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WSNs are an important component of the Internet of Things (IoT), and the research on their routing protocols has always been a hot topic in academia. However, in ARTFMRs’ collaborative operation along railway lines, there are common problems such as energy holes, high latency, and uneven energy consumption in LWSNs. To address these issues, this paper proposes a genetic algorithm-optimized energy-aware routing protocol (GAECRPQ). Firstly, a non-uniform deployment strategy of three-line isosceles triangles is constructed to enhance coverage and balance node distribution. Secondly, an energy–distance adaptive weighting mechanism based on a genetic algorithm is introduced for cluster head (CH) selection to reduce energy consumption in hotspots and extend the network lifetime. Finally, a task-aware TDMA dynamic time slot allocation method is proposed, which incorporates the real-time task status of ARTFMRs into communication scheduling to achieve priority transmission under latency constraints. The simulation results show, that compared with six unequal clustering protocols—EADUC, EAUCA, EBUC, EEUC, LEACH, and LEACH-C—the three-line isosceles triangle deployment has a wider coverage area, and the GAECRPQ protocol increases the network lifetime by 7.4%, the lifetime by 40%, and reduces the average latency by 55.77%, 53.07%, 47.61%, 39.87%, 52.08%, and 50.48%, respectively. This verifies that GAECRPQ has good performance in terms of network lifetime and energy utilization efficiency, providing a practical solution for the collaborative operation of ARTFMRs in railway maintenance scenarios.

With the advent of Industry 4.0, the seamless integration of industrial systems and unmanned technologies has significantly accelerated the development of smart industries. However, the research on task allocation for railway maintenance robots remains limited, particularly with respect to optimizing costs and efficiency within smart railway systems. To address this gap, the present study explores multi-robot task allocation for automated orbital bolt maintenance, aiming to enhance operational efficiency by minimizing both makespan and total travel distance for all robots. To achieve this, an improved hybrid algorithm combining NSGA-II and MOPSO is proposed. Initially, a dynamic task planning method, tailored to the specific conditions of railway operations, is developed. This method uses the coordinates of track bolts to extract environmental features, enabling the dynamic partitioning of task areas. Subsequently, a multi-elite archive strategy is introduced, along with an adaptive mechanism for adjusting crossover and mutation probabilities. This ensures the preservation and maintenance of multiple solutions across various Pareto fronts, effectively mitigating the premature convergence commonly observed in traditional NSGA-II algorithms. Moreover, the integration of the MOPSO algorithm strikes a balance between local and global search capabilities, thereby enhancing both optimization efficiency and solution quality. Finally, a series of experiments, conducted with varying task sizes and robot quantities during the railway maintenance window, validate the effectiveness and improved performance of the proposed algorithm in addressing the multi-robot task allocation problem.

Social media fatigue (SMF), which refers to social media users’ tendency to withdraw from social media because of feeling overwhelmed, is closely related to individuals’ social life and well-being. Many studies focused on understanding SMF and exploring its enablers and influences. However, few pieces of research administered a standard measurement of SMF. This study aimed to develop and validate a measure of SMF, and a cross-sectional survey was conducted among 1599 participants in total. Semi-structured interviews of 30 participants were firstly conducted as a pilot study, and an initial version of the social media fatigue scale (SMFS) with 24 items was generated. Then, both exploratory factor analysis (N = 509) and confirmatory factor analysis (N = 552) as well as reliability and validity analysis (N = 508) were conducted and a 15-item SMFS was finally developed. The results demonstrated that: 1) SMF was a multi-dimension concept including a cognitive aspect, an emotional aspect and a behavioral aspect; 2) the three-dimensional structure of the SMFS (cognitive-behavioral-emotional structure) fitted the data well; 3) the McDonald’s Omega coefficients for the SMFS was 0.83, suggesting that the SMFS was reliable; 4) criterion validity was satisfactory as indicated by both the significant correlations between self-rated scores of fatigue and total SMFS scores and the significant regression model of SMF on social media privacy, social media confidence, and negative feeling after comparison. Based on the Limited Capacity Model, the present study expanded SMF from a unidimensional model to a three-dimension model, and developed a 15-item SMFS. The study enriched the existing knowledge of SMF, and coined a reliable and valid tool for measuring it. Besides, concluding the typical characteristics of SMF, the study may provide some inspiration for both researchers and social media managers and operators in mitigating SMF.

