Brushless Direct Current (BLDC) motors are widely recognized for their high torque, efficiency, and reliability, making them a core component in electric vehicle (EV) applications. However, the continuous operation of these motors at elevated speeds and under extreme temperatures often leads to wear and tear in the motor’s coils, increasing the risk of faults such as open-circuit conditions. To address these challenges, this paper presents the development of a robust fault detection and protection system designed specifically for BLDC motors. The system is integrated into an advanced real-time monitoring platform that continuously tracks motor performance, providing early fault detection and ensuring proactive maintenance. The methodology focuses on designing a monitoring circuit that captures real-time current and voltage signals from the motor. These signals are processed using voltage comparators and low-pass filters to detect anomalies such as open-circuit faults in the motor drive system. The proposed online platform facilitates remote, continuous monitoring, allowing operators to identify faults early and prevent potential failures, thus reducing downtime. Extensive experimental validation shows that the system accurately detects faults with minimal latency, offering a cost-effective and scalable solution for BLDC motor-driven electric vehicles. Additionally, the system enhances operational efficiency by ensuring early fault detection, which allows for timely intervention before catastrophic motor failure occurs. This real-time fault detection platform presents a significant improvement over conventional methods, offering higher reliability, reduced maintenance costs, and extended motor life, making it ideal for integration in electric vehicle systems and other industrial applications.

Oxide materials with a non-centrosymmetric structure exhibit bulk photovoltaic effect (BPVE) but with a low cell efficiency. Over the past few years, relatively larger BPVE coefficients have been reported for two-dimensional (2D) layers and stacks with asymmety-induced spontaneous polarization. Despite these achievements, the full potential of TMDs in the BPVE has not yet been realized, and several fundamental issues remained to be resolved. One is the typical non-Ohmic contacts between metals and TMDs due to the strong pinning effect of the Fermi levels. The 2^nd is that previous devices have typically adopted top contact (TC) electrodes, which may not fully utilize the polarization within TMDs. The 3^rd is that the typical way to break the in-plane symmetry for inducing BPVE in these 2D materials is through non-scalable method to add external strain. Here, we report a crucial breakthrough in boosting the BPVE in 3R-MoS₂ by adopting edge contact (EC) geometry using bismuth semimetal electrode. In clear contrast to the typically used top contact (TC) geometry, the EC metal which strongly adheres to the edges and the subtrates can induce a pronounced tensile strain to the 3R-MoS₂, and the lateral contact geometry allows to completely access to in-plane polarization from underneath layers reachable by light, leading to >100 times of BPVE enhancement in photocurrent. We further design a 3R-MoS₂/WSe₂ heterojunction to demonstrate constructive coupling of BPVE with the conventional photovoltaic effect, indicating their potential in photodetectors and photovoltaic devices.

Dielectric ceramic materials are widely used in pulse power systems, energy storage capacitors, inverters and ultrasound medical equipment due to their high power density, fast charge/discharge rate and good thermal stability. Since the demand for high-performance energy storage capacitors is rapidly growing, advanced dielectric ceramic materials are urgently needed. In this work, lead-free high-entropy ferroelectric ceramics are developed as novel dielectric materials, opening up a new window to improve the performance of energy storage capacitors. Firstly, wet chemical methods were used to prepare high-entropy perovskite (ABO₃) nano-powders, achieving a uniform mixture of multi-component metal elements with equal molar ratios and reducing the sintering temperature of the ceramics; the A-site or B-site of the ABO₃ structure was randomly occupied by 4-5 metal ions, successfully alternating the energy storage properties of high-entropy ferroelectric ceramics. Secondly, high-entropy ferroelectric films with a thickness of 200-800 nm were prepared by spin coating, which have an ultra-high breakdown electric field (>10.0 MV/cm); Through tuning the electric field endurance, saturation polarization and remnant polarization of the high-entropy ferroelectric films, the energy storage density can be increased to as high as 16.0 J/cm³. The results demonstrate that the high-entropy ferroelectric ceramics could be promising dielectrics for energy storage application.

The Internet of Things (IoT) has revolutionized technology by enabling seamless connectivity and automation across various domains. However, this rise introduces unprecedented security risks, from data breaches to physical harm. This project aims to develop a comprehensive IoT Security Risk Management System (IoT-SRMS) to identify, assess, and mitigate these security risks. Utilizing advanced risk assessment methodologies and real-time threat detection, the proposed IoT-SRMS is expected to significantly enhance IoT security management.

