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Tight assembly of stator windings with no insulation paper damage is a manufacturing challenge. We evaluate different sets of parts according to the following parameters: magnet wire thickness, stator slot smoothness, length of the straight magnet wire after the slot end, and type and amount of insulation cap at the end of the slot. These parameters have discrete values with small differences between them. The damage criterion is the decrease of the insulation paper breakdown voltage after assembly/disassembly of parts, assembled in a small set of designed experiments. Parameter values, i.e., levels at individual experiments are set by an orthogonal experiment matrix. Repetition of each experiment provides statistical significance. Data analysis shows that the additive model alone is not sufficient due to the high correlation of the parameters' influences. We extend the model to include interparameter influences, which we model by adding a virtual parameter. The extended additive model generates parameter values that do not degrade the insulation paper breakdown voltage within the manufacturing process. These values are verified by repetitions of the control experiment.

In this paper, the dynamic tracking technique is applied to analyze the triggering factors of online public opinion, modify the feature subsets and establish a basic database of sensitive information. The test of statistical characteristics of the evolution process of public opinion is realized by using the prediction of time series by the ANOVA algorithm, and on this basis, the smoothness of the model is realized by testing the parameters. In the process of establishing the quantitative index system, the seasonal difference algorithm is introduced to predict the development trend of public opinion on hot topics to realize the visualization model design. Finally, in order to verify the validity of the model, an experiment was conducted with Event A as an example. The results show that users can set a total of 4 keywords with an average word frequency of 0.03 Hz. The prediction accuracy in different periods can reach 88.73% on average, and the adaptation value can reach 0.8 percentage points on average. Thus, it can be seen that the trend analysis model of online public opinion evolution constructed in this paper can quantitatively analyze the mechanism of public opinion segmentation evolution and the trend prediction problem and provide a theoretical reference for the government to govern public opinion.

The new generation of pavement technology with the goal of longevity is an important supporting technology that can promote the achievement of sustainable development of high-speed roadways. To further investigate the evolution trend of long-life pavement performance, this paper paved experimental sections to explore the correlations between pavement structure combinations and pavement performances. This paper presented four experimental sections with different pavement structures, asphalt concrete layer thicknesses, and pavement materials. Then, this paper analyzed the effects of the seasonal factors, pavement structure, and lanes on the deflection value and rut depth from three dimensions by the Pearson correlation coefficient (PCC). Finally, this paper used the analysis of variance (ANOVA) to analyze the relationships between the layer thickness of various materials in the pavement structure and the pavement performances, including the deflection value, international roughness index (IRI), texture depth (TD), British Pendulum Number (BPN), sideway force coefficient (SFC), rut depth, and disease area. The results showed that the seasonal factors significantly affected the deflection values of pavement structures with PCCs of 0.61, 0.72, 0.53, and 0.78. The high temperatures increased the average deflection values by 22.85%, 72.88%, 77.61%, and 88.13%, respectively. Under the influence of high temperature in summer and traffic loads, the increased ranges of average rut depth were −0.2%, 4.89%, 9.56%, and 7.31%, respectively. The results of ANOVA showed that the pavement structure type and thickness of each structural layer significantly affected the deflection value, and there also was a strong correlation between the pavement structure type, thickness, BPN, and SFC with p-values less than 0.05. Increasing the thickness of the asphalt surface was beneficial for reducing the area of defects, while laying the semi-rigid base layer was beneficial for maintaining the deflection value and rut depth at a lower level.

The purpose of this paper was to introduce the orthogonal design and its quantitative data analysis of variance and the SAS implementation. From the perspective of degrees of freedom， the orthogonal design could be divided into the saturated orthogonal design and the unsaturated orthogonal design. From the perspective of the number of factor levels， the orthogonal design could be divided into the same level orthogonal design and the mixed level orthogonal design. From the perspective of normalization， the orthogonal design could also be divided into the standard orthogonal design and the non-standard orthogonal design. Quantitative data from the standard orthogonal designs could be analyzed by the conventional methods， while quantitative data from the non-standard orthogonal designs needed to be improved. Based on three examples， this paper realized the quantitative data analysis of variance with the standard orthogonal design without repeated experiments and with repeated experiments by means of the SAS software.

The purpose of this paper was to introduce the nested design and its quantitative data analysis of variance and the SAS implementation. If one of the following two characteristics existed in a specific experimental study， a nested design could be considered to arrange the experiment. Firstly， there was a nested relationship between factors in natural attributes. Secondly， with professional knowledge as the basis， the impact of each factor on the quantitative observation results was divided into primary and secondary. The first feature mentioned above meant that the factors related to the subjects had the conditions for grouping and regrouping. The second feature mentioned above meant that the status of each factor was unequal. In the variance analysis of quantitative data， the calculation formulas of variable error mean square was required to use. Based on four examples and with the help of the SAS software， this paper implemented the univariate analysis of variance for the quantitative data of the nested design， and gave the detailed explanations for the output results of SAS software.

The purpose of the paper was to introduce the calculation formulas and the SAS implementation of the analysis of variance for the quantitative data of the crossover design. In the calculation， three test statistics were involved， namely Ftreatment， Fstage and Findividual. They were three test statistics used to evaluate the statistical significance of the effect of the treatment factor， the stage factor， and the individual factor on the quantitative outcome variable， respectively. In general， it was assumed that there was no or negligible interaction among the three factors in a crossover design， so there was no need to evaluate whether the interaction term was statistically significant. With the help of SAS software， this paper conducted the univariate analysis of variance for the quantitative data of crossover designs for three examples of 2×2 crossover design， 3×3 crossover design and three-stage crossover design， and presented the calculation results and drew the statistical and professional conclusions.

