Search Results (1,973)

To address the disconnect between macro-quantity planning and micro-spatial allocation at the township level during rapid urbanization, this study developed a coupled model framework based on Multi-Objective Planning (MOP) and the Future Land-Use Simulation (FLUS) model, using Longhu Town as a case study. First, economic and ecological benefit coefficients were calibrated via the Grey Prediction Model and equivalent factor method to define three scenarios: Economic Priority (EPS), Ecological Protection (EcPS), and Balanced Development (BDS). Second, an Artificial Neural Network (ANN) was employed to quantify driving factors, coupled with self-adaptive Cellular Automata (CA) for spatial allocation in 2030. The results indicate that: (1) The model exhibits high reliability for small-scale simulation, with a Kappa coefficient of 0.95 and a Figure of Merit (FoM) of 0.29. (2) Strategic orientations lead to distinct spatial differentiation: under the EPS, urban–industrial land expands significantly northwestward (+16.60%), causing fragmented erosion of cropland; the EcPS achieves a 5.27% increase in forest land and ecological restoration through strict quantitative constraints; the BDS realizes the synergy of urban clustering and ecological enhancement with a marginal urban increase (0.72%). (3) The eastern urban sectors and northeastern cropland belts are identified as future land-use conflict hotspots. The “quantity-space” collaborative optimization path proposed in this study provides a scientific basis and dynamic simulation tool for refined territorial spatial management at the township scale. Full article

In practice, village planning often suffers from an “emphasis on plan preparation but neglect of implementation”, a challenge that is especially evident in Sichuan Province, China, where highly diverse landforms and uneven development foundations make one-size-fits-all evaluation approaches difficult to apply. This study aims to develop a locally adaptable and operational method to quantify village planning implementation effectiveness control, enabling cross-type comparison and bottleneck diagnosis. We construct a three-level indicator system spanning eight domains—baseline control, land-use layout and construction, ecological protection and restoration, industrial development, infrastructure, public service facilities, living environment, and disaster prevention and mitigation—and determine indicator weights using the Analytic Hierarchy Process (AHP). To capture both compliance and progress, a dual-path scoring strategy is employed: constraint-based indicators are assessed using a threshold method by comparing current values (T1) with planning standards/thresholds (T2), while expectation-based indicators adopt a progress-ratio method incorporating baseline values before plan preparation (T0), current status (T1), and targets (T2). Three representative villages—Gaohuai (peri-urban integration), Sanlongchang (agglomeration and upgrading), and Lianmeng (characteristic protection)—are examined. Results show medium-to-high comprehensive scores (81–85) with pronounced type differences: Gaohuai ranks highest (85.37), whereas Sanlongchang is lowest (81.40), and Lianmeng is intermediate (83.71). Comparative diagnosis reveals shared bottlenecks driven by the superposition of “quota–space–ecological constraints”, alongside type-specific weaknesses requiring differentiated control strategies. The proposed framework offers a replicable, multi-source-data-oriented tool for implementation monitoring and adaptive policy adjustment. The novelty lies in reframing village plan implementation evaluation as implementation control effectiveness under a baseline-constrained planning system, while operationalizing a dual-path, unified-scale scoring scheme with a type-screenable indicator library for cross-type comparison and checklist-oriented diagnosis. Full article

Accurate prediction of the zero-sequence impedance ($Z_0$) of three-legged zigzag grounding transformers is essential for ground-fault protection and power-quality performance, yet manufacturer analytical estimations often have limited accuracy. This paper investigates how accurately $Z_0$ can be predicted using 3D finite element method (FEM) models based on the stored magnetic energy approach and how modeling the metallic tank and nonlinear core B–H behavior affects $Z_0$ relative to analytical calculations and laboratory measurements. Two 3D FEM models are developed for a three-legged zigzag grounding transformer, incorporating the nonlinear core characteristic; impedance boundary conditions are used to efficiently account for tank-induced currents while reducing computational cost. The FEM results are compared with laboratory tests and with the analytical method used by manufacturers. The proposed models achieve errors below 4% with respect to the nominal $Z_0$ and outperform the analytical approach. The contributions are a validated 3D FEM methodology that resolves zero-sequence flux paths under fault conditions and a practical modeling tool that improves grounding transformer design and ground-fault protection settings in modern power systems. Full article

