Search Results (36,965)

Avian reovirus (ARV) causes viral arthritis and leads to considerable economic losses in the poultry industry. In this study, six ARV strains of distinct genotypes (FJNP01–FJNP06) were isolated from commercial broiler farms. Through gene sequencing and pathogenicity assessment, we analyzed the genetic evolution and pathogenic characteristics of the σC, P10, σB, μB, and λC genes. Pathogenicity tests revealed that inoculation with FJNP01–FJNP06 by footpad or oral gavage induced symptoms in specific-pathogen-free (SPF) chickens, including mortality and growth retardation. Among the isolates, FJNP04 (genotype IV) showed the highest pathogenicity, causing increased mortality, weight loss, and severe lesions in the footpads and bursa of Fabricius, followed by FJNP05 and FJNP02. The pathogenicity of FJNP06 varied by inoculation route, with enhanced pathogenicity observed following oral gavage. In contrast, FJNP01 and FJNP03 demonstrated relatively low pathogenicity. Identity analysis indicated that σC and P10 were highly variable, σB was relatively conserved, while μB and λC displayed considerable divergence. Phylogenetic analysis placed FJNP01–FJNP06 into genotypes I to Ⅵ, respectively, forming six distinct branches on the σC and P10 phylogenetic trees, yet clustering more closely on the σB, μB, and λC trees. The pathogenicity of different genotypes of ARV varies, among which FJNP04 (genotype IV) exhibits the strongest pathogenicity. Genetic sequence analysis revealed that σC and P10 are highly variable, σB is relatively conserved, while μB and λC display a wide range of variation. This study provides insights into the genetic variation and pathogenic characteristics of ARV and serves as a reference for future research. Full article

Wheat processing generates large volumes of co-products, particularly wheat bran (WB) and wheat germ (WG), which remain underutilized despite their high content of dietary fiber, phenolic compounds, bioactive peptides, and lipophilic antioxidants. Although their composition and processing have been widely investigated, an integrated and application-oriented evaluation of these fractions remains limited. This review provides a structured and critical analysis of WB, raw and defatted WG, and wheat germ oil (WGO), linking composition, processing strategies, and functional performance within a unified framework. Conventional and emerging technologies, including enzymatic hydrolysis, fermentation, thermomechanical treatments, and supercritical CO₂ extraction, are discussed in terms of selectivity, impact on techno-functional properties, and scalability. An evidence-grading approach is introduced to distinguish bioactivities supported by chemical assays, cell-based models, animal studies, or human data, enabling a more rigorous interpretation of health-related effects. Across applications, these co-products have been incorporated into food systems and related sectors, primarily showing improvements in nutritional composition, oxidative stability, and product performance under experimental conditions. However, translation to an industrial scale remains constrained by techno-economic limitations, regulatory requirements, and stability challenges. This work highlights the need for integrated processing strategies aligned with industrial feasibility to support the development of sustainable cereal biorefineries. Full article

Ship collision accidents remain a critical concern in maritime safety because of their potential to cause operational disruption as well as environmental and economic damage in areas with dense shipping activity. Complex traffic interactions, differences in vessel characteristics, and dynamic environmental conditions make collision risk increasingly difficult to manage using traditional navigation measures alone. This paper presents a structured review of ship collision research, focusing on collision impacts, collision avoidance strategies, risk assessment methodologies, and safety index development. The review synthesizes reported collision cases and their environmental consequences, examines commonly used analytical frameworks including probabilistic, data-driven, and multicriteria approaches, and discusses recent developments in AIS-based analysis, sensor-based monitoring, and intelligent prediction techniques. The analysis identifies several methodological gaps in existing studies. Collision avoidance methods and risk assessment models are often developed independently, while their integration with safety index frameworks remains limited. In addition, safety index formulations differ considerably in terms of indicator selection and modeling approaches, which reduces comparability between studies conducted in different waterways. The findings highlight how different analytical approaches contribute to maritime safety evaluation at strategic, operational, and real-time levels and provide insights for developing more integrated safety assessment frameworks to support navigation risk monitoring in high-traffic maritime environments. Full article

