Search Results (5,239)

Global agriculture generates more than 5 billion tonnes of post-harvest crop residues each year, most of which remain unused for energy production. Within the broader landscape of advanced biomass and waste conversion technologies (thermochemical and biochemical pathways), producing biomethane from agricultural residues represents a complementary waste-to-energy route that converts decentralized feedstock into a standardized energy carrier. Mobilizing this agro-biomass for biogas/biomethane production via the anaerobic digestion of crop residues offers a promising instrument for decarbonizing agriculture, reducing greenhouse gas emissions, and advancing a circular bioeconomy. This study provides a techno-economic, environmental, and market assessment of biomethane production from post-harvest residues—specifically wheat and barley straw and maize stover—in Ukraine. We estimate the feedstock potential of crop residues and substantiate environmentally permissible removal levels accounting for soil organic matter requirements; we also characterize the role of digestate and biochar amendments in improving soil fertility, increasing mineral nitrogen availability, and enhancing crop yields. The results indicate substantial greenhouse gas mitigation potential relative to fossil natural gas. Practical recommendations are proposed to scale biomethane production from crop residues as part of Ukraine’s agricultural sustainability strategy. Under current cost and policy assumptions, many biomethane projects in Ukraine approach commercial viability, particularly in regions where damaged gas infrastructure creates local demand for a decentralized gas supply. The paper evaluates market assessment and investment feasibility of crop-residue biomethane scenarios under cost, regulatory, and infrastructure constraints. Overall, the findings suggest that agricultural residues can serve as a key feedstock for decarbonizing agriculture and biomethane-based energy systems in Ukraine. Full article

Global transition toward sustainable micro-mobility is an essential aspect of Saudi Vision 2030; however, high car dependency remains a significant barrier to public health and safety targets. In this context, this study explores the factors determining the adoption of electric bicycles (e-bikes) in Al-Qunfudhah, Saudi Arabia. The present research used a convenience sampling strategy through an online survey conducted via social media and texting, utilizing a designed questionnaire of 10 sections delivered to 171 participants, alongside a 5-point Likert scale. Additionally, the scientific validation and analysis were conducted utilizing internal consistency, validity and scale reliability via statistical analysis. The findings indicated a significant intention–action disparity; while respondents demonstrate a strong psychological intention to adopt e-bikes within 12 months (an average of 3.51), real household ownership was relatively low at 11.1%. In addition, a significant 71.9% of participants use private vehicles for short-distance travel (<5 km), influenced by an average bus stop distance of 21.22 km. The hierarchy of barriers indicates infrastructure and security as the main barrier, particularly the absence of dedicated bike lanes, and concerns regarding traffic safety. In contrast, a perception of physical fitness, and interpersonal interaction behave as significant facilitators. Public health data reveals an average weekly activity of 109.77 min, significantly lower than worldwide recommendations; however, 66.7% of individuals believe e-bikes may address the difference. The statistical evaluation acknowledged the questionnaire’s robustness, with significant Pearson correlation coefficients (p < 0.01) demonstrating internal consistency validity and Cronbach’s alpha values between 0.71 and 0.88 indicating high scale reliability, demonstrating a scientifically stable framework for assessing the measured behavioral determinants. The research recommends the establishment of shaded, dedicated micro-mobility networks and the enforcement of safety regulations to promote a healthy, multi-modal urban ecosystem. Full article

