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Abstract Image classification algorithms are commonly based on the Independent and Identically Distribution (i.i.d.) assumption, but in practice, the Out‐Of‐Distribution (OOD) problem widely exists, that is, the contexts of images in the model predicting are usually unseen during training. In this case, existing models trained under the i.i.d. assumption are limiting generalisation. Causal inference is an important method to learn the causal associations which are invariant across different environments, thus improving the generalisation ability of the model. However, existing methods usually require partitioning of the environment to learn invariant features, which mostly have imbalance problems due to the lack of constraints. In this paper, we propose a balanced causal learning framework (BCL), starting from how to divide the dataset in a balanced way and the balance of training after the division, which automatically generates fine‐grained balanced data partitions in an unsupervised manner and balances the training difficulty of different classes, thereby enhancing the generalisation ability of models in different environments. Experiments on the OOD datasets NICO and NICO++ demonstrate that BCL achieves stable predictions on OOD data, and we also find that models using BCL focus more accurately on the foreground of images compared with the existing causal inference method, which effectively improves the generalisation ability.

Abstract Aims/Introduction Regulatory T cells (Tregs) have protected against many cardiovascular diseases. This study was intended to explore the effect of Tregs on diabetic cardiomyopathy (DCM) using a db/db mouse model. Materials and methods Eight‐week‐old male db/db mice were randomly divided into four groups: the control group, administered 200 μL phosphate‐buffered saline; the small‐dose Treg group, administered 105 Tregs; the large‐dose Treg group, administered 106 Tregs; and the PC group, administered 100 μg anti‐CD25 specific antibody (PC61) and 106 Tregs. After 12 weeks, mice were euthanized. Transthoracic echocardiography was carried out at the beginning and end of the experiment. Relevant basic experiments to evaluate the effects of Tregs on DCM were carried out. Results Echocardiography showed that the impaired diastolic and systolic functions were significantly improved in mice administered large‐dose Tregs. Large‐dose Tregs significantly ameliorated myocardial hypertrophy and fibrosis, and decreased hypertrophic gene expression and collagen deposition. The protective effects of Tregs on diabetic hearts were associated with decreased oxidative stress, inflammatory response and apoptosis. In addition, Tregs promoted the activation of the phosphatidylinositol 3‐kinase–protein kinase B signaling pathway, whereas they inhibited extracellular signal‐regulated kinase 1/2 and Jun N‐terminal kinase phosphorylation, which might be responsible for the cardioprotective role of Tregs against DCM. Conclusions Tregs ameliorated myocardial hypertrophy and fibrosis, improved cardiac dysfunction, and protected against DCM progression in db/db mice. The mechanisms involved a decrease of inflammatory response, oxidative stress and apoptosis, which might be mediated by phosphatidylinositol 3‐kinase–protein kinase B and mitogen‐activated protein kinase pathways. Hence, Tregs might serve as a promising therapeutic approach for DCM treatment.

Abstract As a protective component of HV equipment, the primary function of a surge arrester is to mitigate the impact of surge voltages. When a surge arrester fails, the equipment it protects becomes vulnerable to damage. This study integrates chaotic systems with Convolutional Neural Networks (CNN) to diagnose faults in HV surge arresters. The Partial Discharge (PD) test was initially performed on six HV surge arrester fault models. The Discrete Wavelet Transform (DWT) was performed for filtering the PD signals. Subsequently, the Chen‐Lee chaotic system converted the filtered PD signals into a dynamic error scatter diagram, creating a feature map of various fault states. This feature map was then used as the input layer to train the CNN model. The results demonstrate that the proposed CNN achieved an accuracy of 97.0%, outperforming AlexNet and traditional methods using Histograms of Oriented Gradients (HOG) combined with Support Vector Machine (SVM), Decision Tree (DT), Backpropagation Neural Network (BPNN), and K‐Nearest Neighbor (KNN). This study also incorporates the LabVIEW graphic control software with a fault diagnosis system for HV surge arresters. The PD data can identify the fault type in real‐time, enhancing power equipment maintenance efficiency.

Abstract To address the problem that the process deviation of dry‐type air‐core reactors cannot be effectively detected, electrical parameters of reactor are numerically analysed at different frequencies. A new method of process deviation detection by the power factor frequency characteristic is proposed. Considering on‐site power frequency interference, a harmonic analysis method is proposed, which is based on the integer multiple of the power frequency cycle, to calculate the power factor. The results show that the resistance loss remains relatively constant at power frequency and gradually increases at higher frequencies with the number of deviation turns increases. The eddy current loss remains basically unchanged at different turn deviations and frequencies. The change laws of resistance loss and the resistance loss power factor are the same. In a double logarithmic coordinate system, resistance loss power factor changes from linear to non‐linear. The non‐linearity degree can be used to detect the process deviation. The test results are consistent with the theory, and the correctness of the detection method is verified.

Abstract Infrared and visible image fusion involves merging the advantages of infrared and visible images to generate a composite image that encompasses thermal radiation as well as intricate texture details. Infrared and visible image fusion has garnered increasing attention, with numerous fusion methods proposed. However, how to fairly perceive the performance of fused image remains a contentious topic. This paper is dedicated to solving this problem from two perspectives (e.g., subjective and objective aspects). Firstly, an infrared and visible fusion image quality assessment dataset was constructed, including 60 pairs of infrared and visible images captured in various scenes, along with 540 fusion images with different types and degrees of distortions. Additionally, a subjective evaluation dataset of 16,200 subjective scores by 30 participants was further provided for the fused image. Secondly, to overcome the challenging assessment for infrared and visible fusion images without a real reference image, an interesting multi‐domain pseudo‐reference image quality assessment model (MPIQAM) is proposed, by comprehensively considering the thermal radiation information distortion, texture information distortion, and overall naturalness of the fused image. The proposed MPIQAM was compared with 18 mainstream objective metrics, and the experimental findings showcased a commendable level of competitiveness.

Abstract This paper addresses the challenge of maximizing power capture from new energy sources, including coal, wind, solar, and hydroelectric power, which often lack sufficient inertia support. This deficiency can lead to frequency instability and cascading failures within the power system. A cooperative optimization model for the start‐up of multiple power supplies, designed to enhance the integration of new energy sources while maintaining system stability is proposed. The model incorporates primary frequency modulation and the intrinsic inertia support capabilities of self‐synchronous voltage source field stations, considering dynamic frequency constraints. Additionally, it employs new energy units with primary frequency modulation to provide inertia support during curtailment, particularly when conventional units cannot meet frequency standards due to existing constraints. Extensive simulations and comparative analyses demonstrate that the proposed model improves new energy utilization by up to 37.5% and reduces operational costs by approximately 16%, enhancing overall operational efficiency in high energy consumption scenarios.

Abstract In an era characterized by the rapid proliferation of distributed flexible resources (DFRs), the development of customized energy management and regulation strategies has attracted significant interest from the field. The inherent geographical dispersion and unpredictability of these resources, however, pose substantial barriers to their effective and computationally tractable regulation. To address these impediments, this paper proposes a deep reinforcement learning‐based distributed resource energy management strategy, taking into account the inherent physical and structural constraints of the distribution network. This proposed strategy is modelled as a sequential decision‐making framework with a Markov decision process, informed by physical states and external information. In particular, targeting the community energy management system for critical public infrastructure and community holistic benefits maximization, the proposed approach proficiently adapts to fluctuations in resource variability and fluctuating market prices, ensuring intelligent regulation of distributed flexible resources. Simulation and empirical analysis demonstrate that the proposed deep reinforcement learning‐based strategy can improve the economic benefits and decision‐making efficiency of distributed flexible resource regulation while ensuring the security of distribution network power flow.

