Search Results (1,166)

This study explores and evaluates different methodologies for designing the edge-of-traveled-way turning paths at right-angle at-grade intersections, with emphasis on low-speed maneuvers involving large design vehicles. Three geometric approaches are examined as follows: the standard AASHTO configuration, the German RAS-K-1 triple-radius method, and a clothoid-based transition curve design. Simulations using representative design vehicles, conducted under speeds ≤ 15 km/h, are used to assess each method’s performance in terms of spatial efficiency, steering continuity, and lateral clearance. The findings suggest that while the AASHTO asymmetric compound curve offers an effective balance between clearance and compactness, clothoid curves may improve transition smoothness and provide an alternative option for designing the edge-of-traveled-way turning paths at right-angle at-grade intersections. Full article

The potential for structural health monitoring (SHM) in fibre-reinforced polymers (FRPs) using electrical resistance measurements (ERMs) has gained increasing attention, particularly in carbon fibre-reinforced polymers (CFRPs). Most existing studies are limited to single-axis measurements on coupon-scale specimens, whereas industrial applications demand scalable solutions capable of monitoring large areas, with more complex sensing configurations. Structural health monitoring (SHM) of carbon fibre-reinforced polymers (CFRPs) using electrical resistance measurements offers a low-cost, scalable sensing approach. However, predicting surface resistance between arbitrarily placed electrodes on unidirectional (UD) CFRP laminates remains challenging due to anisotropic conductivity and geometric variability. This study introduces a practical analytical model based on two geometry-dependent parameters, effective width and effective distance, to estimate resistance between any two electrodes arbitrarily placed on UD CFRP laminates with 0° or 90° fibre orientations. Validation through finite element (FE) simulations and experimental testing demonstrates good matching, confirming the model’s accuracy across various configurations. Results show that the dominant electrical current path aligns with the fibre direction due to the material’s anisotropic conductivity, allowing simplification to a single-axis resistance model. The proposed model offers a reliable estimation of surface resistance and provides a valuable tool for electrode array configuration design in CFRP-based SHM. This work contributes to enabling low-cost and scalable electrical sensing solutions for the real-time monitoring of composite structures in aerospace, automotive, and other high-performance applications. Full article

We recast cooperative localization and scheduling in heterogeneous multi-agent systems through the lens of symmetry and symmetry breaking. On the geometric side, the Fisher Information Matrix (FIM) objective is invariant to rigid Euclidean transformations of the global frame, while its maximization admits symmetric optimal sensor formations; on the algorithmic side, heterogeneity and task constraints break permutation symmetry across agents, requiring policies that are sensitive to role asymmetries. We model communication as a random graph and quantify structural symmetry via topology metrics (average path length, clustering, betweenness) and graph automorphism-related indices, connecting these to estimation uncertainty. We then design a hybrid reward for reinforcement learning (RL) that is equivariant to agent relabeling within roles yet intentionally introduces asymmetry through distance/FIM terms to avoid degenerate symmetric configurations with poor observability. Simulations show that (i) symmetry-aware, FIM-optimized path planning reduces localization error versus symmetric but non-informative placements; and (ii) controlled symmetry breaking in policy learning improves robustness and data rate–reward trade-offs over baselines. Our results position symmetry/asymmetry as first-class design principles that unify estimation-theoretic invariances with learning-based coordination in complex heterogeneous networks. Under DDPG training, the total data rate (SDR) reaches $6.63\pm 0.97$ and the average reward per step (ARPS) is $-80.70\pm 6.94$, representing improvements of approximately $11.8\%$ over the baseline $(5.93\pm 3.51)$ and $11.1\%$ over SAC $(5.97\pm 2.66)$, respectively. The network’s mean shortest-path length is $L=1.721$, and the average betweenness centrality of the coordination nodes is ≈0.098. Moreover, the FIM-optimized path-planning strategy achieves the lowest localization error among all evaluated policies. Full article