Mobile data collectors (MDCs) play a very important role in Internet of Things (IoT) sensing networks. However, ensuring their trustworthiness against insider threats, such as on–off attacks and spatiotemporal fabrication, remains a critical challenge. Existing trust evaluation methods frequently struggle with these threats due to insufficient evidence dimensions and the inability to quantify behavioral stability. To address these limitations, this paper proposes an enhanced proactive trust evaluation system based on stability sequence extraction (E-PTES-S). E-PTES-S improves the evaluation accuracy by integrating five factors of evidence, stability-computation mechanisms, and an adaptive weight allocation scheme to maintain robustness even when proactive verification data is scarce. In addition to the usual interaction and proactive verification indicators, regional consistency (${\mathcal{T}}_{RC}$) and task timeliness (${\mathcal{T}}_{TT}$) are introduced to mitigate location falsification and transmit-time deviations more rigorously. Then, a sliding window technique is used to obtain an integrated evidence sequence, which includes a new continuous stability sequence (${\mathcal{F}}_{CSS}$) and traditional credible, untrustworthy, and uncertain sequences. This continuous stability sequence adds a variance-based incentive scheme to measure behavioral stability. Finally, the normalized trust value is derived from multiple indicators including multidimensional spatiotemporal evidence and stability metrics. Experimental results show that the proposed E-PTES-S achieves a normal node detection rate of 98.7% under complex dynamic conditions, outperforming the baseline PTES and Trust-SIoT algorithms by approximately 9% and 1%, respectively, while also improving the cumulative data collection profit by 4.8%. Furthermore, robustness analysis demonstrates that E-PTES-S exhibits excellent robustness against physical-layer uncertainties, successfully sustaining an 84.4% detection rate even under severe environmental shadowing. Full article

Casablanca, Morocco’s most populous and economically dynamic metropolis, is undergoing rapid and unregulated expansion, leading to accelerated agricultural land artificialization, landscape fragmentation, and growing socio-environmental vulnerability in peri-urban territories. This study investigates the spatio-temporal dynamics of urban expansion within a 40 km buffer around the city, using multi-temporal Landsat imagery (2015–2025), a GIS-based framework, and supervised classification. Four land-cover classes were extracted (urban, vegetation, forest and water) enabling a diachronic comparison of land transformation processes. Two spatial indicators were mobilized to quantify urban dynamics: the Average Urban Expansion Rate (AUER) and the Urban Expansion Intensity Index (UEII). Results reveal that urban areas expanded by up to 387.9% in some communes, with 15 exceeding an AUER of 25% and 17 falling within the “very high development” category based on UEII thresholds. Land artificialization was most intense along southern and southeastern peripheries, notably Deroua, Tit Mellil, Had Soualem, and Sidi Moussa Ben Ali, resulting in severe fragmentation of agricultural land. The classification of communes into four profiles (fast, slow, consolidated, and stable) highlights varying degrees of territorial vulnerability. By integrating demographic trends (2014–2024), the study exposes mismatches between population growth and land consumption, underscoring the urgent need for integrated spatial diagnostics and governance reforms toward sustainable peri-urban land management. Full article

Labor shortages and the need for increased productivity have accelerated the development of robotic harvesting systems for greenhouse crops; however, reliable operation under fruit occlusion and clustered arrangements remains a major challenge, particularly due to the limited integration between perception and motion planning modules. The paper presents the design and experimental validation of an autonomous robotic system for greenhouse tomato harvesting. The proposed platform integrates a rail-guided mobile base, a six-degrees-of-freedom robotic manipulator, and an adaptive end effector with a hybrid vision framework that combines convolutional neural networks and watershed-based segmentation to enable robust fruit detection and localization under occluded conditions. The proposed approach enables improved separation of overlapping fruits and provides accurate spatial localization through stereo vision combined with IMU-assisted camera-to-robot coordinate transformation. An occlusion-aware trajectory planning strategy was developed to generate collision-free manipulation paths in the presence of leaves and stems, enhancing harvesting safety and reliability. The system was trained and evaluated using a dataset of real greenhouse images supplemented with synthetic data augmentation. Experimental trials conducted under practical greenhouse conditions demonstrated a fruit detection precision of 96.9%, recall of 93.5%, and mean Intersection-over-Union of 79.2%. The robotic platform achieved an overall harvesting success rate of 78.5%, reaching 85% for unobstructed fruits, with an average cycle time of 15 s per fruit in direct harvesting scenarios. The rail-guided mobility significantly improved positioning stability and repeatability during manipulation compared with fully mobile platforms. The results confirm that integrating hybrid perception with occlusion-aware motion planning can substantially improve the functionality of robotic harvesting systems in protected cultivation environments. The proposed solution contributes to the advancement of automation technologies for greenhouse vegetable production and supports the transition toward more sustainable and labor-efficient agricultural practices. Full article

