Search Results (37,103)

This study presents a systematic bibliometric analysis and critical review of assessment indicators and multi-criteria decision-making methods for rural energy systems from 2010 to 2025. It examines the evolving definitions and regional variations in these indicators and methods. The research hotspots of rural energy systems have shifted from basic rural electrification to multi-dimensional assessment indicators and hybrid multi-criteria decision-making methods. The assessment indicators for rural energy systems demonstrate a marked imbalance, dominated by economic and technical dimensions. Specifically, economic evaluations for rural energy systems frequently utilize net present cost and levelized energy cost, shifting from static capital comparisons to comprehensive lifecycle assessments. Meanwhile, loss of power supply probability is identified as the primary inherent constraint among technical assessment indicators for rural energy systems. Geographically, assessment indicators for rural energy systems priorities exhibit significant divergence. Developing regions prioritize basic power supply and affordability, whereas developed regions focus on grid stability and market risk resilience. In addition, environmental evaluations for rural energy systems remain fixated on carbon emissions. Developed nations emphasize global climate benefits, while developing nations focus on localized dividends like indoor air quality improvement. Critically, despite an increasing focus on rural livelihoods, social indicators remain systematically marginalized in rural energy systems, leading to the neglect of local requirements and increasing technical risks. The field of rural energy system assessment is advancing toward multi-criteria decision-making indicators. Future methodologies must integrate robust, dynamic adaptive mechanisms that respond to evolving developmental priorities in order to effectively address inherent data scarcity and complex socio-economic uncertainties of rural energy systems. Full article

Heavy industrial vehicles operating in aluminum smelters are exposed to severe thermal, mechanical, and environmental stresses, which increase the likelihood of failure and unplanned downtime. This study proposes an Economic Risk Priority Number (ERPN) framework to address the limitations of the conventional Risk Priority Number (RPN) used in Failure Mode and Effects Analysis (FMEA). A five-year maintenance dataset (2019–2024), comprising 2303 corrective work orders from 58 heavy equipment units, was analyzed. The classical RPN approach prioritized failure modes mainly according to occurrence and detectability, identifying the wheel and hydraulic subsystems as the most critical. In contrast, the proposed ERPN framework integrates economic impact through maintenance cost, manpower cost, and production loss, resulting in the engine subsystem being ranked as the most critical. The most severe engine failure caused an estimated financial loss of approximately USD 1.92 million due to extended downtime and repair costs. Root cause analysis identified coolant loss, low oil pressure, and excessive vibration as the main contributors to catastrophic engine failure, supported by diagnostic evidence and repeated alarm patterns. Statistical validation performed using the Kruskal–Wallis test confirmed significant differences among subsystem risk distributions for both RPN (χ² = 846.07, df = 4, p < 0.0001) and ERPN (χ² = 131.69, df = 4, p < 0.0001). The findings demonstrate that ERPN provides a more economically meaningful framework for maintenance prioritization and offers a practical decision-support tool for reducing operational risk in aluminum smelter fleets. Full article

Copper (Cu) contamination poses severe threats to agricultural productivity and food safety, particularly affecting economically important crops such as rapeseed (Brassica napus L.). This study investigated the protective effects of selenium nanoparticles (SeNPs) against Cu toxicity in four B. napus cultivars. Exposure to Cu (200 μM) caused severe reductions in growth and photosynthetic efficiency while significantly elevating oxidative stress markers across all cultivars. Application of SeNPs (25 μM) effectively mitigated these adverse effects, improving biomass, restoring chlorophyll content, and enhancing photosynthetic performance compared to Cu-stressed plants. SeNP treatment significantly enhanced antioxidant enzyme activities, with corresponding upregulation of antioxidant gene expression. Secondary metabolite profiling revealed cultivar-specific responses, with sensitive cultivar Zheda 622 exhibiting metabolic adaptation and higher volatile organic compound (VOC) accumulation, while tolerant cultivar Zheda 635 maintained metabolic stability. PCA analysis demonstrated distinct metabolic clustering patterns, reflecting differential stress-responsive strategies. The study demonstrates that SeNPs attenuate Cu-induced toxicity through integrated mechanisms encompassing diminished Cu acquisition, augmented antioxidant defense systems, and comprehensive metabolic reprogramming. Cultivar-specific responses highlighted substantial genetic variation in tolerance mechanisms across B. napus genotypes. These findings substantiate SeNPs as a viable and efficacious nanomaterial for sustainable agronomic management in Cu-contaminated edaphic environments. The approach offers dual benefits of improved crop productivity and reduced Cu accumulation, ensuring enhanced food safety. Full article