Subjective, imprecise evaluation of lower extremity function hinders the effective treatment of gait impairments in Parkinson’s disease (PD). Markerless motion capture (MMC) offers opportunities for integrating objective biomechanical outcomes into clinical practice. However, validation of MMC biomechanical outcomes is necessary for clinical adoption of MMC technologies. This project evaluated the criterion validity of a custom MMC algorithm (CART-MMC) against gold-standard 3D motion capture (Traditional-MC) and its known-groups validity in differentiating PD from healthy controls (HC). Sixty-two individuals with PD and 29 HCs completed a stepping in place paradigm. The trials were recorded by an augmented reality headset with embedded RGB and depth cameras. The CART-MMC algorithm was used to reconstruct a 3D pose model and compute biomechanical measures of lower extremity performance. CART-MMC outcomes were statistically equivalent, within 5% of Traditional-MC, for measures of step count, cadence, duration, height, height asymmetry, and normalized path length. CART-MMC captured significant between-group differences in step height, height variability, height asymmetry, duration variability, and normalized path length. In conclusion, CART-MMC provides valid biomechanical outcomes that characterize important domains of PD lower extremity function. Validated biomechanical evaluation tools present opportunities for tracking subtle changes in disease progression, informing targeted therapy, and monitoring treatment efficacy. Full article

In the field of cultural architecture design, the deep impact mechanisms of wooden material design perception on users’ psychological experiences have not yet been fully elucidated. The interior environmental design of concert halls, as venues for immersive artistic experiences, especially the use of natural materials such as wood, is considered a key factor shaping audience perception and experience. However, existing research has largely focused on the acoustic performance of or visual preferences for wooden materials, while there remains a lack of mechanistic explanations for how wooden design perception systematically enhances users’ overall satisfaction through a series of internal psychological processes. Based on the “stimulus–organism–response” theoretical framework, this study proposes a chain mediation model aimed at exploring how perception of wooden design in concert halls enhances user satisfaction by promoting users’ flow experience and subsequently strengthening their place attachment. Through a cross-sectional survey of 1017 audiences with actual experience in wooden concert halls and analysis of the data using covariance-based structural equation modeling, the findings reveal that: (1) perception of wooden design has a significant direct positive effect on user satisfaction; (2) both flow experience and place attachment independently mediate the influence of wooden design perception on user satisfaction; (3) there exists a significant chain mediation path: “perception of wooden design → flow experience → place attachment → user satisfaction”. This study validates, from an architectural psychology perspective, the role of flow and place attachment as consecutive psychological mechanisms. The research provides empirical evidence for architects to use wood as a psychological intervention tool in cultural spaces, transforming material selection from an aesthetic consideration into a systematic design strategy with measurable psychological outcomes. Full article

