Factorial Design Analysis Research Articles

Tensile and Elastic Performance of Cotton/Lycra Core Spun Denim Yarn

Various cotton/lycra core spun yarns are prepared in modified ring frame. The core of the yarn consists of a 70 dtex lycra filament and sheath consists of staple cotton fibers. The cotton/lycra core spun yarns are explored for the suitable application of stretch denim fabric. The tensile and elastic performance properties of the prepared stretch yarns are evaluated. The influence of various parameters viz. pre-load, linear density, stretch percentage, twist multiplier and applied load during tensile testing, on the properties of the stretch yarns are investigated by employing a full factorial design and statistical analysis.

Using the data mining method to assess the innovation gap: A case of industrial robotics in a catching-up country

It is critical for “catching-up” countries to narrow innovation gaps with developed countries by developing emerging industries. This research introduces a data-mining based method to systematically assess the national innovation gap that is specifically for emerging industries. The method examines the five key attributes of emerging industries, including the ownership of platform technologies, globalization intention, international knowledge position, university-industry linkage, and cross-disciplinary technology development. In particular, this method combines data-mining with experts' knowledge to build patent-training examples, and then uses a support vector machine-based classifier to single out all high-quality patents for each innovation attribute. Based on the selected high-quality patents, the authors utilize a factorial design analysis to systematically evaluate the innovation gap between countries. This method can significantly reduce measurement bias of traditional single patent indicators. In addition, it also can robustly adjust measuring weights in response to the specifics of each innovation attribute, while traditional multi-attribute evaluation methods cannot. As a result, this research empirically shows that China' industrial robot sector has apparent innovation gaps compared to developed economies, specifically in university-industry linkage, cross-disciplinary competence, and globalization intention, and this calls for the attention of policy makers and industrial experts.

Arsenic and selenium removal from water using biosynthesized nanoscale zero-valent iron: A factorial design analysis

Abstract Environmentally friendly method is reported for the synthesis of nanoscale zero-valent iron using plant extract as a reducing agent. The biosynthesized nanoparticles were characterized using FESEM, EDS, XRD, FT-IR and TGA. A factorial design analysis was conducted to determine the influence of different factors affecting the removal of arsenic (As) and selenium (Se) from water using biosynthesized nanoscale zero-valent iron. The factors investigated include; pH, adsorbent dosage, initial concentration, contact time and shaker speed. It was observed that the effect of adsorbent dosage was most significant for the removal of arsenic from water. At 95% confidence level, dosage variation showed more than 30% increase in removal efficiency when it was varied between 10 mg and 100 mg. About 95% of arsenic removal was recorded using 100 mg adsorbent at solution pH 3. For selenium removal, the effect of adsorbent dosage showed a relatively positive influence. About 90% removal efficiency was recorded at Se solution of pH 3 which signifies the applicability of the material for real water samples.

Statistical evaluation of factors affecting the laboratory rutting susceptibility of asphalt mixtures

Rutting or plastic deformation is one of most frequently observed distress types in the wheel paths of flexible road pavements. This distress may cause the pavement structure to be attributable to early failure by expediting the deterioration due to load and environmental detrimental agents. Therefore, it is ideal to simulate the rutting performance of different asphalt mixtures in laboratory-controlled conditions before exposing them to aggressive field conditions. Different rutting simulation computer-controlled equipments are available to predict the mixture performance in laboratory at nearly similar conditions to actual in-service pavements. Cooper wheel tracking equipment is one such precise equipment. Mostly, the wearing course is likely to be damaged by this distress type in flexible asphalt pavements. Therefore, in this research study, thirty representative wearing course mixtures commonly used in road pavements were tested for rutting distress using Cooper wheel tracker. Three aggregate sources, three binders from two sources, five mixture gradations and two temperature conditions were included in the testing scheme. Mixtures were evaluated based on the terminal value of rut depth. Consistent ranking of mixtures has been observed for variable combinations of aggregates, binders, temperatures and gradations. Flakiness index (FI) of aggregates, binder penetration values, per cent coarse material retained on 4.75 mm sieve and temperature values are considered as independent variables based on past researches. The significance of these independent variables on the dependent variable of rut depth has been analysed by analytical tools of general full factorial design analysis. It has been concluded that temperature has highest significance for the rut depth prediction, followed by FI of aggregates, bitumen penetration value and percentage of coarse fraction.

