In this article, an attempt is made to propose a novel method of lifetime distributions with maximum flexibility using a popular T–X approach together with an exponential distribution, which is known as the New Generalized Logarithmic-X Family (NGLog–X for short) of distributions. Additionally, the generalized form of the Weibull distribution was derived by using the NGLog–X family, known as the New Generalized Logarithmic Weibull (NGLog–Weib) distribution. For the proposed method, some statistical properties, including the moments, moment generating function (MGF), residual and reverse residual life, identifiability, order statistics, and quantile functions, were derived. The estimation of the model parameters was derived by using the well-known method of maximum likelihood estimation (MLE). A comprehensive Monte Carlo simulation study (MCSS) was carried out to evaluate the performance of these estimators by computing the biases and mean square errors. Finally, the NGLog–Weib distribution was implemented on four real biomedical datasets and compared with some other distributions, such as the Alpha Power Transformed Weibull distribution, Marshal Olkin Weibull distribution, New Exponent Power Weibull distribution, Flexible Reduced Logarithmic Weibull distribution, and Kumaraswamy Weibull distribution. The analysis results demonstrate that the new proposed model performs as a better fit than the other competitive distributions.

In this paper, we investigate the behavior of dust ion acoustic solitary waves (DIASWs) with arbitrary amplitudes in a magnetized anisotropic dusty plasma that includes inertial hot ion fluid, electrons following a Kappa distribution, and negatively charged dust particles in the background. An ambient magnetic field aligns with the x-direction, while the wave propagation occurs obliquely to the ambient magnetic field. In the linear regime, two distinct modes, namely fast and slow modes, are observed. We employ the Sagdeev pseudo-potential method to analyze the fundamental properties of arbitrary amplitude DIASWs. Additionally, we examine how various physical parameters influence the existence and characteristics of symmetric planar dust ion acoustic solitary structures (DIASs). The characteristics of the solitary structures are greatly influenced by the dust concentration, the electrons superthermality (spectral) index, the obliquity parameter, the magnetic field, the parallel ion pressure and the perpendicular ion pressure. The results show that the amplitude and width of both compressive and rarefactive DIASWs are sensitive to the degree of electron superthermality and dust concentration. Additionally, it is shown that the propagation features of DIASWs are highly affected by the parallel component of ion pressure as compared to perpendicular component of ion pressure.

The Hotelling $T^2$ statistic is used to compare the mean vectors of two independent multivariate Gaussian distributions. Nevertheless, this statistic is highly sensitive to outliers and is not suitable for high-dimensional datasets where the number of variables exceeds the sample size. This study introduces a robust permutation test based on the minimum regularized covariance determinant (MRCD) estimator to address these limitations of the two-sample Hotelling $T^2$ statistic. Simulation studies were performed to evaluate the proposed test’s empirical size, power, and robustness. Additionally, the test was applied to both uncontaminated and contaminated Alzheimer’s Disease datasets. The findings from the simulations and real data examples provide clues that the proposed test can be effectively used with high-dimensional data without being impacted by outliers. Finally, an R function within the “MVTests” package was developed to implement the proposed test statistic on real-world data.

The globe has been in a chaos state since a corona-virus (SARSCoV2) first appeared in December 2019. It was helpful to utilize an isolation strategy with quarantine to slow down the spread of disease. As a result, individuals stayed indoors instead of engaging in their regular daily activities outside. The goal of this study is to examine the connections and potential mediatory pathways between mental health issues, how people perceive their illnesses, and disorders of anxiety and depression. This aim of this study is to use various machine learning approaches to predict, classify, and detect depression risk factors in two districs of Khyber Pukhtunkhwa (KPK), Pakistan. In this paper, machine learning methods i.e., Random Forest and LASSO have been used for feature selection. Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Least Absolute Shrinkage Selection Operation (LASSO), and Random Projection ensembles (RP) have been used to assess the performance of the LASSO and Random Forest by identifying important features. The results show that LASSO has performed better than the other methods. Additionally, the clustering technique is also utilized to detect different hot spots in the population by considering the data as an unsupervised issue.

The concept of the hybrid structure, as an extension of both soft sets and fuzzy sets, has gained significant attention in various mathematical and decision-making domains. In this paper, we delve into the realm of hemirings and investigate the properties of hybrid h-bi-ideals, including prime, strongly prime, semiprime, irreducible, and strongly irreducible ones. By employing these hybrid h-bi-ideals, we provide insightful characterizations of h-hemiregular and h-intra-hemiregular hemirings, offering a deeper understanding of their algebraic structures. Beyond theoretical implications, we demonstrate the practical value of hybrid structures and decision-making theory in handling real-world problems under imprecise environments. Using the proposed decision-making algorithm based on hybrid structures, we have successfully addressed a significant real-world problem, showcasing the efficacy of this approach in providing robust solutions.