Search Results (3,220)

Against the backdrop of China’s dual carbon goals, cross-regional low-carbon power transmission from large-scale clean energy bases is a pivotal direction for energy transition. Formulating their power delivery curves requires precise alignment with the load demand characteristics of receiving provinces and the coordinated operation of hydropower, wind power, photovoltaic (PV) power, and pumped-storage hydropower (PSH). To address the limitations of existing methods, such as the lack of linearized modeling for core operational constraints, low solution efficiency and inadequate integration of multi-energy coupling constraints, this paper proposes a tailored linearized optimization modeling approach. By adopting auxiliary variables, binary variables and the Big M method, core constraints including PSH pumping power supply, stepwise power delivery and multi-energy coordinated operation are linearized. A monthly rolling linear optimization model is constructed with triple objectives: minimizing the renewable curtailment rate and the absolute error between delivery and load curves, and maximizing delivered electricity volume. Multi-objective coordinated optimization is realized via the linear weighted summation method, and the model is solved with the Gurobi solver. Case validation on an integrated hydro–wind–solar clean energy base in Southwest China and its corresponding receiving provincial power grid shows that the proposed method effectively improves the curve matching degree, controls the wind–PV curtailment rate within around 12% (engineering tolerance), and strictly meets engineering safety constraints such as PSH operation and HVDC transmission requirements. Comprehensive optimization of the three objectives is achieved when the weight coefficients for curtailment rate, load matching error and delivered electricity volume are set to 0.3–0.8, 0.1–0.2 and 0.1–0.6, respectively. This method resolves the problems of traditional nonlinear models being disconnected from engineering practice and low solution efficiency, providing a reliable technical reference for the refined dispatching of cross-regional power transmission and scientific formulation of power delivery curves for clean energy bases. Full article

As urban rail transit systems evolve within the Industrial Internet of Things (IIoT), the intelligent recovery of regenerative braking energy becomes critical for energy efficiency. However, the existing train operation optimizations primarily focus on time-domain synchronization, frequently neglecting the spatial impedance constraints of the DC traction network. This oversight creates a discrepancy between theoretical energy matching and actual absorption. To address this, this paper proposes a spatiotemporal synergistic optimization framework integrating the analysis of electrical energy transmission factors and train relative motion. First, a dynamic multi-node circuit model based on Kirchhoff’s laws is established to characterize train fleet operations. By evaluating electrical energy transmission factors, the current distribution ratio and line impedance loss are identified as primary determinants of absorption efficiency. This physically quantifies the coupling among instantaneous energy distribution, transmission loss, and source-load relative distance. Second, a time-domain integration-based gradient analysis framework is formulated to deconstruct the energy gradient into amplitude and directional components. By mapping the relative position and speed of interacting trains, their relative motion states are systematically categorized. Subsequently, an adaptive gradient optimization strategy based on these motion states is introduced, which fine-tunes dwell times to precisely guide train trajectories into a low-impedance “optimal window” for energy absorption. Finally, a case study using operational data from Luoyang Metro Line 1 validates the proposed framework. Results demonstrate that the framework achieves dual spatiotemporal matching of braking and traction trains, outperforming the traditional fixed timetable and improving the regenerative braking energy absorption rate by approximately 13%. Full article

Herringbone gear pairs are critical in high-speed aerospace transmissions, where windage power loss significantly impacts efficiency and thermal management. This study proposes a prediction method that decomposes the total windage loss into five components based on structural features: the tooth, end, circumferential, and relief groove surface losses for both gears, and the meshing extrusion loss. Theoretical models for each component are established to form a complete prediction method using fluid–structure interaction principles. CFD simulations analyze the velocity, pressure, and energy fields around the gear pair, with windage loss integrated via fluid torque on gear surfaces. Results indicate that windage loss escalates rapidly and becomes non-negligible when the driving gear speed exceeds 7000 rpm. The prediction model demonstrates strong agreement with CFD simulations, with a maximum relative error of 13.6%. Analysis reveals that the driving gear contributes the largest share of the total gear pair loss, with meshing extrusion accounting for 20.1–23.6%. For a single herringbone gear, the tooth surface is the primary source of loss (~83%), followed by the end surface (~8%), while relief groove and circumferential losses remain below 10%. This research provides a validated theoretical foundation for optimizing efficiency and thermal control in high-speed aerospace gear systems. Full article