In the industrial internet environment, the operation and control of production equipment have become increasingly complex, and their performance directly affects the efficiency, benefits and sustainable development of manufacturing enterprises. From the three-dimensional perspective of “asset-application-maintenance”, this paper constructs a performance analysis framework for the operation and control of production equipment, systematically identifies the combination of core factors affecting performance, and fills the research gap in the current lack of empirical analysis from the configuration perspective in this field. On the basis of data from 82 manufacturing enterprises, the fsQCA method was used to identify three performance improvement paths: the high-load output mode, the lean management and control mode, and the low-failure operation mode. These paths clarify the equivalent approaches to achieve high performance in the operation and control of production equipment under the interaction of multiple factors. On this basis, the study demonstrates the operability and effectiveness of the proposed strategies in actual industrial scenarios through empirical verification in a manufacturing workshop of aero-engine transmission units. In contrast to existing studies, this study introduces the fsQCA method in the field of industrial equipment management and control for the first time to reveal the influencing paths; its originality and methodology have significant innovative significance. The research results provide new ideas and methodological guidance for enterprise managers to improve the performance of production equipment operations and controls in the industrial internet environment, which helps to enhance the sustainable development capability of manufacturing enterprises. Full article

A major challenge that will confront our society in the coming years is the socioecological transition. This involves a profound, systemic shift in how human societies interact with ecological systems. Beyond merely becoming “greener” or adding new technologies, it is about reorganising economies, lifestyles, institutions and cultural values to align with the planet’s ecological limits. The change also requires transforming the fundamental structure of societies to ensure their deep interconnection and compatibility with natural flows and ecological systems. To this end, it is valuable to explore the small, scattered practices which are currently leading to new organisational solutions or socioecological improvements. These initiatives are often regarded as forms of community resistance, adopting various approaches and strategies, which result in a disparate array of configurations. A comprehensive approach is thus needed to identify common patterns of development. A set of meaningful practices was analysed. The sample actions all took place in the urban context of Seville, a city located in Southwestern Europe and spanned various arenas driven by the transition to sustainability. Following the principles of qualitative research and a case study design, we adopted a qualitative method based on open-ended interviews, emphasising situated knowledge and collective construction of meaning. Moreover, a methodological approach based on interviews and further categorisation was followed to describe and organise ideas, motivations, risks, outcomes, as well as how the experiences evolved. The findings revealed that the core motivation driving the initiative in its initial phases is key. Outcomes nevertheless vary significantly depending on the initiative objectives. Generally, actions focused on specific elements—such as defending precise locations or activities—tend to be more successful and abundant. But the ones based on professional developments end up being somewhat stifled since they depend on the market to succeed. However, most rely somehow on public subsidies or support from public institutions, and their activities tend to diminish when such resources are reduced or withdrawn. The question is therefore how to make these initiatives more resilient in the future. The socioecological transition offers a path to strengthen social cohesion, empower collective action, and generate locally rooted and ecologically sustainable alternatives. Building community resilience—the capacity of local communities to adapt, recover and thrive amid these challenges—is, therefore, essential. Full article

The pattern of the front contact metallization critically influences solar cell efficiency. This study introduces a novel explicit geometry optimization approach for designing the front contact metallization patterns. In the proposed approach, the front contact patterns are represented by wide Bezier curves with variable widths, where each curve’s geometry is defined by both control points and control circles. The control point coordinates and the control circle radii are taken as design variables. To ensure physical feasibility during the design process, one of the end control points of each curve is fixed at the current extraction point. Unlike geometry optimization techniques employing fixed-width Bezier curves, our approach provides enhanced design flexibility through continuous width modulation along the front contact paths. Simulation experimental validation across the simple solar cell geometries demonstrates the proposed method’s superior performance relative to both the solid isotropic material with penalization (SIMP) approach and geometry optimization method using a fixed-width Bezier. Furthermore, the optimized front contact metallization structures outperform the conventional H-pattern designs. Specifically, for a solar cell with a size of 3.5 cm, compared to a solar cell with conventional H-pattern front contact electrodes, the conversion efficiency, open-circuit voltage, short-circuit current, and fill factor of the solar cell with curve-shaped front contact metallization are relatively increased by 0.415%, 0.0011 V, and 5.091 A·m⁻², and 0.904%, respectively, while the material coverage ratio is reduced by 1.974%. The methodology’s versatility is further evidenced by its successful adaptation to free-form solar cell configurations. Full article