The purpose of this paper was to introduce the model， calculation formulas and the SAS implementation of the univariate analysis of variance for the quantitative data with randomized complete block design. In the calculation， two test statistics were involved， namely FA and FB. Among them， the subscript "A" represented the experimental factor， and the subscript "B" represented the block factor （i.e.， the important non-experimental factor）. In general， it was assumed that there was no or negligible interaction between the two factors in a randomized block design， so there was no need to assess whether the interaction term was statistically significant. Therefore， it was not necessary to do repeated experiments under each combination of two factors. With the help of SAS software， this paper conducted the analysis of variance for the quantitative data with randomized complete block design for two instances without and with repeated experiments， gave the calculation results， and made the statistical and professional conclusions.

The purpose of this paper was to introduce the model， calculation formulas and the SAS implementation of the analysis of variance for the quantitative data with balanced incomplete block design. In the calculation， two test statistics were involved， namely FA and FB. Among them， the subscript "A" represented the experimental factor， and the subscript "B" represented the block factor B （i.e.， the important non-experimental factor）. In general， it was assumed that there was no or negligible interaction between the two factors in a balanced incomplete block design， so there was no need to evaluate whether the interaction term was statistically significant. Therefore， it was not necessary to do repeated experiments under each combination of two factors. With the help of SAS software， this paper conducted the analysis of variance for the quantitative data with balanced incomplete block design on two examples， and presented the calculation results and made the statistical and professional conclusions.

Applying a statistical test to check statistical assumptions offers a positive or negative response regarding the veracity of the issued hypothesis. In case of variance analysis it’s necessary to apply a post hoc test to determine differences within the group. Statistical estimation using confidence levels provides more information than a statistical test, it shows the high degree of uncertainty resulting from small samples and builds conclusions in terms of "marginally significant" or "almost significant (p being close to 0,05) . The case study shows how the statistical estimation completes the application form the analysis of variance test and Tukey test.

The purpose of this paper was to introduce the prerequisites， basic ideas， calculation formulas and the SAS implementation of a single-factor multi-level design quantitative data univariate analysis of variance. The prerequisites included the independence， normality and homogeneity of variance. The core of the basic idea was the decomposition of the sum of squares of the total deviations for the mean. The test statistic F was constructed through the between-group mean square divided by the within-group （or called error） mean square. The result of analysis of variance was a general evaluation of the difference among all means of a factor with the whole levels. When it was found that the difference among all means of the factor was statistically significant， a specific approach needed to be adopted for the multiple comparisons about the multiple means of the factor. With the help of the SAS software， the paper performed the analysis of variances for two examples， and used three approaches to make the multiple comparisons among all means of a factor in one of the examples.

The purpose of the paper was to introduce the test for homoscedasticity and the SAS implementation. The test of homogeneity of variance could be divided into the following three categories， ①analysis of variance directly based on comparison of variances， ②analysis of variance based on mean comparison was adopted for the new data from the variable transformation of the original data， ③the method of the χ2 test was used to analyze the quantitative raw data which followed the normal distribution. In the first category， a test statistic that followed the F distribution was constructed directly based on the variance ratio of the two samples. In the second category， there were a variety of different variable transformation approaches for the original data， and the new data after the transformation， which was viewed as the univariate quantitative data collected from a single-factor with multi-level design， was analyzed by using one-way ANOVA. In the third category， the χ2 test statistic was constructed for quantitative data that followed the normal distribution. The paper was based on SAS software to test the homogeneity of variances of three examples， and explained the output results.

The purpose of the paper was to introduce the multiple comparison method among multiple means and the SAS implementation. The multiple comparison approaches could be subdivided into the pairwise comparisons， the comparisons of all treatment groups with a control group， the comparisons of the mean of each treatment group with the average of all groups， the approximate and simulation-based approach， the multi-stage testing and Bayesian method. Except for the Bayesian approach， the difference between other multiple comparison methods lied in the types of error that were controlled. Error types could be roughly divided into the following three categories， the comparisonwise error rate， the experimentwise error rate and the maximum experimentwise error rate. The multiple comparison methods constructed based on the control of different error rates were not all the same in the strength of inference to draw conclusions. This paper used the SAS software to analyze the examples and explained the output results.

Due to the influence of snow cover and soil freeze–thaw processes in the early spring, the characteristics of spring drought in cold regions are different from those in other regions. This paper takes Qiqihar, a cold and semiarid grain production area in Northeast China, as the study area. Based on factors such as snow cover in the early spring period and precipitation, temperature, evapotranspiration, and soil moisture during the spring sowing period, a projection pursuit model with analysis of variance (ANOVA PPM) was used to analyze the spatiotemporal variation characteristics of regional spring drought. For the ANOVA PPM, based on the grouping of the projection points, local aggregation is measured by the mean square within groups, and overall dispersion is measured by the mean square between groups, thus achieving comparability and balance between overall dispersion and local aggregation. The spatial analysis of spring drought showed that, from a spatial perspective, there were significant spatial differences in spring drought in different subregions, and spring drought was closely related to latitude, with significant differences occurring at different latitudes. The northern region is relatively light, while the central and southern regions are more severe. From a temporal perspective, there are significant differences in spring droughts between different decades (1980 s, 1990 s, 2000 s and 2010 s). Overall, spring drought first intensifies and then decreases.

Abstract Polymers are used in various industrial applications due to their ease of production, light weight, and ductility. Fillers such as clays are added to polymers to improve a range of factors such as material processing, thermal properties, fire retardance and cost. However, adding clays may negatively impact the mechanical performance of the composite. In addition, manufacturing parameters, for example, number of extrusions, press time, and so forth may also have an influence on the resulting composite system. This study performs a statistical analysis on a set of previously obtained experimental results, which investigated the influence of various manufacturing, material, and testing parameters on the composite mechanical properties. Exploratory data and statistical analysis techniques are applied to the historical tensile test data to gain insight into the influence on mechanical properties as well as the relationships and interactions between the parameters. Specifically, it is shown that clay loading does not have a statistically significant effect on the composite mechanical properties, which is contrary to literature. Another surprising result is the poor performance of the clay that is compatible with high‐density polyethylene compared to the clay that is compatible with poly vinyl chloride. The contribution of this paper is to demonstrate the usefulness of applying statistical analysis on a large volume of data to understand the diverse correlations between the different variables.

