Search Results (1,218)

Network protector units (NPUs) are crucial parts of the protection of secondary networks to effectively isolate faults occurring on the primary feeders. When a fault occurs on the primary feeder, there is a path of the fault current going through the service transformers that causes a negative flow of current on the NPU connected to the faulted feeder. Conventionally, NPUs rely on the direction of current with respect to the voltage to detect faults and make a correct trip decision. However, the conventional NPU logic does not allow the reverse power flow caused by distributed energy resources installed on secondary networks. The communication-assisted direct transfer trip logic for NPUs can be used to address this challenge. However, the communication-assisted scheme is exposed to some vulnerabilities arising from the disruption or corruption of the communicated data that can endanger the reliable operation of NPUs. This paper evaluates the impact of the malfunction of the communication system on the operation of communication-assisted NPU logic. To this end, the impact of packet modification and denial-of-service cyberattacks on the communication-assisted scheme are evaluated. The evaluation was performed using a hardware-in-the-loop (HIL) co-simulation testbed that includes both real-time power system and communication network digital simulators. This paper evaluates the impact of the cyberattacks for different fault scenarios and provides a list of recommendations to improve the reliability of communication-assisted NPU protection. Full article

The purpose of this paper is to study the shear performance of reinforced concrete corbels under a synergistic change in the main stirrup inclination angle to explore the synergistic mechanism of the main reinforcement and the stirrup inclination angle, and to evaluate the applicability of existing design specifications. The shear performance test was carried out by designing RC corbel specimens with an inclination angle of the main reinforcement and stirrup. The test results show that a 15° inclination scheme significantly improves the shear performance: the yield load is increased by 28.3%, the ultimate load is increased by 23.6%, the strain of the main reinforcement of the 15° specimen is reduced by 51.3%, the stirrup shows a delayed yield (the yield load is increased by 11.6%) and lower strain level (250 kN is reduced by 23.7%), and the oblique reinforcement optimizes the internal force transfer path and delays the reinforcement yield. A CDP finite element model was established for verification, and the failure mode and crack propagation process of the corbel were accurately reproduced. The prediction error of ultimate load was less than 2.27%. Based on the test data, the existing standard method is tested and a modified formula of the triangular truss model based on the horizontal inclination angle of the tie rod is proposed. The prediction ratio of the bearing capacity is highly consistent with the test value. A function correlation model between the inclination angle of the steel bar and the bearing capacity is constructed, which provides a quantitative theoretical tool for the optimal design of RC corbel inclination parameters. Full article

To analyze and enhance the transient stability of a hydro-wind-PV VSC-HVDC transmission system, this paper establishes a transient stability analytical model and proposes strategies for stability improvement. Based on the dynamic interaction mechanisms of multiple types of power sources, an analytical model integrating GFM converters, GFL converters, and SGs is first developed. The EAC is employed to investigate how the factors such as current-limiting thresholds and fault locations influence transient stability. Subsequently, a parameter tuning method based on optimal phase angle calculation and delayed control of current-limiting modes is proposed. Theoretical analysis and PSCAD simulations demonstrate that various factors affect transient stability by influencing the PLL of converters and the electromagnetic power of synchronous machines. The energy transfer path during transient processes is related to fault locations, parameter settings of current-limiting modes in converters, and the operational states of equipment. The proposed strategy significantly improves the transient synchronization stability of multi-source coupled systems. The research findings reveal the transient stability mechanisms of hydro-wind-PV VSC-HVDC transmission systems, and the proposed stability enhancement method combines theoretical innovation with engineering practicality, providing valuable insights for the planning and design of such scenarios. Full article

