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When insulation defects arise in cables， they trigger the occurrence of sub-cycle disturbance voltage， making it imperative to extract the disturbance voltage features for timely defect detection and ensuring safe operation. To address this need， a method for extracting cable sub-cycle disturbance voltage features is introduced. Firstly， the cable’s sub-cycle disturbance voltage waveform is obtained through testing， and two characteristic parameters， namely time and amplitude， are defined for the disturbance waveform. Subsequently， voltage amplitude changes are rapidly tracked using the strong tracking Kalman filter （STKF）. Adaptive parameters are established to suppress fluctuations in the parameters of the Gaussian mixture model （GMM）， facilitating the extraction of disturbance voltage characteristic parameters through the combined use of STKF and GMM. The algorithm is then tested using disturbance voltage data obtained from experiments， and the effects of adaptive parameters， sampling frequency， and the detection algorithm’s dead zone within the Gaussian mixture model on the extraction of characteristic parameters are analyzed. MATLAB simulation results demonstrate that the computational outcomes remain relatively stable when the voltage sampling frequency is set at 2.25 kHz and above. This validation underscores the method’s capability to extract characteristic parameters related to cable sub-cycle disturbance voltage.

Effective prevention strategies for post-COVID complications are crucial for patients, clinicians, and policy makers to mitigate their cumulative burden. This study evaluated the association of modifiable lifestyle factors (smoking, alcohol intake, BMI, physical activity, sedentary time, sleep duration, and dietary habits) with COVID-19 multisystem sequelae, death, and hospitalization in the UK Biobank cohort (n = 68,896). A favorable lifestyle (6-10 healthy factors; 46.4%) was associated with a 36% lower risk of multisystem sequelae (HR, 0.64; 95% CI, 0.58-0.69; ARR at 210 days, 7.08%; 95% CI, 5.98-8.09) compared to an unfavorable lifestyle (0-4 factors; 12.3%). Risk reductions spanned all 10 organ systems, including cardiovascular, coagulation, metabolic, gastrointestinal, kidney, mental health, musculoskeletal, respiratory disorders, and fatigue. This beneficial effect was largely attributable to direct lifestyle impacts independent of corresponding pre-infection comorbidities (71% for any sequelae). A favorable lifestyle was also related to the risk of post-COVID death (HR 0.59, 0.52-0.66) and hospitalization (HR 0.78, 0.73-0.84). These associations persisted across acute and post-acute infection phases, irrespective of hospitalization status, vaccination, or SARS-CoV-2 variant. These findings underscore the clinical and public health importance of adhering to a healthy lifestyle in mitigating long-term COVID-19 adverse impacts and enhancing future pandemic preparedness. The impact of modifiable lifestyle factors on the risk of post-COVID-19 sequelae is not well understood. Here, the authors perform a prospective cohort study amongst UK Biobank participants and show that a healthy lifestyle is associated with lower risk of post-COVID-19 sequelae across multiple organ systems. [ABSTRACT FROM AUTHOR]

Background: Townes-Brocks syndrome (TBS) is a rare genetic disorder characterized by imperforate anus, dysplastic ears, thumb malformations, and other abnormalities. Previous studies have revealed that mutations in the SALL1 gene can disrupt normal development, resulting in the characteristic features of Townes-Brocks syndrome. Spalt-like transcription factors (SALLs) are highly conserved proteins that play important roles in various cellular processes, including embryonic development, cell differentiation, and cell survival. Over 400 different variants or mutations have been reported in the SALL1 gene in individuals with TBS. Most of these variants lead to the formation of premature termination codons (PTCs), also known as nonsense mutations. The majority of these PTCs occur in a specific region of the SALL1 gene called the "hotspot region", which is particularly susceptible to mutation. Methods: In this study, we conducted whole-exome sequencing on a three-generation Chinese family with anorectal malformations. Results: We identified a novel heterozygous mutation (chr16:51175376:c.757 C > T p.Gln253*) in the SALL1 gene. Molecular analysis revealed a heterozygous C to T transition at nucleotide position 757 in exon 2 of the SALL1 (NM_002968) gene. This mutation is predicted to result in the substitution of the Gln253 codon with a premature stop codon (p.Gln253*). The glutamine-rich domain forms a long alpha helix, enabling the mutant protein to interact with the wild-type SALL1 protein. This interaction may result in steric hindrance effects on the wild-type SALL1 protein. Conclusions: Our findings have expanded the mutation database of the SALL1 gene, which is significant for genetic counseling and clinical surveillance in the affected family. Furthermore, our study enhances the understanding of Townes-Brocks syndrome and has the potential to improve its diagnosis and treatment. [ABSTRACT FROM AUTHOR]

