Analysis Of Variance Approach Research Articles

Assessing the impact of cannabis use on freeway driving performance and practices: A comparative analysis with placebo and alcohol-influenced driving

Objective The objectives of this study were 1) to identify the effects cannabis has on driving performance and individual motor practices when on the freeway compared to placebo and 2) to bring context to the effects of cannabis on driving by comparing effect sizes to those of alcohol. Methods Data for analysis was collected from a study of fifty-three participants with a history of tetrahydrocannabinol (THC) cannabis use who completed three visits in randomized order (placebo (0% THC), 6.18% THC, and 10.5% THC). Data for the alcohol analysis was from a subset of eighteen of these participants with a history of recent alcohol use that completed a fourth alcohol visit that targeted a .05 g/210L breath alcohol content (BrAC) during the drive. Comparisons were made using an analysis of variance approach with the SAS General Linear Models Procedure. Cohen’s d effect sizes were calculated for the cannabis and alcohol conditions relative to placebo for both the full sample and alcohol subset. Results Standard deviation of lane position (SDLP) for cannabis significantly increased compared to placebo and the effect size was comparable to that of alcohol at .05 BrAC. Lane departures for cannabis significantly increased relative to placebo as did the time out of the lane. Cannabis use resulted in an increased amount of time at 10% or more below the speed limit for the 6.18% THC condition. Relative to alcohol, cannabis produced more time at slower speeds and less time at speeds more than 10% above the speed limit. Conclusions Multiple factors of lateral and longitudinal vehicle control on the freeway showed statistical significance. Drivers under the influence of cannabis exhibited higher rates of driving errors but also showed more cautious behaviors such as generally lower speeds on the freeway. Compared with alcohol, effect sizes varied. For longitudinal control, there were larger effect sizes for alcohol with speed effects in opposite directions, but relatively equivalent effect sizes for lateral control and driving errors associated with lane keeping.

An integrated optimization and ANOVA approach for reinforcing concrete beams with glass fiber polymer

Concrete beams are commonly used in construction projects to provide structural support. Reinforcing these beams with steel and Glass Fiber Reinforced Polymer (GFRP) can increase their strength and durability. Steel reinforcement is a traditional method used for decades, while GFRP is a newer material that offers several advantages, including corrosion strength and lighter weight. Combining both materials to reinforce concrete beams can result in a stronger and more resilient structure, making it an ideal choice for many construction projects. In this study, the behavior of reinforced concrete beams with steel and GFRP reinforcement is numerically investigated, and the effect of steel percentage and GFRP percentage on the mechanical strength and energy response of the beam is determined using the Response Surface Methodology (RSM). Using the ABAQUS software, a widely used Finite-Element Analysis (FEA), the numerical modeling is first verified, and then targeted analyses are performed on the beam using the tests specified by the RSM. Then, based on the strength and energy of the beam, a two-variable Analysis of Variance (ANOVA) and two-objective optimization are performed to investigate the effect of changing each parameter of steel percentage and GFRP percentage on beam strength and energy. Several laboratory studies have been conducted on the behavior of concrete beams with steel and GFRP bars, but no research has statistically and numerically examined their behavior. We also simultaneously optimized both strength and energy parameters based on the ratio of steel and GFRP and compared them with numerical values. We show an interaction relationship between steel and GFRP ratio in the strength and energy of concrete beams. In the beam with hybrid reinforcement, with the increase of steel and GFRP ratios, the amount of strength and energy does not increase linearly, and there is a quadratic relationship between them.

Taguchi and ANOVA-based Optimization of CNC Milling Parameters for Aluminium 7075 Alloy

This study is aimed at optimizing the CNC milling parameters for Aluminium 7075 alloy, using Taguchi and Analysis of Variance (ANOVA) approaches. The high-strength alloy has applications in the automobile and aerospace industries. The cutting depth, speed of milling and rate of feed were adjusted to methodically enhance the machining performance and efficiency; surface roughness was studied in these metrics. Taguchi and ANOVA approaches were helpful in optimizing the parameters to lower cost, improve machining quality and enhance the productivity of Aluminium 7075 alloy.

