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The purpose of this article is to study the link between mean shift and inflation coefficient when the underlying null hypothesis is rejected in the analysis of variance (ANOVA) application after the preliminary test on the model specification.

A new approach is proposed to study the link between mean shift and inflation coefficient when the underlying null hypothesis is rejected in the ANOVA application. First, we determine this relationship from the general perspective of Six Sigma methodology under the normality assumption. Then, the approach is extended to a balanced two-stage nested design with a random effects model in which a preliminary test is used to fix the main test statistic.

The features of mean-shifted and inflated (but centred) processes with the same specification limits from the perspective of Six Sigma are studied. The shift and inflation coefficients are derived for the two-stage balanced ANOVA model. We obtained good predictions for the process shift, given the inflation coefficient, which has been demonstrated using numerical results and applied to case studies. It is understood that the proposed method may be used as a tool to obtain an efficient variance estimator under mean shift.

In this work, as a new research approach, we studied the link between mean shift and inflation coefficients when the underlying null hypothesis is rejected in the ANOVA. Derivations for these coefficients are presented. The results when the null hypothesis is accepted are also studied. This needs the help of preliminary tests to decide on the model assumptions, and hence the researchers are expected to be familiar with the application of preliminary tests.

After studying the proposed approach with extensive numerical results, we have provided two practical examples that demonstrate the significance of the approach for real-time practitioners. The practitioners are expected to take additional care before deciding on the model assumptions by applying preliminary tests.

The proposed approach is original in the sense that there have been no similar approaches existing in the literature that combine Six Sigma and preliminary tests in ANOVA applications.

The purpose of this study is to investigate and evaluate the impact of varying proportions of reinforcement on the mechanical properties of a modified Al₂O₃-LM6 cast composite under self-pouring temperature conditions. This study aims to determine the optimal mixture proportion of fine powders of Al, Si and xAl₂O₃ (with x values of 2%, 3% and 4%) through the application of design of experiment (DoE) and statistical analysis using the Minitab software. This study also involved evaluating the microstructural estimation and other physical properties of the cast composite to understand the combined effect of the reinforcement proportion on the material’s properties.

The researchers initially mixed the powders through ball milling and then compacted the moisture-free powder mix in a closed steel die. The resulting preforms were heated at the self-pouring temperature in an inert environment to fabricate the final cast composite. By applying DoE and performing an analysis of variance (ANOVA), the researchers sought to optimize the mixture proportion that would yield the best mechanical properties.

The experimental results indicated that a mixture combination of 83.5% Al blended with 12.5% Si and 4% Al₂O₃ led to the greatest improvement in mechanical properties, specifically in terms of increased density, hardness and impact strength. The ANOVA further supported the interaction effect of each processing parameter on the observed results. The results of this study offer valuable insights for the fabrication of modified Al₂O₃-LM6 cast composites under self-pouring temperature conditions. The identified optimal mixture proportion provides guidance for manufacturing processes and material selection to achieve improved mechanical properties in similar applications.

This study focuses on a specific composite material consisting of modified Al₂O₃ and LM6. Although Al₂O₃ and LM6 have been studied individually in various contexts, the combination of these materials and their impact on mechanical properties under self-pouring temperature conditions is a novel aspect of this research. The researchers use DoE methodology, along with statistical analysis using Minitab software, to optimize the mixture proportion and analyze the data. This systematic approach allows for a comprehensive exploration of the parameter space and the identification of significant factors that influence the mechanical properties of the composite.

This study aims to investigate the impact of titanium oxide (TiO₂) filler on the coefficient of friction (COF) and specific wear rate (SWR) in flax fiber reinforced epoxy composites (FFRCs) under abrasive wear conditions utilizing the Taguchi approach. The primary objective is to enhance wear resistance and promote the development of sustainable materials for various applications.

Epoxy/flax composites with varying TiO₂ filler content (0–8 wt%) are fabricated through the hand layup method. Subsequently, wear testing is conducted following ASTM G99-05 standards. The Taguchi design of experiments (DOE) and analysis of variance (ANOVA) are utilized for statistical analysis.

Results indicate a significant improvement in abrasive wear properties with the incorporation of TiO₂ filler. The COF is found to be most influenced by the normal load (55.19%), followed by grit size, wt% TiO₂ filler and sliding distance. SWR is found to be most influenced by the grit size (42.92%), followed by wt% TiO₂, normal load and sliding distance. Notably, the Taguchi model aligns well with experimental results, demonstrating its efficacy in predicting the abrasive wear behavior of FFRCs.

This research introduces a novel hybrid composite that combines TiO₂ filler and flax fibers, showcasing their potential to enhance the tribological properties of epoxy composites. The study offers valuable insights into optimizing abrasive wear test variables in natural fiber-reinforced composites using Taguchi DOE and ANOVA, crucial for improving the performance of sustainable materials in engineering applications.