Air pollution has become a pressing global challenge that threatens ecological security, public health, and sustainable socioeconomic development, prompting extensive academic and policy attention on air pollution control and environmental governance. To systematically clarify the knowledge structure, evolutionary trends, and interdisciplinary characteristics of this field, this study employs bibliometric methods combined with CiteSpace, VOSviewer, and Tableau tools for in-depth analysis of the global literature published in the last 25 years. Key dimensions including keyword clustering, co-occurrence networks, national cooperation patterns, journal co-citation relationships, and policy evaluation methodology evolution are explored. The results reveal that research output in this field has maintained sustained rapid growth, with distinct interdisciplinary integration across environmental science, economics, energy engineering, and public health. Notably, the evolutionary path of research themes presents a clear transformation: shifting from early emphasis on “emission standards” and “end-of-pipe treatment” to market-oriented policy instruments such as “carbon tax” and “carbon emission trading”, and further expanding toward systematic solutions including “green finance” and “collaborative environmental governance”. In terms of policy evaluation methodologies, there is a developmental trend from single-indicator monitoring to integrated assessment frameworks combining quasi-experimental approaches (e.g., difference-in-differences, regression discontinuity design) and multi-model coupling. Furthermore, national collaboration analysis identifies China as a core hub in the global research network, while European and American countries maintain advantages in research impact. While this observation is based on absolute metrics, a data normalization approach (e.g., by population) reveals more distinct relative differences and a complementary global dynamic: China’s scale-driven output aligns with large-scale, engineering-intensive governance challenges, whereas the markedly higher per capita research impact of Western nations reflects a deeper focus on policy innovation and systemic mechanisms. Burst term detection highlights emerging frontiers such as the “Porter hypothesis”, reflecting growing focus on the synergistic relationship between environmental regulation, green innovation, and economic development. This study also identifies critical research gaps, including insufficient attention on cross-regional pollution transport policy coordination and emergency policy evaluation under extreme weather conditions. The findings provide a comprehensive academic map of global air pollution control and environmental governance research, offering valuable insights for optimizing environmental policy design, promoting interdisciplinary collaboration, and guiding future research directions in this field. Full article

The silkworm (Bombyx mori) is essential to sericulture and is also becoming a key model organism in genomics and agriculture. For decades, genetic studies of the silkworm were limited by inefficient and inflexible genome tools. CRISPR genome editing allows precise and scalable alterations to genes regulating development, physiology, and industrial traits. This review summarizes silkworm genome-editing breakthroughs, highlighting CRISPR’s evolution from simple gene knockouts to large-scale genome-wide screening. We highlight how these advancements contribute to disease resistance, higher yields, and the development of new silk-based materials, as well as how they influence the development and growth rate of the sericulture. The creation of high-quality reference genomes, pangenomes, and genome-wide screening systems has made the silkworm a major model for integrating multiple biological datasets and approaches, such as genomic, transcriptomic, and proteomic. By considering the unique biological characteristics of the silkworm, this provides new insights for research on silk biology, piRNA synthetic biology, and hormonal signaling regulation. Finally, we examine new areas at the intersection of CRISPR, pangenomics, and artificial intelligence (AI) and suggest future paths for molecular breeding, pest control, and synthetic biology. Moreover, AI-assisted prediction of CRISPR outcomes is utilized to inform the design of targeted trait modifications, representing an approach to enhancing biomanufacturing efficiency and eco-friendly silk production. Together, these advances have made the silkworm a flexible genetic platform and an important part of sustainable agriculture and biomanufacturing. Full article

This paper presents an optimization framework for determining safe and efficient evacuation paths in complex industrial facilities. The proposed approach models the evacuation process through a timed flow network that captures both the structural characteristics of the layout and the temporal evolution of emergency conditions. The formulation accommodates real-time updates, enabling dynamic re-routing when certain areas or connections become inaccessible. Computational experiments on large-scale instances demonstrate the scalability of the model and its ability to provide complete evacuation plans under increasing demand. The results confirm predictable relationships between population size, time horizon, and evacuation completion, supporting its use as a decision support tool for both strategic planning and operational response. Full article

Seagrass meadows are essential coastal ecosystems that provide key ecological services, including carbon sequestration, sediment stabilization, and shoreline protection. Increasing threats from natural and anthropogenic stressors highlight the need for efficient, reproducible, and non-invasive monitoring solutions. This study evaluates the performance of low-cost commercial drones for seagrass assessment in shallow coastal waters, with an emphasis on freely accessible mission-planning and photogrammetric workflows. Field surveys were conducted along the Calabrian coast (southern Italy), where automated flight paths were generated using the software WaypointMap, and high-resolution orthophotos were generated using the WebODM software and subsequently analyzed in QGIS for seagrass patch detection, mapping, and surface estimation. The methodological pipeline is described in detail to facilitate full reproducibility. Compared with traditional diver-based methods, this workflow offers faster data collection, broader spatial coverage, and minimal environmental disturbance. Although some limitations remain, the results demonstrate that combining low-cost drones with open-source tools provides a practical and scalable solution for routine monitoring. This approach has strong potential for integration into routine coastal habitat assessment, supports early impact detection, and contributes to evidence-based conservation and management strategies. Full article