Using statistical models to depict the response of multi-timescale drought to forest cover change across climate zones

Abstract. The interaction between forest and climate exhibits regional differences due to a variety of biophysical mechanisms. Observational and modeling studies have investigated the impacts of forested and non-forested areas on a single climate variable, but the influences of forest cover change on a combination of temperature and precipitation (e.g., drought) have not been explored, owing to the complex relationship between drought conditions and forests. In this study, we use historical forest and climate datasets to explore the relationship between forest cover fraction and drought from 1992–2018. A set of linear models and an analysis of variance approach are utilized to investigate the effect of forest cover change, precipitation and temperature on droughts across different timescales and climate zones. Our findings reveal that precipitation is the dominant factor (among the three factors) leading to drought in the equatorial, temperate and snow regions, while temperature controls drought in the arid region. The impact of forest cover changes on droughts varies under different precipitation and temperature quantiles. Precipitation modulates forest cover's impact on long-term drought in the arid region, while temperature modulates the impact of forest cover changes on both short- and long-term drought in the arid region as well as only on long-term drought in the temperate region. Forest cover can also modulate the impacts of precipitation and temperature on drought. High forest cover leads to a combined effect of precipitation and temperature on long-term drought in arid and snow regions, while precipitation is the only dominant factor in low forest cover conditions. In contrast, low forest cover triggers a strong combined effect of precipitation and temperature on drought in the temperate region. Our findings improve the understanding of the interaction between land cover change and the climate system and further assist decision-makers to modulate land management strategies in different regions in light of climate change mitigation and adaptation.

Optimization of wire spark erosion machining of Grade 9 titanium alloy (Grade 9) using a hybrid learning algorithm

Manufacturing has grown challenging because of the increased usage of harder materials, such as titanium alloys, in many industries, such as aerospace, automobiles, and marine. Conventional material removal procedures are not suitable for these tough materials due to their increased hardness and slow machinability. Wire Electrical discharge machining (WEDM) is a modern approach for material removal, particularly for harder materials, such as titanium alloys, nickel alloys, hard particle reinforced metal matrix composites, etc. The research design was performed by deeming the independent factors, such as duration of pulse and applied current. The removal rate of material, surface roughness of the machined region, dimensional deviation, and tolerance errors in form/orientation are considered performance metrics. Taguchi’s approach was engaged to assess the process variables, and the importance of the process factors was established using analysis of variance approach. The purpose of this research is to create an AI based decision making tool, which can be utilized to anticipate the various parameters that impact the WEDM material removal process. The discoveries of the present exploration allowing the manufacturers to make better-informed decisions with a developed model’s capability by demonstrating that the model’s predicted values were in close confirmation to the actual values.

Quantifying the contributions of powder mixing mechanisms using a combined proper orthogonal decomposition and analysis of variance approach

In manufacturing, powder mixing processes are vital for ensuring product quality. The mixing progress and efficiency are determined based on the fundamental convection and diffusion mechanisms. While mixers are believed to have a unique primary mixing mechanism, recent findings from our group have verified that the main mechanism can change as the mixing progresses. The transitions were successfully captured using a new method incorporating proper orthogonal decomposition (POD) into the discrete element method simulation, proving POD as a valuable tool for mechanism identification. Nevertheless, the existing POD method cannot quantitatively evaluate these mechanisms, hindering a comprehensive analysis of their magnitudes and transitions. This study combines analysis of variance (ANOVA) with POD to solve the problem, establishing a POD-ANOVA framework to quantify the degree of contribution of the mechanisms. The capability of POD-ANOVA is assessed in the transverse mixing of a rolling drum. For a quantitative evaluation of the mechanism magnitudes, POD-ANOVA is performed over the entire mixing process (denoted as Standard POD-ANOVA). The convection and diffusion rates are then derived from the overall mixing rate. Validations show that the two rates corroborate well with common indicators of mechanism intensities. Furthermore, Standard POD-ANOVA is applied over sequential time domains to track mechanism transitions; however, it is found to be insufficiently precise. Thus, a new time-windowing POD is implemented, leading to Windowed POD-ANOVA. Over short time windows, the improved method can effectively quantify the transitions. Consequently, the proposed methods enable a quantitative evaluation of powder mixing mechanisms scientifically for the first time.

