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Moisture is the primary factor leading to the deterioration of transformer oil. Real-time detection of transformer oil moisture content holds significant importance to help power companies monitor the transformer’s operational status, track the change curve of the oil moisture content, identify water ingress causes, and implement preventive measures. However, transformer oil has the characteristics of low moisture content and complex chemical composition, which pose a substantial challenge for sensor monitoring, and require the prepared sensor to be chemically resistant and highly sensitive to moisture. To address these challenges, in this paper, a parallel plate oil moisture sensor with high sensitivity and high chemical resistance based on PES and PI was developed using the MEMS process. The sensor incorporates PES as the chemical resistance layer and PI as the moisture sensitivity layer. The parallel plate design not only optimizes sensitivity by effectively utilizing electric field lines but also allows the passage of water while isolating oil molecules, further enhancing the chemical resistance of the sensor. Experimental results in air demonstrate that the prototype sensor exhibits high sensitivity (~1.26 pF/% RH) across the full humidity range of 0-100% RH. Moreover, the sensor can withstand exposure to a wide range of chemical corrosive gases, including acetone, ammonia, etc. Tests in transformer oil reveal that the sensor has a resolution better than 200 ppm and can effectively measure moisture in the range of 0-1400 ppm, demonstrating very high sensitivity (4 pF/100 ppm) and rapid response (10/23 s).

A direct bidirectional current discharge path between the input/output (I/O) and ground (GND) is essential for the robust protection of charging device models (CDM) in the tightly constrained design parameters of advanced low-voltage (LV) processes. Dual-direction silicon controlled rectifiers (DDSCRs) serve as ESD protection devices with high efficiency unit area discharge, enabling bidirectional electrostatic protection. However, the high trigger voltage of conventional DDSCR makes it unsuitable for ASICs used for the preamplification of biomedical signals, which only operate at low supply voltage. To address this issue, a self-biased triggered DDSCR (STDDSCR) structure is proposed to further reduce the trigger voltage. When the ESD pulse comes, the external RC trigger circuit controls the PMOS turn-on by self-bias, and the current release path is opened in advance to reduce the trigger voltage. As the ESD pulse voltage increases, the SCR loop opens to establish positive feedback and drain the amplified current. Additionally, the junction capacitance is decreased through high-resistance epitaxy and low-concentration P-well injection to further lower the trigger voltage. The simulation results of LTspice and TCAD respectively demonstrate that ESD devices can clamp transient high voltages earlier, with low parasitic capacitance and leakage current suitable for ESD protection of high-speed ports up to 1.5 V under normal operating conditions.

In recent years, Vision Transformers (ViTs) have gained significant traction in the field of hyperspectral image classification due to their advantages in modeling long-range dependency relationships between spectral bands and spatial pixels. However, after stacking multiple Transformer encoders, challenges pertaining to information degradation may emerge during the forward propagation. That is to say, existing Transformer-based methods exhibit certain limitations in retaining and effectively utilizing information throughout their forward transmission. To tackle these challenges, this paper proposes a novel dual-branch spatial–spectral Transformer model that incorporates similarity propagation (DBSSFormer-SP). Specifically, this model first employs a Hybrid Pooling Spatial Channel Attention (HPSCA) module to integrate global information by pooling across different dimensional directions, thereby enhancing its ability to extract salient features. Secondly, we introduce a mechanism for transferring similarity attention that aims to retain and strengthen key semantic features, thus mitigating issues associated with information degradation. Additionally, the Spectral Transformer (SpecFormer) module is employed to capture long-range dependencies among spectral bands. Finally, the extracted spatial and spectral features are fed into a multilayer perceptron (MLP) module for classification. The proposed method is evaluated against several mainstream approaches on four public datasets. Experimental results demonstrate that DBSSFormer-SP exhibits excellent classification performance.