Background: The Kingdom of Saudi Arabia has developed rigorous strategies to control and prevent the spread of COVID-19. However, the effectiveness of these measures in containing and mitigating the epidemic has yet to be studied. This paper aims to assess the efficiency of preventive policy initiatives that Saudi Arabia has taken to reduce the spread of COVID-19, which was rapid and progressive in nature. Information on the effectiveness of measures applies to help the Saudi government adjust policy responses when considering which measures to relax once the epidemic is controlled. Methods: Data for this study were retrieved via publicly available data sources such as the Saudi Arabia Ministry of Health and the government's official news agency—Saudi Press Agency (SPA) websites. Other datasets, such as prevention measures, were gathered from the Country Policy Tracker website. Our dataset's time component extends over 590 consecutive days from 20 January 2020–31 August 2021. Moreover, a mixed-method approach combining COVID-19 data and prevention measures was adopted to assess preventative measures practice. We compiled the dataset used in this study in a Microsoft Excel database. The significance of observed differences among implementing effective strategies was determined using ANOVA and Mixed methods approach. Noticeably, the statistical analysis was performed using the open-source statistical system R version 4.2 (available at http://cran.r-project.org). Results: Our analysis showed that only three out of the 32 (9.4%) measures significantly reduced the spread of COVID-19. Our results also show substantial variations in the spread of COVID-19 associated with preventive measures in Saudi Arabia. There was a significant positive correlation between activating and massive testing in communities and cases of COVID-19 (measure effect = 923.086 and p

The objective of this paper was to investigate the Cutting Speed, Feed Rate and Depth of Cut to predict Tool wear during Turning of AISI 1040 Medium Carbon Steel Blanks using Artificial Neural Network Approach. The significance of the cutting parameters was investigated using the Analysis of Variance and results revealed the feed rate as the most influential factor, followed by the interaction of cutting speed and depth of cut. The Artificial Neural Network model exhibited notable correlation coefficients (R) in training (0.81301), validation (0.99932), and test (0.99922) datasets, with an overall coefficient of 0.86662, affirming the model's efficacy in predicting tool wear. The minimum predicted tool wear (0.1007mm) was observed at a 0.50mm depth of cut, cutting speed of 200m/min, and feed rate of 0.15mm/rev, demonstrating a close alignment with the observed data. The ANN predictions effectively capture the intricate relationship between tool wear and process parameters, substantiated by high correlation coefficients.

This paper first investigates the use of the ESP teaching mode in English teaching today and then constructs an English teaching mode based on ESP blended learning to enhance reading comprehension ability. We then examined the sample students’ overall reading skills in terms of content summarization, word decoding, and content analysis. Lastly, we conducted a comparative analysis to investigate the impact of the ESP teaching mode on the reading comprehension skills of English majors. We compared the students’ performance in each type of reading comprehension question and their proficiency in each reading comprehension module before and after the teaching, using independent samples t-tests and chi-square test methods. After the implementation of ESP English teaching, the p-values of students’ performance in reading comprehension main idea questions, factual details questions, inferential judgment questions, and guessing the meaning of words were 0.018, 0.008, 0.041, and 0.004, respectively, with the p-values less than 0.05. This study provides supplemental assessment data for students’ reading comprehension learning, which can have a positive effect on improving their reading levels and increasing their confidence in answering reading comprehension questions.

This paper first established a multiple linear regression model and estimated the model parameters using the least squares estimation method. Then, it analyzes the employment and entrepreneurship abilities of college students in higher vocational colleges and universities and analyzes the differences in the level of employment and entrepreneurship ability of students in different grades. Then, the impact of artificial intelligence technology on employment and entrepreneurship ability was analyzed in combination with relevant measurement indexes at home and abroad, and finally, based on the results of the analysis, the strategies for improving the employment and entrepreneurship ability of college students in higher vocational colleges were proposed from multiple aspects. The results show that for every 1% increase in the level of AI technology, the total number of people employed in the labor employment market rises by 0.48, and the employment income rises by 6.56. This study has certain reference significance for balancing the “positive effect” and “negative effect” brought by the development of artificial intelligence on labor and employment, and provides realistic and feasible basis for the introduction of employment policy and talent development policy adapted to the development of artificial intelligence.

This paper uses the method of statistical analysis to study the interpersonal relationship feelings, teaching ability, teacher responsibility, professional ethics, work commitment, and work values of young teachers and to explore the impact of different gender, education, school, and other factors on the professional quality of young teachers. Through the analysis of the current situation of young teachers’ professional quality, we summarize the problems in the training and management of teachers and put forward targeted improvement suggestions to construct a new path for the training and management of young teachers. The study shows that young teachers in the age group of 28-35 have a lower work commitment score. The mean values of the teacher-student relationship and relationship with parents of young teachers are 3.375 and 2.685, respectively, and there are problems in the teacher-student relationship and relationship with parents. This study has practical value and guides training young teachers and managing colleges and universities.

This paper first discusses the basic functions and basic ways of network security education, then explores the basic characteristics of students’ social mentality, analyzes the main influencing factors of college students’ healthy social mentality, and the influence of network security education on college students’ social mentality, and based on this, proposes an innovative path of network security parenting. Then, the students’ network security literacy and healthy social mentality were analyzed for differences, and the influence of network security education on their critical thinking ability was studied using ANOVA. The use of heterogeneity analysis was used to investigate changes in students’ healthy social mindset at both high and low levels of cybersecurity education. The results showed that the effect of cybersecurity education on students’ total critical thinking ability was significant at the 1% level, F=3.219, η2=0.013. Students’ healthy social mindset was affected by both high and low cybersecurity education levels, with substantial coefficients of 0.00189 and 0.00152, respectively. The present study systematically examines the mechanism of the influence of cybersecurity education on college students’ social mindset and the path of influence, which helps to enrich the research paradigm on college students’ social mentality.