Experimental optimization on mechanical properties of 18Ni-300 maraging steel produced by direct metal laser sintering

Maraging steel material characteristics are greatly affected by build orientation and scanning strategies during manufacturing. This research aims to examine the effect of various build orientations and scan strategies on the ultimate strength and microhardness of 18 Ni-300 grade maraging steel. The maraging steel is made using a technology called direct metal laser sintering. Using the Analysis of Variance approach, the effects of processing factors like build orientation, scan techniques, and type of process on ultimate strength, microhardness, and roughness are optimized. The properties tend to be greater in the horizontal orientation when using the XY scan strategy as a result of thermal history variations,. The mechanical performances of maraging steel components in terms of strength values observed were lower in the vertical position compared to all other orientations. The tests are carried out using an L27 orthogonal array and a Taguchi design of experiment. Build orientation XZ, scan method XY, and Solution-aging-aging(SAAT) has the most significant influence on the final ultimate strength, microhardness, and roughness of all the input factors. A2B2C3 is the optimal combination level of control factors. Additionally, a percentage inaccuracy was identified after conducting confirmation tests on the most prominent experimental data.

Resistance welding of thermoplastic composites with nanofiber films prepared by electrospinning technique

In resistance welding of thermoplastic composites, a stainless-steel (SS) heating element (HE) is usually applied with current to provide heat to melt the thermoplastic polymer and remains at the joint. In this study, electrospinning technique was proposed to produce nanofiber films on the SS HE to achieve the hole pre-filling in the mesh and to provide a resin-rich zone in the bondline to enhance resin impregnation and void reduction, which improved the mechanical interlocking of the joints. The lap shear strength (LSS) of welded joints with nanofiber films was increased by 2.3 times to 36.98 MPa in comparison to the film-free joints and the indefinite fatigue life of the joint was reported at 20% of the static LSS. Taguchi method and Analysis of Variance approach were used to investigate the influence of the process parameters on the LSS. A comparative analysis of the welding curves, defects distributions, and fractographic evolutions was carried out for welded joints with and without the nanofiber films. With nanofiber films, the main failure modes of the joints changed from HE debonding to interlayer debonding.

Forest protection costs in sustainable forest management. The example of Poland

Purpose: The main aim of the study is to analyze the costs of forest protection incurred by Poland's State Forests and to present the activities performed as part of forest protection. The specific objective is to present the method and place of disclosing and reporting information on actions to prevent damage to the forest and on the costs of forest protec-tion on a general level. Methodology/approach: The considerations in the study are presented against the views presented in the literature on the subject by selected authors. The methods of critical analysis and synthesis, inference, and a case study were used. The data for the analyses were taken from the financial and economic reports of the State Forests for the years 2016–2019, from the statistical studies „Forestry”, and reports obtained as part of the application submitted to the State Forests for the reuse of public sector information. Pearson's linear correlation analysis was used, as well as an agglomeration grouping method – Ward's method – which uses the analysis of variance approach in its procedures. Findings: The scale of forest protection measures varied between State Forests in the analyzed period. The highest costs for forest protection in total are borne by forest dis-tricts located in the area of State Forests in Wrocław, and the lowest costs for forest protection in total are borne by forest districts located in the area of State Forests in Kra-ków. The smallest area protected against insects covers forest districts located in the State Forests in Krakow. With the increase in the costs of forest protection, State Forest’s own costs also increase. The highest relationship was noted in 2017, with a correla-tion coefficient of 0.86. Research limitations: The research sample is limited to one country. Originality/value: The presented voice in the discussion fills the research gap in terms of information about State Forests’ activities to protect the forest and the subsequent costs incurred. The use of Ward’s method allowed similar regional directorates of State Forests to be grouped from the perspective of forest protection costs and protective measures (area approach), which is the basis for extended research in this area from the point of view of the natural diversity of these units (narrowing the research and conducting it in similar State Forests units).

