature searches were conducted in Google Scholar, Scopus and PubMed using targeted keywords and MESH terms, including “ageing in Africa”, “ageing in Sub-Saharan Africa”, “healthy ageing in Africa”, “healthy ageing in Sub-Saharan Africa”, “population ageing in Africa”, “population ageing in Sub-Saharan Africa”, “age-friendly environment in Africa”, and “age-friendly environment in Sub-Saharan Africa.” The authors argue that rapid population ageing in SSA is outpacing existing informal care arrangements, necessitating a strategic shift towards the development of age-friendly environments and more coordinated healthy ageing interventions to bridge the widening gap between demographic change and geriatric support systems. This paper underscores the necessity of proactive, evidence-based policy implementation to secure the well-being of SSA’s burgeoning older population. Full article

The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system stability. Simulation results demonstrate that the proposed fuzzy–PID controller effectively maintains a steady charging voltage and minimizes power losses by modulating switching frequency. Additionally, the system shows resilience to rapid changes in irradiance and load, improving energy efficiency and extending battery life. This hybrid approach outperforms conventional PID and static control methods, offering enhanced adaptability for renewable-integrated EV infrastructure. The study contributes to sustainable mobility solutions by optimizing the interaction between solar energy and EV charging, paving the way for smarter, grid-friendly, and environmentally responsible charging networks. These findings support the potential for the real-world deployment of intelligent controllers in EV charging systems powered by renewable energy sources This study is purely simulation-based; experimental validation via hardware-in-the-loop (HIL) or prototype development is reserved for future work. Full article

The increasing complexity of multi-scale energy systems makes robust anomaly detection essential to ensure system resilience and operational continuity. Recent advances in DL enable effective modeling of high-dimensional, non-linear energy data by capturing latent spatio-temporal patterns. In this paper, we proposed an optimized deep reinforcement learning–generative adversarial network (ODRL-GAN) framework for reliable anomaly detection in multi-scale energy systems. The integration of DRL and GAN brings a key innovation: while DRL enables adaptive decision-making under dynamic operating conditions, GAN enhances detection by reconstructing normal patterns and exposing subtle deviations. To further strengthen the model, a novel multi-objective chimp optimization algorithm (NMOChOA) is employed for hyper-parameter tuning, improving accuracy, and convergence. This design allows the ODRL–GAN to effectively capture high-dimensional spatio-temporal dependencies while maintaining robustness against diverse anomaly patterns. The framework is validated on two benchmark datasets, PSML and LEAD1.0, and compared against state-of-the-art baselines including transformer, deep belief network (DBN), convolutional neural network (CNN), gated recurrent unit (GRU), and support vector machines (SVM). Experimental results demonstrate that the proposed method achieves a maximum detection accuracy of 99.58% and recall of 99.75%, significantly surpassing all baselines. Furthermore, the model exhibits superior runtime efficiency, faster convergence, and lower variance across trials, highlighting both robustness and scalability. The optimized DRL–GAN framework provides a powerful and generalizable solution for anomaly detection in complex energy systems, offering a pathway toward secure and resilient next-generation energy infrastructures. Full article

Driven by the dual imperatives of global energy transition and sustainable development goals, lithium-ion batteries, as critical energy storage carriers, have seen the assessment of their lifecycle reliability and durability become a core issue underpinning the sustainable operation of clean energy systems. Grounded in a multidimensional perspective of sustainable development, this study aims to establish a quantifiable and monitorable battery reliability evaluation framework to address the challenges to lifespan and performance sustainability faced by batteries under complex multi-stress coupled operating conditions. Lithium-ion batteries are widely used across various fields, making an accurate assessment of their reliability crucial. In this study, to evaluate the lifespan and reliability of lithium-ion batteries when operating in various coupling stress environments, a multi-stress collaborative accelerated model(MCAM) considering interaction is established. The model takes into account the principal stress effects and the interaction effects. The former is developed based on traditional acceleration models (such as the Arrhenius model), while the latter is constructed through the combination of exponential, power, and logarithmic functions. This study firstly considers the scale parameter of the Weibull distribution as an acceleration effect, and the relationship between characteristic life and stresses is explored through the synergistic action of primary and interaction effects. Subsequently, a multi-stress maximum likelihood estimation method that considers interaction effects is formulated, and the model parameters are estimated using the gradient descent algorithm. Finally, the validity of the proposed model is demonstrated through simulation, and numerical examples on lithium-ion batteries demonstrate that accurate lifetime prediction is enabled by the MCAM, with test duration, cost, and resource consumption significantly reduced. This study not only provides a scientific quantitative tool for advancing the sustainability assessment of battery systems, but also offers methodological support for relevant policy formulation, industry standard optimization, and full lifecycle management, thereby contributing to the synergistic development of energy storage technology across the economic, environmental, and social dimensions of sustainability. Full article