Abstract Background For patients with advanced non‐small cell lung cancer (NSCLC) who have received frontline immunochemotherapy, subsequent treatment options are limited. As the first dual programmed cell death‐1 (PD‐1)/cytotoxic T lymphocyte‐associated antigen‐4 bispecific antibody approved globally, cadonilimab demonstrated potential antitumor activity in advanced NSCLC patients resistant to anti‐PD‐1/PD‐L1 antibodies. Methods We retrospectively collected efficacy and safety data from advanced NSCLC patients treated with cadonilimab‐based regimens in later therapy lines. Results A total of 41 advanced NSCLC patients refractory to anti‐PD‐1/PD‐L1 therapy were enrolled. More than half of the patients received cadonilimab‐based regimen as a fourth or later line of treatment. At the data cutoff date, treatment efficacy could be evaluated in 23 patients. One patient (4.3%) achieved partial response, eight patients (34.8%) experienced stable disease, and 14 patients (60.9%) progressed. The objective response rate and disease control rate were 4.3% and 39.1%, respectively. The median progression‐free survival for all evaluated patients was 108.0 days. Due to the short follow‐up period, the median overall survival has not yet been reached. Treatment‐related adverse events (TRAEs) and immune‐related AEs occurred in 63.4% and 22% patients, respectively. The most common TRAEs included gamma‐glutamyl transferase elevation (17.1%), coughing (14.6%), and fatigue (12.2%). Five patients (12.2%) experienced grade ≥3 TRAEs. Conclusions In this heavily pretreated cohort of advanced NSCLC patients, cadonilimab‐based regimens showed moderate antitumor efficacy with a generally tolerable and manageable safety profile. However, more evidence is needed to support the administration of cadonilimab in NSCLC patients refractory to previous anti‐PD‐1/PD‐L1 therapy.

Early cancer diagnosis from bisulfite‐treated cell‐free DNA (cfDNA) fragments requires tedious data analytical procedures. Here, we present a deep‐learning‐based approach for early cancer interception and diagnosis (DECIDIA) that can achieve accurate cancer diagnosis exclusively from bisulfite‐treated cfDNA sequencing fragments. DECIDIA relies on transformer‐based representation learning of DNA fragments and weakly supervised multiple‐instance learning for classification. We systematically evaluate the performance of DECIDIA for cancer diagnosis and cancer type prediction on a curated dataset of 5389 samples that consist of colorectal cancer (CRC; n = 1574), hepatocellular cell carcinoma (HCC; n = 1181), lung cancer (n = 654), and non‐cancer control (n = 1980). DECIDIA achieved an area under the receiver operating curve (AUROC) of 0.980 (95% CI, 0.976–0.984) in 10‐fold cross‐validation settings on the CRC dataset by differentiating cancer patients from cancer‐free controls, outperforming benchmarked methods that are based on methylation intensities. Noticeably, DECIDIA achieved an AUROC of 0.910 (95% CI, 0.896–0.924) on the externally independent HCC testing set in distinguishing HCC patients from cancer‐free controls, although there was no HCC data used in model development. In the settings of cancer‐type classification, we observed that DECIDIA achieved a micro‐average AUROC of 0.963 (95% CI, 0.960–0.966) and an overall accuracy of 82.8% (95% CI, 81.8–83.9). In addition, we distilled four sequence signatures from the raw sequencing reads that exhibited differential patterns in cancer versus control and among different cancer types. Our approach represents a new paradigm towards eliminating the tedious data analytical procedures for liquid biopsy that uses bisulfite‐treated cfDNA methylome.

Arthroscopic treatments of hip synovial osteochondromatosis are mostly performed under traction, resulting in neurovascular injury or iatrogenic damage to the labrum or cartilage. This study aimed to assess the effectiveness of outside‐in hip arthroscopy without traction in treating hip synovial osteochondromatosis. This retrospective study was conducted on a series of patients with hip synovial osteochondromatosis treated using outside‐in hip arthroscopy without traction in our hospital between 2018 and 2020. Plain radiography and magnetic resonance imaging (MRI) scans were obtained. The Harris hip score (HHS), hip range of motion (ROM), and visual analog scale (VAS) scores were analyzed. The preoperative scores and last follow‐up scores were compared with a paired‐sample t test. The complications and recurrence postsurgery were recorded. This study included five patients (three male and two female) with an average age of 41 years (range 28–54 years). The mean follow‐up time was 25.2 months (range 18–36 months). All patients experienced groin pain relief and improved ROM. The mean VAS score was significantly lower postoperatively (0.4 ± 0.5) than preoperatively (3.2 ± 0.8) (p

Objective The craniovertebral junction (CVJ) presents intricate anatomical challenges. In severe or irreducible malformations, complications such as reduction loss and fixation failure may occur, necessitating revision surgery. The posterior facet joint distraction and fusion (PFDF) technique, offers a solely posterior approach for revisions. Hence, we delineate varied revision scenarios, proposing surgical strategies and technical details to enhance outcomes and mitigate risks, thereby enriching the neurosurgical community's repertoire. Methods This was a retrospective cohort study, analyzed patient data from Xuanwu Hospital, between 2017 to 2023. All patients had a history of surgical treatment for CVJ malformations, and experienced failure or loss of reduction. The distance from the odontoid process tip to the Chamberlain's line (DCL), the atlantodental interval (ADI), clivus‐canal angle, cervicomedullary angle, width of subarachnoid space, CVJ area, and width of syrinx were used for radiographic assessment. Japanese Orthopaedic Association (JOA) scores and SF‐12 scores were used for clinical assessment. Independent sample t‐tests were employed. A significance level of p

Objective Osteoporosis is a severe bone disease with a complex pathogenesis involving various immune processes. With the in‐depth understanding of bone immune mechanisms, discovering new therapeutic targets is crucial for the prevention and treatment of osteoporosis. This study aims to explore novel bone immune markers related to osteoporosis based on single‐cell and transcriptome data, utilizing bioinformatics and machine learning methods, in order to provide novel strategies for the diagnosis and treatment of the disease. Methods Single cell and transcriptome data sets were acquired from Gene Expression Omnibus (GEO). The data was then subjected to cell communication analysis, pseudotime analysis, and high dimensional WGCNA (hdWGCNA) analysis to identify key immune cell subpopulations and module genes. Subsequently, ConsensusClusterPlus analysis was performed on the key module genes to identify different diseased subgroups in the osteoporosis (OP) training set samples. The immune characteristics between subgroups were evaluated using Cibersort, EPIC, and MCP counter algorithms. OP's hub genes were screened using 10 machine learning algorithms and 113 algorithm combinations. The relationship between hub genes and immunity and pathways was established by evaluating the immune and pathway scores of the training set samples through the ESTIMATE, MCP‐counter, and ssGSEA algorithms. Real‐time fluorescence quantitative PCR (RT‐qPCR) testing was conducted on serum samples collected from osteoporosis patients and healthy adults. Results In OP samples, the proportions of bone marrow‐derived mesenchymal stem cells (BM‐MSCs) and neutrophils increased significantly by 6.73% (from 24.01% to 30.74%) and 6.36% (from 26.82% to 33.18%), respectively. We found 16 intersection genes and four hub genes (DND1, HIRA, SH3GLB2, and F7). RT‐qPCR results showed reduced expression levels of DND1, HIRA, and SH3GLB2 in clinical blood samples of OP patients. Moreover, the four hub genes showed positive correlations with neutrophils (0.65–0.90), immature B cells (0.76–0.92), and endothelial cells (0.79–0.87), while showing negative correlations with myeloid‐derived suppressor cells (negative 0.54–0.73), T follicular helper cells (negative 0.71–0.86), and natural killer T cells (negative 0.75–0.85). Conclusion Neutrophils play a crucial role in the occurrence and development of osteoporosis. The four hub genes potentially inhibit metabolic activities and trigger inflammation by interacting with other immune cells, thereby significantly contributing to the onset and diagnosis of OP.

