With the rapid advancement of the Internet of Things and edge intelligent computing, the demand for low-power, high-energy-efficient computing systems has become increasingly urgent. The traditional von Neumann architecture suffers from poor energy efficiency in data-intensive tasks due to the 'memory wall' problem. Neuromorphic computing, as an emerging paradigm that mimics the biological brain's information-processing methods, offers highly promising solutions to overcome energy-efficiency bottlenecks through event-driven operations, integrated sensing, storage, and computation, and novel devices such as memristors. This paper systematically analyses and summarises the latest research achievements in neuromorphic computing across hardware devices, system architectures, and optimisation strategies. It first presents fundamental hardware implementation options for simulating neurons and synapses, highlighting memristors' benefits in low-power synaptic plasticity. Then the system-level low-power architectures are discussed-including the event-driven paradigm and compute-in-memory concept integration. Finally, taking IoT edge nodes as an example application scenario of energy-efficient neuromorphic systems, it also outlines current major issues confronting technologies along with future development directions.

The accurate prediction of power losses in ultra-high-voltage (UHV) AC transmission systems is critical for ensuring the economic and efficient operation of modern power grids. This paper investigates the temperature-dependent fluctuations in line losses—particularly those induced by corona discharge—under varying ambient conditions. To this end, this paper proposes a comprehensive loss-prediction framework integrating Spearman rank correlation analysis with a particle swarm optimization–enhanced extra-trees (PSO-ET) model. The methodology is validated using operational data from 1000 kV UHV transmission projects in the Fujian–Zhejiang region. First, Spearman correlation analysis confirms temperature as the dominant meteorological factor influencing line losses. Subsequently, the PSO algorithm is employed to globally optimize three key hyperparameters of the ET model: tree count, maximum tree depth, and the humidity threshold used to dichotomize operating conditions into "dry" and "high-humidity" regimes. This enables precise decoupled training under distinct environmental states. Experimental results demonstrate that the proposed model achieves an average absolute error (MAE) of only 5.1315 MW on the independent test set. Further sensitivity analysis reveals that the gradient of line loss with respect to temperature peaks at approximately 7.6 °C—indicating a pronounced nonlinear response. Crucially, this inflection coincides with the onset of surface condensation, thereby uncovering a previously underappreciated surge effect: in low-temperature, high-humidity environments, condensation on conductor surfaces significantly amplifies corona losses. This study provides a robust, data-driven foundation for real-time loss monitoring and energy-efficient scheduling of UHV transmission lines under complex and dynamic meteorological conditions.

The aim of this study is to examine the aerodynamic characteristics of a 2018 Formula 1 car rear wing, which is equipped with a Drag Reduction System (DRS), by applying a two-dimensional computational fluid dynamics (CFD) approach, particularly when the DRS is partially activated. The study applied a commercial CFD code, ANSYS Fluent, based on a k-omega Shear Stress Transport (SST) turbulence closure model, to solve the flow problem. The CFD code was applied to a 2018 Formula 1 car rear wing to solve the aerodynamic problem for different DRS activation ratios, i.e., 0%, 25%, 50%, 75%, and 100%, at three different free-stream velocities, i.e., 50 m/s, 70 m/s, and 90 m/s. The results obtained in this study indicated that although aerodynamic forces are proportional to the square of the free-stream velocity, lift and drag coefficients remain almost constant for a wide range of free-stream velocities. The results reveal a "sweet spot" where a 25% opening of the DRS resulted in a 50% reduction in drag and a 20-22% increase in downforce compared to a fully closed DRS position due to a "slot effect" delaying the flow separation. The results obtained in this study revealed that the highest opening of the DRS, i.e., 100% DRS, resulted in a 75% reduction in drag but caused a significant reduction in downforce.

The performance of robot control systems directly influences their effectiveness across various industries, agriculture, services, and other sectors. Traditional PID control is widely used due to its simple structure and ease of implementation. However, when facing complex nonlinear systems, time-varying parameters, and external disturbances, its control accuracy and robustness significantly decrease. This paper analyzes the limitations faced by classical PID control in robot applications, with a focus on exploring how fuzzy PID control can adaptively adjust PID parameters by introducing fuzzy inference mechanisms, thereby improving the system's adaptability in uncertain environments. Furthermore, it reviews the integration strategies of various intelligent optimization algorithms, modern control methods, and PID control, including particle swarm optimization (PSO), genetic algorithm (GA), active disturbance rejection control (ADRC), and PID composite control. The results demonstrate the significant effects of these methods in improving robot trajectory tracking accuracy, vibration suppression, and anti-interference ability. It further found that by integrating intelligent algorithms with advanced control strategies, the performance of PID controllers can be systematically improved, providing an effective technical path for the reliable operation of robot control systems in complex dynamic environments.

With the boom of IoT, neuromorphic computing, and implantable biomedical electronics, ultra-low-power integrated circuit design is a critical focus. Operating MOSFETs in the subthreshold region offers a practical solution via exponential drain current–gate voltage dependence, enabling ultra-low-power operation. However, subthreshold behavior is strongly influenced by physical and technological factors, complicating the reliable design of devices and circuits. This paper reviews MOSFET subthreshold operation from a device-physics perspective. It explores subthreshold conduction physics, emphasizing diffusion-dominated transport and the exponential current–voltage relationship. A core current model is analyzed to clarify key parameters, such as the thermal voltage and gate-channel coupling efficiency. The impacts of temperature, device structure, fabrication processes, and material properties on subthreshold characteristics are discussed, along with representative low-power analog modules and applications, including IoT sensing and neuromorphic computing. This work finds that thermal effects, electrostatic control, and material-dependent carrier behavior determine subthreshold performance. Integrating device physics, characteristic modulation, and applications into a unified framework, it provides design insights and highlights challenges for robust ultra-low-power system development.

