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.

Planning the tourism areas is performed by considering the potentials of these areas, and this will lead to greater co-ordination among the tourists. As a result, the effects of overpopulation in some places that will result in environmental costs can be minimized. This research is based on the main objective of tourism capacity building according to some of the most effective attractions of urban tourists and using advanced statistical models. The research method is descriptive-analytical and in terms of gathering data is a survey study. The data analysis was performed using SPSS software version 19. The results of the research indicated that the criteria for capacity building of urban walkways in the province in order are Hamedan, Malayer, Kabudarahang, Nahavand, Toyserkan, Razan, Asadabad and Bahar respectively. After prioritization, the understudied areas were determined in terms of tourism infrastructure based on which Hamadan and Malayer had a very good, Kabudarahang and Nahavand middle, and Tuyserkan weak and the cities of Razan, Asadabad and Bahar very weak tourism infrastructures.

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.

It is an important issue that what is the most effective service or efficient designing part as a distinctive factor between a tourism app and its competitors in the highly competitive environment in this field? In this survey, as the first step, the Tourism Mobile Apps (TMAs) functional criteria (success factors) are extracted by an appropriate literature review and the opinion of experts who have competitive intelligence knowledge. Then a case study is done on five famous tourism apps in the accommodation sector using the Fuzzy ANOVA. According to the achieved results, users believe that there isn’, t any difference between considered applications in criteria such as “, Reliability of Mtechnology”, , “, Perceived ease of use”, , “, Perceived Trust”, , “, Perceived Cost Transparency”,and “, security issues”, . Consequently, the first group is identified as critical success criteria and has more influence on user satisfaction and, subsequently, the success of TMA. Introduction The tourism industry is the most profitable part of the economy in advanced countries and has undeniable effects on economic, social, cultural, and ecological areas (Sadeghi et al., 2019). Today, information technology and using different mobile applications attract many tourists to this market. Numerous competitors, further making the choosing process for users difficult, create a set of qualified services for consumers. This research is a case study on top five accommodation (as one of the most important sections of the tourism industry) applications. Therefore, it extracted the performance criteria initially and then investigated the similarity rate in fulfilment levels of those mobile apps. In other words, the final purpose of the current study is to identify the critical success factors through performance adaptation of success TMAs to each other between success criteria. In this regard, the previous research related to TMA is investigated to achieve an initial success framework. Materials and Methods The best method for investigating differences between several groups of data is ANOVA. Likewise, Fuzzy ANOVA is a powerful and efficient variance analysis method for fuzzy data. This study evaluates the fulfilment levels of 10 performance criteria in top 5 TMA in Iran. Initially, 20 performance criteria were extracted, then mitigated to 10 factors, using the Friedman test. A Likert scale questionnaire in five linguistic variables was provided and distributed between experts for this aim. The total average of criteria is achieved at 2. 82, and it means all the criteria by an average less than 2. 82 will be omitted from the initial list. Five TMAs by the most active installation are identified at the next step. Respectively, these mobile apps are Alibaba, Snaptrip, AP, Booking. com, and Trip Advisor. Before considering the analysis of achieved data from experts, it’, s needed to calculate the reliability and validity of questionnaires. For this purpose, a Likert scale questionnaire is designed in the present study, and the validity and reliability of criteria are calculated using discriminant/convergent validity and Cronbach alpha. Furthermore, a secondary approach (composite reliability) is also used to evaluate the reliability. This process is implemented using SmartPLS software. Finally, the comparison between TMAs is made by using the fuzzy ANOVA approach. Discussion and Result The distributed questionnaire was answered by 10 experts who were selected by using the purposive sampling method. All the respondents at least have one year experience of working with a TMA. The collected data by respondents requires validity and reliability evaluation. The achieved results prove the questionnaire reliability and validity of the criteria. The result is demonstrated in table 1. At the next step, the fuzzy ANOVA approach proves that in five criteria, the relationship p of SSTR ,ρ,SSEis true and verifies the zero hypnosis, and for the others, the correct equation is 𝑆, 𝑆, 𝑇, 𝑅,> ρ,SSE that proves the null one. Conclusion The final output of this study demonstrates that a TMA for success achievement must focus on “, Perceived value of M-technology”, , ”,Perceived ease of use”, , ”,Perceived Trust”, , ”,Perceived Cost Transparency”,and “, Security issues”,because all these criteria were implemented equally in all of five TMA.

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.

Parametric tests are inferential tests having high statistical power and ability to address any gathered data in the form of complex designs. They mostly assume data to have a natural distribution and samples to have Homogeneous variances. Nonparametric inferential tests are procedures that have few assumptions about data, especially its distribution and a low power compared with parametric procedures. ANOVA is one of the most powerful parametric statistical tests for testing statistical hypotheses, together with different designs and models and related procedures. This technique is used to uncover the main and interactive effects of independent variables (called "factors") on a dependent variable. ANOVA parametric and nonparametric tests are very helpful statistical procedures that can be used in researches related to cities by its fundamental nature, extensively. In the current research we have used One-Way Between-Subjects ANOVA and its nonparametric test (Kruskal-Wallis H) as well as related procedures on three different factors(with three ,four and five levels), having two Balanced(with equal sample sizes of groups) and Unbalanced designs, and by the use of two dependent variables (Percapita and transformed data based on data characteristics), according to the gathered data from the city level with the aim of comparing percapita of services at spatial dimension  of  areas . After finding the significant factor, we used various contrast tests in order to describe the nature of group differences in a parametric procedure using unplanned and planned comparisons i.e. by dedicating different weights on factor levels based on previous hypotheses. With the significant factor of quintuplet areas, we used planned (a priori) simple and compound linear, deviation and special contrasts as well as Post hoc unplanned Pairwise Multiple Comparisons and Range tests. In order to appraise the structure and study the effect and strength of a relationship and discover the differences through the insignificant factor levels of quadruplet areas, we used planned simple, polynomial, deviation and simple and compound linear contrasts. Generally, results showed that the distribution of civic functions of services in spatial dimension of areas are not optimal and have not been distributed equally. Meanwhile, parametric test of One-Way ANOVA and related tests offered reliable results through the convenient transformation of data especially with a small sample size.