The emergence of soybean diseases represents a formidable obstacle to the sustainable progression of the rapidly evolving soybean industry, which endeavors to achieve elevated productivity and enhanced crop excellence. Prompt and accurate disease prognostication is imperative for efficacious management protocol, as it contributes to limiting pathogen proliferation. This investigation examined the impact of various meteorological factors on the incidence of Soybean Yellow Mosaic Virus (SYMV) in Pantnagar, Uttarakhand. Six multivariate frameworks were assessed, encompassing Stepwise Multiple Linear Regression (SMLR), Artificial Neural Network (ANN) and Elastic Net (ELNET), employing both original climatic parameters and calculated weather indices to predict disease severity. For model construction, 80% of the dataset was allocated for training purposes while the remaining 20% was reserved for validation procedures. Among all examined frameworks, the ANN model incorporating weather indices (ANN-WI) exhibited exceptional predictive performance, attaining a Normalized Mean Square Error (nRMSE) of only 3.08% and an R2 coefficient of 0.99 during the calibration phase. Based on performance metrics, the frameworks were ranked as follows: ANN-WI ≈ ANN-W > ELNET-W > ELNET-WI > SMLR-WI > SMLR-W. The results definitively demonstrate that ANN-based frameworks, especially those incorporating weather indices, significantly exceeded alternative modeling techniques within the investigated area.

This article considers a machine maintenance problem. When the machine fails after a stochastic period, reducing its capacity to a proportion of the nominal level. In this degraded capacity state, three maintenance and repair policy include, continue at 50% capacity, imperfect maintenance and increase the capacity of the machine to 80% or perfect replacement and increase the capacity of the machine to the initial stateare available for evaluation. By modeling the system as a discrete-time Markov chain and analyzing the probability transition matrix between the system states, the costs associated in each state can be evaluated. The objective function representing the average cost per unit time of production is calculated to determine the optimal maintenance policy.

Progressively first failure censored (PFFC) data plays a pivotal role in reliability theory and life-testing experiments due to its ability to provide comprehensive insights into the reliability of systems and components. This approach facilitates more accurate estimation of reliability metrics and provides valuable insights into the performance and longevity of systems in life-testing experiments. In this article, we explore both classical and Bayesian approaches to estimate the model parameter and reliability characteristics of the xgamma distribution utilizing data from the PFFC dataset. In classical estimation, we analyze maximum likelihood estimators (MLEs) and derive asymptotic confidence intervals (ACIs). Within the Bayesian framework, we evaluate Bayes estimators using both non-informative and gamma informative priors, employing the squared error loss function (SELF) and utilizing Lindley approximation alongside the Metropolis-Hasting (M-H) algorithm. Furthermore, we construct highest probability density (HPD) intervals using the M-H algorithm. To assess the effectiveness of each estimation method, we conduct numerical computations through a simulation study. Lastly, we analyze a real dataset to demonstrate the practical utility of the xgamma distribution within a censoring framework.

In this research article, we present a comprehensive analysis of a repairable M/M/1/K queueing system that incorporates a threshold-based recovery policy to address server breakdowns, catastrophic events, and customer behaviors such as reneging and balking. In this model, the server fails only when at least one customer is present, and recovery is initiated once the number of customers in the queue reaches a specified threshold T (1≤T<K). We derive closed-form expressions for the system’s steady-state solutions using successive over-relaxation. The study develops critical system characteristics, including the number of customers in the system, the probability of the server being busy, the effective arrival rate, and the expected waiting time. We formulate a cost model to determine the optimal threshold value, system capacity, and service rate that minimize the total cost, which includes repair costs, downtime expenses, and revenue loss. Optimization is performed using Newton-Quasi method. The findings offer valuable insights into queue system design and management, aiding decision-makers in optimizing cost-effectiveness and enhancing overall system performance.

This paper presents a novel analysis of a standby redundancy system under real conditions. The study considers the presence of a real commutator and failure detector in the system. Through a comprehensive failure mode analysis, mathematical relationships between different module characteristics are established. The results of investigation provide valuable insights for manufacturers, allowing them to evaluate the Mean Time To Failure (MTTF) of the system during the design phase and make informed decisions regarding the selection of failure rates for the detector and commutator. Overall, this work contributes to the effective operation of standby redundancy systems in practical applications.