Total knee arthroplasty (TKA) is a well‐established treatment for end‐stage knee osteoarthritis. However, in patients with concomitant extra‐articular deformities, conventional TKA techniques may lead to unsatisfactory outcomes and higher complication rates. This review summarizes the application of navigated TKA for treating knee osteoarthritis with extra‐articular deformities. The principles and potential benefits of computer navigation systems, including improved component alignment, soft tissue balancing, and restoration of mechanical axis, are discussed. Research studies demonstrate that navigated TKA can effectively correct deformities, relieve pain, and improve postoperative joint function and quality of life compared with conventional methods. The advantages of navigated TKA in terms of surgical precision, lower complication rates, and superior functional recovery are highlighted. Despite challenges like the learning curve and costs, navigated TKA is an increasingly indispensable tool for achieving satisfactory outcomes in TKA for knee osteoarthritis patients with extra‐articular deformities.

Abstract Background The solid pattern is a highly malignant subtype of lung adenocarcinoma. In the current era of transitioning from lobectomy to sublobar resection for the surgical treatment of small lung cancers, preoperative identification of this subtype is highly important for patient surgical approach selection and long‐term prognosis. Methods A total of 1489 patients with clinical stage IA1‐2 primary lung adenocarcinoma were enrolled. Based on patient clinical characteristics and lung imaging features obtained via deep learning, highly correlated diagnostic factors were identified through LASSO regression and decision tree analysis. Subsequently, a logistic model and nomogram were constructed. A restricted cubic spline (RCS) was used to calculate the optimal inflection point of quantitative data and the differences between the groups. Results The three‐dimensional proportion of solid component (PSC), sex, and smoking status was identified as being highly correlated diagnostic factors for solid predominant adenocarcinoma. The logistic model had good prediction efficiency, and the area under the ROC curve was 0.85. Decision curve analysis demonstrated that the application of diagnostic factors can improve patient outcomes. RCS analysis indicated that the proportion of solid adenocarcinomas increased by 4.6 times when the PSC was ≥72%. A PSC of 72% is a good cutoff point. Conclusion The preoperative diagnosis of solid‐pattern adenocarcinoma can be confirmed by typical imaging features and clinical characteristics, assisting the thoracic surgeon in developing a more precise surgical plan.

Abstract Huff‐n‐puff (HnF) is a crucial technology for effectively enhancing the oil recovery (EOR) of tight oil reservoirs. Soaking period is the primary platform for injection medium interacting with formation fluid and reservoir rock in HnF. Elucidating the micro‐percolation mechanism of the soaking period is immensely significant for guiding oilfield production practices. The present study established a physical simulation method combining HnF experiments with nuclear magnetic resonance to reveal the microscopic percolation mechanisms, including water, fracturing fluid, and surfactant. Furthermore, the impacts of soaking time, HnF cycles, wettability, and pore structure on oil recovery degree were quantified. The results demonstrate the crucial significance of wettability and pore structure in the soaking period. The dominant mechanism during water HnF in reservoirs characterized by well‐connected pore networks and minimal clay pores is micropore imbibition, while conversely, macropore displacement plays a predominant role. The oil recovery degree of fracturing fluid HnF primarily relies on mitigating solid‐fluid forces within macropores. The surfactant HnF in preferential water‐ and oil‐wet reservoirs is primarily governed by oil films stripped from macropore walls and micropore imbibition, respectively. Specifically, water and fracturing fluid HnF are suitable for shorter soaking time and fewer HnF cycles, whereas the surfactant HnF exhibits an inverse relationship.

Abstract In this study, a new method is proposed to quantitatively determine the extraction of boreholes under the effect of time. The main purpose of this method is to divide the gas drilling holes in the monitoring time into two stages: the early stage of extraction and the middle stage of extraction. The early stage of extraction uses the gas pressure difference to detect the gas tightness of the drilling holes and determine the type of gas leakage of the drilling holes. Moreover, the unqualified drilling holes are repaired immediately to ensure that all the drilling holes in the middle stage of the monitoring are the qualified holes at the early stage of the inspection. In the middle of extraction, the average attenuation rate of the fitting attenuation curve of pure gas extraction in the extraction borehole is calculated by monitoring, the growth rate of the linear fitting of the pure air leakage is calculated, and their ratios are used as the basis for judging the repair value of the gas leak borehole and the gas leak borehole. This quantitative evaluation method is used to monitor the extraction effect of four test holes in a field test conducted at Ping Coal Mine 10. The results indicate that, after adopting in situ downhole repair technology in the early stage of extraction, the average air leakage of Borehole No. 4, which failed due to air leakage in the borehole, decreased from 9.33 to 1.26 L/min (a reduction of 86.5%). Similarly, that of Borehole No. 3, which failed due to penetration of fissures in the surrounding rock, had an air leakage drop from 8.35 to 1.34 L/min (a reduction of 84.0%). These improvements demonstrate the remarkable effectiveness of the method. The quantitative evaluation method established in this study is not only simple to operate but also stable and reliable. Thus, it is feasible to be applied in the field, which provides important technical support for the improvement in gas extraction effect during coal mine safety production.

Abstract Most of the mine roadways in China are located in layered rock mass. To study the mechanical properties of a layered rock mass, the uniaxial compression test was carried out on the layered composite rock mass composed of sand and paraffin. The results showed that the locations of the high‐ and low‐strength rocks were independent of the strength of the layered composite rock. The main failure site was not affected by the combination mode. Failure was mainly concentrated in the low‐strength rock. The strengths of low‐ and high‐strength rocks determined the lower and upper limits of the strength of the layered rock, respectively. When the thickness of the layered high‐strength composite rock was >60%, the layered composite rock strength tended to be high; conversely, layered composite rock strength lowered the rock strength, and with increasing thickness, the layered composite rock strength was significantly enhanced. From the perspective of energy conversion, the effect of the thickness of the high‐strength rock mass on the strength of the layered composite rock mass was analyzed.

Abstract Exploiting the hierarchical dependence behind user behaviour is critical for click‐through rate (CRT) prediction in recommender systems. Existing methods apply attention mechanisms to obtain the weights of items; however, the authors argue that deterministic attention mechanisms cannot capture the hierarchical dependence between user behaviours because they treat each user behaviour as an independent individual and cannot accurately express users' flexible and changeable interests. To tackle this issue, the authors introduce the Bayesian attention to the CTR prediction model, which treats attention weights as data‐dependent local random variables and learns their distribution by approximating their posterior distribution. Specifically, the prior knowledge is constructed into the attention weight distribution, and then the posterior inference is utilised to capture the implicit and flexible user intentions. Extensive experiments on public datasets demonstrate that our algorithm outperforms state‐of‐the‐art algorithms. Empirical evidence shows that random attention weights can predict user intentions better than deterministic ones.

