Search Results (38,186)

The increasing number of end-of-life (EOL) products has raised new challenges for sustainable manufacturing, especially when recycling efficiency, structural modularity and worker well-being must be considered simultaneously. From the perspective of symmetry and asymmetry in mechanical product design, this study proposes a Design for human-centric Modular Recycling (DFHMR) approach to improve EOL product recycling while reducing ergonomic risks in disassembly operations. In the proposed framework, functional similarity, structural correspondence and spatial association among components are used to characterize symmetry-oriented modular relationships, whereas asymmetric factors such as disassembly difficulty, carbon emissions, recycling profit and worker-related ergonomic risks are incorporated to describe the heterogeneity of practical recycling processes. A multi-objective optimization model is developed to maximize green disassembly performance and intra-module relevance while minimizing inter-module coupling and human-factor risks. To solve the constrained modular design problem, an enhanced social engineering optimizer (SEO) is introduced to balance global exploration and local exploitation. A turbo reducer case study is conducted to validate the proposed model, and comparative experiments with several multi-objective optimization algorithms demonstrate the effectiveness and robustness of the enhanced SEO. The results indicate that the DFHMR framework can provide decision-makers with a set of balanced modular recycling schemes, offering a practical reference for symmetry-oriented, sustainable and human-centered mechanical design under Industry 5.0. Full article

Urban river networks face significant ecological challenges due to intensive urbanization. Traditional assessment methods focus mainly on individual rivers and overlook cross-scale connections. To fill this research gap, the study refined the Urban Riverscape Conditions-based Assessment for Management Needs (URBAN) framework and developed a dual-scale assessment system covering the entire river network and individual rivers. It evaluates hydrology, geomorphology, ecology, and the waterfront public service dimension. Taking the Qingxi area of Shanghai as a case study, this study integrated multi-source data and adopted field investigations, the analytic hierarchy process (AHP) and principal component analysis (PCA) to collect field data, calculate indicator weights, and extract dominant functional factors. The results show that the overall comprehensive health score of the study area is 59.39, classified as average; the river network scale scores 58.34, and the 21 monitored rivers achieve an average score of 61.80. The assessment identifies clear advantages in hydrological and geomorphological conditions, whereas waterfront public services and river morphological diversity are still deficient. Overall, this system demonstrates good operability and scientific validity, providing practical technical approaches for sustainable urban river network management and supporting refined watershed governance. Full article

Ammonia is a key chemical for fertilizers, industrial processes, and emerging energy applications, yet its conventional production via the Haber–Bosch process is associated with high energy demand and significant greenhouse gas emissions. In this context, electrochemical routes for ammonia synthesis have attracted increasing attention as a potential sustainable alternative, enabling nitrogen conversion under milder conditions and using renewable electricity. This review examines recent advances in electrochemical ammonia production, focusing on nitrogen reduction mechanisms, catalyst development, and electrochemical system design. The main reaction pathways for nitrogen activation are analyzed, together with the role of electrocatalysts in determining activity and selectivity. Progress in catalyst engineering, electrolyte optimization, and reactor configuration is discussed, with particular emphasis on strategies to mitigate competing reactions such as hydrogen evolution. In addition, alternative approaches based on nitrate reduction are considered due to their promising performance and potential integration with wastewater treatment. Unlike many recent reviews primarily focused on catalyst development or individual reaction pathways, this review provides an integrated perspective encompassing nitrogen reduction, nitrate reduction, electrolyte engineering, reactor architectures, and techno-economic considerations, thereby highlighting the interdependence between materials design, reaction environment, and system-level integration for scalable electrochemical ammonia synthesis. Full article