This study develops a framework for personalized learning path planning that incorporates individual students’ learning behaviors, current learner status, and mastery of knowledge points with varying degrees of difficulty. Utilizing a big data-driven model, the framework calculates the difficulty of each knowledge point specifically tailored for individual students. Subsequently, it assesses the learners’ current status through an analysis of historical learning records, enabling the strategic planning of personalized educational trajectories. Finally, the correlation analysis and independence test were used to compare the differences in students’ academic performance, learning confidence, and learning needs between personalized learning and traditional learning modes. After using the personalized learning model, the student achievement outcome data showed F(1, 145) = 41.805, p = 0.013, η2 = 0.213. There was a significant difference in the personalized learning model on the students’ student confidence, F(1, 120) = 7.364, p = 0.021, η2 = 0.007. The customized learning pathway designed in this paper can promote the achievement of personalized learning goals for students.

In this paper, a non-linear stochastic model was constructed to introduce psychological capital as a random disturbance and predict students’ entrepreneurial intentions. Then, we conducted a differential analysis of students’ psychological capital on indicators of gender, family economic status and parents’ attitudes toward their children’s entrepreneurship, and also tested the mediating effects of innovation efficacy and perceived behavioral control to reveal the inner mechanism of the psychological capital of college students’ innovation and entrepreneurship. Finally, a psychological support system for college students’ entrepreneurship was constructed, including three dimensions a positive organizational system, positive emotional experience, and positive personality traits. The study showed that the direct effect of psychological capital on entrepreneurial intention was significant, β = 0.15, t = 3.19, p < 0.01, and overall significant. Innovative efficacy significantly and positively predicted perceived behavioral control β=0.48, t=12.67, p

This paper first investigates cognitive psychology and art design, divides cognitive memory into sensory memory, short-term memory and long-term memory according to cognitive psychology, and investigates art design's needs, psychology and creative thinking ability. Then, an art design is proposed that uses image processing technology. The first step in designing an art design using image processing technology is to use an image processing algorithm. Based on the image luminance equalization processing in art design, image fusion and wavelet noise reduction processing are carried out, and ANOVA cognitive psychology and art design experimental data are used to realize art design human-computer interaction. The results show that the analysis of the model with artistic design creative thinking variability as the dependent variable found that the R2 of the model was 0.036, and the adjusted R2 was 0.023. F=2.633, p=0.015, and the grade level had a significant effect on artistic design creative thinking variability. This study is a comprehensive and thorough cognitive psychology and art and design relationship that can contribute to development.

Testing the equality of several independent group means is a common statistical practice in the social sciences. The traditional analysis of variance (ANOVA) is one of the most popular methods. However, the ANOVA F test is sensitive to violations of the homogeneity of variance assumption. Many alternative tests have been developed in response to this problem of the F test. These tests include some modifications of the ANOVA F test and others based on the structured means modeling technique. This paper provides a SAS macro for testing the equality of group means using thirteen methods including the regular ANOVA F test. In addition, this paper summarizes the results of a simulation study that compares the performance of these tests in terms of their Type I error rate under different conditions, especially under violations of the homogeneity of variance assumption.

Voltage sags are power quality disturbances mainly caused by faults with a highly random nature. For this reason, the severity and frequency of occurrence of voltage sags is typically analyzed by means of probabilistic methods, such as Monte Carlo simulations, where different probability distributions are selected to model the uncertain inputs (e.g., fault impedance, fault location, fault rate, etc.). The selection and parameterization of the appropriate probability functions involves many difficulties to ensure that they represent the distribution of input variables in a realistic way and so, many different approaches can be found in literature. This paper studies the sensitivity of voltage sags severity to the uncertainty in the input models in order to focus modeling effort on the input variables that are more critical and effectively have an influence on voltage sags levels, whereas non-influential parameters can be neglected or modeled by means of simplified hypothesis. The method proposed is based on Design of Experiments and Analysis of Variance (ANOVA) and is able to discriminate with a statistic confidence level whether the uncertainty in the input significantly affects voltage sags severity indices or not. The effect of uncertainty on voltage sags indices SARFI90 and SARFI70 has been evaluated in realistic scenarios for IEEE test networks of 24 and 118 buses and in the power system of Ecuador with 357 nodes. General trends have been also established that help to understand the effect of the modeling of input parameters on the estimated number of sags.

Cyber security has been drawing massive attention in recent years due to human reliance on new technology, and systems. Therefore, securing these systems against cyber-attacks has become an essential task nowadays. The advanced persistent threat is one of the most sophisticated cyber-attacks in which malicious actors gain unauthorized access to a network and remain undiscovered for a lengthy period of time. The number of recorded advanced persistent threat attacks and threats to organizations intensifies. One method used in detecting advanced persistent threat attacks is machine learning. However, this method has not been covered in many previous kinds of research due to the lack of datasets covering an entire advanced persistent threat attack lifecycle. Thus, this paper aims to build a new dataset that covers the complete life cycle of an advanced persistent threat attack to detect them in different stages such as normal, reconnaissance, initial compromise, lateral movement, and data exfiltration activities. The newly collected dataset is based on advanced persistent threat attacks using tactics, techniques, procedures, and indicators of compromise. Then, it is applied to a proposed machine learning model employing eXtreme gradient boosting and analysis of variance feature selection method. The model was compared to other traditional classifiers: random forest, decision tree, and K-nearest neighbor. Based on the accuracy score of the proposed model 99.89% using only 12 features, it proved to be more powerful and efficient than the other classifiers in detecting advanced persistent threat attacks. The dataset used in this research is newly built based on advanced persistent threat attack behaviors, which will aid organizations in detecting advanced persistent threat attack activity efficiently. The experimental evaluation proved that the proposed method effectively detects advanced persistent threat attacks at different stages.

