Case study using analysis of variance to determine groups’ variations

The paper discusses the symmetric balanced incomplete block designs (SBIBD’s) and further verifies whether treatment adjustment in SBIBD is necessary using analysis of variance (ANOVA) method. Fortunately, the data collected for the analysis were in the same group of the balanced incomplete-block designs (BIBD’s), that is, symmetric balance incomplete block design (SBIBD).The ANOVA method is applied differently on the two sets of data arising from the symmetric balanced incomplete block designs (SBIBDs). In this work, the basic interest is to verify empirically whether treatment adjustment is necessary in SBIBDs with data from National Root Crop Research Institute (N.R.C.R.I), Umudike, AbiaState. To achieve this, we considered treatment (not adjusted), treatment (adjusted), and block (adjusted) in the ANOVA method. The study discovered that if ANOVA method is applied on the symmetric balanced incomplete block design (SBIBD) when a treatment is not significant, it turns out to be significant when the same treatment is adjusted. It obvious from the experiments that were carried out that the adjustment of the treatment in SBIBD’s is very necessary when the ANOVA method is applied. Apart from the improvement in the level of significance of the result, the error is also greatly reduced when treatment is adjusted.

The Taguchi and analysis of variance (ANOVA) methods were applied to investigate the effects of the structural and operational parameters on the heat transfer performance of a vertical sinter cooling packed bed. The analysed parameters were the gas flow rate, the air inlet temperature, the sinter inlet temperature, the cooling bed diameter and the cooling bed height, all of which contain three levels. The purpose was to improve the heat transfer performance of a vertical sinter cooling bed. A numerical analysis model was established to assess the heat transfer performance with respect to the varying parameters and their different levels. This mathematical model was validated by using data from practical industrial processes. The Taguchi method for the L27 (35) orthogonal design experiment was selected to evaluate the impacts of the design parameters on the heat transfer performance and to acquire the optimum combination of parameters. The analysis of variance was applied to assess the impact weights and the order of significance of the design parameters. The results show that the sinter inlet temperature and cooling bed diameter have great influences and impact the exergy of the wasted heat recovery by 61.65% and 23.31%, respectively. However, the gas flow rate and the air inlet temperature have small effects on the response. Furthermore, the air and sinter inlet temperatures have the most significant impacts on the efficiency of heat transfer by 68.83% and 23.31%, respectively. The optimal parameter combination (A1B1C3D3E3) was obtained, and the optimal results were validated by confirmation tests.

This research is about the roundness characteristics of the turning process parameters on St 42 material using the Taguchi and ANOVA (Analysis of Variance) methods. A cylindrical product requires a good roundness value to ensure its quality. One of the factors that determine the quality of these products is the machining process parameters. In this study, optimization of the turning process parameters using the Taguchi and ANOVA methods was carried out on the roundness characteristics of the workpiece. The workpiece used is Steel St 42. The lathe used is the Krisbow Precision Gear Head Machine 410X1000MM. After data analysis using the Taguchi method, the parameters with the optimum roundness value of 7.385 µm were 510 rpm spindle rotation, 1mm depth of cut, and coolant opening valve. The results of the ANOVA analysis show the percent contribution of each parameter, respectively, as follows: spindle rotation 26.97%, depth of cut 24.82%, and coolant 38.63%.

In this study, the effect of various parameters on the power coefficient (CP) performance of a vertical axis wind turbine (VAWT) was optimized using the Taguchi method. The optimization was conducted with the Taguchi method, employing three control factors: blade-spoke connection (BSC), turbine blade pitch angle (β), and turbine blade pitch direction (φ). Subsequently, the analysis of variance (ANOVA) method as employed to determine the contribution ratio of each control factor. Regression analysis (RA) was applied to develop an empirical equation predicting the CP of the VAWT, incorporating the control factors. The results indicated an optimal parameter configuration of BSC = 0.5c, β = 2°, and φ = (-), which maximizes the system performance. The performance of the optimal model was observed to exceed that of the conventional VAWT (B1) by 5.82%. Using the ANOVA method, the contribution of parameters on the CP performance of the VAWT was ranked as follows: φ > BSC > β. The φ parameter has the most significant effect at 82.07%, whereas the β parameter exhibits the least effect of 1.17%. Moreover, the predictive accuracy of the developed regression model was validated, yielding R2 values of 0.9221 for the training data and 0.9908 for the test data.