With the rapid development of the automotive industry, particularly the year-on-year growth in sales of new energy vehicles, automobile outer panel materials have shown a trend toward high-strength lightweight solutions. Regarding steel for outer panels, existing research has paid less attention to the UF steel that has entered the market in recent years. Moreover, studies on the similarities and differences in deformation behavior among various outer panel steels are lacking. In this study, room-temperature tensile tests at 5% and 8% strain were conducted in accordance with the stamping deformation range on commonly used ultra-low carbon automotive outer panel steels of comparable strength grades, namely, UF340, HC180BD, and DX53D+Z. Prior to deformation, the three materials exhibited similar texture components, predominantly characterized by the γ-fiber texture beneficial for deep drawing, and their room-temperature tensile deformation behaviors were fundamentally identical. After transverse tensile deformation, the textures concentrated towards {111}<112> texture. After 8% deformation, UF340 demonstrated a more rapid stress increase and a higher degree of work hardening. This phenomenon is attributed to the presence of the precipitate free zone (PFZ) near grain boundaries in the UF340, which facilitates the continuous generation of dislocations at grain boundaries during deformation, leading to a rapid increase in dislocation density within the grains. Consequently, this induces accelerated work hardening under small-strain conditions. This mechanism enables UF steels to achieve a strength level comparable to that of bake-hardened (BH) steels, exhibiting a significant performance advantage. Full article

In the context of sustainable development and the growing emphasis on decent work and productivity, understanding the human factors that shape employee performance has become a central concern for organizations and policymakers. While intelligence has long been linked to work outcomes, existing research remains fragmented and predominantly focused on single dimensions, offering limited insight into how different forms of intelligence interact across employees’ career life cycles. Addressing this gap, the present study advances a multi-dimensional perspective on intelligence and examines its relevance for sustainable employee productivity, thereby contributing to the human resource management literature and to the achievement of Sustainable Development Goal 8 (Decent Work and Economic Growth). The study assesses the impact of five types of intelligence (cognitive—IQ, emotional—EQ, physical—PQ, vitality—VQ, and social—SQ) on employee productivity across distinct career life cycle stages. The research was conducted in two phases: (1) measurement of intelligence dimensions and employee productivity using standardized psychometric instruments, including MSCEIT V2.0, the Guilford–O’Sullivan test, the Eysenck test, the Chekhov vitality method, and biological age indicators; (2) statistical analysis of the relationships between intelligence, productivity, and career stages using open-source Python tools. Empirical data were collected from enterprises in the Ukrainian construction industry. The findings demonstrate that the influence of intelligence on productivity varies across career stages. Emotional intelligence emerges as a consistently significant factor throughout the employee life cycle, while other intelligence dimensions exhibit stage-specific effects. These results confirm the dynamic and non-uniform nature of intelligence–productivity relationships. The study provides practical insights for sustainable human resource management by highlighting the need for stage-sensitive development strategies that align intelligence profiles with career phases. Implementing such targeted approaches can enhance employee productivity, organizational effectiveness, and long-term economic sustainability, thereby supporting progress toward SDG 8. Full article

