Search Results (10,074)

Pinus yunnanensis Franch. is a native pioneer and economically important tree in Yunnan Province in China. In this study, over 1400 annual seedlings were used. Following national or regional official seedling quality standards, seedlings were classified into three grades, namely Grade I, Grade II, and Grade III by using mean ± 1/2 standard deviation method according to the height of seedlings (H ± 1/2σ). Morphological traits including seedling height, ground-line diameter, root length, and root average diameter were measured from September 2022 to December 2023 for each grade. A power-law allometric growth model was constructed, and the standardized major axis method was used to analyze the allometric relationships between plant height and ground-line diameter as well as between root length and root average diameter. The results showed that higher grade seedlings exhibited stronger synergistic plasticity, accelerating allometric growth and enhancing phenotypic plasticity. A significant positive correlation was found between plant height and ground-line diameter growth rates, with ground-line diameter showing greater plasticity. Grade I seedlings demonstrated clear advantages, with mean allometric rates of 0.5860 for plant height versus ground-line diameter and 1.6315 for root length versus root system. The phenotypic plasticity index for ground-line diameter was high across all three grades, but actual thickening varied by grade due to different initial diameters, with Grade I and II seedlings growing much more than Grade III. For plant height, the index ranged from 0.3 to 0.8, with values of 0.6–0.7 for Grade I, 0.3–0.7 for Grade II, and 0.6–0.8 for Grade III. These findings provide a scientific basis for evaluating seedling quality, breeding, reproduction, and improving survival and growth in later-stage afforestation. Full article

Understanding the spatial hierarchy, distribution patterns, and driving mechanisms of A-level tourist attractions is essential for optimizing tourism resource allocation and promoting sustainable regional development. This study integrates core–periphery theory with a sustainability perspective to examine hierarchical differentiation of China’s A-level tourist attractions, using 15,699 POI data points collected in 2024 and applying the nearest neighbor index (NNI), kernel density estimation, spatial autocorrelation analysis, and the geographical detector model. The results indicate that these attractions exhibit an unbalanced spatial distribution characterized by a “dense east and sparse west” pattern, with the Hu Huanyong Line (Hu Line) as an important spatial boundary, showing east–west hierarchical disparities. The attractions demonstrate a clustered distribution pattern, although the degree of agglomeration decreases as attraction grades increase. Spatial associations exhibit a pattern of coordination in eastern regions and polarization in western regions, forming a three-tier spatial hierarchy of core–sub-core–periphery. Population density exhibits the strongest explanatory power. Interaction detector results reveal grade-dependent differences. 2A attractions show weak factor associations, whereas 5A attractions are more strongly linked to resource endowment, population density, and economic development. These findings advance the theoretical understanding of the hierarchical spatial structure and differentiated development mechanisms of tourist attractions. Full article

Long-term energy system planning depends on projections of future wind turbine characteristics, yet existing approaches rely on either costly expert elicitation or deterministic trend extrapolation without formal uncertainty quantification. We present a Bayesian logistic framework that models the temporal evolution of hub height, rotor diameter, and specific power as physically constrained growth and decay processes, producing full posterior predictive distributions via Markov Chain Monte Carlo sampling. The framework is validated across three major onshore wind markets: Austria (534 turbines, 2000–2025), Germany (31,202 turbines, 1988–2026), and the United States (71,457 turbines, 1986–2025); spanning different market structures, regulatory environments, and data availability. Systematic benchmarking against linear, polynomial, and maximum-likelihood alternatives demonstrates superior hindcast performance, particularly for long-range projections where physical saturation constraints become relevant. Prior sensitivity analysis reveals that posteriors are robust for data-rich regions but honestly reflect prior influence for small datasets, identifying where expert knowledge is essential. We extend the framework to climate-aware energy yield estimation by propagating turbine posteriors through synthetic power curves and site-specific wind resource projections under SSP2-4.5 and SSP5-8.5, decomposing the total uncertainty into technology and climate components. When climate uncertainty is measured by scenario spread alone, technology uncertainty dominates. However, accounting for the full inter-model spread across 13 CMIP6 global climate models reveals that climate uncertainty becomes substantial (14–56%) and region-dependent, underscoring that both sources require explicit quantification. The open-source pipeline is designed for direct adoption in energy system planning workflows. Full article