The application of orchard inspection robots has become increasingly widespread. How-ever, achieving autonomous navigation in unstructured environments continues to pre-sent significant challenges. This study investigates the Simultaneous Localization and Mapping (SLAM) navigation system of an orchard inspection robot and evaluates its performance using Light Detection and Ranging (LiDAR) technology. A mobile robot that integrates tightly coupled multi-sensors is developed and implemented. The integration of LiDAR and Inertial Measurement Units (IMUs) enables the perception of environmental information. Moreover, the robot’s kinematic model is established, and coordinate transformations are performed based on the Unified Robotics Description Format (URDF). The URDF facilitates the visualization of robot features within the Robot Operating System (ROS). ROS navigation nodes are configured for path planning, where an improved A* algorithm, combined with the Dynamic Window Approach (DWA), is introduced to achieve efficient global and local path planning. The comparison of the simulation results with classical algorithms demonstrated the implemented algorithm exhibits superior search efficiency and smoothness. The robot’s navigation performance is rigorously tested, focusing on navigation accuracy and obstacle avoidance capability. Results demonstrated that, during temporary stops at waypoints, the robot exhibits an average lateral deviation of 0.163 m and a longitudinal deviation of 0.282 m from the target point. The average braking time and startup time of the robot at the four waypoints are 0.46 s and 0.64 s, respectively. In obstacle avoidance tests, optimal performance is observed with an expansion radius of 0.4 m across various obstacle sizes. The proposed combined method achieves efficient and stable global and local path planning, serving as a reference for future applications of mobile inspection robots in autonomous navigation. Full article

This paper presents a method to design and optimize a mimetic, multi-band antenna for direction-finding applications based on multiple IoT mobile nodes for protecting sensitive areas. A set of 84 antenna configurations were selected based on possible resonant paths and simulated using a Method of Moments (MoM)-based tool to compute resonant frequencies, VSWR, and gain across three frequency bands centered on 433 MHz, 877.5 MHz, and 2.4 GHz. Compared to a brute-force approach requiring 8¹⁴ full-wave simulations, our technique dramatically reduces computing time by performing only 84 simulations, followed by a fine-tuning procedure targeting the antenna segments with the highest contribution to the error figure. The final design provides good gain and VSWR figures over almost all the frequency ranges of interest. Full article

Carbon capture and power-to-X are becoming increasingly relevant in the context of decarbonization and supply security. Actinobacillus succinogenes is capable of transforming CO₂ into succinate, whereby product formation is significantly limited by the availability of NADH. The aim of this work was to further develop a bioelectrochemical system (BES) in order to provide additional reduction equivalents and thus increase yield and titer. To this end, a new BES configuration was established. A conventional stirred tank reactor (STR) is coupled via a bypass to an H-cell, in which the redox mediator neutral red (NR) is electrochemically reduced and then returned back to the bioreactor. The indirect electron transfer decouples the electrochemical reduction from the biology and results in increased intracellular availability of NADH. The present approach resulted in an increase in yield from 0.64 g·g⁻¹ to 0.76 g·g⁻¹, corresponding to an increase of 18%. At the same time, a titer of 16.48 ± 0.19 g·L⁻¹ was achieved in the BES, compared to 12.05 ± 0.18 g·L⁻¹ in the control. The results show that the mediator-assisted, partially decoupled BES architecture significantly improves CO₂-based succinate production and opens up a scalable path to the use of renewable electricity as a reduction source in power-to-X processes. Full article

