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In this study, to solve the low accuracy of multi-space-based sensor joint tracking in the presence of unknown noise characteristics, an adaptive multi-sensor joint tracking algorithm (AMSJTA) is proposed. First, the coordinate transformation from the target object to the optical sensors is considered, and the observation vector-based measurement model is established. Then, the measurement noise characteristics are assumed to be white Gaussian noise, and the measurement covariance matrix is set as a constant. On this premise, the traditional iterative extended Kalman filter is applied to solve this problem. However, in most actual engineering applications, the measurement noise characteristics are unknown. Thus, a forgetting factor is introduced to adaptively estimate the unknown measurement noise characteristics, and the AMSJTA is designed to improve the tracking accuracy. Furthermore, the lower bound of the proposed algorithm is theoretically proved. Finally, numerical simulations are executed to verify the effectiveness and superiority of the proposed AMSJTA.

This paper studies the problem of joint deployment, user association, channel, and resource allocation in unmanned aerial vehicle‐enabled access network. Since different user equipments performing different tasks and have different data rate requirements, the priority‐based traffic fairness problem is investigated. This problem, however, is a mixed integer nonlinear programming problem with NP‐hard complexity, making it challenging to be solved. To address this issue, a self‐organized and distributed framework “sense‐as‐you‐fly” based on the decomposition process, which divides the original problem into several subproblems, is proposed. Assuming without central controller, we derive the closed‐form resource allocation scheme and propose distributed many‐to‐one matching to optimize user association subproblem. Considering the coupled characteristics, the multi‐unmanned aerial vehicle deployment and channel allocation subproblems are modelled as a local altruistic game. The existence of Nash equilibrium is proved with the aid of exact potential game and efficient best response learning‐based algorithm is proposed. The original problem is finally addressed by solving the sub‐problems alternately and iteratively. Simulation results verify its effectiveness. By jointly optimizing multidimensional variables, the proposed algorithm unlocks network performance gains, especially in resource‐limited regimes. This paper investigates the priority‐aware traffic fairness problem in a unmanned aerial vehicle‐enabled wireless access network. A self‐organized and distributed framework is proposed where multi‐unmanned aerial vehicle deployment, user association, channel, and resource allocation are jointly considered. The performance is analysed, and extensive simulations verify the effectiveness and robustness of the proposed algorithm.

As women age, especially after menopause, cardiovascular disease (CVD) prevalence rises, posing a significant global health concern. Regular exercise can mitigate CVD risks by improving blood pressure and lipid levels in postmenopausal women. Yet, the optimal exercise modality for enhancing vascular structure and function in this demographic remains uncertain. This study aims to compare five exercise forms to discern the most effective interventions for reducing cardiovascular risk in postmenopausal women. The study searched PubMed, Web of Science, Cochrane, EBSCO, and Embase databases. It conducted a network meta-analysis (NMA) of randomized controlled trials (RCTs) on five exercise interventions: continuous endurance training (CET), interval training (INT), resistance training (RT), aerobic combined with resistance training (CT), and hybrid-type training (HYB). Outcome measures included carotid artery intima-media thickness (IMT), nitric oxide (NO), augmentation index (AIx), pulse wave velocity (PWV), and flow-mediated dilatation (FMD) of the brachial artery. Eligible studies were assessed for bias using the Cochrane tool. A frequentist random-effects NMA was employed to rank exercise effects, calculating standardized mean differences (SMDs) with 95% confidence intervals (CIs). The analysis of 32 studies ( = 1,427) indicates significant increases in FMD with CET, INT, RT, and HYB in postmenopausal women. Reductions in PWV were significant with CET, INT, RT, CT, and HYB. AIx decreased significantly with INT and HYB. CET, INT, and CT significantly increased NO levels. However, no significant reduction in IMT was observed. SUCRA probabilities show INT as most effective for increasing FMD, CT for reducing PWV, INT for decreasing AIx, CT for lowering IMT, and INT for increasing NO in postmenopausal women. The study demonstrates that CET, INT, RT, and HYB have a significant positive impact on FMD in postmenopausal women. Furthermore, all five forms of exercise significantly enhance PWV in this population. INT and HYB were found to have a significant positive effect on AIx in postmenopausal women, while CET, INT, and CT were found to significantly improve NO levels. For improving vascular function in postmenopausal women, it is recommended to prioritize INT and CT exercise modalities. On the other hand, as CET and RT were not ranked at the top of the Sucra value ranking in this study and were less effective than INT and CT as exercise interventions to improve vascular function in postmenopausal women, it is not recommended that CET and RT be considered the preferred exercise modality.