Application of full-factorial design in the synthesis of polypropylene-g-poly(glycidyl methacrylate) functional material for metal ion adsorption

The graft polymerization of glycidyl methacrylate onto electron beam pre-irradiated polypropylene (PP) nonwoven fabric was optimized using a 43 full factorial design analysis. The analysis yielded a polynomial equation that relates the linear, quadratic and interaction effects of the independent parameters to degree of grafting (Dg). The linear terms (i.e. absorbed dose, reaction time and monomer concentration), quadratic terms of time and concentration, and interaction term between absorbed dose and time were determined as significant independent parameters based from analysis of variance (ANOVA). The optimum grafting time and absorbed dose to achieve 150% Dg at 5% monomer concentration were 3.5h and 39.8kGy, respectively. The pristine PP, polypropylene-g-poly(glycidyl methacrylate) (PP-g-PGMA) and functionalized grafted materials were characterized using ATR-FTIR spectroscopy, thermogravimetric analysis and scanning electron microscopy. The affinities of the synthesized adsorbents towards the target metal ions at pH 4 were established to be in the following order: Cr(VI) >> Pb(II) ~ Cd(II) for the amine functionalized PP-g-PGMA; and Pb(II) > Cd(II) > Cr(VI) for the carboxylic acid functionalized PP-g-PGMA.

The Effect of Varying Mixing Temperatures and Baking Level on the Quality of Pilot Scale Anodes—A Factorial Design Analysis

Identifying optimum anode baking level and mixing temperature are important when producing high quality anodes. The effect of varying mixing temperature and baking level were investigated in terms of the resulting apparent anode density, specific electrical resistivity (SER), air permeability, coefficient of thermal expansion (CTE), air reactivity, and CO2 reactivity. Six pilot-scale anodes were prepared at Hydro Aluminium using a single source petroleum coke and <2 mm coke fractions. A coal tar pitch was used with Mettler softening point of 119.1 °C. The aggregate was mixed at 150 °C or 210 °C and baked to a low, medium, or high baking level. A 22 full-factorial design analysis was performed to determine the response of the analyzed properties to the applied mixing and baking temperature. Apparent density, SER, and air permeability were found to be highly dependent on mixing temperature. Apparent density and SER were also slightly affected by baking level. CTE was found to be independent of both baking level and mixing temperature. Air reactivity was found to be mainly dependent on baking level, while CO2 reactivity was dependent on both mixing temperature and baking level.

Sorption of cyanide from aqueous medium by coffee husk: Response surface methodology

Cyanide is a very toxic compound that is released into the environment through industrial effluents; therefore, it needs to be removed prior to wastewater discharge. Adsorption is one of the most widely used treatment methods for removing contaminants from water and wastewater. In present research work response surface methodology was successfully employed for optimization and analysis of the adsorption process. Batch mode experiments were performed to determine the adsorption equilibrium of cyanide in aqueous solution. At optimum condition initial concentration (10 mg/l), contact time (1 h), adsorbent dose (1 g) and pH (8), 90.6% of cyanide adsorption was obtained. The factorial design and RSM analysis demonstrated that the experimental and the predicted values from the models were in agreement.

Statistical Analysis and Modelling of an Yb: KGW Femtosecond Laser Micro-drilling Process

Femtosecond laser drilling is capable of economically drilling a number of closely located holes on micro-scale. Different characteristics or acceptance criteria like geometric and metallurgical factors are used to judge the quality of the drilled holes.This paper aims to evaluate the quality of micro-holes, which were drilled by Yb:KGW laser end-pumped by high-power diode bars. The geometric factors included hole roundness, hole taper and variation in hole entrance diameter. The metallurgical factors included recast layer and micro-cracks. The process parameters that most affected the hole quality were identified by the analysis of a full factorial design. Optimum hole characteristics on stainless steel sheet were studied by the ANOVA analysis. ANN model provided better insight into the best conditions for process adjustment, making possible simultaneous optimization of multiple responses.