Osteoarthritis (OA) is a prevalent clinical condition affecting the entire joint, characterized by its multifactorial etiology and complex pathophysiology. The onset of OA is linked to inflammatory mediators produced by the synovium, cartilage, and subchondral bone, all of which are closely tied to cartilage degradation. Consequently, OA may also be viewed as a systemic inflammatory disorder. Emerging studies have underscored the significance of T cells in the development of OA. Notably, imbalances in Th1/Th2 and Th17/Treg immune cells may play a crucial role in the pathogenesis of OA. This review aims to compile recent advancements in understanding the role of T cells and their Th/Treg subsets in OA, examines the immune alterations and contributions of Th/Treg cells to OA progression, and proposes novel directions for future research, including potential therapeutic strategies for OA.

Bilayer graphene can host topological currents that are robust against defects and are associated with the electron valleys. It is now shown that electric fields can tune this topological valley transport over long distances at room temperature. Valley pseudospin, the quantum degree of freedom characterizing the degenerate valleys in energy bands 1 , is a distinct feature of two-dimensional Dirac materials 1 , 2 , 3 , 4 , 5 . Similar to spin, the valley pseudospin is spanned by a time-reversal pair of states, although the two valley pseudospin states transform to each other under spatial inversion. The breaking of inversion symmetry induces various valley-contrasted physical properties; for instance, valley-dependent topological transport is of both scientific and technological interest 2 , 3 , 4 , 5 . Bilayer graphene is a unique system whose intrinsic inversion symmetry can be controllably broken by a perpendicular electric field, offering a rare possibility for continuously tunable topological valley transport. We used a perpendicular gate electric field to break the inversion symmetry in bilayer graphene, and a giant nonlocal response was observed as a result of the topological transport of the valley pseudospin. We further showed that the valley transport is fully tunable by external gates, and that the nonlocal signal persists up to room temperature and over long distances. These observations challenge the current understanding of topological valley transport in a gapped system, and the robust topological transport may lead to future valleytronic applications.

Objective In this systematic review and meta-analysis, we evaluated and compared the efficacy and safety of platelet-rich plasma injection into corticosteroid injection in the treatment of tendinopathy. Methods We searched PUBMED, EMBASE, Cochrane Library, SCOPUS, and Web of Science to identify randomized controlled trials on the PRP injection versus CS injection in treatment of tendinopathy.The meta-analysis was performed using the Revman 5.4 software. Result We found 27 RCT studies with a total of 1779 patients enrolled. 8 rotator cuff injuries, 7 humeral external epicondylitis, 10 plantar fasciitis, and 2 tenosynovitis. The results of the meta-analysis showed that there were no significant group differences in the results of patients with rotator cuff injury comparing the pain visual analog scale score and functional measures at 1 month after receiving injection treatment. After three months of receiving PRP treatment, the VAS scores showed greater improvement compared to the CS group(OR = -1.64,95%CI [-2.97,-0.31], P  = 0.02), while there was no statistically significant difference in shoulder joint function between the two groups at the 3–6 month post-treatment mark. Patients with plantar fasciitis showed no significant differences in VAS and AOFAS scores after receiving PRP or CS injections at 1 and 3 months. However, at the 6-month mark, the PRP group demonstrated significantly better VAS and AOFAS scores compared to the CS group(OR = -1.41,95%CI [-1.88,-0.44], P  < 0.00001; OR = 7.19,95%CI [2.41,11.91], P  = 0.003). 1 month after CS injection in patients with tenosynovitis, the VAS score was lower than that of the PRP group; patients with elbow epicondylitis had better improved upper limb function rating scale scores 1 month after CS injection compared to the PRP group. In patients with tenosynovitis, the VAS scores were superior to the CS group six months after PRP treatment(OR = -0.72,95%CI [-1.04,-0.40], P  < 0.00001); similarly, patients with lateral epicondylitis exhibited better VAS, DASH scores than the CS group three and twelve months post-PRP treatment(OR = -9.76,95%CI [-10.89,-8.63], P  = 0.0002; OR = -0.97,95%CI [-1.87,-0.06], P  < 0.0001; OR = -18.03,95%CI [-31.61,-4.46], P  = 0.009). Conclusion PRP can effectively improve pain and functional impairment in patients with tendinopathy, and its mid-term efficacy is superior to that of corticosteroids. However, the long-term efficacy remains to be further clinically verified.