To address the cracking problem caused by the weak tensile performance of concrete, this study investigates the failure mechanisms of basalt fiber-reinforced concrete under different fiber contents, single-blend, and mixed-blend schemes through splitting tensile tests and discrete element method (DEM) simulations. The tests employ 0.1–0.3% of 18 mm single-blend fibers and 6 mm:12 mm:18 mm (3:4:3) mixed-blend schemes, and PFC software is used to simulate crack propagation in fiber-reinforced concrete. The results show that the optimal 0.2% content of 18 mm single-blend fibers enhances the splitting tensile strength by 10.8%, whereas excessive 0.3% content reduces the strength by 7.8% due to poor dispersion. The mixed-blend scheme, via gradient crack-resisting effects of multi-scale fibers, increases the strength by 7.43% compared with the single-blend group at the same fiber content. DEM simulations reveal that fibers delay crack propagation through stress concentration transfer: single-blend fibers render tortuous crack paths, while mixed-blend fibers form three-dimensional crack networks, transforming the failure energy dissipation mode from single pull-out to multi-stage consumption. This research provides theoretical basis and optimization strategies for the anti-cracking design of basalt fiber-reinforced concrete. Full article

Preformed porous media (PPM) technology has emerged as a transformative approach to enhance heat and mass transfer in vacuum freeze-drying (VFD) of agricultural and food products. This review systematically analyzes recent advances in PPM research, with particular focus on spray freeze-drying (SFD) as the dominant technique for precision pore architecture control. Empirical studies confirm PPM’s efficacy: drying time reductions of 20–50% versus conventional VFD while improving product quality (e.g., 15% higher ginsenoside retention in ginseng, 90% enzyme activity preservation). Key innovations include gradient porous structures and multi-technology coupling strategies that fundamentally alter transfer mechanisms through: resistance mitigation via interconnected macropores (50–500 μm, 40–90% porosity), pseudo-convection effects enabling 30% faster vapor removal, and radiation enhancement boosting absorption by 40–60% and penetration depth 2–3 times. While inherent VFD limitations (e.g., low thermal conductivity) persist, we identify PPM-specific bottlenecks: precision regulation of pore structures (<5% size deviation), scalable fabrication of gradient architectures, synergy mechanisms in multi-field coupling (e.g., microwave-PPM interactions). The most promising advancements include 3D-printed gradient pores for customized transfer paths, intelligent monitoring-feedback systems, and multiscale modeling bridging pore-scale physics to macroscale kinetics. This review provides both a critical assessment of current progress and a forward-looking perspective to guide future research and industrial adoption of PPM-enhanced VFD. Full article

In the context of global climate change and growing food security challenges, this study provides a comprehensive analysis of the yields of three staple crops (wheat, corn and rice) in the Yellow River Basin of China, employing multiple quantitative analysis methods including the Mann–Kendall trend test, center of gravity transfer model and hotspot analysis. Our research integrates yield data covering these three crops from 72 prefecture-level cities across the Yellow River Basin, during 2000 to 2023, to systematically examine the temporal variation, spatial variation and spatial agglomeration characteristics of the yields. The study uses GeoDetector to explore the impacts of natural and socioeconomic factors on changes in crop yields from both single-factor and interactive-factor perspectives. While traditional statistical methods often struggle to simultaneously handle complex causal relationships among multiple factors, particularly in effectively distinguishing between direct and indirect influence paths or accounting for the transmission effects of factors through mediating variables, this study adopts Structural Equation Modeling (SEM) to identify which factors directly affect crop yields and which exert indirect effects through other factors. This approach enables us to elucidate the path relationships and underlying mechanisms governing crop yields, thereby revealing the direct and indirect influences among multiple factors. This study conducted an analysis using Structural Equation Modeling (SEM), classifying the intensity of influence based on the absolute value of the impact factor (with >0.3 defined as “strong”, 0.1–0.3 as “moderate” and <0.1 as “weak”), and distinguishing the nature of influence by the positive or negative value (positive values indicate promotion, negative values indicate inhibition). The results show that among natural factors, temperature has a moderate promoting effect on wheat (0.21) and a moderate inhibiting effect on corn (−0.25); precipitation has a moderate inhibiting effect on wheat (−0.28) and a moderate promoting effect on rice (0.17); DEM has a strong inhibiting effect on wheat (−0.33) and corn (−0.58), and a strong promoting effect on rice (0.38); slope has a moderate inhibiting effect on wheat (−0.15) and a moderate promoting effect on corn (0.15). Among socioeconomic factors, GDP has a weak promoting effect on wheat (0.01) and a moderate inhibiting effect on rice (−0.20), while the impact of population is relatively small. In terms of indirect effects, slope indirectly inhibits wheat (−0.051, weak) and promotes corn (0.149, moderate) through its influence on temperature; DEM indirectly promotes rice (0.236, moderate) through its influence on GDP and precipitation. In terms of interaction effects, the synergy between precipitation and temperature has the highest explanatory power for wheat and rice, while the synergy between DEM and precipitation has the strongest explanatory power for corn. The study further analyzes the mechanisms of direct and indirect interactions among various factors and finds that there are significant temporal and spatial differences in crop yields in the Yellow River Basin, with natural factors playing a leading role and socioeconomic factors showing dynamic regulatory effects. These findings provide valuable insights for sustainable agricultural development and food security policy-making in the region. Full article

