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Modelling and optimizing soil-tool interaction parameters for tillage operations is crucial for developing efficient and precise tools. This study focused on a specific draft of sweep tools in the soil bin filled with vertisol, considering factors such as tool geometry, cone index, working depth and operational speed. Data analysis showed that the range of specific draft values, from 9.51 to 38.95 kN/m2. Machine learning models, including artificial neural network (ANN), support vector machine (SVM), bagged trees (BT) and boosted trees (BoT), were developed using experimental data to predict the specific draft of sweep tools with hyperparameter configuration. The developed machine learning models have also been compared with the predictive multiple linear regression (MLR) model, and it was found that the predictive performance of the machine learning models was better than the MLR model during training and testing. The fine-tuned ANN model achieved impressive statistical performance with the lowest mean absolute error (MAE) of 0.489 kN/m2, root mean square error (RMSE) of 0.619 kN/m2, standard error of prediction (SEP) of 3.462% and highest coefficient of determination (R2) of .99 during testing. R2 values for BT, BoT, SVM and MLR models were .97, .96, .94 and .83, respectively, for specific draft predictions. The findings from this study have practical implications for optimizing sweep tool design and improving tillage operation. Manufacturers and farmers can benefit from predictive modelling using machine learning to design and select appropriate tillage tools for specific soil conditions. This approach can lead to improved soil health, increased yields and reduced costs. [ABSTRACT FROM AUTHOR]

Background and Aim: The risk of acquiring hepatitis B virus (HBV) infection through exposure to blood or its products is highest amongst health care workers (HCWs). Despite potential risks, a proportion of HCWs never get vaccinated. India is second to China in the numbers of people with chronic HBV. This study aimed to investigate the vaccination practices and the prevalence of HBV infection in HCWs in India. Methods: A total of 2162 HCWs were screened for the presence of serological markers of HBV and hepatitis C virus (HCV). Occult HBV infection was tested by detection of HBV-DNA for surface and core regions by nested polymerase chain reaction in HBsAg-negative and IgG anti-hepatitis core antigen–positive subjects. Results: Only 1198 (55.4%) of the 2162 HCWs screened had been vaccinated; and 964 (44.6%) were not vaccination-status conscious; of these HCWs, 600 (27.7%) had never been vaccinated and 364 (16.4%) were unaware of their vaccination status. Protective (> 10 IU/mL) anti-hepatitis B surface (anti-HBs) antigen titers were seen in only 61.7%. The anti-HBs titers were found to be lower with the passage of time; the median anti-HBs titers in subjects who were vaccinated > 10 years ago were significantly lower than those who had been vaccinated < 5 years ago ( P < 0.001). One percent of HCWs were HBsAg-positive, and 24.7% of 700 HCWs screened had past exposure (IgG-anti-HBc-positive). Occult HBV was detected in 5% of 120 positive subjects with past exposure; all had anti-HBs titers > 10 IU/mL. Conclusions: Even today, 28% HCWs in India are unvaccinated and 17% are unaware of their vaccination status. This data suggests that use of hepatitis B immune globulin be mandatory in needle-pricked HCWs in India, and that implementation of awareness strategies is urgent. Since the anti-HBs titers decline in a fair proportion, there is justification for giving a booster dose of vaccine 10 years after primary vaccination to HCWs in India. [ABSTRACT FROM AUTHOR]