Tourism development has, in the past decades, brought new opportunities and challenges to residents’ livability in urban destinations, due to mobility, landscape and environmental quality effects. Quality of life may comprise, inter alia, a clean environment, historic atmosphere, cultural identity or a relaxed inner city. In recent years, uncontrolled tourism has led to ‘overcrowding’ and has prompted ‘mixed feelings’ on tourism among residents, despite clear economic benefits. Clearly, tourism takes place in a conflicting domain with different local actors. There is a rising fear in many historic cities that the long-run effects of mass tourism may be detrimental to the locals. This study seeks to examine local tensions among different interest groups in the tourism sector as a result of negative externalities such as decay of local livability, traffic congestion, or quality decline in the supply of tourism attractions. In this paper a novel supply-oriented concept, Local Leisure Resources, is put forward to uncover the externality effects of tourism and tourism mobility on urban livability, as well as the moderating effect of intra-city destination mobility of visitors. This concept will be tested for sustainability challenges in urban areas in China. Our empirical modeling analysis, based on data from 247 Chinese tourist places over the years 2008–2018, shows that local leisure resources have a clear mediating effect on the relationship between tourist visits and quality of life in urban destinations. The internal mobility appears to have a positive moderating effect on the role of diverse local leisure resources in supporting place-based livability of various local groups of actors involved. This research highlights the complex mechanism of tourism development on urban livability and environmental landscapes from the new concept of local leisure resources. It provides a solid basis and reference for sustainable development strategies for local policy actors regarding local destination livability. Full article

Offshore industries depend solely on diesel-based power generation systems or mainland grids, which are expensive and carbon-intensive. The demand for renewable energy-based offshore DC microgrids (MGs) has significantly increased due to rising fuel prices, high costs of fuel transportation and storage, extreme operation and maintenance expenses, and associated carbon emissions. This research study optimises the size of an offshore DC MG that integrates wave, solar, energy storage, and diesel, utilising real-world data from a specific geographical location (latitude −33.525587 and longitude 114.772211), thereby accurately representing the availability of renewable energy sources. An algorithm is designed to optimise the utilisation of highly variable renewable sources via battery-based energy management, resulting in optimal energy dispatch. Utilising economic performance metrics, such as levelised cost of energy (LCoE) and net present value (NPV), this research aims to minimise the energy, operating, and greenhouse gas emission costs while maximising the economic feasibility of the system. A sensitivity analysis is performed to determine the impact of fuel prices, discount rates, and system lifespans on the feasibility of the system. The findings demonstrate that the proposed renewable-based offshore DC MG can substantially reduce fuel consumption (93%), operational expenses (77.56%), and carbon emissions (89.50%) compared with a diesel-only system for offshore platforms, while improving the sustainability and reliability of power supply for aquaculture and marine activities. In addition, the proposed renewable-energy-based offshore DC MG achieves a lower LCoE (0.5649 $/kWh) and a higher NPV (2.987 × ${10}^4$ $) than a conventional diesel-based power generation system for offshore industries. The results provide a decision-making framework for the design and implementation of renewable energy-based offshore DC MGs. Full article

Background: Physical activity (PA), physical fitness (PF), and motor skills (MS) play crucial roles in overall health and well-being, particularly in early childhood, when habits that affect future health are formed. Methods: This study, involving 299 children (150 boys, 149 girls, mean age 6.9 ± 0.96 years), explored the variance explained by external factors such as socioeconomic status (SES), body composition (BC), sex, and geographical location on motor-related physical fitness (MRPF) and health-related physical fitness (HRPF) in children. Using a variety of assessments, including demographics, anthropometric data, BIA, ActiGraphs, the 20 m shuttle run, 10 and 20 m speed tests, and test items from the Körperkoordinations test für Kinder (KTK) and the TGMD-2, a multiple stepwise regression analysis using SPSS (v 28.0) identified the associated factors. Results: The variables tested show modest explained variance for HRPF, MRPF, and MS, with the largest cumulative explained variance of 26.4%. The explained variances for MRPF and MS were lower (medium to small) than the significant, medium-to-large, explained variances for HRPF. Body fat percentage (BF%), moderate-to-vigorous physical activity (MVPA), parental education and income, and BMI emerged as substantial contributors to HRPF, explaining 12.1% to 26.4% of the variance. Sex, BF%, and quintile status were the most influential associated factors for MRPF, and for MS, BMI and sex emerged as the strongest contributors. Conclusions: These findings underscore the importance of holistic approaches that consider individual factors, such as MVPA, body composition (BC), PA levels, sex, and broader social and economic contexts, to promote children’s well-being. The study emphasises the need for comprehensive strategies to address the multifaceted associations with children’s physical development. Full article