Accurate path-loss characterization in the upper mid-band is critical for 5G/6G outdoor planning, yet classical deterministic expressions lose fidelity at 18 GHz, and purely data-driven regressors offer limited physical insight. We present a physics-guided residual learner that couples a calibrated two-ray model with an XGBoost regressor trained on the deterministic residuals. To enlarge the feature space without promoting overfitting, synthetic samples obtained by perturbing antenna height and ground permittivity within realistic bounds are introduced with a weight of $w=0.3$. The methodology is validated with narrowband measurements collected along two straight 25 m corridors. Under cross-corridor transfer, the hybrid predictor attains $0.59$–$0.62$ dB RMSE and $R^2\ge 0.996$, reducing the error of a pure-ML baseline by half and surpassing deterministic formulas by a factor of four. Small-scale analysis yields decorrelation lengths of 0.23 m and 0.41 m; a cross-correlation peak of unity at $\Delta =0.10$ m confirms the physical coherence of both corridors. We achieve <1 dB error using a small set of field measurements plus simple synthetic data. The method keeps a clear mathematical core and can be extended to other priors, NLOS cases, and semi-open hotspots. Full article

This study investigates the spatiotemporal optimization of small-scale emergency infrastructure in high-density urban environments, using nucleic acid testing sites in Macao as a case study. The objective is to enhance emergency responsiveness during future public health crises by aligning infrastructure deployment with dynamic patterns of population influx. A behaviorally informed spatial decision-making framework is developed through the integration of kernel density estimation, point-of-interest (POI) distribution, and origin–destination (OD) path simulation based on an Ant Colony Optimization (ACO) algorithm. The results reveal pronounced temporal fluctuations in testing demand—most notably with crowd peaks occurring around 12:00 and 18:00—and highlight spatial mismatches between existing facility locations and key residential or functional clusters. The proposed approach illustrates the feasibility of coupling infrastructure layout with real-time mobility behavior and offers transferable insights for emergency planning in compact urban settings. Full article

A three-dimensional numerical simulation of hot air anti-icing was conducted on the full-annular realistic model of engine intake components, comprising the intake ducts, intake casing, struts, axial flow casing, and zero-stage guide vanes, based on the intermittent maximum icing conditions and the actual engine operating parameters. The simulation integrated multi-physics modules, including air-supercooled water droplet two-phase flow around components, water film flow and heat transfer on anti-icing surfaces, solid heat conduction within structural components, hot air flow dynamics in anti-icing cavities, and their coupled heat transfer interactions. Simulation results indicate that water droplet impingement primarily localizes at the leading edge roots and pressure surfaces of struts, as well as the leading edges and pressure surfaces of guide vanes. The peak water droplet collection coefficient reaches 4.2 at the guide vane leading edge. Except for the outlet end wall of the axial flow casing, all anti-icing surfaces of intake components maintain temperatures above the freezing point, demonstrating effective anti-icing performance. The anti-icing characteristics of the intake components are governed by two critical factors: cumulative heat loss along the hot air flow path and heat load consumption for heating and evaporating impinging water droplets. The former induces a 53.9 °C temperature disparity between the first and last struts in the heating sequence. For zero-stage guide vanes, the latter factor exerts a more pronounced influence. Notable temperature reductions occur on the trailing edges of three struts downstream of the hot air flow and at the roots of zero-stage guide vanes. Full article