Reducing agricultural carbon emissions is an important component of China’s efforts to achieve its carbon peaking and carbon neutrality goals. As an important policy oriented financial instrument, green credit can facilitate lower agricultural carbon intensity by directing resources more efficiently across regions and encouraging low carbon transformation in agriculture. Using panel data for 30 Chinese provinces from 2005 to 2022, this study measures agricultural carbon emission intensity (ACEI) from six sources. It then examines the spatial spillover effects, transmission channels, and nonlinear characteristics associated with green credit by using a spatial Durbin framework, mediation analysis, and panel threshold model. The results indicate that: (1) green credit development is significantly associated with lower ACEI; (2) green credit exhibits significant spatial spillover effect, being associated with lower ACEI both within a province and in neighboring provinces; (3) green credit exhibits marked regional heterogeneity in its impact on ACEI: it shows both direct and spillover effects in the eastern region, only spillover effects in the central region, and only direct effects without effective diffusion in the western region; (4) green credit is associated with lower ACEI through industrial structure upgrading and lowering agricultural energy consumption intensity; (5) green credit has a single threshold effect on ACEI based on its own development level. After crossing the threshold, the emission intensity reduction effect weakens but remains significant. These results offer empirical evidence for refining green credit arrangements and advancing coordinated agricultural emission reduction across regions. Full article

A pressing challenge of modern wireless networks is ensuring stable radio coverage inside buildings, where radio signal propagation is significantly complicated by the influence of building structures. Reinforced concrete walls, floor slabs, internal partitions, and energy-efficient windows with metallized coatings create substantial obstacles to the propagation of electromagnetic waves, causing reflection, absorption, and scattering. As a result, areas with weakened coverage are formed inside buildings, leading to deterioration in mobile communication quality and reduced data transmission rates. This study presents an experimental investigation of the received signal strength of mobile operators inside a multi-storey residential complex. An analysis was conducted to evaluate the impact of building height, architectural features, and construction materials on radio signal propagation. In addition, the frequency bands used in 4G LTE and 5G networks by mobile operators were examined. It was found that LTE networks mainly operate in the 1.8–2.1 GHz frequency range, whereas 5G networks operate in the n77 band (3.6–3.7 GHz), which provides higher data throughput but is characterized by greater signal attenuation when propagating inside buildings. To address this issue, a Distributed Antenna System (DAS) based on GPON technology was implemented in the studied building. The placement of antenna equipment on the roof enabled the efficient reception of the signal from the base station and its subsequent distribution inside the building through an internal antenna network. The measurement results demonstrated that the deployment of a GPON-based DAS significantly improves the received signal level and ensures more uniform radio coverage inside indoor environments. The obtained results confirm that the use of distributed antenna systems is an effective solution for compensating signal losses caused by the shielding effect of building structures and can significantly improve the quality of mobile communications in dense urban environments. The results show that the RSRP level in indoor environments without DAS decreases to approximately −100 to −110 dBm, while after deployment of the GPON-based DAS, it improves to −45 to −75 dBm. This corresponds to a signal gain of up to 40–50 dB, ensuring stable connectivity and significantly improved data transmission performance. Full article

The evolution from the Internet of Things (IoT) to the Internet of Vehicles (IoV) has expanded intelligent connectivity across embedded systems while increasing cybersecurity risks arising from large scale data exchange and device heterogeneity. As IoV environments become more dynamic and safety critical, centralized Intrusion Detection Systems (IDSs) face constraints related to latency, privacy exposure, and bandwidth overhead. These limitations motivate a transition to edge-enabled IoV architectures, where localized vehicular and anchor nodes supported by edge servers enable decentralized processing, enhanced privacy, and reduced communication load. To address these operational challenges, this paper proposes FedX (Federated Explainable Ensemble Intrusion Detection System), a privacy-preserving and explainable federated ensemble IDS that integrates XGBoost and LightGBM models across resource-constrained edge vehicles and roadside units (RSUs) to enable collaborative, low-latency anomaly detection without sharing raw data. By applying adaptive weighting based on model confidence and resource availability, FedX enhances robustness and efficiency while enabling explainable decisions via SHAP and LIME analysis, which highlights reliance on key features (flow duration, speed, RPM) for high-confidence (>97%) intrusion alerts grounded in domain-specific behavior. Privacy is further enforced through Gaussian differential privacy and secure aggregation to mitigate inference and inversion attacks. Experiments on the CICIoV2024 dataset show that FedX achieves 99.1% accuracy, outperforming existing federated ensemble IDS models by up to 2.1%. The system reduces communication overhead by 17% relative to full synchronization through adaptive weighted transmission and secure aggregation. It maintains negligible accuracy loss (<1.5%) under a strong privacy budget ($ϵ$ = 1.1). The deployment of proposed IDS on Raspberry Pi 4 underscores its efficacy for edge computing. Experimental results indicate that adaptive weighting yields a 1.8% performance increase, while resource profiling shows 45% lower CPU utilization and over 50% lower power consumption compared with centralized baselines. The findings demonstrate that FedX, combined with explainable AI enables trustworthy, interpretable, and energy-efficient intrusion detection for secure next-generation Edge-enabled IoV networks. Full article