Biomass pyrolysis has emerged as a flexible platform for converting low-value residues into higher-value energy carriers (bio-oil, biochar and gas) and carbon-rich materials, with realistic potential for negative emissions when biochar is deployed in long-lived sinks. Over the last decade, three developments have driven the field forward: first, a finer mechanistic understanding of devolatilization and secondary reactions; second, major improvements in analytical techniques for characterising feedstocks and products; and third, more rigorous techno-economic and life-cycle assessments that place pyrolysis in a broader energy-system context. Recent experimental work on forestry and agro-industrial residues has clarified how biomass composition, ash chemistry and operating conditions jointly govern product yields, energy content and stability. Parallel advances in GC×GC–MS, high-resolution mass spectrometry, NMR and thermogravimetric methods have shifted the discussion from bulk “bio-oil” and “char” to families of molecules and well-defined structural domains, which can be deliberately targeted by reactor and catalyst design. Data-driven models, ranging from support vector machines applied to TGA curves to ANFIS and random forests for yield prediction, are now accurate enough to support process screening and multi-objective optimisation. At the system level, commercial fast pyrolysis biorefineries report overall useful energy efficiencies on the order of 80–86%, while slow pyrolysis configurations centred on biochar can be economically viable when carbon storage and co-products are appropriately valued. Thermodynamic analyses confirm that indirect gasification via fast-pyrolysis oil sacrifices some energy and exergy efficiency relative to direct solid-biomass gasification but may offer logistical and integration advantages. This review synthesises recent work on (i) feedstock and process characterisation; (ii) state-of-the-art analytical methods for bio-oil, biochar and gas; (iii) modelling and machine-learning tools; and (iv) energy-system deployment of pyrolysis products. Throughout, the emphasis is on how characterisation and modelling inform concrete design choices and on the trade-offs that arise when pyrolysis is considered as part of a wider decarbonisation portfolio. By integrating laboratory-scale characterisation with system-level modelling, this review aligns biomass pyrolysis with several United Nations Sustainable Development Goals (SDGs). The optimisation of thermochemical conversion pathways for forestry and agro-industrial residues directly supports SDG 7 (Affordable and Clean Energy) by enhancing the efficiency of bio-oil and syngas production. Furthermore, the deployment of biochar as a stable carbon sink for negative emissions and soil amendment addresses SDG 13 (Climate Action) and SDG 15 (Life on Land). By converting low-value waste streams into high-value energy carriers and chemicals within a circular bioeconomy framework, the research further contributes to SDG 12 (Responsible Consumption and Production) and SDG 9 (Industry, Innovation and Infrastructure). Full article

Flood damage assessment remains challenging, as conventional flood risk management mainly relies on hydraulic hazard maps that do not explicitly reproduce observed damage patterns. Recent advances in remote sensing and machine learning (ML) enable the integration of environmental and socio-economic data with historical impact information to improve flood damage modeling. This study proposes an explainable machine learning framework for flood damage susceptibility mapping, using observed institutional damage records from the 2011 and 2013 flood events combined with 17 geospatial flood risk factors (FRFs) representing hazard, exposure, and vulnerability. This approach enables the capture of non-linear relationships between flood damage and FRFs. For comparison purposes, the same framework was also applied using hydraulically modeled flood extents corresponding to return periods of 30, 200, and 500 years. The framework was tested along the Basilicata Ionian coast in southern Italy, a Mediterranean region characterized by complex geomorphology, intense rainfall events, and recurrent flood impacts. An eXtreme Gradient Boosting (XGBoost) model was trained using 17 FRFs related to hazard, exposure, and vulnerability at a spatial resolution of 20 m. The model achieved high performance with an accuracy of 0.988, an F1-score for the minority class of 0.860, and an ROC-AUC (test) of 0.996. High to very high flood damage probability was predicted in approximately 4.1% of the study area, mainly in low-lying floodplains near river corridors and infrastructure. SHAP-based explainability analysis revealed that damage susceptibility was predominantly driven by hazard and exposure factors: Drainage density (17.10%), Railway distance (16.33%), and Elevation (15.42%), extreme precipitation (Max rainfall, 10.66%) and Street distance (7.51%), with socio-economic vulnerability contributing less than 4%. The observed damage target exhibited clear threshold-like patterns (e.g., sharp risk increases below ~25/35 m elevation or within ~150/200 m of road infrastructure), contrasting with the smoother, continuous gradients produced by hydraulic scenarios. This analysis identified the most influential predictors and their response ranges. The proposed framework complements hydraulic hazard mapping by explicitly modeling observed flood damage, supporting flood risk assessment in flood-prone coastal regions. Full article