The health and operational continuity of emergency responders are fundamental pillars of sustainable and resilient disaster management systems. These personnel operate in high-risk environments, exposed to intense physical, environmental, and psychological stress. This makes it crucial to monitor their health to safeguard their well-being and performance. Traditional methods, which rely on intermittent, voice-based check-ins, are reactive and create a dangerous information gap regarding a responder’s real-time health and safety. To address this sustainability challenge, the convergence of the Internet of Things (IoT) and wearable biosensors presents a transformative opportunity to shift from reactive to proactive safety monitoring, enabling the continuous capture of high-resolution physiological and environmental data. However, realizing a field-deployable system is a complex “system-of-systems” challenge. This review contributes to the field of sustainable emergency management by analyzing the complete technological chain required to build such a solution, structured along the data workflow from acquisition to action. It examines: (1) foundational health sensing technologies for bioelectrical, biophysical, and biochemical signals; (2) powering strategies, including low-power design and self-powering systems via energy harvesting; (3) ad hoc communication networks (terrestrial, aerial, and space-based) essential for infrastructure-denied disaster zones; (4) data processing architectures, comparing edge, fog, and cloud computing for real-time analytics; and (5) visualization tools, such as augmented reality (AR) and heads-up displays (HUDs), for decision support. The review synthesizes these components by discussing their integrated application in scenarios like firefighting and urban search and rescue. It concludes that a robust system depends not on a single component but on the seamless integration of this entire technological chain, and highlights future research directions crucial for quantifying and maximizing its impact on sustainable development goals (SDGs 3, 9, and 11) related to health, sustainable cities, and resilient infrastructure. Full article

This paper investigates how CAP investment subsidies influence agricultural productivity in Greece using time-series data from 2000 to 2023. The analysis focuses on whether subsidies intended to stimulate investment in agricultural infrastructure and technology have a tangible effect on productivity. Employing econometric methods such as the Vector Autoregressive Model (VAR) and Granger causality testing, this study explores the short- and long-term impacts of these subsidies. Findings suggest that CAP subsidies have a significant and positive influence on agricultural productivity, with more notable effects in regions that have adopted technological advancements. These results provide valuable insights for policymakers looking to optimise CAP reforms and ensure sustainable agricultural growth in Greece. Full article

E-bikes are emerging as a competitive alternative to private cars in both urban and suburban contexts, enhancing accessibility to daily amenities and aligning with the proximity-oriented principles of X-minute city development. However, empirical knowledge remains limited regarding e-bike adopter profiles, trip purposes, influencing factors, and modal substitution patterns, particularly in urban Global South contexts. This exploratory pilot study employs correlation analysis and exploratory factor analysis to examine the sociodemographic characteristics of e-bike users in Surabaya, identify trip behavior patterns, and uncover potential determinants associated with e-bike usage within the X-minute city framework. Based on a sample of 71 e-bike users, the preliminary findings reveal notable socioeconomic patterns in e-bike adoption, with lower-income inner-city residents, particularly women in informal employment, emerging as early adopters. Additionally, two potential influence dimensions are identified: utilitarian trip chaining and active mobility infrastructure. While these findings require validation through larger-scale studies, they suggest potential for e-bikes to expand feasible X-minute city catchments and support low-carbon mobility transitions in similar Global South contexts. Full article

Flooding in Bengaluru areas such as Kodigehalli, Hebbal, and Nagavara has led to severe disruptions, including traffic congestion, infrastructure damage, and health risks. To address this issue, we have proposed a smart flood alert and communication system, integrating Internet of things (IoT), artificial intelligence (AI), and smart infrastructure solutions. The system helps by giving information about real-time water level sensors, AI-driven flood prediction models, automated emergency coordination, and a mobile-based citizen reporting platform. Through cloud-based data processing, predictive analytics, and smart drainage management, this solution aims to enhance early warnings, reduce emergency response time, and improve urban flood resilience. It yields up to an 80% reduction in alert delays, a 50% faster emergency response, and improved community safety. This project seeks collaboration with government agencies, technology firms, and community stakeholders to implement a pilot plan, ensuring a scalable and sustainable flood mitigation strategy for Bengaluru. Full article

The CE has attracted widespread interest due to the urgent issues of climate change and resource depletion. The implementation of the CE within small- and medium-sized enterprises (SMEs) is crucial, as they play a major economic role globally, including significant contributions to CO₂ emissions and resource depletion in developing countries. The existing literature has primarily examined the factors that hinder and enable the adoption of the CE in SMEs in developed nations. The most critical gap in the previously mentioned literature is the very slow adoption of the CE in developing nations, attributable to a limited understanding of the drivers of its adoption. Through a systematic literature review (PRISMA 2020) and content analysis, we identified 52 potential drivers, which were refined to 33 final drivers using the Fuzzy Delphi Method (FDM) with 20 experts from six developing countries. These drivers were categorized using the Triple Bottom Line (TBL) framework and prioritized through IVIF-BWM with 15 experts. The results show that 33 drivers of CE adoption are classified into three groups: economic, environmental, and social/organizational. Environmental drivers accounted for 53.02% of the total weight, followed by social/organizational (24.85%) and economic (22.14%) drivers. The three most significant drivers identified were from the environmental drivers category, while purely financial drivers ranked notably lower than expected. These findings suggest that policymakers in developing countries should prioritize regulatory frameworks and environmental infrastructure over financial incentives alone and that SME owners prioritize resource security and compliance for CE transition. Full article