The aim of this meta-analysis was to investigate the effect of exercise intervention on glycolipid metabolism in overweight and obese adolescents. A systematic review and meta-analysis of randomized trials were conducted. The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was registered (ID: CRD42024623686). Electronic searches were performed using the following databases: Web of Science, PubMed, Scopus, Cochrane and Embase. Randomized controlled trials of exercise interventions were included. Data on fasting blood glucose (FBG), fasting insulin (FINS), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) before and after exercise interventions were extracted for overweight and obese adolescents. Risk of bias was assessed using the Cochrane risk of bias tool. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) tool was used to evaluate the quality of the evidence. Standardized mean differences (SMDs) were calculated to compare differences between exercise and conventional control groups. Subgroup analyses were performed to assess whether effects differed by exercise type, intervention duration, supervision, and intervention frequency. A total of 984 participants (576 in experimental groups and 408 in control groups) were included across 26 studies. The analysis revealed that exercise interventions significantly improved key metabolic parameters: FBG (SMD: -0.42 95% CI [-0.73 to -0.12]), FINS (SMD: -0.81 95% CI [-1.13 to -0.49]), TC (SMD: -0.18 95% CI [-0.34 to -0.01]), TG (SMD: -0.46 95% CI [-0.56 to -0.25]), LDL-C (SMD: -0.28 95% CI [-0.44 to -0.12]), and HDL-C (SMD: 0.26 95% CI [0.11-0.40]). Subgroup analysis indicated that exercise type, supervision and intervention frequency influenced the effectiveness. The analysis suggests that exercise interventions improve glycolipid metabolism in adolescents with overweight and obesity. Continuous endurance training demonstrated greater efficacy in improving blood glucose parameters, whereas hybrid-type exercise showed advantages in improving lipid metabolism. Engaging in three supervised training sessions weekly may be the optimal approach to enhance glycolipid metabolism in obese adolescents. These findings provide evidence for clinicians and healthcare professionals ( ., exercise physiologists, physical therapists) to guide exercise prescriptions for obese adolescents, thereby preventing worsening metabolic imbalances.

Background Knee osteoarthritis is a common degenerative joint disease involving multiple pathological processes, including energy metabolism, cartilage repair, and osteogenesis. To investigate the alterations in critical metabolic pathways and differential proteins in osteoarthritis patients through metabolomic and proteomic analyses and to explore the potential mechanisms underlying synovial osteogenesis during osteoarthritis progression. Methods Metabolomics was used to analyze metabolites in the synovial fluid and synovium of osteoarthritis patients (osteoarthritis group: 10; control group: 10), whereas proteomics was used to examine differential protein expression. Alkaline phosphatase activity was assessed to evaluate osteogenesis. Results Upregulation of the tricarboxylic acid cycle: Significant upregulation of the tricarboxylic acid cycle in the synovial fluid and synovium of osteoarthritis patients indicated increased energy metabolism and cartilage repair activity. Arginine metabolism and collagen degradation: Elevated levels of ornithine, proline, and hydroxyproline in the synovial fluid reflect active collagen degradation and metabolism, contributing to joint cartilage breakdown. Abnormal Phenylalanine Metabolism: Increased phenylalanine and tyrosine metabolite levels in osteoarthritis patients suggest their involvement in cartilage destruction and osteoarthritis progression. Synovial osteogenesis: Increased expression of type I collagen in the synovium and elevated alkaline phosphatase activity confirmed the occurrence of osteogenesis, potentially driven by the differentiation of synovial fibroblasts, mesenchymal stem cells, and hypertrophic chondrocytes. Relationships between differential proteins and osteogenesis: FN1 and TGFBI are closely associated with synovial osteogenesis, while the upregulation of energy metabolism pathways provides the energy source for osteogenic transformation. Conclusions Alterations in energy metabolism, cartilage repair, and osteogenic mechanisms are critical. The related metabolites and proteins have potential as diagnostic and therapeutic targets for osteoarthritis.