Postmenopausal osteoporosis (PMOP) is a condition in women caused by estrogen deficiency, characterized by reduced bone mass and increased fracture risk. Fatty acid-binding protein 4 (FABP4), a lipid-binding protein involved in metabolism and inflammation, has emerged as a key regulator in metabolic disorders and bone resorption; however, its direct role in PMOP remains unclear. Here, we show that serum FABP4 levels in PMOP patients negatively correlate with bone mineral density, a trend also observed in ovariectomized mice. FABP4 promotes osteoclast formation and bone resorption without affecting osteoblast differentiation. The FABP4 inhibitor BMS309403 suppresses osteoclast differentiation by modulating calcium signaling and inhibiting the Ca 2+ -Calcineurin-NFATc1 pathway. Oral BMS309403 increases bone mineral density in ovariectomized mice, though less effectively than alendronate. Notably, bone-targeted delivery of BMS309403 achieves comparable efficacy to alendronate. In this work, we demonstrate that FABP4 is a critical mediator in PMOP and that its inhibition offers a promising therapeutic strategy. This study identifies FABP4 as driver of postmenopausal osteoporosis and shows that targeting FABP4 with a small-molecule inhibitor or bone-targeted nanoparticles can reduce bone loss and offers a potential new treatment approach.

Defect detection in rail fasteners constitutes a fundamental requirement for ensuring safe and reliable railway operations. Confronted with increasingly demanding inspection requirements of modern rail networks, traditional manual visual inspection methods have proven inadequate. To achieve accurate, efficient, and intelligent detection of rail fasteners, this paper presents an enhanced YOLOv5m-based defect detection model. Firstly, a dual-attention mechanism comprising Squeeze-and-Excitation and Coordinate Attention modules is employed to enhance the model. Secondly, the network architecture is redesigned by adopting MobileNetv3 as the backbone while incorporating structures with Ghost Shuffle Convolution (GSConv) modules and lightweight upsampling operators to reduce computational overhead. Finally, the original CIoU loss function in YOLOv5 is replaced with SIoU to accelerate convergence rate during training. Experimental results on a custom-built rail fastener dataset comprising 6500 images demonstrate that the enhanced model achieves 96.5% mAP and 17.9 FPS, surpassing the baseline by 3.1% and 2.1 FPS, respectively. Compared to existing detection models, this solution exhibits higher accuracy, faster inference, and lower memory consumption, providing critical technical support for edge deployment of rail fastener defect detection systems.

The stochastic fluctuation characteristics of wind speed can significantly affect the control performance of train suspension systems. To enhance the running quality of trains in non-stationary wind fields, this paper proposes a semi-active control method for trains based on fuzzy rules of non-stationary wind fields. Firstly, a dynamic model of the train and suspension system was established based on the CRH2 (China Railway High-Speed 2) high-speed train and magnetorheological dampers. Then, using frequency–time transformation technology, the non-stationary wind load excitation and train response patterns under 36 common operating conditions were calculated. Finally, by analyzing the response patterns of the train under different operating conditions, a comprehensive control rule table for the semi-active suspension system of the train under non-stationary wind fields was established, and a fuzzy controller suitable for non-stationary wind fields was designed. To verify the effectiveness of the proposed method, the running smoothness of the train was analyzed using a train-semi-active suspension system co-simulation model based on real wind speed data from the Lanzhou–Xinjiang railway line. The results demonstrate that the proposed method significantly improves the running quality of the train. Specifically, when the wind speed reaches 20 m/s and the train speed reaches 200 km/h, the lateral Sperling index is increased by 46.4% compared to the optimal standard index, and the vertical Sperling index is increased by 71.6% compared to the optimal standard index.