This work studies the impact of electromagnetic fields on the degradation of agricultural land. During the analysis of available sources of information, it was found that in the scientific publications by domestic authors, there is no mention of such a factor of land degradation as the action of electromagnetic fields (EMF) of man-made origin. Numerous scientific works by foreign experts present the results of experimental studies that confirm the negative impact of electromagnetic radiation (EMR) of power lines (PL) on agricultural land. The application of mathematical methods of modelling the dynamics of the distribution of the number of microorganisms or other soil components in the irradiation zone of power lines can be the basis for biomonitoring in the system of construction technologies to protect agricultural land from electromagnetic pollution. The paper presents statistical models designed to assess the impact of electromagnetic fields on pollution of agricultural land and conduct appropriate biomonitoring. The dependence of the number of fungi in the soil on the distance to the 110 kV power line at different seasons and the dynamics of the intensity of adaptation processes in the soil at different distances from the power line (PL) was constructed. The methods of regression, variance and cluster analysis were usedin the development of models, and the corresponding elasticity functions were constructed. It is shown that the application of modern data processing methods allows obtaining additional information even from the alreadypublished results of work performedby various researchers.

With the promotion of advanced manufacturing concepts such as green, high efficiency and intelligence, the shortboard and development bottleneck of traditional forming technology are increasingly prominent. The traditional forming technology is difficult to meet the actual needs of modern production. Special forming methods have emerged. The piercing process of sheet assisted by magnetic medium is an innovative attempt. However, there are many factors that affect the process, and the multi-field coupling further improves the process complexity, so it is very important to design the experimental scheme scientifically and reasonably. In this paper, through the design of orthogonal experiment, the piercing experiments are carried out under the condition of various process parameters, and then with the help of the grey correlation theory analysis, the relative rollover and the width of the relative bright zone are taken as the evaluation indexes. Through the analysis of range and analysis of variance, to find the key factors affecting the quality of piercing, determine the best combination of process parameters, and verify the accuracy of the results by confirming the test. The results show that: the best process combination scheme is A2B3C2D3. The experiments were carried out according to the recommended optimization scheme, the relative rollover reduced to 1.19%, and the proportion of the width of relative bright area reaches 64.06%. This paper provides scientific guidance for the research and quality improvement of piercing technology assisted by magnetic medium.

【Objective】 A porous filter is often used in micro-irrigation systems to filter debris and sand particles. Its performance depends on many factors. The purpose of this paper is to present a method to optimize its operating conditions. 【Method】 The studies were based on a micro-pressure filter. We took water pressure loss, sand interception rate and filtration efficiency as assessment criteria. In the experiments we measured and calculated the combined impact of inlet flow, sediment content, water-separator type, and filter area on operation of the filter. The results were processed using the analysis of range and variance. 【Result】 The factors that affected the head loss of muddy water across the filter were ranked in the descending order of inlet flow > sediment content > filter area > water separator type. The factors that impacted the mass of sand trapped by the filter were ranked in the descending order of sediment content > filter area > water separator type > inlet flow rate. The factors that affected the total filtration efficiency were ranked in the descending order of filter area > sediment content > water separator type>inlet flow rate. 【Conclusion】 The two factors that affected water head loss across the filter, sediment filtration and total filtration efficiency most are sediment content and filter area. Comprehensive analysis shows that the optimal operating conditions for the filter are sediment concentration 0.36 kg/m3, inflow rate 4 m3/h, water separator type Type 3, filter area 2 060 cm2.

The purpose of this paper was to introduce the fractional factorial design and its quantitative data analysis of variance and the SAS implementation. The fractional factorial designs were very similar to the factorial designs and the orthogonal designs， but they had some differences. The fractional factorial design required significantly fewer combinations of levels than the factorial design of the same size， and even saved sample size than the orthogonal design of the same size. In general， the precision of the results obtained by a fractional factorial design was lower than an orthogonal design and much lower than a factorial design. The fractional factorial design was suitable for the trial tests with many experimental factors， and its main purpose was to explore experimental factors that had a greater impact on the quantitative experimental results. When performing ANOVA and regression analysis on quantitative data with a fractional factorial design， it should be clear which factors or interactions had confounded effects.

Multiple comparisons tests (MCTs) are performed several times on the mean of experimental conditions. When the null hypothesis is rejected in a validation, MCTs are performed when certain experimental conditions have a statistically significant mean difference or there is a specific aspect between the group means. A problem occurs if the error rate increases while multiple hypothesis tests are performed simultaneously. Consequently, in an MCT, it is necessary to control the error rate to an appropriate level. In this paper, we discuss how to test multiple hypotheses simultaneously while limiting type I error rate, which is caused by α inflation. To choose the appropriate test, we must maintain the balance between statistical power and type I error rate. If the test is too conservative, a type I error is not likely to occur. However, concurrently, the test may have insufficient power resulted in increased probability of type II error occurrence. Most researchers may hope to find the best way of adjusting the type I error rate to discriminate the real differences between observed data without wasting too much statistical power. It is expected that this paper will help researchers understand the differences between MCTs and apply them appropriately.

The purpose of this paper was to introduce the calculation formulas and the SAS implementation of the analysis of variance of the univariate quantitative data with the Latin square design. The Latin square design could be divided into two categories： the general Latin square design and the Greek Latin square design. The former could be used for the experimental situation with one experimental factor and two block factors， the latter could be used for the experimental situation with two experimental factors and two block factors. In fact， Latin square designs could be further subdivided by whether or not the repeated experiments were performed and whether the block factor was a single individual type. Generally speaking， in addition to satisfying the requirements of "independence， normality and homogeneity of variance"， the interaction between all factors was required to be non-existent or negligible when performing an analysis of variance on the quantitative data with Latin square design. When the quantitative data did not meet the preconditions mentioned above， it was recommended to use a mixed-effects model to build the model and solve it， or to solve the estimated values of the parameters in the ANOVA model based on the generalized estimating equation method.

College majors for high school students are mostly predicted by Grade Point Average (GPA) scores and standardized tests. Both predictors are applied by the regional government of Kurdistan, Iraq. The objectives of this research were to characterize dynamics that determine performance of high school students, in terms of both the standardized Wzary test and overall high school GPA, and the influence on their subsequent selection of college majors. Whether students who choose the same major could be assumed to have the similar cognitive abilities, on average, was also investigated by this paper. Data was collected via a questionnaire that was distributed to university students. The questionnaire sought to collect information on the academic performance in high school, socioeconomic status at the time, and parents’ education status. It was established that biology major students had a distinct higher GPA compared to other departments in the faculty of Education, and through multiple linear regression, it was established that mother’s education level, mode of transport to school, place of residence, and student’s age are some of the predictors of high school GPA.

