Search Results (4,617)

This study investigated how different organic fertilization practices affect productivity, stability, and nitrogen use efficiency in the rotation systems of the Hetao Irrigation District. The research was based on a long-term field experiment (2015–2025), with a chemical fertilizer-only treatment as the control (CK). Four organic fertilization treatments were evaluated: farmyard manure application (CM), straw incorporation (CS), green manure cultivation and incorporation (CG), and a combined green manure plus straw treatment (CGS). Based on three consecutive years of observations (2023–2025), the impacts of these treatments on crop yield, yield composition and stability, plant nitrogen accumulation and allocation, and nitrogen use efficiency were systematically analyzed. Both CM and CS significantly increased maize equivalent yield (MEY) compared with the other treatments, by 33.68–66.04% and 16.05–24.21%, respectively. CM’s productivity advantage was primarily driven by higher biomass accumulation, whereas CS’s advantage was largely due to improvements in the harvest index. In terms of stability, CM exhibited the lowest coefficient of variation (CV), indicating the highest static stability, while CS showed a regression coefficient (b_i) close to 1, indicating stronger dynamic stability. CM also significantly enhanced total plant nitrogen accumulation, nitrogen recovery efficiency (NRE), and nitrogen use efficiency (NUE), while optimizing nitrogen allocation to grain. CS significantly improved nitrogen internal efficiency (NIE), promoting more efficient conversion of absorbed nitrogen into grain yield. CG and CGS did not show clear advantages across productivity, stability, or most nitrogen use efficiency-related indices. Overall, in the Hetao Irrigation District, farmyard manure application is an effective strategy for achieving both high and stable yields, whereas straw incorporation offers stronger environmental adaptability. Both practices represent practical and effective approaches for improving the sustainability of rotation systems. Full article

In research on electro-hydraulic servo systems, nonlinearity deeply affects dynamic performance, such as the output of hydraulic actuators and the generation of control signals, leading to response hysteresis and control complexity. Moreover, during the control process, changes in the external environment and component loss lead to model parameter distort, which reduces control capability. To address these challenges, this paper conducts a structural transformation on the traditional dynamic surface controller in combination with the fast finite-time stability theorem and proposes a novel finite-time dynamic surface control strategy, which can not only overcome the differential explosion phenomenon in the recursive backstepping iterative process but also enhance the transient dynamic response speed. Furthermore, the neural network adaptive algorithm is adopted to handle the negative dynamic effect caused by parametric uncertainty. The theoretical results are verified by the Lyapunov stability method and numerical simulation. Full article

Wireless Sensor Networks (WSNs) are widely used in environmental monitoring, disaster early warning, and smart grids. However, sensor nodes face strict energy limitations. Unbalanced energy consumption and hotspots severely shorten the network lifetime. To address these problems, this paper proposes an optimized Spotted Hyena Optimization-Energy-Efficient Non-Uniform Clustering algorithm (SHOE) for cluster head selection and data transmission. The algorithm has three main innovations: combining a bio-inspired metaheuristic with an improved EEUC (Energy-Efficient Unequal Clustering) multi-hop relay and a Gaussian distribution model for non-uniform node deployment; designing a multi-dimensional fitness function considering energy, distance, and node location; and introducing empty cluster and isolated node repair mechanisms to balance exploration and exploitation. Specifically, the multi-dimensional fitness function guides the heuristic search process towards high-quality cluster head candidates, while the empty cluster and isolated node repair mechanisms dynamically rectify abnormal network structures, ensuring the robustness of the final architecture optimized by the bio-inspired framework. Simulations in MATLAB show that SHOE outperforms LEACH (Low-Energy Adaptive Clustering Hierarchy), PSOE (Particle Swarm Optimization with Evolutionary Strategy), PL-EBC (Probabilistic Localized Energy-Balanced Clustering), and CGWOA (Chaotic Grey Wolf Optimization Algorithm) in reducing node death, saving energy, and extending network lifetime. It improves adaptability to non-uniform distribution and optimizes energy balance, thus enhancing the efficiency and stability of WSNs. Full article