With the accelerated development of economic globalization, it is of great significance to strengthen the ability to measure, evaluate, and warn of systemic financial risks for preventing and defusing financial risks. Thus, this research established the Time-Varying Parameter Factor-Augmented Vector Autoregression model (TVP-FAVAR), combined with the Markov Regime Switching Autoregressive Model, to dynamically measure China’s systemic financial risk. The network public opinion index is constructed and introduced into the financial risk early warning system to capture the dynamic impact of market sentiment on financial risks. After testing the nonlinear causal relationship between financial indicators based on the transfer entropy method, the Transformer deep learning model is applied to build a financial risk early warning system, and the performance is compared to traditional methods. The experimental results showed that (1) the trend of the systemic financial risk index based on the dynamic measurement of the TVP-FAVAR model fitted the actual situation well and that (2) the Transformer model public opinion index could fully and effectively mine the nonlinear relationship between data. Compared to traditional machine learning methods, the Transformer model has significant advantages in stronger prediction accuracy and generalization ability. This study provided a new technical path for financial risk early warning and has important reference value for improving the financial regulatory system. Full article

Targeted alpha therapy (TAT) has recently emerged as a highly promising approach for the management of metastatic castration-resistant prostate cancer (mCRPC), especially in patients with disease progression despite standard treatments. Among alpha-emitter radiopharmaceuticals, actinium-225-labelled prostate-specific membrane antigen ([225Ac]Ac-PSMA) has shown remarkable potential due to its high linear energy transfer (LET), short path length, and ability to induce potent, localised cytotoxic effects. This review summarises current clinical evidence regarding [225Ac]Ac-PSMA radioligand therapy (RLT), emphasising its efficacy, safety profile, and position relative to beta-emitter therapy with lutetium-177 ([177Lu]Lu-PSMA). Data from compassionate-use programs and small clinical trials demonstrate that [225Ac]Ac-PSMA produces significant biochemical and imaging responses, including > 50% declines in prostate-specific antigen (PSA) and lesion regression on [68Ga]Ga-PSMA PET/CT, even in heavily pre-treated mCRPC cohorts. Xerostomia, renal toxicity, and haematological adverse effects remain the main safety challenges, necessitating optimisation of patient selection, dosing strategies, and salivary gland protection protocols. Compared with [177Lu]Lu-PSMA, [225Ac]Ac-PSMA appears effective even in cases of beta-refractory disease, highlighting its complementary role rather than a competitive alternative. However, limited availability, high production costs, and the lack of large-scale, randomised trials hinder widespread clinical adoption. Future directions include combination protocols, improved radiopharmaceutical design, and trials evaluating its use in earlier disease stages. This review provides a comprehensive overview of the clinical aspects of [225Ac]Ac-PSMA RLT and its evolving role in advanced prostate cancer management. Full article