The optimisation design of agricultural machinery is shifting from offline, experience-driven engineering towards adaptive, data-driven, and closed-loop intelligent optimisation. Conventional approaches based on computer-aided engineering (CAE), empirical testing, mathematical modelling, and static multi-objective optimisation have provided an important engineering foundation, but they remain limited under unstructured field conditions involving soil heterogeneity, crop variability, climatic disturbance, and nonlinear machinery–environment interactions. This review systematically examines the evolution of intelligent optimisation design for agricultural machinery from conventional simulation-based methods to artificial intelligence (AI)- and digital twin (DT)-enabled paradigms. First, mathematical modelling, response surface methodology, discrete element method (DEM), computational fluid dynamics (CFD), multi-body dynamics (MBD), heuristic algorithms, and early AI-assisted surrogate optimisation are reviewed to clarify their contributions and limitations. Second, frontier enabling technologies are analysed, including agriculture-specific large models, generative AI, lightweight edge intelligence, deep reinforcement learning (DRL), embodied AI, federated learning (FL), and privacy-preserving computing. Third, system-level applications integrating DT and AI are discussed, with emphasis on full-lifecycle machinery optimisation, device–edge–cloud collaborative control, multi-agent fleet coordination, predictive maintenance, and Agriculture 5.0-oriented intelligent equipment systems. Key deployment bottlenecks are further identified, including sim-to-real inconsistency, virtual–physical mismatch in DTs, edge-side trade-offs among accuracy, latency, energy consumption, and cost, insufficient validation standards, and economic adoption barriers. Finally, a 2025–2030 roadmap is proposed, highlighting large-model–DT closed loops, control biomimetics, green low-carbon optimisation, and trustworthy human–machine symbiosis for sustainable Agriculture 5.0. Full article

The objectives of this study are to examine the direct relationships among perceived ethics, perceived sustainability, customer trust, customer engagement, and customer loyalty; and to investigate the mediating roles of customer trust and customer engagement in explaining the relationship between ethical and sustainability perceptions and customer loyalty. Using the Stimulus–Organism–Response (SOR) framework and the Theory of Planned Behavior (TPB) as theoretical foundations, this research examines how ethical and sustainability perceptions within social commerce environments influence customers’ psychological states and behavioral responses. A quantitative approach was used, involving data collection from 360 Thai consumers who had previously bought sustainable fashion items through social media. The proposed model was then evaluated using partial least squares structural equation modeling (PLS-SEM). The results suggest that consumers’ evaluations of seller ethics significantly enhance their perceptions of product sustainability, customer trust, and engagement. Furthermore, perceived sustainability of fashion products affects both trust and engagement. Customer trust subsequently promotes both engagement and loyalty; however, customer engagement exhibits the most substantial direct effect on customer loyalty. Mediation analysis confirms the essential functions of trust and engagement in mediating the impacts of ethical and sustainability perceptions on loyalty. These findings highlight the importance of ethical transparency and proactive customer engagement in fostering trust and long-term customer loyalty within social media-based sustainable fashion commerce. Therefore. This study provides both theoretical and practical insights for sustainable fashion enterprises functioning within digital contexts. Full article

This study addresses second-home ownership not merely as a form of tourism accommodation or real estate investment, but as a home-building process intersecting with local life, belonging, daily practices, and sustainable destination governance. While the economic, environmental, and community impacts of second-homes have been extensively discussed in the literature, how individuals perceive their primary and secondary homes differently in terms of the bodily, material, vibrant, imaginary, and emotional dimensions of home has been examined in a limited number of studies. This research analyzes paired data obtained through a two-stage online questionnaire from 223 British participants who own a secondary home in the Mugla–Ortaca region and a primary home in the United Kingdom. The 18-item Home Scale was used as the measurement tool. Confirmatory factor analysis, reliability–validity analyses, measurement invariance, and paired-samples t-tests were applied. The findings show that the bodily home difference was not statistically significant at the conventional 0.05 threshold, whereas primary-home scores were significantly higher in the material, vibrant, imaginary, and emotional home dimensions. The small to small-medium effect sizes suggest that the results should be interpreted cautiously as an asymmetrical home-building process rather than as evidence of a hierarchical superiority of the primary home. The study proposes a planning approach that does not view second home owners as merely transient consumers in sustainable coastal–rural destinations, but rather considers social sustainability, service planning, seasonality management, and local community engagement channels together. Full article

