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Antibody-mediated autoimmune pathologies like membranous nephropathy are difficult to model, particularly in the absence of local target antigen expression in model organisms such as mice and rats; as is the case for phospholipase A2 receptor 1 (PLA2R1), the major autoantigen in membranous nephropathy. Here, we generated a transgenic mouse line expressing the full-length human PLA2R1 in podocytes, which has no kidney impairment after birth. Beginning from the age of three weeks, these mice spontaneously developed anti-human PLA2R1 antibodies, a nephrotic syndrome with progressive albuminuria and hyperlipidemia, and the typical morphological signs of membranous nephropathy with granular glomerular deposition of murine IgG in immunofluorescence and subepithelial electron-dense deposits by electron microscopy. Importantly, human PLA2R1-expressing Rag2–/–mice, which lack mature and functioning B and T lymphocytes, developed neither anti-PLA2R1 antibodies nor proteinuria. Thus, our work demonstrates that podocyte expression of human PLA2R1 can induce membranous nephropathy with an underlying antibody-mediated pathogenesis in mice. Importantly, this antibody-mediated model enables proof-of-concept evaluations of antigen-specific treatment strategies, e.g., targeting autoantibodies or autoantibody-producing cells, and may further help understand the autoimmune pathogenesis of membranous nephropathy.

The aim of this study is to establish an objective evaluation method for the VR glasses in the design process using fit test panels. Based on head width-length index and head height-length index in Chinese head and face 3D database, we get 9 categories using Fuzzy C-Means Clustering (FCM). Then, a fit test panels including 20 samples was established. According to the subjective evaluation of 20 subjects, the fit assessment scale of 5 regions was established. Then, the virtual fit assessment of a digital model of VR glasses padding in the design process and the traditional expert experience evaluation of its 3D printing model are carried out. The Results obtained by the objective evaluation method and the subjective evaluation were consistent. Results showed that the objective evaluation method was effective and had the characteristics of simplicity, rapidity and accuracy. It provides an effective decision tool for fitness prediction and improvement of the VR glasses padding in the design process.

CFRP circular cell honeycomb is becoming increasingly important in aerospace due to its low density, high specific stiffness, impact resistance and microwave absorption performance. However, high-quality machining of CFRP circular cell honeycomb still represents a significant challenge due to its anisotropic, heterogeneity and discontinuous characteristics. This paper is presented to summarize the machining damage and analyze its cause. In this paper, the exit angle and MRR are defined to describe the effect of discontinuous structure on material removal process, the machining damage of CFRP circular cell honeycomb is classified based on the morphology and scale, the end face machining experiments are carried out to study the effect of machining method (milling and grinding), geometry of tools, exit angle and MRR on damage. The results show that milling produces larger tearing and less burr than grinding. The exit angle influences the curvature radius of fiber that determines fiber fracturing significantly, and side face burr occurs as exit angle and curvature radius of fiber increases. The MRR is larger in cut in and cut out stage of circular cell, tearing and delamination occurs respectively, but the chamfer or fillet of the grinding wheel tetrad material removal process and strength the stiffness of machining area, damage was suppressed. Considering the frequency and the size of damage may occur systematically, grinding wheel with chamfer or fillet is recommended to process CFRP circular cell honeycomb.

In the multi-cutting of metallic materials, the machined surface modification generated in the preceding cutting process, could further influence the cutting characteristic in the subsequent cutting. This paper investigates the effect of the work-hardened layer (WHL) on the subsequent cutting characteristics in the multi-cutting process of 49Fe49Co2V alloy. Firstly, the WHL features in terms of dislocation density, micro-hardness, and plastic deformation in the subsurface are studied experimentally. A modified Johnson-Cook (MJC) constitutive model with the consideration of dislocation density is developed to describe the dynamic behavior for the WHL. The FE model of an orthogonal cutting process is developed with the implementation of MJC model. In this model, the preceding cutting-induced dislocation density distribution is defined in the WHL. The cutting force is measured experimentally and used to verify the FE model. The cutting force, cutting temperature and stress distribution for the WHL and the bulk material are compared. It is found that in the multi-cutting with small cutting depths, the effect of dislocation density distribution in WHL on the cutting characteristics should be taken into consideration. The study could provide guidance to determination of the machining parameters in the multi-cutting.