Slant-path propagation of laser beams through atmospheric turbulence produces beam spreading and jitter that must be rapidly predicted for system design and performance assessment. Existing scaling laws are mainly derived for horizontal paths and single-parameter variations, which limits their accuracy and applicability to realistic engagement geometries. Here, we construct a comprehensive wave-optics database for 1.064 μm truncated Gaussian beams with a 1 m aperture by traversing initial beam quality factor β₀, propagation distance L, elevation angle θ, turbulence strength Cₙ², and tracking jitter. From 46,800 turbulence-only cases, we extract the 63.2% encircled-power expansion factor and quantify the coupled influence of β₀, L, and θ on the turbulence term coefficient A in the scaling expression. A compact 3–10–1 feedforward neural network is trained to map (β₀, L, θ) to A, achieving a coefficient of determination R² = 0.948. Additional simulations without turbulence show that the jitter term coefficient B is nearly invariant over the considered parameter range, with an average value B = 3.69. Combining these results yields a unified scaling law for linear beam spreading on horizontal and slant paths. Comparison with full-wave-optics simulations demonstrates that the proposed law reproduces horizontal-path results and significantly reduces prediction errors at θ = 60° relative to existing models, providing an efficient tool for beam-quality prediction and performance evaluation in atmospheric laser propagation. Full article

The escalating global surge in Waste Electrical and Electronic Equipment (WEEE) necessitates the strategic deployment of recycling facilities within resilient, multi-period networks. However, existing planning methodologies falter due to the non-stationary spatiotemporal volatility of e-waste generation, the high reconfiguration costs associated with path-dependent infrastructure, and the “curse of dimensionality” inherent in large-scale dynamic optimization. To address these challenges, we propose LAtt-PR, an innovative hybrid reinforced adaptive optimization framework. The methodology integrates a spatiotemporal attention-based neural network, combining Multi-Head Attention (MHA) for spatial correlation with Long Short-Term Memory (LSTM) units for temporal dependencies to accurately capture and predict fluctuating demand patterns. At its core, the framework employs Deep Reinforcement Learning (DRL) as a high-level action proposer to prune the expansive search space, followed by a Particle Swarm Optimization (PSO) module to perform intensive local refinement, ensuring both global strategic foresight and numerical precision. Experimental results on large-scale instances with 150 nodes demonstrate that LAtt-PR significantly outperforms state-of-the-art benchmarks. Specifically, the proposed framework achieves a solution quality improvement of 76% over traditional metaheuristics Genetic Algorithm (GA)/PSO and 55% over pure DRL baselines Deep Q-Network(DQN)/Proximal Policy Optimization (PPO). Furthermore, while maintaining a negligible optimality gap of less than 4% relative to the exact solver Gurobi, LAtt-PR reduces computational time to just 16% of the solver’s requirement. These findings confirm that LAtt-PR provides a robust, scalable, and efficient decision-making tool for optimizing resource circularity and environmental resilience in volatile, real-world recycling logistics. Full article