Abstract Synchronous stability is crucial for the safety and operation of AC power systems. However, most of the current researches focused on the stability of grid‐connected converters, and that of renewable equipment still lacked. In this article, the impact of the additional inertia control (AIC) on the permanent magnet synchronous generator (PMSG) is studied. It is found that with the AIC, the machine‐side converter dynamics of the PMSG cannot be ignored, and the system dominant dynamics shifts from the electromagnetic to electromechanical timescales. This article develops a simplified model for the single‐PMSG infinite‐bus system with the AIC within the electromechanical timescale, and reveals the transient synchronization stability mechanism from three aspects: the machine‐network interface, transient dominant variable, and interaction between the synchronization loop and the power imbalance loop. Finally, this article analyzes the swing characteristics of the PMSG system, and uncovers the relationship between the energy transmission and synchronization. These findings are supported by wide experimental verification and can provide the deeper physical insight and theoretical basis for the transient synchronous stability analysis of renewable‐dominated new‐type power systems.

Abstract Owing to the widespread integration of renewable distributed energy resources (DERs), the system frequency stability has been jeopardized by the non‐inertial and stochastic units caused by power electronic devices. The integrated energy system (IES) that combines multi‐vector energy resources can provide energy compensation among sub‐systems in a coordinated fashion to further alleviate the volatility on the electric grid. Under the framework of IES, a virtual power plant (VPP) can aggregate multi‐entities and multi‐vector energy resources to participate in the frequency regulation service while pursuing profit maximization. A three‐stage optimal scheduling model of IES‐VPP that fully considers the cycle life of energy storage systems (ESSs), bidding strategies and revenue settlement has been proposed in this paper under the modified PJM frequency regulation market framework to motivate the aggregated resources to respond to the frequency regulation market actively. The simulation results under various scenarios have verified the feasibility and effectiveness of the proposed model through techno‐economic analysis, the advantages of IES‐VPP have been demonstrated compared with a single vector energy system.

Abstract Virtual power plants (VPPs) have been widely recognized as a key enabler for energy system neutrality. The communication traffic of a VPP fundamentally indicates its activeness in interacting with the power system, thus providing a new dimension in depicting the behaviour characteristics of distributed energy resources in VPPs. Therefore, the prediction of communication traffic is significant in improving the control efficiency of VPPs. However, due to the involvement of numerous interactive agents characterised by both individual randomness and coordinative characteristics, traditional prediction models are no longer capable of fitting VPP communication traffic effectively. Therefore, a novel prediction model is introduced that enhances the prediction accuracy by integrating long short‐term memory (LSTM) and variational mode decomposition (VMD). This model employs VMD as the initial step for extracting the intrinsic modes from the traffic sequence, thereby mitigating the impact of incidental noise. Then, LSTM is applied to fit each intrinsic mode individually. Additionally, considering the outer influencing factors, the attention mechanism is incorporated. Finally, all sub‐prediction algorithms are neatly integrated as a whole prediction model. The proposed model is evaluated through simulation prediction using realistic VPP communication traffic data, and the results demonstrate its effectiveness.

Abstract Most current RM approaches are developed for fixed bottlenecks. However, the number and locations of bottlenecks are usually uncertain and even time‐varying due to some unexpected phenomena, such as severe accidents and temporal lane closures. Thus, the RM approach should be able to enhance traffic flow stability by effectively handling the time‐delay effect and fluctuations in traffic flow rate caused by uncertain bottlenecks. This study proposed a novel approach called deep reinforcement learning with curriculum learning (DRLCL) to improve ramp metering efficacy under uncertain bottleneck conditions. The curriculum learning method transfers an optimal control policy from a simple on‐ramp bottleneck case to more challenging bottleneck tasks, while DRLCL agents explore and learn from the tasks gradually. Four RM control tasks were developed in the modified cell transmission model, including typical on‐ramp bottleneck, fixed downstream bottleneck, random‐location bottleneck, and multiple bottlenecks. With curriculum learning, the entire training process was reduced by 45.1% to 64.5%, while maintaining a similar maximum reward level compared to DRL‐based RM control with full learning from scratch. Specifically, the results also demonstrated that the proposed DRLCL‐based RM outperformed the feedback‐based RM due to its stronger predictive ability, faster response, and higher action precision.

Abstract For the multistage progression of prostate cancer (PCa) and resistance to immunotherapy, tumour‐associated macrophage is an essential contributor. Although immunotherapy is an important and promising treatment modality for cancer, most patients with PCa are not responsive towards it. In addition to exploring new therapeutic targets, it is imperative to identify highly immunotherapy‐sensitive individuals. This research aimed to establish a signature risk model, which derived from the macrophage, to assess immunotherapeutic responses and predict prognosis. Data from the UCSC‐XENA, GEO and TISCH databases were extracted for analysis. Based on both single‐cell datasets and bulk transcriptome profiles, a macrophage‐related score (MRS) consisting of the 10‐gene panel was constructed using the gene set variation analysis. MRS was highly correlated with hypoxia, angiogenesis, and epithelial‐mesenchymal transition, suggesting its potential as a risk indicator. Moreover, poor immunotherapy responses and worse prognostic performance were observed in the high‐MRS group of various immunotherapy cohorts. Additionally, APOE, one of the constituent genes of the MRS, affected the polarisation of macrophages. In particular, the reduced level of M2 macrophage and tumour progression suppression were observed in PCa xenografts which implanted in Apolipoprotein E‐knockout mice. The constructed MRS has the potential as a robust prognostic prediction tool, and can aid in the treatment selection of PCa, especially immunotherapy options.

Abstract To devise more scientifically rational interruptible load contracts, this paper introduces a novel model for interruptible load contracts within modern electric power systems, grounded in data mining techniques. Initially, user characteristics are clustered using data mining technology to determine the optimal number of clusters. Building on this, the potential for different users to participate in interruptible load programs is analysed based on daily load ratios, yielding various user‐type parameters. Furthermore, the paper develops an interruptible load contract model that incorporates load response capabilities, enhancing the traditional interruptible load contract model based on principal‐agent theory through considerations of user type parameters and maximum interruptible load limits. The objective function, aimed at maximizing the profits of the electric company, is solved, and lastly, through the use of real data, a case study analysis focusing on commercial users with the strongest load response capabilities is conducted. The results affirm the efficacy of the proposed model.