Neighborhood-scale decarbonization is essential to achieving urban climate neutrality, yet existing methods often rely on complex, technology-intensive models that are difficult to implement in aging urban areas. This study introduces a simplified smart analysis method and decision-support framework to facilitate net-zero energy and emissions transitions at the neighborhood level through impactful, low-disruption interventions. Applied to a mixed-use neighborhood in Izmir, Türkiye, part of the European Union Mission for Climate-Neutral and Smart Cities, the methodology evaluates four intervention strategies: rooftop Photovoltaic systems, air-source heat pumps, solar-powered LED street lighting, and repurposing idle public spaces. The analysis quantifies energy demand, CO₂ emissions, and economic performance based on standardized data and incremental renovation scenarios. Results show that a gradual renovation approach, with a 10% annual replacement rate for heating systems, full rooftop Photovoltaic deployment, and street lighting retrofitting, can achieve a net-zero energy balance in 6–7 years. Redirecting fossil fuel and electricity subsidies to support renewable technologies makes these interventions economically viable within the same period. This framework demonstrates that neighborhood-scale climate neutrality can be attained without extensive structural changes, providing a replicable tool for cities with similar conditions aiming to meet European Union climate targets. Full article

In Seoul, neighborhood-scale commercial districts, known as Golmok commercial districts, are small-scale retail areas focused on local daily life but also play a significant role in the city’s economy. Existing classification strategies for supporting Seoul’s Golmok commercial districts primarily rely on static, administrative data, failing to sufficiently capture actual citizen usage patterns. This deficiency limits the effectiveness of revitalization efforts. This study employs a two-timescale analysis of de facto population data to build a more dynamic typology of Seoul’s Golmok commercial districts. An unsupervised machine learning approach, specifically time-series K-means clustering, was applied to both weekly (short-term) and multi-year (long-term) time series data. This enabled us to classify 1090 districts into 16 distinct types. This more granular typology reveals significant heterogeneity masked by the Seoul Metropolitan Government’s current system, which groups these districts into only four broad categories. Our results show that while a minority of districts maintain stable activity, many exhibit patterns of long-term decline or significant fluctuation, underscoring the diverse and dynamic nature of these areas. The short-term analysis also captures temporal variations in population activity. The proposed typology may offer a foundation for near real-time monitoring and more proactive policy interventions to support urban economic sustainability. Full article

Macrobrachium nipponense is one of the major economic species in freshwater aquaculture in China. As an important component of aquaculture ecosystem, microorganisms participate in key processes such as material cycling and water quality regulation, exerting significant impacts on the cultured organisms. In this study, high-throughput sequencing of the 16S rRNA, 18S rRNA, and ITS regions was employed to comparatively analyze the characteristics of microbial communities before and during the cultivation period, combined with correlation analysis of environmental factors. The results showed that the dominant microbial groups in the prawn pond water were Proteobacteria, Cyanobacteria, Ascomycota, Basidiomycota, Chlorophyta, and Arthropoda. The microbial community structure differed significantly between the pond water during the culture period and the pre-culture external river baseline: manifested as an increase in the relative abundances of Cyanobacteria, Chytridiomycota, and zooplankton, and a decrease in the abundances of Ascomycota, Basidiomycota, and Chlorophyta. Analysis of LEfSe revealed that the low-nitrogen pond was enriched with taxa such as Muribaculaceae; the high-nitrogen pond was enriched with taxa such as Cyanobium_PCC-6307; and the control pond was enriched with taxa such as CL500-29_marine_group. Functional prediction indicated that heterotrophic metabolism-related functions dominated the microbial communities. The abundance of fungal pathogens was significantly higher in the low-nitrogen group, while potential pathogenic bacteria were enriched in the high-nitrogen group. Ammonia nitrogen is a core environmental factor associated with differences in microbial community structure. The findings of this study can provide theoretical references and data support for water quality optimization and the construction of healthy aquaculture models in freshwater shrimp and crab farming waters. Full article

