Energy-efficient and reliable dual closed-loop DC control system for intelligent electric vehicle charging infrastructure.

This study presents an innovative dual closed-loop DC control system for intelligent electric vehicle (EV) charging infrastructure, designed to address the challenges of high power factor, low harmonic pollution, and high efficiency in EV charging applications. The research implements a three-level Pulse Width Modulation (PWM) rectifier with a diode-clamped topology and Insulated-Gate Bipolar Transistors (IGBTs), achieving a power factor of 0.99, a total harmonic distortion (THD) of 1.

Polyacrylamide/sodium alginate double network hydrogel with easily repairable superhydrophobic surface for strain sensor resistant to fluid interference.

Constructing an easily repairable hydrophobic layer on the hydrogel surface that confers resistance to liquid interference remains a great challenge for hydrogel strain sensors. In this paper, superhydrophobic hydrogel sensors were prepared by driving hydrophobic organically modified silica (o-SiO) nanoparticles to the surface of polyacrylamide/sodium alginate (PAM/SA) double network hydrogels by a weak ultrasonic field in o-SiO/cyclohexane dispersion. The hydroxyl groups present on the surface of o-SiO are able to form hydrogen bonds with hydrogels, which in turn form a strong surface hydrophobic layer on its surface.

Psychological factors and biochemical indicators influencing sleep disturbance of patients with primary biliary cholangitis in China: a cross-sectional survey analysis.

Objective: The impact of primary biliary cholangitis (PBC) on sleep disturbance is relevant to treatment decision-making processes. Studies on sleep disturbance in Chinese patients with PBC are still lacking.

Methods: We analyzed and compared the health-related quality of life (HRQoL) of 107 PBC patients by using the Pittsburgh Sleep Quality Index (PSQI) questionnaire, Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire 9 (PHQ-9), Short Form (36) Health Survey Questionnaire (SF-36), Fatigue Visual Analog Scale (F-VAS).

Using novel deep learning models for rapid and efficient assistance in monkeypox screening from skin images.

Monkeypox, a communicable disease instigated by the monkeypox virus, transmits through direct contact with infectious skin lesions or mucosal blisters, posing severe complications such as pneumonia, encephalitis, and even fatality. Traditional clinical diagnostics, heavily reliant on the discerning judgment of clinical experts, are both time-consuming and labor-intensive, with inherent infection risks, underscoring the critical need for automated, efficient auxiliary diagnostic models. In response, we have developed a deep learning classification model augmented by self-attention mechanisms and feature pyramid integration, employing attentional strategies to amalgamate image features across varying scales and assimilating knowledge from the VGG model to selectively capture salient features.