Search Results (1,170)

Enhancing capacity utilization (CU, hereinafter referred to as CU) is crucial for effectively solving the overcapacity problem, optimizing industrial structure, and promoting premium economic development. While extensive academic research has been conducted on CU, supply chain finance (SCF, hereinafter referred to as SCF) and its influence on corporate capacity constraints remain largely unexplored. This study carefully examines how SCF affects corporate CU and the transmission mechanism, with a focus on China’s A-share listed businesses (2010–2023). The result shows that SCF improves businesses’ CU. After applying various robustness and endogeneity tests, the findings still hold that SCF largely affects the growth in CU throughby alleviating financing constraints, reducing internal agency costs, enhancing technological innovation, and improving inefficient investment. Further analysis indicates that close supply chain relationships, lower supply chain efficiency and non-state ownership, higher industry competition, a high marketization level, and a high level of financial development all enhance the “de-capacity” effect of SCF. Besides enriching the theoretical framework of SCF’s economic impacts, this research develops an operational solution to mitigate production overcapacity, a long-standing structural issue in China’s manufacturing industries, and provides a solid theoretical support for SCF to strengthen the foundation of the real economy and spearhead the sustainable, productivity-driven development of China’s economic landscape. Full article

In complex urban wireless environments, unmanned aerial vehicle–mobile edge computing (UAV-MEC) systems face challenges like link blockage and single-antenna eavesdropping threats. The traditional single reconfigurable intelligent surface (RIS), limited in collaboration, struggles to address these issues. This paper proposes a double-RIS cooperative UAV-MEC optimization scheme, leveraging their joint reflection to build multi-dimensional signal paths, boosting legitimate link gains while suppressing eavesdropping channels. It considers double-RIS phase shifts, ground user (GU) transmission power, UAV trajectories, resource allocation, and receiving beamforming, aiming to maximize secure energy efficiency (EE) while ensuring long-term stability of GU and UAV task queues. Given random task arrivals and high-dimensional variable coupling, a dynamic model integrating queue stability and secure transmission constraints is built using Lyapunov optimization, transforming long-term stochastic optimization into slot-by-slot deterministic decisions via the drift-plus-penalty method. To handle high-dimensional continuous spaces, an end-to-end proximal policy optimization (PPO) framework is designed for online learning of multi-dimensional resource allocation and direct acquisition of joint optimization strategies. Simulation results show that compared with benchmark schemes (e.g., single RIS, non-cooperative double RIS) and reinforcement learning algorithms (e.g., advantage actor–critic (A2C), deep deterministic policy gradient (DDPG), deep Q-network (DQN)), the proposed scheme achieves significant improvements in secure EE and queue stability, with faster convergence and better optimization effects, fully verifying its superiority and robustness in complex scenarios. Full article

Amphibians are particularly vulnerable to hydric stress due to their permeable skin, biphasic life cycle, and strong dependence on aquatic and moist terrestrial environments. In the Northwestern Mediterranean Basin—one of Europe’s most climate-sensitive regions—the intensification of droughts associated with climate change poses a critical threat to amphibian populations. Increased aridification, either due to higher temperatures or to more frequent, prolonged, and severe drought episodes, can affect both aquatic and terrestrial life stages, directly altering breeding opportunities, larval development, post-metamorphic survival, and dispersal capacity. This review aims to gather and synthesize current knowledge on the ecological, physiological, and demographic impacts of drought on amphibians of the Northwestern Mediterranean across habitat types, including ephemeral ponds, permanent water bodies, lotic systems, and terrestrial landscapes, including a final section on possible mitigation actions. Drought-induced shifts in hydroperiod can drastically reduce reproductive success and accelerate larval development with fitness consequences while, on land, desiccation risk and habitat degradation could limit access to refugia and fragment populations by reducing structural connectivity. These environmental constraints are compounded by the interactions between drought and emerging infectious diseases. We discuss the current knowledge on how chytrid fungi (Batrachochytrium dendrobatidis and B. salamandrivorans) and ranaviruses may respond to temperature and moisture regimes, and how drought may affect their transmission dynamics, host susceptibility, and pathogen persistence. In these cases, microbiome disruption, pollutant concentration, and increased contact rates between species may amplify disease outbreaks under dry conditions, but a better understanding of the multifactorial effects of drought on amphibian biology and disease ecology is needed for predicting species vulnerability, identifying high-risk populations, and guiding future conservation and management strategies in Mediterranean environments. Full article

