Search Results (2,419)

As an efficient clean energy technology, water electrolysis for hydrogen production has its efficiency limited by the sluggish oxygen evolution reaction (OER) kinetics, which drives the demand for the development of high-performance anode OER catalysts. This work constructs bimetallic (Al, Mn) co-doped nanoporous spinel CoFe₂O₄ (np-CFO) with a tunable structure and composition as an OER catalyst through a simple two-step dealloying strategy. The as-formed np-CFO (Al and Mn) features a hierarchical flaky configuration; that is, there are a large number of fine nanosheets attached to the surface of a regular micron-sized flake, which not only increases the number of active sites but also enhances mass transport efficiency. Consequently, the optimized catalyst exhibits a low OER overpotential of only 320 mV at a current density of 10 mA cm⁻², a minimal Tafel slope of 45.09 mV dec⁻¹, and exceptional durability. Even under industrial conditions (6 M KOH, 60 °C), it only needs 1.83 V to achieve a current density of 500 mA cm⁻² and can maintain good stability for approximately 100 h at this high current density. Theoretical simulations indicate that Al and Mn co-doping could indeed optimize the electronic structure of CFO and thus decrease the energy barrier of OER to 1.35 eV. This work offers a practical approach towards synthesizing efficient and stable OER catalysts. Full article

The accelerating integration of electric vehicles (EVs) into contemporary transportation infrastructure has underscored significant limitations in traditional charging paradigms, particularly in accommodating heterogeneous user requirements within dynamic operational environments. This study presents a differentiated optimization framework for EV charging strategies through the systematic classification of user types. A multidimensional decision-making environment is established for three representative user categories—residential, commercial, and industrial—by synthesizing time-variant electricity pricing models with dynamic carbon emission pricing mechanisms. A bi-level optimization architecture is subsequently formulated, leveraging deep reinforcement learning (DRL) to capture user-specific demand characteristics through customized reward functions and adaptive constraint structures. Validation is conducted within a high-fidelity simulation environment featuring 90 autonomous EV charging agents operating in a metropolitan parking facility. Empirical results indicate that the proposed typology-driven approach yields a 32.6% average cost reduction across user groups relative to baseline charging protocols, with statistically significant improvements in expenditure optimization (p < 0.01). Further interpretability analysis employing gradient-weighted class activation mapping (Grad-CAM) demonstrates that the model’s attention mechanisms are well aligned with theoretically anticipated demand prioritization patterns across the distinct user types, thereby confirming the decision-theoretic soundness of the framework. Full article

Plastic pollution is a growing environmental and social concern, particularly in Southeast Asia, where urban rivers serve as key pathways for transporting waste to marine environments. This scoping review examines 110 peer-reviewed studies to understand how plastic pollution in waterways is being researched, addressed, and reconceptualized. Drawing from the literature across environmental science, technology, and social studies, we identify four interconnected areas of focus: urban pollution pathways, innovations in monitoring and methods, community-based interventions, and interdisciplinary perspectives. Our analysis combines qualitative synthesis with visual mapping techniques, including keyword co-occurrence networks, to explore how real-time tools, such as IoT sensors, multi-sensor systems, and geospatial technologies, are transforming the ways plastic waste is tracked and analyzed. The review also considers the growing use of novel theoretical frameworks, such as post-phenomenology and ecological materialism, to better understand the role of plastics as both pollutants and ecological agents. Despite progress, the literature reveals persistent gaps in longitudinal studies, regional representation, and policy translation, particularly across the Global South. We emphasize the value of participatory models and community-led research in bridging these gaps and advancing more inclusive and responsive solutions. These insights inform the development of plastic tracker technologies currently being piloted in Vietnam and contribute to broader sustainability goals, including SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 14 (Life Below Water). Full article

We establish a rigorous kinetic-theoretical framework to analyze causal propagation in thermal transport phenomena within relativistic ideal fluids, building a more rigorous framework based on the kinetic theory of gases. Specifically, we provide a refined derivation of the wave equation governing thermal and density fluctuations, clarifying its hyperbolic nature and the associated characteristic propagation speeds. The analysis confirms that thermal fluctuations in a simple non-degenerate relativistic fluid satisfy a causal wave equation in the Euler regime, and it recovers the classical expression for the speed of sound in the non-relativistic limit. This work offers enhanced mathematical and physical insights, reinforcing the validity of the hyperbolic description and suggesting a foundation for future studies in dissipative relativistic hydrodynamics. Full article