This paper provides a comparative analysis of the economic crises in Argentina and Lebanon to derive policy-relevant lessons for the design of IMF-supported adjustment programs in fragile economies. Using a structured comparative case study approach, the study examines crisis dynamics, policy responses, and socioeconomic outcomes across both countries, with particular attention given to exchange rate collapse, banking sector distress, public debt, inflation, unemployment, and poverty. The findings suggest that programs centered primarily on macroeconomic stabilization and fiscal austerity, without adequate attention to institutional capacity, social protection, and debt restructuring, risk deepening economic contraction and social vulnerability. The Argentine experience shows that IMF-supported adjustment in institutionally fragile environments may fail to restore confidence or deliver sustainable recovery when reform sequencing is weak and complementary domestic policies are absent. For Lebanon, where the crisis is deeper and compounded by governance failures and geopolitical instability, IMF engagement appears necessary but insufficient on its own. The paper concludes that a sustainable recovery requires a hybrid strategy combining external financial support with country-specific reforms, including exchange rate unification, banking sector restructuring, debt resolution, stronger governance, and targeted social protection. Full article

This study recognizes education as an investment and estimates the private rates of return to upper secondary education in Greece, overall, by type (general or vocational) and by gender. Earnings data were collected through primary research using stratified sampling from the private sector of the economy. The analysis is based on the Mincer method and is complemented by machine learning methods, including Support Vector Regression, Random Forests, and Extreme Gradient Boosting. The empirical analysis shows that investing in upper secondary education (general and vocational) is profitable. The private rates of return in upper general secondary education are higher than those in vocational education, and female graduates exhibit higher returns than male graduates. Machine learning models achieve modest improvements in predictive performance, as reflected in higher adj. R² values and lower prediction errors. However, the estimated rates of return remain broadly consistent with those obtained from the Mincer method. This convergence suggests that the Mincer specification captures the core structural relationship between education and earnings, while machine learning models primarily enhance predictive accuracy without substantially altering the estimated economic returns. This finding highlights the robustness of the traditional econometric framework and clarifies the complementary role of machine learning techniques in empirical labor economics. Full article

The current study provides a comprehensive overview of the socioeconomic and sociodemographic conditions of Vietnamese fishing crews, who form the backbone of the nation’s marine capture fisheries but remain among the most vulnerable labor groups. Based on interviews with 2037 captains and crew members across six coastal provinces, the study examines demographic characteristics, education, working conditions, legal arrangements, and income determinants. Results show that the fishing labor force is entirely male, predominantly middle-aged, and characterized by limited formal education and long occupational experience. Employment relationships are largely informal and verbal, leaving crews without labor protection, social or health insurance, or contractual stability. Statistical analysis revealed significant income disparities between captains and crew members, between inshore and offshore fleets, and among fisheries and provinces. Fishing experience and professional certification were positively correlated with income, highlighting the importance of skill development. The findings underscore the urgent need for socioeconomic policies that formalize labor contracts, expand insurance coverage, promote vocational training, and modernize fishing technologies. These measures, combined with income diversification and community welfare programs, are critical to improving the well-being, safety, and resilience of Vietnam’s fishing workforce and advancing sustainable marine economic development. This study provides valuable baseline information on an underrepresented segment of the commercial fishing industry, informing fisheries managers and policymakers in designing future development programs that account for the socioeconomic and demographic conditions of fishing crews. Full article