From Many to One: Consensus Inference in a MIP

AbstractA Model Intercomparison Project (MIP) consists of teams who estimate the same underlying quantity (e.g., temperature projections to the year 2070). A simple average of the ensemble of the teams' outputs gives a consensus estimate, but it does not recognize that some outputs are more variable than others. Statistical analysis of variance (ANOVA) models offer a way to obtain a weighted frequentist consensus estimate of outputs with a variance that is the smallest possible. Modulo dependence between MIP outputs, the ANOVA approach weights a team's output inversely proportional to its variance, from which optimally weighted estimates follow. ANOVA weights can also provide a prior distribution for Bayesian Model Averaging of the MIP outputs when external evaluation data are available. We use a MIP of carbon‐dioxide‐flux inversions to illustrate the ANOVA‐based weighting and subsequent frequentist consensus inferences.

Assessing Response Readiness to Health Emergencies: A Spatial Evaluation of Health and Socio-Economic Justice in Pakistan.

COVID19 pandemic has put the global health emergency response to the test. Providing health and socio-economic justice across communities/regions helps in resilient response. In this study, a Geographic Information Systems-based framework is proposed and demonstrated in the context of public health-related hazards and pandemic response, such as in the face of COVID19. Indicators relevant to health system (HS) and socio-economic conditions (SC) are utilized to compute a response readiness index (RRI). The frequency histograms and the Analysis of Variance approaches are applied to analyze the distribution of response readiness. We further integrate spatial distributional models to explore the geographically-varying patterns of response readiness pinpointing the priority intervention areas in the context of cross-regional health and socio-economic justice. The framework’s application is demonstrated using Pakistan’s most developed and populous province, namely Punjab (districts scale, n = 36), as a case study. The results show that ~ 45% indicators achieve below-average scores (value < 0.61) including four from HS and five from SC. The findings ascertain maximum districts lack health facilities, hospital beds, and health insurance from HS and more than 50% lack communication means and literacy-rates, which are essential in times of emergencies. Our cross-regional assessment shows a north–south spatial heterogeneity with southern Punjab being the most vulnerable to COVID-like situations. Dera Ghazi Khan and Muzaffargarh are identified as the statistically significant hotspots of response incompetency (95% confidence), which is critical. This study has policy implications in the context of decision-making, resource allocation, and strategy formulation on health emergency response (i.e., COVID19) to improve community health resilience.

Establishing Statistical Correlation Between Sensor Signature Features and Lubricant Solid Particle Contamination in a Spur Gearbox

This paper aims to predict the severity of solid particle contaminants present in the lubricant in a Spur Gearbox using Vibration, Acoustic Emission, and Sound Signature features. Sensor signatures are acquired at various contaminant conditions of lubricant with different speed and load conditions. Statistical Features are extracted in the time domain, and feature ranking is carried out using the analysis of variance approach. Statistical models are developed using the selected features of Sound, Acoustic Emission, and Vibration separately and fusing the features in the feature level. Decision Trees and Support Vector Machine algorithms are used in this study to build statistical models. AE features have a good correlation with lubricant conditions compared to sound and vibration features. The feature-level fusion approach predicts the lubricant conditions with an accuracy of more than 99%. The feature-level fusion models built using dominant features are computationally efficient without compromising the prediction ability.

Green strength properties of waterjet abrasive waste as potential composition in green mould by Taguchi and ANOVA approach

The sand casting process still continues today due to the cost-effectiveness of materials and processes. There is a wide variety of castings related to composition and size, but silica sand is widely available from coastal line mining and has a negative impact on the environment. Moreover, waste from waterjet cutting of non-ferrous and ferrous metals is practically unhazardous and may potentially be used in sand casting mould. The aim of this paper is to optimize the proportion of coal dust, water and bentonite added to the silica sand mixture and the waterjet cutting abrasive waste as a new way of handling waste with the potential to be used in sand casting manufacturing. The method used was L9 orthogonal array optimization and the composition was qualitatively measured using a green compression strength test and a green shear strength test. Factors were evaluated using the analysis of variance (ANOVA) to find the the critical factors while confirmation test was conducted for the optimal material proportion. The study concluded that the ideal ratio for silica sand mixture with waterjet abrasive waste is bentonite-12%, coal dust-5%, and water-7% for green compression strength while bentonite-12%, coal dust-6%, water-7% for green shear strength. With proper selection, the incorporation of waterjet abrasive waste into the green sand mixture is promising to potentially be used in green sand mould casting without undermine the quality of mould.