Multi-response optimization of impact strength and elongation at break of nanocomposites based on polypropylene/polyethylene binary polymer matrix in the presence of titanium dioxide nanofiller

In this article, the effect of adding titanium dioxide (TiO2) nanoparticles on impact strength and elongation at break of polypropylene/linear low-density polyethylene (PP/LLDPE) binary polymer matrix has been investigated. A 23 factorial design analysis was conducted to screen significant factors influencing the impact strength and elongation at break of polymer nanocomposites based on PP/LLDPE matrix. Three factors in two levels including LLDPE at 20 and 40 wt%, TiO2 nanoparticles at 2 and 4 wt%, and styrene–ethylene–butylene–styrene (SEBS as coupling agent) at 0 and 3 wt% were chosen for adding to matrix, and eight experiments were conducted for each response. Optimization of these factors was done for predicting maximum impact strength and elongation at break. It was observed that nanoparticles decreased impact strength. The presence of high levels of LLDPE and SEBS led to an increase in impact strength and elongation at break. From the analysis of variance, the most important parameters affecting impact strength and elongation at break were determined, and satisfactory prediction regression models were derived. In addition, the optimal condition of LLDPE and SEBS for the best combination of response variables was obtained at high level and for TiO2 at middle level.

Investigation of the Combustion Performance of Residues from Vineyards and Processing Industry via Fluidized Bed Experiments, Factorial Design, and Modeling

ABSTRACTResidues from vineyards and wine/spirits industry, as being abundant in South European countries, were investigated for their application in thermal processes for energy production. The thermal behavior and the reactivity of the fuels and their blends were determined by thermogravimetric analysis tests. The combustion performance of the residues in terms of efficiency and emissions was studied by carrying out experiments in a fluidized bed unit. The effects of excess air ratio, fuel loading, and blending ratio were examined and a factorial design and modeling was developed in order to provide optimal values of process variables for minimizing emissions. The results showed that CO emissions were low, SO2 emissions were negligible, while NOx emissions from grape husks were high. A mitigation of CO and NOx levels could be achieved by decreasing excess air from 50% to 30% or reducing reactor loading. By increasing the percentage of grape husks in the mixtures, flue gas emissions were increased, however, combustion efficiency was maintained at high levels. Factorial design analysis and modeling indicated that the optimum combustion and emission performance was accomplished when the combustor was fed with vine shoots at reduced loading and excess air below 40%.

A Statistical Analysis of Epoxy Polymer Reinforced with Micro Ceramic Particles

A significant amount of research has been focused on the use of ceramic nano/micro particles to enhance the strength and stiffness of polymeric matrices. This work evaluates the effect of Portland cement or crystalline silica (quartz) particle inclusions into epoxy polymer. Two experiments were conducted based on a full factorial design analysis. Experiment I investigated the effect of Portland cement amount (ASTM III), two types of hardeners (HY 951 and 956) and two curing times (7 and 28 days) on the compressive behaviour and density of particulate composites. Experiment II evaluated the incorporation of quartz or cement particles by mixing different mass fraction levels, considering 28 days of curing time and HY 951 hardener. The samples were prepared in a randomized manufacturing process and tested in compression. The mechanical properties were significantly affected by the type of hardener used. Both particles can enhance the compressive strength and stiffness of the composites.

Asymmetric flow field flow fractionation with light scattering detection – an orthogonal sensitivity analysis