This study investigates the processing temperature characteristics and etching behavior of fused silica using an atmospheric pressure microwave plasma jet. The temperature distribution within the processing region was measured in real time via infrared thermography. The effects of microwave input power, argon flow rate, and CF₄ flow rate on the processing temperature were systematically examined using a single-factor approach. Experimental results reveal a strong positive correlation between the plasma temperature and microwave power. The temperature initially rises and then declines with increasing argon flow, peaking at 3 slm, while it increases and eventually stabilizes with higher CF₄ flow. Fixed-point etching demonstrates that the etching rate increases with rising processing temperature. Furthermore, heat accumulation during prolonged dwell time leads to a nonlinear increase in the removal rate. This effect can be effectively mitigated by employing a multi-segment processing strategy, enabling more stable and controllable material removal. The effectiveness of this processing method has also been verified on a fused quartz sub-mirror. Full article

Recent developments and trends in power systems have increased the importance of dynamic modeling and monitoring of system components. Increased penetration of renewable energy sources and battery storage systems makes grid operation challenging. Being environment-friendly and fast-responding, hydroelectric power plants will participate in the generation as a balancing factor while introducing inertia. They will operate dynamically—as a reserve in frequency regulation and load-generation balancing— due to the intermittent characteristics of wind and photovoltaics (PVs). Therefore, their condition monitoring and health assessment should be performed regularly or in real time to ensure that the plant is ready whenever needed. In this research, a data-driven condition monitoring method is introduced in which the health status of the water conduit system is assessed from the turbine’s startup process. The proposed “PbyGate Analysis” method briefly obtains the expected behavior and healthy/anomalous operation regions from the historical data. Then the unit is monitored in real time with the online SCADA measurements. The method is developed and tested on three different hydroelectric turbine data. Startups are tagged as healthy or anomalous with 84.5% accuracy. Full article

In therapeutic applications, ultrasound is widely used in physiotherapy, tissue repair, and cancer treatment. Regarding cancer treatment, as an emerging field for technology, significant research efforts have been devoted to the area of ultrasound therapy. The derived energy from beams can be deposited in tissues not only through heating but also through non-thermal mechanisms, whereby cancer cells are subject to cell death. Ultrasound-induced heating can generate localized temperature elevations within biological tissues, making it a subject of interest for thermal therapeutic applications. Nevertheless, excessive temperature elevations outside the primary exposure region may result in undesirable thermal effects within the surrounding tissue. In this study, we used continuous 3 MHz ultrasound waves at the powers of 0.4 to 1.4 W on ex vivo chicken breast tissue in a water bath to prevent fluctuations in temperature. The process was also numerically modeled with a maximum error of 0.4% from the measured data. Temperature measurements revealed a significant difference between the region of maximum acoustic pressure along the beam axis and deeper tissue locations (in some cases, above 3.5 °C). These findings indicate that temperature gradients can develop within homogeneous tissue during ultrasound exposure, emphasizing the importance of controlling acoustic power and exposure conditions. Moreover, increasing the temperature was significant during the first moments of treatment, which highlights the importance of precise controls for rate and precision in therapy. The numerical simulations also showed that increasing acoustic power elevates tissue temperature while simultaneously producing a less uniform temperature distribution. These observations may be useful for the optimization of future ultrasound-based thermal treatment strategies; however, direct clinical extrapolation requires further investigation using physiologically representative tissue models. Full article

Taking 563 national key rural tourism villages across 12 provinces, autonomous regions, and municipalities in western China as the research object, this study integrates multi-source data on physical geography, transportation location, socioeconomic conditions, and historical culture based on the ArcGIS platform. It comprehensively applies kernel density analysis, spatial autocorrelation analysis, buffer analysis, Spearman correlation analysis, Geodetector, and the relative enrichment index to examine the spatial distribution characteristics of these villages and their associated spatial factors. The results show that key rural tourism villages in western China exhibit an overall clustered and uneven distribution, forming a spatial pattern characterized by “high concentration in core areas, extension along secondary corridors, and sparse distribution across vast hinterlands.” The core agglomeration areas are mainly located in the Sichuan Basin, the Chongqing metropolitan area, and the Guanzhong Plain. In terms of physical geography, the distribution of key villages shows certain spatial associations with major river basins, low-slope areas, and low-relief terrain. In terms of human factors, population density and road network density are important associated factors, and the combined population–transportation conditions have strong explanatory power for the spatial differentiation of key village density. With regard to historical culture, folk-custom inheritance villages and red-culture heritage villages account for relatively high proportions, while different cultural types show certain regional agglomeration or corridor-like distribution characteristics. The findings can provide references for zoned optimization, transportation connectivity, cultural resource integration, and coordinated regional development of key rural tourism villages in western China. Full article