This paper examines the impact of load on the operational time and maintenance cost of mechanically repairable machines. Three different levels of load with multiplicative impact on the hazard rate of the failure distribution were applied to the working of a cassava grinding machine using a two-parameter Weibull distribution with respective hazard and cumulative hazard functions. Their effect on the preventive maintenance (PM) and replacement schedules revealed that at above maximum load level, the length of the machine’s operational time decreased drastically compared to the decrease at maximum load level and relative decrease at the below maximum load level when compared to the machine’s operational time at the minimum load level. The application of load also results in frequent preventive maintenance actions and an increase in machine downtime for a given cost ratio. This implies that the influence of load on the PM and replacement maintenance schedule of mechanically repairable machines is essential to the design and operation of such machines. The results also provide maintenance engineers with an operational guide for PM and replacement maintenance actions in order to prevent failure maintenance and increase the machine’s availability for enhanced productivity.

In this study, we have developed a Ratio type estimator in Stratified sampling to estimate the population average of study variable by using the information of a concomitant variable. By utilizing Taylor’s series, we have derived the expressions for Bias and MSE upto first degree of approximation. In numerical illustration, employing a real data set, we have demonstrated that the proposed estimator has highest Percentage relative efficiency when compared to the considered existing estimatrs. Furthermore, we have demonstrated that Separate ratio type estimators have the highest relative efficiency when contrasted with the Combined ratio type estimators.

In today’s world where competition is increasing, the focus of all efforts on meeting production targets at the lowest possible cost has increased the need for maximum benefit from existing machinery/equipment. In this context, availability, reliability and maintainability analysis have become the basic tools to be used in meeting this need. In this study, reliability, maintainability and availability values were determined using the operation and repair data (2019) of large capacity electric trucks used in the Manisa-Soma coal field. First of all, each truck was analyzed and then the analysis of the entire truck system was performed. In this context, RAM analysis of all trucks was performed and then the general reliability of the entire truck system was calculated by considering the working order in the enterprise. According to this; The truck with the highest reliability is the truck with door number 555 and the reliability rate is 77.04%, and the truck with the lowest reliability is the truck number 556 with a rate of 26.31%. As a result of the maintainability analysis, the truck with the 553 door number had the highest maintainability with a rate of 73.52%. The truck numbered 549 has the lowest maintainability rate with a rate of 42.21%. As a result of the system analysis, the reliability rate was determined as 24% in R (1000) minutes, 59% in R (500) minutes and 83% in R (250) minutes.

The ranked set sampling (RSS) is an effective scheme popularly used to produce more precisely estimators. Despite its popularity, RSS suffers from some drawbacks which includes high sensitivity to outliers and it cannot sometimes be applicable when the population is relatively small. To overcome these limitations, varied L ranked set sampling (VLRSS) is recently introduced. It is shown that VLRSS scheme enjoys with many interesting properties over RSS and also encompasses several existing RSS schemes. In addition, it is also helpful for providing precise estimates of several population parameters. To fill this gap, this article extends the work and address the estimation of based ℛ on VLRSS when the strength and stress both follow exponential distribution. Maximum likelihood approach as well as Bayesian method are considered for estimating ℛ. The Bayes estimators are obtained by using gamma distribution under general entropy loss function and LINEX loss function. The performance of the estimators based on VLRSS are investigated by a simulation study as well as a real dataset relevant to industrial field. The results reveal that the proposed estimators are more efficient relative to their analogues estimators under L ranked set sampling given that the quality of ranking is fairly good.

Statisticians often use auxiliary information at an estimation stage to increase efficiencies of estimators. In this article, we suggest modified ratio- and product-type estimators utilizing the known value of the coefficient of variation of the auxiliary variable for a time-based survey. Further, to excel the performance of the suggested estimators, we utilize information from the past surveys along with the current surveys through hybrid exponentially weighted average. We obtain expressions for biases and mean square errors of the suggested estimators. The conditions, under which the suggested estimators have less mean square errors than that of other existing estimators, are also obtained. The results obtained through an empirical analysis examine the use of information from past surveys along with current surveys and show that the mean square errors and biases of the suggested estimators are less than that of the existing estimators. For example: for a sample size 5, mean square error and bias of the suggested ratio-type estimator are (0.0414,0.0065) which are less than (0.5581,0.0944) of the existing Cochran (1940) estimator, (0.4788,0.0758), of Sisodia and Dwivedi (1981) estimator and (0.0482,0.0082) of Muhammad Noor-ul-Amin (2020) estimator. Similarly, mean square error and bias of the suggested product- type estimator are (0.0025,−0.0006) which are less than (0.0612,−0.0096) of the existing Murthy (1964) estimator, (0.0286,−0.0071), of Pandey and Dubey (1988) estimator and (0.0053,−0.0008) of Muhammad Noor-ul-Amin (2020) estimator.