This paper examines how county-level government in China formulates and implements solar photovoltaic (PV) policies through an adaptive-governance lens, using Lin’an District (Hangzhou) as a case study. Drawing on multi-level policy document analysis and 30 semi-structured interviews with government officials, developers, grid actors and experts, we identify three stages of local PV development (rooftop diffusion; rapid utility-scale expansion; and market-oriented regulatory adjustment). Key governance innovations include a district PV task force, an industry alliance, and a dual acceptance safety mechanism that together accelerated deployment while managing technical and political risks. We show how adaptive governance operates within an authoritarian, hierarchical system by combining top-down targets with bottom-up development and stakeholder coordination. The findings illuminate practical trade-offs between market liberalization and regulatory control, and provide transferable lessons for other developing countries pursuing decentralized renewable energy transitions. Full article

Unmanned aerial vehicles (UAVs) are widely utilized for environmental monitoring, precision agriculture, infrastructure inspection, and various defense missions, including reconnaissance and surveillance. Their cybersecurity is essential because any compromise of communication, navigation, or control systems can cause mission failure and introduce significant safety and security risks. Therefore, this paper examines the existing literature on UAV cybersecurity and highlights that most previous surveys focus on listing different types of attacks or communication weaknesses, rather than evaluating the problem from a control systems perspective. Considering control systems is important because the safety and performance of a UAV depend on how cyberattacks affect its sensing, decision-making, and actuation loops; modeling these attacks and their impact on system behavior provides a clearer foundation for designing secure, resilient, and stable control strategies. Based on a comprehensive review of the literature, it presents a mathematical framework for characterizing common cyberattacks on UAV communication and sensing layers, including time-delay switch, false data injection, denial of service, and replay attacks. To demonstrate the impacts of these attacks on UAV control systems, a simulation of a two-UAV leader-follower multi-agent system is conducted in MATLAB. Defense algorithms from the existing literature are then organized into a hierarchical framework of prevention, detection, and mitigation, with detection and mitigation further categorized into model-based, learning-based, and hybrid approaches that combine both. The paper concludes by summarizing key findings and highlighting challenges with current defense strategies, including those insufficiently addressed in existing research. Full article

Elevators are critical vertical transportation systems in modern urban infrastructure; however, their intricate mechanical and electrical configurations render them highly susceptible to safety-critical failures. Although various automated diagnostic techniques have been proposed, many data-driven approaches exhibit limited generalizability due to their insufficient consideration of physical fault mechanisms and strong dependence on facility-specific training data. To overcome these limitations, this study presents a rule-based automated diagnostic framework for elevator state recognition that prioritizes reliability, real-time performance, and interpretability. The proposed approach explicitly integrates physically meaningful fault characteristics and dominant frequency components into the diagnostic process, and employs predefined expert rules derived from established standards to classify fault states in an automated manner. The effectiveness of the proposed method is verified using real operational data collected from an in-service elevator, demonstrating improved diagnostic accuracy and computational efficiency compared to conventional manual inspection procedures. The proposed framework provides a practical and scalable solution for intelligent elevator condition monitoring and is expected to serve as a foundational technology for future smart maintenance and preventive safety systems. Full article