With the large-scale grid integration of renewable energy sources such as wind power and photovoltaics, power system net load fluctuations have become significantly more severe, imposing higher demands on system flexibility. Traditional optimal transmission switching (OTS) models require the simultaneous optimization of continuous and discrete variables, resulting in high computational complexity that renders them unsuitable for daily real-time scheduling in large-scale power systems. This paper develops a flexible real-time rolling optimization scheduling model that incorporates OTS and proposes a two-stage fast solution framework based on deep neural networks (DNN). In the offline training phase, a multilayer perceptron-based DNN is trained using load and renewable generation data to rapidly and accurately predict the optimal line switching scheme. In the online application phase, the network topology predicted by the DNN transforms the original mixed-integer linear programming problem into a standard linear programming problem, substantially reducing computational complexity and solution time. Case studies on the modified IEEE 118-bus and IEEE 300-bus systems show that the proposed method achieves high prediction accuracy, reduces solution time by up to 117 times, and maintains nearly identical system operating costs to the physics-driven approach in the majority of cases. The results demonstrate that the proposed approach effectively balances computational efficiency and economic performance, verifying the practical value of optimal transmission switching in enhancing large-scale renewable energy accommodation and overall power system flexibility. Full article

Edge Internet of Things (IoT) blockchain deployments increasingly rely on continuous transaction ingestion from resource-constrained IoT devices to nearby edge gateways over heterogeneous wireless links. In this setting, transient channel noise and packet corruption can inject invalid payloads into the edge processing pipeline and trigger unnecessary buffering, parsing, and, most critically, computationally expensive cryptographic operations such as digital signature verification. This leads to wasted computation, increased latency, and reduced energy efficiency at the edge, particularly under dense IoT traffic. This paper presents an energy-aware and fault-tolerant Quantum-Dot Cellular Automata (QCA)-based integrity pre-filter for IoT-to-edge blockchain transaction ingestion. At the circuit level, we adapt and modify a previously reported fault-tolerant five-input majority gate (MV5) structure and use it as a robust primitive for nanoscale integrity-screening circuits. Building on this modified MV5, we design a set of QCA integrity blocks, including a parity checker, a compact XNOR gate circuit, a parity-bit generation circuit, and a sender-to-channel/receiver nano-communication integrity workflow suitable for early screening of corrupted payloads. Compared with the best previously reported baseline considered in this study, the modified MV5 achieves 76.47% tolerance to single-cell omission defects, corresponding to a 17.47 percentage-point increase and an approximately 29.61% relative improvement over the prior 59% omission-tolerance result, while preserving 100% tolerance against extra-cell deposition defects. At the system level, the proposed circuit is discussed as a potential early screening stage for edge-IoT blockchain transaction ingestion. A bounded analytical model is used to estimate the possible reduction in unnecessary signature-verification workload under assumed corruption and detection conditions. This analysis is not intended as a deployment-level validation; full edge-node implementation, throughput measurement, queueing-delay evaluation, real traffic traces, retransmission behavior, and empirical signature-verification profiling remain future work. The proposed parity/chunk-parity pre-filter is designed for low-cost detection of random transmission-induced corruption and does not replace cryptographic authentication, hashing, digital signatures, CRC-based detection, or blockchain validation. All proposed designs are validated using QCADesigner tools. Full article

