Risk and related factors of elbow musculoskeletal diseases: a nationwide cross-sectional survey in China.

OBJECTIVES: Although studies have shown that work-related musculoskeletal disorders (WMSDs) are common and continue to be a major source of disability and work time loss, there are few reports about elbow WMSDs. The aim of this study was to explore the prevalence and associated factors of elbow WMSDs. METHODS: The valid questionnaires of 57501 workers from 15 different industries nationwide were collected and the c2 test and logistic regression analysis were applied to reveal the prevalence and risk factors of elbow WMSDs. RESULTS: The findings indicated that the overall prevalence of elbow WMSDs among workers was 7.3%. However, the prevalence of elbow WMSDs in toy manufacturing was 21.3%, which was significantly higher than that in other industries (P < .05). Logistic regression analysis showed that age 40 and above, married, very poor health, left-handedness, lifting weights (more than 20 kg each time), work requiring upper limb or hand force, work in an uncomfortable position, repetitive operations within 1 minute, using vibrating tools, work involving cold, cool draughts, or temperature changes, work being completed in the same workshop, work being done outdoors, frequent dealings with customers, 2 shifts, often working overtime, staff shortage, and often working for colleagues were risk factors for elbow WMSDs. A higer education level, monthly income, and enough rest time were protective factors for elbow WMSDs. CONCLUSIONS: Toy manufacturing is a high-risk industry for elbow WMSDs. Promotion of education about ergonomics should be strengthened, and workers' ergonomics awareness should be improved to reduce the impact of WMSDs.

Learning a Low-Dimensional Representation of a Safe Region for Safe Reinforcement Learning on Dynamical Systems.

For the safe application of reinforcement learning algorithms to high-dimensional nonlinear dynamical systems, a simplified system model is used to formulate a safe reinforcement learning (SRL) framework. Based on the simplified system model, a low-dimensional representation of the safe region is identified and used to provide safety estimates for learning algorithms. However, finding a satisfying simplified system model for complex dynamical systems usually requires a considerable amount of effort. To overcome this limitation, we propose a general data-driven approach that is able to efficiently learn a low-dimensional representation of the safe region. By employing an online adaptation method, the low-dimensional representation is updated using the feedback data to obtain more accurate safety estimates. The performance of the proposed approach for identifying the low-dimensional representation of the safe region is illustrated using the example of a quadcopter. The results demonstrate a more reliable and representative low-dimensional representation of the safe region compared with previous works, which extends the applicability of the SRL framework.

Effect of noise on hearing loss among workers in a fastener manufacturing enterprise / 预防医学

Objective @#To investigate the current status of hearing loss in a fastener manufacturing enterprise, and to analyze its influencing factors, so as to provide insights into occupational disease prevention and control. @*Methods@#The occupational health examination data of noise exposed workers and the workplace occupational disease hazard factors detection data in a fastener manufacturing enterprise in Jiaxing City in 2022 were collected through the Occupational Disease and Occupational Health Hazard Factors Detection System of China Disease Prevention and Control Information System, and factors affecting the development of high-frequency noise-induced hearing loss (HFNIHL) and speech-frequency noise-induced hearing loss (SFNIHL) were analyzed. @*Results@#Totally 625 workers were investigated, with a median age of 44.00 (interquartile range, 13.00) years and a median length of service of 8.00 (interquartile range, 9.00) years, and including 519 men (83.04%) and 106 women (16.96%). There were 309 workers with single noise exposure (49.44%) and 316 workers with joint noise exposure (50.56%), and 518 workers exposed to noise with the normalized continuous A-weighted sound pressure level equivalent to a 40 h working week (LEX,40 h) that exceeded the national standard (82.88%). The detection rates of HFNIHL and SFNIHL were 49.12% and 35.04%, respectively. Multivariable logistic regression analysis indicated that males (OR=10.528, 95%CI: 5.271-21.025), length of service of 10 years and longer (OR=2.451, 95%CI: 1.599-3.759), LEX,40 h of >85 dB (A) (OR=2.227, 95%CI: 1.318-3.764) and joint noise exposure (OR=3.002, 95%CI: 2.080-4.334) were associated with an increased risk of HFNIHL, and male (OR=9.400, 95%CI: 4.211-20.985), LEX,40 h of >85 dB (A) (OR=2.305, 95%CI: 1.345-3.951), and joint noise exposure (OR=3.880, 95%CI: 2.677-5.623) were associated with an increased risk of SFNIHL.@*Conclusion@#Gender, length of service, noise intensity and exposure mode are factors affecting the risk of HFNIHL, while gender, noise intensity and exposure mode are factors affecting the risk of SFNIHL.

