With the rapid spread of the pandemic due to the coronavirus disease 2019 (COVID-19), the virus has already led to considerable mortality and morbidity worldwide, as well as having a severe impact on economic development. In this article, we analyze the state-level correlation between COVID-19 risk and weather/climate factors in the USA. For this purpose, we consider a spatio-temporal multivariate time series model under a hierarchical framework, which is especially suitable for envisioning the virus transmission tendency across a geographic area over time. Briefly, our model decomposes the COVID-19 risk into: (i) an autoregressive component that describes the within-state COVID-19 risk effect; (ii) a spatiotemporal component that describes the across-state COVID-19 risk effect; (iii) an exogenous component that includes other factors (e.g., weather/climate) that could envision future epidemic development risk; and (iv) an endemic component that captures the function of time and other predictors mainly for individual states. Our results indicate that maximum temperature, minimum temperature, humidity, the percentage of cloud coverage, and the columnar density of total atmospheric ozone have a strong association with the COVID-19 pandemic in many states. In particular, the maximum temperature, minimum temperature, and the columnar density of total atmospheric ozone demonstrate statistically significant associations with the tendency of COVID-19 spreading in almost all states. Furthermore, our results from transmission tendency analysis suggest that the community-level transmission has been relatively mitigated in the USA, and the daily confirmed cases within a state are predominated by the earlier daily confirmed cases within that state compared to other factors, which implies that states such as Texas, California, and Florida with a large number of confirmed cases still need strategies like stay-at-home orders to prevent another outbreak.

Zeros in compositional data are very common and can be classified into rounded and essential zeros. The rounded zero refers to a small proportion or below detection limit value, while the essential zero refers to the complete absence of the component in the composition. In this article, we propose a new framework for analyzing compositional data with zero entries by introducing a stochastic representation. In particular, a new distribution, namely the Dirichlet composition distribution, is developed to accommodate the possible essential‐zero feature in compositional data. We derive its distributional properties (e.g., its moments). The calculation of maximum likelihood estimates via the Expectation‐Maximization (EM) algorithm will be proposed. The regression model based on the new Dirichlet composition distribution will be considered. Simulation studies are conducted to evaluate the performance of the proposed methodologies. Finally, our method is employed to analyze a dataset of fluorescence in situ hybridization (FISH) for chromosome detection.

Concrete compressive strength is a fundamental indicator of the mechanical properties of High-Performance Concrete (HPC) with multiple components. Traditionally, it is measured through laboratory tests, which are time-consuming and resource-intensive. Therefore, this study develops a machine learning-based regression framework to predict compressive strength, aiming to reduce experimental costs and resource usage. Under three different data preprocessing strategies—raw data, standard score, and Box–Cox transformation—a selected set of high-performance ensemble models demonstrates excellent predictive capacity, with both the coefficient of determination (${\mathrm{R}}^2$) and explained variance score (EVS) exceeding 90% across all datasets, indicating high accuracy in compressive strength prediction. In particular, stacking ensemble (${\mathrm{R}}^2$-$0.920$, $\mathrm{EVS}$-$0.920$), XGBoost regression (${\mathrm{R}}^2$-$0.920$, $\mathrm{EVS}$-$0.920$), and HistGradientBoosting regression (${\mathrm{R}}^2$-$0.913$, $\mathrm{EVS}$-$0.914$) based on Box–Cox transformation data show strong generalization capability and stability. Additionally, tree-based and boosting methods demonstrate high effectiveness in capturing complex feature interactions. Furthermore, this study presents an analytical workflow that enhances feature interpretability through visualization techniques—including Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE), and SHapley Additive exPlanations (SHAP). These methods clarify the contribution of each feature and quantify the direction and magnitude of its impact on predictions. Overall, this approach supports automated concrete quality control, optimized mixture proportioning, and more sustainable construction practices.

In this paper, we propose a new multivariate Laplace distribution from normal variance mixture models, called as Type II multivariate Laplace distribution. Unlike the original multivariate Laplace distribution whose all components must have the same value for the mixing variate, the random components in the new distribution could have different value for its own mixing variate and are correlated only through the dependence structure of the normal random vector. Thus, it contains the multiplication of independently identical distribution univariate Laplace distributions as a special case if the normal covariance matrix is diagonal. A tractable stochastic representation is used to derive the probability density function and other statistical properties. The maximum likelihood estimates of parameters via an ECM (expectation/conditional maximization) algorithm and the Bayesian methods are derived. Some simulation studies are conducted to evaluate the performance of the proposed methods. Applications in two real data sets indicate that the Type II multivariate Laplace distribution could have a better performance and is distinct from the original one.