Quantum dot light-emitting diodes (QLEDs) are strong contenders in display technology due to their excellent color purity and wide color gamut. Nevertheless, the strong acidity and hygroscopicity of the conventional hole injection layer (HIL) PEDOT:PSS lead to ITO anode corrosion and unbalanced carrier injection, severely limiting device efficiency and stability. In this work, MoO₃ prepared by sol–gel method was employed as an interfacial interlayer between ITO and PEDOT:PSS to construct a hybrid dual-hole injection system, and the effects of annealing temperature (100–170°C) on film morphology and device performance were systematically studied. Results reveal that the MoO3interlayer effectively blocks ITO corrosion by PEDOT:PSS and optimizes hole injection via energy level matching. The optimized red QLED with MoO3annealed at 130°C exhibits outstanding performance: the maximum current efficiency (CEmax) reaches 121.21 cd/A, the maximum external quantum efficiency (EQEmax) is 17.06%, and the maximum brightness climbs up to 97340 cd/m². More importantly, the T50 lifetime at 100 cd/m² is extended to 8511 h, which is nearly 7 times that of the standard PEDOT:PSS–based device. This study offers a reliable interface engineering strategy for fabricating high–efficiency, long–lifetime all–solution–processed QLEDs, laying a foundation for their industrial applications in advanced displays and lighting.

This paper focuses on the problem of power system inertia decline caused by changes in the composition and form of system inertia in power systems with a high penetration of non-synchronous power sources. Aiming at the scenario of high penetration of non-synchronous power sources, this paper expands the concept of synchronous generator inertia, proposes the inertia support characteristics of wind and photovoltaic power sources, and defines the composition and connotation of the equivalent inertia of the power system. Taking the IEEE 3-generator 6-node system with a high penetration of new energy (including photovoltaic and wind power) as a case study, the paper conducts optimal power flow calculation and unit commitment calculation, and then completes the system inertia evaluation. The research verifies the reliability of the proposed scheme, which effectively improves the equivalent inertia of the system, and provides theoretical support for the inertia regulation and operation optimization of new power systems.

With the rapid development of the Internet of Things (IoT) and microelectronics, smart home systems are becoming increasingly important. This paper examines the hardware foundations of embedded systems as applied to smart home environments, with a particular focus on the integration, operating principles, and practical deployment of sensors and actuators. Specifically, this paper investigates the technical characteristics of key hardware components including temperature and humidity sensors, smoke sensors, direct current (DC) motors, servo motors, and stepper motors. The study adopts a literature review methodology, systematically analyzing existing academic research, technical documentation, and engineering case studies to evaluate the roles of these components within embedded smart home platforms such as Arduino, STM32, and ESP32. This paper finds that embedded systems provide an efficient, low-cost, and highly adaptable hardware backbone for smart home applications, and that the effective integration of diverse sensors and actuators is central to enabling automation, energy efficiency, and user safety in residential environments. These findings contribute to a clearer understanding of the hardware design principles underlying modern smart home systems and offer reference value for future research and engineering practice in this domain.

As the traditional Von Neumann architecture computing system with separate storage and computing units no longer meets people's needs, the realization of the integrated storage and computing technology that combines the two is extremely urgent. However, due to the inherent limitations of traditional silicon-based materials, carbon-based materials, with their superior properties, have become the ideal material for realizing the integrated computing and storage technology. At present, this technology is widely applied in fields such as three-dimensional integrated circuits, sensor detection, and digital computing. This review will systematically describe the integration techniques of carbon-based materials in memory-computing integrated chips, providing a detailed analysis of their development and challenges in digital circuits, computing, and sensing applications. Furthermore, this paper explores corresponding solutions and outlines the future prospects for carbon-based compute-in-memory chips. In addition, this study has important theoretical value for promoting fundamental innovations in post-Moore era integrated circuit architectures, providing a feasible material basis and technological pathway for breaking through the traditional bottleneck of computing energy efficiency. At the same time, the potential application of carbon-based computing-in-memory chips in cutting-edge fields such as artificial intelligence also lays the base for the realization of high-performance, low-power information systems.

As carbon - neutrality targets progressed and social electricity consumption witnessed a significant rise, traditional thermal power could no longer satisfy the development requirements of the energy field; wind and photovoltaic power, two typical kinds of clean renewable energy sources, experienced remarkable output fluctuations because of natural constraints like meteorological situations and seasonal changes; although standalone pumped - storage technology was firmly established in actual application, it had inherent defects such as inescapable energy loss and the incapacity to keep high - efficiency power generation for an extended period, becoming a crucial obstacle to constructing a stable clean - energy power supply system; the combined wind - solar - hydro - storage system could effectively stabilize the output instability of wind and photovoltaic power and compensate for the efficiency shortcomings of standalone pumped - storage, emerging as a central pattern suitable for the multi - energy complementary development of clean energy; this article selected four main integrated wind - solar - hydro - storage systems as research subjects, analyzed their operating mechanisms, advantages and disadvantages, essential elements for site selection, as well as cost and economic characteristics, and sorted out specific optimization and cost - reduction strategies for each system, consequently providing practical technical references for the optimal design of these systems and the engineering practice of clean - energy multi - energy complementarity.