The Internet of Things (IoT) has ushered in a new era of connectivity, transforming how we interact with the digital world and revolutionizing industries ranging from healthcare and manufacturing to transportation and smart cities. By interconnecting billions of devices, sensors, and systems, IoT has enabled unprecedented levels of automation, efficiency, and convenience. The naturally interconnected nature of IoT ecosystems, coupled with the diverse range of devices and communication protocols, creates complex attack surfaces that adversaries can exploit. Security vulnerabilities in IoT devices, such as weak authentication, insecure communication channels, and lack of update mechanisms, pose serious threats to data privacy, system integrity, and user safety.

As IoT adoption continues to grow across industries and sectors, the need for effective risk management strategies to mitigate these security risks becomes paramount. A robust IoT Security Risk Management System (IoT-SRMS) is essential for identifying, assessing, and mitigating security threats in IoT ecosystems, ensuring the integrity, confidentiality, and availability of connected devices and data.

The detection and classification of stress is essential for advancing mental health diagnosis and improving human well-being. Accurate stress assessment can lead to early intervention, personalized treatment plans, and improved quality of life. By identifying levels and types of stress, healthcare professionals can better understand and treat the underlying causes, promoting mental resilience and overall health. This study presents a novel method for stress detection and classification using electroencephalogram (EEG) data combined with neural network modeling. We propose a multi-layer neural network architecture optimized to analyze EEG frequency bands and extract stress-related biomarkers with high precision. The methodology includes preprocessing EEG signals to reduce noise using blind source separation (BSS) techniques, followed by feature extraction focusing on frequency bands associated with stress responses. The neural network is trained on labeled EEG datasets to classify stress levels, demonstrating significant accuracy and outperforming conventional classifiers. This method is implemented on an embedded Raspberry Pi system to capture and analyze data in real-time. The results, stemming from the integration of BSS, neural networks, and the embedded system, indicate that this approach offers a reliable and efficient means for stress detection, with potential applications in mental health monitoring and adaptive biofeedback systems. This work contributes to the field by introducing an innovative, data-driven model that enhances the precision and scalability of EEG-based stress classification.

We fabricated a prototype for SiC trench MOSFET with intergrated trench Junction Barrier Schottky Diode (TJBS) to realize low reverse on-state voltage drop (V_{R_on}) and low switching power loss under different temperatures. A TJBS is integrated at the sidewall of a trench nearby the gate trench to inactivate the PN body diode, which not only reduces V_{R_on}, but also eliminates the conductance modulation effect during the free-wheeling state. Thus, the reverse recovery charge will not increase with the increase of temperature, which ensures that the reverse recovery loss for the free-wheeling diode and turning-on loss (E_on) for the nearby MOSFET almost remain unchanged under high temperature operation. The fabricated SiC trench MOSFET with cell pitch of 8 um shows a R_on,sp of 5.95 mΩ·cm² and V_{R_on} of 2.6 V at 300 A/cm². Further TCAD optimization based on the measured data shows that a low R_on,sp of 2.52 mΩ·cm² and V_{R_on} of 2.04 V can be obtained when shrinking the cell pitch to 5 um. Compared with a simulated CoolSiC^TM MOSFET, the proposed SiC trench MOSFET shows that E_on and turning-off loss (E_off) at 25 °C are reduced by 32.8% and 80.7%, respectively. The total power loss is reduced by 54.5% to 61.8% when temperature increases from 25 °C to 175 °C.

Gallium nitride (GaN) and other III-nitride semiconductors have the advantages of continuous adjustable direct bandgap from near-infrared to deep-ultraviolet, strong polarization, high breakdown field strength, fast electron saturated drift velocity, and high power density. They have excellent performance in visible light and ultraviolet light LEDs and laser diodes, as well as power electronics and RF devices. Due to the price and size limitation of III-nitride substrates, heterogeneous epitaxial III-nitride materials on silicon, sapphire and SiC substrates of III-nitride materials has attracted attention from the industry.
This report focuses on the key technologies for large-size silicon-based III-nitride epitaxy. The 8-inch silicon-based GaN HEMT epitaxial wafers grown using metal-organic chemical vapor deposition (MOCVD) demonstrate industry-leading vertical breakdown voltages. Furthermore, to enhance the RF device performance of 8-inch Si-based GaN epitaxial wafers, we developed a large-size AlN-on-Si template epitaxial process utilizing molecular beam epitaxy (MBE). By employing MBE technical advantages such as lower growth temperature, we successfully mitigated the formation of parasitic conductive layers at the AlN/Si interface, thereby decreasing RF loss. Finally, we provide a brief overview of efforts undertaken by Hubei JFS Laboratory regarding n+ GaN regrowth, as well as other compound semiconductor materials such as SiC, InP and GaAs.