Cytospora chrysosperma is a common opportunistically parasitic fungus that mainly infects forest trees, severely restricting the development of the fruit and forest industry. Surfactin is a secondary metabolite produced by Bacillus species and exhibits antifungal activity; Although the core antifungal mechanism of surfactin against plant pathogens has been extensively studied, our study found that surfactin can target the tricarboxylic acid cycle of C. chrysosperma. This study aimed to investigate the potential mechanism underlying the inhibitory effect of surfactin on C. chrysosperma. The results showed that surfactin had a significant inhibitory effect on C. chrysosperma, with a half-maximal effective concentration of 0.787 ± 0.045 mg/mL and a minimum inhibitory concentration of 2 mg/mL. Morphological observations revealed that surfactin significantly disrupted the morphology and ultrastructure of C. chrysosperma hyphae. FDA/PI staining indicated that surfactin affected the cell membrane integrity of C. chrysosperma, while DCFH-DA fluorescent staining and antioxidant enzyme activity assays demonstrated the accumulation of reactive oxygen species in hyphal cells following surfactin treatment. Additionally, the reduction in adenosine triphosphate content, as well as the decreased activities of ATPase and succinate dehydrogenase, suggested that energy production might be inhibited. Finally, MDC staining showed the occurrence of autophagosomes in C. chrysosperma hyphae after surfactin treatment, which may lead to hyphal death. Transcriptome analysis revealed that surfactin impaired the normal biosynthesis of the C. chrysosperma cell membrane and interfered with the tricarboxylic acid cycle by binding to citrate synthase, resulting in intracellular energy metabolism disorders. This study provides new insights into the potential mechanism by which surfactin inhibits hyphal growth of C. chrysosperma. Full article

Fluidized bed drying has been widely applied in the food industry due to its high heat and mass transfer rates. In this study, the impact of drying temperatures (50, 60, 70 and 80 °C) in a fluidized bed on the physicochemical, functional, morpho-structural, and thermal properties of avocado seed starch was evaluated. The process yield for all temperatures ranged from 52.3 to 58.5% (p > 0.05), with a starch content of 59.20–60.9 g/100 g, amylose content of 28.85–31.84 g/100 g, and amylopectin content of 29.13–30.37 g/100 g. Additionally, all samples showed high water, milk, and oil absorption capacity (>90%), low solubility (5.22–8.35%), good flow characteristics, and swelling power greater than 50%. There was also a greater release of water (syneresis) after 168 h of storage, regardless of the drying temperature, which likewise did not influence the texture parameters. The granules had a smooth surface, without cracks or cavities, predominantly oval and partially rounded, being classified as type B. In the FT-IR analysis, no new functional groups were observed, only a reduction in peak intensity with increasing drying temperature. Finally, the thermal properties indicated high conclusion temperatures (>130 °C), with gelatinization enthalpy in the range of 14.18 to 15.49 J/g, reflecting its thermal resistance and structural integrity under heat conditions. These results demonstrated that fluidized bed drying is an alternative technique for drying avocado seed starch pastes. Full article

In the era of the digital economy, AI technology is regarded as a key driver in promoting the development of new quality productive forces of enterprises. Based on the theories of creative destruction and resource allocation, this study selects Chinese enterprise-level data from 2009 to 2022 as the research sample, constructs enterprise new quality productivity indicators through text analysis and machine learning methods, and explores the impact of artificial intelligence on new quality productivity. The study results show that AI technology significantly improves the new quality productivity of enterprises. Further research found that enterprise director background, digital industry agglomeration and financial agglomeration positively moderated the relationship between AI and new quality productivity. Heterogeneity analysis shows that the enabling effect of AI technology on new quality productivity is more significant in high-tech enterprises, state-owned enterprises and enterprises with strong policy support. Through empirical analysis, this study verifies the facilitating effect of AI technological innovation on enterprises’ new quality productivity, which provides important insights for enterprises in emerging economies to achieve the development of new quality productive forces in digital transformation. Full article

Water reservoirs are critical components of hydrological systems that mitigate floods and droughts, but their long-term performance under climate change and variable socioeconomic conditions remain insufficiently documented. This study examines the Bahlui River basin (northeastern Romania), where 17 reservoirs constructed mainly between the 1960s and 1980s have been operational for more than five decades. Using the most recent technical reservoir reports, land-use evolution, and present operational functions, the contribution of man-made reservoirs to flood attenuation and drought buffering over time was appraised. Flood mitigation is the most consistent and reliable function, with peak-flow reductions commonly exceeding 60–90% of design discharges at the basin scale. Engineered drought mitigation functions (irrigation and industrial water supply) have decreased significantly as a result of socioeconomic changes started in 1989. However, the gradual expansion of green infrastructure, such as wetlands and riparian vegetation, has improved passive water retention and low-flow buffering capacity. These unanticipated developments have resulted in variable levels of hybrid hydrological resilience. The findings show that, while artificial reservoirs have strong flood-control capacity over long periods of time, their contribution to drought mitigation is increasingly dependent on the integration of ecological components, emphasizing the importance of green-gray interactions in long-term reservoir management. Full article