In this paper, we investigate the modeling and control of networked discrete-event systems (DESs), where a supervisor is connected to the plant via an observation channel and the control commands issued by the supervisor are delivered to the actuator of the plant via a control channel. Communication delays exist in both the observation channel and the control channel. First, a novel modeling framework for the supervisory control of DESs subject to observation delays and control delays is presented. The framework explicitly models the interaction process between the plant and the supervisor over the communication channels. Compared with the previous work, a more accurate “dynamics” of the closed-loop system is specified. Under this framework, we further discuss how to estimate the states of the closed-loop system in the presence of observation delays and control delays. Based on the state estimation, we synthesize an optimal supervisor on the fly to maximize the controlled behaviors while preventing the system from leaving the desired behaviors under communication delays. We compare the proposed supervisor with the supervisor proposed in the literature and show that the proposed supervisor is more permissive. As an application, we show how the proposed approach can be applied to manage vehicles in a signal intersection. Finally, we show how to extend the proposed framework to model a system whose actuators and sensors are distributed at different sites.

Correspondence to Dr Yan Wang; yan.wang@siat.ac.cn ; Dr Qian Xie; qian.xie@szu.edu.cn Obesity is recognised as a risk factor for triggering rheumatoid arthritis (RA), and it can worsen joint deformities1 and diminish the quality of life in patients with RA.2 The reduction of body weight in obese individuals is believed to alleviate RA symptoms.3 Body mass index (BMI) serves as the primary standard for evaluating obesity.4 An increase in BMI by 1 SD notably elevates the incidence rate of RA, suggesting a causal link between higher BMI and an increased risk of developing RA.5 The association between BMI and obesity is straightforward, as a higher BMI typically indicates a greater risk of obesity.4 Obesity is clinically defined as having a BMI of 30 kg/m2 or greater.4 Here, we established collagen-induced arthritis (CIA) models in mice using both regular and high-fat diets (HFDs) to see if HFD can induce severe RA symptoms in mice. Furthermore, the HFD sham group displayed significantly higher levels of cholesterol (CHO) and high-density lipoprotein (HDL) compared with the regular diet sham group, whereas the HFD CIA group exhibited similar serum levels of CHO, HDL, low-density lipoprotein and triglycerides compared with the regular diet CIA group (figure 1C). (C) Cholesterol (CHO), high-density lipoprotein (HDL), low-density lipoprotein (LDL) and triglyceride (TG) levels were measured at the end of the experiment.

Studies have rarely investigated the association between extrinsic motivation and social media fatigue. This study aims to examine the mediating role of Fear of missing out (FOMO) and problematic social media use in the association between extrinsic academic motivation and social media fatigue. A total of 399 college students (43% males) completed measures of extrinsic academic motivation, FOMO, problematic social media use, and social media fatigue. The results showed that FOMO mediated the relationship between extrinsic academic motivation and problematic social media use; problematic social media use mediated the association between FOMO and social media fatigue; extrinsic academic motivation fostered social media fatigue either through FOMO or problematic social media use, or through these two factors together; and the indirect mediation effects between extrinsic academic motivation and social media fatigue through problematic social media use were larger than the single mediation effect of FOMO and their serial mediation effects. In addition, the indirect effects of the three subconstructs of extrinsic academic motivation (external regulation, introjected regulation, and identified regulation) on social media fatigue follow a trend of gradual decline. The findings and implications of this study are presented and discussed.

Cyberbystanders are generally defined as witnesses of cyberbullying performed through electronic media. They might support the perpetrator ( reinforcer ), help the victim ( defender ), or do nothing ( outsider ). Limited research has investigated the different roles of cyberbystanders and the factors that influence their behavior. This study aimed to advance the understanding of cyberbystander behavior by focusing on the role moral disengagement plays in the relationship between empathy and behavior. Four hundred and thirty-five participants (45% males) with an average age of 30 years completed the Interpersonal Reactivity Index-C, the Moral Disengagement Scale, and the Cyberbystander Behavior Questionnaire. The results indicate that gender and age have non-significant associations with cyberbystander roles. Both cyberbullying perpetration and victimization had significant effects only on reinforcer behavior. Moreover, moral disengagement mediates relationships between either emotional or cognitive empathy and reinforcer/defender/outsider roles, respectively. The significance and limitations of these results are discussed.