This paper deals with the design and the robustness analysis of a model predictive control (MPC) for the position tracking of primary flight movables driven by electro-mechanical actuators. This study is, in particular, focused on a rotary electro-mechanical actuator (EMA) by UMBRAGROUP, employing a patented mechanical transmission based on a differential ball-screw mechanism characterized by a huge gear ratio. To obtain a baseline reference, conventional PID regulators were initially optimized by using multi-objective cost functions based on tracking accuracy, load disturbance rejection, and power consumption. The position regulator was then replaced by an MPC regulator, designed to balance performance, computational resources, and safety constraints. A nonlinear physics-based simulation model of the EMA, entirely developed in the Matlab–Simulink environment and validated with experiments, was used to compare the two control strategies. The simulation results in both the time and frequency domains highlight that the MPC solution provides faster and more accurate position tracking, improved dynamic stiffness, and reduced power absorption. Finally, the robustness against model uncertainties of the MPC was addressed by imposing random and combined deviations of model parameters from the nominal values (via Monte Carlo analysis). The results demonstrate that the implementation of MPC control laws could enhance the stability and the reliability of EMAs, thus supporting their application for safety-critical flight control functions. Full article

Loop shaping the controller for high-order systems, especially in the presence of flexible transmission components such as elastic shafts, gearboxes, and belts commonly found in servo systems, poses significant challenges. Therefore, developing a non-parametric, versatile tuning algorithm that adapts to multi-order systems is essential for general control applications. This article first obtains the frequency characteristics of plants through a frequency sweep. Then, based on preset frequency domain specifications, the boundaries representing disturbance rejection and stability constraints are defined in the complex plane with explicit mathematical and graphical expressions. Subsequently, a system of equations is developed based on the tangency between the open-loop curve of the system and the boundaries in the complex plane. On this basis, a versatile tuning algorithm is designed to calculate parameters of a PI controller cascaded with a low-pass filter that ensures the system meets the preset constraints. The proposed approach does not rely on parametric modeling, and the zeros and poles of the controller can be flexibly placed. Experimental validation is carried out on mechanical platforms. Full article

Advancements in battery technology, marked by reduced costs and enhanced efficiency, are steadily making electric vehicles (EVs) more accessible to consumers. This trend is fueling global growth in EV fleet sizes, allowing EVs to compete directly with internal combustion engine vehicles. However, this rapid growth in EV numbers is likely to introduce challenges to the power grid, necessitating effective load management strategies. This work proposes an optimization method where EV load management is integrated into the Transmission Constrained Unit Commitment Problem (TCUCP). A Differential Evolution (DE) variant, enhanced with heuristic repair sub-algorithms, is employed to address the TCUCP. The heuristic sub-algorithms, adapted from earlier approaches to the simpler Unit Commitment Problem (UCP), are updated to incorporate power flow constraints and ensure the elimination of transmission line violations. Additionally, new repair mechanisms are introduced that combine priority lists with grid information to minimize violation. The proposed formulation considers EVs as both flexible loads and energy sources in a large urban environment powered by two grid nodes, accounting for the vehicles’ daily movement patterns. The algorithm exhibits exceptionally fast convergence to a feasible solution in fewer than 150 generations, despite the nonlinearity of the problem. Depending on the scenario, the total production cost is reduced by up to 45% within these generations. Moreover, the results of the proposed model, when compared with a MILP algorithm, achieve values with a relative difference of approximately 1%. Full article