Objective The role of hypoalbuminemia throughout the course of chronic periprosthetic joint infection (PJI) remains poorly understood. This study aimed to determine the prevalence and risk factors of hypoalbuminemia in periprosthetic joint infection (PJI) patients and to explore the association between hypoalbuminemia and treatment outcomes. Methods This retrospective cohort study included 387 PJI cases who underwent two‐stage exchange arthroplasty between January 2007 and August 2020, of which 342 were reimplanted. The mean follow‐up period was 7.9 years. Multivariate logistic regression analyses were performed to identify risk factors for hypoalbuminemia and to assess the effect of hypoalbuminemia at 1st‐ and 2nd‐stage exchange on the treatment outcome. Furthermore, the impact of dynamic changes in hypoalbuminemia was investigated. Results The prevalence of hypoalbuminemia at 1st‐ and 2nd‐stage exchange was 22.2% and 4.7%, respectively. Patients with age ≥ 68 years and those with isolation of Staphylococcus aureus, Streptococcus, or Gram‐negative bacteria exhibited a higher risk of hypoalbuminemia. Hypoalbuminemia at 1st‐stage was significantly related to treatment failure (OR = 3.3), while hypoalbuminemia at 2nd‐stage raised the OR to 10.0. Patients with persistent hypoalbuminemia at both the 1st‐ and 2nd‐stage exchanges had a significantly higher rate of treatment failure than patients with hypoalbuminemia at the 1st‐stage but normal albumin levels at the 2nd‐stage exchange (55.6% vs 20.0%, p = 0.036). Conclusion One in five patients with chronic PJI exhibits hypoalbuminemia. Hypoalbuminemia is more likely to develop in patients of advanced age and those infected by specific highly virulent organisms. Also, our results highlight the close association between hypoalbuminemia and treatment outcomes.

Objective The unclear clinical outcomes of two different zero‐profile implants with different number of screws in hybrid surgery restricts the choice of patient‐specific implants. This study aims to compare two different implants on its postoperative subsidence, motion stabilization and clinical outcomes. It also provides references to the most reasonable implant choice in fusion surgery. Methods This was a retrospective study. From February 2014 to March 2022, 173 patients who underwent hybrid surgery were included. Among them, 122 received surgery with a four screw implant, while 51 received a two screw implant. We analyzed the significance of patient‐specific factors, radiographic factors and clinical outcomes. The Wilcoxon rank sum test, t tests/analysis of variance (ANOVA) test and stepwise multivariate logistic regression were adopted for statistical analysis. Results No statistically significant difference was observed between the two screw and four screw groups in terms of immediate, middle, and long‐term stability and fusion rate (p > 0.05). However, the two screws group had higher FSU height subsidence at 3, 6, and 12 months postoperatively and higher rates of significant subsidence at three and 6 months postoperatively (p

Abstract Despite advancement in anti‐seizure medications, 30% of patients continue to experience recurrent seizures. Previous data indicated the antiepileptic properties of melatonin and its agonists in several animal models. However, the underlying mechanisms of melatonin and its agonists on cellular excitability remain poorly understood. In this study, we demonstrated the electrophysiological changes of two main kinds of ion channels that are responsible for hyperexcitability of neurons after introduction of melatonin agonists‐ ramelteon (RAM). In Neuro‐2a cells, the amplitude of voltage‐gated Na+ (INa) and delayed‐rectifier K+ currents (IK (DR)) could be suppressed under RAM. The IC50 values of 8.7 and 2.9 μM, respectively. RAM also diminished the magnitude of window Na+ current (INa (W)) elicited by short ascending ramp voltage, with unchanged the overall steady‐state current–voltage relationship. The decaying time course of INa during a train of depolarizing pulses arose upon the exposure to RAM. The conditioning train protocol which blocked INa fitted the recovery time course into two exponential processes and increased the fast and slow time constant of recovery the presence of RAM. In pituitary tumor (GH3) cells, INa amplitude was also effectively suppressed by the RAM. In addition, GH3‐cells exposure to RAM decreased the firing frequency of spontaneous action potentials observed under current‐clamp conditions. As a result, the RAM‐mediated effect on INa was closely associated with its ability to decrease spontaneous action potentials. Collectively, we found the direct attenuation of INa and IK (DR) caused by RAM besides the agonistic action on melatonin receptors, which could partially explain its anti‐seizure activity.

Abstract Laser wireless energy transmission is a widely utilized method, yet its efficiency is constrained by a variety of factors. In order to improve the conversion efficiency of the receivers of the laser wireless power transmission (LWPT) system, the square elliptic hyperboloid (SEH) concentrating module designed for LWPT system receivers is developed. By analysing the I–V characteristic curves from the results of the experiments and employing non‐linear parameter regression, a corrected battery characteristic curve was derived within a specific laser irradiation range. On this basis, an optical–thermal–electric multi‐field coupling characteristic model was developed. The finite element method is used to simulate the multi‐field coupling characteristics and conversion efficiency of the receiving end under diverse working conditions (including different rotation angles and different divergence angles) of the concentrating photovoltaic module. Research shows: First, the larger the divergence half‐angle β of the laser beam, the more obvious the improvement of the effective optical efficiency of the system by the SEH concentrator. Second, the short‐circuit current and the maximum output power of the PV cell at the receiving end are significantly improved by the SEH concentrator, and the improvement effect is more obvious with the increase of the divergence angle and the rotation angle. Third, SEH concentrators did not significantly affect the fill factor of PV cells.

Abstract Aims Zinc‐finger protein 418 (ZNF418) has been confirmed to be expressed in myocardial tissue. However, the role and mechanism of ZNF418 in pathological myocardial remodelling after myocardial infarction (MI) have not been reported. This study was to elucidate the effect and mechanism of ZNF418 on ventricular remodelling after MI in mice. Methods and results MI mice and H9c2 cardiomyocytes were used to conduct in vivo and in vitro experiments, respectively. ZNF418 expression was regulated by adeno‐associated virus 9 and adenovirus vectors. Pathological analysis, echocardiography, and molecular analysis were performed. ZNF418 was down‐regulated in the left ventricular tissues of post‐MI mice. In contrast, ZNF418 overexpression decreased mortality and improved cardiac function in MI mice. The MI mice exhibited a significantly increased cross‐sectional area of myocardial cells and elevated protein expression levels of myocardial hypertrophy markers ANP, BNP, and β‐MHC (all P

Abstract In recent years, multi‐view clustering (MVC) has had significant implications in the fields of cross‐modal representation learning and data‐driven decision‐making. Its main objective is to cluster samples into distinct groups by leveraging consistency and complementary information among multiple views. However, the field of computer vision has witnessed the evolution of contrastive learning, and self‐supervised learning has made substantial research progress. Consequently, self‐supervised learning is progressively becoming dominant in MVC methods. It involves designing proxy tasks to extract supervisory information from image and video data, thereby guiding the clustering process. Despite the rapid development of self‐supervised MVC, there is currently no comprehensive survey analysing and summarising the current state of research progress. Hence, the authors aim to explore the emergence of self‐supervised MVC by discussing the reasons and advantages behind it. Additionally, the internal connections and classifications of common datasets, data issues, representation learning methods, and self‐supervised learning methods are investigated. The authors not only introduce the mechanisms for each category of methods, but also provide illustrative examples of their applications. Finally, some open problems are identified for further investigation and development.