Given the severe global traffic safety challenges—including threats to human lives and socioeconomic impacts—this study analyzes visual behavior to promote sustainable transportation, improve road safety, and reduce resource waste and pollution caused by accidents. Four typical road sections, namely, turning, straight ahead, uphill, and downhill, were selected, and the eye movement data of 23 drivers in different driving stages were collected by aSee Glasses eye-tracking device to analyze the visual gaze characteristics of the drivers and their transfer patterns in each road section. Using Markov chain theory, the probability of staying at each gaze point and the transfer probability distribution between gaze points were investigated. The results of the study showed that drivers’ visual behaviors in different road sections showed significant differences: drivers in the turning section had the largest percentage of fixation on the near front, with a fixation duration and frequency of 29.99% and 28.80%, respectively; the straight ahead section, on the other hand, mainly focused on the right side of the road, with 31.57% of fixation duration and 19.45% of frequency of fixation; on the uphill section, drivers’ fixation duration on the left and right roads was more balanced, with 24.36% of fixation duration on the left side of the road and 25.51% on the right side of the road; drivers on the downhill section looked more frequently at the distance ahead, with a total fixation frequency of 23.20%, while paying higher attention to the right side of the road environment, with a fixation duration of 27.09%. In terms of visual fixation, the fixation shift in the turning road section was mainly concentrated between the near and distant parts of the road ahead and frequently turned to the left and right sides; the straight road section mainly showed a shift between the distant parts of the road ahead and the dashboard; the uphill road section was concentrated on the shift between the near parts of the road ahead and the two sides of the road, while the downhill road section mainly occurred between the distant parts of the road ahead and the rearview mirror. Although drivers’ fixations on the front of the road were most concentrated under the four road sections, with an overall fixation stability probability exceeding 67%, there were significant differences in fixation smoothness between different road sections. Through this study, this paper not only reveals the laws of drivers’ visual behavior under different driving environments but also provides theoretical support for behavior-based traffic safety improvement strategies. Full article

Aluminum (Al) stress is an important factor that inhibits crop growth in acidic soils and poses a threat to pumpkin (Cucurbita moschata) production. In this study, we investigated the effect of endophyte (endophyte) strain J01 of blueberry (Vaccinium uliginosum) on the growth, development, and transcriptional regulatory mechanisms of pumpkin under aluminum stress. The results showed that the blueberry endophyte strain J01 significantly increased the root length of pumpkin under aluminum stress, promoted the growth of lateral roots, and increased root vigor; strain J01 reduced the content of MDA and the relative conductivity in the root system; strain J01 enhanced the activities of superoxide dismutase and catalase in the root system but inhibited ascorbate peroxidase activity. Transcriptome analysis further revealed that strain J01 significantly regulated the expression of key genes associated with aluminum tolerance, including the upregulation of transporter protein genes (aluminum-activated malate transporter and aquaporin), affecting the gene expression levels of genes encoding antioxidant enzymes (ascorbate peroxidase and glutathione S-transferase) and cell wall modification genes (xyloglucan endotransglucosylase/hydrolase and pectin methylesterase). This study provides a theoretical basis and practical guidance for using microbial resources to improve aluminum tolerance in cucurbit crops. Full article

The aim of this paper is to tailor the geometry and material parameters of a three-layer railway track model to achieve favorable properties for the circulation of high-speed trains at very high velocities. The three layers imply that the model should have three critical velocities for resonance. However, in many cases, some of these values are missing and must be replaced by pseudo-critical values. Since no resonance occurs at pseudo-critical velocities, even in the absence of damping, deflections never reach infinity. By using optimization techniques, it is possible to adjust the model’s parameters, so that the increase in vibrations remains minimal and does not pose a real danger. In this way, circulation velocities could be extended beyond the critical value, thereby increasing the network capacity and, consequently, improving the competitiveness of rail transport compared to other modes of transportation, thus contributing to decarbonization. The presented results are preliminary and require further analysis and validation. Several optimization techniques are implemented, leading to the establishment of designs that already have rather high pseudo-critical velocities. Further research will show how these theoretical findings can be utilized in practice.  Full article

The multiscale interactive system composed of wind, leaves, and droplets serves as a critical dynamic unit in precision orchard spraying. Its coupling mechanisms fundamentally influence pesticide transport pathways, deposition patterns, and drift behavior within crop canopies, forming the foundational basis for achieving intelligent and site-specific spraying operations. This review systematically examines the synergistic dynamics across three hierarchical scales: Droplet–leaf surface wetting and adhesion at the microscale; leaf cluster motion responses at the mesoscale; and the modulation of airflow and spray plume diffusion by canopy architecture at the macroscale. Key variables affecting spray performance—such as wind speed and turbulence structure, leaf biomechanical properties, droplet size and electrostatic characteristics, and spatial canopy heterogeneity—are identified and analyzed. Furthermore, current advances in multiscale modeling approaches and their corresponding experimental validation techniques are critically evaluated, along with their practical boundaries of applicability. Results indicate that while substantial progress has been made at individual scales, significant bottlenecks remain in the integration of cross-scale models, real-time acquisition of critical parameters, and the establishment of high-fidelity experimental platforms. Future research should prioritize the development of unified coupling frameworks, the integration of physics-based and data-driven modeling strategies, and the deployment of multimodal sensing technologies for real-time intelligent spray decision-making. These efforts are expected to provide both theoretical foundations and technological support for advancing precision and intelligent orchard spraying systems. Full article