Human Activity Recognition (HAR) using wearable sensors plays a critical role in applications such as healthcare, sports monitoring, and rehabilitation. Traditional approaches typically rely on centralized models that aggregate and process data from multiple sensors simultaneously. However, such architecture often suffers from high latency, increased communication overhead, limited scalability, and reduced robustness, particularly in dynamic environments where wearable systems operate under resource constraints. This paper proposes a distributed neural network framework for HAR, where each wearable sensor independently processes its data using a lightweight neural model and transmits high-level features or predictions to a central neural network for final classification. This strategy alleviates the computational load on the central node, reduces data transmission across the network, and enhances user privacy. We evaluated the proposed distributed framework using our publicly available multi-sensor HAR dataset and compared its performance against a centralized neural network trained on the same data. The results demonstrate that the distributed approach achieves comparable or superior classification accuracy while significantly lowering inference latency and energy consumption. These findings underscore the promise of distributed intelligence in wearable systems for real-time and energy-efficient human activity monitoring. Full article

Minimizing the risk of disease transmission, disseminating superior genetics, and reducing transportation costs are recognized advantages of embryo biotechnologies. These advantages make the development of a minimally invasive and repeatable procedure in pigs enticing, but simultaneously magnify the anatomical constraints. For decades, the swine industry has struggled to establish a universal procedure to collect pre-implantation embryos from pigs due to their long and convoluted uterine horns (UHs). Thus, the objectives were to evaluate the benefits of employing a transitional surgical model by shortening UH tissue using a 40 cm ipsilateral resection and assess the compensatory ovulatory response following an ovariectomy. The surgery was deemed successful as the UH was resected and the contralateral UH was fully ligated. The dam- and sire-line gilts exhibited ovarian hypertrophy between surgery and slaughter on the remaining ovary, illustrated by an increase in the number of corpora lutea (13.4 and 3.0 vs. 27.2 and 12; p < 0.05, respectively) and intact ovary weight (11.9 and 7.7 vs. 25.9 vs. 38.7 g; p < 0.05, respectively). This research is a vital step in assessing whether this interim surgical approach serves as a valuable method to advance the development of non-surgical techniques to collect pre-implantation embryos in pigs. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper proposes a real-time pricing optimization framework for large-scale EV charging networks incorporating renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The core objective is to dynamically determine spatiotemporal electricity prices that simultaneously reduce system peak load, improve renewable energy utilization, and minimize user charging costs. A rigorous mathematical formulation is developed integrating over 40 system-level constraints, including power balance, transmission capacity, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber resilience. Real-time electricity prices are treated as dynamic decision variables influenced by charging station utilization, elasticity response curves, and the marginal cost of renewable and grid-supplied electricity. The problem is solved over 96 time intervals using a hybrid solution approach, with benchmark comparisons against mixed-integer programming (MILP) and deep reinforcement learning (DRL)-based baselines. A comprehensive case study is conducted on a 500-station EV charging network serving 10,000 vehicles integrated with a modified IEEE 118-bus grid model and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar and wind profiles are used to simulate realistic operational conditions. Results demonstrate that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% improvement in renewable energy utilization, and user cost savings of up to 30% compared to baseline flat-rate pricing. Utilization imbalances across the network are reduced, with congestion mitigation observed at over 90% of high-traffic stations. The real-time pricing model successfully aligns low-price windows with high-renewable periods and off-peak hours, achieving time-synchronized load shifting and system-wide flexibility. Visual analytics including high-resolution 3D surface plots and disaggregated bar charts reveal structured patterns in demand–price interactions, confirming the model’s ability to generate smooth, non-disruptive pricing trajectories. The results underscore the viability of advanced optimization-based pricing strategies for scalable, clean, and responsive EV charging infrastructure management in renewable-rich grid environments. Full article