Poplar (Populus) trees are indispensable to various industries and environmental sustainability efforts. They are widely utilized for paper production, timber, and windbreaks, while also playing a significant role in carbon sequestration. Given their economic and ecological importance, the effective management of diseases is crucial. Convolutional Neural Networks (CNNs), renowned for their ability to process visual data, are pivotal in accurately detecting and classifying plant diseases. This study presents a domain-specific dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, ensuring geographic diversity and broader applicability. The dataset includes four disease classes, i.e., “Parsha (Scab),” “Brown spotting,” “White-Gray spotting,” and “Rust,” which represent common afflictions in these regions. To advance research efforts, this dataset will be made publicly accessible, providing a valuable resource for the scientific community. Leveraging the cutting-edge YOLOv9c model, a state-of-the-art CNN architecture, we applied the Histogram Equalization technique as a preprocessing step to enhance the image quality to increase the accuracy of disease detection. This method not only improves the diagnostic performance of the model but also provides a scalable solution for monitoring and managing poplar diseases. By ensuring the health of poplar trees, this approach supports the sustainability of these critical resources. To our knowledge, this is the first publicly available dataset specifically focused on diseased poplar leaves, making it a significant contribution to global research efforts. It offers an invaluable resource for researchers and practitioners, enabling further advancements in early disease detection and sustainable forestry management. Full article

The growing demand for renewable energy, together with the need to mitigate climate change and promote more sustainable agriculture systems, has stimulated interest in energy crops. In this context, invasive alien plant species (IAPS), which have progressively colonized abandoned farmland, degraded ecosystems, and marginal areas, represent a key bioresource. IAPS have a dual nature combining high ecological invasiveness and fast growing rate with notable energetic potential. These aspects have generated a still ongoing debate among farm managers, ecologists, and policymakers regarding their role within the future bioeconomy. The present study provides a review of the IAPS black locust (Robinia pseudoacacia L.) on its real benefits as a source of bioenergy, ecological impact, and the management strategies adopted. We examine the trade-offs between containment efforts and use for renewable bioenergy production, particularly in marginal areas where few alternatives exist. This review highlights the need for stratified site-specific approaches that balance biodiversity conservation with bioresource exploitation. Finally, this study also contributes to the ongoing discussion on whether IAPS should be regarded primarily as a management challenge or a multifunctional bioresource, as in the production of bioenergy. Full article

Flotation is a fundamental unit operation in mineral processing; however, achieving high selectivity while reducing the environmental impact of reagents remains a major challenge in phosphate ore beneficiation. Conventional depressants often exhibit limited selectivity and may pose environmental concerns, highlighting the need for sustainable alternatives. This study reports, for the first time, the application of starch nanostructures derived from potato pulp processing residues as a depressant in phosphate flotation, representing an innovative and eco-friendly approach. An exploratory and experimental methodology was adopted, including nanostarch synthesis via acid hydrolysis followed by centrifugation and sonication, as well as comprehensive physicochemical characterization. The primary objective was to evaluate the selective depressant performance of the nanomaterial in apatite–calcite flotation systems. The synthesized nanostructures exhibited particle diameters ranging from 179 to 443.6 nm. Microflotation tests conducted in a Hallimond tube using pure mineral samples under alkaline conditions (pH ≈ 9), at a depressant dosage of 500 mg/L and in combination with a plant-based fatty acid collector, revealed a pronounced selectivity window, resulting in an approximately 77% difference in flotation recovery between apatite and calcite. These findings demonstrate that nanostarch derived from agro-industrial residues is a promising, biodegradable, and sustainable depressant capable of enhancing selectivity in phosphate flotation. The results contribute to the advancement of greener mineral processing Technologies, although Further studies are required to elucidate the underlying interaction mechanisms. Full article