Ice accumulation on critical infrastructure surfaces threatens operational safety in aviation, power transmission, and transportation systems. Conventional anti-icing and deicing strategies, such as chemical deicers and energy-intensive active heating, have inherent drawbacks. These include environmental pollution, high energy consumption, and low efficiency. In recent years, photothermal-responsive extremely water-repellent surfaces have attracted widespread attention. They can harvest renewable solar energy and achieve efficient anti-icing and deicing through tailored interfacial wetting properties. This review summarizes photothermal extremely water-repellent surfaces based on the “water as a lubricating layer” strategy. This strategy reduces ice adhesion strength and enables low-energy deicing. It works by forming a continuous lubricating film via photothermally induced interfacial meltwater. We discuss photothermal conversion mechanisms and strategies to enhance performance for stable lubricating film formation. We also analyze the stagewise physics of anti-icing and deicing, focusing on the interfacial tribological behavior of the water film. Key engineering challenges are addressed, including mechanical durability and all-weather applicability. Finally, we clarify future research directions for industrial translation. This review aims to provide theoretical insights and technical pathways for developing next-generation anti-icing and deicing surfaces that are efficient, eco-friendly, and sustainable. Full article

The development of offshore wind energy has increased the demand for reliable submarine transmission systems. In South Africa, research remains constrained due to the lack of operational offshore wind farms, despite favorable geographical conditions and persistent energy challenges such as load-shedding. Submarine cable faults, primarily caused by manufacturing deficiencies, environmental factors, and human activities, contribute significantly to system downtime while accounting for only a small portion of overall installation costs. This study reviews submarine cable fault identification, classification, pre-determination, and localization techniques. Conventional methods, including time-domain reflectometry, the Murray loop, the Varley loop, and impulse-based techniques, are reviewed alongside artificial neural network models, such as convolutional and deep learning architectures. Findings imply that traditional techniques offer low error margins but lack the accuracy needed for pinpointing exact faults, as faults may extend over several kilometers. In contrast, neural network-based methods, particularly when integrated with signal processing methods, significantly improve fault classification and localization accuracy. The study concludes that hybrid approaches combining conventional diagnostic techniques with neural networks offer a robust framework for submarine cable fault analysis, providing real-world solutions to enhance reliability and efficiency in future offshore wind transmission systems. Full article

Mitochondrial dysfunction is a key contributor to cognitive impairment, directly affecting neuronal viability, synaptic function, and energy metabolism. In the central nervous system, where energy demand is particularly high, disturbances in mitochondrial dynamics, including impaired oxidative phosphorylation (OxPhos), increased reactive oxygen species (ROS) production, and reduced ATP availability, can compromise synaptic transmission and accelerate cognitive decline. These alterations are commonly observed in neurodegenerative diseases such as Alzheimer’s (AD) and Parkinson’s (PD), in which mitochondrial dysfunction is closely associated with oxidative stress and neuroinflammatory processes. This review aims to investigate the role of mitochondrial dysfunction in cognitive impairment and the effects of physical exercise as a non-pharmacological strategy to mitigate these alterations. Current evidence indicates that exercise promotes mitochondrial biogenesis through activation of the AMPK/PGC-1α pathway, enhances oxidative metabolism, and improves mitochondrial efficiency. Furthermore, exercise reduces oxidative stress and inflammation while stimulating the release of neurotrophic factors, such as brain-derived neurotrophic factor which support neurogenesis, synaptic plasticity, and neuronal survival. Overall, these findings reinforce the importance of mitochondrial integrity in maintaining cognitive function and highlight physical exercise as a promising strategy to counteract mitochondrial dysfunction and delay the progression of neurodegenerative diseases. Full article

This study investigated the influence of hydrocarbon feedstock composition evolved from slow pyrolysis of polypropylene (PP) and polystyrene (PS) and plasma gas flow rate on the carbon black and hydrogen production yields and quality. The temperature distribution and feedstock flow within the carbon black formation zone with plasma were supplementarily modeled using computational fluid dynamics. TG-FTIR-GC/MS was employed to analyze thermal degradation patterns of plastics and to estimate the composition of volatile intermediates of plastics’ slow pyrolysis. Produced CB was characterized, encompassing physical, structural, and compositional properties using thermogravimetric analysis, CHNS analysis, scanning electron microscopy–energy dispersive spectroscopy, transmission electron microscopy, Brunauer-Emmett-Teller, and Raman spectroscopy. The results revealed that both feedstocks yield CB with comparable structural characteristics; however, PS-derived (aromatic-rich) volatiles produce significantly higher CB yields, whereas PP-derived (aliphatic) volatiles favor hydrogen formation. Differences in carbon structure were also observed, with PP-derived CB exhibiting a higher degree of graphitic ordering compared to the more disordered CB obtained from PS. The optimal flow rate of plasma gas was identified as 6.1 L/min. Increasing the flow rate to 7.2 L/min led to reduced conversion efficiency for PP-derived long-chain hydrocarbons. Overall, the findings demonstrate the potential of this approach for the co-production of high-quality carbon black and hydrogen from plastic waste. Full article

