Search Results (3,326)

The structural integrity of bogie frames is a critical factor in the safety and reliability of railway rolling stock, requiring advanced assessment methods to handle complex, multi-axial stress states. This research presents a robust numerical framework for the preliminary fatigue evaluation of a metro bogie frame, integrating high-fidelity Finite Element Analysis (FEA) with the Findley multi-axial fatigue criterion. The methodology overcomes the limitations of traditional uniaxial verification methods by employing a localized critical plane approach, implemented through a proprietary computational code. The investigation simulates a realistic operational scenario by superimposing a static vertical load of 15 tons per side with dynamic components derived from on-track accelerometric data. This integrated loading condition enables a precise reproduction of the “rotating” stress states typically encountered in service. Global structural analysis identified critical transverse welded joints as high-stress concentration zones, which were then subjected to a detailed multi-axial investigation. By correlating the extracted stress tensors with the resistance category included in the reference standard, over a regulatory life of 10 million cycles, a maximum cumulative damage index of 0.4602 was recorded. The results demonstrate that while the frame possesses adequate structural reserves, nearly half of its fatigue life is consumed in localized nodes. This methodology provides a reliable and computationally efficient tool for the structural health monitoring and development of innovative railway geometries, offering a superior predictive capability that remains scarcely utilized by major rolling stock manufacturers. Full article

This study investigates how the initial oxygen fraction in a confined space affects post-vent combustion, gas composition, and pressure hazards during thermal runaway (TR) of 58 Ah prismatic Li(Ni_0.8Co_0.1Mn_0.1)O₂ lithium-ion cells. Thermal abuse experiments were conducted in a 250 L sealed chamber under five initial oxygen fractions (20%, 15%, 10%, 5%, and 0% O₂), with synchronized measurements of cell temperature, vent-jet temperature, chamber pressure, voltage, and post-event gas composition. A first-vent event occurred reproducibly at a cell surface temperature of approximately 155 °C, followed by TR onset at about 170 °C. Although the onset temperatures were only weakly affected by ambient oxygen concentration, the post-vent hazard escalation depended strongly on oxygen availability. As the initial oxygen fraction increased from 0% to 20%, the peak vent-jet temperature increased from 353 °C to 1172 °C, and the peak chamber pressure rose from 90.7 kPa to 523.1 kPa. Gas chromatography showed that H₂, CO₂, CO, CH₄, and C₂H₄ were the dominant gaseous products. Lower oxygen fractions promoted retention of combustible species, whereas higher oxygen fractions enhanced oxidation and increased the CO₂/CO ratio. An oxygen-participation parameter, η, was introduced to quantify the fraction of initially available chamber oxygen consumed during post-vent oxidation. The increase in η was positively associated with oxygen-involved heat release and chamber overpressure. When the accessible oxygen fraction was limited to 10% or below, secondary combustion and pressure buildup were markedly suppressed, although a localized near-field thermal hazard remained significant around 10% O₂. These results provide quantitative guidance for enclosure inerting, vent management, and post-vent hazard mitigation in high-energy lithium-ion battery systems. Full article

The crystal structure regulation of Ti₃O₅ by Al₂O₃ doping and its effect on the optical properties of TiO₂ films prepared by electron beam evaporation were systematically studied. Ti₃O₅ coating materials with different Al₂O₃ doping contents (0–50 at%) were prepared by vacuum melting, and the corresponding TiO₂ films were deposited on K9 glass substrates via electron beam vacuum evaporation. The phase structure, phase transition temperature, chemical composition and optical properties of the materials and films were characterized by XRD, DSC, EDS, XPS, UV-Vis and AFM. Results show that Al₂O₃ doping induces the phase transition of Ti₃O₅ from a room-temperature stable β-phase to a high-temperature stable λ-phase, with complete transition at 5 at% doping. Al³⁺ with a smaller ionic radius causes lattice contraction and local distortion of Ti₃O₅, enabling stabilization at room temperature of the λ-phase. For TiO₂ films, 12.5 at% doping is the optimal state with the stable composition transfer under this condition. With the increase in Al₂O₃ doping content, the refractive index and extinction coefficient of TiO₂ films decrease continuously, while the optical band gap and surface roughness show an increasing trend. The changes in optical properties are mainly ascribed to the low refractive index of Al₂O₃, lattice compressive strain effect and oxygen vacancy passivation induced by Al³⁺. This study clarifies the regulation effect of Al₂O₃ doping on Ti₃O₅ phase transition and TiO₂ film optical properties, and provides theoretical basis and experimental reference for the doping modification of TiO₂ films and their practical applications in consumer electronics and optical filter devices. Full article