Oblique detonation engines have been proposed for hypersonic propulsion because detonation-based heat addition can, in principle, provide rapid energy release with reduced total-pressure penalties. We investigate non-premixed injection/mixing of an RP-3 aviation-kerosene surrogate in a constrained supersonic channel and its impact on oblique-detonation initiation, stabilization, and static pressure gain. Numerical simulations are performed for a Mach 8 inflow representative of a 30 km altitude condition using an OpenFOAM v7-based reacting-flow solver. We analyze the pressure-gain process following detonation onset, quantify the effects of the inducer-ramp angle, and qualitatively assess the predicted initiation/stabilization trends against direct-connect hot-fire experiments. The results show that non-premixed injection into a supersonic crossflow yields limited mixing over the available mixing length and results in a strongly stratified inflow to the combustor. In the constrained passage, a train of reflected shocks forms and progressively reduces the total-pressure recovery factor along the mixing section, which asymptotically approaches ~0.49. In the combustor, the inducer-ramp angle controls whether and how a stabilized oblique detonation can be established. For a 25° ramp, no self-sustained ODW is obtained under the present conditions, whereas stabilized ODWs are observed for 30° and 35° ramps, exhibiting abrupt and smooth topologies, respectively. These initiation thresholds and stabilized morphologies show qualitative consistency with the direct-connect observations. Due to fuel stratification, pressure gain varies among streamlines but consistently follows a “primary compression–plateau–secondary pressure rise” sequence; the secondary stage contributes approximately 17.54–27.98% of the static pressure rise. Full article

The transition towards sustainable urban mobility requires planning approaches that integrate accessibility, social inclusion, environmental quality, and stakeholder preferences, particularly in medium-sized cities, where mobility challenges differ from those of large metropolitan areas. However, comparative evidence on how different stakeholder groups prioritize sustainable mobility strategies in such cities remains limited. This paper addresses this gap by applying a comparative Multi-Actor Multi-Criteria Analysis (MAMCA) to two medium-sized European cities: Cáceres, Spain, and Coimbra, Portugal. The analysis involved five stakeholder groups (citizens, entrepreneurs, public institutions, mobility operators, and academics) and used a common framework comprising five objectives, fifteen sub-objectives, and eight strategic alternatives for each city. The results show that both cities share strong priorities related to accessibility for vulnerable groups, safety, environmental quality, and public space. However, their preferred strategic pathways differ. In Coimbra, the highest support is associated with pedestrian infrastructure, public space improvements, and integrated spatial planning, whereas in Cáceres, the leading priorities are public transport connectivity, territorial integration, and accessibility for vulnerable groups. The study confirms the usefulness of MAMCA as a transferable decision-support framework for incorporating stakeholder preferences into sustainable mobility planning. Full article

Effective project selection is a critical determinant of success for Design for Six Sigma (DFSS), particularly in automotive environments defined by high technical complexity and constrained resources. Because these selection tasks involve competing priorities, they are fundamentally multi-criteria decision-making (MCDA) problems that directly impact a company’s economic performance. This paper proposes a hybrid decision-support framework that integrates the Analytic Hierarchy Process (AHP) with a normalized scoring model. In this approach, classical AHP pairwise comparisons are used to derive consistent criteria weights, while project alternatives are evaluated on a 1–10 normalized scale to ensure the model remains scalable and practical for an industrial setting. The framework was empirically validated through a case study in an automotive company evaluating twelve DFSS project concepts. The results reveal that experts prioritize Product Quality (33%) and Cost/Functionality (33%) above all other factors, with these two criteria accounting for 66% of the total decision weight. Furthermore, the study established classification rules where projects scoring above 7.2 showed high implementation potential, while those below 5.2 were frequently discontinued. This structured approach enables a transparent and justifiable prioritization process that supports economic and operational sustainability by significantly reducing wasted engineering hours and prototype costs. Full article