Firstly, this paper reviews the fundamental theories of solid surface wettability and contact angle hysteresis. Subsequently, it further introduces four typical wettability-engineered surfaces with low hysteresis (superhydrophobic, superamphiphobic, super-slippery, and liquid-like smooth surfaces). Finally, it focuses on the latest research progress in the field of droplet manipulation on open planar surfaces with engineered wettability. To achieve droplet manipulation, the core driving forces primarily stem from natural forces guided by bioinspired gradient surfaces or the regulatory effects of external fields. In terms of bioinspired self-propelled droplet movement, this paper summarizes research inspired by natural organisms such as desert beetles, cacti, self-aligning floating seeds of emergent plants, or water-walking insects, which construct bioinspired special gradient surfaces to induce Laplace pressure differences or wettability gradients on both sides of droplets for droplet manipulation. Moreover, this paper further analyzes the mechanisms, advantages, and limitations of these self-propelled approaches, while summarizing the corresponding driving force sources and their theoretical formulas. For droplet manipulation under external fields, this paper elaborates on various external stimuli including electric fields, thermal fields, optical fields, acoustic fields, and magnetic fields. Among them, electric fields involve actuation mechanisms such as directly applied electrostatic forces and indirectly applied electrocapillary forces; thermal fields influence droplet motion through thermoresponsive wettability gradients and thermocapillary effects; optical fields cover multiple wavelengths including near-infrared, ultraviolet, and visible light; acoustic fields utilize horizontal and vertical acoustic radiation pressure or acoustic wave-induced acoustic streaming for droplet manipulation; the magnetic force acting on droplets may originate from their interior, surface, or external substrates. Based on these different transport principles, this paper comparatively analyzes the unique characteristics of droplet manipulation under the five external fields. Finally, this paper summarizes the current challenges and issues in the research of droplet manipulation on the open planar surfaces and provides an outlook on future development directions in this field. Full article

Information and communication technologies (ICTs) are becoming increasingly important for sustainable rural development through the smart village concept. This study aims to model ICT’s potential for public services management in European rural areas. It identifies ICT applications across rural service domains, analyzes how these technologies address specific rural challenges, and evaluates their benefits, implementation barriers, and future prospects for sustainable rural development. A qualitative content analysis method was applied using purposive sampling to analyze 79 peer-reviewed articles from EBSCO and Elsevier databases (2000–2024). A deductive approach employed predefined categories to systematically classify ICT applications across rural public service domains, with data coded according to technology scope, problems addressed, and implementation challenges. The analysis identified 15 ICT application domains (agriculture, healthcare, education, governance, energy, transport, etc.) and 42 key technology categories (Internet of Things, artificial intelligence, blockchain, cloud computing, digital platforms, mobile applications, etc.). These technologies address four fundamental rural challenges: limited service accessibility, inefficient resource management, demographic pressures, and social exclusion. This study provides the first comprehensive systematic categorization of ICT applications in smart villages, establishing a theoretical framework connecting technology deployment with sustainable development dimensions. Findings demonstrate that successful ICT implementation requires integrated urban–rural cooperation, community-centered approaches, and balanced attention to economic, social, and environmental sustainability. The research identifies persistent challenges, including inadequate infrastructure, limited digital competencies, and high implementation costs, providing actionable insights for policymakers and practitioners developing ICT-enabled rural development strategies. Full article

Background: To address the uncertainties of renewable energy power generation, the disorderly charging characteristics of electric vehicles, and the high electricity cost of the power grid in expressway service areas, a method of economic dispatch optimization based on the improved particle swarm optimization algorithm is proposed in this study. Methods: Mathematical models of photovoltaic power generation, energy storage systems, and electric vehicles were established, thereby constructing the microgrid system model of the power load in the expressway service area. Taking the economic cost of electricity consumption in the service area as the objective function and simultaneously meeting constraints such as power balance, power grid interactions, and energy storage systems, a microgrid economy dispatch model is constructed. An improved particle swarm optimization algorithm with time-varying parameters of the inertia weight and learning factor was designed to solve the optimal dispatching strategy. The inertia weight was improved by adopting the Gaussian decreasing method, and the asymmetric dynamic learning factor was adjusted simultaneously. Findings: Field case studies demonstrate that, compared to other algorithms, the improved Particle Swarm Optimization algorithm effectively reduces the operational costs of microgrid systems while exhibiting accelerated convergence speed and enhanced robustness. Value: This study provides a theoretical mathematical reference for the economic dispatch optimization of microgrids in renewable-integrated transportation systems. Full article