In the pursuit of sustainable and flexible electronics, polymer-based conductive films offer a promising solution due to their biodegradability, mechanical flexibility, and cost-effective fabrication. This study presents the development of a highly conductive and flexible nanocomposite material based on polyaniline-grafted chitosan (PANI-g-Chs) and Vinavil (Vi, a vinyl glue specifically designed for enhancing the sealability of textiles and paper), serving as a matrix for applications in flexible electronics. The PANI-g-Chs nanocomposite was synthesized via in situ oxidative polymerization, where chitosan nanoparticles (Chs) served as a stabilizing template to prevent PANI aggregation, reducing the particle size from 1700 nm (pristine PANI) to 180 nm (PANI-g-Chs). The resulting composite exhibited exceptional electrical conductivity (77.79 S/m at 25 wt% PANI-g-Chs). Hall effect measurements showed that the carrier mobility increased up to 1162.7 cm²/V·s and the carrier density rose to 6.5.1017 cm⁻³, confirming efficient charge transport and network formation. Mechanical analysis revealed a 300% increase in the storage modulus for PANI-g-Chs, and thermal studies confirmed stability up to 300 °C. Optical characterization showed a reduced bandgap (3.6 eV) and extended π-conjugation, which are critical for optoelectronic applications. Application tests demonstrated stable conductivity under mechanical deformation, highlighting the material’s potential for use in flexible electronics, sensors, and sustainable conductive coatings. This work offers a viable alternative to conventional conductive polymers. Full article

Background: Breast cancer-related lymphedema (BCRL) is one of the most debilitating long-term morbidities after axillary lymph node dissection (ALND), severely impairing quality of life through reduced mobility, independence, and chronic burden, especially in older women. Axillary reverse mapping (ARM) aims to preserve upper extremity lymphatics while maintaining oncologic safety. Evidence in older adult populations with long-term follow-up remains limited. Methods: This retrospective cohort study included 138 female patients (median age 72.5 years) undergoing ALND for invasive breast cancer between January 2018 and January 2024. Patients were divided into ARM (n = 72) and non-ARM (n = 66) groups. BCRL was graded 0–3 according to adapted International Society of Lymphology (ISL) criteria (2013 consensus document). Assessments were performed preoperatively and at 3, 6, 12, 24, 36, 48, and 60 months using blinded circumference measurements and bioimpedance spectroscopy. Results: Baseline characteristics were comparable. Mean follow-up was 46.5 ± 8.8 months. Clinically relevant BCRL (Grades 2–3) was dramatically lower in the ARM group (18.1% vs. 60.6%, p < 0.0001), while subclinical changes (Grade 1) were similar (31.9% vs. 27.3%, p = 0.55). Kaplan–Meier analysis showed significantly better clinically relevant lymphedema-free survival with ARM (log-rank p = 0.00019), with curve separation after 30–40 months—indicating a sustained long-term benefit for quality of life in this frail population. Recurrence rates were comparable (8.3% vs. 10.6%, p = 0.776). Multivariable Cox regression confirmed ARM as an independent protective factor (adjusted HR 0.22, 95% CI 0.11–0.44, p < 0.0001). Conclusions: In older women with breast cancer, ARM significantly reduces clinically relevant lymphedema—a major determinant of long-term quality of life—without compromising oncologic safety. These findings support the routine consideration of ARM during ALND to preserve upper-extremity function, mobility, and independence in this vulnerable population, thereby balancing aggressive oncologic treatment with enhanced long-term quality of life and reduced treatment-related morbidity. Full article