The suspension system of high-speed maglev trains has composite time-delay factors, such as inductance delay and control circuit latency, which lead to a decrease in the tracking and robustness of the current control loop. Based on the study of electromagnetic characteristics of suspension systems, this paper proposes a composite anti-time-delay current loop based on adaptive parameter optimization. First, a finite element analysis model of the suspension electromagnet is constructed to analyze the changes in suspension force and inductance of the suspension electromagnet. A self-tuning PI current loop is constructed to achieve time-varying parameter matching. Second, to tackle the inherent time delays and disturbances in the control loop, a predictive PI control algorithm combined with an extended state observer (ESO) is introduced, which effectively estimates and compensates for disturbances and phase lags. Furthermore, a parameter optimization strategy based on the adaptive differential evolution (ADE) algorithm is proposed to address the difficulties in current loop tuning. The results demonstrate that compared to traditional current loop strategies, the dynamic performance of the designed composite anti-time-delay current loop is significantly improved, enhancing the current following control capability of the suspension system under complex operating conditions. Full article

Driven by the global energy transition and carbon neutrality goals, hybrid power systems have become a core technical path for energy conservation and carbon reduction in the transportation and power sectors, and the performance of energy management strategies directly determines the system’s overall energy efficiency. Traditional energy management methods have inherent bottlenecks of high model dependence and poor adaptability, making it difficult to satisfy real-time decision-making requirements under complex operating conditions. Deep Reinforcement Learning (DRL) provides an innovative solution to this technical bottleneck, and has become a cutting-edge research direction in this field. However, existing reviews have not yet constructed a full-chain analysis framework covering its algorithms, applications, verification, challenges and prospects. Focusing on the engineering application of DRL in the real-time energy management of hybrid power systems, this paper systematically sorts out domestic and international research results up to the first quarter of 2026. The core quantitative findings of this review are as follows: (1) DRL-based strategies can achieve 93–99.5% of the Dynamic Programming (DP) theoretical global optimum in fuel economy, which is 5–25% higher than rule-based methods; (2) DRL strategies only have 3.1–4.8% performance degradation under unseen operating conditions, which is significantly better than the 10.3–14.7% degradation of the Equivalent Consumption Minimization Strategy (ECMS); (3) Actor–Critic (AC) algorithms (Twin Delayed Deep Deterministic Policy Gradient (TD3)/Soft Actor–Critic (SAC)) have become the mainstream in this field, with a 3–5 times higher sample efficiency than value function-based algorithms; and (4) offline DRL and transfer learning can reduce the training time of DRL strategies by more than 80% while maintaining equivalent optimization performance. This paper first analyzes the essential attributes and core technical challenges of hybrid power system energy management; second, classifies DRL algorithms from the perspective of control engineering and analyzes their technical characteristics; third, disassembles the application design logic of DRL around four major scenarios: land vehicles, water vessels, aerial vehicles and fixed microgrids; fourth, summarizes the mainstream verification platforms and evaluation systems; fifth, analyzes core bottlenecks and cutting-edge solutions; and finally, prospects the development trends of next-generation intelligent energy management systems combined with cross-fusion technologies. This paper aims to build a complete technical system map for this field and promote the engineering deployment and practical application of intelligent energy management technologies integrating data and knowledge. Full article

Mobile robot navigation remains challenging when fast convergence, collision avoidance and deployability must be satisfied simultaneously. The original Q-learning with Artificial Potential Field (QAPF) paradigm is extended in this paper with three coordinated mechanisms that together yield a reported-horizon convergence reduction of approximately four orders of magnitude (from $\sim 3\times {10}^6$ episodes to $\sim 200$ to 230 episodes under the present protocol) and an internal-ablation collision-rate reduction of approximately one order of magnitude ($6.2\%$ to $0.3\%$), and that open a new capability frontier covering dynamic obstacles, multi-robot coordination, energy-aware velocity modulation and embedded-deployable inference timing. The first mechanism is a potential-based reward-shaping schedule whose unclipped fixed-weight form follows the policy-invariant shaping theorem, while the implemented clipped and time-varying form is used as an empirically stable approximation. Under the present experimental protocol, the reported convergence horizon is reduced from the $\sim 3\times {10}^6$ episodes reported for the original QAPF formulation to approximately 200 to 230 episodes; this comparison is protocol-dependent and is not claimed as a controlled one-to-one runtime speedup. The second mechanism is a discrete Control Barrier Function (CBF)-inspired action filter (thediscrete filter described in this paper is inspired by the continuous-time CBF literature, but does not carry a forward-invariance proof; it is used as an empirical safety mechanism rather than as a formal Control Barrier Function in the formal continuous-time sense) with per episode visit memory by which the held-out collision rate is reduced from $6.2\%$ for QAPF alone to $0.3\%$ while $93.8\%$ task completion is maintained, where this collision-rate comparison is internal to the QAPF ablation because the prior QAPF reference does not report a comparable held-out collision metric. The third mechanism is a set of extensions to dynamic obstacles, two-robot cooperative navigation under a centralized scheme (with an explicit $\mathcal{O}(N^2)$ scaling-cost analysis and three decentralization strategies for fleets beyond the small-N regime), curriculum learning and energy-aware velocity modulation. Disturbance robustness tests, empirical timeout/stagnation detection for unreachable-goal cases, i7 reference inference timing with projected embedded-device latencies, multi-axis generalization over obstacle density and grid size, scalability analysis for centralized multi-robot coordination and a scope comparison against A* and RRT* are added by the revised evaluation. Across 30 independent seeds on held-out static maps, $94.5\pm 2.1\%$ success is achieved by adaptive QAPF while $93.8\pm 2.3\%$ success with $0.3\pm 0.4\%$ collisions is achieved by QAPF+CBF. Under a separate finite robustness suite, $85.0\pm 4.1\%$ success is retained by QAPF+CBF in the combined disturbance regime. The timing study indicates that the 20 Hz real-time threshold is comfortably exceeded by all methods on the measured i7 reference platform and by all projected embedded-device equivalents. The results show that a lightweight and safety-oriented navigation policy for grid-based mobile-robot settings can be provided by APF-guided tabular reinforcement learning when it is paired with a discrete safety filter and a clarified energy and robustness analysis. Full article