The heat shock transcription factor (HSF) family is a core regulatory component for plants in response to adversity stress and plays a pivotal role in regulating plant reactions to abiotic stress. Lanzhou lily (Lilium davidii var. unicolor) is an economically and horticulturally important bulbous crop widely cultivated in Northwest China, and its growth and yield are severely threatened by high-temperature stress during the growing season. Although HSF genes have been extensively and thoroughly investigated in other plant species, their functional characterization in lilies remains elusive. In this study, a total of 41 LdHSF genes were identified from the genome of Lilium davidii var. unicolor using bioinformatics approaches. The proteins encoded by these genes exhibited considerable variations in the number of amino acids (aa), as well as distinct isoelectric points (pI) and instability indices. Phylogenetic analysis classified these 41 LdHSF genes into three subfamilies (A, B and C). Promoter analysis revealed that the promoters of most LdHSF genes were rich in light-responsive cis-elements. Meanwhile, the promoters of some genes were highly abundant in hormone-responsive cis-elements, whereas those of other genes were enriched in stress-responsive cis-elements. Gene expression heatmaps and transcriptomic data demonstrated that the expression patterns of LdHSF genes showed significant differences in various tissues and under heat treatment. Based on transcriptomic and RT-qPCR data, we further screened out LdHSF10 and LdHSF40 as the major genes responding to heat stress. Functional experiments verified that these two genes encoded nuclear-localized proteins with transcriptional activity. Collectively, these findings lay a solid foundation for elucidating the molecular mechanisms underlying the regulation of heat tolerance by HSF transcription factors (TFs) in lilies in future research. Full article

Sustainable development results from the harmonious integration of economic growth, social equity, and environmental sustainability. Building on available risk analysis capacities, this study employs risk perception as a diagnostic tool to evaluate the adequacy of decision-making regarding environmental sustainability in vulnerable human settlements under a changing climate in the Dominican Republic. Using the perceived risk profile approach and a specially designed questionnaire, the research explores issues related to climate change and sustainability, targeting a population composed of decision-makers and professionals engaged in risk assessment. The findings reveal a systematic underestimation of risk across most perception variables, as well as a generally low collective risk perception. The study’s methodological framework enables the identification of proactive measures to strengthen knowledge and performance among decision-makers and stakeholders involved in advancing sustainable development in Dominican human settlements. Full article

Against the backdrop of deepening coordinated policy governance, the systemic synergy between digitalization and green transformation policies and their impact on corporate ESG performance has become a key issue requiring urgent exploration. Unlike existing studies that focus on the effects of individual policies, this paper adopts a policy system synergy framework to systematically investigate the impact of the coordinated implementation of big data administrative reform and low-carbon city pilot policies on corporate ESG performance. Using a sample of Chinese A-share listed companies from 2010 to 2022, this study applies a multi-period difference-in-differences (DID) method for empirical analysis. The findings show that the systemic synergy between digital and green policies significantly enhances corporate ESG performance, with this promoting effect substantially stronger than that of single pilot policies. Further causal re-identification using a double machine learning (DML) approach verifies the robustness of the baseline conclusion. Heterogeneity analysis indicates that the synergistic effect of digital and green policies is more pronounced in firms with higher levels of digital transformation, greater patient capital, and heavier tax burdens. Mechanism tests reveal that digital–green policy synergy improves ESG performance by enhancing external supervision from government, society, and the market, increasing green government subsidies, and incentivizing firms to engage in green innovation. At the same time, policy system synergy also reduces firms’ perceived uncertainty regarding economic policies and stabilizes their expectations, further enhancing ESG performance. This paper extends the research on the determinants of corporate ESG performance from the perspective of system synergy governance, providing new empirical evidence for understanding the interaction mechanisms between digital governance and green transformation policies. Full article

