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Considering the variety of Internet of Things (IoT) device types and access methods, it remains necessary to address the security challenges we currently encounter. Physical layer security (PLS) can offer streamlined security solutions for the next generation of IoT networks. Presently, we are witnessing the application of intelligent technologies including machine learning (ML) and artificial intelligence (AI) for precise prevention or detection of security breaches. Active eavesdropping detection is a physical layer security-based method that can differentiate wireless signals between wireless devices through feature classification. However, the operation of numerous IoT devices operate in environments characterized by low signal-to-noise ratios (SNR), and active eavesdropping attack detection during communication is rarely studied. We assume that the wireless system comprising an access point (AP), K authorized users and a proactive eavesdropper (E), following the framework of transforming wireless signals at AP into organized datasets that this article proposes a BP neural network model based on deep learning as a classifier to distinguish eavesdropping and non-eavesdropping attack signals. By conducting experiments under SNRs, the numerical results show that the proposed model has stronger robustness and detection accuracy can significantly improve the up to 19.58% compared with the reference approach, which show the superiority of our proposed method.

China has a large population of elderly in rural areas. Village doctors are acting as health-care gatekeepers for the rural elderly in China and are encouraged to provide more long-term care for patients with chronic diseases such as atrial fibrillation (AF). The data of AF registries from the rural elderly are limited. The present registry aims to provide contemporary data on the current AF-related health status of the rural elderly and the gaps in management of AF by village doctors. This study has two phases. The first phase is a cross-sectional study of AF screening in two rural towns of eastern China. All the residents aged [greater than or equal to]65 years are eligible and will be invited to attend a government-led health examination or an in-house AF screening program. The AF detection rate, the awareness of AF and the usage of oral anticoagulants and smartphones by AF patients, and the ability to diagnose and manage AF by village doctors will be assessed. Participants with AF detected in the first phase are eligible for the second phase. A variety of modes of intensified education will be provided to all AF patients and their family members to enrich their AF-related knowledge. Their village doctors will be offered a lecture-based training program focusing on Atrial fibrillation Better Care (ABC) pathway. Follow-up will be conducted for 1 year. The primary endpoint is the composite of all stroke and all-cause mortality. The first phase of AF screening was conducted between April 2019 and June 2019, and 18,712 participants with the mean age of 73.1[+ or -]5.8 years were enrolled. The second phase that includes 810 patients with AF, started on 1 May 2019. This study will provide a perspective of primary care system and would indirectly reflect the current status of chronic disease care in rural China. Keywords: atrial fibrillation, elderly, village doctors, rural China

Introduction Metabolic syndrome (MetS) and atrial cardiomyopathy (AtCM) are recognized as risk factors for cardiovascular disease, including stroke. We aimed to determine the combined impact of MetS and AtCM on stroke mortality. Methods Participants were selected from the Third National Health and Nutrition Examination (NHANES III) Survey. MetS was defined according to the Adult Treatment Panel III, while AtCM was defined as deep terminal negativity of the P wave in V1 (DTNPV1). Survey‐weighted Firth penalized Cox analysis was performed to determine the adjusted HRs and 95% CIs of stroke mortality by MetS‐AtCM status, including metabolically healthy without AtCM (MHNA; reference), metabolically unhealthy without AtCM (MUNA), metabolically healthy with AtCM (MHA), metabolically unhealthy with AtCM (MUA). Results A total of 4315 participants were included in the analysis. Throughout the follow‐up, the rates of stroke mortality increased across the MetS‐AtCM status categories: 1.56, 2.78, 4.68, and 8.24 per 1000 person‐years in MHNA, MUNA, MHA, and MUA groups, respectively. Compared to the MHNA participants, MUA were at a higher risk of stroke mortality (HR = 3.33, 95% CI 1.24–8.94, p = 0.018). Stroke mortality showed a non‐significant upward trend in both MUNA (HR = 1.57, 95% CI 0.96–2.59, p = 0.074) and MHA (HR = 1.61, 95% CI 0.52–5.00, p = 0.401) groups. Conclusions Our findings suggest a potential joint association of MetS and AtCM on stroke mortality. Further RCTs are warranted to evaluate the efficacy of anticoagulation in preventing ischemic stroke and reducing stroke mortality among individuals with both MetS and AtCM. Metabolic syndrome and atrial cardiomyopathy each show a positive but statistically non‐significant association with stroke mortality, but their coexistence confers a synergistically higher risk.

