Lesional Intractable Epileptic Spasms in Children: Electroclinical Localization and Postoperative Outcomes

To analyze the influence of seizure semiology, electroencephalography (EEG) features and magnetic resonance imaging (MRI) change on epileptogenic zone localization and surgical prognosis in children with epileptic spasm (ES) were assessed. Data from 127 patients with medically intractable epilepsy with ES who underwent surgical treatment were retrospectively analyzed. ES semiology was classified as non-lateralized, bilateral asymmetric, and focal. Interictal epileptiform discharges were divided into diffusive or multifocal, unilateral, and focal. MRI results showed visible local lesions for all patients, while the anatomo-electrical-clinical value of localization of the epileptogenic zone was dependent on the surgical outcome. During preoperative video EEG monitoring, among all 127 cases, 53 cases (41.7%) had ES only, 46 (36.2%) had ES and focal seizures, 17 (13.4%) had ES and generalized seizures, and 11 (8.7%) had ES with focal and generalized seizures. Notably, 35 (27.6%) and 92 cases (72.4%) showed simple and complex ES, respectively. Interictal EEG showed that 22 cases (17.3%) had bilateral multifocal discharges or hypsarrhythmia, 25 (19.7%) had unilateral dominant discharges, and 80 (63.0%) had definite focal or regional discharges. Ictal discharges were generalized/bilateral in 71 cases (55.9%) and definite/lateralized in 56 cases (44.1%). Surgically resected lesions were in the hemisphere (28.3%), frontal lobe (24.4%), temporal lobe (16.5%), temporo-parieto-occipital region (14.2%), and posterior cortex region (8.7%). Seizure-free rates at 1 and 4 years postoperatively were 81.8 and 72.7%, respectively. There was no significant difference between electroclinical characteristics of ES and seizure-free rate. Surgical treatment showed good outcomes in most patients in this cohort. Semiology and ictal EEG change of ES had no effect on localization, while focal or lateralized epileptiform discharges of interictal EEG may affect lateralization and localization. Complete resection of epileptogenic lesions identified via MRI was the only factor associated with a positive surgical outcome

Efficient Table-Based Masking with Pre-processing

Masking is one of the most investigated countermeasures against sidechannel attacks. In a nutshell, it randomly encodes each sensitive variable into a number of shares, and compiles the cryptographic implementation into a masked one that operates over the shares instead of the original sensitive variables. Despite its provable security benefits, masking inevitably introduces additional overhead. Particularly, the software implementation of masking largely slows down the cryptographic implementations and requires a large number of random bits that need to be produced by a true random number generator. In this respect, reducing the< overhead of masking is still an essential and challenging task. Among various known schemes, Table-Based Masking (TBM) stands out as a promising line of work enjoying the advantages of generality to any lookup tables. It also allows the pre-processing paradigm, wherein a pre-processing phase is executed independently of the inputs, and a much more efficient online (using the precomputed tables) phase takes place to calculate the result. Obviously, practicality of pre-processing paradigm relies heavily on the efficiency of online phase and the size of precomputed tables. In this paper, we investigate the TBM scheme that offers a combination of linear complexity (in terms of the security order, denoted as d) during the online phase and small precomputed tables. We then apply our new scheme to the AES-128, and provide an implementation on the ARM Cortex architecture. Particularly, for a security order d = 8, the online phase outperforms the current state-of-the-art AES implementations on embedded processors that are vulnerable to the side-channel attacks. The security order of our scheme is proven in theory and verified by the T-test in practice. Moreover, we investigate the speed overhead associated with the random bit generation in our masking technique. Our findings indicate that the speed overhead can be effectively balanced. This is mainly because that the true random number generator operates in parallel with the processor’s execution, ensuring a constant supply of fresh random bits for the masked computation at regular intervals

Line Loss Calculation and Optimization in Low Voltage Lines with Photovoltaic Systems Using an Analytical Model and Quantum Genetic Algorithm

