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Objective To explore the correlation between metabolic syndrome (MS) and noise-induced hearing loss (NIHL) in noise-exposed workers. Methods Using the convenient sampling method, a total of 850 male noise-exposed workers in an automobile manufacturing enterprise were studied. The questionnaire survey and occupational health examination were combined to collect general demographic data, physical examination data, and other information. According to the results of high-frequency hearing loss, the noise-exposed workers were divided into the hearing loss group (326 workers) and the normal hearing group (524 workers). The single-factor analysis was used to compare the above data differences between the two groups. The binary multiple-factor logistic regression model was used to analyze the influencing factors of NIHL in noise-exposed workers. Results The non-compliance rate of noise intensity in the enterprise was 39.47% (15/38), with the highest occurrences in the welding and painting posts. The morbidity of hearing loss was 38.35% (326/850), and the MS morbidity was 27.53% (234/850). The frequency of obesity, high-level triglyceride, high-level fasting blood glucose, hypertension, MS,and the contact noise level in the hearing loss group were higher than those in the normal hearing group (P < 0.05). The results of multiple-factor logistic regression showed that, compared with the workers with a noise exposure duration ≤ 5 years, the risk of NIHL in the workers with a noise exposure duration of 11 - 15 years or > 15 years increased to 1.553 and 1.727 times, respectively (P < 0.001). Compared with the workers without MS, the risk of NIHL in the workers with MS increased to 1.863 times (P < 0.001). The risk of NIHL increases by 1.567 times for each 1 dB(A) increase in noise level (P < 0.001). Conclusions There was a correlation between MS and NIHL. In addition to emphasizing control of noise level in the workplace, attention should also be paid to adopting a low-sugar, low-salt, and low-sodium diet, reducing the intake of saturated fatty acids, and strengthening physical exercise to effectively control the blood pressure, blood sugar, and blood lipid levels of workers so as to reduce the risk of NIHL. [ABSTRACT FROM AUTHOR]

Introduction: Service robot technology is increasingly gaining prominence in the field of artificial intelligence. However, persistent limitations continue to impede its widespread implementation. In this regard, human motion pose estimation emerges as a crucial challenge necessary for enhancing the perceptual and decision-making capacities of service robots., Method: This paper introduces a groundbreaking model, YOLOv8-ApexNet, which integrates advanced technologies, including Bidirectional Routing Attention (BRA) and Generalized Feature Pyramid Network (GFPN). BRA facilitates the capture of inter-keypoint correlations within dynamic environments by introducing a bidirectional information propagation mechanism. Furthermore, GFPN adeptly extracts and integrates feature information across different scales, enabling the model to make more precise predictions for targets of various sizes and shapes., Results: Empirical research findings reveal significant performance enhancements of the YOLOv8-ApexNet model across the COCO and MPII datasets. Compared to existing methodologies, the model demonstrates pronounced advantages in keypoint localization accuracy and robustness., Discussion: The significance of this research lies in providing an efficient and accurate solution tailored for the realm of service robotics, effectively mitigating the deficiencies inherent in current approaches. By bolstering the accuracy of perception and decision-making, our endeavors unequivocally endorse the widespread integration of service robots within practical applications., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Tang and Zhao.)