Search Results (122,543)

This study develops an integrated decision-support framework to advance green supply chain management (GSCM) by systematically linking Environmental, Social, and Governance (ESG) practices, environmental product innovation, corporate performance, and strategic alternatives. Employing the Analytic Network Process (ANP), the proposed model captures complex interdependencies and feedback relationships across life-cycle value chain stages, enabling a holistic evaluation of sustainability-oriented strategies. A Delphi panel comprising 15 experts from academia, industry, and government is used to validate the evaluation criteria and network structure. The empirical results indicate that eco-friendly design, energy and resource efficiency, and carbon–climate management are the most influential drivers shaping green supply chain performance. Moreover, operational and sustainability performance are found to exert greater strategic importance than short-term financial performance, highlighting GSCM as a long-term capability-building approach rather than a cost-centered initiative. To enhance analytical adaptability, this study proposes a conceptual extension integrating neural feature extraction (NFE) signals with ANP-based expert weights. The NFE module is not empirically trained or validated; rather, it illustrates a theoretically consistent mechanism for incorporating data-driven feature signals into structured multi-criteria decision frameworks. Empirical validation of the NFE component is proposed as a future research direction. Full article

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder in the aging population. Drug repurposing provides a cost-effective strategy to identify novel therapeutics that may mitigate age-associated pathologies. Here, we report the therapeutic potential of fluoxetine, a selective serotonin reuptake inhibitor commonly used as an antidepressant, in alleviating cognitive impairment and AD-like pathology in 5xFAD mice, a transgenic model of familial AD. Chronic fluoxetine administration significantly ameliorated anxiety-like behavior and cognitive deficits in 5xFAD mice, as assessed by open field, Y-maze, and novel object recognition tests. Fluoxetine treatment was associated with reduced amyloid plaque deposition in the hippocampus and cortex, attenuation of microglial activation, and decreased expression of inflammatory cytokines. At the molecular level, fluoxetine increased phosphorylation of GSK3β at Ser9, which was associated with enhanced CREB phosphorylation and upregulation of the α-secretase ADAM10. These effects were further examined in SH-SY5Y neuronal cells, where CREB phosphorylation and ADAM10 expression were significantly modulated by GSK3β inhibition, whereas CaMKII inhibition had no detectable effect under our experimental conditions. Our findings suggest that fluoxetine modulates amyloid-associated signaling pathways in the 5xFAD model, in part through regulation of the GSK3β-CREB signaling framework. These results provide mechanistic insight into how fluoxetine may influence APP processing in an amyloid-driven pathological context, although further studies are required to clarify its translational implications in human AD. Full article

Logistics centers play a significant role in regional economic growth and development by optimizing logistics chains, minimizing transportation and transfer costs, shortening transit times, and enabling centralized management through support services. Intermodal transportation is an important function that enables goods to be transported efficiently using multiple modes of transport at logistics centers. This study examines 12 operational logistics centers in Türkiye, evaluating five types of transportation: unimodal (highway, railway) and intermodal (highway/railway, highway/airway, and highway/marine). The assessment considers four key criteria (transportation cost, carbon emissions, transportation risk, and transportation time) under various transportation distance and volume scenarios. The Fuzzy AHP method is employed to weight these criteria, and a goal programming model is developed to optimize transport mode selection. Among the evaluated transport modes, air transportation was not selected in any scenario due to its high cost and carbon emissions, aligning with the study’s focus on cost-efficiency and sustainability. The findings provide scenario-based recommendations for the most suitable transportation modes at each logistics center, contributing to more efficient and sustainable logistics operations. Full article