As interest in sustainable food consumption grows, local food has emerged as a key strategy for enhancing environmental sustainability, regional economic vitality, and food system resilience. This study examines how consumers’ sustainable value perceptions influence willingness to pay (WTP) and future purchase intention for local food within an integrated analytical framework. Using survey data from 400 consumers in South Korea, sustainable value perceptions were measured across social, environmental, and economic dimensions. Exploratory factor analysis and cluster analysis were used to identify consumer segments, while one-way ANOVA and binomial logistic regression analyzed differences in WTP and purchase intention. The results identify four consumer clusters and show that social and environmental value perceptions are positively associated with higher WTP and stronger purchase intention, whereas economic value perception does not significantly affect purchase intention. Prior purchase experience further reinforces purchase intention. This study contributes by proposing and empirically validating an integrated framework linking value perceptions, consumer segmentation, WTP, and purchase intention. The findings highlight the importance of value-based communication, information transparency, and experience-oriented strategies in promoting sustainable local food systems and strengthening agri-food supply chain resilience. Full article

Electrified railways are major industrial electricity consumers. The Grid-Source-Storage-Vehicle (GSSV) system supports a more sustainable railway power supply by improving local renewable energy utilization, strengthening multi-source energy coordination, and promoting low-carbon development. However, existing rule-based energy management strategies (EMS) remain limited in their ability to support the efficient coordinated operation of the GSSV system. Moreover, under strong source-load fluctuations, conventional optimization-based EMS often fail to provide sufficiently reliable and responsive decision-making for real-time operation of GSSV systems. To address these issues, this paper proposes a real-time EMS based on a rule-guided enhanced non-dominated sorting genetic algorithm (RG-NSGA-II). First, based on the GSSV architecture, the operating modes of the system under different working conditions are systematically analyzed, and a corresponding rule-based EMS is designed. Then, a multi-objective optimization model considering system economic performance and grid power-intake fluctuation is formulated. Furthermore, a coordination mechanism between the rule-based EMS and the optimization EMS is developed. By embedding power commands generated by the rule-based EMS into the optimization EMS and regulating their activation through a time threshold, the proposed method improves the reliability, economic efficiency, and real-time performance of the EMS. Finally, the proposed method is validated, and the results show that the proposed real-time EMS ensures effective utilization of RE, improves power coordination efficiency and operational adaptability under fluctuating operating conditions, and delivers tangible environmental and economic sustainability benefits for electrified railway power supply systems. Full article

Rapid and reliable tea classification is valuable for routine product screening, yet conventional sensory or physicochemical methods are subjective or time-consuming. Electronic nose (E-nose) sensing provides a fast alternative, but performance often degrades under domain shifts caused by different tea types, commercial categories, or acquisition conditions. This study proposes MGDA-Net, a multi-granularity domain adversarial network for cross-domain tea classification using E-nose time-series signals. MGDA-Net learns local temporal dynamics via a CNN branch and global contextual dependencies via a self-attention branch, and fuses them through an adaptive gating module. A branch-level adversarial alignment strategy is introduced to reduce source–target discrepancy at both local and global feature levels. A three-stage training procedure, consisting of source pretraining, adversarial alignment, and target fine-tuning, enables knowledge transfer from a labeled green tea source-domain to two target tasks. Experiments on oolong tea commercial-category classification (6 classes) and jasmine tea retail price-level classification (8 classes) show that MGDA-Net achieves mean accuracies of 99.31 ± 0.69% and 99.38 ± 0.51% over 10 independent runs, substantially outperforming all compared baseline methods. Ablation studies, feature-space analyses, and label-efficiency experiments further confirm the contribution of each component and show that MGDA-Net maintains mean accuracies above 87% when only 40% of the target-domain labels are used for fine-tuning. These findings suggest that MGDA-Net is a promising approach for cross-domain tea classification using E-nose data. Full article