Abstract Background Rasch analysis with a focus on Differential Item Functioning (DIF) is increasingly used for examination of psychometric properties of health outcome measures. To take account of DIF in order to retain precision of measurement, split of DIF-items into separate sample specific items has become a frequently used technique. The purpose of the paper is to present and summarise recent advances of analysis of DIF in a unified methodology. In particular, the paper focuses on the use of analysis of variance (ANOVA) as a method to simultaneously detect uniform and non-uniform DIF, the need to distinguish between real and artificial DIF and the trade-off between reliability and validity. An illustrative example from health research is used to demonstrate how DIF, in this case between genders, can be identified, quantified and under specific circumstances accounted for using the Rasch model. Methods Rasch analyses of DIF were conducted of a composite measure of psychosomatic problems using Swedish data from the Health Behaviour in School-aged Children study for grade 9 students collected during the 1985–2014 time periods. Results The procedures demonstrate how DIF can be identified efficiently by ANOVA of residuals, and how the magnitude of DIF can be quantified and potentially accounted for by resolving items according to identifiable groups and using principles of test equating on the resolved items. The results of the analysis also show that the real DIF in some items does affect person measurement estimates. Conclusions Firstly, in order to distinguish between real and artificial DIF, the items showing DIF initially should not be resolved simultaneously but sequentially. Secondly, while resolving instead of deleting a DIF item may retain reliability, both options may affect the content validity negatively. Resolving items with DIF is not justified if the source of the DIF is relevant for the content of the variable; then resolving DIF may deteriorate the validity of the instrument. Generally, decisions on resolving items to deal with DIF should also rely on external information.

DC motors have wide acceptance in industries due to their high efficiency, low costs, and flexibility. The paper presents the unique design concept of a multi-objective optimized proportional-integral-derivative (PID) controller and Model Reference Adaptive Control (MRAC) based controllers for effective speed control of the DC motor system. The study aims to optimize PID parameters for speed control of a DC motor, emphasizing minimizing both settling time (Ts ) and % overshoot (% OS) of the closed-loop response. The PID controller is designed using the Ziegler Nichols (ZN) method primarily subjected to Taguchi-grey relational analysis to handle multiple quality characteristics. Here, the Taguchi L9 orthogonal array is defined to find the process parameters that affect Ts and %OS. The analysis of variance shows that the most significant factor affecting Ts and %OS is the derivative gain term. The result also demonstrates that the proposed Taguchi-GRA optimized controller reduces Ts and %OS drastically compared to the ZN-tuned PID controller. This study also uses MRAC schemes using the MIT rule, Lyapunov rule, and a modified MIT rule. The DC motor speed tracking performance is analyzed by varying the adaptation gain and reference signal amplitude. The results also revealed that the proposed MRAC schemes provide desired closed-loop performance in real-time in the presence of disturbance and varying plant parameters. The study provides additional insights into using a modified MIT rule and the Lyapunov rule in protecting the response from signal amplitude dependence and the assurance of a stable adaptive controller, respectively.

Purpose: This paper aims to check the contribution of Logistics Information Systems – LIS to supply chain optimization in the steel industry group of Bachir Rachid. This group that owns eight independent companies and numerous points of sale and warehouses, has to be connected by such advanced systems to ensure effectiveness and profitability. Methodology: A questionnaire was distributed among a group of employees, and the collected data was later analyzed by SPSS 25 and complemented by further structured and unstructured interviews. We have realized an analysis of variance to evaluate the impact of LIS on logistics activities, supply chain management, and integration as well as optimization. The test of Kruskal-Wallis H was chosen due to the non-linearity of the data. Results: This work proves that Logistics Information Systems contribute to the performance of logistics activities, supply chain integration, and supply chain optimization through the enhancement of the efficiency of supply chain management and by the reduction of supply chain costs. The theoretical contribution: The contribution made to the theory of logistics and supply chain management lies in confirming previous findings on the contribution of LIS to logistics and supply chain optimization in Algeria, and more specifically, in the steel industry.

Introduction. In addition to grain, a grain heap of rye may contain poisonous ergot sclerotia. Modern grain cleaning machines do not isolate ergot sclerotia in one technological process because of the similarity of physical properties in linear dimensions. Isolation of ergot sclerotia from rye grain in one technological process is possible through the use of aqueous solutions of inorganic salts. The purpose of the study is to determine the optimum elevation of the loading hopper relative to the liquid surface. The data obtained contribute to increasing the quality of the technological process of the machine being developed. Materials and Methods. The paper considers the delivery of rye grain flow from the loading hopper outlet into the liquid by varying the specific grain load, liquid density and the delivery height. To set up the experiments, the experiment design methods have been used. The experimental data have been processed using the statistical package Statgraphics Plus 5.1. Results. The estimation of the effective elevation of the loading hopper outlet relative to the liquid surface when delivering grain flow has been carried out. There have been obtained regression models for the fraction of grains, which did not submerge and rose up to the liquid surface with air bubbles. Discussion and Conclusion. It has been found that the density of the aqueous salt solution has a significant effect on the percentage of grains, which did not submerge and rose up to the liquid surface with air bubbles. The smallest values of at different density of the liquid and specific grain load are achieved at a grain delivery height 56.0 ∙ 10–3 m.

Transparent reporting is essential for the critical evaluation of studies. However, the reporting of statistical methods for studies in the biomedical sciences is often limited. This systematic review examines the quality of reporting for two statistical tests, t-tests and ANOVA, for papers published in a selection of physiology journals in June 2017. Of the 328 original research articles examined, 277 (84.5%) included an ANOVA or t-test or both. However, papers in our sample were routinely missing essential information about both types of tests: 213 papers (95% of the papers that used ANOVA) did not contain the information needed to determine what type of ANOVA was performed, and 26.7% of papers did not specify what post-hoc test was performed. Most papers also omitted the information needed to verify ANOVA results. Essential information about t-tests was also missing in many papers. We conclude by discussing measures that could be taken to improve the quality of reporting.