Abstract The intricate causes of Alzheimer's disease (AD) hinder effective, lasting treatment. Although the dietary modulation of the brain–gut axis was explored for AD therapy, the exact mechanism remains unclear. This study suggested that 140 days of the whey protein hydrolysate (WPH) intake could attenuate the AD pathologic symptoms in APP/PS1 transgenic mice via a bidirectional action of the gut microbe–SCFA (short‐chain fatty acid)–brain axis. Behavioral tests demonstrated that high‐dose WPH (WPH‐H, 100 mg/kg body weight [bw]) improved passive and recognition memory in mice. Furthermore, WPH‐H significantly reduced amyloid beta 1–42 (Aβ1–42) levels in serum (p

Abstract In a distributed multi‐station system, the observations received by local radar nodes for a single target will have a large signal‐to‐noise ratio (SNR) bias due to inconsistent radar cross‐sections from distinct angles, different distances from the target, various local interference such as harsh weather, and dissimilar background noise. Integrating heterogeneous information in dynamic and uncertain environments can be challenging for the fusion centre. Moreover, the particles in the basic particle filter (PF) may degrade after many iterations, making it difficult to achieve accurate target state estimation in the local tracking process. To address these issues, the authors propose a novel method named DS‐UPF based on the Dempster–Shafer (DS) theory and the unscented particle filter (UPF). By updating the important density function, the UPF efficiently suppresses particle degradation. The weighted Basic Probability Assignments (BPAs) are proposed and integrated under the new synthesis formula. The weight‐modified DS method restrains the impact of significant local estimation errors on weighted BPAs fusion result, improving robustness without local interference prior knowledge. The experimental results demonstrate that the DS‐UPF outperforms the unscented Kalman filter, PF, and UPF in tracking tasks under various local interference. This indicates that the proposed algorithm can improve estimation precision in dynamic and uncertain environments.

Abstract Silicone rubber (SiR) is commonly used in reinforced insulation parts for high‐voltage direct current (HVDC) cable accessories due to its excellent insulation, elasticity, and high‐temperature resistance. HVDC cable accessories always suffer from the local electric field concentration due to the electrical conductivity mismatch between reinforced insulation and main insulation, which can ultimately lead to electric breakdown. The non‐linear conductive composites based on SiR have the ability to adaptively adjust the distribution of the electric field in cable accessories. This is expected to solve the problem of localised electric field concentration. The zinc oxide (ZnO) and glycidyl methacrylate (GMA) are used as fillers and grafted modifier respectively to improve the non‐linear electrical conductivity of ZnO/SiR‐GMA composites. The results indicate that grafting GMA can increase electrical conductivity of SiR, while doping ZnO filler enables SiR to have non‐linear conductivity characteristics. The combination of doping and grafting modification of the composites achieves excellent non‐linear conductive properties at lower ZnO filler content. Additionally, the mechanical properties of the modified composites are enhanced. The simulation results indicate that ZnO/SiR‐GMA is the most effective material for homogenising the electric field when used as reinforced insulation for cable intermediate joints.

Abstract High conductivity loss and low breakdown strength of traditional polypropylene (PP) film in the high‐temperature environment are the key factors limiting the application of metallised film capacitors (MFCs). The effect of cyclic olefin copolymers (COCs) with different glass transition temperatures (Tgs) on the dielectric performance of PP/COC composites at high temperature are studied. The results showed that the addition of COC enhanced the intermolecular interaction force, which led to the reduction of defects in the films. In addition, COC with high Tg inhibits the movement of molecular chains and enhances the thermal stability of the film, which limits the transport of carriers at high temperatures. The conductivity and breakdown strength of the modified films (with a Tg of 134°C for COC) at 125°C are 91.6% lower and 45.7% higher, respectively, than that of pure PP. This method shows great potential in improving the dielectric properties of PP at high temperatures.

Abstract Background The aim of the present study was to investigate the function of novel circular RNA hsa_circ_0036683 (circ‐36683) in non‐small cell lung cancer (NSCLC). Methods RNA sequencing was used to screen out differentially expressed miRNAs. Expression levels of miR‐4664‐3p and circ‐36683 were evaluated in lung carcinoma cells and tissues by quantitative reverse transcription‐polymerase chain reaction (qRT‐PCR). The effects of miR‐4664‐3p and circ‐36683 on proliferation and migration were assessed using cell counting kit‐8 (CCK‐8), wound healing and transwell migration assays and xenograft experiments. The targeting relationship of circ‐36683/miR‐4664‐3p/CDK2AP2 was assessed by luciferase reporter assays, western blot, qRT‐PCR and argonaute2‐RNA immunoprecipitation (AGO2 RIP). Co‐immunoprecipitation (Co‐IP), 5‐ethynyl‐2′‐deoxyuridine (EdU) staining and CCK‐8 were used to validate the indispensable role of CDK2AP2 in suppressing cell proliferation as a result of CDK2AP1 overexpression. Results By RNA sequencing, miR‐4664‐3p was screened out as an abnormally elevated miRNA in NSCLC tissues. Transfection of miR‐4664‐3p could promote cell proliferation, migration and xenograft tumor growth. As a target of miR‐4664‐3p, CDK2AP2 expression was downregulated by miR‐4664‐3p transfection and CDK2AP2 overexpression could abolish the proliferation promotion resulting from miR‐4664‐3p elevation. Circ‐36683, derived from back splicing of ABHD2 pre‐mRNA, was attenuated in NSCLC tissue and identified as a sponge of miR‐4664‐3p. The functional study revealed that circ‐36683 overexpression suppressed cell proliferation, migration and resulted in G0/G1 phase arrest. More importantly, the antioncogenic function of circ‐36683 was largely dependent on the miR‐4664‐3p/CDK2AP2 axis, through which circ‐36683 could upregulate the expression of p53/p21/p27 and downregulate the expression of CDK2/cyclin E1. Conclusion The present study revealed the antioncogenic role of circ‐36683 in suppressing cell proliferation and migration and highlighted that targeting the circ‐36683/miR‐4664‐3p/CDK2AP2 axis is a promising strategy for the intervention of NSCLC.

Abstract The wide‐angle electromagnetic scattering problems can be rapidly solved by using the method of moments in conjunction with compressive sensing theory. The method mainly has two computational parts: measurement and recovery. To further enhance the performance, an improved orthogonal matching pursuit algorithm with a randomised atom selection process is proposed, significantly reducing the computational complexity of the recovery part. Additionally, a restart mechanism is designed to eliminate possible errors that might occur during the random process, ensuring the accuracy of recovery results. Numerical experiments with objects of different shapes validate the effectiveness and efficiency of the proposed scheme.

Abstract Aims Chronic liver disease (CLD) is increasingly recognized as a significant global public health threat, with morbidity and mortality rates remaining high. Evidence suggests that sarcopenia independently increases the risk of CLD and negatively impacts various clinical outcomes, including survival, quality of life, and the emergence of additional complications in patients with CLD. This study aimed to give a bibliometric analysis to examine the correlation between sarcopenia and CLD from a literature perspective. Methods To understand the structure of this research field, we employed VOSviewer. The research on the correlation between long‐term liver disease and sarcopenia was obtained from the Web of Science Core Collection. VOSviewer 1.6.19.0 was utilized to examine and illustrate these publications, encompassing yearly patterns in the domain, focal points of research, significant articles, authors, journals, and organizations. Moreover, according to the results of the cluster analysis of keywords, we further searched and classified related studies to discuss. Results This study provides a comprehensive analysis of current research trends, international collaboration models, fundamental understandings, key focus areas, and future research areas in the field of sarcopenia and CLD by reviewing publications from January 1, 2000 to December 31, 2023. Over the past 24 years, research in the field of sarcopenia and CLD has deepened, with a gradual increase in publications and citations from various countries, institutions, and authors. Keyword analysis of sarcopenia and CLD indicates that current research predominantly focuses on several key areas, including obesity, metabolic syndrome, hepatic steatosis, insulin resistance, inflammation, and nutrition therapy. Conclusion This study provided a visual representation of the current research on the correlation between CLD and sarcopenia, including publication trends, global collaboration patterns, and research hotspots. This research contributes significantly by summarizing and discussing current research trends in sarcopenia and CLD, offering valuable insights into the complex relationship, and highlighting research trends, collaborations, and future directions in clinical treatment.