Background/Objectives: Despite the decades-old ban on lead in fuel, plumbing, consumer goods, industrial processes, and various materials, it remains a public health threat due to its persistent nature. Zebrafish (Danio rerio) are highly effective for modeling several disorders, including those affecting neurological and behavioral functions, and are well-suited for assessing the impact of environmental toxins like lead. This study aimed to investigate the neurodevelopmental effects of embryonic lead exposure using the zebrafish model system. Methods: Embryos were exposed to lead acetate (PbAc) at concentrations ranging from 0.3 to 0.7 µg/mL using an exposure window of 6 to 48 h post-fertilization (hpf). Results: PbAc exposure produced sublethal teratogenic effects in a subset of larvae across concentrations, including tail and spinal deformities, craniofacial abnormalities, and uninflated swim bladder observed at 7 dpf. At 3 days post-fertilization (dpf), spontaneous circle swimming behavior suspected to be seizure-like was observed in the lead-exposed larvae and was more pronounced under light conditions in a dose-dependent manner. Electrophysiological recordings confirmed that larvae exhibiting circle swimming behavior had heightened neural activity, indicating a potential seizure-like phenotype driven by lead exposure. Conclusions: Our findings suggest that embryonic lead exposure leads to morphological defects and seizure susceptibility, demonstrating lead’s neurotoxic potential during early development. Seizure-like behaviors occurred in a non-linear concentration-dependent manner with a photosensitive component, and elevated baseline neural excitability was confirmed by local field potential (LFP) recordings. Full article

Honey is one of the most valued natural food products, yet it remains highly vulnerable to fraud through mislabeling and adulteration, practices that mislead consumers and compromise food safety. We develop a low-cost and portable electronic nose (e-nose) system for classifying locally available honey brands in the Philippines. The system integrates an array of eight MQ gas sensors to detect volatile organic compounds (VOCs), with an Arduino Mega 2560 handling data acquisition and a Raspberry Pi 5 executing data processing and classification. An Extreme Gradient-Boosted Decision Tree (XGBoost) algorithm was applied to analyze the VOC profiles of three honey brands, each with 38 samples, resulting in a total dataset of 114 samples. The dataset was divided into training, testing, and validation sets to assess the system’s classifying and predictive performance, with accuracy evaluated using a 3 × 3 confusion matrix. The results showed that the system effectively distinguished between honey brands, achieving a validation accuracy of 87.50%, corresponding to 21 out of 24 correctly identified validation trials. Full article

The dominant agri-food system’s well-documented failures—biodiversity loss, deepening rural inequalities, and the erosion of small-scale farming livelihoods—have elevated SSE initiatives and social innovation in the agri-food sector and bioeconomy from a niche policy concern to a structural priority. This paper examines how SSE arrangements drive meaningful transformation in agri-food chains while advancing sustainable development at local and regional scales. Through a narrative review of interdisciplinary peer-reviewed literature and key institutional sources, the paper synthesizes evidence that SSE initiatives generate transformation through three interconnected mechanisms: (a) the reconfiguration of governance structures; (b) the deepening of producer–consumer relationships through spatial proximity and relational transparency; and (c) the more equitable redistribution of value across agri-food territories. These findings suggest that place-based SSE models occupy a central—rather than peripheral—role in sustainability transitions and local development. The paper presents a structured analytical framework linking SSE practices to agri-food chain transformation and develops nine concrete policy implications for scaling and sustaining SSE innovations through coordinated collaboration among public, private, and social economy stakeholders. The findings contribute to a sharper understanding of the conditions under which SSE-driven models can foster sustainable, socially inclusive, and community-oriented agri-food systems and of why the solidarity dimension, rather than organisational form alone, is the decisive criterion for identifying genuinely transformative initiatives. Full article

