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In this paper, a high speed slotted solid-rotor induction motor (SSRIM) with rated power of 15 kW and rated speed of 120krpm is studied, and its electromagnetic performance and rotor mechanical structure are optimized. First, according to the empirical formula of motor design, the volume size of the motor is determined. Then, by constructing a two-dimensional finite element model, the slot matching scheme and coil pitch are optimized, and the influence of different slot matching scheme and coil pitch on the output torque and harmonics of the motor is compared. Then, the influence of rotor slot size on electromagnetic and mechanical properties of the motor is described in detail. Finally, a finite element model is constructed to verify the mechanical strength of the rotor, and the influence of temperature on the deformation and stress of the rotor is explored. Through the analysis, it is found that the slot matching scheme has a great influence on the torque ripple of the motor, and the torque ripple can be reduced by selecting the slot matching scheme reasonably. Secondly, the size of the rotor slot has a great impact on the electromagnetic and mechanical properties of the motor, and it is necessary to comprehensively consider and jointly optimize its size.

Staphylococcus aureus stands as one of the most pervasive pathogens given its morbidity and mortality worldwide due to its roles as an infectious agent that causes a wide variety of diseases ranging from moderately severe skin infections to fatal pneumonia and sepsis. S. aureus produces a variety of exotoxins that serve as important virulence factors in S. aureus-related infectious diseases and food poisoning in both humans and animals. For example, staphylococcal enterotoxins (SEs) produced by S. aureus induce staphylococcal foodborne poisoning; toxic shock syndrome toxin-1 (TSST-1), as a typical superantigen, induces toxic shock syndrome; hemolysins induce cell damage in erythrocytes and leukocytes; and exfoliative toxin induces staphylococcal skin scalded syndrome. Recently, Panton–Valentine leucocidin, a cytotoxin produced by community-associated methicillin-resistant S. aureus (CA-MRSA), has been reported, and new types of SEs and staphylococcal enterotoxin-like toxins (SEls) were discovered and reported successively. This review addresses the progress of and novel insights into the molecular structure, biological activities, and pathogenicity of both the classic and the newly identified exotoxins produced by S. aureus.

In response to the uncertainty of renewable energy output and the fluctuation of load, this paper proposes a hybrid energy storage management strategy based on the State of Charge (SOC) to smooth power fluctuations and thereby improve the power quality of photovoltaic energy storage DC microgrids. Firstly, a hybrid algorithm for power tracking control is formed by incorporating the Particle Swarm Optimization (PSO) algorithm into the variable step-size Incremental Conductance (INC) method, thereby optimizing the maximum power point tracking control system of the photovoltaic system. Then, a first-order filter is employed for the initial allocation of demand power. Taking the SOC of supercapacitors and energy storage batteries as a reference, a secondary power allocation energy management strategy based on rule-based control is proposed to ensure the service life and application safety of the hybrid energy storage system. Finally, simulation experiments are conducted in MATLAB/Simulink 23.2 (R2023b). The results indicate that the proposed energy management strategy can maintain the SOC of the hybrid energy storage system at a reasonable level and effectively smooth DC bus voltage fluctuations.

The traditional air-assisted side-deep fertilization device has some problems, such as inaccurate control system parameters and poor precision in variable fertilization. It seriously affects the application and popularization of the device. Aiming at the above problems, this paper wanted to realize the precise fertilizer discharge control of an air-assisted side-deep fertilization device. This paper designs an electronically controlled fertilization system based on a PID controller from the past. The system model was constructed in MATLAB, and the mathematical model and transfer function model of a stepper motor, the mathematical model of fertilizer discharge, and the stepper motor rotational speed were established too. In order to improve the accuracy of precise fertilizer discharge control system parameters, the system parameters were optimized based on the particle swarm optimization algorithm and the control system tuner toolbox. We had established a validation test platform to test the performance of a precise fertilizer discharge control system. In the actual experiment, the maximum stability coefficient of variation was 0.91% at the target fertilizer discharge mass level of 350 g/min, and the maximum error of fertilizer discharge was 4.14% at 550 g/min of the target fertilizer discharge mass level. By analyzing the test results of the precise fertilizer discharge control system, the new precise fertilizer discharge control system had good fertilizer discharge stability and could also meet the technical specification for quality evaluation of fertilization machinery (NY/T 1003-2006). This research can improve the fertilizer discharge accuracy of the air-assisted side-deep fertilization control system.

Electric-drive-reconstructed onboard chargers (EDROCs), also known as electric-drive-reconstructed power management systems, are a promising alternative to conventional onboard chargers due to their characteristics of low cost and high power density. The model predictive control offers a fast dynamic response, simple implementation, and the ability to control multiple targets simultaneously. In this paper, a two-stage multi-vector model predictive current control (MPCC) of a six-phase EDROC for solar-powered electric vehicles (EVs) is proposed. Firstly, the topology for the EDROC incorporating a six-phase symmetrical permanent magnet synchronous machine (PMSM) is introduced, and the operation principles of the DC charge mode, the drive mode, and, especially, the in-motion charge mode are analyzed in detail. After that, a two-stage multi-vector MPCC method is proposed by using the multi-vector MPC technique and designing a two-stage MPC structure to eliminate the regulation of the weighting factor of the MPC. Finally, the effectiveness of the proposed method is verified on a self-designed 2 kW EDROC platform.