Noncoplanar spin textures give rise not only to unusual magnetic structures but also to emergent electromagnetic responses stemming from scalar spin chirality, such as the topological Hall effect. In this study, we theoretically investigate nonreciprocal transport phenomena induced by noncoplanar magnetic orderings through microscopic model analyses. By focusing on meron–antimeron spin textures that exhibit local scalar spin chirality while maintaining vanishing global chirality, we demonstrate that the electronic band structure becomes asymmetrically modulated, which leads to the emergence of nonreciprocal transport. The present mechanism arises purely from the noncoplanar magnetic texture itself and requires neither net magnetization nor relativistic spin–orbit coupling. We further discuss the potential relevance of our findings to the compound Gd₂PdSi₃, which has been suggested to host a meron–antimeron crystal phase at low temperatures. Full article

Intelligent driving is a key technology in the field of automotive manufacturing due to its advantages in environmental protection, energy efficiency, and economy. However, since the intelligent driving model is an uncertain multi-input multi-output dynamic system, especially in an interactive environment, it faces uncertainties such as non-uniform trial lengths, unknown nonlinear parameters, and unknown control direction. In this paper, an adaptive iterative learning control method is proposed for vehicle systems with non-uniform trial lengths and asymmetric control gain matrices. Unlike the existing research on adaptive iterative learning for non-uniform test lengths, this paper assumes that the elements of the system’s control gain matrix are asymmetric. Therefore, the assumption made in traditional adaptive iterative learning methods that the control gain matrix of the system is known or real, symmetric, and positive definite (or negative definite) is relaxed. Finally, to prove the convergence of the system, a composite energy function is designed, and the effectiveness of the adaptive iterative learning method is verified using vehicle systems. This paper aims to address the challenges in intelligent driving control and decision-making caused by environmental and system uncertainties and provides a theoretical basis and technical support for intelligent driving, promoting the high-quality development of intelligent transportation. Full article

The excellent moisture transport properties of yarns play a crucial role in improving the liquid moisture transfer behavior within textiles and maintaining their thermal-wet comfort. However, the current research on the moisture management performance of fabrics made from yarns with excellent liquid transport properties primarily compares the wicking results, without considering the varying requirements of testing conditions due to differences in human sweating rates during daily activities. Moreover, the understanding of moisture transport mechanisms in yarns within fabrics under different testing conditions remains insufficient. In this study, two types of twisted combination yarns, composed of hydrophobic profiled polyester filaments and hydrophilic spun yarns to form a hydrophobic-hydrophilic gradient along the axial direction of the yarn, were developed and compared with profiled polyester filaments to understand the liquid migration behaviors in the knitted fabrics formed by these yarns. Results showed that hydrophobic profiled polyester filament yarn demonstrated superior liquid transport performance with infinite saturated liquid supply (vertical wicking test). In contrast, the twisted combination yarns exhibited better moisture diffusion properties under limited liquid droplet supply conditions (droplet diffusion test and moisture management test). These contradictory findings indicated that the amount of liquid moisture supply in testing conditions significantly affected the moisture transport performance of yarns within fabrics. It was revealed that the liquid moisture in the twisted combination yarns migrated through capillary wicking for moisture transfer. Under an infinite saturated liquid supply condition, the higher the content of hydrophilic fibers in the spun yarns, the greater the amount of moisture transferred, demonstrating an excellent liquid transport performance. Under the limited liquid droplet supply conditions, both the volume of liquid water and the moisture absorption capacity of the yarn jointly influence internal moisture migration within the yarn. It provided a theoretical reference for testing the internal moisture wicking performance of fabrics under different states of human sweating. Full article

In the era of “carbon dioxide peaking and carbon neutrality”, low-carbon development of road traffic and transportation has now become a rigid demand in China. Considering the fact that socioeconomic and demographic characteristics vary significantly across Chinese cities, proper city-level transportation development strategies should be established. Using detailed data from cities at prefecture level and above in China, this study investigates the spatially heterogeneous effects of various factors on road traffic CO₂ emissions. Another theoretical issue is concerned with the analytic method for zonal CO₂ emission modeling. We combine the concepts of geographically weighted regression (GWR) and machine learning for nonparametric regression, proposing a modified random forest (RF) algorithm, named “geographically weighted random forest” (GWRF). Our empirical analysis indicates that, when an appropriate weight parameter is applied, GWRF is able to achieve significantly superior performance compared to both the traditional RF and GWR methods. Moreover, the influences of various explanatory variables on CO₂ emissions differ across cities. These findings suggest that low-carbon transportation strategies should be customized to reflect regional heterogeneity, rather than relying on a unified national policy. Full article