The effective closure of the Strait of Hormuz on 28 February 2026 disrupted approximately 20 million barrels (bbl) per day of crude oil transit, constituting the largest supply shock in global oil market history. This study quantifies the resulting economic losses under three blockade-duration scenarios and evaluates the Northern Sea Route (NSR) as a partial mitigation mechanism through a novel framework integrating sustainable supply chain resilience (SSCR), the Triple Bottom Line (TBL), and the United Nations Sustainable Development Goals (SDGs). A 3 × 3 scenario matrix crossing three blockade durations with three NSR utilization levels estimates global and country-level impacts using data from the International Energy Agency (IEA), the International Monetary Fund (IMF), and the Centre for High North Logistics (CHNL). Even under maximum feasible NSR utilization, net environmentally adjustment mitigation offsets only 1.1–3.6% of total global losses, demonstrating that the Northern Sea Route functions as marginal insurance rather than a viable substitute for Hormuz-dependent supply chains. Global Gross Domestic Product (GDP) losses range from USD 330 billion to USD 2.2 trillion, with South Korea (68–70% Middle East crude dependency) and Japan (approximately 95%) disproportionately affected. After TBL environmentally adjustment monetizing CO₂, black-carbon, and icebreaker costs, the NSR mitigates 1.1–3.6% of total losses, functioning as insurance rather than substitution. The SDG assessment reveals a fundamental trade-off: the NSR offsets energy-security losses (SDGs 7, 9) but worsens climate and marine outcomes (SDGs 13, 14). Theoretically, this study proposes “alternative maritime route availability” as a conceptual extension of supply chain resilience (SCRes) capabilities and outlines a sustainability-adjusted resilience score (SARS) framework that, pending further validation, could serve as a replicable assessment tool. These findings underscore that accelerating the energy transition remains the most effective long-term response to chokepoint vulnerability. Full article

Vanilla planifolia, a high-value tropical orchid, is significantly impacted by viral pathogens that threaten its cultivation and productivity. This study employs metagenomic techniques to detect and characterize the viral communities associated with V. planifolia in South Florida. Using high-throughput RNA sequencing, the Cymbidium mosaic virus (CymMV) and Vanilla latent virus (VLV) were prevalent in the plant system, with CymMV being the dominant viral species. Phylogenetic analysis of the CymMV coat protein gene revealed notable genetic divergence in the Homestead isolate, forming a distinct clade from global reference strains, suggesting local adaptation or host-specific evolution. Viral distribution across the plant system revealed higher viral loads in stem tissue, consistent with their role in systemic transport, whereas leaves exhibited greater viral richness, likely due to increased environmental exposure. The low abundance of other viral species, including Garlic viruses and Senna severe yellow mosaic virus, suggests that V. planifolia may harbor a broader range of viral taxa than previously characterized, though these findings represent a preliminary survey of symptomatic plants rather than a comprehensive regional assessment. This study underscores the value of metagenomic approaches for uncovering both well-characterized and novel viruses in plant systems and highlights the need for expanded sampling and continuous viral surveillance to guide disease management strategies in economically important crops such as vanilla. Full article

Credit risk assessment under severe class imbalance requires both structured imbalance correction and principled decision rules, yet most studies treat these as independent steps. This study develops a general integrated three-layer framework for credit risk assessment under class imbalance. The first layer introduces Sequential Hybrid Data Oversampling (SHDO), which sequentially applies five complementary oversampling techniques to enrich minority-class representation in mixed-type credit data. The second layer formulates credit approval as a decision-theoretic optimisation problem: a closed-form optimal threshold is derived under asymmetric costs, extended to constrained portfolios via a Lagrangian formulation with Karush–Kuhn–Tucker conditions, and further extended to minimax-robust decision making under estimation uncertainty. The third layer compares eleven classifiers under a unified evaluation protocol with an ablation isolating the effect of SHDO. The framework is empirically validated on the Home Credit Default Risk dataset, which is used as an illustrative case study rather than defining the scope of the contribution. On the held-out test set, XGBoost trained with SHDO achieves the highest minority-class F1 (0.254), while gradient-boosted models collectively attain ROC-AUC values of 0.713–0.750, outperforming classical baselines (0.540–0.620). The ablation confirms that without SHDO, all models exhibit near-zero minority-class recall despite adequate ranking ability. SHAP analysis on XGBoost confirms that the learned risk structure aligns with established creditworthiness determinants. The decision framework converts these probability estimates into analytically justified approval thresholds responsive to economic parameters, institutional constraints, and estimation uncertainty. Full article