Unmanned Aerial Vehicles (UAVs) exhibit significant potential in enhancing the wireless communication coverage and service quality of Mobile Edge Computing (MEC) systems due to their superior flexibility and ease of deployment. However, the rapid growth of tasks leads to transmission queuing in edge networks, while the uneven distribution of user nodes and services causes network load imbalance, resulting in increased user waiting delays. To address these issues, we propose a multi-UAV collaborative MEC network model based on Non-Orthogonal Multiple Access (NOMA). In this model, UAVs are endowed with the capability to dynamically offload tasks among one another, thereby fostering a more equitable load distribution across the UAV swarm. Furthermore, the integration of NOMA is strategically employed to alleviating the inherent queuing delays in the communication infrastructure. Considering delay and energy consumption constraints, we formulate a task offloading strategy optimization problem with the objective of minimizing the overall system delay. To solve this problem, we design a delay-optimized offloading strategy based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. By jointly optimizing task offloading decisions and UAV flight trajectories, the system delay is significantly reduced. Simulation results show that, compared to traditional approaches, the proposed algorithm achieves a delay reduction of 20.2%, 9.8%, 17.0%, 12.7%, 15.0%, and 11.6% under different scenarios, including varying task volumes, the number of IoT devices, UAV flight speed, flight time, IoT device computing capacity, and UAV computing capability. These results demonstrate the effectiveness of the proposed solution and offloading decisions in reducing the overall system delay. Full article

To meet the escalating demands of massive data transmission, the next generation of wireless networks will leverage the multi-access edge computing (MEC) architecture coupled with multi-access transmission technologies to enhance communication resource utilization. This paper presents queue stability-constrained reinforcement learning algorithms designed to optimize the transmission control mechanism in MEC systems to improve both throughput and reliability. We propose an analytical framework to model the queue stability. To increase transmission performance while maintaining queue stability, queueing delay model is designed to analyze the packet scheduling process by using the M/M/c queueing model and estimate the expected packet queueing delay. To handle the time-varying network environment, we introduce a queue stability constraint into the reinforcement learning reward function to jointly optimize latency and queue stability. The reinforcement learning algorithm is deployed at the MEC server to reduce the workload of central cloud servers. Simulation results validate that the proposed algorithm effectively controls queueing delay and average queue length while improving packet transmission success rates in dynamic MEC environments. Full article

Integrated hydro–wind–solar power generators (IPGs) in China face multi-timescale bidding challenges across provincial forward–spot markets, which are further compounded by hydropower nonconvexity and transmission constraints. This study proposes a stochastic optimization model addressing uncertainties from wind–solar generation and spot prices through scenario-based programming, integrating three innovations: average-day dimensionality reduction to harmonize monthly–hourly decisions, local generation function approximation to linearize hydropower operations, and transmission-aware coordination for cross-provincial allocation. Validation on a southwestern China cascade hydropower base transmitting power to eastern load centers shows that the model establishes hydropower-mediated complementarity with daily “solar–daytime, wind–nighttime” and seasonal “wind–winter, solar–summer” patterns. Furthermore, by optimizing cross-provincial power allocation, strategic spot market participation yields 46.4% revenue from 30% generation volume. Additionally, two transmission capacity thresholds are found to guide grid planning: 43.75% capacity marks the economic optimization inflection point, while 75% represents technical saturation. This framework ensures robustness and computational tractability while enabling IPGs to optimize profits and stability in multi-market environments. Full article