To solve the problem of infrared (IR) small target tracking loss or error caused by factors such as scale changes, motion blur, and occlusion, this article proposes a multistrategy fusion tracking algorithm using an IR small target segmentation network (IRSTNet) as the detection head, which mainly includes six strategies: target pixel clustering, target feature threshold adjustment, large-area search, small-area tracking, gate tracking, and coordinate solution. First, candidate targets are obtained through the IRSTNet and pixel clustering strategy. Second, the range of candidate targets is further reduced through a threshold adjustment strategy. Then, real-time tracking of IR small targets is achieved through large-area search, small-area tracking, and wave gate tracking strategies. Finally, the longitude, latitude, and altitude of the tracked target are obtained through the coordinate calculation strategy. Both qualitative and quantitative experiments based on real IR small target sequences verify that our algorithm can achieve more satisfactory performances in terms of success rate, precision, and robustness compared with other typical visual trackers. In addition, we have deployed the tracking algorithm proposed in this study on the Orange Pi 5 embedded platform, and the tracking speed meets the real-time requirements.

The world’s electric power grids have evolved over the last 120 years from a single power line to today’s large networks. The evolution will continue at an accelerated rate with extensive smart grid development worldwide. For example, the U.S. government has set a goal of reaching 100% carbon pollution-free electricity by 2035, while the U.S. Department of Energy shared a goal to deploy 30 and 110 GW of offshore wind by 2030 and 2050, respectively. To meet such ambitious goals, a significant percentage of electricity will come from intermittent renewable sources and be supplied to a vast number of loads that will actively respond to grid conditions and incentive signals. This development is largely driven by environmental and economic factors, such as reducing carbon emissions and reducing electricity costs for consumers. These energy resources and responsive loads lead to new uncertain behaviors and dynamics that the grid has never seen and that were not considered in its design. Operating such a dynamic grid with sufficient reliability and efficiency is a monumental challenge. Figure 1 presents three challenges faced by software tools used in power systems planning and operation along with solutions.

Accurate models of generation systems are critical for maintaining reliable and secure grid operations. In this paper, a novel and systematic approach is proposed to identify and calibrate the generation system problematic parameters using continuous wavelet transform (CWT) and advanced deep-learning technology. The phasor measurement unit (PMU) data are used through “event playback” to check whether the parameter calibration is required, and if yes, a group of suspicious parameters will be identified as the primary problematic parameter candidates (PPCs). These primary PPCs are randomly perturbed to generate the event playback simulation data, which are used by the CWT and convolutional neural networks (CNNs) to further narrow down the primary PPCs into a smaller set of candidates. Then, the identified candidates are perturbed again to generate massive event playback simulation data for training a parameter calibration neural network. We designed a multi-output neural network structure to find the mappings between the perturbed parameters and the simulation data using both CNN and long short-term memory (LSTM) models. Finally, the well-trained and tested CNN-LSTM model is used to estimate the accurate value of the suspicious parameters with actual PMU measurements. The proposed CNN-LSTM network can accurately and reliably estimate the generation-system problematic parameters, and has better performance when compared to other machine-learning methods, such as the multilayer perceptron network and the conditional variational autoencoder method. The accuracy and effectiveness of the proposed approach have been validated through simulation and real-world data.

The roughness of the contact surface exerts a vital role in rubbing. It is still a significant challenge to understand the microscopic contact of the rough surface at the atomic level. Herein, the rough surface with a special root mean square (RMS) value is constructed by multivariate Weierstrass–Mandelbrot (W–M) function and the rubbing process during that the chemical mechanical polishing (CMP) process of diamond is mimicked utilizing the reactive force field molecular dynamics (ReaxFF MD) simulation. It is found that the contact area A/A0is positively related with the load, and the friction force Fdepends on the number of interfacial bridge bonds. Increasing the surface roughness will increase the friction force and friction coefficient. The model with low roughness and high lubrication has less friction force, and the presence of polishing liquid molecules can decrease the friction force and friction coefficient. The RMS value and the degree of damage show a functional relationship with the applied load and lubrication, i.e., the RMS value decreases more under larger load and higher lubrication, and the diamond substrate occurs severer damage under larger load and lower lubrication. This work will generate fresh insight into the understanding of the microscopic contact of the rough surface at the atomic level.