Naturally occurring halloysite nanotubes (HNTs), a clay mineral characterized by a unique dual-charge architecture, offer a promising strategy for enhancing the performance of composite polymer electrolyte (CPE). In this work, HNTs are introduced as a low-cost, functional filler to simultaneously address two key limitations of poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP)-based CPE: low ionic conductivity and inadequate lithium-ion transference number. The negatively charged outer surface of HNTs facilitates Li⁺ transport, while the positively charged inner lumen confines anions such as TFSI⁻. Controlled acid etching (6 M HCl, 12 h) further optimizes this structure by removing surface impurities and enlarging the lumen, thereby enhancing both charge-directed ion transport pathways. The resulting HNT-modified CPE achieves a high ionic conductivity of 6.1 × 10⁻⁴ S⋅cm⁻¹ and a Li⁺ transference number of 0.73. When assembled into Li||CPE||LiFePO₄ cells, the electrolyte enables stable cycling over 300 cycles at 0.2C, retains 119.2 mAh/g at 2C, and delivers 85.7 mAh/g even at 5C, demonstrating excellent cycling stability and rate capability. This study reveals the potential of mineral-derived nanomaterials, with their inherent structural and physicochemical properties, to serve as key functional components in high-performance batteries. Full article

This study presents a performance and techno-economic evaluation of a 24 kWp grid-connected rooftop photovoltaic system in Yeşilova, Burdur, Türkiye, based on measured operational data from 2024. Beyond conventional software comparisons, this research establishes a validated benchmarking protocol for medium-scale rooftop PV systems by quantifying the divergence between measured data and predictive modeling under fluctuating seasonal conditions. Measured results were compared with energy yield predictions from PVsyst and HelioScope. Key performance indicators, including final yield, performance ratio (PR), and capacity factor, were evaluated alongside main loss components. The system produced an annual energy output of 33,977.5 kWh, corresponding to an average PR of 75.7% and a capacity factor of 16.99%. Simulation results show deviations from measured values, with PVsyst moderately overestimating and HelioScope underestimating the annual yield. Thermal effects were identified as the dominant contributor to performance losses, particularly during elevated summer temperatures. A techno-economic assessment indicates a payback period of 8.4 years, a levelized cost of electricity (LCOE) of 0.0485 US$/kWh, and an internal rate of return (IRR) of 15.58%. These findings underline the importance of validating simulation-based assessments with site-specific measurements to improve the reliability of photovoltaic system performance and investment evaluations. Full article

Sheep farming length of stay in the feedlot directly influences system profitability, mainly due to the high cost of feed. Thus, the use of predictive models based on body measurements is an important tool to define the optimal slaughter point and the ideal feedlot period. Thus, the aim was to evaluate predictive models of meat cuts and tissue carcasses concerning weight at slaughter (WS), loin eye area (LEA), and subcutaneous fat thickness (SFT) obtained by ultrasound of the lumbar region of lambs. The WS and ultrasound measurements were obtained from a pre-slaughter collection of 45 lambs, divided into five groups, each weighing 15, 20, 25, 30, or 35 kg, with nine replications per group. Three regression models were evaluated: WS, LEA, and SFT (independent variables) and the cuts yield or tissue composition (dependent variable). Increasing WS resulted in greater carcass weight and commercial cuts. Above 15 kg body weight, bone weight showed little or no increase (allometric coefficient = 0.06), whereas muscle and fat tissues increased steadily, with allometric coefficients of 0.25 and 0.12, respectively. The commercial cuts showed a high and significant correlation with WS and LEA. The muscle and bone proportion of the leg had a significant (p < 0.10) correlation with SFT. For the weight of commercial cuts estimates, the inclusion of LEA and/or SFT with WS did not improve the coefficient of determination but made the predictions equivalent to the measured values. There were high determination coefficients when WS was only used to predict muscle, fat, and bone weight, but it was not efficient in predicting the muscle/fat and muscle/bone ratios and the percentage of tissues. The WS was the variable that best explained the weight and tissue content. The inclusion of LEA and/or SFT made little improvement to the predictive models. Full article