Although through tied-arch bridges exhibit strong structural robustness, collapse incidents triggered by the progressive failure of hangers still occasionally occur. Given that such bridges are unlikely to collapse due to the damage of a single or multiple hangers under the serviceability limit state, this study focuses on the failure safety limit state. Using the Nanfang’ao Bridge with inclined hangers and the Liujiang Bridge with vertical hangers as case studies, this paper investigates the dynamic response and failure modes of the residual structures when single or multiple hangers fail and initiate progressive collapse of all hangers. The results demonstrate that the configuration of hangers significantly influences the distribution of structural importance coefficients and the load transmission paths. Under identical failure scenarios, the Nanfang’ao Bridge with inclined hangers remains stable after the failure of four hangers without experiencing progressive collapse, whereas the Liujiang Bridge with vertical hangers undergoes progressive failure following the loss of only three hangers, which indicates that inclined hanger configurations offer superior resistance to progressive collapse. Based on the aforementioned analysis, the LS-DYNA Simple–Johnson–Cook damage model was employed to simulate the collapse process. The extent of damage and ultimate failure modes of the two bridges differ significantly. In the case of the Nanfang’ao Bridge, following the progressive failure of the hangers, the bridge deck system lost lateral support, leading to excessive downward deflection. The deck subsequently fractured at the mid-span (1/2 position) and collapsed in an inverted “V” shape. This failure then propagated to the tie bar, inducing outward compression at the arch feet and tensile stress in the arch ribs. Stress concentration at the connection between the arch columns and arch rings ultimately triggered global collapse. For the Liujiang Bridge, failure initiated with localized concrete cracking, which propagated to reinforcing bar yielding, resulting in localized damage within the bridge deck system. These observations indicate that progressive stay cable failure serves as the common initial triggering mechanism for both bridges. However, differences in the structural configuration of the bridge deck systems, the geometry of the arch ribs, and the constraint effects of the tie bar result in distinct failure progression patterns and ultimate collapse behaviors between the two structures. Thereby, design recommendations are proposed for through tied-arch bridges, from the aspects of the hanger, arch rib, bridge deck system, and tie bar, to enhance the resistance to progressive collapse. Full article

AC/DC parallel transmission is a critical approach for large-scale centralized transmission. Existing assessments of power transfer capability in AC/DC corridors rarely incorporate comprehensive security and stability constraints, potentially leading to overestimated results. This paper investigates a grid-forming renewable energy system integrated via AC/DC parallel transmission. First, the transmission section’s power transfer limit under N-1 static security constraints is determined. Subsequently, analytical conditions satisfying synchronization and frequency stability constraints are derived using the equal area criterion and frequency security indices, revealing the impacts of AC/DC power allocation and system parameters on transfer capability. Finally, by integrating static security, synchronization stability, and frequency stability constraints, an operational region for secure AC/DC power dispatch is established. Based on this region, an optimal power allocation scheme maximizing the corridor’s transfer capability is proposed. The theoretical framework and methodology enhance system transfer capacity while ensuring AC/DC parallel transmission security, with case studies validating the theory’s correctness and method’s effectiveness. Full article

In the era of Industry 5.0, future wireless networks are increasingly shifting from traditional symmetric architectures toward heterogeneous and asymmetric paradigms, driven by the demand for diversified and dynamic services. This architectural evolution gives rise to complex and asymmetric flows, such as the coexistence of periodic and burst flows with varying latency, jitter, and deadline constraints, posing new challenges for deterministic transmission. Traditional time-sensitive networking (TSN) is well-suited for periodic flows but lacks the flexibility to effectively handle dynamic, asymmetric traffi. To address this limitation, we propose a two-stage asymmetric flow scheduling framework with dynamic deadline control, termed A-TSN. In the first stage, we design a Deep Q-Network-based Dynamic Injection Time Slot algorithm (DQN-DITS) to optimize slot allocation for periodic flows under varying network loads. In the second stage, we introduce the Dynamic Deadline Online (DDO) scheduling algorithm, which enables real-time scheduling for asymmetric flows while satisfying flow deadlines and capacity constraints. Simulation results demonstrate that our approach significantly reduces end-to-end latency, improves scheduling efficiency, and enhances adaptability to high-volume asymmetric traffic, offering a scalable solution for future deterministic wireless networks. Full article