Canestrato di Castel del Monte (CCM) is a traditional sheep cheese from the Abruzzo region of Italy, strongly linked to local pastoral practices and characterized by high cultural and commercial value. Ensuring its authenticity is therefore essential to protect both producers and consumers. In this study, Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric modelling was investigated for the classification of traditional sheep cheeses. A dataset of approximately 2000 spectra obtained from Canestrato di Castel del Monte (CCM), low-ripening CCM, and Pecorino Toscano was analyzed using different modelling strategies. Partial Least Squares Discriminant Analysis (PLS-DA) and Sequential Preprocessing through Orthogonalization combined with Linear Discriminant Analysis (SPORT-LDA) were first applied to simultaneously separate the three categories. Subsequently, a class-modelling approach based on Soft Independent Modelling of Class Analogy (SIMCA) was used to authenticate CCM and low-ripening cheeses. The discriminant models achieved excellent classification performance: accuracies close to 100% for CCM and low-ripening CCM and around 95% for Pecorino Toscano. SIMCA provided reliable rejection of non-target samples, although with lower sensitivity compared to discriminant approaches. Overall, the results demonstrate that ATR-FTIR spectroscopy coupled with appropriate chemometric modelling represents a powerful strategy for the authentication and classification of traditional sheep cheeses. Full article

Sustainable land stewardship is increasingly essential in South Africa’s wine-producing regions (WPR), where climate variability, ecological sensitivity, and economic pressures interact to shape vineyard management practices. This study synthesizes data from 107 vineyard websites and 20 in-depth stakeholder interviews to examine how sustainability is conceptualized and practiced across the region. Results show that growers prioritize biodiversity conservation, soil health, and water-efficient management as foundational to long-term resilience, with widespread adoption of practices such as mulching, cover cropping, habitat restoration, and integrated pest management. Website-derived data reveal substantial participation in sustainability certifications, including the Integrated Production of Wine (IPW) program and WWF Conservation Champions, although implementation depth varies among producers. Interviews underscore that climate extremes—particularly drought—have intensified reliance on soil-moisture conservation and adaptive irrigation strategies. Producers also identified escalating input costs, shifting markets, and export barriers as central economic challenges, contributing to diversified business models that include tourism and direct-to-consumer sales. Collectively, these findings demonstrate that sustainable viticulture in South Africa’s WPR is shaped by dynamic interactions between environmental stewardship and economic adaptation. Strengthening collaboration and aligning local practices with global sustainability frameworks can enhance the region’s ecological resilience and support the long-term viability of its wine industry. Full article

The adoption of instrumented wheelsets on diagnostic trains offers the possibility of continuous monitoring of wheel–rail contact forces. The collection of large datasets can be exploited for diagnostic purposes, aiming to localize specific track defects, allowing significant improvements in terms of safety and maintenance costs. Machine learning (ML) techniques can be used to automate anomaly detection. In this work, the authors compare the application of various ML algorithms based on the identification of different frequency or time-based features of analyzed signals. To perform the activity, a significant number and variety of local defects have been included in the recorded data. From a practical point of view, the insertion of real known defects into an existing line is extremely time-consuming, expensive, and not immune to safety issues. On the other hand, the design of anomaly detection algorithms involves the usage of relatively extended datasets with different faulty conditions. The authors propose deliberately adding real contact force profiles of healthy lines to a mix of synthetic signals, which substantially reproduce the behavior and the variability of foreseen faulty conditions. The results of this work, although preliminary and still to be completed, offer a contribution to the scientific community both in terms of obtained results and adopted methodologies. Full article