Adaptive Neuro-Fuzzy Inference System (ANFIS) can analyze the factors and factor levels affecting the subject of interest in many branches such as technology, production, health, social and education, depending on the many rules it creates and with a very small experimental error (RMSE). and modelling. It is also applied in the field of agriculture, especially for the solution of problems such as agricultural field selection or technological product development. On the other hand, classical statistical methods are generally used in due diligence studies in a certain time period, such as product cultivation. Experimental design methods or in other words analysis of variance (ANOVA) methods come first among these methods. With the experiments modeled by ANOVA, the factors affecting the subject of interest and the levels of these factors are analyzed according to a single rule of the method used. Since the Root Mean Square Error (RMSE) of the model formed by the multiple rules of ANFIS versus the single rule of ANOVA is much smaller, it gives stronger results. Modeling agricultural products with ANFIS depending on time will support data mining studies in this field. In this study, first both ANOVA and ANFIS methods were briefly explained, and then the data of a due diligence study carried out in agriculture were modeled by both methods and similar findings were obtained. However, mostly the standard deviation (RMSE) values of ANFIS were found to be smaller than ANOVA. In addition, the relationships between ANFIS outputs and real measurements were examined.

Rutting is one of the main distresses in the asphalt pavements, especially in higher summer temperatures and/or under heavy loads. Efforts were made in this study to investigate rutting failures on Jing-Zhu expressway in China to determine the cause and the source of the rutting. The characteristic of the rutting on each asphalt pavement layer were analyzed by surveying the rutting depth, excavating transverse profile sections, and boring samples in the typical sections of expressways where different degrees of rutting damage were observed. It was found that 60% of the rutting was generated by the asphalt middle course layer and the remaining 40% came from the other surface layers, which was consistent with the mechanical analysis of the asphalt pavement that showed that the shear stress in middle asphalt course layer was the highest in magnitude, under load or temperature. With the combination of the indoor rutting tests on the samples from a corresponding location of the road shoulders, the influence of the high temperature stability of each asphalt pavement layer on rutting was analyzed using the method of ANOVA (Analysis Of Variance). The results indicated that the stability of the middle course layer impacted the rutting development the most. Finally, the relationship between the RD (rut depth) and the DS (dynamic stability) of each asphalt pavement layer was established using a nonlinear regression method. The governing criterion for the DS at the bottom course asphalt mixture was studied and recommendation made based on regression analysis.

The SS methodology is being widely used as a quality management model. Focusing on Zero Defects production, the tool that support DNP used in the planning phases, that precede manufacturing is DFSS methodology, suitable for production processes with SS requirement. There is a contradictory question regarding the differences and complementarities between DFSS tool and the SS methodology: to conclude about what is the best strategic decision by two companies, in which one uses the SS and the other uses the DFSS; else more, if it would be a competitor at a higher level with regard to the experience and knowledge, by using both. The case study indicates that given the intrinsic characteristics of the company, common to other Portuguese Small, Medium Enterprises (SMEs), it is clear that the use of DFSS tool turns out to be the most effective, especially when the client is a big company. Keywords: Design for Six Sigma (DFSS); New products development; Six Sigma (SS).

The epikarsts of five covered karst areas in the Bakony region are compared. The comparison is based on the specific resistances of the bedrock resistance averages measured by vertical electrical sounding (VES). The average largest specific resistance differences (per area) and the mean values (per profile) were calculated to investigate the characteristics of the epikarst. The mean values of specific resistance were compared using analysis of variance (ANOVA) to determine whether the specific resistances of the areas showed significant differences. The studied karst areas can be categorised as high or low specific resistance areas, and the ANOVA method could be applied to three areas based on the available data. It can be concluded that the mean values of their specific resistances are significantly different. The mean values and standard deviation of the specific resistances of the different areas were analysed. It is described that the karst receives less water in the case of areas with lower specific resistance, while it receives more water in the case of areas with higher specific resistance. Areas with a high specific resistance are less karstified, while areas with a low specific resistance are more karstified. Low specific resistances and small mean values indicate a higher degree of cavity formation in the epikarst, while the decrease in mean values and standard deviation indicates increasing uniformity of the degree of cavity formation in the epikarst. The different degree of cavity formation is explained by the different karstification rates and the exposure of different time of certain areas.