This paper reviews and analyzes three types of vertical-axis wind rotors: the classic Savonius, spiral Savonius, and Darrieus designs. Using numerical modeling methods, including computational fluid dynamics (CFD), their aerodynamic characteristics, power output, and efficiency under different operating conditions are examined. Key parameters such as lift, drag, torque, and power coefficient are compared to identify the strengths and weaknesses of each rotor. Results highlight that the Darrieus rotor demonstrates the highest efficiency at higher wind speeds due to lift-based operation, while the spiral Savonius offers improved stability, smoother torque characteristics, and adaptability in turbulent or low-wind environments. The classic Savonius, though less efficient, remains simple, cost-effective, and suitable for small-scale urban applications where reliability is prioritized over high performance. In addition, the study outlines the importance of blade geometry, tip speed ratio, and advanced materials in enhancing rotor durability and efficiency. The integration of modern optimization approaches, such as CFD-based design improvements and machine learning techniques, is emphasized as a promising pathway for developing more reliable and sustainable vertical-axis wind turbines. Although the primary analysis relies on numerical simulations, the observed performance trends are consistent with findings reported in experimental studies, indicating that the results are practically meaningful for design screening, technology selection, and siting decisions. Unlike prior studies that analyze Savonius and Darrieus rotors in isolation or under heterogeneous setups, this work (i) establishes a harmonized, fully specified CFD configuration (common domain, BCs, turbulence/near-wall treatment, time-stepping) enabling like-for-like comparison; (ii) couples the transient aerodynamic loads p(θ,t) into a dynamic FEA + fatigue pipeline (rainflow + Miner with mean-stress correction), going beyond static loading proxies; (iii) quantifies a prototype-stage materials choice rationale (aluminum) with a validated migration path to orthotropic composites; and (iv) reports reproducible wake/torque metrics that are cross-checked against mature models (DMST/actuator-cylinder), providing design-ready envelopes for small/medium VAWTs. Overall, the work provides recommendations for selecting rotor types under different wind conditions and operational scenarios to maximize energy conversion performance and long-term reliability. Full article

Entrepreneurship is widely recognized as a critical engine of economic growth. This is especially true in rural areas, where resources, policy support, and talent pools are often constrained. Stimulating entrepreneurial vitality in these regions has thus become an urgent policy and research priority. This study adopts an inclusive growth perspective, selecting six key elements—economic level, industrial structure, financial development, educational condition, medical condition, and social security—to construct a theoretical model exploring the configuration pathways that drive rural entrepreneurial activity. Using fuzzy set qualitative comparative analysis (fsQCA), the study examines 982 rural regions in China and draws the following conclusions: (1) None of the six key elements is a necessary condition for rural entrepreneurial activity. (2) The “finance and healthcare-driven” type (C1), “industrial and educational balance” type (C2), “financial and educational synergy” type (C3), and “industrial and healthcare support” type (C4) are the configuration paths to achieve high rural entrepreneurial activity. The findings provide both theoretical and practical insights for stimulating entrepreneurship in rural China. Specifically, they highlight how different developmental configurations can activate local entrepreneurial ecosystems, expand employment and entrepreneurship opportunities for vulnerable groups, and contribute to sustainable poverty alleviation. Full article

A university campus is a composite built environment integrating research, daily life, culture, and ecological green space. Its landscape elements shape environmental perception and overall spatial quality. This study assesses spatial quality by identifying key features and optimizing their joint effects across three perceptions: safety, comfort, and belonging. Using a Chinese campus, we captured street-view images, applied semantic segmentation to quantify elements (grass, trees, buildings, roads, sidewalks), and used explainable machine learning with data augmentation to identify the features most relevant to these perceptions. This study then employed fuzzy-set Qualitative Comparative Analysis (fsQCA) to reveal configuration pathways that enhance spatial quality. Results show that data augmentation mitigates class imbalance and improves prediction accuracy. Key features include sky, river, bridge, people, grass, and sidewalks, and path analysis indicates that greater sky openness and higher densities of people, roads, sidewalks, and grass, together with fewer buildings, cars, and bare earth, enhance safety, comfort, and belonging. This study delivers globally transferable design rules and a replicable, policy-ready workflow that enables evidence-based campus upgrades across diverse regions. Full article