Impact of MWCNT in CFRP composite during end milling process

ABSTRACT Incorporation of nanofillers upto a certain weight percentage in polymeric matrix has been established to enhance the performance of composites. Machining is an integral part of any manufacturing process and the effect of machining on nanocomposites has been a subject of various studies. The present study investigates the influence of multi-walled carbon nanotube (MWCNT), spindle speed (SS), and feed rate (FR), on end milling performances, such as, delamination factor (DF) and surface roughness (Ra) by using the TOPSIS methodology. The investigation has showed that incorporation of MWCNTs in the CFRP composites optimizes the machining performances. The investigation, using multi-objective TOPSIS-based analysis of variance approach, showed that SS is the most governing factor, followed by FR and MWCNT wt%, which influence the overall performance of the composites. Field emission scanning electron microscope (FESEM) has been used to show the dispersion of MWCNT in the epoxy matrix. Improvisation on machining surface characteristics along with optimal performance parameters are accomplished successfully.

Effect of infill and density pattern on the mechanical behaviour of ABS parts manufactured by FDM using Taguchi and ANOVA approach

Purpose: The present work aims to investigate the effect of many infill patterns (rectilinear, line, grid, triangles, cubic, concentric, honeycomb, 3D honeycomb) and the infill density on the mechanical tensile strength of an Acrylonitrile Butadiene Styrene (ABS) test specimen manufactured numerically by FDM. Design/methodology/approach: Computer-Aided Design (CAD) software has been used to model the geometry and the mesostructure of the test specimens in a fully automatic manner from a G-code file by using a script. Then, a Numerical Design of Experiments (NDoE) has been carried out by using Taguchi method and the Analysis of Variance (ANOVA). The tensile behaviour of these numerical test specimens has been studied by the Finite Element Analysis (FEA). Findings: The FEA results showed that a maximal Ultimate Tensile Strength (UTS) was reached by using the ‘concentric’ infill pattern combined with an infill density of 30%. The results also show that the infill pattern and the infill density are significant factors. Research limitations/implications: The low infill densities of 20% and 30% that have already been used in many previous studies, we have also applied it in order to reduce the time of the simulations. Indeed, with high infill density, the simulations take a very excessive time. In an ongoing study, we predicted higher percentages. Practical implications: This study provided an important modelling tool for the design and manufacture of functional parts and helps the FDM practitioners and engineers to manufacture strong and lightweight FDM parts by choosing the optimal process parameters. Originality/value: This study elucidated the effect of various infill patterns on the tensile properties of the test specimens and applied for the first time a NDoE using numerical test specimens created by the mesostructured approach, which considerably minimized the cost of the experiments while obtaining an error of 6.8% between the numerical and the experimental values of the UTS.

Experimental analysis on carbon nanotube embedded GFRP composites during AWJM

ABSTRACT With steadily rising expanse of the engineering sector, demand for composite materials has been growing progressively over the years. Particularly, glass fiber reinforced polymer (GFRP) composites are being increasingly applied in various industries in the aerospace, automobile, and construction sectors due to their lightweight and superior performances. The present investigation examines the effect of traverse rate, jet pressure, standoff distance, and multi-walled carbon nanotubes (MWCNT), mixed with epoxy/glass fabric nanocomposites, on abrasive water jet machining (AWJM) responses, such as kerf taper (KT), surface roughness (Ra), material removal rate (MRR), entrance and exit delamination factor (DF) by using the Taguchi approach. The study has established that the addition of MWCNT in the GFRP laminates optimizes the machining responses. The investigation, using analysis of variance approach, revealed the most dominant parameter, which influences the machining performance of the composites. A significant enhancement in AWJM responses could be attained with optimal parameters. And also validates using surface examinations.

