Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System

Unmanned aerial vehicles (UAVs) can be applied in many Internet of Things (IoT) systems, e.g., smart farms, as a data collection platform. However, the UAV-IoT wireless channels may be occasionally blocked by trees or high-rise buildings. An intelligent reflecting surface (IRS) can be applied to improve the wireless channel quality by smartly reflecting the signal via a large number of low-cost passive reflective elements. This article aims to minimize the energy consumption of the system by jointly optimizing the deployment and trajectory of the UAV. The problem is formulated as a mixed-integer-and-nonlinear programming (MINLP), which is challenging to address by the traditional solution, because the solution may easily fall into the local optimal. To address this issue, we propose a joint optimization framework of deployment and trajectory (JOLT), where an adaptive whale optimization algorithm (AWOA) is applied to optimize the deployment of the UAV, and an elastic ring self-organizing map (ERSOM) is introduced to optimize the trajectory of the UAV. Specifically, in AWOA, a variable-length population strategy is applied to find the optimal number of stop points, and a nonlinear parameter a and a partial mutation rule are introduced to balance the exploration and exploitation. In ERSOM, a competitive neural network is also introduced to learn the trajectory of the UAV by competitive learning, and a ring structure is presented to avoid the trajectory intersection. Extensive experiments are carried out to show the effectiveness of the proposed JOLT framework.Comment: 11 pages, 7 figures, 4 table

A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing

This paper studies a multi-user cooperative mobile edge computing offloading (CoMECO) system in a multi-user interference environment, in which delay-sensitive tasks may be executed on local devices, cooperative devices, or the primary MEC server. In this system, we jointly optimize the offloading decision and computation resource allocation for minimizing the total energy consumption of all mobile users under the delay constraint. If this problem is solved directly, the offloading decision and computation resource allocation are generally generated separately at the same time. Note, however, that they are closely coupled. Therefore, under this condition, their dependency is not well considered, thus leading to poor performance. We transform this problem into a bilevel optimization problem, in which the offloading decision is generated in the upper level, and then the optimal allocation of computation resources is obtained in the lower level based on the given offloading decision. In this way, the dependency between the offloading decision and computation resource allocation can be fully taken into account. Subsequently, a bilevel optimization approach, called BiJOR, is proposed. In BiJOR, candidate modes are first pruned to reduce the number of infeasible offloading decisions. Afterward, the upper level optimization problem is solved by ant colony system (ACS). Furthermore, a sorting strategy is incorporated into ACS to construct feasible offloading decisions with a higher probability and a local search operator is designed in ACS to accelerate the convergence. For the lower level optimization problem, it is solved by the monotonic optimization method. In addition, BiJOR is extended to deal with a complex scenario with the channel selection. Extensive experiments are carried out to investigate the performance of BiJOR on two sets of instances with up to 400 mobile users. The experimental results demonstrate the effectiveness of BiJOR and the superiority of the CoMECO system

GluNet: A Deep Learning Framework For Accurate Glucose Forecasting

For people with Type 1 diabetes (T1D), forecasting of \red{blood glucose (BG)} can be used to effectively avoid hyperglycemia, hypoglycemia and associated complications. The latest continuous glucose monitoring (CGM) technology allows people to observe glucose in real-time. However, an accurate glucose forecast remains a challenge. In this work, we introduce GluNet, a framework that leverages on a personalized deep neural network to predict the probabilistic distribution of short-term (30-60 minutes) future CGM measurements for subjects with T1D based on their historical data including glucose measurements, meal information, insulin doses, and other factors. It adopts the latest deep learning techniques consisting of four components: data pre-processing, label transform/recover, multi-layers of dilated convolution neural network (CNN), and post-processing. The method is evaluated in−silico for both adult and adolescent subjects. The results show significant improvements over existing methods in the literature through a comprehensive comparison in terms of root mean square error (RMSE) (8.88 ± 0.77 mg/dL) with short time lag (0.83 ± 0.40 minutes) for prediction horizons (PH) = 30 mins (minutes), and RMSE (19.90 ± 3.17 mg/dL) with time lag (16.43 ± 4.07 mins) for PH = 60 mins for virtual adult subjects. In addition, GluNet is also tested on two clinical data sets. Results show that it achieves an RMSE (19.28 ± 2.76 mg/dL) with time lag (8.03 ± 4.07 mins) for PH = 30 mins and an RMSE (31.83 ± 3.49 mg/dL) with time lag (17.78 ± 8.00 mins) for PH = 60 mins. These are the best reported results for glucose forecasting when compared with other methods including the neural network for predicting glucose (NNPG), the support vector regression (SVR), the latent variable with exogenous input (LVX), and the auto regression with exogenous input (ARX) algorithm