Sustainable development is a central objective for contemporary firms. It involves both long-term organizational resilience and improved environmental, social, and governance (ESG) performance. Structural tools that support long-term stability and strategic continuity play a critical role in achieving these goals. However, their adoption depends on the interaction between formal and informal institutional forces. Drawing on institutional theory, this study applies fuzzy-set qualitative comparative analysis (fsQCA) to data from Chinese listed firms. We examine how four institutional dimensions jointly shape structural tool adoption: governance structure, intergenerational heterogeneity, institutional and cultural context, and market-driven and mimetic forces. Structural tools facilitate governance consolidation and leadership succession, which are essential for sustainable development. Our findings show that no single institutional condition is sufficient to trigger adoption. Instead, multiple conditions must combine to enable firms to implement structural tools. The seven configurations identified reveal diverse governance paths across different institutional contexts, including complementary, substitutive, and conflicting relationships between formal and informal institutions. We also find clear causal asymmetry: the conditions that promote adoption differ fundamentally from those that inhibit it. Structural tools provide an institutional foundation for balancing short-term pressures with long-term sustainability commitments. Firms lacking these mechanisms face greater risks of leadership succession failure and long-term instability. Additional analyses using mean difference tests and fixed-effects models further confirm that structural tool adoption significantly enhances both sustainable development capacity and ESG performance. Overall, this study advances institutional theory. It shows how the interaction between formal and informal institutions shapes governance choices. It also explains how governance structures are linked to sustainable development outcomes. Full article

Construction management plays a fundamental role in the sustainability of construction projects, as its primary objective is to enhance cost-effectiveness and efficient resource utilization. One of the main challenges encountered at the early stages of a project’s lifecycle, particularly during the planning phase, is the development and agreement of construction schedules among the stakeholders involved. The tools employed for time planning and scheduling during both the planning and construction phases should therefore be capable of modeling complex environments and supporting dynamic updates in response to resource constraints. Petri nets are known for their capability of modeling complex systems, such as resource management. Their use in project management is essential for resource constraint problems. This paper investigates the use of Petri Nets as a tool for the time scheduling of engineering and construction projects. A case study is presented and modeled using Timed Petri nets, enabling dynamic adaptation under time and resource constraints. Through simulation performed with the ROMEO (v3.10.6) software, the study identifies the critical paths and determines the total project duration under various scenarios of sensitivity by adjusting specific project parameters. The results demonstrate the effectiveness of Petri nets in project management and the benefits they offer when used in modeling complex systems, identifying critical activities and calculating resource constraints and time deadlines. Full article

To address the issues of excessive sheet metal thinning and geometric deviation in single point incremental forming (SPIF), this paper proposed a bi-objective process parameter optimization framework for Al1060 sheet based on a multilayer perceptron (MLP) surrogate model and an improved multi-objective grey wolf optimization (IMOGWO) algorithm. Finite element simulations based on ABAQUS were conducted to generate a dataset considering variations in tool radius, initial sheet thickness, tool path strategy, step depth and forming angle. The trained MLP was used as the objective function in the optimization process to enable the rapid prediction of forming quality. The IMOGWO algorithm, enhanced by the Spm chaotic mapping initialization, an improved convergence coefficient updating mechanism and associative learning mechanism, was then employed to efficiently search for Pareto optimal solutions. For a truncated conical component case, optimal parameter sets were selected from the Pareto front via the entropy-weighted TOPSIS method for order preference by similarity to an ideal solution. Experimental verification showed close agreement with the simulated results, with relative errors of only 0.58% for the thinning rate and 3.10% for the geometric deviation. This validation demonstrates the feasibility and potential of the proposed method and its practical potential for improving the quality of SPIF forming. Full article

Feedrate planning is a critical process in computer numerical control (CNC) machining, playing a key role in ensuring machining quality and improving efficiency. This paper proposes a feedrate planning method based on feedrate profile shaping to satisfy machine axis constraints, including axis velocity, acceleration, and jerk limits. First, the five-axis machining path is represented using parametric curves. By combining the geometric characteristics of the path with machine axis velocity constraints, the upper bound of the feedrate under static constraints is derived. On this basis, machine axis acceleration and jerk constraints are further incorporated to establish feedrate planning criteria, thereby obtaining a distribution of feasible points that satisfies dynamic constraints. Then, a feedrate curve is generated using a profile shaping strategy based on the feasible point distribution, and further optimized through a corner shaping method. As a result, the planned feedrate strictly satisfies machine axis constraints along the entire tool path while ensuring continuity and smoothness of the feedrate profile. Finally, the effectiveness and reliability of the proposed method are validated through simulations of the parametric curve and experimental machining of an impeller blade. Full article