Nine sets of fin parameter combinations, including a plain tube control group, were modeled. Simulations were performed under steady-state conditions using the EWT Realizable k-ε turbulence model, with benzene and water as working fluids, while accounting for temperature-dependent thermophysical properties. Flow field distribution, temperature profile, Nusselt number, and pressure drop in the shell side of the heat exchanger were analyzed. Response surface methodology was employed to systematically evaluate the coupled effects of fin height and fin spacing on thermal performance. The results indicate that annular fins significantly enhance heat transfer by inducing secondary flow and disrupting the thermal boundary layer. Compared to the smooth tube, the finned tubes increased the Nusselt number (Nu) by up to 28.6% and the total heat transfer rate by 13.55%, while the pressure drop (ΔP) increased by approximately 9.81% to 16.5%. The analysis revealed that fin height is the dominant factor affecting performance, whereas fin spacing plays a regulatory role. As the fins became taller or denser, the temperature field evolved from stable stratification to intense mixing and eventually to local disorder. The study identified an optimal parameter range for engineering applications. A fin height of 2–3 mm combined with a spacing of 10–15 mm achieves the best balance between heat transfer enhancement and flow resistance. Specifically, the combination of h = 3 mm and s = 10 mm yielded the highest Energy Efficiency Coefficient (EEC) of 1.567. This configuration is recommended for large-flow, pressure-drop-sensitive systems, such as those found in petrochemical plants or long-distance heat transmission applications. Full article

This study presents a comprehensive investigation into the optimal deployment of Unified Power Flow Controllers (UPFCs) to enhance voltage stability and reduce power losses in the Sudanese national grid. With the increasing demand for electricity driven by population growth, urban expansion, and industrial development, modern power systems require advanced control strategies to ensure reliable and efficient operation. In this work, the Line Stability Index (Lmn) is employed as a key indicator to identify the most critical transmission lines prone to voltage instability. Based on this index, optimal locations for UPFC installation are determined. Furthermore, an Optimal Power Flow (OPF) framework is utilized to calculate the control parameters of the UPFC devices, aiming to minimize system losses while maintaining operational constraints. The proposed methodology is validated using a real large-scale network model of the Sudanese power system implemented in MATLAB (24b) and NEPLAN (v10) environments. The results demonstrate that installing seven UPFC devices leads to a significant improvement in voltage profiles, maintaining all bus voltages within ±5% of nominal values. Additionally, the system experiences a reduction in total active and reactive power losses by 6.96% and 0.74%, respectively. These findings highlight the effectiveness of UPFC-based control strategies in improving system stability, enhancing transmission efficiency, and supporting the integration of future energy resources. Full article

The use of differential pressure energy for green hydrogen and ammonia comes with significant safety challenges. Two zero-emission technical schemes—one based on magnetic coupling transmission and another based on dual magnetic fluid seals—were proposed and designed. The energy performance of both schemes was first analyzed for a DN200 pipe using the DWSIM software (Version 8.6.6). Subsequently, the levelized cost of electricity and the dynamic payback period were evaluated and compared. The results show that the magnetic coupling transmission scheme exhibits relatively low energy efficiency (54.9–61.7%), whereas the scheme based on dual magnetic fluid seals is more complex yet achieves higher energy efficiency (65.8–67.1%). The levelized electricity cost of both schemes under a differential pressure of 0.5 MPa is estimated to be lower than the feed-in tariff of coal-fired power plants in China, and the dynamic payback period is estimated to be less than 5.5 years. Overall, both schemes provide benefits in energy savings and profitability. These schemes warrant further experimental investigation and pilot testing. Full article