Driven by advanced capabilities of fifth-generation (5G) mobile networks, vehicle-to-everything (V2X) communication is transitioning from theoretical concept to practical implementation. However, the inherent characteristics of vehicular environments—high mobility, dynamic topology and dense traffic scenarios—pose significant challenges in meeting the stringent and diverse quality-of-service (QoS) requirements for V2X applications. This article explores the application of network slicing technology, supported by Network Function Virtualization (NFV) and Software Defined Networking (SDN). The requirements for network slicing in the context of the Internet of Vehicles are analyzed. In response to these needs, the article further studies the specific applications of network slicing technology in these scenarios. Finally, the article discusses the challenges faced by network slicing in the Internet of Vehicles and looks forward to future prospects such as intelligent management, cross industry cooperation, and standardization. The findings reveal that effective implementation of network slicing can significantly enhance the performance and reliability of V2X communication, paving the way for safer and more efficient vehicular networks.

As a key process in agricultural production, the demand for high-efficiency and high-quality tomato harvesting is continuously rising with the rapid advancement of smart agriculture. However, conventional rigid manipulators often fail to meet the needs of intelligent harvesting due to the limitations of adaptability, dexterity, and safety. To address these challenges, this study proposes a silicone-based fin-ray soft gripper inspired by bio-mimetic fin structures. The gripper is designed to adaptively deform when grasping tomatoes, thereby enhancing grip stability while minimizing mechanical damage. The gripper’s mechanical performance is evaluated through SolidWorks modeling and built-in simulation analysis. Static analysis result shows uniform stress distribution, free-end displacement for adaptability, and controlled strain along ribs, confirming its flexibility and reliability under load conditions. Additionally, a closed-loop control system integrated with tactile sensors is developed to enable adaptive force regulation, further improving the intelligence of the harvesting process. The stress, displacement, and strain analyses demonstrate that under a 30N load, the designed gripper exhibits uniform stress distribution, excellent flexibility, and favorable mechanical properties. Compared to conventional grippers, this design offers significant improvements in notable flexibility, safety, and operational efficiency. This research provides an innovative end-effector solution for tomato-harvesting robots and serves as a valuable technical reference for the automated harvesting of other crops. It further demonstrates the significant application potential and scalability of agricultural harvesting grippers.

With autonomous driving technology playing an increasingly important role in intelligent transportation systems, how to improve ride comfort while ensuring safety has become an urgent challenge. This paper proposes a multi-objective optimization approach for autonomous driving based on reinforcement learning. By designing a multi-objective reward function that integrates rewards based on position, speed, direction, and acceleration, the method aims to balance driving efficiency and ride comfort. The Proximal Policy Optimization (PPO) algorithm is employed for training on the high-fidelity simulation platform MetaDrive, and experiments in multiple scenarios verify the effectiveness of the proposed approach. The experimental results show that as the comfort penalty coefficient in the reward function changes, the success rates for left turns, straight driving, and right turns exhibit a non-linear trend of first increasing then decreasing, with the best performance achieved when the parameter value is 0.001. This fully demonstrates the critical impact of parameter selection on the performance of autonomous driving strategies. It provides an optimization solution for reinforcement learning-based autonomous driving decision-making that balances safety and ride comfort, and offers a reference for subsequent related research.

Excavators, as specialized equipment for energy extraction and construction in engineering machinery, play an important role in the quality and effectiveness of construction. With the gradual introduction of the concept of green environmental protection into society, energy conservation and material reduction have become one of the main development directions of excavators. This article takes a small excavator as an example to study the optimization design of its boom from the perspective of mechanical structure optimization design. SolidWorks is used to create a three-dimensional model of the boom in the excavator's working device. Ansys Workbench is used to perform finite element static analysis on the boom, obtaining the stress situation and hazardous conditions under various working conditions. The optimized position is determined through the topology optimization module. Design a response surface optimization simulation test group using Design Expert, and generate an optimization regression equation for the boom based on the results. Under the conditions of satisfying stiffness and strength, the optimal solution for boom optimization was obtained through this equation. A comprehensive and feasible method and approach are proposed for the optimization design of excavator boom by modeling the optimal solution and ultimately analyzing and verifying its correctness.

Due to their unique mechanical properties, auxetic (negative Poisson's ratio) materials have attracted significant attention. In particular, they exhibit outstanding performance in automotive engineering by offering advantages such as excellent energy absorption, cushioning, vibration reduction, noise mitigation, and lightweight characteristics. This paper summarizes the fundamental concepts and mechanical properties of such metamaterials, with a focus on their applications in the automotive sector, including energy-absorbing and vibration-damping structures, suspension systems, and intelligent components. Additionally, the main fabrication methods and current limitations of auxetic materials are reviewed. Finally, future development directions and strategies for their application in the automotive field are proposed, providing a reference for the design of new automotive structures and their industrial implementation.