Heating, ventilation, and air conditioning (HVAC) systems struggle to maintain optimal thermal comfort because perception is subjective and varies significantly across individuals. Traditional uniform cooling strategies often overlook demographic diversity, leading to inequitable comfort outcomes and inefficient building operations. To address this limitation, this study analyzed a web-based survey of 366 university occupants using a partial proportional odds model with multiple imputation and inverse-frequency weighting. Interaction terms, specifically Age–Activity, Gender–Clothing, and Age–Clothing, were included to assess combined effects that reflect demographic disparities in adaptive capacity. The results show that clothing insulation, activity, age, gender, race/ethnicity, and space type significantly influence thermal responses. Notably, male occupants were more than three times as likely to report feeling too warm (odds ratio [OR] = 3.24), whereas older adults exhibited significantly lower odds of reporting feeling too warm (OR = 0.42). Substantial variation was observed across racial and ethnic groups (ORs ranging from 2.4 to 6.5). These findings highlight the limitations of traditional population-average comfort approaches and provide valuable scientific insights for demand-response-ready HVAC strategies that adjust temperature setpoints dynamically without sacrificing comfort. By offering accurate, real-time estimates across diverse thermal ranges, these occupant-centric models reduce HVAC energy use and associated emissions at the building scale while supporting ancillary services for flexible load shifting and smarter coordination within low-carbon electric grids. Ultimately, incorporating demographic and contextual diversity into building controls reduces unnecessary cooling waste while promoting thermal equity, establishing a human-centric foundation for sustainable built environments. Full article

This study addresses the problem of robust video-based tracking of laboratory rats in open-field and Y-maze experiments under challenging acquisition conditions, including non-uniform illumination, low contrast, and heterogeneous recording setups. Existing approaches based on classical image processing or deep learning often fail to maintain stable localization under such conditions or require large, annotated datasets. We propose a hybrid tracking framework that combines an improved motion–appearance voting mechanism with consistency-constrained optimization for open-field experiments, together with a comparative deep learning-based detection strategy for Y-maze analysis. The proposed method introduces (i) adaptive dual-threshold motion extraction, (ii) directionally constrained temporal validation, and (iii) a robustness-driven fusion of motion and appearance cues. Experimental results demonstrate that the proposed approach achieves reliable tracking with a maximum localization error below 10 pixels under severe illumination variations. In the Y-maze scenario, a comparative evaluation of multiple detectors (YOLOv5, YOLOv9, YOLO12, Faster R-CNN) highlights the trade-off between accuracy and inference time, with YOLOv9 providing the best balance. The main contribution consists of enabling robust behavioral quantification in low-quality experimental conditions using limited training data, bridging the gap between classical tracking robustness and deep learning flexibility. Full article

