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Epilepsy is a common chronic neurologic disorder, with about 30%‐40% of patients suffering from recurrent seizures despite receiving enough antiseizure medication. Growing evidence of extensive structural and functional research has revealed that epilepsy is a network disorder, and the network investigation is crucial in the presurgical evaluation. This narrative review is aimed at providing an overview of the conceptual development of epileptic surgery, the epileptic network formation, and the network‐oriented strategy in clinical practice. We searched the PubMed database before October 2022 with no language or regional restrictions for studies of network exploration in epilepsy patients. The study eligibility criteria were original connectivity or network research studies using neuroimaging or neurophysiological approaches, focusing on seizure localization, neuropsychological evaluation, neuromodulation, or surgical prognosis. The initial search revealed 2046 studies. The selection process presented 746 papers that meet the inclusion criteria. The results indicated that epilepsy was characterized by the alternation of brain networks, which account for the clinical, electrographic, and neurocognitive phenomena related to human seizures and changes across different disease stages. Hubs have garnered significant attention due to their extensive connections, which make them prone to facilitating the spread of seizures to various brain regions. Unlike the seizure onset zone, the removal of the majority of these ictogenic nodes demonstrated a significant correlation with positive outcomes. Therefore, performing a network analysis during the epilepsy presurgical evaluation seems essential and will influence surgical decisions. It is hoped that studying the network of epilepsy will enhance our ability to determine the most appropriate surgical strategy for patients with epilepsy, thus contributing to success in terms of effective seizure control with maximal sparing or even improvement of brain functioning. [ABSTRACT FROM AUTHOR]

Due to the complexity of the underwater environment, existing methods for underwater target detection present low precision on small or dense targets. To address these issues, a novel method is proposed for underwater target detection based on YOLOv5s (You Only Look Once version 5 small), which aims to improve the precision and robustness. In this study, an efficient feature extraction network is introduced to extract significant features, and a novel attention mechanism with deformable convolution is designed to improve the feature representation. Subsequently, an adaptive spatial fusion operation is introduced at the neck of YOLOv5s to facilitate feature fusion from various layers. By integrating low-level features with high-level features, the adaptive fusion feature pyramid network effectively integrates global semantic information and decreases the semantic gap between features from various layers, contributing to the high detection precision. Comprehensive experiments demonstrate that the proposed method achieves an mAP50 of 86.97% on the Underwater Robot Professional Contest of China 2020 dataset, 3.07% higher than YOLOv5s. Furthermore, the proposed method achieves a detection precision of 76.0% on the PASCAL VOC2007 dataset, surpassing several outstanding methods. [ABSTRACT FROM AUTHOR]

In this paper, according to the requirement of safety design of IoT(Internet of Things) operating system, combined with the research and engineering practice of classical linear temporal logic, logic programming and theory of formal model detection, a methodology for safety design of IoT operating system is proposed, and the engineering prototype is validated. Practice has proved that the effect of this method is in line with expectations. It is not only suitable for the safety design of IoT operating system, but also can be further extended to other areas of software product design with high safety requirements.

To better harvest the kinetic energy of the human and broaden the energy harvest frequency band, a nonlinear piezo-electromagnetic composite human energy harvester (NPE-HEH) is proposed. A magnetic repulsion force between the two groups of magnets makes the energy harvester nonlinear. The excitation experiment and the actual experiment of the human are carried out for the harvester. The excitation experiment results show that there is an optimal resistance value of the harvester to maximize the output power value. When the excitation acceleration is 0.4 g and the excitation frequency is 9 Hz, the output voltage value and the output power of the electromagnetic part of the energy harvester are 0.86 V and 2.47 mW respectively, and the output performance is excellent. When the energy harvester is installed in a backpack with a moving speed of 9 km/h, it can generate 0.7 mW of power. When the energy harvester is placed on the leg, the output performance is good and the output power can reach 1.3 mW. The energy harvester can efficiently harvest energy at low frequencies. This harvester is efficient at low frequencies, compact in size, and easy to carry, making it highly suitable for human vibration energy harvesting applications. [ABSTRACT FROM AUTHOR]

A transverse flux linear motor is a special type of linear motor with a high thrust force density, and it has broad application prospects in the field of linear direct-drive systems. In the process of oil production, the vibration of the linear motor poses a significant amount of harm to the system due to its special slender structure. This paper focuses on the electromagnetic vibration of a transverse flux permanent magnet linear submersible motor (TFPMLSM). Firstly, the no-load air gap flux density is calculated based on the field modulation principle. Secondly, the radial electromagnetic force (REF) of the TFPMLSM is calculated, and the finite element method (FEM) is used to analyze the time-space and spectral characteristics of the REF. Then, the influence of secondary eccentricity on the frequency spectrum of the REF is further concluded. Finally, the natural frequencies of each vibration mode are calculated using the modal superposition method and the influence of the REF on the motor vibration is obtained through magnetic-structural coupling analysis. The research results found that the motor does not cause resonance at low speeds, and the fundamental frequency of REF has the greatest impact on electromagnetic vibration. [ABSTRACT FROM AUTHOR]

A novel linear permanent magnet vernier generator (LPMVG) for small-power off-grid wave power generation systems is proposed in this paper. Firstly, in order to reduce the cogging force and the inherent edge effect of the linear generator, a staggered tooth modular structure is proposed. Secondly, in order to improve the output power and efficiency of the LPMVG and reduce the fluctuation coefficient of electromagnetic force, the relationship between the parameters of the generator is studied, and a method combining multi-objective optimization and single parameter scanning based on the response surface model and particle swarm optimization algorithm is proposed to obtain the optimal structural parameters of the generator. Thirdly, the output power and efficiency of the optimized generator are calculated and analyzed based on the two-dimensional finite element method, and the effectiveness of the multi-objective optimization design method based on the response surface model and particle swarm optimization algorithm is verified. Finally, a prototype is developed, and the calculated results and the measured results are shown to be in good agreement. [ABSTRACT FROM AUTHOR]