The inverse Ramos-Louzada distribution (IRLD), a novel one-parameter distribution designed to handle real data with hazard rates shaped like an upside-down bathtub, is presented in this work. The IRLD can be used for many applications and has asymmetric and unimodal density shapes. We derive some of the IRLD's main statistical characteristics, such as moments, incomplete moments, inverse moments, probability-weighted moments, quantile functions, extropy measurements, and stochastic ordering.} Focusing on the inference procedures, including the maximum likelihood, the least squares, the weighted least squares, the Cramér-von-Mises, the maximum product of spacing, the Anderson-Darling, the right-tail Anderson-Darling, the left-tailed Anderson-Darling, minimum spacing absolute distance, and minimum spacing absolute log distance, have been used to estimate the parameter of the IRLD. {\color{black} An extensive simulation analysis was performed to compare the performance of various estimates based on some measures of accuracy. Using the partial and general ranks of all estimation methods for various parameter combinations, we show that the maximum likelihood approach is the best estimation strategy, followed by the maximum product of the spacings. We examined real data set to illustrate the potential applications of the proposed distribution. The findings demonstrate that the provided distribution can fit the data more accurately than competing distributions.

A less expensive and time-consuming option to simple random sampling (SRS) is the ranked set (RSS). Frequently, the RSS method produces more accurate estimators than those employed by SRS. The Chris-Jerry distribution (C-JD) is a new heavy-tailed distribution that helps model many data in human endeavors. The flexibility of the C-JD in capturing various hazard rate shapes ensures its applicability to a wide range of real-world data. This article's primary objective is to examine how well fifteen different estimation techniques, such as maximum and minimum spacing distances methods, Kolmogorov method, and variations of the method of the minimum distances, perform when compared to the maximum likelihood method for parameter estimation of the C-JD based on RSS. We conduct comprehensive simulation research and assess the efficacy of various estimates based on RSS and SRS using many criterion measures. To identify the optimal estimating strategy, partial and overall ranks of the mean estimates, mean squared errors, maximum absolute differences, mean absolute relative errors, average absolute biases, average absolute differences and average squared absolute error based on both designs are provided. The results of our study indicate that the maximum likelihood technique consistently outperforms other strategies, as evidenced by the overall rankings. Because RSS is more efficient than SRS, it is a more effective sampling method with lower accuracy measurements. To explain more, an actual data set about the fatigue life of a certain kind of Kevlar epoxy strand subjected to a constant continuous load at a pressure level of 90 % till the strand fails was examined.

Accurate prediction of photocatalytic degradation in TiO₂ reactors remains challenging due to the time-consuming, labor-intensive, and costly nature of experimental investigations, as well as the limited ability of conventional models. The degradation of benzoic acid was investigated in TiO₂-immobilized tubular plug-flow reactors under artificial UV radiation. The effects of reactor diameter and flow rate on the reaction rate constant were experimentally investigated. The results showed a constant reaction rate under artificial UV irradiation, which is attributed to higher electron photoactivation resulting in high photocatalytic activity. Three machine learning models, namely kernel extreme learning machine (KELM), Crested Porcupine Optimizer–Support Vector Regression (CPO-SVR), and Harris Hawks Optimizer (HHO)–SVR, were designed and compared for accurate prediction of reaction rates. The Pearson correlation coefficient (PCC), Willmott index (WI), Nash–Sutcliffe efficiency (NSE), and Legates–McCabe index (LM) were used to evaluate model performance. The highest predictive accuracy, resulting in the best PCC, WI, NSE, and LM values, was obtained from the HHO-SVR model, with PCC values of 0.990 and 0.990, WI values of 0.999 and 0.999, NSE values of 0.999 and 0.998, and LM values of 0.982 and 0.971 during training and testing, respectively. The CPO-SVR model also demonstrated good predictive performance and outperformed the KELM model on its own. A five-fold cross-validation ensured the stability and uncertainty of the HHO-SVR model. Shapley Additive Explanations (SHAP) analysis was used to determine the relative importance of operating parameters that affect the reaction rate over time, thereby enhancing model interpretability. The results showed the predominant factors controlling photocatalytic degradation and how these factors influenced the model predictions.

JAIT is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology.

The primary aims of this research is to reduce marijuana use within the general population, addressing its harmful effects and its status as an illegal substance that poses ongoing risks to public health in developing countries. To tackle this issue, we have developed a mathematical model that translates real-world marijuana consumption into mathematical terms using first-order nonlinear ordinary differential equations. A key objective of this study is to modify the NERAPH "Non-smokers, Experimental-smokers, Recreational-smokers, Addicts, Hospitalized individuals, and Prisoners" model by incorporating optimal control measures. This modified model is designed to estimate the initial rate of marijuana transmission within these groups. To implement effective control strategies, we conduct sensitivity analyses to identify the most impactful parameters. These findings enable us to recommend targeted control variables for the most sensitive parameters, leading to the development of an optimal control model aimed at reducing marijuana consumption as efficiently as possible. Finally, we compare the outcomes of the basic and optimal control models through numerical results to evaluate the effectiveness of each approach. This comparison provides a clear measure of how optimal control techniques enhance the model's ability to reduce marijuana use more effectively than the existing basic model.