The Benefits of Longitudinal Data and Multilevel Modeling to Measure Change in Adventure Education Research

Background: A common critique of adventure education research methodology is the overreliance on pre-/post-study designs to measure change. Purpose: This paper compares and contrasts two methods of data analysis on the same adventure education data set to show how these distinct approaches provide starkly different results and interpretation. Methodology/Approach: Using secondary data analysis, we employ a longitudinal data set of the social skill development of urban middle school students who participated in an adventure education program over the course of three academic years. First, change was assessed using a pre-/post-design by a traditional analysis of variance (ANOVA). Next, change was assessed with a six-wave, longitudinal design using multilevel modeling. Findings/Conclusions: Results show that the multilevel modeling approach revealed nonlinear change in the social skill development of middle school students, resulting in more accurate, nuanced estimation of change in social skill development than the ANOVA approach. Implications: The use of longitudinal data and multilevel modeling can be a useful methodological approach and statistical tool for adventure education researchers to not only address the criticisms of quantitative research in adventure education, but more importantly, provide a more thorough understanding of the impact of adventure education on human development.

Estimation of Macrodispersivity in Bounded Formations by Circulant Embedding and Analysis of Variance

AbstractWe present a new method to estimate the displacement covariance and macrodispersivity for solute transport in bounded formations. Here we use circulant embedding, which is based on fast Fourier transform and is much more efficient than eigen‐decomposition, for the factorization of random spatial fields. We compute the displacement covariances by the analysis of variance approach, and introduce an interpolation process to significantly reduce the number of forward simulations. Once the effect of each eigenvector on the displacement covariance is obtained, it is unnecessary to rerun the simulator for different spatial covariance functions or anisotropy ratios, which saves a large amount of computational effort. The proposed method is validated in various tests in two‐dimensional and three‐dimensional examples and accurately matches the results from the Monte Carlo simulation. It is found that the longitudinal dispersivity is less sensitive to the impervious boundaries, while the transverse and vertical dispersivities are greatly affected. The method is applied to the Borden site and provides a better explanation of the observed data after considering the effect of vertical boundaries. These results show that our method could serve as a promising tool for studying and predicting the characteristics of solute transport in heterogeneous media.

Experimental investigation of surface roughness and chip morphology during machining of austenitic stainless steel 303 with pvd coated (TiAlN) insert

Optimizing the influencing parameters of turning operation is a precious thing which determines the desired level of quality in Material Removal Rate (MRR) and Surface Roughness (SR). In this investigation, Taguchi based Grey Relational Analysis (GRA) has been used optimizing the effective process parameters such as depth of cut, feed rate and cutting speed on Stainless Steel 303 (SS 303) material while the specimen is turned with PVD (TiAlN) coated cutting tool. The experimental combinations have been planned based on Taguchi’s Design of Experiments technique. The optimal input parameter combination has been identified as 600 m min−1 spindle speed, 0.1 mm rev−1 feed rate and 0.2 mm depth of cut. The most influencing factor affecting the overall responses of turning process has been found out using Analysis of Variance approach as spindle speed (37.91%). Second-order mathematical models have been developed using Design Expert software for all the considered output responses, which provide the experimental values within the considered range of process parameters even without performing the actual experimentation. 3D surface plots have been generated and analyzed for interpreting the correlation between the process variables. SEM images taken on the machined surfaces and chips have been analyzed and presented in order to validate the optimization results.

Autoregressive parametric modeling combined ANOVA approach for label-free-based cancerous and normal cells discrimination

Label free based methods received huge interest in the field of bio cell characterizations because they do not cause any cell damage nor contribute any change in its compositions. This work takes a close outlook of cancerous cells discrimination from normal cells utilizing parametric modeling approach. Autoregressive (AR) modeling technique is used to fit the measured optical transmittance profiles of both cancer and normal cells. The transmitted light intensity, when passes through the cells, gets affected by their intercellular compositions and membrane properties. In this study, four types of cells: lung-cancerous and normal, liver-cancerous and normal, were suspended in their corresponding medium and their transmission characteristics were collected and processed. The AR coefficients of each type of the cell were analyzed with the statistical technique called Analysis of variance (ANOVA), which provided the significant coefficients. The poles extracted from the significant coefficients resulted in an improved demarcation for normal and cancer cells. These outcomes can be further utilized for cell classification using statistical tools.