Coastal regions played a key role in the emergence of Early Neolithic cultures. Fluctuating sea levels shaped prehistoric human migration, settlement patterns, and adaptation strategies. The lower reaches of the Min River in Fujian were a major centre of activity. During the Middle to Late Neolithic, marine communities such as the Keqiutou (6500–5500 cal. a BP) and Tanshishan (5500–4300 cal. a BP) cultures flourished. However, the scarcity of earlier remains has limited understanding of Early Neolithic life before 8000 cal. a BP. We dated stratigraphic layers at the newly excavated Niutoushan site using radiocarbon dating and optically stimulated luminescence (OSL). OSL results indicate the site’s Neolithic culture layer between 9.3 ± 0.7 ka and 8.1 ± 0.5 ka, with radiocarbon dates clustering around 8300–7000 cal. a BP. Based on the younger bounds of the dating results and kernel density estimation, the Neolithic remains at the site are dated to approximately 8000–7000 cal. a BP, identifying Niutoushan as one of the earliest Neolithic sites in the region. Combined with sea-level reconstructions, the findings suggest that the rapid Early Holocene sea-level rise drove human migration along China’s eastern coast before 8000 cal. a BP. The Niutoushan culture was influenced by Neolithic cultures from northern coastal regions and potentially by those located to its south across the exposed Taiwan Strait from the Last Glacial Maximum to the Early Holocene. This points to complex interactions among Early Neolithic cultures in both northern and southern coastal China, warranting further investigation for validation. Full article

This article analyzes the state of water resources in the Guercif Plain (Morocco) under the combined effects of drought and increasing consumption pressures. The study adopts a quantitative and analytical approach based on climatic and hydrological data, demographic information, and Landsat satellite imagery. The main findings reveal pronounced rainfall variability with an overall declining tendency, with drought years accounting for approximately 58% of the observation period. This climatic context has been accompanied by strong interannual fluctuations in the discharge of Oued Melloulou, with a slight long-term declining trend, along with a continuous and accelerating groundwater decline in the Tafrata aquifer at an average rate of 0.98 m per year. The analysis also indicates an estimated urban water deficit approaching 77% under peak demand conditions in 2025. Furthermore, NDVI-based analysis of satellite imagery highlights a marked expansion of irrigated areas in the Guercif Plain, increasing from about 2% of the total plain area in 1985 to approximately 9% in 2020. This vegetation expansion is largely associated with irrigation development, suggesting increasing pressure on groundwater resources rather than recovery linked to rainfall conditions. Overall, the findings raise critical concerns regarding the long-term sustainability of water resources and underscore the need for integrated and adaptive water-management strategies under persistent drought conditions. Full article

Speech Emotion Recognition (SER) is a key enabling technology for advanced human–computer interaction and affective computing. This paper presents an adaptive hybrid SER framework that combines a deep neural feature extraction module with a heterogeneous ensemble of machine learning classifiers, including XGBoost, Support Vector Machines (SVMs), and Random Forest. To overcome the limitations of static fusion strategies, a confidence-gated meta-classification mechanism is introduced to dynamically weight the contribution of each base classifier according to its instance-level reliability. The proposed approach is evaluated on two widely adopted benchmark datasets, EmoDB and SAVEE, achieving competitive accuracies of 98.88% and 91.92%, respectively. Experimental results demonstrate that the proposed fusion strategy significantly improves robustness against inter-speaker variability and emotional ambiguity, while maintaining low computational complexity suitable for real-time implementation. These findings highlight the effectiveness of the proposed framework as a robust and efficient solution for speech emotion recognition. While the model is evaluated on benchmark datasets, it is intended as a foundational component for future emotion-aware systems, including applications in human–computer interaction. Full article

Under strict spatial constraints and environmental interference, autonomous navigation of vessels in inland bridge-restricted waterways demands precise coordination between collision avoidance and trajectory tracking. To meet these operational demands, an integrated framework that directly combines spatiotemporal risk assessment with dynamic control execution is developed. Based on a digital traffic model integrating bridge piers and channel boundaries, collision risks are evaluated by combining trajectory-predicted time to safe distance with the velocity obstacle interval. Such a formulation quantifies the actual spatial difficulty of evasion rather than relying solely on temporal urgency. Driven by this continuous assessment, a time-series rolling strategy calculates feasible maneuvering intervals, generating trajectories that comply strictly with inland navigation rules and physical vessel limits. Subsequently, an adaptive model predictive control algorithm executes these commands, implicitly compensating for the localized hydrodynamic disturbances typical of bridge areas. The effectiveness of the architecture is validated through comprehensive simulations covering rule-based encounters and complex multi-vessel scenarios. Quantitative results indicate that under wind and current disturbances, the maximum route tracking deviation is constrained below 53 m, while the minimum encounter distance with target ships is consistently maintained above 51 m. These performance metrics confirm the capacity to execute safe, rule-compliant maneuvers while preserving high navigational precision in confined inland environments. Full article