Tornadoes pose growing threats to both communities and the built environment, yet few studies have quantified how spatial characteristics of the built environment interact with social and economic factors while influencing tornado impacts. This paper introduces an integrated metric that combines tornado risk and exposure to evaluate localized disaster impact. Focusing on Florida’s Panhandle, we examine how housing density and affordability, network connectivity, and urban form efficiency, together with demographic and socioeconomic attributes, shape tornado impacts at the U.S. census block group (CBG) level. To address spatial autocorrelation and non-stationarity, five statistical models were compared, including both global and local spatial regressions. The findings indicate that multiscale geographically weighted regression (MGWR) most effectively captures the spatial heterogeneity of tornado impacts. Built-environment and affordability factors show clear spatial heterogeneity— smart location indexand housing cost burden (h_ami) are positively associated with tornado impact in CBGs near Tallahassee and parts of Pensacola—suggesting amplified impacts in location-efficient urban areas where exposure is concentrated and affordability stress may limit preparedness and recovery. In contrast, network density is negatively associated with the impact of key clusters, consistent with the idea that denser, more redundant road networks can reduce canopy-weighted disruption by providing alternative routes for emergency access and restoration. Overall, these findings can inform our understanding of how the built environment influences tornado exposure, offering critical insights for planners and policymakers seeking to strengthen communities against tornadoes. Full article

Unmanned Aerial Vehicle imagery often suffers from severe spatial detail degradation due to sensor limitations and motion blur, hindering downstream vision tasks. To address this, we propose a lightweight super-resolution framework leveraging a Tiny-Transformer backbone enhanced by a multi-domain feature fusion strategy. Specifically, we jointly model spatial structural semantics and frequency domain texture priors via a cross-domain fusion attention mechanism, enabling coordinated restoration of global consistency and local details. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on standard benchmarks, achieving significant gains in Peak Signal-to-Noise Ratio and structural similarity index while maintaining low computational cost. Notably, the model exhibits superior robustness in reconstructing high-frequency textures common in aerial scenes. This work provides an efficient, deployable solution for enhancing visual fidelity in resource-constrained applications such as urban planning and precision agriculture. Full article

Dust generated during coal mining and transportation poses serious threats to miners’ health, operational safety, and the surrounding environment. However, comprehensive review studies on dust suppression in coal mines remain limited, particularly those integrating dust movement laws with numerical simulation approaches. This review presents a systematic and reproducible analysis of dust control methods in coal mines with a particular focus on numerical simulation. Current research progress and development trends are summarized from three aspects: structural optimization of dust suppression devices, optimization of operating conditions, and ventilation system design. Existing studies indicate that structural improvements mainly concentrate on nozzle geometry, diameter, installation position, and spraying distance, while operating condition optimization primarily involves pressure regulation. Due to the complexity and high cost of full-scale experimental platforms, ventilation system optimization is largely achieved through numerical simulation, supplemented by field measurements. Studies based purely on numerical simulations remain limited in addressing the chemical modification of dust removers; however, with the advancement of molecular dynamics techniques, this area may represent a promising direction for future research. Full article

Arsenic (As) pollution has become a global concern, and the search for effective and sustainable As remediation methods has attracted much attention. Sustainable and cost-effective technologies for As remediation are essential to protect public health. This study aligns with the United Nations Sustainable Development Goals (SDGs), specifically SDG 6 (Clean Water and Sanitation) and SDG 12 (Responsible Consumption and Production), by transforming agricultural waste into value-added biochar for environmental remediation. Currently, studies on the remediation of As pollution using iron-modified biochar (Fe-BC) and cerium-modified biochar (Ce-BC) have demonstrated promising application potential. Although there is an established research foundation regarding their remediation performance and mechanisms, comparative studies evaluating their performance and mechanisms under unified experimental conditions remain limited. As in this study, Fe-BC and Ce-BC were prepared and systematically investigated. The As remediation performance and mechanisms of the two biochars were compared and analyzed through material characterization, aqueous adsorption experiments, and soil remediation assessments. The results showed that the specific surface areas of Fe-BC and Ce-BC were 94.380 m²·g⁻¹ and 36.388 m²·g⁻¹, respectively, both higher than that of the original biochar (BC). The Langmuir and Freundlich models adequately fitted the As adsorption processes of all three materials. Fe-BC and Ce-BC exhibited a tendency toward monolayer adsorption for As(III). The Freundlich distribution coefficient K_F of Fe-BC was 0.1604, which was higher than that of BC and Ce-BC, indicating superior adsorption performance for As(III). In the pot experiment, when Fe-BC and Ce-BC were applied at 5%, the As content in ryegrass decreased by 78.38% and 77.15%, respectively. Fe-BC reduced the available As content in soil by 63.1% and decreased As accumulation in ryegrass by 78.38%. The reduction in available As content achieved by Fe-BC was greater than that achieved by Ce-BC. Fe(III) oxides supported on Fe-BC immobilized As through complexation and precipitation mechanisms. Fe⁰ and Fe₃O₄ in the materials altered the redox potential of the local microenvironment, affecting the transformation and stabilization of As species. Ce-BC primarily oxidized As(III) to As(V), and Ce⁴⁺ facilitated the formation of CeAsO₄ precipitates due to its high redox potential. Full article