Abstract Electric vehicles (EVs) and small photovoltaic (PV) installations advance residential power grids by lowering charging costs and fostering eco‐friendly operations. Yet, the variable nature of EV charging presents challenges to grid reliability. This research introduces a Monte Carlo‐based simulation for predicting EV charging loads and a systematic charging method that integrates a ‘green electricity’ pricing scheme with a joint optimization model for PV and EV management. By applying an improved ant lion optimizer (IALO) algorithm enriched with differential evolution features, an optimization strategy that markedly enhances grid performance is devised. In a park scenario, this ‘green electricity’ model reduced the mean square error of EV charging load by 11.82%, smoothed the power load curve, and improved grid stability. When compared with particle swarm optimization (PSO) and grey wolf optimizer (GWO) algorithms, the IALO algorithm boosted overall revenue by 16.8% and 12.8%, increased PV utilization by 162.3% and 37.1%, and significantly cut carbon emissions by 159.6% and 31.6%, respectively. These outcomes affirm the financial, environmental, and functional benefits of our proposed approach.

Abstract Effective extraction of building from remote sensing images requires both global and local information. Despite convolutional neural networks (CNNs) excelling at capturing local details, their intrinsic focus on local operations poses challenge in effectively extracting global features, especially in the context of large‐scale buildings. In contrast, transformers excel at capturing global information, but compared to CNNs, they tend to overly rely on large‐scale datasets and pre‐trained parameters. To tackle the challenge, this paper presents the multi‐scale and dual‐branch feature fusion network (MDTrans). Specifically, the CNN and transformer branches are integrated in a dual‐branch parallel manner during both encoding and decoding stages, local information for small‐scale buildings is extracted by utilizing Dense Connection Blocks in the CNN branch, while crucial global information for large‐scale buildings is effectively captured through Swin Transformer Block in the transformer branch. Additionally, Dual Branch Information Fusion Block is designed to fuse local and global features from the two branches. Furthermore, Multi‐Convolutional Block is designed to further enhance the feature extraction capability of buildings with different sizes. Through extensive experiments on the WHU, Massachusetts, and Inria building datasets, MDTrans achieves intersection over union (IoU) scores of 91.36%, 64.69%, and 79.25%, respectively, outperforming other state‐of‐the‐art models.

Abstract In view of the fact that dynamic stall models in the wind energy industry such as ONERA model, Beddoes–Leishman model, and Snel model are mostly semi‐empirical models, and the determination of empirical time constants has a great influence on the model accuracy. To optimize the time constant in dynamic stall model and improve the prediction accuracy of unsteady aerodynamics, the influence of boundary layer and pressure lag on the unsteady performance of the S809 airfoil under 2D flow conditions is explored using a simple semi‐empirical dynamic stall engineering model. The proposed model consists of four first‐order differential equations accounting for attached flow and dynamic separation flow of trailing edge based on the Theodorsen theory. A validation is carried out by the wind tunnel experiment in the Key Laboratory of Wind and Solar Energy Utilization Technology of the Ministry of Education at Inner Mongolia University of Technology. The main conclusions are as follows. The time constants for lag in pressure and boundary layer both have a great influence on the unsteady lift coefficient. When the mean angle of attack is relatively small and the airflow is between the attached flow and the separated flow, appropriately reducing the time constant can make the prediction results closer to the experimental values. When the mean angle of attack is relatively large and the airflow is in condition of fully separated flow, the time constant value can be appropriately increased. The influence of pressure lag and boundary layer lag on the unsteady drag coefficient is not significant.

ABSTRACT Background Tremor‐dominant (TD) and nontremor‐dominant (NTD) Parkinson's disease (PD) showed different responses to rehabilitation. However, the neural mechanism behind this remains unclear. Methods This cohort study explores changes in motor function, brain activation, and functional connectivity following 2 weeks of rehabilitation in TD‐PD and NTD‐PD patients, respectively. A total of 11 TD‐PD patients, 24 NTD‐PD patients, and 21 age‐matched healthy controls (HCs) were included. At baseline, all participants underwent functional magnetic resonance imaging (fMRI) while performing the foot tapping task. Motor symptoms, gait, balance, and task‐based fMRI were then evaluated in patients before and after rehabilitation. Results Compared to HCs, TD‐PD patients showed increased activity in the left inferior frontal gyrus and the right insula, and NTD‐PD patients showed increased activations in the left postcentral gyrus and decreased within‐cerebellar connectivity at baseline. Rehabilitation improved motor function in PD patients regardless of motor subtype. TD‐PD patients showed increased recruitments of the sensorimotor cortex and the bilateral thalamus after rehabilitation, and NTD‐PD patients showed increased cerebellar activation and within‐cerebellar connectivity that was associated with better motor performance. Conclusions This study demonstrated that rehabilitation‐induced brain functional reorganization varied by motor subtypes in PD, which may have important implications for making individualized rehabilitation programs. Trial Registration ClinicalTrials.gov identifier: ChiCTR1900020771

Background The clinical characteristics and survival outcomes of patients who underwent concomitant coronary artery bypass grafting during septal myectomy have not been well studied. Methods and Results We reviewed patients who underwent both septal myectomy and coronary artery bypass grafting from 2009 to 2020. Causes of concomitant grafting and their impact on survival were analyzed. The median follow‐up period was 5.1 years. A total of 320 patients underwent both grafting and myectomy. Of these, 69.7% and 28.1% underwent grafting attributed to atherosclerotic coronary artery disease and myocardial bridging, respectively. Patients who underwent grafting for coronary artery disease tended to be older, had a longer bypass time, and required more grafts compared with patients undergoing procedures because of myocardial bridging (all P95% and improved functional class of >90%.

Abstract Background and Aim Glycated albumin (GA) is a biomarker monitoring glycemia 2–4 weeks before stroke onset. This study was designed to explore the association between GA levels with poststroke outcomes in patients with acute ischemic stroke or transient ischemic attack (TIA). Method Participants with ischemic stroke or TIA who had a baseline GA measurement were included in the Third China National Stroke Registry study. The effect of GA on stroke recurrence, poor functional outcomes, and combined vascular events was examined during the 1‐year follow‐up period. Multivariate Cox and logistic regression models were performed to evaluate the association. Discrimination tests were used to examine the incremental predictive value of GA when incorporating it into the conventional model. Results A total of 3861 participants were enrolled. At the 3‐month follow‐up, the elevated GA level was associated with an increased risk of poor functional outcomes (adjusted odds ratio [OR], 1.45; 95% confidence interval [CI], 1.01–2.09). A similar increase was observed for stroke recurrence (adjusted hazard ratio [HR], 1.56; 95% CI, 1.09–2.24), poor functional outcomes (adjusted OR, 1.62; 95% CI, 1.07–2.45), and combined vascular events (adjusted HR, 1.55; 95% CI, 1.09–2.20) at the 1‐year follow‐up. In nondiabetic patients, the association between GA and poor functional outcomes was more pronounced (adjusted OR, 1.62; 95% CI, 1.05–2.50). Adding GA into the conventional model resulted in slight improvements in predicting poor functional outcomes (net reclassification improvement [NRI]: 12.30% at 1 year). Conclusion This study demonstrated that elevated GA levels in serum were associated with stroke adverse outcomes, including stroke recurrence, poor functional outcomes, and combined vascular events, in patients with ischemic stroke or TIA.