With the increasing integration of distributed photovoltaic (PV) generation into distribution networks, challenges such as power reverse flow and high line losses have emerged, leading to greater uncertainty in power systems. To address these issues, this paper presents an analytical model for calculating line losses in low-voltage distribution networks with PV generation, utilizing power flow calculations. A simulation model of a 15 node low-voltage network is developed using SIMULINK to validate the accuracy of the analytical model under the scenario of uniform load distribution (ULD). Additionally, a line loss optimization algorithm based on quantum genetic algorithms (QGA) is proposed for low-voltage distribution networks with distributed PV generation, along with an optimization model. The objective function of the optimization model is based on the reduction in line losses resulting from the integration of the PV system. The example results demonstrate the consistency between the line loss optimization using QGA and the analytical results, highlighting the significant advantages of QGA in terms of speed and accuracy. This research provides valuable insights for line loss optimization in low-voltage distribution networks with distributed PV generation and serves as a theoretical reference for future studies in this field

Integrated IMU with Faster R-CNN Aided Visual Measurements from IP Cameras for Indoor Positioning

Considering the radio-based indoor positioning system pertaining to signal degradation due to the environmental factors, and rising popularity of IP (Internet Protocol) cameras in cities, a novel fusion of inertial measurement units (IMUs) with external IP cameras to determine the positions of moving users in indoor environments is presented. This approach uses a fine-tuned Faster R-CNN (Region Convolutional Neural Network) to detect users in images captured by cameras, and acquires visual measurements including ranges and angles of users with respect to the cameras based on the proposed monocular vision relatively measuring (MVRM) method. The results are determined by integrating the positions predicted by each user&rsquo;s inertial measurement unit (IMU) and visual measurements using an EKF (Extended Kalman Filter). The results experimentally show that the ranging accuracy is affected by both the detected bounding box&rsquo;s by Faster R-CNN height errors and diverse measuring distances, however, the heading accuracy is solely interfered with bounding box&rsquo;s horizontal biases. The indoor obstacles including stationary obstacles and a pedestrian in our tests more significantly decrease the accuracy of ranging than that of heading, and the effect of a pedestrian on the heading errors is greater than stationary obstacles on that. We implemented a positioning test for a single user and an external camera in five indoor scenarios to evaluate the performance. The robust fused IMU/MVRM solution significantly decreases the positioning errors and shows better performance in dense multipath scenarios compared with the pure MVRM solution and ultra-wideband (UWB) solution

Exploring associations between eHealth literacy, cyberchondria, online health information seeking and sleep quality among university students: A cross-section study

Background: University students are increasingly inclined to use the Internet for health-related purposes, and their sleep problems are becoming increasingly prominent. Currently, the relationship between sleep quality and online health-related searches is poorly understood. The aim of this study was to exam the associations of sleep quality, Internet use, eHealth literacy, online health information seeking and cyberchondria in the sample of Chinese university students. Methods: A total of 2744 students completed self-reported questionnaires online containing the Pittsburgh Sleep Quality Index (PSQI), eHealth Literacy Scale, Online Health Information Seeking, Cyberchondria Severity Scale (CSS) and questions regarding sleep duration, Internet use, health status, and demographic information. Results: The prevalence of poor sleep quality (PSQI >7) among the university students was 19.9% and 15.6% students slept less than 7 h per day. As time spent on online daily and playing phone before bed increased, the prevalence of sleep disturbance gained. Sleep disturbance was significantly associated with cyberchondria (OR = 1.545, p = 0.001), health status [good (OR = 0.625, p = 0.039), poor (OR = 3.128, p = 0.010), and fair (OR = 1.932, p = 0.001)]. Sleep quality, online health information seeking and eHealth literacy positively influenced with cyberchondria. Compared to 7–8 h sleep duration, online health information seeking (OR = 0.750, p = 0.012) was significantly associated with ≥8 h sleep duration. Conclusion: Our findings highlighted poor health status, too much time spent on online daily and high cyberchondria level might decrease sleep quality in the sample of Chinese university students, further suggesting the need for developing interventions based on online health-related searches for improving sleep quality among university students

Exploring associations between social media addiction, social media fatigue, fear of missing out and sleep quality among university students: A cross-section study.