As global interest in sustainable energy solutions intensifies, inverted chalcogenide solar cells have gained significant attention for their promising combination of cost-effectiveness, efficiency, and customizable light-absorption capabilities. This review provides a comprehensive analysis of the structure, working principles, manufacturing approaches, and performance enhancement techniques for these photovoltaic systems. The device architecture generally features a multilayer design: a transparent conductive oxide substrate, a hole-selective transport layer, a photoactive chalcogenide semiconductor, an electron-collecting transport layer, and a metallic back contact. When exposed to sunlight, the absorber layer generates charge carriers that undergo efficient separation at material interfaces, with electrons migrating through the ETL and holes via the HTL to generate electrical output. Advanced deposition methods such as spin-coating and thermal evaporation are critically compared, highlighting the trade-offs between production scalability and layer morphology control in solution-based fabrication processes. The discussion further addresses recent innovations in interface engineering, bandgap tuning, and defect passivation that collectively contribute to improved power conversion efficiencies. By evaluating both fundamental mechanisms and practical manufacturing considerations, this analysis offers insights into the development roadmap for next-generation thin-film photovoltaic technologies.

The installed capacity and electricity production from renewable energy sources, including wind power and photovoltaics, have been consistently rising, facilitating the ecological transformation of the energy framework. Nonetheless, the unpredictability and variability of renewable energy sources provide problems to the power system, necessitating the evolution of the contemporary power system into a smart and efficient framework to harness the potential of demand-side resources. This study focuses on the load of electric vehicles (EVs) and analyzes their usage and charging habits through surveys. A demand response model that takes into account users' travel and battery degradation is established. This model aims to reduce the grid peak-to-valley difference, improve system stability, and reduce operating costs. Meanwhile, this paper also pays attention to electric vehicle aggregators and establishes a demand response model to refine the charging power curve, maximizing their interests. The results show that electric vehicles, as a high-quality demand response resource, can effectively promote the stability of the power system and the consumption of renewable energy. Electric vehicle aggregators can also obtain greater benefits by optimizing their scheduling, providing users with higher-quality demand response services.

Facing the goals of “carbon peak” and “carbon neutrality”, offshore wind turbines hold significant application value and expansion prospects. However, the complex marine environment, characterized by high salinity, humidity, and fluctuating temperatures, poses severe corrosion challenges, making corrosion protection a critical aspect of offshore wind turbine maintenance. This paper systematically analyzes the zonal characteristics and mechanisms of marine corrosion, highlighting the unique corrosion risks in different zones such as the splash zone and tidal zone. It also summarizes the limitations of existing protection technologies, including traditional coatings and cathodic protection, which often face issues like high maintenance costs and insufficient durability. The paper then focuses on the innovative applications of intelligent and digital technologies in corrosion monitoring and protection, such as advanced sensors and real-time data analytics, which can enhance the effectiveness of corrosion management. Additionally, it discusses the potential benefits of integrating these technologies with renewable energy systems to further optimize operational efficiency. Finally, it proposes directions for technological breakthroughs in specific areas, providing theoretical support for the safe and efficient operation of offshore wind turbines.

Wind energy prediction is crucial for efficient management and dispatch of power systems, and accurate prediction can optimize the integration of renewable energy sources and can be a good solution for energy security. This study proposes a chaos-enhanced bi-directional LSTM model for wind power forecasting that incorporates chaotic signals from Lorentz attractors to enhance the feature representation of time series data and combines the powerful contextual information capturing capability of bi-directional LSTM. The goal of this research is to develop a model that outperforms traditional as well as current mainstream machine learning aspects of prediction methods. The method splices the generated slices of chaotic signals with the input sequences in the feature dimension and captures the forward and backward time dependencies using bi-directional LSTM, and finally feeds its output into the fully connected layer for final prediction. The experimental results show that the proposed model outperforms several current mainstream machine prediction models with a MAPE of only 0.5979% and an RMSE of 4.2, which demonstrates the effectiveness and superiority of the model in wind power prediction.

This paper systematically investigates the failure mechanisms of gate oxides in silicon carbide (SiC) MOSFETs, aiming to enhance gate reliability. Through long-term high-temperature dynamic bias (HTDB) stress experiments on 4H-SiC MOSFETs, the degradation behavior of gate oxides under various stress conditions is revealed. The study demonstrates that the energy-level-dependent distribution of interface state density (Dit) significantly impacts device performance. By optimizing nitric oxide (NO) post-oxidation annealing processes, the threshold voltage (Vth) drift and capacitance-voltage (C-V) hysteresis are effectively reduced by 35% and 22%, respectively. Furthermore, a novel interface state characterization method combining high-low frequency C-V measurements is proposed, providing enhanced accuracy in evaluating gate oxide quality under bias stress. These findings offer critical theoretical insights and process optimization strategies for improving SiC MOSFET reliability in extreme environments (elevated temperatures >200°C, high-frequency operations >100 kHz), thereby accelerating the commercialization of SiC power devices.