In the context of advancing sustainable urban development, the spatial organization of industries plays a critical role in shaping environmental quality, economic vitality, and public health. This study examines the health effects of furniture enterprises agglomeration in Chinese cities, using a unique dataset combining point-of-interest (POI) big data and micro-level survey responses from 13,217 individuals. The results show that a one-unit increase in furniture enterprises agglomeration intensity is associated with a 0.656-unit improvement in physical health and a 0.060-unit improvement in mental health. These benefits are driven by three synergistic mechanisms: environmental improvement, income growth, and enhanced public health services. However, the health gains are unevenly distributed, with greater benefits observed in less-developed cities and among vulnerable groups such as low-skilled and middle-aged workers. We further reveal divergent effects between specialized and diversified agglomeration patterns, moderated by environmental regulation. Our findings underscore the need for health-oriented industrial policies that align with sustainable urban planning, emphasizing spatial adaptation, targeted support for vulnerable populations, and innovative regulatory approaches to foster both industrial growth and resident well-being. Full article

To reduce the computational cost associated with traditional moving heat source methods, a segmented approach is proposed for simulating the welding process of T-joints in large-scale infrastructure steel modules. Firstly, the hole-drilling method was employed to measure the welding residual stresses in a 2400 mm T-joint. Subsequently, a three-dimensional finite element model was established in ABAQUS, and a user-defined subroutine for the segmented moving heat source was developed in Fortran to calculate the welding residual stresses. The numerical simulation results were compared with experimental data, showing high consistency and further validating the accuracy of the finite element model. Furthermore, the distribution patterns of residual stresses along the thickness direction and the effects of different welding sequences on temperature, stress, and deformation were investigated to optimize the welding sequence. The results indicated that the residual stresses along the weld seam exhibited a compressive–tensile–compressive distribution, with the maximum tensile stress reaching approximately 347 MPa. Additionally, the simulation results demonstrated that the double-ellipsoidal heat source method was computationally intensive and failed to provide accurate results for long weld seams, whereas the segmented moving heat source approach reduced the computation time to only 38 h. Moreover, different welding sequences had a significant impact on residual stresses and deformation. Through comprehensive analysis, it was found that Case 1 (sequential welding in the forward direction) achieved the best performance in minimizing welding residual stresses and deformation. Full article

As the digital economy increasingly integrates with the real economy, the goal of high-quality economic development in China has become increasingly clear. Promoting high-quality regional economic development through the integration of the digital and real economies holds significant practical importance for achieving Chinese modernization. This study selects panel data from 2013 to 2023 for 31 provinces, autonomous regions, and municipalities directly under the central government in China. It employs the entropy method to measure the development levels of both the digital and real economies in each province. It uses a coupling coordination degree model to gauge their level of integration. By constructing bidirectional fixed-effects models, mediating effect models, and spatial econometric models, this study explores the impact of Digital–real Integration (DRI) on regional High-Quality Development (HQD). The findings reveal that DRI promotes high-quality regional economic development, with a 1% increase in DRI leading to a 4.810% increase in high-quality regional economic development. Meanwhile, this effect exhibits significant regional disparities. During this process, industrial structure upgrading and scientific and technological innovation serve as mediating factors, with coefficients of 1.249 and 10.562, respectively, for every 1% increase in DRI. Moreover, DRI exhibits significant spatial spillover effects, benefiting neighboring regions. Based on these findings, the paper proposes targeted recommendations, including strengthening digital infrastructure to lay a solid foundation for integrated development, implementing an innovation-driven strategy to master core technologies, optimizing production factor allocation to amplify DRI’s driving force, breaking regional economic barriers, and adopting dynamic, differentiated development strategies tailored to local conditions. These measures aim to fully harness DRI’s potential in advancing high-quality regional economic growth, offering empirical insights for coordinated regional development. Full article