Morocco’s recent legalization of industrial and medicinal cannabis has created a rapidly expanding seed-oil sector whose sustainability has yet to be fully assessed. This study applies an environmental life cycle assessment (LCA) in accordance with ISO 14040:2006 and ISO 14044:2006, complemented by a qualitative social responsibility assessment based on ISO 26000:2010, aiming to evaluate the life cycle sustainability of Moroccan cannabis seed oil. Three representative processing chains, traditional artisanal presses, producer cooperatives and regulated industrial plants are compared using a functional unit of 1 kg of cold-pressed oil packaged for local distribution. Inventory data were drawn from field measurements and interviews and were modeled in OpenLCA with background datasets from Ecoinvent 3.8 and Agribalyse v3.1. Impact assessment used the ReCiPe 2016 (H) method at the midpoint level across nine categories (climate change, fossil resource scarcity, water use, freshwater eutrophication, terrestrial acidification, land occupation, carcinogenic, non-carcinogenic human toxicity, and fine particulate matter formation). Sensitivity analyses varied seed yield, electricity mix and transport distances by ±20% to gauge uncertainty. Results show that the cooperative scenario achieves the lowest impacts across nearly all categories because of higher extraction yields (3 kg seed per kg oil), lower energy use (0.54 kWh kg⁻¹ oil) and more effective co-product recovery. In contrast, artisanal extraction requires approximately 1 kg of additional seed input per functional unit compared to optimized scenarios, significantly increasing upstream environmental burdens and causing upstream agricultural burdens to multiply. Industrial facilities perform comparably to cooperatives if powered by renewable electricity. Integrating a semi-quantitative social responsibility assessment reveals that legalization has markedly improved organizational governance, labor conditions, consumer protection and community involvement. Cooperatives display the most balanced social performance, whereas industrial plants excel in governance and quality control. A set of recommendations, including drip irrigation, cultivar improvement, co-product valorisation, renewable energy adoption, eco-designed packaging and cooperative governance, is proposed to enhance the environmental and socio-economic sustainability of Morocco’s emerging cannabis seed-oil industry. Full article

(1) Background: Exploring regional foods can help consumers expand their options for consuming diverse food products in various forms. This could enhance human health in local populations. (2) Methods: The present study evaluated the physicochemical composition of quince powder using standard analytical methods. Color parameters were determined using a PCE-CSM colorimeter equipped with a xenon lamp; the antioxidant activity via DPPH, ABTS, FRAP, and CUPRAC methods; the sorption capacity (at 10 °C, 25 °C, 40 °C and a_w from 0.1 to 0.9) through the static gravimetric method; and monolayer moisture content (MMC) with the BET model. The isotherms were fitted via modified Chung–Pfost, Halsey, Henderson and Oswin models. (3) Results: The approximate physico-chemical composition of laboratory-produced quince powder (dried at 45 °C for 10 h) was: proteins—1.27 g, carbohydrates—75.80 g, fats—0.49 g, fibers—21.50 g, ash—2.31 g, and nutritional value—355.65 kcal. The color analysis indicated limited non-enzymatic browning. Antioxidant activity was confirmed by all four methods. The three-parametric Halsey model is recommended to describe the representative S-shaped isotherms from type II. The MMC for the adsorption process ranged from 14.41% d.b. to 7.09% d.b., and for the desorption process, it ranged from 13.11% d.b. to 7.80% d.b.; (4) Conclusions: This study presents a quince powder as a convenient form for both storage and consumption, emphasizing its value as a rich source of bioactive compounds and its suitability for home production and regular inclusion in a healthy daily diet. Full article

Background: Global water scarcity, intensified by climate change, demands optimization of irrigation systems consuming 70% of freshwater resources. Despite significant investments in modernizing irrigation infrastructure from open channels to pressurized networks, performance often falls below expectations. Objective: This study develops an integrated diagnostic and simulation framework for evaluating and improving large-scale pressurized irrigation systems by adapting the Mapping System and Services for Pressurized Irrigation (MASSPRES) methodology. Methods: The framework integrates three components: (1) demand flow dynamics determination using stochastic modelling; (2) hydraulic performance simulation incorporating multiple flow regimes; and (3) performance analysis using relative pressure deficit and reliability indicators. The methodology combines deterministic soil water balance calculations with stochastic farmer behaviour modelling. Results: Application to the Sinistra Ofanto irrigation scheme revealed localized pressure deficits during peak demand periods. The rehabilitation strategy restored full hydraulic feasibility of the network, increasing the proportion of hydraulically satisfied operating configurations from 62% to 100% under peak demand conditions and ensuring adequate pressure at all 317 hydrants across the system. Conclusions: The methodology provides robust decision support for cost-effective rehabilitation, ensuring reliable water delivery while promoting water-energy efficiency. Full article