"Gallium Nitride (GaN) materials exhibit a wide bandgap, elevated electron mobility, high breakdown electric fields, substantial output power capabilities, and excellent thermal stability. AlGaN/GaN high electron mobility transistors (HEMTs), leveraging GaN materials, are capable of operating at elevated voltages with minimal on-resistance and have emerged as a focal point of research in the domain of microwave power devices and circuits over the past decade."

Conventional AlGaN/GaN HEMTs are primarily depletion-mode devices (threshold voltage Vth < 0V). The need for a negative gate-on voltage makes the design of depletion-mode HEMTs more complex than enhancement-mode devices (Vth > 0V). Current methods to achieve enhancement include trench gate technology, p-GaN technology, gate fluorine ion implantation, and common-source common-gate cascode configurations. Etching a trench gate reduces the distance between the gate and channel, enhancing control over the channel and increasing the device's threshold voltage. The p-GaN technique maintains the original two-dimensional electron gas (2DEG) channel while providing high electron mobility that enhances transconductance. Both methods require high-quality GaN etching techniques. Low-damage etching of GaN trench gates can reduce gate leakage; simultaneously, selective low-damage etching of p-GaN minimizes 2DEG loss and improves output characteristics. Thus, achieving low-damage and precise depth control in GaN etching is a key challenge in fabricating GaN enhancement-mode HEMTs.

This report focuses on low-damage GaN trench etching and highly selective P-GaN etching technologies, introducing their fundamental principles, optimization methods, and final results from various technical perspectives and applications, with the aim of positively impacting subsequent device fabrication processes and performance.

This paper presents a novel method for 3D printing materials using a six-axis robotic arm, which offers enhanced precision and flexibility for additive manufacturing applications. Traditional 3D printing systems often face scalability, material versatility, and spatial reach limitations, particularly in industrial settings. Integrating the UR5 robotic arm into the 3D printing process expands the potential for producing complex geometries and larger structures with a broader range of materials. The method combines advanced path-planning algorithms with the UR5’s six-axis flexibility, allowing for precise control over deposition patterns and material distribution. This study focuses on developing a robotic 3D printer using a six-axis robotic arm, outlining a structured approach to achieve precise, multi-angled printing capabilities. Key steps include designing a custom print head or adapting an existing extruder, integrating a slicer to generate G-code from the 3D model, and developing a path-planning algorithm to generate collision-free paths, optimizing for speed, accuracy, and efficiency. Calibration and testing are essential to fine-tune accuracy, while safety and monitoring systems are implemented to ensure stable operation. Finally, iterative testing and refinement optimize the setup for reliable, high-quality 3D printing on complex surfaces. Experimental results demonstrate significant improvements in print accuracy and structural integrity compared to conventional methods, with potential applications in fields such as aerospace, automotive, construction, and biomedical engineering.

Objective: This study investigated the relationship between Q angle, lower limb posture during dynamic movements (walking and jumping), and potential injury risk using a novel "triangle" metric derived from 3D coordinate data obtained from inertial measurement units and the Perception Neuron motion capture system .
Methods: Twenty-four physically active participants without lower limb injury history participated in this cross-sectional study. Participants performed walking and jumping trials while wearing IMUs and the PN system. Three-dimensional coordinates were obtained from sensors positioned on the lateral aspect of the femur, infragenicular region, and dorsal surface of the foot to calculate Q angle and the triangle metric. Larger triangle areas were hypothesized to indicate higher injury risk .

Results: A weak negative correlation was found between Q angle and triangle area overall (r = -0.24, p = 0.227). During walking, this correlation was negligible (r = -0.11, p = 0.702), while during jumping, a moderate negative correlation was observed (r = -0.54, p = 0.047). Q angle did not differ significantly between walking and jumping (t(13) = 1.20, p = 0.252), with mean Q angles of 91.65° during walking and 90.65° during jumping. The triangle area, representing the proposed injury risk metric, was not significantly different between jumping and walking (t(13) = -0.09, p = 0.928), with mean areas of 0.0219 during walking and 0.0220 during jumping.
Conclusion: The triangle metric, derived from thigh, shank, and foot sensor data, showed a moderate negative correlation with Q angle during jumping, suggesting a potential relationship between lower limb posture and injury risk. However, the lack of significant differences in Q angle and triangle area between walking and jumping indicates that these movements may not present substantially different injury risks in our sample. Further research is needed to validate these findings and explore the clinical significance of the observed correlations.