Optimal operating parameter determination based on fuzzy logic modeling and marine predators algorithm approaches to improve the methane production via biomass gasification

Surface roughness optimization in laser machining of stainless steel 304 using response surface methodology

In palmprint recognition, researchers can extract ridges, singular points and minutia points as features from high resolution images while in low resolution images they generally extract principal lines, wrinkles and texture. And now almost all research concentrates on low resolution images for civil and commercial applications. This is also the focus of this paper. In this paper, a new method based on 2D hybrid Log-Gabor filter, D-S evidence theory and ANOVA (Analysis Of Variance) method is proposed for feature extraction. In palmprint images, 2D hybrid Log-Gabor filter is constructed by a number of weighted 2D Log-Gabor filters with different orientations to extract texture mixed-phase feature. And the integration of ANOVA method and D-S evidence theory is then employed for detecting directional line feature by dealing with the uncertain weak edges and the difficulty in threshold selection from multi-images. Finally, a decision based on the measurement of a modified Hamming distance is obtained. Experiments on the Polyu-Online-Palmprint Database show competitive performance.

The paper is devoted to the construction of a regression model describing the dynamics of the cattle population in Ukraine and to the development of a forecast for 2026. Descriptive statistics, correlation–regression and analysis of variance methods were applied, and the model parameters were estimated using the least squares method. The normality of the distribution was tested using the JarqueBera criterion, while model adequacy was assessed by the coeffi cient of determination and residual analysis. Based on data for 2015–2024, a linear model was developed, indicating an average annual decline in the cattle population of about 211 thousand head. The forecast for 2026 is approximately 1,574 thousand head, with a defi ned confi dence interval. The results confi rm a persistent decline in the cattle population in Ukraine and are consistent with European trends. The model is suitable for analytical purposes and strategic planning under conditions of economic instability and martial law. The aim of the study is to construct a regression model of the dynamics of the cattle population in Ukraine and to form a forecast for 2026 based on a limited time series of statistical data. Methods. The study employed methods of descriptive statistics, correlation–regression analysis, and analysis of variance. Model parameters were estimated using the least squares method. The Jarque–Bera test was used to verify the normality of the data distribution. Model adequacy and statistical reliability were evaluated using the coeffi cient of determination, correlation index, and residual analysis. Calculations and forecasting were performed using Microsoft Excel. Results. Based on statistical data for 2015–2024, a linear regression model of cattle population dynamics in Ukraine was constructed, characterized by a high goodness of fi t (coeffi cient of determination of 0.9874). The Jarque–Bera test confi rmed the normality of the data distribution. A quantitative estimate of the average annual decline in the cattle population was obtained, amounting to about 211 thousand head. A point forecast and a confi dence interval for the projected cattle population in 2026 were constructed. Conclusions. The constructed regression model is statistically signifi cant and can be used to forecast the dynamics of the cattle population in Ukraine under conditions of a limited information base. The projected number of cattle in 2026 is approximately 1,574 thousand head. The obtained results have practical value for strategic planning of the agricultural sector and for managerial decision-making under conditions of economic instability and martial law.

A typical microarray data of ovarian cancer consists of the expressions of tens of thousands of genes on a genomic scale. To avoid higher computational complexity, we want to find the most likely differentially expressed gene that best explain the effects of tumor/cancer for ovarian cancer. In this paper, we derive a hybrid approach for extracting and evaluating informative genes from microarray data of ovarian cancer. Furthermore, the expression patterns of extracted genes are used to identify tumor/cancer for ovarian cancer. In the proposed approach, the method analysis of variance (ANOVA) will, using P-value, test gene expressions that are significantly different in microarray data. The genetic algorithm is applied to extract genes and then the support vector machine is processed to identify tumor/cancer for ovarian cancer. We show that this extracted set of genes can be used to significantly identify ovarian tumors (OVT) and ovarian cancers (OVCA).