Characterization of a New HIV-1 CRF01_AE/B Recombinant Virus Form Among Men Who Have Sex with Men in Shanghai, China.

To date, there are 16 types of CRF01_AE/B circulating recombinant forms identified, and most of them are distributed in Asian countries such as China, Malaysia, and Singapore. Previous HIV molecular epidemiological surveys showed that CRF01_AE (27.6%) and B (9.6%) subtypes are predominant strains in mainland of China. At the same time, the HIV-1 virus spreads faster in the men who have sex with men (MSM) population than in other risk groups. In Shanghai district, ∼66.0% of newly reported cases were infected through homosexual transmission. In this study, we report a novel recombinant strain of CRF01_AE/B. The near full-length genome phylogenetic tree showed that the strain clustered with the CRF01_AE reference sequence and placed in the peripheral position within the branch of the CRF01_AE strain. Subregional evolutionary results indicated that the CRF01_AE subtype was derived from cluster 4 of CRF01_AE, which is mainly distributed in northern China. The subtype B was correlated with the U.S./Europe B, which are widely prevalent in the Chinese MSM population. In recent years, a large number of recombinant forms between CRF01_AE and B strains are continuously emerging in China. Therefore, understanding the current epidemic recombinant forms will have significant implications for prevention and treatment of HIV/AIDS.

Characterization of a New HIV-1 CRF01_AE/CRF07_BC Recombinant Virus in Tianjin, China.

HIV is notorious for its rapid evolution since its transmissions from monkey to human. Currently, HIV contains multiple subtypes, circulating recombinant forms (CRFs) and unique recombinant forms (URFs). Here, from an HIV-positive mother and her child in Tianjin, China, we identified a novel HIV-1 second-generation recombinant virus (TJ20170316 and TJ20170317) between CRF01_AE and CRF07_BC. Near full-length genomes (NFLGs) were obtained from both samples, and they shared very close sequences, except some point mutations. Phylogenetic analyses of the NFLGs showed that they consist of CRF01_AE backbone and part CRF07_BC sequences. Recombinant Identification Program and Simplot software identified four breakpoints in gag, pol, vif, and tat genes in TJ20170316, totally different from other reported CRFs and URFs. The emergence of such URFs in Tianjin, China, highlights the complexity of HIV-1 epidemic and more measures should be taken to prevent HIV transmissions.

An Inverse Optimal Control Approach to Explain Human Arm Reaching Control Based on Multiple Internal Models.

Human motor control is highly efficient in generating accurate and appropriate motor behavior for a multitude of tasks. This paper examines how kinematic and dynamic properties of the musculoskeletal system are controlled to achieve such efficiency. Even though recent studies have shown that the human motor control relies on multiple models, how the central nervous system (CNS) controls this combination is not fully addressed. In this study, we utilize an Inverse Optimal Control (IOC) framework in order to find the combination of those internal models and how this combination changes for different reaching tasks. We conducted an experiment where participants executed a comprehensive set of free-space reaching motions. The results show that there is a trade-off between kinematics and dynamics based controllers depending on the reaching task. In addition, this trade-off depends on the initial and final arm configurations, which in turn affect the musculoskeletal load to be controlled. Given this insight, we further provide a discomfort metric to demonstrate its influence on the contribution of different inverse internal models. This formulation together with our analysis not only support the multiple internal models (MIMs) hypothesis but also suggest a hierarchical framework for the control of human reaching motions by the CNS.

A Hybrid Framework for Understanding and Predicting Human Reaching Motions.

Robots collaborating naturally with a human partner in a confined workspace need to understand and predict human motions. For understanding, a model-based approach is required as the human motor control system relies on the biomechanical properties to control and execute actions. The model-based control models explain human motions descriptively, which in turn enables predicting and analyzing human movement behaviors. In motor control, reaching motions are framed as an optimization problem. However, different optimality criteria predict disparate motion behavior. Therefore, the inverse problem-finding the optimality criterion from a given arm motion trajectory-is not unique. This paper implements an inverse optimal control (IOC) approach to determine the combination of cost functions that governs a motion execution. The results indicate that reaching motions depend on a trade-off between kinematics and dynamics related cost functions. However, the computational efficiency is not sufficient for online prediction to be utilized for HRI. In order to predict human reaching motions with high efficiency and accuracy, we combine the IOC approach with a probabilistic movement primitives formulation. This hybrid model allows an online-capable prediction while taking into account motor variability and the interpersonal differences. The proposed framework affords a descriptive and a generative model of human reaching motions which can be effectively utilized online for human-in-the-loop robot control and task execution.