Programmable Logic Controllers (PLC) are frequently used by a good number of companies like steel plants, biscuit manufacturing companies, etc. Various plants/companies use two PLC at a time: one operative, and the other as a hot standby to avoid big losses. Analysis of the reliability, and profit of a hot standby PLC system is of great importance; and hence the present paper examines such a system wherein two PLC are working in master-slave fashion. Initially, the master unit is operative, and the slave unit is in hot standby. The slave unit can also fail, but with a lower failure rate than the master unit. The master unit has the priority of operation ∓mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp;mp; repair over the slave unit. While operating, the latest information from the master unit keeps on transferring to the slave unit. There are three types of failure: minor, major-repairable, and major-irreparable. The ordinary repairman who stays with the system repairs the minor failures. The expert repairman who is available upon demand repairs the major failures. Various measures of the system effectiveness, such as the mean time to system failure, steady-state availability, busy period of the ordinary as well as expert repairmen, expected number of replacements, etc. are obtained by using semi-Markov processes, and regenerative point Techniques. Profit incurred to the system is evaluated, and a graphical study is also made. Real data from an industrial application is used in this study.

This study proposes an innovative framework integrating geographic information systems (GISs), water quality index (WQI) analysis, and advanced machine learning (ML) models to evaluate the prevalence and impact of organic and inorganic pollutants across the urban–industrial confluence zones (UICZ) surrounding the National Capital Territory (NCT) of India. Surface water samples (n = 118) were systematically collected from the Gautam Buddha Nagar, Ghaziabad, Faridabad, Sonipat, Gurugram, Jhajjar, and Baghpat districts to assess physical, chemical, and microbiological parameters. The application of spatial interpolation techniques, such as kriging and inverse distance weighting (IDW), enhances WQI estimation in unmonitored areas, improving regional water quality assessments and remediation planning. GIS mapping highlighted stark spatial disparities, with industrial hubs, like Faridabad and Gurugram, exhibiting WQI values exceeding 600 due to untreated industrial discharges and wastewater, while rural regions, such as Jhajjar and Baghpat, recorded values below 200, reflecting minimal anthropogenic pressures. The study employed four ML models—linear regression (LR), random forest (RF), Gaussian process regression (GPR), and support vector machines (SVM)—to predict WQI with high precision. SVM_Poly emerged as the most effective model, achieving testing CC, RMSE, and MAE values of 0.9997, 11.4158, and 5.6085, respectively, outperforming RF (0.9925, 29.8107, 21.7398) and GPR_PUK (0.9811, 68.4466, 54.0376). By leveraging machine learning models, this study enhances WQI prediction beyond conventional computation, enabling spatial extrapolation and early contamination detection in data-scarce regions. Sensitivity analysis identified total suspended solids as the most critical predictor influencing WQI, underscoring its relevance in monitoring programs. This research uniquely integrates ML algorithms with spatial analytics, providing a novel methodological contribution to water quality assessment. The findings emphasize the urgency of mitigating the fate and transport of organic and inorganic pollutants to protect Delhi’s hydrological ecosystems, presenting a robust decision-support system for policymakers and environmental managers.

The operating time of a diesel locomotive indicates its mechanical states to a certain scale. Though, even the same variant of diesel locomotive with the same types of operating hours shows different technical states in different types of operative environments. Therefore, it is quite intricate to obtain the data for entire life or general life prediction methods are required to study the physical failure mechanism. In pursuant of the discussed cases, mathematical model of diesel locomotive’s life estimation with respect to downward trend and neural networks is evolved in the study. Primarily, the parameters corresponds to downward trends are selected and the sample data is to be standardized & then the principal component analysis method is being used to simplify different parameters to a pervasive parameter. Method of interpolation is being used for the parameter’s time series data as the train data of neural network. Finally, the life span estimation model of the locomotive engine based on neural network is evolved.

Requirement for sustainable development is constantly rising and civilization is at a point where population must learn to see beyond its animal instincts and d