Traditional Chinese villages are important carriers of rural heritage, collective memory, vernacular landscapes, and living cultural traditions. However, rapid urbanization, agricultural modernization, climate change, and tourism development have increasingly threatened their spatial integrity and cultural continuity, highlighting the need for evidence-based conservation and adaptive management. This study examines the spatial distribution patterns and associated factors of 8155 national-level traditional villages in China. An integrated spatial analytical framework was developed by combining kernel density estimation, spatial autocorrelation analysis, Geodetector, and multiscale geographically weighted regression (MGWR). The results show that: (1) traditional villages are unevenly distributed across China and form a distinct “three-core and multi-node” spatial pattern, with major high-density clusters concentrated in several cross-provincial regions and secondary clusters distributed in other heritage-rich areas; (2) the spatial differentiation of traditional village density is statistically associated with natural, cultural, and socioeconomic factors, among which temperature and precipitation show the strongest explanatory power, while cultural endowment, ecological quality, and socioeconomic variables show more context-dependent associations; and (3) compared with OLS and conventional GWR, MGWR improves model performance by capturing spatially heterogeneous and scale-dependent relationships through variable-specific bandwidths. These findings provide national-scale empirical evidence for differentiated conservation planning and support the integration of traditional village protection with rural revitalization, cultural heritage conservation, and sustainable regional development. Full article

“Power-to-ammonia” is widely regarded as a viable solution for large-scale consumption of wind and solar power, as well as for deep decarbonization in the energy and chemical sectors. However, the intermittent nature of renewable energy requires ammonia synthesis systems to operate across a wide and varying range of loads, posing challenges to their economic viability. To address this, we develop a simulation and optimization methodology for ammonia reactor operation under varying loads. Firstly, a high-fidelity reactor model is developed based on the reactor’s structural characteristics by incorporating reaction kinetics and thermodynamic mechanisms. This reactor model is then integrated with compression and separation units. To ensure computational efficiency, surrogate models are developed to approximate the ammonia synthesis and flash separation units. A case study of an ammonia plant with a nominal production rate of 100,000 tons/year is conducted to demonstrate the effectiveness of the proposed method. The results indicate that the feasible operation region of the reactor narrows significantly as the system production load decreases. System operation parameters, including reactor inlet temperature, reactor pressure, and ammonia separation temperature, are optimized for the ammonia synthesis loop over a wide operating window from 30% to 100% of nominal capacity. It is recommended to increase the system inlet temperature as the production load decreases, thereby compensating for the reduced heat release per unit product resulting from the decreased system pressure. Full article

Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale. Full article

Aiming at addressing the problem whereby the traditional time-optimal trajectory planning based on the steady-state torque–speed characteristic cannot fully exploit the short-term dynamic output performance of the servo permanent magnet synchronous motor (SPMSM), a time-optimal trajectory planning method for the SPMSM based on the short-term dynamic feasible region constraint is proposed to effectively improve the response speed. Firstly, the dynamic trapezoidal domain operation boundary is obtained by analyzing the motor working point variation curve and considering factors such as the working temperature and trajectory control, which constitutes the torque–speed value and the dynamic constraint mechanism of trajectory planning. Secondly, based on the energy consumption model, the average thermal power is used to represent the torque overload limit condition, and a dynamic constraint method based on the short-term dynamic torque–speed operation boundary is proposed. Then, in order to reduce the computational load in the online millisecond-level response, a time-optimal trajectory optimization algorithm based on sequential least squares is proposed to calibrate the positioning time of the time-optimal trajectory under different working temperatures and angles. Finally, a simulation and experimental comparisons of the time-optimal trajectories under different angles and working temperatures are carried out to verify the effectiveness of the proposed method. Full article

The article presents a systematic and critical theoretical–methodological review and conceptual synthesis of the region as a fundamental analytical category and the central subject matter of regional geography. The primary objective of the study is to critically re-examine and conceptually redefine the region through an ontological and epistemological analysis of classical and contemporary geographical paradigms. The study is based on a qualitative interpretative methodology that combines analytical–synthetic, historical–genetic, comparative, critical, and conceptual approaches in order to examine the ontological and epistemological foundations of the region within classical and contemporary geographical thought. The region is conceptualized as a complex, multilayered, and dynamic socio-spatial entity whose ontological status has continuously evolved—from the essentialist notion of an objective spatial reality characteristic of classical geographic paradigms toward a relational and constructivist concept shaped by the interaction of social practices, political processes, and identity articulations within contemporary theoretical frameworks. Attention is also given to the epistemological foundations of regional knowledge, linking various modalities of the production and interpretation of scientific knowledge. Furthermore, the paper examines the roles of power, knowledge, identity, and institutionalization in the formation of regions, as well as the significance of centripetal and centrifugal forces in maintaining or destabilizing regional coherence. The research challenges traditional concepts of the region and proposes its redefinition in accordance with contemporary approaches that conceptualize it as an open, fluid, and context-dependent analytical framework. In conclusion, from the perspective of new regional geography, the region is interpreted as an emergent relational configuration whose understanding requires a broad interdisciplinary and critical approach. Full article