The purpose of this study to identify and optimize the machining of polyvinyl butyral (PVB) material for industrial uses. The research is based on input machining parameters of rotary ultrasonic machining for better understand the output response surface roughness (SR) property of polyvinyl butyral (PVB) by using the Taguchi approach. The grey relational grade analysis (GRG) is also implemented to resolve the complex interrelationship of SR data for optimization and predicting and validate the results.

In experimental work, the input parameters, namely, concentration, abrasives, power rate, grit size, tool material and hydrofluoric (HF) acid has been selected. The experiment’s design was created using MINITAB Software; the L27 orthogonal array was selected for the experimentation. SR was examined with the GRG technique for process optimization. On the other hand, for single parameter optimization analysis of variance (ANOVA) has been used.

ANOVA optimization technique gives the best result on concentration (40%) of abrasive (Al2O3+SiC+B4C), power rate (40%), grit size (600), HF acid (1.5%) and tool material (D2 alloy) are the optimal parameters to provide the slightest degree of SR. GRG optimization of multi-response parameter setting: 40% concentration, SiC+B4C mixed abrasive slurry, 40% of power rating, 280 grit size, 0.5% HF acid and high-speed tool steel tool material gives better results. The SR of PVB glass material improved by 20% after grey relational analysis.

There are several practical applications in a variety of material processing sectors, including metallurgy, machinery, electronics and transportation. These real-world applications have produced substantial and discernible economic benefits.

The analytical and optimization results will be used in the various material processing sectors, including metallurgy, machinery, electronics and transportation.

The ANOVA and grey theory approaches offer the reader a primary picture of the machining research and process parameter optimization. Combined abrasive slurry of Al₂O3+SiC+B₄C with a high power-rating exhibits lower SR. Similarly, grit size is vital; larger grits produce better SR. Ra – 0. 611 m is the lowest SR value at the hole found in trial 25 after the experimentation.

The purpose of this study is to investigate the impact of titanium oxide (TiO₂) filler on the abrasive wear properties of bamboo fiber reinforced epoxy composites (BFRCs) using a Taguchi approach. The study aims to enhance the abrasive wear resistance of these composites by introducing TiO₂ filler as a potential reinforcement, thus contributing to the development of sustainable and environmentally friendly materials.

This study focuses on the fabrication of epoxy/bamboo composites infused with TiO₂ particles within the Wt.% range of 0–8 Wt.% using hand layup techniques. The resulting composites were subjected to wear testing according to ASTM G99-05 standards. Statistical analysis of the wear results was carried out using the Taguchi design of experiments (DOE). Additionally, an analysis of variance (ANOVA) was used to determine the influential control factors impacting the specific wear rate (SWR) and coefficient of friction (COF).

The study illuminates how integrating TiO₂ filler enhances abrasive wear in epoxy/bamboo composites. Statistical analysis of SWR highlights abrasive grit size (grit) as the most influential factor, followed by normal load, Wt.% of TiO₂ and sliding distance. Analysis of the COF identifies normal load as the primary influential factor, followed by grit, Wt.% of TiO₂ and sliding distance. The Taguchi predictive model closely aligns with experimental results, validating its reliability. The morphological study revealed significant differences between the unfilled and TiO₂-filled composites. The inclusion of TiO₂ improved wear resistance, as evidenced by reduced surface damage and wear debris.

This research paper aims to integrate TiO₂ filler and bamboo fibers to create an innovative hybrid composite material. TiO₂ micro and nanoparticles show promise as filler materials, contributing to improved tribological properties of epoxy composites. The utilization of Taguchi’s DOE and ANOVA for statistical analysis provides valuable guidance for academic researchers and practitioners in optimizing control variables, especially in the context of natural fiber reinforced composites.

Social engineering is crucial in today’s digital landscape. As technology advances, malicious individuals exploit human judgment and trust. This study explores how age, education and occupation affect individuals’ awareness, skills and perceptions of social engineering.

A quantitative research approach was used to survey a diverse demographic of Egyptian society. The survey was conducted in February 2023, and the participants were sourced from various Egyptian social media pages covering different topics. The collected data was analyzed using descriptive and inferential statistics, including independent samples t-test and ANOVA, to compare awareness and skills across different groups.

The study revealed that younger individuals and those with higher education tend to research social engineering more frequently. Males display a higher level of awareness but score lower in terms of social and psychological consequences as well as types of attacks when compared to females. The type of attack cannot be predicted based on age. Higher education is linked to greater awareness and ability to defend against attacks. Different occupations have varying levels of awareness, skills, and psychosocial consequences. The study emphasizes the importance of increasing awareness, education and implementing cybersecurity measures.

This study’s originality lies in its focus on diverse Egyptian demographics, innovative recruitment via social media, comprehensive exploration of variables, statistical rigor, practical insights for cybersecurity education and diversity in educational and occupational backgrounds.

This study aims at assessing item fairness in students' evaluation of teaching based on students' academic college using measurement invariance analysis (MI).