Asymmetric flow field flow fractionation (AF4) has several instrumental factors that may have a direct effect on separation performance. A sensitivity analysis was applied to ascertain the relative importance of AF4 primary instrument factor settings for the separation of a complex environmental sample. The analysis evaluated the impact of instrumental factors namely, cross flow, ramp time, focus flow, injection volume, and run buffer concentration on the multi-angle light scattering measurement of natural organic matter (NOM) molar mass (MM). A 2(5-1) orthogonal fractional factorial design was used to minimize analysis time while preserving the accuracy and robustness in the determination of the main effects and interactions between any two instrumental factors. By assuming that separations resulting in smaller MM measurements would be more accurate, the analysis produced a ranked list of effects estimates for factors and interactions of factors based on their relative importance in minimizing the MM. The most important and statistically significant AF4 instrumental factors were buffer concentration and cross flow. The least important was ramp time. A parallel 2(5-2) orthogonal fractional factorial design was also employed on five environmental factors for synthetic natural water samples containing silver nanoparticles (NPs), namely: NP concentration, NP size, NOM concentration, specific conductance, and pH. None of the water quality characteristic effects or interactions were found to be significant in minimizing the measured MM; however, the interaction between NP concentration and NP size was an important effect when considering NOM recovery. This work presents a structured approach for the rigorous assessment of AF4 instrument factors and optimal settings for the separation of complex samples utilizing efficient orthogonal factional factorial design and appropriate graphical analysis.

Influence of the Process Parameters on the Foam Fractionation Treatment of Olive Mill Wastewater

Olive mill wastewater (OMW) is a high organic load waste produced from the three-phase decanter system used in the processing of olives for oil that has phytotoxic and antibacterial phenolic compounds, which makes this waste to resist biological degradation. Foam fractionation has not yet been studied with regard to its potential for treatment of such wastewaters. In the present study, this technique was investigated in a simple model system. The effect of different operational conditions, namely, surfactant concentration, process time, pH and gas flow rate were evaluated in this study. Statistical analysis of the fractional factorial design revealed that the surfactant concentration, pH and the gas flow rate are the most influential process parameters. Low surfactant concentration and pH of 3-4 were found to be advantageous in terms of good chemical oxygen demand (COD) removal. More than 80% of COD was removed through a single stage foam fractionation process. This method has proved to be a feasible technique for the OMW effluent treatment.

Competitive adsorption and optimization of binary mixture of textile dyes: A factorial design analysis

In this paper, a study of simultaneous adsorption of mixture dyes of Basic Blue 41 and Basic Yellow 28 in binary system was done using two types of activated carbon and compared with a single dye system in a batch mode. A competitive adsorption between the two cationic dyes was observed and it was noticed that Basic Blue 41 was favored. Kinetics of each dye in single and binary systems was found to follow pseudo-second-order rate kinetic model, with a good correlation (higher than 0.99). The single component equilibrium data were analyzed using the Langmuir and Freundlich isotherms. Overall, the Langmuir isotherm showed a better fitting for all adsorptions under investigation in terms of correlation coefficient. As the binary adsorption is competitive, extended Langmuir models could not predict the binary component isotherm well. The essential parameters which affect the removal efficiency of binary mixture solution such as pH, temperature and adsorbent type were optimized using full factorial design methodology. Effect of parameters and interaction were analyzed using Pareto chart, main effect and interaction effect. In various industrial effluents like textile industries and plant-produced water, dyes are existent in mixture form. So, this work might be of great benefit in knowing to remove the used dyes.

English

Polymer modified concrete is one of the alternative material of construction to obtain better performance in strength and durability of concrete. There are some types of PMC such as natural rubber, synthetic rubber, modified rubber and asphalt. In this work, copolymer of natural rubber styrene and copolymer of natural rubber methacrylate were used as PMC added to Portland cement concrete and Portland Pozzolan cement concrete up to 1 wt%. The aims of this work were to investigate the effects of curing methods (air curing and saturated lime curing) and addition of plasticizer on the compressive strength of concrete. Interactions among parameters were observed using factorial design analysis with Minitab (software) and strength value in concrete age of 28 days as a yield. The result showed that by saturated lime curing, calcium hydroxide as side product of cement hydration could be reacting with pozzolanic material of cement. So, there would be more calcium silicate hydrate (CSH) crystal that improve bond in the interfacial zone. It was found that the saturated lime curing produced concrete with compressive strength 20% higher than concrete produced by air curing. This result was supported by results from Scanning Electron Spectroscopy (SEM) that prove there were compactness factor of concrete structure. It was also found that plasticizer did not significantly influence compressive strength of concretes. Key words: Polymer modified concrete, copolymer of natural rubber, strength development, saturated lime curing.