This paper aims to determine whether the color of based-plant resin called, by the manufacturer, eco-resin has an influence on the dimensions and geometric accuracy of the 3D-printed part. The analysis of flatness, straightness and dimensions deviations was carried out with high-precision measurement systems, and according to current standards regarding linear dimensions and geometrical tolerances. A coordinate measuring machine with contact probes was used to measure the printed part’s physical characteristics, and analysis of variance and response surface design methods were used for the data analysis. The printing experiment was carried out for each color. After that, the measurement of the printed parts and the study of the data were performed. The first finding is that for black and clear eco-resin, there are problems with the printing of the supports. Based on standard data for the range of nominal lengths of the part for linear dimensions, flatness and straightness, the measurement results can be included in different tolerance classes within standard value limits. The best value of the printed structure was obtained for clear eco-resin. The paper demonstrates that the impact of the color of the eco-resin is more important than the supports density for all the studied features. Based on 3D measurements, the optimal values for each of the eco-resin colors regarding the flatness, straightness and linear dimensions deviations of the 3D printed part were also determined.

Asphalt pavement maintenance section classification is an important prerequisite for accurately determining asphalt pavement maintenance needs and formulating accurate maintenance plans. This paper introduces the three-dimensional (3D) ground-penetrating radar (GPR) pavement internal crack rate index on the basis of an original road surface performance data matrix, and the dimensionality of the road section classification data matrix was reduced through the principal component technique. An analysis of variance was used to compare the significance of the differences in the results for road section classification using different clustering methods and different clustering data and to investigate the influence of the clustering method, principal component technique and crack rate index on the maintenance road section classification results. The results showed that the principal component technique could reduce the dimensionality of the data matrix by 33% and retain more than 84% of the information. There was a genetic relationship between the clustering data and the technical characteristics of the classified sub-sections, and the internal crack rate was important for the characterisation of internal defects in asphalt pavement sub-sections and the determination of maintenance needs. The results of section classification varied considerably between clustering methods, and the choice of clustering method had a relationship to the pavement maintenance objectives. The dynamic clustering method combined with principal component analysis could significantly improve the significance of the differences in the clustering results, effectively improving the division of maintenance sections.

In-depth studies of the topological properties of information and telecommunication networks contribute to the understanding of their functional capabilities, including stability. The study of the stability of complex networks to failures in operation when their components fail is based on modeling by sequentially removing nodes or edges of the network (percolation). The paper presents a comparative analysis of sequential and stochastic variants of percolation of network nodes and statistical estimates of the complex two-criterion network stability coefficient. During the study, methods for calculating the average path length based on graph theory were used. In the statistical analysis of the network stability, we applied the analysis of variance and pairwise comparisons according to the Tukey criterion, based on the provisions of the theory of mathematical statistics. The simulation is performed using the Barabashi–Albert and Erdős–Rényi random graph models. The difference between the method of stochastic percolation and sequential percolation is shown. The performed statistical analysis proved the influence of the factor changing the structure of networks on their stability due to stochastic percolation. The dynamics of network stability reduction under stochastic percolation for different types of networks is shown. It is revealed that in some cases, for example, in networks with high density, the stochastic percolation method is the most preferable one. The study shows the possible options for assessing the stability of networks without a priori knowledge about the type of connections between nodes and with a priori knowledge about the type of connections between nodes. In the former case, knowing the number of network nodes, one can calculate the limit values of stability, in the same way as if the nodes were deleted accidentally. The latter option can be used to calculate the stability of networks that are subject to random node failures, for example, when diagnosing technical systems.

This paper presents an application of design of experiments techniques to determine the optimized parameters of artificial neural network (ANN), which are used to estimate force from Electromyogram (sEMG) signals. The accuracy of ANN model is highly dependent on the network parameters settings. There are plenty of algorithms that are used to obtain the optimal ANN setting. However, to the best of our knowledge they did not use regression analysis to model the effect of each parameter as well as present the percent contribution and significance level of the ANN parameters for force estimation. In this paper, sEMG experimental data are collected and the ANN parameters based on an orthogonal array design table are regulated to train the ANN. Taguchi help us to find the optimal parameters settings. Next, analysis of variance (ANOVA) technique is used to obtain significance level as well as contribution percentage of each parameter to optimize ANN’s modeling in human force estimation. The results indicated that design of experiments is a promising solution to estimate the human force from sEMG signals.

Duplex stainless steel (DSS)-2205 offers high strength compared to normal steels; however, currently need for rapid production of high-quality parts in bulk quantities has led to high competitiveness. So characterizations of machining process parameters of DSS-2205 are essential. This paper focuses on finding the optimum turning parameters for cutting force(CF), cutting temperature(CT), and surface roughness(SR) in turning of DSS-2205 using hybrid(Al2O3+CuO) nano cutting fluid under minimum quantity lubrication technique. The experiments were conducted based on the face-centered composite design (CCF) in three levels of four factors, namely volume concentration of nanoparticles, Cutting speed, feed rate, and Depth of Cut (DOC). The analysis of variance (ANOVA) was used to inspect the most significant turning parameters on responses. The feed rate is the most influential parameter for cutting force & surface roughness and DOC for cutting temperature. Multi-response optimization was carried out under the desirability function approach (DFA). The optimum turning parameters for minimizing the responses are volume concentration of nanoparticles 0.3 %, speed 50 m/min, feed rate 0.051 mm/rev and DOC 0.4 mm.

This paper uses the Multidimensional Fatigue Inventory (MFI-16) to investigate the fatigue status of pilots, and the reliability and validity of the scale are tested by Cronbach’s α and exploratory factor analysis. The founding shows that mild fatigue and above accounted for 67.7%. For further quantify the impact of different flights on pilots’ fatigue, research improves the fatigue coefficient model based on the results of pilot fatigue feeling questionnaire. Combined with multifactor analysis of variance and multiple linear regression, it is found that the independent variables have different and positive effects on the dependent variables, and there is no multicollinearity. Through the actual test, its accuracy is improved by 16.7% compared with the original model.