As the digital economy advances, information infrastructure has become a core engine for driving green economic transition and optimizing sustainable land systems. However, its heterogeneous governance effects on different types of pollutants and spatial spillover mechanisms remain insufficiently explored. This study draws on the theoretical framework of the dynamic game between scale and technique effects. It utilizes the PSTR model and the SDM to systematically investigate the nonlinear and spatial synergistic impacts of information infrastructure. The analysis covers aggregate information infrastructure and its structural subdivisions, including traditional and new information infrastructure. To ensure empirical rigor, this study introduces a Bartik instrumental variable constructed via the shift share approach and thoroughly eliminates endogeneity interference through the Control Function Approach and a core variable lagging strategy. The empirical research reveals three core findings. Firstly, after crossing the initial extensive scale effect dominated by physical construction, the profound technique effect dominates long-term environmental governance. Secondly, new-type information infrastructure demonstrates a superior capacity for long-term environmental governance and land use efficiency compared to traditional telecommunications. Finally, spatial spillover analysis indicates that although PM2.5 exhibits strong cross-regional physical contagion, the current environmental dividends of information infrastructure remain highly localized due to regional administrative data silos, lacking significant cross-regional synergistic spillover effects. This study provides a solid empirical basis for formulating differentiated digital spatial governance frameworks, breaking interprovincial data factor barriers, and preventing the physical expansion trap of traditional infrastructure. Full article

The increasing presence of contaminants of emerging concern (CECs) in surface and groundwater is a global concern due to their toxicity, persistence, and bioaccumulation, which lead to undesired effects. Conventional wastewater treatment processes are unable to remove these CECs, necessitating advanced treatment strategies to remove them effectively. Among advanced strategies, photocatalytic membrane treatment has attracted considerable interest among researchers. This review critically examines the fundamental principles governing the performance of photocatalytic membranes. It identifies significant challenges, including photocatalyst leaching, light accessibility, intermediates’ toxicity, and scalability of synthesis and immobilisation techniques. It explains why these factors significantly hinder long-term stability, scalability, and practical deployment of photocatalytic membrane systems and provides potential solutions. Through gap analysis, the review has identified rigorous techno-economic analysis, real-world wastewater validation, and systematic toxicity assessment of degradation intermediates as areas of further study. These targeted actions provide clear pathways to enhance the viability, safety, and commercial readiness of photocatalytic membrane systems. Full article

While Environmental, Social, and Governance (ESG) rating divergence poses a barrier to accurate sustainability measurement and sustainable investment, how internal managerial cognition addresses this external market misalignment remains underexplored. To address the research question of how executive focus shapes market consensus on corporate sustainability, this study integrates the Attention-Based View and Signaling Theory to examine the potential mitigating role of Top Management Team (TMT) environmental attention on ESG rating divergence. Utilizing high-dimensional fixed-effects regressions and textual analysis, we analyze a sample of Chinese A-share non-financial listed firms from 2015 to 2023. Empirical results indicate that a transparent and forthcoming managerial environmental focus helps reduce rating divergence, thereby partially aligning informational baselines. This cognitive alignment can act as an information calibrator, particularly when environmental issues match the firm’s core industry materiality, and this association appears more pronounced in regions with stringent environmental regulations. Robustness checks support the notion that substantive, quantitative sustainability disclosures driven by executive attention assist in alleviating informational misalignment among external rating agencies. These findings offer socio-economic and policy insights for advancing sustainable development, suggesting that regulators could consider encouraging structured sustainability reporting to support the role of executive cognition in standardizing ESG measurements. Full article

Global value chains have recently changed structurally (“servicification”)—that is, service sectors’ involvement in global value chain processes has become more intensive. We quantify services trade restrictions’ contribution to underdevelopment of global value chain servicification across Asian economies—an underexplored area. The study applies the “structural” gravity trade model and constructs panel data based on the 2025 Trade in Value Added and the Services Trade Restrictiveness Index database developed by the Organization for Economic Co-operation and Development. The empirical analysis covers five major service sectors—trade, transport, I&C, finance, and professional services. First, global value chain servicification remains relatively underdeveloped in most emerging and developing Asian economies, particularly across several service categories. Second, services trade restrictions’ presence significantly and negatively affects global value chain servicification’s extent in these economies. Third, these restrictive measures account for approximately 30–60% of servicification’s observed underdevelopment. Regarding policy implications, removing or easing such trade restrictions could substantially promote global value chain servicification, enhancing productivity and integration for emerging and developing Asian economies. Full article