The encoder–decoder paradigm has emerged as the prevailing framework in medical image segmentation, and recent studies within this paradigm have demonstrated its remarkable effectiveness for lesion delineation. However, because the encoder compresses high-dimensional inputs and the decoder must reconstruct the target from the encoder’s limited latent representation, a fixed encoder–decoder pipeline inevitably introduces a semantic gap between the two stages. To bridge this gap, we present MAFormer, a novel U-shaped network tailored for medical image segmentation. Specifically, we design a Multi-scale Dependency Feature Construction (MDFC) module that refines the skip-connection pathway to fuse semantic information across hierarchical levels. In addition, we propose an Attention Representation Reinforcement Module (ARRM) that strengthens encoder–decoder semantic alignment via bidimensional similarity computation and a hierarchical masking strategy. Extensive experiments on GlaS, Synapse and ISIC2018 datasets confirm that MAFormer consistently surpasses state-of-the-art encoder–decoder methods on both large and small scale datasets. In particular, it achieves higher Dice scores, underscoring the effectiveness of MAFormer in improving overall segmentation accuracy.

Background Excessive prolongation of the PR interval indicates the potential for atrioventricular (AV) asynchrony, resulting in severe impairment of cardiac function. Case presentation. A 72-year-old man presented to the cardiology department with a history of worsening shortness of breath and chest tightness over the past 3 years. The electrocardiogram (ECG) showed sinus rhythm with a prolonged PR interval of 400 ms. The echocardiogram revealed mild mitral valve regurgitation with mitral E-A fusion during ventricular diastole. The patient received left bundle branch area pacing to shorten the AV conduction time. Conclusion In patients with symptomatic AV block, reflected by an excessively prolonged PR interval, prompt decision-making regarding cardiac pacing therapy can help relieve clinical symptoms and enhance the patient's quality of life.

Heterostructured TiO 2 @MXene rich in oxygen vacancies defects (VO-TiO 2 @MXene) has been developed to construct an electrochemical sensing platform for imidacloprid (IMI) determination. For the material design, TiO 2 nanoparticles were firstly in situ grown on MXene and used as a scaffolding to prevent the stack of MXene nanosheets. The obtained TiO 2 @MXene heterostructure displays excellent layered structure and large specific surface area. After that, electrochemical activation is utilized to treat TiO 2 @MXene, which greatly increases the concentration of surface oxygen vacancies (VOs), thereby remarkably enhancing the conductivity and adsorption capacity of the composite. Accordingly, the prepared VO-TiO 2 @MXene displays excellent electrocatalytic activity toward the reduction of IMI. Under optimum conditions, cyclic voltammetry and linear sweep voltammetry techniques were utilized to investigate the electrochemical behavior of IMI at the VO-TiO 2 @MXene/GCE. The proposed sensor based on VO-TiO 2 @MXene presents an obvious reduction peak at -1.05 V(vs. Hg|Hg 2 Cl 2 ) with two linear ranges from 0.07 - 10.0 μM and 10.0 - 70.0 μM with a detection limit of 23.3 nM (S/N= 3). Furthermore, the sensor provides a reliable result for detecting IMI in fruit and vegetable samples with a recovery of 97.9-103% and RSD≤ 4.3%. Graphical abstract A sensitive electrochemical sensing platform was reported for imidacloprid (IMI) determination based on heterostructured TiO 2 @MXene rich in oxygen vacancy defects.

In the realm of advanced technology, deep learning capabilities are harnessed to analyze and predict novel data, once it has absorbed existing information. When applied to the sphere of education, this transformative technology becomes a catalyst for innovation and reform, leading to advancements in teaching modes, methodologies, and curricula. In light of these possibilities, the application of deep learning technology to teaching resource recommendations is explored in this article. Within the context of the study, a bespoke recommendation algorithm for teaching resources is devised, drawing upon the integration of deep learning and cognitive diagnosis (ADCF). This intricately constructed model consists of two core elements: the Multi-layer Perceptron (MLP) and the Generalized Matrix Factorization (GMF), operating cohesively through stages of linear representation and nonlinear learning of the interaction function. The empirical analysis reveals that the ADCF model achieves 0.626 and 0.339 in the hits ratio (HR) and the Normalized Discounted Cumulative Gain (NDCG) respectively due to the traditional model, signifying its potential to add significant value to the domain of teaching resource recommendations.