The sample of this study consists of 17,270 undergraduate students from 12 different academic colleges. SET survey consists of 20 Likert-type items distributed to four factors: planning, instruction, management and assessment was used to collect the data. The Lavaan R package with confirmatory factor analysis (CFA) was used to evaluate measurement invariance (MI). Four models of CFA were investigated and assessed: the configural model, the metric model, the scalar model and the residual invariance model. ANOVA was used to test the differences in SET according to academic colleges.

MI analysis showed that the four levels of MI models are supported. ANOVA test showed that means of SET total scores are statistically different according to students' academic colleges. College of “Education” has the highest SET mean (88.64 out of 100), and all the differences between the College of Education’s SET mean and other colleges' SET means are statistically significant.

The study recommends that higher education institutions test the MI of SET according to academic colleges and then use colleges with the highest SET at the university level as internal benchmarking to develop and enhance their teaching practices.

This study is probably the only study that tested MI according to students' colleges before testing the differences between colleges in SET. If MI is not supported, then the comparisons between academic colleges are not applicable.

This study examines the underlying relationships between the critical factors of building information modeling (BIM) implementation and the factors' groupings among architecture, engineering and construction (AEC) organizations in Saudi Arabia. The objectives of the study are to (1) identify the critical factors for BIM implementation, (2) analyze the interrelationships between the critical factors and (3) compare the critical factors between the different organizational characteristics.

First, potential factors were identified through a systematic literature review and interviews with AEC professionals. Then, a questionnaire survey was sent to AEC professionals and the collected data were analyzed using the following techniques and tests: mean score ranking, standard deviation, normalized value, factor analysis (FA), analysis of variance (ANOVA) and post-hoc Tukey test.

The analyses show that 14 factors are critical for BIM implementation in Saudi Arabia. The top critical factors include the existence of standard contracts on data security and user confidentiality, consistent views on BIM among stakeholders and the availability of guidelines for implementing BIM. Of the 14 critical factors, 9 can be grouped into 4 underlying factors: environmental, governmental, legal and organizational. The analysis shows that the criticality of the most critical factors grouped by the FA varies between different levels of BIM competency. Finally, the presence of public–private partnerships (PPPs) in realizing BIM projects is a new and emerging critical factor for BIM implementation in Saudi Arabia.

This study differs from prior works on BIM implementation in Saudi Arabia by using FA to explore the underlying relationships among factors of BIM implementation and the factors' groupings. Based on the FA results, a roadmap for implementing the BIM was developed. These findings will help to purposefully and efficiently customize BIM implementation strategies and initiatives to ensure successful BIM implementation in Saudi Arabia.

The purpose of this article is to investigate the variables affecting heat transfer from the surfaces of a tall building and also the extent of the impact of each of them. Another purpose of this paper is to provide a suitable model for estimating the heat transfer coefficient of the external surfaces of the building according to the impact of variables.

In this study, the Taguchi's approach in the design of the experiments was used to reduce the number of experiments. Percent contributions factors into the overall and surface-averaged Nu of a square prism were obtained by the (ANOVA). The change in Nu by changing either of T, P, angle of attack and V were investigated by the (ANOM). The most significant factors affecting the value Nu were also identified to facilitate the design of thermal systems by eliminating the factors imposing no significant effect on the response in the molding phase. The set of conditions under which the air properties remained unchanged was identified. Five correlations were formulated to predict Nu.

Models used in BES, in which the effects of T, P, A and geometrical effects are not accounted for, are not reliable. The air pressure was found to impose no significant effect on the overall Nu of the considered square prism. Studied in the range of 274–303 K, the air temperature imposed a significant effect on the overall Nu. The results of ANOVA show the significant role of Re to predict Nu of tall buildings.

This article is taken from a doctoral dissertation.

This study aims to investigate the optimal process parameters of the wire-cut electrical discharge machining (WCEDM) for the machining of the GZR-AA7475 hybrid metal matrix composite (HMMC). HMMCs are prepared with 2 Wt.% graphite and 4 Wt.% zirconium dioxide reinforced with aluminium alloy 7475 (GZR-AA7475) composite by using the stir casting method. The objective is to enhance the mechanical properties of the material while preserving its unique features. WCEDM with a 0.18 mm molybdenum wire electrode is used for machining the composite.

To conduct experimental studies, a Taguchi L27 orthogonal array was adopted. Input variables such as peak current (I_p), pulse-on-time (T_ON) and flushing pressure (P_F) were used. The effect of process parameters on the output responses, such as material removal rate (MRR), surface roughness rate (SRR) and wire wear ratio (WWR), were investigated. The grey relational analysis (GRA) is used to obtain the optimal combination of the process parameters. Analysis of variance (ANOVA) was also used to identify the significant process parameters affecting the output responses.

Results from the current study concluded that the optimal condition for grey relational grade is obtained at T_ON = 105 µs, I_p = 100 A and P_F = 90 kg/cm². Peak current is the most prominent parameter influencing the MRR, whereas SRR and WRR are highly influenced by flushing pressure.

Identifying the optimal process parameters in WCEDM for machining of GZR-AA7475 HMMC. ANOVA and GRA are used to obtain the optimal combination of the process parameters.