BackgroundSocial media use has been linked to poor sleep outcomes among university students in the cyber age, but the association between the negative consequences of social media use and sleep problems is not yet well understood. The present study investigated the relationships among social media usage, social media fatigue (SMF), fear of missing out (FoMO), social media addiction (SMA) and sleep quality in university students.MethodAn online survey was administered to 2744 respondents that included the Pittsburgh Sleep Quality Index (PSQI); questionnaires evaluating FoMO, SMF, and SMA; and questions regarding sleep duration, social media use, health status, and demographic information.ResultA total of 19.9% of respondents suffered from sleep disturbance. A total of 15.6% of participants had sleep durations less than 5 h, and 21.6% of subjects had sleep durations longer than 9 h. Sleep quality was positively associated with SMF (OR = 1.387, 95% CI: 1.103~1.743), and SMA (OR = 1.415, 95% CI: 1.118~1.791). The relationship between FoMO and sleep disturbance was not significant. Compared to a sleep duration > 9 h, SMF increased the risk of shorter sleep durations [5-6 h sleep (OR = 2.226, 95% CI: 1.132~4.375), 6-7 h sleep (OR = 1.458, 95% CI: 1.061~2.002), and 7-8 h sleep (OR = 1.296, 95% CI: 1.007~1.670)]. FoMO and SMA did not significantly affect sleep duration. In addition, SMA (OR = 3.775, 95% CI: 3.141~4.537), FoMO (OR = 3.301, 95% CI: 2.753~3.958), and sleep disorders (OR = 1.284, 95% CI: 1.006~1.638) increased SMF.ConclusionUpon experiencing negative consequences of social media use, such as SMF and SMA, university students were likely to experience sleep problems. Further research exploring the interventions that improve sleep and alleviate negative consequences of social media use should be conducted

The Performance of Electronic Current Transformer Fault Diagnosis Model: Using an Improved Whale Optimization Algorithm and RBF Neural Network

With the widely application of electronic transformers in smart grids, transformer faults have become a pressing problem. However, reliable fault diagnosis of electronic current transformers (ECT) is still an open problem due to the complexity and diversity of fault types. In order to solve this problem, this paper proposes an ECT fault diagnosis model based on radial basis function neural network (RBFNN) and optimizes the model parameters and the network size of RBFNN simultaneously via an improved whale optimization algorithm (WOA) to improve the classification accuracy and robustness of RBFNN. Since the classical WOA is easy to fall into a locally optimal performance, a hybrid multi-strategies WOA algorithm (CASAWOA) is proposed for further improvement in optimization performance. Firstly, we introduced the tent chaotic map strategy to improve the population diversity of WOA. Secondly, we introduced nonlinear convergence factor and adaptive inertia weight to enhance the exploitation ability of the WOA. Finally, on the premise of ensuring the convergence speed of the algorithm, we modified the simulated annealing mechanism in order to prevent premature convergence. The benchmark function tests show that the CASAWOA outperforms other state-of-the-art WOA algorithms in terms of convergence speed and exploration ability. Furthermore, to validate the performance of ECT fault diagnosis model based on CASAWOA-RBFNN, a comprehensive analysis of eight fault diagnosis methods is conducted based on the ECT fault samples collected from the detection circuit. The experimental results show that the CASAWOA-RBFNN achieves an accuracy of 97.77% in ECT fault diagnosis, which is 9.8% better than WOA-RBF and which shows promising engineering practicality

General characteristics and Internet use of the participants (n = 2744).

General characteristics and Internet use of the participants (n = 2744).</p

Factors associated with SMF among university students.

Factors associated with SMF among university students.</p