Approximately one quarter of the European Union’s (EU’s) CO₂ emissions originate from the transport sector, of which road transport, such as cars and heavy-duty vehicles, contributes roughly 72%. Moreover, according to the European Automobile Manufacturers’ Association, 92% of cars in the EU are internal combustion engine vehicles powered by fossil fuels. Therefore, boosting the adoption of Electric Vehicles (EVs) is considered one of the most prominent solutions for reducing GHG emissions and achieving the EU’s climate targets. To increase EV adoption and fulfil the demand of EV users, adequate EV Charging Stations (EVCSs) are required. Nevertheless, since most EVCSs are supplied by electricity grids that remain predominantly fossil fuel-based, their operation entails substantial indirect GHG emissions. A prominent approach to reducing grid-related emissions is integrating renewable energy sources (RESs) with EVCSs, thereby lowering emissions and alleviating grid stress. Although promising, the energy, economic, and environmental (3E) benefits of this integration remain insufficiently explored. Therefore, this study develops and applies a 3E optimisation framework to assess the feasibility and performance of RES-powered EVCS at NOVA University Lisbon (UNL). Data was collected from the UNL parking area, such as time of arrival, and time of departure. Also, a rule-based algorithm was developed to curate data and estimate the EVCS load profile. Furthermore, HOMER optimisation software was employed to evaluate four scenarios, including (i) an EVCS based on PV, Wind Turbine (WT), and the grid, (ii) an EVCS based on PV and the grid, (iii) an EVCS based on WT and the grid, and (iv) an EVCS based only on energy withdrawal from the grid (base scenario). Under the adopted techno-economic assumptions, in the most optimised scenario, economic and environmental analyses illustrate significant improvements over the base scenario: CO₂ emissions are five times lower, and cost of energy is significantly lower, resulting in significantly lower EV charging costs for users. The results demonstrate that, through developed feasibility studies, researchers, decision-makers, and stakeholders can reach better conclusions about EVCS planning and management. Full article

The rising cost of conventional protein sources such as soybean meal has prompted the search for sustainable and economical alternatives in aquafeeds. Sugar beet pulp (SBP), an abundant by-product of the sugar industry, possesses nutritional potential but is limited by its high fiber and anti-nutritional factors. Solid-state fermentation (SSF) offers a promising approach to enhance its nutritive value and functional properties. This study evaluated the effects of dietary inclusion of mixed microbial solid-state fermented beet pulp (FBP) on the growth, systemic health and intestinal function of juvenile yellow catfish (Pelteobagrus fulvidraco). First, orthogonal optimization determined Lactiplantibacillus plantarum:Saccharomycopsis fibuligera:Bacillus subtilis = 1:3:3 as the optimal ratio, significantly improving the nutritional profile of FBP. Based on this optimized FBP, an 8-week feeding trial, five isonitrogenous and isolipidic diets were formulated by replacing 0–12% soybean meal with FBP. The results demonstrated that 9% FBP inclusion yielded optimal growth performance and significantly improved muscle texture. At the systemic level, FBP supplementation reduced serum lipid markers and liver enzyme activities while enhancing antioxidant capacity. At the intestinal level, FBP promoted intestinal health by increasing key digestive enzyme (lipase, trypsin, amylase) activities, stimulating villus development, and improving intestinal antioxidant status. Furthermore, gut microbiota analysis revealed that dietary FBP supplementation significantly modulated intestinal microbial composition, with notable enrichment of genera such as Leucobacter. In conclusion, FBP is a multi-functional ingredient that enhances growth, product quality, systemic physiology, and intestinal health in yellow catfish aquaculture. These findings provide a viable strategy for the sustainable utilization of agricultural by-products in aquafeeds. Full article