Since tires from end-of-life vehicles are not entirely biodegradable and pose a serious environmental problem, their disposal has become a significant global environmental concern. One technique to decrease these environmental issues is incorporating waste rubber to make sustainable green concrete. This study examined the usage of waste supplementary cementitious materials (SCMs) such as fly ash (FA), metakaolin (MK), marble powder (MP), slag (SL), and silica fume (SF) for surface precoating of crumb rubber (CR) to improve the mechanical properties of the produced crumb rubber concrete (CRC) by strengthening the bond between CR and cement paste in the interfacial transition zone (ITZ). The CR replaced (0, 15%, and 25%) of sand by weight in the preparation of CRC mixtures. A total of eleven CRC mixes were cast to investigate the fresh properties, compressive strength, and splitting tensile strength. In addition, the compressive stress-strain curve was investigated, and peak stress, peak strain, energy absorption, toughness, and modulus of elasticity have been evaluated. The outcomes showed that precoating CR using FA, followed by MK, has the strongest effect on increasing CRC compressive performance. The 25% substitution of sand with FA-treated CR increased compressive strength after 28 days, splitting tensile strength, peak stress, toughness, and modulus of elasticity by 34.7%, 23.7%, 34.8%, 26.1%, and 25.2%, respectively, in comparison to the same percentage of untreated CR. The proposed approach demonstrates a viable pathway for integrating waste materials and SCM-based technologies to develop high-performance, sustainable cementitious composites. Full article

Italy is one of Europe’s largest consumers of wood pellets, while domestic production remains comparatively limited. In parallel, wood chips (WC) represent a strategic biofuel for power generation, where particle size distribution (PSD) affects handling and storage. Conventional PSD assessment relies on time-consuming methodology. This study proposes a patent-pending image-processing approach (Shadow Size Distribution—SSD analysis) for PSD classification of WC under bulk conditions. One hundred samples were characterized via both standard analysis and SSD. PSD data were aggregated into fine and coarse macro-fractions and used to define binary class labels. Multivariate analyses (PERMANOVA, PCA) and Support Vector Classifier (SVC) models were employed to evaluate the discriminative capability of SSD features. PCA revealed coherent relationships between PSD macro-variables and key shadow descriptors, particularly shadow number and area. The best SVC configuration achieved 0.77 test accuracy, with strong recall for coarse samples. Although overall performance was constrained by dataset size and imbalance, the results demonstrate that SSD features retain meaningful granulometric information, supporting further development toward automated, in-line PSD monitoring systems. From a sustainability perspective, the proposed SSD-based approach enables faster and potentially in-line monitoring of biomass quality, supporting more efficient combustion processes, reduced emissions, and improved resource management in bioenergy systems. Full article

Research on cattle feeding and nutrition has increasingly integrated animal welfare considerations in response to evolving scientific, societal, and production challenges. This study aimed to characterise the global scientific landscape on this topic through a comprehensive bibliometric analysis. A structured methodological framework was applied using the Web of Science database, covering the period from 2009 to 2025, limited to literature published in English, Spanish, and Portuguese. The analysis followed five stages: research design, data collection, analysis, visualisation, and interpretation, using a broad search strategy combining terms related to cattle production, nutrition, feeding, health, stress, and welfare. Bibliometric indicators and science mapping techniques were implemented using the Bibliometrix package in R (Biblioshiny), including collaboration network analysis, keyword co-occurrence, thematic evolution, and Bradford’s Law to identify core journals. In total, 424 documents were analysed. The results showed sustained growth in scientific production, particularly from 2016 onwards, indicating consolidation of the field. Output was concentrated in a limited number of countries, institutions, and journals, supported by increasingly interconnected collaboration networks. Thematic trends revealed a shift towards integrative approaches linking nutrition with stress, health, and productivity, positioning nutrition as a key tool to enhance welfare and efficiency, although behavioural and socio-economic aspects remain underrepresented. Full article