Support Vector Machine for Behavior-Based Driver Identification System

We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security application is more effective. In this paper, we first describe our experimental platform for collecting and modeling human driving behaviors. Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. The experimental results of learning algorithms and evaluation are described

Case Report: Toripalimab: a novel immune checkpoint inhibitor in advanced nasopharyngeal carcinoma and severe immune-related colitis

Toripalimab, a specific immune checkpoint inhibitor targeting the programmed death 1 (PD-1) receptor, represents a novel immunotherapeutic approach for advanced nasopharyngeal carcinoma, showing promising curative potential. However, it is not without drawbacks, as some patients experience immune-related adverse events (irAEs) associated with this treatment, and there remains a limited body of related research. Here, we present a case of advanced nasopharyngeal carcinoma in a patient who developed colitis as an irAE attributed to Toripalimab. Subsequent to Toripalimab treatment, the patient achieved complete remission. Notably, the development of colitis was accompanied by inflammatory manifestations evident in colonoscopy and pathology results. Further investigation revealed cytomegalovirus (CMV) infection, detected through immunohistochemistry in 11 colon biopsies. Subsequent treatment with ganciclovir and steroids resulted in symptom relief, and colonoscopy indicated mucosal healing. Our case highlights the association between irColitis induced by Toripalimab and CMV infection. Toripalimab demonstrates remarkable efficacy in treating advanced nasopharyngeal carcinoma, albeit with a notable risk of irAEs, particularly in the form of colitis. The link between symptoms and endoscopic pathology findings in irColitis is noteworthy. Standardized biopsy procedures can effectively confirm the diagnosis of CMV infection. Our findings may provide valuable guidance for managing acute CMV infection and irAEs associated with Toripalimab in the treatment of nasopharyngeal carcinoma in the future

Current therapy option for necrotizing enterocolitis: Practicalities and challenge

Necrotizing enterocolitis (NEC) is one of the most prevalent neonatal gastrointestinal disorders. Despite ongoing breakthroughs in its treatment and prevention, the incidence and mortality associated with NEC remain high. New therapeutic approaches, such as breast milk composition administration, stem cell therapy, immunotherapy, and fecal microbiota transplantation (FMT) have recently evolved the prevention and the treatment of NEC. This study investigated the most recent advances in NEC therapeutic approaches and discussed their applicability to bring new insight to NEC treatment

Physics-Informed Generative Adversarial Network-Based Modeling and Simulation of Linear Electric Machines

The demand for fast magnetic field approximation for the optimal design of electromagnetic devices is urgent nowadays. However, due to the lack of a publicly available dataset and the unclear definition of each parameter in the magnetic field dataset, the expansion of data-driven magnetic field approximation is severely limited. This study presents a physics-informed generative adversarial network (PIGAN), as well as a permanent magnet linear synchronous motor (PMLSM)-based magnetic field dataset, for fast magnetic field approximation. It includes the current density, material distribution, electromagnetic material properties, and other parameters of the electric machine. Physics-informed loss functions are utilized in the training process, making the output governed by Maxwell’s equation. Different slot-pole combinations of the PMLSM are involved in the dataset to extend the generalization of PIGAN. Some indicators for the further evaluation of magnetic approximation performance, including image-based metrics and calculation methods for the performance of electric motors, are presented in this study. Some challenges of magnetic field approximation using PIGAN are also discussed. The effectiveness of the physics-informed method is verified by comparing the magnetic field approximation results and the performance analysis results of the PMLSM with FEM, and the speed of PIGAN is approximately 40 times faster than that of FEM, while the accuracy is similar

Physics-Informed Generative Adversarial Network-Based Modeling and Simulation of Linear Electric Machines

The demand for fast magnetic field approximation for the optimal design of electromagnetic devices is urgent nowadays. However, due to the lack of a publicly available dataset and the unclear definition of each parameter in the magnetic field dataset, the expansion of data-driven magnetic field approximation is severely limited. This study presents a physics-informed generative adversarial network (PIGAN), as well as a permanent magnet linear synchronous motor (PMLSM)-based magnetic field dataset, for fast magnetic field approximation. It includes the current density, material distribution, electromagnetic material properties, and other parameters of the electric machine. Physics-informed loss functions are utilized in the training process, making the output governed by Maxwell’s equation. Different slot-pole combinations of the PMLSM are involved in the dataset to extend the generalization of PIGAN. Some indicators for the further evaluation of magnetic approximation performance, including image-based metrics and calculation methods for the performance of electric motors, are presented in this study. Some challenges of magnetic field approximation using PIGAN are also discussed. The effectiveness of the physics-informed method is verified by comparing the magnetic field approximation results and the performance analysis results of the PMLSM with FEM, and the speed of PIGAN is approximately 40 times faster than that of FEM, while the accuracy is similar