This paper demonstrates an experimental scrutiny into turning process of hot work tool steel AISI H21 under dry machining plight. In this paper, face centered central composite design concealed by response surface methodology is practiced and analysis of variance is implemented to analyze the eloquent benefaction of machining parameters on responses. To access accommodate between the surface roughness and the MRR, an approach for concurrent optimization of multi-objective characteristics based on comprehensive desirability function is employed. The multi objective optimization concludes a spindle speed of 1599.568 rpm, feed rate of 0.262 mm/rev and depth of cut of 2 mm.

The experimental work of this paper leads to electrical discharge machining (EDM). A system for machining in this process has been developed. Many parameters are studied such as current, pulse on-time, pulse off time of the machine. The main aim of this work is to calculate the metal removal rate (MRR) and electrode wear rate (EWR) using copper, electrodes when machining tool steel H13 specimens of a thickness (4mm).Different current rates are used ranging from (30, 42, and 54) Amp, pulse on-time ranging from (75, 100, and 125) and pulse off time ranging from (25, 50, and 75) found that high current gives large electrode wear and metal removal rate and. The experiment design was by Taguchi Method. From an analysis of variance (ANOVA) the more active influence of input factors on the outputs is currently for metal removal rate (MRR) (58%) and electrode wear rate (EWR) (57)

Sandwich panels (also known as insulated panels) have been traditionally used for industrial buildings and warehouses, but nowadays are being increasingly a favorable choice in building construction, mainly in wall cladding and roofing systems. This paper presents the results of an experimental and statistical comparative analysis of Fire Reaction development in sandwich panels consisting of steel sheeting and Polyisocyanurate (PIR) foam core. All these PIR core sandwich panels with joints kept the same dimensions (1000 mm × 1500 mm) + (500 mm × 1500 mm), but different thicknesses (30, 50, 100 and 150 mm). Five Single Burning Item (SBI) tests were carried out on individual PIR sandwich panels with vertical joints and their results were compared between themselves. It was possible to observe through an analysis of variance that there is an influence of the sample thickness in the individual results of the SBI test parameters; however, this variability has no significant influence on the Fire Reaction performance of the samples. Overall, the importance of these alternative sandwich panels is the increase in performance in the constructive processes and the offered comfort through its thermal insulation characteristics.

The redesign of the Common Agricultural Policy (CAP) allows for more room to address issues related to stabilising farmers' income and developing their viability in areas facing natural constraints (ANC). Maintaining income levels, developing farm economies in rural areas, and encouraging competitive agricultural practises are the challenges facing the new CAP. ANCs in the Czech Republic are characterised by a lower level of income compared to areas outside ANCs and their generally prevailing specialisation in livestock production, which has been facing a relatively turbulent development in the last decade. The main aim of this paper is to evaluate the economic viability with regard to the level of natural disadvantage and with regard to farm specialisation. The database of Farm Accountancy Data Network (FADN) was used for assessment; the authors built the Farm Economic Viability indicator, which is based on modified Farm Net Value Added. The differences between the farm groups were tested through analysis of variance. Significantly lower viability was found in ANCs compared to farms outside ANCs. Field crops achieved significantly higher levels, both in and outside ANCs. The most threatened group of farms are grazing livestock in ANCs.

This paper presents the optimization of cutting parameters for MRR, tool wear and surface roughness characteristics in machining of ADC12 alloy. Milling experiments were performed on CNC Milling machine using uncoated carbide inserts. The objective is to minimize the surface roughness, tool wear and maximize the MRR. Process parameters that included speed (1000, 2000, 3000) rpm, feed (0.05, 0.075, 0.1) m/min and Depth of Cut (DOC) of (0.5, 1.0, 1.5) mm were varied for three levels to determine the test cases with the help of Taguchi’s Orthogonal Array (L9), following which, surface roughness, MRR and tool wear was calculated. MRR and tool wear showed similar trends where the responses increase with speed, feed and DOC, whereas the surface roughness tends to increase with feed but lowers on increasing the speed and DOC. Minimum surface roughness and tool wear obtained was 0.25 µm and 0.001 mm respectively, whereas maximum MRR obtained was 0.1144 mm3/min. Speed had major influence on MRR and tool wear whereas, feed had highest impact on surface roughness. ANOVA results also depicted the same rankings. Regression analysis was performed to estimate the surface roughness, MRR and tool wear which was further validated using confirmation tests.

The technic of the processing of the meteorological data for conclusion on the presence of the time trends in the quantitative characteristics of the weather on the example of the analysis of the average yearly atmospheric temperature change at the meteorological station Minsk from 1989 is presented. The average yearly atmospheric temperature received from the measurements is approximated by the least square error method in the linear time dependence regression function. The linear time dependence regression function received in such a way has some positive growing (positive trend). The aim of this paper is to clarify the significance of this growth. For this aim, the usage of the regression analysis with its procedures of hypotheses testing is proposed. First of all, the performing of the demands presented to the regression analysis is checked: normality of the distribution of the disturbance and the homogeneity of the variance (dispersion) of the disturbance. The normality of the distribution of the disturbance was checked and confirmed by the Kolmogorov test. The homogeneity of the dispersion of the disturbance was checked and confirmed both by checking the hypotheses on the equality of the dispersions of two normal distributions and by the Smirnov test for checking the hypotheses on the equality of two distributions. For checking the significance of the positive trend of the yearly mean temperature, the hypotheses on the significance of the coefficients of the linear regression function by the Student t-statistics and the hypothesis on the linear connection presence by the analysis of variance were checked. As the result, the insignificance of the positive linear trend from 1998 to 2016 and from 1998 to 2017 and its significance from 1998 to 2018 and from 1998 to 2019 on the level of significance 0.05 for mean average yearly atmospheric temperature at the meteorological station Minsk was stated.