Background Despite improvement in treatment strategies for atrial fibrillation (AF), a significant proportion of patients still experience recurrence after ablation. This study aims to propose a novel algorithm based on Transformer using surface electrocardiogram (ECG) signals and clinical features can predict AF recurrence. Methods Between October 2018 to December 2021, patients who underwent index radiofrequency ablation for AF with at least one standard 10-second surface ECG during sinus rhythm were enrolled. An end-to-end deep learning framework based on Transformer and a fusion module was used to predict AF recurrence using ECG and clinical features. Model performance was evaluated using areas under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy and F1-score. Results A total of 920 patients (median age 61 [IQR 14] years, 66.3% male) were included. After a median follow-up of 24 months, 253 patients (27.5%) experienced AF recurrence. A single deep learning enabled ECG signals identified AF recurrence with an AUROC of 0.769, sensitivity of 75.5%, specificity of 61.1%, F1 score of 55.6% and overall accuracy of 65.2%. Combining ECG signals and clinical features increased the AUROC to 0.899, sensitivity to 81.1%, specificity to 81.7%, F1 score to 71.7%, and overall accuracy to 81.5%. Conclusions The Transformer algorithm demonstrated excellent performance in predicting AF recurrence. Integrating ECG and clinical features enhanced the models’ performance and may help identify patients at low risk for AF recurrence after index ablation.

Background Deep terminal negativity of the P‐wave in V1 (DTNPV1) was considered if the absolute value of the depth of the negative phase was >100 μV in the presence of a biphasic P‐wave in V1. In this study, we aimed to determine the association between DTNPV1, a simpler P‐wave index, and the risk of stroke. Methods We compared P‐wave indices between participants with and without a self‐reported history of stroke in the United States Third National Health and Nutrition Examination Survey (NHANES III). The association between DTNPV1 and stroke was quantified with logistic regression models. Results In total, 7732 participants were included (307 with a history of stroke). Patients with stroke had deeper terminal negativity of the P‐wave in V1 (52.3 ± 33.9 μV vs. 41.4 ± 27.0 μV, p < .001). After adjustment, DTNPV1 was associated with an increased risk of stroke (OR: 1.63, 95% CI: 1.03–2.60, p = .038). This association appeared to be stronger in people aged <75 years (interaction p = .023), and in those without heart failure (interaction p = .018) or ischemic heart disease (interaction p = .014). In contrast to the participants with 0 or ≥2 risk factors, in those with 1 risk factor, stroke prevalence was significantly different among the three categories of terminal negativity of the P‐wave (0 μV, >0 μV but ≤100 μV and > 100 μV) in V1 (2.8%, 3.3%, and 10.3%, respectively, p = .005). Conclusion In NHANES III, DTNPV1 was associated with a higher prevalence of stroke, suggesting that DTNPV1 might be a convenient marker to distinguish the risk of stroke. DTNPV1 might be a simpler P‐wave index to distinguish stroke risk. The association between DTNPV1 and stroke risk was stronger in people age <75 years and in those without heart failure or ischemic heart disease. The association between DTNPV1 and stroke risk was stronger in people at intermediate stroke risk than those at low or high risk.

To explore the diagnostic efficacy of tomosynthesis spot compression (TSC) compared with conventional spot compression (CSC) for ambiguous findings on full-field digital mammography (FFDM). In this retrospective study, 122 patients (including 108 patients with dense breasts) with ambiguous FFDM findings were imaged with both CSC and TSC. Two radiologists independently reviewed the images and evaluated lesions using the Breast Imaging Reporting and Data System. Pathology or at least a 1-year follow-up imaging was used as the reference standard. Diagnostic efficacies of CSC and TSC were compared, including area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The mean glandular dose was recorded and compared for TSC and CSC. Of the 122 patients, 63 had benign lesions and 59 had malignant lesions. For Reader 1, the following diagnostic efficacies of TSC were significantly higher than those of CSC: AUC (0.988 vs. 0.906, P  = 0.001), accuracy (93.4% vs. 77.8%, P  = 0.001), specificity (87.3% vs. 63.5%, P  = 0.002), PPV (88.1% vs. 70.5%, P = 0.010), and NPV (100% vs. 90.9%, P  = 0.029). For Reader 2, TSC showed higher AUC (0.949 vs. 0.909, P  = 0.011) and accuracy (83.6% vs. 71.3%, P  = 0.022) than CSC. The mean glandular dose of TSC was higher than that of CSC (1.85 ± 0.53 vs. 1.47 ± 0.58 mGy, P  < 0.001) but remained within the safety limit. TSC provides better diagnostic efficacy with a slightly higher but tolerable radiation dose than CSC. Therefore, TSC may be a candidate modality for patients with ambiguous findings on FFDM.