To provide the population with healthy nutrition is an urgent problem of any country. According to experts of the World Health Organization, one of the most important factors determining the health and well-being of the population is healthy nutrition. In the context of growing interest in healthy eating, an in-depth study of nutrition practices becomes particularly important, as food norms, traditions and habits are an integral part of everyday life. Healthy nutrition is an actual problem of modern Georgia. The nutrition of the majority of the Georgian population is unbalanced, incomplete and irregular, therefore, their food supply structure remains unsatisfactory. Under the current economic conditions, many Georgian consumers are switching from natural food products to their cheaper substitutes in order to save money. This trend has a negative impact on the health of the population as a whole. Studying the attitude of consumers towards healthy nutrition is the decisive issue of social marketing. In the literature of social marketing, much attention is paid to researching how modern life impacts on the practice ofnutrition and what changes occur regarding to the nutrition in everyday life. Social marketing is the use of marketing concepts in a program designed to influence the voluntary behavior of target audiences in order to improve health in the society. Social marketing is focused on people, their wishes and needs, aspirations, lifestyle and freedom of choice aiming aggregate behavior change. Social marketing uses traditional marketing instruments to promote healthy attitudes and behaviors. One of the important factors of changing healthy behavior is increasing awareness and knowledge in food labeling among the general public. Most part of the population in the world uses the labels on food packaging to make healthier choices. For improving the healthy choice of the consumer,it is important to get the consumer into the habit of checking the label. The ability to choose prepackaged food based on information obtained on its label requires knowledge and ability to read, understand and interpret information. There are many studies about the impact of food labeling on healthy nutrition, but such studies have not been carried out in Georgian reality. To solve this problem, we conducted a marketing research. Qualitative and quantitative methods have been chosen for studying and respectively, the study consisted of two steps. At the first step the focus group technique has been choosing for the qualitative survey and hypothesis formulation. In the second step the respondents were conducted online and face to face survey through the country. A systematic random sampling method was used.The research tool we chose a questionnaire that consisted of several structured questions. The questionnaire contained the information on the consent and confidentiality of the respondent, as well the study explanation and the filling instructions. A five-point Likert scale was employed. The self-administered survey method was used to avoid errors caused by the subjectivity of the interviewer. Based on this the survey results were analyzed using statistical software SPSS for windows. Along with research methodology we used variance analysis method – ANOVA. In the work, on the basis of variance analysis, is established dependencies of the influence of consumer awareness about the appointment and requirement of food labeling, as well as the reliability of packaging information on healthy nutrition.

Introduction: Osteoporosis is a disease of bones and it has been associated with various risk factors such as female gender, advanced age and smoking habit. However, evidence has shown that the true figures between osteoporosis and smoking are conflicted. Aim: This study was aimed to screen individuals for potential risk factors related to osteoporosis and to assess whether smoking habit is associated with osteoporosis development in Primary care population aged 45 years and over. Materials and Methods: A screening program for osteoporosis was conducted in two Primary care settings in the island of Crete – Greece, during a 3-month period (April to June 2013). We studied 400 participants aged 45 years old and over for risk-factors associated with osteoporosis development using the ONE Minute Osteoporosis Risk Test. Participants were screened for to 19 risk-factors related to osteoporosis such as fracture, diagnosis of Thyroid, Alcohol Consumption and smoking responding to Yes or No. Statistically, chi square and/or Variance analysis were used for the descriptive characteristics of the study participants and the significance of association among demographic characteristics. The method of variance analysis was applied to determine associations between risk factors and demographic characteristics. Bivariate associations were described using Odds Ratio (OR) with 95% Confidence Intervals (CI). P-values≤0.05 was considered significant using the SPSS version 21.0. Results: The mean age of participants (50% males) was 61.94 ± 11.6. Males were smoked significantly more often (75.5% vs. 43%, p<0.001) than females and, current smokers both females (OR=1.12; 95%CI: 0.25-1.99, p=0.012) and, males (OR=0.85; 95%CI: 0.10-1.59, p=0.027) were significantly more likely to report an increased number of risk-factors for osteoporosis compared to never smokers. We also found that obese participants were less likely to report an increased number of risk-factors (OR=-1.87; 95%CI: -3.54, -0.20, p=0.029) compared to reference category suggesting that obesity was a protective factor for osteoporosis, but this association was significant only for males. Conclusion: Although a number of risk-factors did not differ among non-smokers, former and current smokers, current smokers had a greater likelihood of reporting a number of risk-factors related to osteoporosis, suggesting that smoking is an important risk factor for osteoporosis development.