The availability of an ever‐expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi‐dimensional parameter space and massive data sets involved is emblematic of complex, resource‐intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data‐driven approach and subset of artificial intelligence, is a potential game‐changer, enabling a cheaper – yet more efficient – alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine‐assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area. The family of 2D materials is an unprecedented platform for materials by design, thanks to their ever‐expanding material portfolio with rich internal degrees of freedom. The study provides a comprehensive overview of the recent progress, challenges and emerging opportunities in a frontier research area that exploits machine learning—a very powerful data‐driven approach and subset of artificial intelligence—for 2D materials.

The toxicity of lead ions in halide perovskite absorbing materials is the main bottleneck for practical application. To replace the traditional lead halide perovskites by environmental friendly double perovskite, computational tools based on density functional theory were employed to predict the intrinsic properties of potential double perovskites to efficiently and rapidly find more double perovskites with properties suitable for optoelectronic applications. Screening homovalent alternatives for B and X‐site ions in vacancy‐ordered double perovskite Cs2BX6 for solar cell applications and photocatalyst was done using Perdew–Burke–Ernzerhof and Heyd–Scuseria–Ernzerhof functional with spin‐orbit coupling. Three empirical factors and formation enthalpy were used to evaluate the stability of 30 materials at different temperatures. Finally, the Cs‐based vacancy‐ordered double perovskites with suitable bandgap for optoelectronic applications can thus be obtained. Using computational techniques, this study can also provide theoretical guidance for the rational design of possible double perovskite materials with improved photocatalytic characteristics. Screening homovalent alternatives for B and X‐site ions in vacancy‐ordered double perovskite Cs2BX6 for solar cell applications and photocatalyst was done using Perdew–Burke–Ernzerhof and Heyd–Scuseria–Ernzerhof functional with spin‐orbit coupling. Three empirical factors and formation enthalpy were used to evaluate the stability of 30 materials at different temperatures. Finally, the Cs‐based vacancy‐ordered double perovskites with suitable bandgap for optoelectronic applications can thus be obtained. Using computational techniques, this study can also provide theoretical guidance for the rational design of possible double perovskite materials with improved photocatalytic characteristics.

This paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density.A multi-laboratory study in the form of a community challenge assesses the quality of models that can be produced from cryo-EM maps using different software tools, the reproducibility of models generated by different users and the performance of metrics used for model validation.

The underlying mechanism of chronic hepatitis B virus (HBV) functional cure by interferon (IFN), especially in patients with low HBsAg and/or young ages, is still unresolved due to the lack of surrogate models. Here, we generate a type I interferon receptor humanized mouse (huIFNAR mouse) through a CRISPR/Cas9-based knock-in strategy. Then, we demonstrate that human IFN stimulates gene expression profiles in huIFNAR peripheral blood mononuclear cells (PBMCs) are similar to those in human PBMCs, supporting the representativeness of this mouse model for functionally analyzing human IFN in vivo. Next, we reveal the tissue-specific gene expression atlas across multiple organs in response to human IFN treatment; this pattern has not been reported in healthy humans in vivo. Finally, by using the AAV-HBV model, we test the antiviral effects of human interferon. Fifteen weeks of human PEG-IFNα2 treatment significantly reduces HBsAg and HBeAg and even achieves HBsAg seroconversion. We observe that activation of intrahepatic monocytes and effector memory CD8 T cells by human interferon may be critical for HBsAg suppression. Our huIFNAR mouse can authentically respond to human interferon stimulation, providing a platform to study interferon function in vivo. PEG-IFNα2 treatment successfully suppresses intrahepatic HBV replication and achieves HBsAg seroconversion. There is increasing evidence that treatment of hepatitis B with interferon alpha can be beneficial. Here, Wang et al, present a type 1 interferon receptor humanized mouse model and characterize it as a platform in which to study interferon function in vivo.