Recently, noncontact temperature measurement methods based on infrared face perception have received widely attentions since fever screening plays an important role in the early prediction of respiratory infections, such as SARS, H1N1, and COVID-19. However, the performance of these methods always significantly degrades when facing the changes of environment. Thus, the majority of these methods leverage the block-body and sensors to reduce the influence of environment changes. It is a pity that the increased instrument complexity leads to higher costs and failure rate. To address the aforementioned issues, this article presents a novel fever screening method, named dynamic group difference coding (DGDC), which is based on the analysis about the influencing factors. The key idea of DGDC is to compute the temperature differences between the target person and the recently passed crowd (dynamic group). Specifically, we develop the face temperature encoder (FTE) to describe the face temperature and thus construct the difference matrix of the embedding feature between the target person and the dynamic group. Multilayer perceptions (MLP) are employed to capture the intrinsic information by characterizing the difference matrix in vertical and horizontal directions, respectively. Finally, we provide a dataset of thermal infrared face (TIF) images and conduct extensive experiments to demonstrate the advantages of the proposed method over the competing methods.

ObjectiveTo determine, among patients with axial spondyloarthritis (axSpA), whether the risk of inflammatory bowel disease (IBD) varies between patients treated with biologic therapies and those treated with other therapies and, specifically, whether the risk is higher in patients treated with etanercept (ETN).MethodsThe British Society for Rheumatology Biologics Register in Ankylosing Spondylitis (BSRBR-AS) was used to determine the incidence of IBD during follow-up and to calculate the incidence rate difference (IRD) per 1000 person-years (PY), between biologic treatment and other treatment groups. We then conducted a systematic review, involving observational studies and randomized controlled trials (RCTs), to perform a metaanalysis to quantify the difference in incidence of IBD between treatment groups.ResultsAccording to the BSRBR-AS, among people with axSpA, exposure to biologic therapy was associated with an increased incidence of IBD compared to those who were not exposed to biologic therapy (IRD 11.9, 95% CI 4.3-19.6). This finding was replicated across observational studies but was not seen in placebo-controlled RCTs (IRD 2.2, 95% CI −4.1 to 8.5). Data from the BSRBR-AS do not suggest that excess incidence of IBD is associated with exposure to ETN compared to other anti–tumor necrosis factor (TNF) therapies (IRD −6.5, 95% CI −21.3 to 8.5). RCTs and their extensions suggest a small—yet not statistically significant—absolute increased incidence associated with ETN of between 2.1 and 5.8 per 1000 PY compared to other anti-TNF therapies.ConclusionThere was an excess risk of IBD among persons treated with biologics in observational studies. Only evidence from RCTs suggested that ETN was associated with an increased risk compared to other anti-TNF therapies, albeit with considerable uncertainty.

Efficiently and accurately separating infrared (IR) small targets from complex backgrounds presents a significant challenge. Numerous studies in the literature have proposed various feature fusion modules designed specifically to enhance the extraction of IR small target features. While these designs offer some incremental improvement to the accuracy of IR small target detection, they come at a steep cost of significantly increasing network parameters and floating-point operations per second (FLOPs). Striving for a balance between computational efficiency and model accuracy, we decided to forgo these complex feature fusion modules. Instead, we developed a new lightweight encoding and decoding structure known as the lightweight IR small target segmentation network (LW-IRSTNet). This structure integrates regular convolutions, depthwise separable convolutions, atrous convolutions, and asymmetric convolutions modules. In addition, we devised postprocessing modules, including an eight-neighborhood clustering algorithm and an online target feature adjustment strategy. Experimental results indicate that: 1) the segmentation accuracy metrics of LW-IRSTNet match the best results of 14 state-of-the-art comparative baselines; 2) the parameters and FLOPs of LW-IRSTNet at only 0.16M and 303M, respectively, are significantly smaller in comparison to these baselines; and 3) the postprocessing modules enhance both user-friendliness and the robustness of algorithm deployment. Moreover, LW-IRSTNet has been successfully implemented on both embedded platforms and websites, expanding its range of applications. Utilizing the open neural network exchange (ONNX) framework, neural network processing unit (NPU) acceleration, and CPU multithreaded resource allocation, we have been able to achieve high-performance inference capabilities, as well as online dynamic threshold adjustment with the LW-IRSTNet. The source codes for this project can be accessed at https://github.com/kourenke/LW-IRSTNet.