Wuxi Institute of Technology

Purpose This paper aims to design and investigate a high-precision tunneling magnetoresistance (TMR) noncontact current sensor based on a dual-gap anti-interference structure, to address the susceptibility of traditional TMR current sensors to external interference in complex electromagnetic environments. Design/methodology/approach A systematic methodology integrating theoretical modeling, finite element simulation and experimental validation was used. A mathematical model of the dual-gap magnetic circuit was established based on magnetic Ohm’s law to analyze its differential output mechanism for suppressing common-mode magnetic field interference. Key parameters such as magnetic core cross-section and air gap dimensions (a = b = 7 mm) were optimized through finite element simulation, evaluating magnetic field uniformity and conductor displacement error. Thermal performance and anti-interference capability were experimentally validated under controlled conditions. Findings The optimized dual-gap structure improves magnetic field uniformity to 0.0010 and reduces measurement error caused by conductor displacement to below 0.08%. It also demonstrates superior thermal management, lowering temperature rise by 27.4% compared to a single-gap structure under high load. Under an external interference field of 10 Oe, the dual-gap sensor achieves an output voltage error of 0.014%, outperforming the single-gap design (0.017%). The sensor maintains a linearity of ± 0.5% and an accuracy of ± 1% across a temperature range of –50°C to 70°C. Originality/value This study proposes an innovative dual-gap magnetic circuit structure that effectively enhances anti-interference performance and thermal stability in TMR current sensors. The research provides a practical and reliable design pathway for developing high-precision, robust current sensors suitable for demanding electromagnetic environments.

Rapid prediction of unsteady flow fields around airfoils is crucial for aerodynamic design and optimization of aircraft. However, traditional wind tunnel experiments and numerical simulation methods incur high computational costs for flow field calculations. This paper proposes an improved method based on deep neural operator networks for rapidly predicting unsteady flow fields of different symmetric airfoils under high angle-of-attack conditions. The method uses incoming flow conditions and airfoil parameters as inputs, reconstructing the flow field through spatiotemporal coordinates to achieve joint prediction of pressure and velocity. Experimental results demonstrate that this method accurately captures the unsteady flow field characteristics of different symmetrical airfoils at various high angles of attack. The inference time for a single time-step case takes only about 0.6 s, which is significantly faster than traditional numerical simulation methods. The proposed method can substantially enhance computational efficiency while maintaining flow prediction accuracy, offering a viable new approach for rapid flow field prediction and aerodynamic design optimization of aircraft.

Glioblastoma (GBM), a highly aggressive primary brain tumor, presents substantial treatment challenges due to its resistance to genotoxic therapies and frequent recurrence. Oncogenic alterations significantly impact lipid metabolism in GBM cells. G Protein-Coupled Receptor 40 (GPR40), a receptor for polyunsaturated fatty acids (PUFAs), plays a key role in neural development and neurogenesis. Additionally, ferroptosis induction in GBM relies on PUFA peroxidation within cell membranes. Considering the persistent oxidative stress in the central nervous system, aberrant GPR40 activation in glioma lipid metabolism might suppress ferroptosis, thus contributing to chemotherapy resistance. Transcriptomic analysis of TCGA data revealed upregulated GPR40 expression in malignant gliomas, alongside alterations in ferroptosis-related and drug resistance pathways. To model GBM temozolomide (TMZ) resistance, a TMZ-resistant GL261 cell line was established. Additionally, key ferroptosis markers, including iron metabolism, lipid peroxidation, and glutathione levels, as well as TMZ treatment sensitivity, were assessed. Our findings confirm that GPR40 reduces glioma sensitivity to TMZ chemotherapy by inhibiting ferroptosis. These results highlight the GPR40-ferroptosis regulatory axis as a potential therapeutic target to enhance ferroptosis-induced treatment and overcome TMZ chemotherapy resistance in GBM.

Abstract Background As an emerging information media, the Internet is uncontrollable in many aspects such as information dissemination and speech supervision. Therefore, in online social interaction, incidents such as sensitive vocabulary, verbal violence, and false information occur frequently. And online violence has gradually become an important triggering factor for a series of mental illnesses such as depression. The phenomenon of online violence is directly related to the psychological changes of victims. After being maliciously defamed, victims often experience immense pressure and anxiety, which affects their daily life and physical and mental health. Therefore, the study analyzes the specific impact of online violence incidents on the mental health of college students. The purpose of the study is to explore targeted intervention recommendations to alleviate the negative impact of online violence on the mental health of victims. Methods Conduct a questionnaire survey among students from three universities in a certain city. Firstly, the study used the Symptom Checklist 90 (SCL-90) to investigate the psychological state of college students who have experienced online violence. At the same time, a self-made survey questionnaire related to online violence was used to analyze the influencing factors of college students' online violence behavior. After obtaining the survey results, the study used SPSS 24.0 software for data statistical analysis. Results The experimental results showed that the somatization statistics of college students who suffered from online violence were 1.50 ± 0.52, while the somatization statistics of college students who did not suffer from online violence were 1.34 ± 0.45, and the difference was significant (p<.001). In addition, the average evaluations of depression and anxiety among college students who have experienced online violence are 1.95 ± 0.58 and 1.67 ± 0.59, respectively. The average evaluations of depression and anxiety among college students who did not experience online violence were 1.57 ± 0.61 and 1.42 ± 0.43, respectively, with a significant difference between the two groups (p<.001). The experimental results showed that, except for interpersonal relationships and obsessive-compulsive symptoms, the SCL-90 scores of the sample college students who suffered from online violence were higher than those who did not suffer from online violence. Therefore, online violence can trigger anxiety and depression in victims, which can have a significant negative impact on their mental health. Discussion Research findings indicate that online violence has a significant negative impact on the mental health of college students. Online violence not only increases students' anxiety and depression, but also damages their self-esteem and self-efficacy. In order to alleviate the negative impact of online violence on mental health, research suggests that universities and related institutions need to provide psychological support services, strengthen online literacy education, and enhance college students' ability to identify and respond to online violence. At the same time, it is necessary to strengthen the supervision and punishment of online violence, and create a healthier and more positive online environment. Future research will explore and analyze the role of current policies and regulations in reducing online violence and protecting victims.

This paper combines test paper quality indicators to construct a test paper assembly mathematical model and uses an improved differential evolution algorithm to solve the model. It generates an initial population through uniform search of the question bank and dynamically adjusts the mutation rate and crossover rate based on the fitness values of the population. Building on this, the paper adopts a backward design approach based on the OBE philosophy, focusing on three aspects—curriculum content, instructional organization, and instructional feedback—to construct an educational reform strategy and intelligent manufacturing-oriented teaching model that integrates automation technology, characterized by “two increases, two decreases, and one improvement.” Finally, the model was tested on students from a certain school, leading to the conclusion that among the experimental data on difficulty level score distributions of test papers generated by different algorithms, the algorithm proposed in this paper had the smallest distribution at the difficult level, with a minimum of 7.89, indicating that the paper's test paper generation strategy is more effective. Through empirical research, it was found that after applying the teaching model proposed in this paper, students in the experimental class achieved higher average scores on each question in the test compared to the control class. Additionally, as teaching progressed, students in the experimental class demonstrated a gradually superior trend in their mastery of computational concepts compared to students in the control class.