Indirect Tensile Strength Test on Heterogeneous Rock Using Square Plate Sample with a Circular Hole

An indirect testing method for determining the tensile strength of rock-like heterogeneous materials is proposed. The realistic failure process analysis method, which can consider material inhomogeneity, is applied to model the failure process of the square plate containing a circular hole under uniaxial compression. The influence of plate thickness and applied loads on the maximum tensile stress is investigated, and the tensile strength equation is deduced. Meanwhile, the initial cracking loads are obtained by the corresponding physical tests, and the tensile strengths are determined by substituting the initial cracking loads into the developed tensile strength equation. The values predicted by the newly proposed method are almost identical to those of the direct tensile tests. Furthermore, the proposed method can give the relatively small tensile strength error with the direct tensile test in comparison to the other test methods, which indicates that the proposed method is effective and valid for determining the tensile strength of rock-like heterogeneous materials

Workplace Social Capital and Mental Health among Chinese Employees: A Multi-Level, Cross-Sectional Study

Background: Whereas the majority of previous research on social capital and health has been on residential neighborhoods and communities, the evidence remains sparse on workplace social capital. To address this gap in the literature, we examined the association between workplace social capital and health status among Chinese employees in a large, multilevel, cross-sectional study. Methods: By employing a two-stage stratified random sampling procedure, 2,796 employees were identified from 35 workplaces in Shanghai during March to November 2012. Workplace social capital was assessed using a validated and psychometrically tested eight-item measure, and the Chinese language version of the WHO-Five Well-Being Index (WHO-5) was used to assess mental health. Control variables included sex, age, marital status, education level, occupation status, smoking status, physical activity, and job stress. Multilevel logistic regression analysis was conducted to explore whether individual- and workplace-level social capital was associated with mental health status. Results: In total, 34.9% of workers reported poor mental health (WHO-5,13). After controlling for individual-level sociodemographic and lifestyle variables, compared to workers with the highest quartile of personal social capital, workers with the third, second, and lowest quartiles exhibited 1.39 to 3.54 times greater odds of poor mental health, 1.39 (95% CI: 1.10– 1.75), 1.85 (95% CI: 1.38–2.46) and 3.54 (95% CI: 2.73–4.59), respectively. Corresponding odds ratios for workplace-level social capital were 0.95 (95% CI: 0.61–1.49), 1.14 (95% CI: 0.72–1.81) and 1.63 (95% CI: 1.05–2.53) for the third, second, and lowest quartiles, respectively. Conclusions: Higher workplace social capital is associated with lower odds of poor mental health among Chinese employees. Promoting social capital at the workplace may contribute to enhancing employees’ mental health in China

Task-Load-Aware Game-Theoretic Framework for Wireless Federated Learning

Federated learning (FL) can protect data privacy but has difficulties in motivating user equipment (UE) to engage in task training. This paper proposes a Bertrand-game based framework to address the incentive problem, where a model owner (MO) issues an FL task and the employed UEs help train the model by using their local data. Specially, we consider the impact of time-varying task load and channel quality on UE’s motivation to engage in the FL task. We adopt the finite-state discrete-time Markov chain (FSDT-MC) to predict these parameters during the FL task. Depending on the performance metrics set by the MO and the estimated energy cost of the FL task, each UE seeks to maximize its profit. We obtain the Nash equilibrium (NE) of the game in closed form, and develop a distributed iterative algorithm to find it. Finally, the simulation result verifies the effectiveness of the proposed approach

Disease burden of low back pain attributable to ergonomic risk factors in selected Chinese occupational groups