The parameter variations of magnetically coupled resonant wireless power transmission (MCR‐WPT) system cause frequency splitting and resonant frequency shift, which lead to the system detuning and reduce the transmission efficiency. To solve the problems, a frequency tracking control method based on the phase difference between the primary‐side voltage and the secondary‐side current (Frequency Tracking Control‐the primary‐side voltage and the secondary‐side current, FTC‐PS) in this paper. According to the characteristics that the phase difference between the voltage of transmitting side and the current of receiving side is not affected by frequency splitting, the phase difference is controlled at 90°, so that the system can achieve the maximum transmission efficiency under the condition of frequency splitting and resonant frequency shift. The experimental results show that the method can make the system always work at the natural resonant frequency when the parameters change, and the transmission efficiency is not affected by the frequency splitting phenomenon. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Fault diagnosis plays an essential role in maintaining the safe and stable operation of hydroelectric units. In this paper, an intelligent fault diagnosis method based on radar maps and improved GoogleNet by depthwise separate convolution (DSC) is proposed to address the problems of low recognition accuracy and weak computing speed of fault diagnosis models in the field of hydroelectric unit fault diagnosis at present. Firstly, a one-dimensional signal sequence is obtained and denoised. Secondly, five time-domain features are extracted and radar maps are plotted. Then, an improved GoogleNet intelligent fault diagnosis model based on DSC (DSC-GoogLeNet) is constructed for training and validation. To assess the effectiveness of the proposed model, two case studies are conducted using the simulated dataset of the rotor experimental bench and the actual measured dataset of a domestic hydroelectric power plant. The results demonstrate that the average recognition accuracy of the fault diagnosis method proposed in this paper is as high as 99.04% on the simulated dataset, and even though the recognition accuracy decreases on the actually measurement dataset, it still has a recognition rate of 98.79%. The fault diagnosis performance is better than the other types of comparison models. The results demonstrate that the proposed fault diagnosis method holds significant engineering applicability in the domain of safe operation of hydroelectric units. It effectively addresses the existing challenges in fault diagnosis within this field with accuracy, stability, and efficiency. [ABSTRACT FROM AUTHOR]

In the practical production environment, the complexity and variability of hydroelectric units often result in a need for more fault data, leading to inadequate accuracy in fault identification for data-driven intelligent diagnostic models. To address this issue, this paper introduces a novel fault diagnosis method tailored for unbalanced small-sample states in hydroelectric units based on the Wasserstein generative adversarial network (W-GAN). Firstly, the fast Fourier transform is used to convert the signal from the time domain to the frequency domain to obtain the spectral data, and the W-GAN is trained to generate false spectral data with the same probability distribution as the real fault data, which are combined with the actual data and inputted into the 1D-CNN for feature extraction and fault diagnosis. In order to assess the effectiveness of the proposed model, a case study was conducted using actual data from a domestic hydropower plant, and the experimental results show that the sample features can be effectively enriched via data enhancement performed on small-sample data to improve the accuracy of fault diagnosis, which verifies the effectiveness of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

Aims Pulsed-field ablation (PFA) is a promising new ablation modality to treat atrial fibrillation. However, PFA can cause varying degrees of diaphragmatic contraction and dry cough, especially under conscious sedation. This prospective study presents a method to minimize the impact of PFA on diaphragmatic contraction and dry cough during the procedure. Methods and results Twenty-eight patients underwent PFA for pulmonary vein (PV) and superior vena cava isolation under conscious sedation. Each patient received two groups of ablations in each vein: the control group allowed PFA application during any phase of respiratory cycle, while the test group used respiratory control, delivering PFA energy only at the end of expiration. A rating score system was developed to assess diaphragmatic contraction and dry cough. A total of 1401 control ablations and 4317 test ablations were performed. The test group had significantly lower scores for diaphragmatic contraction (P < 0.01) and dry cough (P < 0.001) in all PVs compared to the control group. The average relative reductions in scores for all PVs were 33–47% for diaphragmatic contraction and 67–83% for dry cough. The percentage of ablations with scores ≧2 for diaphragmatic contraction decreased significantly from 18.5–28.0% in the control group to 0.4–2.6% in the test group (P < 0.001). For dry cough, the percentage decreased from 11.9–43.7% in the control group to 0.7–2.1% in the test group. Conclusion Pulsed-field ablation application at the end of expiration can reduce the severity of diaphragmatic contraction and eliminate moderate and severe dry cough during PV isolation performed under conscious sedation. [ABSTRACT FROM AUTHOR]