To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives. Full article

Bio-based, biodegradable, and renewable polymers offer a promising alternative to traditional synthetic polymers derived from petroleum or other non-renewable resources. However, their use is limited by suboptimal properties and high costs. Incorporating sustainable reinforcements into the polymer matrix significantly improves biopolymer performance while preserving key properties, sustainability, and cost-effectiveness. Bio-based polymeric composites have emerged as a crucial category of biopolymers, playing a key role in advancing a sustainable, circular economy. This review provides an updated overview of bio-based polymer composites and nanocomposites, focusing on reinforcement strategies using natural nanofillers and engineered nanoparticles. We summarize key synthesis and processing methods, discuss structure–property relationships, and highlight recent advances in applications such as food packaging, biomedical devices, energy systems, environmental remediation, 3D printing, and supercapacitors. Polymer nanocomposites are versatile, with their performance depending on the type, size, and interactions between the fillers and the polymer matrix. Progress in metallic, ceramic, carbon-based, natural, and hybrid fillers has improved their properties. Using bio-based polymers and renewable fillers supports sustainability. Natural nanofillers derived from renewable sources and industrial byproducts offer a sustainable approach to developing high-performance, biodegradable nanocomposites. Smart nanocomposites can react to external stimuli by integrating specialized fillers that enhance their mechanical and mobility properties. Shape memory nanocomposites can be remotely activated—using heat, electricity, magnets, or light—enabling advanced applications. Finally, we address major challenges and outline future directions for scalable, circular-material solutions, drawing on perspectives from the circular economy and life cycle assessment (LCA). Full article

Food waste in households means that there is a need to recognize the possibilities of balancing activities in the field of obtaining and managing food products. Activities in this area may concern giving away surplus food to others, purchasing local and organic products, limiting shopping activity. Generation Z, which was included in this research, uses new media, including mobile applications, to a greater extent than other generations. The main objective of the research is to recognize and present the food-sustainable behaviors and attitudes of Generation Z consumers. The study used the analysis of source data, which was the basis for formulating four hypotheses. They were verified in empirical studies conducted using the CAWI method. The collected material was analyzed using, among others, the proprietary index of environmentally and socially sustainable behaviors (ESRBI), the Mann-Whitney test. The studies showed that respondents assessed their food behaviors as irresponsible, with women’s assessment being higher than men’s. A positive correlation was demonstrated between the use of food saving applications and the value of the ESRBI index and individual sustainable behaviors. Respondents positively assessed the initiatives of local authorities and housing cooperatives in the area of creating places for sharing food and organizing community gardens. Full article

Tourism’s digital transformation has reshaped how travelers search for and evaluate destinations. However, relatively little empirical work has examined how user engagement translates into booking intent, especially under the emergent discovery channels mediated by artificial intelligence (AI). This study tests an engagement-driven referral framework using longitudinal behavioral data from a Mediterranean destination portal (April 2022–January 2026; 1.6 million sessions). Engagement depth, measured as average session time, significantly predicts booking intent click rate. Mobile drives 83% of sessions, but desktop users convert at nearly twice the rate (5.69% vs. 3.37%). High traffic, as it turns out, does not equal high commercial intent. Lower-volume international markets routinely outperform the dominant domestic market. The most striking result concerns AI referrals. Traffic arriving from AI assistants converts at 8.26%, more than double the organic search rate of 3.88%, despite shorter sessions, a pattern consistent with compressed decision-making under generative AI. These findings, grounded in real travel portal data, extend engagement theory beyond transactional settings and shed early light on how referrals from AI assistants like ChatGPT or Gemini differ behaviorally from organic search, with practical implications for portal managers, destination marketing organizations (DMOs), and sustainable demand management. Full article

Public transportation (PT) is key to breaking the vicious cycle of private vehicles, a critical sustainability challenge in developing countries. The increase in population raises the number of private cars, and this trend continues. PT plays a vital role in reducing car use, traffic congestion, and environmental pollution. Fare is crucial to the system’s ability to encourage passengers to use PT. It affects mobility, the quality of life, and the sustainability of the system. This study aims to examine Istanbul’s optimal fare system using the BWM (Best–Worst Method) for PT fare for the first time. Furthermore, it is the first study to compare fare structures and criteria for Istanbul, Europe’s second-largest city, where transportation affects quality of life. The most frequently used fare structures and criteria in the literature and practice were weighted by experts using BWM surveys for the Istanbul Metro. The results show that distance-based fare (DBF) (43.7%) is the best fare structure, while flat fare (FF) (12.2%) is the weakest. For the criteria weightings, benefit received (24.4%) and social equity (22.7%) are seen as superior. Finally, the impact of the criterion on the fare structure was demonstrated through analysis, and its importance for experts in evaluating PT was highlighted. Full article