Chronic obstructive pulmonary disease (COPD), as the third leading cause of global mortality, presents complex pathological mechanisms and imposes a substantial health burden. Emerging evidence reveals that olfactory receptors (ORs), traditionally associated with odor detection, exhibit non-canonical regulatory functions in COPD pathogenesis. This review systematically explores ORs’ multidimensional roles: environmental triggers activate specific ORs in specific cells, initiating chronic inflammatory cascades. Persistent inflammation drives irreversible airway remodeling through smooth muscle proliferation and extracellular matrix reorganization. Preclinical and clinical studies demonstrate that OR agonists/antagonists modulate the inflammation-remodeling axis to influence pulmonary function, though their pleiotropic effects complicate therapeutic targeting. The cell type-specific expression patterns and diverse ligand profiles of ORs create unique opportunities for precision interventions, while posing challenges in tissue delivery and receptor efficacy optimization. Future investigations should integrate single-cell omics and artificial intelligence to elucidate OR-mediated dynamic networks, downstream signaling pathways, and their interplay with microbiome-gut-lung axis regulation. This review not only advances our understanding of OR biology in respiratory diseases but also proposes a novel theoretical framework for developing OR-based diagnostic and therapeutic strategies in the early management of COPD.

The application of stretchable strain sensors in human movement recognition, health monitoring, and soft robotics has attracted wide attention. Compared with traditional electronic conductors, stretchable ionic hydrogels are more attractive to organization‐like soft electronic devices yet suffer poor sensitivity due to limited ion conduction modulation caused by their intrinsic soft chain network. This paper proposes a strategy to modulate ion transport behavior by geometry‐induced strain concentration to adjust and improve the sensitivity of ionic hydrogel‐based strain sensors (IHSS). Inspired by the phenomenon of vehicles slowing down and changing lanes when the road narrows, the strain redistribution of ionic hydrogel is optimized by structural and mechanical parameters to produce a strain‐induced resistance boost. As a result, the gauge factor of the IHSS is continuously tunable from 1.31 to 9.21 in the strain range of 0–100%, which breaks through the theoretical limit of homogeneous strain‐distributed ionic hydrogels and ensures a linear electromechanical response simultaneously. Overall, this study offers a universal route to modulate the ion transport behavior of ionic hydrogels mechanically, resulting in a tunable sensitivity for IHSS to better serve different application scenarios, such as health monitoring and human–machine interface.

Aiming at the shortcomings of the existing machine vision pose measurement technology, a pose measurement method based on monocular vision and a cooperative target is proposed. A planar target designed with circles and rings as the main body is dedicated to object pose measurement, and a feature point coordinate extraction and sorting algorithm is designed for this target to effectively extract image features on the target. The RANSAC algorithm and topology-based fitting for the intersection method are used to optimise data processing, further improving the accuracy of feature point coordinate extraction and ultimately achieving high-precision measurement of object poses. The experimental results show that the measurement accuracy of the roll angle perpendicular to the optical axis can reach 0.02°, and the repeatability can reach 0.0004° after removing the systematic error; the measurement accuracy of the pitch angle can reach 0.03°, and the repeatability can go to 0.002° after removing the systematic error. The measurement range of the pitch angle is [−30°, +30°]; the measurement range of the roll angle is [−179°, +179°]. The experimental results show that the system has high measurement accuracy and meets the requirements of high-precision measurement.

Three-dimensional (3D) printed surgical models provide an excellent surgical training option to closely mimic real operations to teach medical students who currently rely largely on visual learning aided with simple suturing pads. There is an unmet need to create simple to complex surgical training programs suitable for medical students. A prospective cohort study was conducted on a group of 16 6th year students. They were randomly divided into two groups for suture training on a basic training pad or on unique 3D-printed intestinal anastomosis models. After 4 weeks of open and laparoscopic surgical training, exams were performed on the standardized 3D-printed model at the end of each stage to assess surgical performance including surgical time and scores. Both groups had similar skills before the start of each stage. In stage 1, both groups showed comparable learning performance, but the 3D model group performed better in Exam 1. In stage 2, the 3D model group took more time but showed significant improvements and outperformed the pad group in Exam 2 in both performance scores and time. Post-training questionnaires indicated increased interest in surgery and technical training among students using 3D models. Realistic 3D-printed models benefit surgical training, expected to become integral in teaching operative skills and techniques to medical students.

With the prosperity of the economy and the continuous expansion of the port area, container trucks have become the main means of transportation on port roads. Traditional traffic flow research mainly focuses on passenger cars. In view of the unique characteristics of container truck traffic flow and the lack of research on conflict-influencing factors for this traffic flow, this paper is committed to filling this research gap. This paper uses drones and YOLOv8 technology to construct a vehicle trajectory dataset in the container truck traffic flow scenario and extracts relevant features of container truck traffic flow from vehicle trajectory data from a macro perspective. For the trajectory data after denoising, the time to collision (TTC) indicator is used to identify conflict events, and then the synthetic minority oversampling technique (SMOTE) is used to obtain four datasets. Machine learning and related classification models are selected for conflict prediction. It is worth noting that the XGBoost model performs better than other models on the four datasets, with an accuracy of 0.86 and an AUC value of 0.933. The Shapley additive explanation (SHAP) theory is used to explain and analyze the model results and compare them with existing studies. The results show that in container truck traffic flow, traffic density is the most important factor affecting conflicts, and conflicts occur more frequently when the traffic density is between 50 and 70 vehicles/km, followed by lane change rate. In contrast, for general traffic flows, studies have shown that speed is the main factor affecting conflicts.