Analysis of variance (ANOVA) is an important method in exploratory and confirmatory data analysis when explanatory variables are discrete and response variables are continues and independent from each other. The simplest type of ANOVA is one-way analysis of variance for comparison among means of several populations. In this paper, we extend one-way analysis of variance to a case where observed data are non-symmetric triangular or normal fuzzy observations rather than real numbers. Meanwhile, a case study on the car battery length-life is provided on the basis on the proposed method.

Determining the personality dimensions of the individuals is very important in the psychological research works. The most well-known example of personality dimensions is the five-factor model (FFM). There are two approaches, manual and automatic, for determining the personality dimensions. In a manual approach, the Psychologists discover these dimensions through the personality questionnaires. As an automatic way, varied personal input types (textual/image/video) of people are gathered and analyzed for this purpose. In this work, we propose a method called DENOVA (DEep learning based on the ANOVA), which predicts FFM using deep learning based on an Analysis of variance (ANOVA) of words. For this purpose, DENOVA first applies ANOVA in order to select the most informative terms. Then DENOVA employs Word2Vec in order to extract document embeddings. Finally, DENOVA uses support vector machine (SVM), logistic regression, XGBoost, and multi-layer perceptron (MLP), as classifiers in order to predict FFM. The experimental results obtained show that DENOVA outperforms on average, 6. 91%, the state-of-the-art methods in predicting FFM with respect to accuracy.

Dynamic Matrix Control (DMC) is a widely used model predictive controller (MPC) in industrial plants. The successful implementation of DMC in practical applications requires a proper tuning of the controller. The available tuning procedures are mainly based on experience and empirical results. This paper develops an analytical tool for DMC tuning. It is based on the application of Analysis of Variance (ANOVA) and nonlinear regression analysis for First Order plus Dead Time (FOPDT) process models. It leads to a simple formula which involves the model parameters. The proposed method is validated via simulations as well as experimental results. A nonlinear pH neutralization model is used for the simulation studied. It is further implemented on a laboratory scale control level plant. A robustness analysis is performed based on the simulation results. Finally, comparison results are provided to show the effectiveness of the proposed methodology.

Powder Metallurgy (PM) is a versatile manufacturing technology widely used for producing diverse metal matrix composites. In this study, PM was employed to fabricate pure magnesium (P Mg) samples, optimizing critical properties such as density (ρ), percent porosity (%P), and microhardness (HV). Unlike previous works, this study uniquely applied a combined Taguchi and ANOVA-based optimization framework to systematically evaluate the influence of three key PM factors: Compaction Pressure (CP), Sintering Temperature (ST), and Sintering Time (St). Experimental runs were designed using Taguchi's L9 orthogonal array, with CP (550, 600, and 650 MPa), ST (455, 525, and 585 °C), and St (30, 45, and 60 minutes) tested at three levels each. The findings revealed that CP level 3 (650 MPa), ST level 2 (525 ºC), and St level 1(30 minutes) were the optimal parameters for maximizing response characteristics. These results represent a significant improvement in density and microhardness compared to prior studies, demonstrating the efficacy of this method in reducing porosity and enhancing the mechanical properties of P Mg. Furthermore, the integration of regression analysis and Pareto visualization provided predictive insights and highlighted the dominant influence of CP on material performance. This approach establishes a reliable methodology for optimizing PM parameters, offering valuable benchmarks for future research and industrial applications.