This study introduces the LESG index, a composite analytical framework designed to assess the structural coherence of national development systems by integrating logistics capability, governance quality, and sustainability performance. Traditional development metrics evaluate these dimensions separately, limiting their ability to capture systemic interactions. Using cross-country data for 123 countries, the LESG index is constructed through normalization procedures and Principal Component Analysis (PCA) to derive a composite indicator reflecting the multivariate structure of the selected dimensions. Cluster analysis is subsequently applied to identify distinct structural development regimes. The results indicate a consistent empirical association between the LESG index and broader development outcomes, while also highlighting heterogeneous configurations of logistics capability, institutional quality, and sustainability performance across countries. These findings suggest that composite indicators can provide useful diagnostic tools for examining the structural alignment of development conditions beyond single-dimension metrics. The LESG framework contributes an integrated perspective for analyzing national development systems and offers a basis for future research on the structural conditions supporting sustainable economic transformation. Full article

Seaweeds are photosynthetic organisms with ecological, social, and economic significance, and they serve as effective bioindicators in marine ecosystems. This study assessed trace element concentrations in beach-cast seaweeds collected from four beaches along the Espírito Santo coast in southeastern Brazil—an area impacted by mining-related contamination. Samples of Zonaria tournefortii (J.V. Lamouroux) Montagne and Sargassum natans (Linnaeus) Gaillon, gathered during low tide (July–August 2022), were analyzed for 15 elements. Statistical analysis using the Kruskal–Wallis test revealed significant interspecific differences in the accumulation of several metals. Aluminum (Al), iron (Fe), and magnesium (Mg) were the most abundant (>100 mg/kg), while minor elements (<100 mg/kg) included barium (Ba), arsenic (As), zinc (Zn), vanadium (V), nickel (Ni), chromium (Cr), copper (Cu), lead (Pb), cobalt (Co), cadmium (Cd), silver (Ag), and mercury (Hg). Elemental profiles exceeded those reported in other global regions and closely resembled iron ore tailings. Most elements had relatively higher concentrations on the beaches of Imigrantes, in the north of the state. These findings are the first for beach-cast seaweeds in this region, suggesting that this contamination indicates the legacy of the mining industry from southeastern Brazil. Full article

This study estimates China’s methane (CH₄) emissions from 43 specific emission sources in 2020 and projects future trends through 2050 under two scenarios: Current Legislation (CLE) and Maximum Technically Feasible Reduction (MFR). The analysis utilises the Greenhouse gas and Air pollution Interactions and Synergies (GAINS) model methane framework, incorporating updated province-level activity data to capture the pronounced regional heterogeneity inherent in emission profiles and mitigation capacities. The results reveal a national CH₄ budget of 1114 MtCO₂e in 2020, with the energy sector (59%) and agriculture (28%) emerging as the primary contributors. A substantial technical mitigation potential is identified; by 2050, emissions could be curtailed by up to 48% relative to the CLE scenario, representing a 46% reduction from 2020 levels. The energy and waste sectors emerge as the primary contributors to this potential. Specifically, coal mining CH₄ abatement constitutes 58% of the energy sector’s total reduction potential, while enhanced solid waste management accounts for 97% of the mitigation within the waste sector. Key measures include ventilation air methane (VAM) oxidation and pre-mining degasification, as well as anaerobic digestion and recovery and utilization for energy use. Owing to regional disparities in hydrothermal conditions (representing the combined influence of temperature and moisture), demographic status, economic development, the most effective mitigation strategies vary across provinces. For example, pre-mining degasification and VAM oxidation are most impactful in major coal-producing regions such as Shanxi, Inner Mongolia, and Shaanxi. In contrast, anaerobic digestion, recovery and utilization, and waste incineration play a dominant role in more economically developed and densely populated provinces such as Jiangsu, Shandong and Zhejiang. By delineating region-specific technological priorities, this study quantifies the maximum technical mitigation potential for China and offers guidance for other nations facing similar mitigation challenges. Full article