Human factors engineering simulated analysis in administrative, operational and maintenance loops of nuclear reactor control unit using artificial intelligence and machine learning techniques.

The nuclear reactor control unit employs human factor engineering to ensure efficient operations and prevent any catastrophic incidents. This sector is of utmost importance for public safety. This study focuses on simulated analysis of specific areas of nuclear reactor control, specifically administration, operation, and maintenance, using artificial intelligence software. The investigation yields effective artificial intelligence algorithms that capture the essential and non-essential components of numerous parameters to be monitored in nuclear reactor control. The investigation further examines the interdependencies between various parameters and validates the statistical outputs of the model through attribution analysis. Furthermore, a Multivariant ANOVA analysis is conducted to identify the interactive plots and mean plots of crucial parameters interactions. The artificial intelligence algorithms demonstrate the correlation between the number of vacant staff jobs and both the frequency of license event reports each year and the ratio of contract employees to regular employees in the administrative domain. An AI method uncovers the relationships between the operator failing rate (OFR), operator processed errors (OEE), and operations at limited time frames (OLC). The AI algorithm reveals the interdependence between equipment in the out of service (EOS), progressive maintenance schedule (PRMR), and preventive maintenance schedules (PMRC). Effective machine learning neural network models are derived from generative adversarial network (GAN) algorithms and proposed for administrative, operational and maintenance loops of nuclear reactor control unit.

Prevalence of work-related musculoskeletal disorders among professional rickshaw drivers in Aligarh, Uttar Pradesh, India.

BACKGROUND: Professional rickshaw driving is a seemingly sedentary occupation and involves many risk factors for work-related musculoskeletal disorders (WMSD). OBJECTIVE: To assess the prevalence of work-related musculoskeletal disorders and its associated risk factors among professional rickshaw drivers. METHODS: 263 rickshaw drivers were voluntarily recruited from Aligarh, Uttar Pradesh, India. MSD symptoms in the past 12 months and last 7 days were assessed using a self-modified musculoskeletal questionnaire (Nordic Questionnaire). Chi-square tests and binary logistic regression were performed to analyze associations of MSD symptoms between study variables. RESULTS: 155 (58.9%) study participants reported MSD symptoms in past 12 months, in lower back (n = 126, 81.3%), leg (n = 122, 78.7%), neck (n = 106, 68.4%) and knee (n = 105, 67.7%); and 121 (46.1%) in last 7 days, in leg (n = 107, 88.4%), lower back (n = 102, 84.3%), neck (n = 89, 73.6%), upper back and knees (n = 87, 71.9%). Binary logistic regression showed relationship between working hours, seat and road condition, average load per ride, rickshaw ride duration, and standing driving with MSD symptoms among rickshaw drivers, especially in the lower back, leg/calf muscles, neck and knees. CONCLUSION: The results showed a high prevalence of MSD among all rickshaw drivers, with the neck, lower back, leg/calf muscles and knees being the most affected body parts. In order to avoid adverse effects on the occupational health of rickshaw drivers, ergonomic intervention training is necessary.

Investigating factors affecting musculoskeletal disorders: Predictive models for identifying caregivers at risk.

BACKGROUND: Musculoskeletal disorders (MSDs) are the most common work-related injuries identified among caregivers in the health sector as a high-risk group. OBJECTIVES: The study aimed to investigate the factors that influence musculoskeletal disorders among caregiver and to evaluate the relationship between work and non-work related factors with MSDs. METHODS: Data were collected from 104 caregivers using descriptive design and stratified cluster sampling. The survey included a demographic questionnaire and a Nordic Standardized Musculoskeletal Questionnaire. Logistic regression was performed to determine the risk factors associated with MSDs. The Odds ratio (OR) was calculated to define the influence of each risk factors. In addition, we used forward logistic regression analysis to validate the predictive model. RESULTS: In this cross-sectional survey, the results showed that 70 (67.3%) participants reported MSDs. MSD was highest at the lower back (46%), then knee (15.4%) and shoulder (11.5%). The overall success of the prediction is 90.4% (94.6 for having MSDs). The most important risk factors were manual handling (p < 0.001, odds ratio = 45.64) followed by bending (p = 0.008, odds ratio = 39.4). CONCLUSIONS: The results of this study reaffirmed the high prevalence of work-related MSD among caregiver's primarily in the lower back. The most important risk factors were manual handling, followed by bending / twisting, and handling of an excessive number of patients. Therefore, it is necessary to consider appropriate policies for managing MSDs among caregivers.