Abstract Electrical conductivity is a pivotal biophysical factor for neural interfaces, though optimal values remain controversial due to challenges isolating this cue. To address this issue, conductive substrates made of carbon nanotubes and graphene oxide nanoribbons, exhibiting a spectrum of conductivities from 0.02 to 3.2 S m−1, while controlling other surface properties is designed. The focus is to ascertain whether varying conductivity in isolation has any discernable impact on neural lineage specification. Remarkably, neural‐tissue‐like low conductivity (0.02–0.1 S m−1) prompted neural stem/progenitor cells to exhibit a greater propensity toward neuronal lineage specification (neurons and oligodendrocytes, not astrocytes) compared to high supraphysiological conductivity (3.2 S m−1). High conductivity instigated the apoptotic process, characterized by increased apoptotic fraction and decreased neurogenic morphological features, primarily due to calcium overload. Conversely, cells exposed to physiological conductivity displayed epigenetic changes, specifically increased chromatin openness with H3acetylation (H3ac) and neurogenic‐transcription‐factor activation, along with a more balanced intracellular calcium response. The pharmacological inhibition of H3ac further supported the idea that such epigenetic changes might play a key role in driving neuronal specification in response to neural‐tissue‐like, not supraphysiological, conductive cues. These findings underscore the necessity of optimal conductivity when designing neural interfaces and scaffolds to stimulate neuronal differentiation and facilitate the repair process.

Abstract Background Understanding the trends of the prevalence and incidence rate of chronic obstructive pulmonary disease (COPD) is vital for improving the control and prevention of COPD. We aimed to examine the trends in the prevalence and incidence rate of COPD among adults aged 50 years or older in the United States during 2000–2020. Methods Utilizing data from the Health and Retirement Study, we analyzed COPD prevalence across survey waves and calculated COPD incidence rates between consecutive interview waves, stratified by gender and race. We employed joinpoint regression models to investigate trends in COPD prevalence and incidence. Results The individuals reporting COPD are more likely to be women and Caucasians. The age‐adjusted prevalence of COPD among adults aged 50 years and over showed an increasing trend throughout the study period, spanning from 9.02% in 2000 to 9.88% in 2020 (average biennial percent change [ABPC] = 0.41, 95% confidence interval [CI]: 0.10, 0.71; p = 0.01). The age‐adjusted incidence rate of COPD among adults aged 50 and over showed a decreasing trend throughout the study period 1031.1 per 100,000 person‐years in 2000 to 700.5 per 100,000 person‐years in 2020 (ABPC = −1.63, 95% CI: −2.88, −0.36; p = 0.02). Conclusion Our findings indicate a rising prevalence of COPD among older adults in the United States since 2000, while the incidence rate of COPD has shown a declining trend.

Abstract Cross‐linked polyethylene (XLPE) cables are commonly used for constructing urban power lines due to their superior properties. Insulation defects can cause partial discharge (PD) and electrical tree, which can negatively impact the insulation performance of the cable and even lead to insulation failure. During operation, cables undergo hot and cold cycles, and the temperature of the insulation layer can affect the PD and electrical tree. An experimental platform with a needle‐plate electrode was developed to investigate this phenomenon. The platform was used to detect PD activity and electrical tree propagation in XLPE under a 50 Hz voltage at various temperatures. The results indicate that an increase in insulation temperature leads to an increase in the number of PDs and a decrease in the inception voltage. Simultaneously, it has been observed that a rise in temperature can facilitate the spread of electrical trees. To explicate the aforementioned PD result, a finite element analysis (FEA) model has been developed. Additionally, a molecular dynamics (MD) model of XLPE material was developed to clarify the phenomenon of electrical tree propagation. This study's findings aid in investigating the impact of temperature on XLPE defects, which is critical for assessing power cable performance.

Abstract The dielectric performance of the pristine and the electrothermal‐aged metallised polypropylene capacitors are investigated under different magnetic fields. The energy storage capacity decreases under the magnetic field. At 12 T, the capacitance value of the pristine capacitors decreases by 8.4%–17% and that of the aged capacitors declines by 15%–20%. The dielectric loss is reduced by 2.6%–6.3%, but the equivalent series resistance shows an 8.3%–23.1% growth. The leakage conductivity increases to 202%–354%, which leads to a weakened voltage holding capacity. In addition to the effects of the magnetic fields, the impacts of ageing have also been investigated. The effects of the magnetic field and ageing on the electrical parameters of the capacitors are discussed separately. The molecular chains of the dielectric are oxidised and decomposed during the ageing process, which reduces the insulating properties of the dielectric. The magnetic field inhibits polarisation by changing the polarisation behaviour through Lorentz force and excites the electrons to enhance charge transmission.

Abstract In the anti‐interference of broadband communication radio, interference cancellation performance is seriously affected by the uncertainty of the working frequency selection of the communication system and the direction of hostile interference. The closed‐form solution of the interference cancellation weight value and the signal‐to‐interference noise ratio after the cancellation are derived and simplified to clarify the factors affecting interference cancellation quality. Then, the efficient blind area criterion is established for the low computational complexity in view of cancellation quality, which combines the radio communication index and anti‐interference demand index. Based on this criterion, the minimum blind area model is proposed for robust performance. The experimental results in the auxiliary antenna spacing optimisation are consistent with the theoretical analysis, verifying the effectiveness of the proposed model in dealing with uncertain communication frequency and interference direction. It is very promising in communication interference cancellation.

Abstract A comparative study of two force perception skill learning approaches for robot‐assisted spinal surgery, the impedance model method and the imitation learning (IL) method, is presented. The impedance model method develops separate models for the surgeon and patient, incorporating spring‐damper and bone‐grinding models. Expert surgeons' feature parameters are collected and mapped using support vector regression and image navigation techniques. The imitation learning approach utilises long short‐term memory networks (LSTM) and addresses accurate data labelling challenges with custom models. Experimental results demonstrate skill recognition rates of 63.61%–74.62% for the impedance model approach, relying on manual feature extraction. Conversely, the imitation learning approach achieves a force perception recognition rate of 91.06%, outperforming the impedance model on curved bone surfaces. The findings demonstrate the potential of imitation learning to enhance skill acquisition in robot‐assisted spinal surgery by eliminating the laborious process of manual feature extraction.

Abstract Multi‐object tracking in autonomous driving is a non‐linear problem. To better address the tracking problem, this paper leveraged an unscented Kalman filter to predict the object's state. In the association stage, the Mahalanobis distance was employed as an affinity metric, and a Non‐minimum Suppression method was designed for matching. With the detections fed into the tracker and continuous ‘predicting‐matching’ steps, the states of each object at different time steps were described as their own continuous trajectories. We conducted extensive experiments to evaluate tracking accuracy on three challenging datasets (KITTI, nuScenes and Waymo). The experimental results demonstrated that our method effectively achieved multi‐object tracking with satisfactory accuracy and real‐time efficiency.