Modifying farming practices combined with breeding has the potential to improve water and nutrient use efficiency by regulating root growth, but achieving this goal requires phenotyping the roots, including their architecture and ability to take up water and nutrients from different soil layers. This is challenging due to the difficulty of in situ root measurement and opaqueness of the soil. Using stable isotopes and soil coring, we calculated the change in root water uptake of summer maize in response to planting density and nitrogen fertilization in a 2-year field experiment. We periodically measured root-length density, soil moisture content, and stable isotopes δ 18 O and δD in the plant stem, soil water, and precipitation concurrently and calculated the root water uptake based on the mass balance of the isotopes and the Bayesian inference method coupled with the Markov Chain Monte Carlo simulation. The results show that the root water uptake increased asymptotically with root-length density and that nitrogen application affected the locations in soil from which the roots acquired water more significantly than planting density. In particular, we find that reducing nitrogen application promoted root penetration to access subsoil nutrients and consequently enhanced their water uptake from the subsoil, while increasing planting density benefited water uptake of the roots in the topsoil. These findings reveal that it is possible to manipulate plant density and fertilization to improve water and nutrient use efficiency of the summer maize and the results thus have imperative implications for agricultural production.

Human Activity Recognition (HAR) has had a diverse range of applications in various fields such as health, security and smart homes. Among different approaches of HAR, WiFi-based solutions are getting popular since it solves the problem of deployment cost, privacy concerns and restriction of the applicable environment. In this paper, we propose a WiFi-based human activity recognition system that can identify different activities via the channel state information from WiFi devices. A special deep learning framework, Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN), is designed for accurate recognition. LSTM-CNN is going to be compared with the LSTM network and the experimental results demonstrate that LSTM-CNN outperforms existing models and has an average accuracy of 94.14% in multi-activity classification.

When the conventional Hertz formula is used to extract the elastic modulus, E, of cells based on the compression test using atomic force microscope spherical probe, the inconsistency between the actual situation and the assumption of the formula will lead to a large error. Using the ABAQUS for finite element modeling and analysis, here, a modified Hertz formula was developed to reduce the effects of cell radius, cell thickness, probe radius and compression depth on the extracted E of cells. Experimentally, the insensitivity of the extracted E to the compression region of cell and probe radius reflects the validity of the modified formula. Owing to the poor resolution of spherical probes, it's unlikely to know the actual thickness of cell at the measured point, which can lead to a huge error. Based on the modified formula, we further proposed an approach to control the effect of the uncertainty of cell thickness and ensured that a 10% difference in cell thickness does not incur over 10% variation in the obtained elastic modulus.

To address the problem of modeling the growth rate of coating film thickness when spraying at inclination angle, based on Gaussian sum model, it is proposed to use the elliptic double Gaussian sum model to establish the cumulative model of coating film thickness when spraying at static inclination angle of the spray gun. The differential geometry amplification theorem is used to establish the coating growth rate model with the spraying inclination angle as the variable; after that, the static inclination spraying experiments are carried out, and the coating thickness data of the sampling points are recorded through the spraying experiments, and the Levenberg-Maquart algorithm is used for the least-squares fitting of the model, which results in the static spraying film thickness distribution model. Finally, compared with the elliptic double β model, the fitting accuracy of the elliptic double Gaussian sum model is 6.3% higher than that of the elliptic double β model when spraying at inclination angle by comparing the R-square values, and the elliptic double Gaussian sum model is more capable of obtaining a better fitting accuracy, which further confirms the validity and practicability of the model.

Abrasive wear resulting from the microclastic rock is a common failure phenomenon in the drilling environment that often limits the sealing ability and the service life of seals. In this study, the friction and wear process of fluoro rubber (FKM) seals against 304 stainless steel (SS304) after one single entry of SiO 2 abrasives were investigated. The influence of the changes in particle state on friction coefficient evolution, wear loss evolution, wear morphologies, and wear mechanisms were discussed in detail. The results indicate that the presence of abrasive particles dispersed between the sealing interfaces clearly improves the friction performance of the seal pairs and deteriorates the wear performance of the metal counterpart. The movement and breakage of particles after one single entering into the sealing interface were obtained. And on this basis, the stable wear process can be divided into three stages. In addition, the main causes contributed to this change of wear mechanisms are the random movement and process of continuous breakdown of abrasive particles. Furthermore, the transition of the wear mechanism that clearly describes the wearing behavior of the seal pairs under these abrasive wear conditions was identified. The results of this study enhanced our understanding of the abrasive wear degradation of rubber seal in practical drilling applications.