Background/Objectives: Advanced prostate cancer (PCa) evolves through adaptive mechanisms that sustain tumor growth despite the suppression of androgen receptor (AR) signaling. Accumulating evidence identifies activation of the hepatocyte growth factor (HGF)/MET pathway as a potential driver of PCa progression in advanced disease states characterized by AR-independence and therapeutic resistance. We review the biological and clinical evidence supporting MET as a context-dependent therapeutic target and discuss its implications for patient selection and combination strategies. Methods: A comprehensive narrative review of preclinical, translational, and clinical studies evaluating MET-directed therapies for PCa was performed. Results: Aberrant activation of the HGF–MET axis is frequently driven by autonomous paracrine and autocrine loops that sustain pathway activation during disease progression. MET overexpression is associated with adverse pathological features, increased tumor aggressiveness, bone metastasis, lineage plasticity, and resistance to AR-targeted treatments. Preclinical studies have demonstrated that AR suppression, tumor hypoxia and tumor–microenvironment interactions promote MET upregulation, supporting AR-independent growth and epithelial-to-mesenchymal transition. Clinical trials of MET inhibitors have shown modest activity as monotherapies, with the most consistent biological effects observed in bone-dominant disease. Recent studies indicate greater therapeutic potential when MET inhibition is incorporated into rational combination strategies targeting complementary molecular pathways. Emerging data further indicate that MET activation characterizes a biologically aggressive, AR-low or neuroendocrine-like disease state. These findings support a transition from empiric use of MET inhibitors toward precision, context-driven therapeutic development. Conclusions: MET is not a universal therapeutic target but defines a clinically relevant subset of aggressive, AR-indifferent PCa. Future development should focus on biomarker-guided patient selection and rational combination strategies. Integration of molecular profiling, imaging, and liquid biopsy approaches will be essential to identify patients most likely to benefit from MET-directed interventions. Full article

Accurate and robust control of robotic joints is essential for high-performance robotic systems. However, conventional proportional–integral–derivative (PID) controllers suffer from limited adaptability when applied to brushless direct current (BLDC) motor-driven joints operating under nonlinear and time-varying conditions. To address this issue, this paper proposes a Radial Basis Function (RBF) neural network-enhanced self-tuning PID control strategy. The RBF neural network serves as an online identifier to approximate the nonlinear dynamics of the BLDC motor and to estimate the system Jacobian online. Based on the estimated Jacobian, the PID gains (K_p, K_i, and K_d) are adaptively updated using a gradient descent mechanism, enabling continuous adjustment to varying operating conditions. Simulation and experimental results demonstrate that the proposed method achieves negligible overshoot, faster settling performance, and improved steady-state accuracy compared with conventional PID and PI controllers. In addition, the proposed controller exhibits enhanced disturbance rejection capability and robust performance under abrupt speed variations and start–stop conditions. The proposed approach effectively combines the simplicity of PID control with the adaptability of neural networks, providing a practical and efficient solution for high-precision robotic joint control. Full article

Viticulture is highly vulnerable to weather variability and climate change. Growers increasingly face risks associated with extreme weather events, water scarcity, and emerging pests and diseases. To address these challenges, this study presents the development and implementation of the first operational digital decision support platform (DSP) tailored to Montenegrin vineyards within the MONTEVITIS project. The platform integrates IoT sensor data, national meteorological records and high-resolution global climate datasets to provide real-time monitoring and climate projections for vineyard management. The system was piloted in four vineyards representing diverse microclimatic and soil conditions of Montenegro. Key functionalities include phenology, irrigation and disease alerts supported by a user-friendly dashboard, map-based visualisation tools and data export functions. The pilot deployment demonstrated that combining heterogeneous data streams increases the reliability of outputs and enables timely, site-specific recommendations. Challenges identified during implementation include connectivity limitations, gaps in data and variable levels of digital expertise among growers; however, lessons learned point to the importance of continuous stakeholder engagement and institutional support for sustained use. The MONTEVITIS experience demonstrates how digital agriculture tools can bridge tradition and innovation in viticulture. By fostering collaboration between growers, researchers and policy makers, the platform enables adaptive strategies for climate resilience and sustainable vineyard management. Although the platform has been successfully deployed and tested under pilot conditions, a comprehensive long-term validation of its performance and impact on vineyard decision-making remains part of ongoing future work. Full article