The pathological mechanism of autism spectrum disorder (ASD) exhibits dual heterogeneity: abnormal local energy metabolism and brain-wide high-order topological failure. To synergistically characterize these complex signals captured by advanced neuroimaging sensors, we propose the Quantum-Enhanced Graph Convolutional Wide Residual Network (QG-WRN), a modality-specific, decoupled parallel dual-stream architecture. In the classical branch, to accurately capture the spatial distribution of local metabolic abnormalities, we employ a wide residual network (WRN) to extract amplitude of low-frequency fluctuation (ALFF) features, leveraging its expanded feature channels to effectively mine regional neurodynamic properties. Furthermore, to overcome the representational bottlenecks of classical linear operators in parsing hidden, long-range network connections, we introduce quantum computing, exploiting its exponentially expansive state space and intrinsic low-parameter regularization mechanism. Guided by these properties, the quantum branch utilizes a variational quantum graph convolutional (QGCN) module—featuring a trainable circular encoding strategy and a hardware-efficient 4-qubit configuration—with a 2-layer nested message passing structure to process the functional connectivity (FC) matrix, harnessing quantum interference in Hilbert space to parse complex topology while effectively mitigating overfitting on small-sample medical data. A unified training scheme achieves full-dimensional fusion of node activity and topology. Achieving 68.49% accuracy, our method outperforms 10 classic and recent new baselines, providing a powerful computational intelligence tool for sensor-based ASD clinical diagnosis. Furthermore, interpretability analysis successfully maps core disease hubs to standard AAL116 atlas coordinates, providing a powerful tool for computationally aided ASD diagnosis. Full article

An automated CFD-based workflow for the design, evaluation, and comparative optimization of 2D tidal-stream turbine blade sections is presented for early-stage design exploration. The workflow is intended to efficiently derive an improved section using a consistent and higher fidelity evaluation approach, which is particularly relevant for floating tidal concepts where the effective angle of attack can vary. HEEDS is used to manage a SHERPA optimization loop, while candidate geometries are regenerated in Rhino Grasshopper through a control point parameterization with thickness bounds and smooth interpolation. STAR-CCM+ simulations are executed in an automated manner and the resulting lift and drag responses are returned to HEEDS to evaluate performance over four representative angles of attack, 0, 3, 6, and 9 deg. A total of 1000 design evaluations are conducted for a baseline NACA 63–815 section at Reynolds number 1 × 10⁷, using a two metric formulation that targets high mean lift to drag ratio while limiting the maximum drag coefficient within the same angle set. The optimization history shows rapid early improvement followed by a plateau and identifies a final best design at Design 746. Compared with the original section, the optimized section increases lift and improves the lift-to-drag ratio across the operating range, while keeping the peak drag constrained. Cavitation inception characteristics also improve, with the optimized section delaying inception at the same lift criterion and sustaining a cavitation free state at higher lift for the same cavitation number. Pressure coefficient distributions indicate that these changes are primarily associated with altered suction side loading in the front to mid chord region and modified pressure recovery behavior. A preliminary full 3D RANS CFD rotor comparison under a prescribed rotor geometry further shows that the optimized section can improve rotor power performance in the main operating TSR range, although the benefit becomes limited at high TSR. Full article

Against the backdrop of global carbon neutrality, the cross-regional allocation of green electricity is pivotal for energy transition, yet its impact on inclusive economic growth and regional equity remains contentious. This study addresses the spatial welfare mismatch arising from large-scale power transmission in China. Utilizing provincial panel data from 2006 to 2022 and employing the staggered rollout of Ultra-High Voltage (UHV) lines as a quasi-natural experiment, we apply advanced econometric models, including CS-DID and Bartik instrumental variables, to identify causal effects. Empirical results reveal an asymmetric “cost-benefit separation” effect: while green electricity imports significantly bolster high-quality development (HQD) in eastern recipient regions, exports exert a drag on western provinces by triggering capital outflow, profit deprivation, and ecological load. Consequently, regional HQD gaps exhibit divergence rather than convergence. However, we find that fiscal ecological compensation acts as a critical moderating buffer, effectively reversing this trend and driving conditional convergence and sustainable regional development. Heterogeneity analysis further indicates that market-oriented electricity reforms and “East Data, West Computing” infrastructure mitigate these negative externalities. These findings underscore the necessity of shifting from a purely engineering-focused transmission model to an institutional framework centered on energy justice, offering actionable insights for achieving SDG 7 and SDG 10 synergies. Full article