BackgroundAs traditional chemical and physical hazards as well as associated adverse health outcomes in workplace were wildly controlled in the past half century, the prevalence and disease burden of low back pain (LBP) have drawn more and more attention and become one of the important public health problems in the world. ObjectiveTo analyze the health loss and attributable disease burden of ergonomic risk factors for LBP in two major categories of occupations in China, aiming to provide evidence for formulating effective prevention and control policies of LBP in the workplace. MethodsBased on the methodological framework of the Global Burden of Disease Study (GBD), a meta-analysis was firstly applied to summarize relevant literature results and estimate the prevalence of LBP in two occupational groups (including technicians and associate professionals and machine operators and assemblers) by different age groups in China. Then important epidemiologic parameters (including disability weight, remission rate, and incidence) from GBD 2019 were used to estimate mean duration of disease and age at onset using DisMod II software, and to calculate health loss indexes in the selected occupational groups in China in 2013, such as years lived with disability (YLD) and disability-adjusted life year (DALY) of LBP and its attributable fractions by ergonomic risk factors, which were compared to the outcome of GBD 2013. ResultsAfter the adjustment by DisMod II, the prevalence rate of LBP was 13.00% in technicians and associate professionals (11.25% for males and 14.84% for females) and 14.80% in machine operators and assemblers (13.56% for males and 16.10% for females) in 2013, which increased with age. The DALY rate of LBP was 8.02‰ in technicians and associate professionals (7.68‰ for males and 8.33‰ for females) and 10.34‰ in machine operators and assemblers (10.30‰ for males and 10.44‰ for females), which also showed an overall increasing trend with age. In 2013, the population attributable fraction (PAF) of ergonomic risk factors to LBP was 11.42% in technicians and associate professionals and 29.17% in machine operators and assemblers. The DALY of LBP attributable to ergonomics risk factors was 4498 person-years (2108 person-years for males), with the highest DALY in the 45-49 year group (951 person-years), and the attributable DALY rate was 0.92‰ in technicians and associate professionals. The DALY of LBP attributable to ergonomics risk factors was 48529 person-years (33046 person-years for males), with the highest DALY in the 40-44 year group (10852 person-years), and the attributable DALY rate was 3.02‰ in machine operators and assemblers. Regarding LBP-associated DALY rate, in the 20 years of age and above group, both occupational groups (technicians and associate professionals: 8.06‰, machine operators and assemblers: 10.66‰) showed higher values than the general population (3.55‰). In the 20 years of age and above group, the DALY rates attributable to ergonomic risk factors with the order from high to low were machine operators and assemblers (3.11‰), general population (1.10‰) and technicians and associate professionals (0.92‰).ConclusionThe LBP-associated disease burden is heavier in the two Chinese occupational groups than in general population. Reducing the disease burden of LBP by interventions targeting ergonomic risk factors in machine operators and assemblers is more effective than that in technicians and associate professionals as the results of attributable burden of disease suggest

Predictive value of the domain specific PLA2R antibodies for clinical remission in patients with primary membranous nephropathy: A retrospective study.

BackgroundM-type phospholipase A2 receptor (PLA2R) is a major auto-antigen of primary membranous nephropathy(PMN). Anti-PLA2R antibody levels are closely associated with disease severity and therapeutic effectiveness. Analysis of PLA2R antigen epitope reactivity may have a greater predictive value for remission compared with total PLA2R-antibody level. This study aims to elucidate the relationship between domain-specific antibody levels and clinical outcomes of PMN.MethodsThis retrospective analysis included 87 patients with PLA2R-associated PMN. Among them, 40 and 47 were treated with rituximab (RTX) and cyclophosphamide (CTX) regimen, respectively. The quantitative detection of -immunoglobulin G (IgG)/-IgG4 targeting PLA2R and its epitope levels in the serum of patients with PMN were obtained through time-resolved fluorescence immunoassays and served as biomarkers in evaluating the treatment effectiveness. A predictive PMN remission possibility nomogram was developed using multivariate logistic regression analysis. Discrimination in the prediction model was assessed using the area under the receiver operating characteristic curve (AUC-ROC).Bootstrap ROC was used to evaluate the performance of the prediction model.ResultsAfter a 6-month treatment period, the remission rates of proteinuria, including complete remission and partial remission in the RTX and CTX groups, were 70% and 70.21% (P = 0.983), respectively. However, there was a significant difference in immunological remission in the PLA2R-IgG4 between the RTX and CTX groups (21.43% vs. 61.90%, P = 0.019). Furthermore, we found differences in PLA2R-CysR-IgG4(P = 0.030), PLA2R-CTLD1-IgG4(P = 0.005), PLA2R-CTLD678-IgG4(P = 0.003), and epitope spreading (P = 0.023) between responders and non-responders in the CTX group. Multivariate logistic analysis showed that higher levels of urinary protein (odds ratio [OR], 0.49; 95% confidence interval [CI], 0.26-0.95; P = 0.035) and higher levels of PLA2R-CTLD1-IgG4 (OR, 0.79; 95%CI,0.62-0.99; P = 0.041) were independent risk factors for early remission. A multivariate model for estimating the possibility of early remission in patients with PMN is presented as a nomogram. The AUC-ROC of our model was 0.721 (95%CI, 0.601-0.840), in consistency with the results obtained with internal validation, for which the AUC-ROC was 0.711 (95%CI, 0.587-0.824), thus, demonstrating robustness.ConclusionsCyclophosphamide can induce immunological remission earlier than rituximab at the span of 6 months. The PLA2R-CTLD1-IgG4 has a better predict value than total PLA2R-IgG for remission of proteinuria at the 6th month