Amidst global climate warming and increasingly severe food security challenges, the agroforestry economy, a green ecological industry that balances ecological conservation and economic development, has attracted widespread attention. This study constructs a theoretical analytical framework based on the diamond model to systematically identify key factors influencing the development of the agroforestry economy. Using 56 practical cases from the agroforestry economy in China as samples, the study applies Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to further explore the multiple driving paths of agroforestry economic development and their supporting elements. The research findings show that (1) forest resources, technological innovation, market demand, enterprise forms, related industries, and government support do not constitute necessary conditions for the development of the agroforestry economy. The path to the development of the agroforestry economy exhibits complex and concurrent multi-faceted characteristics. (2) Technological innovation has always been at the core of all configurations, and strengthening technological innovation plays a universal role in enhancing the level of agroforestry economic development. The role of government support in the process of the development of the agroforestry economy is limited. (3) The system identified four driving paths, including the endogenous type, characterized by resource technology enterprises; the collaborative type, characterized by a resource technology market with light promotion by the government; the external expansion type, characterized by market technology enterprises; and the linkage type, characterized by market technology enterprises assisted by related industries. The consistency level of the overall solution reached 0.91, and the coverage was 0.54. It reveals the different driving mechanisms with different combinations of elements for the development of the agroforestry economy. Therefore, each region should strengthen scientific and technological research, innovation, and the transformation and application of research outcomes. It should promote the coordinated development of diverse factors, establish tailored regional development models, and explore suitable pathways for developing the agroforestry economy based on its unique resource endowments. Full article

Green and low-carbon development (GLD) is central to facilitating the high-quality transitional development of economic and social sectors, as well as to the achievement of China’s “dual carbon” goals. The digital economy (DE), a burgeoning economic paradigm, serves as a potent driver for GLD by leveraging its intrinsic strengths in innovation-led growth and cross-sectoral industrial integration. Drawing on the TOE (Technology-Organization-Environment) framework, this study employs dynamic Qualitative Comparative Analysis (QCA) and regression analysis to examine panel data (2014–2023) of 44 core coastal cities in the Yangtze River Economic Belt, aiming to identify the driving paths of GLD. The research results indicate that a single dimension in the DE cannot constitute the necessary condition for regional GLD. Specifically, there are 6 configurational paths for high-level GLD (categorized into “organization-led” and “technology-organization-environment multi-driven” models) and 3 paths for low-level GLD (summarized as “three-dimensional constraint” and “technology-organization deficiency” models). In terms of the driving effect, the technology-organization-environment multi-driven configurational path exerts the strongest promotional effect on regional GLD. This study yields a valuable theoretical foundation for understanding the synergistic role of multidimensional DE elements in driving GLD, while also delivering actionable insights for local governments to identify contextually tailored GLD trajectories. Full article

Within the framework of the Healthy China strategy, daylighting in primary and secondary schools is crucial for students’ health and learning efficiency. Most schools in China still face insufficient and uneven daylighting, along with limited outdoor solar exposure, underscoring the need for systematic optimization. Guided by the “Daylighting School” concept, this study proposes a campus design model that integrates indoor daylighting with outdoor activity opportunities and explores a generative optimization approach. The research reviews daylighting and thermal performance metrics, summarizes European and American “Daylighting School” experiences, and develops three classroom prototypes—Standard Side-Lit, High Side-Lit, and Skylight-Lit—together with corresponding campus layout models. A two-stage optimization experiment was conducted on a high school site in Guangzhou. Stage 1 optimized block location and functional layout using solar radiation illuminance and activity accessibility distance. Stage 2 refined classroom configurations based on four key performance indicators: sDA, sGA, UOD, and APMV-mean. Results show that optimized layouts improved activity path efficiency and daylight availability. High Side-Lit and Skylight-Lit classrooms outperformed traditional Side-Lit in illuminance, uniformity, and glare control. To improve efficiency, an ANN-based prediction model was introduced to replace conventional simulation engines, enabling rapid large-scale assessment of complex classroom clusters and providing architects with real-time decision support for daylight-oriented educational building design. Full article