Conventional radiative transfer (RT) models often adopt the plane-parallel (PP) approximation, which neglects Earth’s curvature and leads to significant optical path errors under large solar or sensor zenith angles, particularly for high-latitude regions and twilight conditions. The spherical Monte Carlo method offers high accuracy but is computationally expensive, and the commonly used pseudo-spherical (PSS) approximation fails when the viewing zenith angle exceeds 80°. With the increasing application of machine learning in atmospheric science, the efficiency and angular limitations of spherical RT simulations may be overcome. This study provides a physical and quantitative foundation for developing a hybrid RT framework that integrates physical modeling with machine learning. By systematically quantifying the discrepancies between PP and spherical RT models under diverse atmospheric scenarios, key influencing factors—including wavelength, solar and viewing zenith angles, aerosol properties (e.g., single scattering albedo and asymmetry factor), and PP-derived radiance—were identified. These variables significantly affect spherical radiative transfer and serve as effective input features for data-driven models. Using the corresponding spherical radiance as the target variable, the proposed framework enables rapid and accurate inference of spherical radiative outputs based on computationally efficient PP simulations. Full article

Three-dimensional cementitious material printing (3DCMP) enables structures with complex geometry to be fabricated. Printed filament quality is significantly affected by the mass distribution at its corners. Although fruitful results have been obtained, a significant gap exists in systematically investigating the impact of comprehensive parameters on this mass distribution. Therefore, the cross-section ratio Φ (Φ = S_o/S_u) of the filament is proposed as a measurement to evaluate the mass distribution at corners. Then, the impacts of printing process parameters, including the tool path radius R, nozzle aspect ratio φ, and relative nozzle travel speed ζ, on the filament mass distribution are investigated using computational fluid dynamics (CFD). The flow mechanism is elaborated using CFD for cementitious material printing at corners. It was found that the material flow mechanism caused by the combined effects of the printing process parameters affects the filament mass distribution significantly. Some material spills out from the overfilled zone to the underfilled zone during the deposition process. Additionally, printing process windows were identified to ensure acceptable printing quality using a support vector machine (SVM). A new printing window is identified using transfer learning, which can save data resources compared to the SVM method. Finally, the experimental results show the feasibility and effectiveness of the proposed methods in printing process window determination. Full article

Autonomous UAV navigation in unknown and complex environments remains a core challenge, especially under limited sensing and computing resources. While most methods rely on modular pipelines involving mapping, planning, and control, they often suffer from poor real-time performance, limited adaptability, and high dependency on accurate environment models. Moreover, many deep-learning-based solutions either use RGB images prone to visual noise or optimize only a single objective. In contrast, this paper proposes a unified, model-free vision-based DRL framework that directly maps onboard depth images and UAV state information to continuous navigation commands through a single convolutional policy network. This end-to-end architecture eliminates the need for explicit mapping and modular coordination, significantly improving responsiveness and robustness. A novel multi-objective reward function is designed to jointly optimize path efficiency, safety, and energy consumption, enabling adaptive flight behavior in unknown complex environments. The trained policy demonstrates generalization in diverse simulated scenarios and transfers effectively to real-world UAV flights. Experiments show that our approach achieves stable navigation and low latency. Full article

As traditional connection studies ignore the unbalanced distribution of connection demand and the variability of connection situations, this results in a poor match between passenger demand and connection mode, increasing passenger travel costs. Combining the economic efficiency of metro network operations with the unique accessibility advantages of ride-hailing services, this study clusters origin and destination points based on different travel needs and proposes four transfer strategies for integrating ride-hailing services with urban rail transit. Four nested strategies are developed based on the distance between the trip origin and the subway station’s service range. A reinforcement learning approach is employed to identify the optimal connection strategy by minimizing overall travel cost. The guided reinforcement learning principle is further introduced to accelerate convergence and enhance solution quality. Finally, this study takes the Fengtai area in Beijing as an example and deploys the Guided Q-Learning (GQL) algorithm based on extracting the hotspot passenger flow ODs and constructing the road network model in the area, searching for the optimal connecting modes and the shortest paths and carrying out the simulation validation of different travel modes. The results demonstrate that the GQL algorithm improves search performance by 25% compared to traditional Q-learning, reduces path length by 8%, and reduces minimum travel cost by 11%. Full article