The freshwater snail Bellamya purificata is both ecologically and economically significant, exhibiting notable sexual dimorphism in growth and nutritional traits that underscore the importance of breeding of monosex stocks. However, the genetic basis of sex determination remains unclear. Herein, genome-wide association studies (GWASs) combined with transcriptomic analysis were conducted to identify sex-linked markers and candidate genes for this species. GWAS generated 571 significantly sex-associated SNPs and 1853 InDels, corresponding to 44 candidate genes. Multiple significant SNP peaks were detected on chromosomes 1 and 2, with mrc2 and mis18bp1 as key candidate genes. A sex-linked InDel marker located within mis18bp1 can distinguish males and females cost-effectively. Genotype analysis of the sex-associated loci revealed that most females were homozygous while males were heterozygous, suggesting that B. purificata has a primarily XX/XY sex determination system. Comparative gonadal transcriptome analyses identified 2996 female-biased and 4281 male-biased genes. Among them, sry, sox8, dmrt1 and dmrt2 may be critical in male sex differentiation, while β-catenin, foxl2, esr1 and nr5a2 may be important in female sex differentiation. Integration of GWAS and transcriptomic data highlighted four pronounced sex-associated candidate genes, including mis18bp1, rnf216, tbx1 and mrc2. These results provide a valuable foundation for elucidating the genetic mechanisms underlying sex determination and for the development of monosex stocks in B. purificata. Full article

Underwater acoustic single-beacon positioning technology achieves localization by integrating vehicle motion with range measurements acquired from acoustic ranging devices, offering advantages such as system simplicity, flexible deployment, and high cost-effectiveness. However, its accuracy is limited by weak initial observability and degraded observation geometry. To address this, a sensor data correction and collaborative optimization framework is proposed. A hybrid outlier rejection strategy first suppresses acoustic multipath and sensor noise. To compensate for systematic sensor errors ignored in conventional Virtual Long Baseline methods, an affine transformation maps the true trajectory to the sensor-indicated one, reformulating error compensation as a correction to virtual beacon coordinates. To further mitigate the accuracy degradation caused by degenerated geometric configurations, this paper proposes a collaborative algorithm that integrates Chan initialization with affine transformation optimization. This approach formulates the positioning problem as an optimization task, simultaneously estimating the position information and affine transformation parameters through iterative refinement to achieve high-precision localization. The process begins with Chan’s algorithm, which provides an initial estimate from the virtual sensor array. This estimate is then refined under affine constraints to achieve high-precision localization. Experimental results show the method improves positioning accuracy by 36.30% compared to baseline algorithms, demonstrating significant performance enhancement. Full article

Low-rank sparse decomposition models have become the mainstream optimization framework for multiframe infrared small target detection. Existing low-rank matrix decomposition approximations typically pre-decompose infrared videos into the product of two low-rank matrices to capture the background’s low-rank characteristics. However, such approximations are not optimal and often result in suboptimal background recovery. To achieve more accurate low-rank recovery, we exploit the intrinsic relationship between low-rank matrices and their generalized inverse matrices, thereby improving conventional decomposition approximations. Moreover, to address the high computational cost of applying low-rank and sparse decomposition models to multi-frame infrared videos, we introduce a robust column-row (CUR) decomposition to accelerate the iterative process, thereby significantly improving computational efficiency. The experimental results show that the proposed method achieves fast detection of small targets in infrared videos while maintaining competitive detection performance. Full article