Research on information and communication technologies (ICTs) in sustainable agriculture has largely been technocentric, focusing on effectiveness, efficiency, and adoption, with limited consideration of end-user perceptions in practice. This study addresses this gap by examining perceptions of mobile gardening apps as accessible ICT tools that may support sustainable behaviours. Based on over 180,000 user reviews from Google Play and the Apple App Store, Contextualized Topic Modeling (CTM) was used to identify key themes and interpret them within the Theory of Consumption Value (TCV) framework. This approach allows for the analysis of functional, emotional, and epistemic dimensions of user experiences based on large-scale, real-world data. The results indicate that functional aspects, such as reliability and usability, dominate app evaluation, but emotional engagement and knowledge acquisition also play a significant role. By combining a data-driven approach with a well-established behavioural framework, this study bridges the gap between technological and user perspectives. It simultaneously extends the application of the TCV to the field of ICT solutions supporting sustainable development and provides practical guidance for designing more effective gardening apps. Full article

Polyvinyl chloride (PVC) is widely used in engineering applications; however, its inherent thermal instability associated with dehydrochlorination limits its processing window and long-term performance. While blending with thermoplastic polyurethane (TPU) and plasticization are common strategies to improve flexibility, their combined influence on the thermal behavior and stability of PVC, particularly when bio-based plasticizers are employed, has not been thoroughly investigated. In this study, the thermal behavior and stability of PVC/TPU blends plasticized with glycerol diacetate monolaurate, a bio-based plasticizer derived from waste cooking oil, were investigated. Dynamic mechanical analysis (DMA) and Fourier transform infrared spectroscopy (FTIR) were used to examine segmental mobility and intermolecular interactions, while scanning electron microscopy (SEM) provided insight into microstructural organization. Thermal stability was evaluated through conductivity-based dehydrochlorination measurements, complemented by thermogravimetric and derivative thermogravimetric analyses (TGA/DTG) to assess degradation behavior. The results showed that neither TPU nor the bio-plasticizer alone improved the resistance of PVC to dehydrochlorination. In contrast, ternary PVC/TPU/bio-plasticizer blends exhibited a pronounced delay in HCl evolution, accompanied by a more homogeneous phase distribution and interaction-driven modification of the molecular environment. TGA/DTG analysis indicated that this stabilization arises from altered degradation kinetics rather than a simple shift in degradation onset. Overall, the findings clarify the thermal behavior of PVC-based blends and demonstrate a sustainable formulation approach for achieving flexible and thermally balanced PVC materials while reducing reliance on potentially toxic phthalate plasticizers. Full article

Road traffic accidents at intersections pose a persistent challenge in Riyadh, Saudi Arabia, contributing significantly to public health burdens and economic losses. Traditional statistical approaches often fail to capture the complex, non-linear interactions among geometric design, traffic parameters, and accident severity. This study develops a multi-methodological machine learning framework to predict intersection accident severity using the Equivalent Property Damage Only (EPDO) metric. Historical data (2017–2023) from Riyadh Municipality for 150 high-risk intersections were analyzed, incorporating predictors such as service road distance (SRD), U-turn distance (UTD), median width (MW), peak hour volume (PHV), heavy vehicle percentage (HV%), and injury/frequency counts. Six algorithms, i.e., Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Linear Regression, and Artificial Neural Network, were compared using a 70/30 train–test split and k-fold cross-validation in this study. The Gradient Boosting model achieved superior performance (R² = 0.89 with MSE = 63.43 and RMSE = 7.96) and was selected for final deployment. SHAP feature importance analysis revealed minor injuries (MIs), serious injuries (SRIs), and fatalities (FAs) as the most important dominant predictors, with geometric factors (UTD, MW) and traffic composition (HV%) providing actionable infrastructure insights. The model ranked intersections and identified the “Jeddah Road with Taif Road” (predicted EPDO = 137.22) as the highest-risk location. Evidence-based recommendations include enforcing the minimum 300 m U-turn buffers with staggering service road exits ≥150 m and restricting heavy vehicles during peak hours. The scalable framework developed in this study supports the data-driven prioritization of safety interventions and aligns with sustainable urban mobility goals and offers transferability to other metropolitan contexts worldwide. Full article