Factorial design analysis applied to the performance of SMS anti-spam filtering systems

Over the last few decades, the advent of telecommunication systems has allowed a growing exchange of electronic messages around the world. Unfortunately, irrelevant and/or unsolicited content corresponds to the majority of this volume of data, and to decide whether to keep or discard each message is a known challenge in the context of machine learning. This paper proposes an anti-spam filtering approach base on linguistic techniques. The real effect of each system parameter is evaluated through design factorial analysis using two different classifiers: first using Support Vector Machine (SVM) and second applying Naive Bayesian (NB) classification. This analysis is detailed and discussed providing a step-by-step guide for developers and users of anti-spam filters. Based on different system metrics, multi-objective optimization is applied in order to obtain the optimal filter setup. Evaluation of anti-spam filter under optimal configuration showed that SVM-based system achieved an accuracy performance above 98% whereas the NB-based system reached 87%. Results also reveal that linguistic techniques are relevant for the NB classifier but do not contribute to improve the SVM-based system performance.

Ensuring the Quality of Evidence: Using the Best Design to Answer Health IT Questions.

The quality of logic in a research design determines the value of the results and our confidence regarding the validity of the findings. The purpose of this contribution is to review the principles of research design as they apply to research and evaluation in health IT. We review the architecture of research design, the definitions of cause, sources of bias and confounds, and the importance of measurement as related to the various types of health IT questions. The goal is to provide practitioners a roadmap for making decisions for their own specific study. The contribution is organized around the Threats to Validity taxonomy and explains how different design models address these threats through the use of blocking, factorial design, control groups and time series analysis. The contribution discusses randomized experiments, and includes regression discontinuity designs and various quasi-experimental designs with a special emphasis on how to improve pre/post designs. At the end, general recommendations are provided for improving weaker designs and general research procedures.

Factorial design analysis for adsorption of Indigo Carmine onto Montmorillonite-Evaluation of the kinetics and equilibrium data

In this study, the potential of montmorillonite for Indigo Carmine (IC) adsorption from aqueous solutions was evaluated. The equilibrium adsorption results for montmorillonite can be successfully modeled by the Langmuir, Freundlich, and Temkin, isotherms. The pseudo- first order and pseudo-second order models were used to describe the kinetic data and rate constants were evaluated. In order to reduce the number of experiments to achieve better IC removal efficiency, two independent sets of full 23 factorial design experiments were carried out. The variables were montmorillonite of 1.25 and 5.00 g/L, IC concentrations of 20 and 200 mg/L and pH of 3 and 7.

Mechanical Properties and Metallurgical Characterization of Dissimilar Welded Joints between AISI 316 and AISI 4340

In the present study, the influence of six different process parameters and three interactions on joint tensile strength, toughness, fusion zone microhardness variation are studied during dissimilar tungsten inert gas welding between austenitic stainless steel AISI 316 and alloy steel AISI 4340. Detailed experimental study using fractional factorial experimental design and subsequent statistical analysis show that higher tensile strength, toughness can be achieved using ER 309 filler material and suitably selecting the other process parameters and heating conditions. Addition of small proportion of hydrogen in shielding gas increases the heat transfer efficiency, melting and subsequent penetration. Preheating of AISI 4340 material reduces the chance of solidification cracking and post-heating helps to improve the joint mechanical property. Microstructural observations show that improper selection of process parameters may lead to micro-pores and degrade the joint quality. Successful joining of the dissimilar materials greatly depends on the selection of optimum process parameters, filler material and shielding gas.

Alleviating Mathematics Anxiety of Elementary School Students: A Situated Perspective

The present study investigates the effects of the situated learning and effortful control on mathematics anxiety of school students. Participants were 99 seventh graders who studied in two schools. Students in one of these were given instruction through the situated learning model, and the students of other school were treated as a control group. Existing instruments, namely, Mathematics Anxiety Scale by Sharma and Sansanwal (2011) and Effortful Control Scale by Lonigan and Phillips (2001) were used to measure mathematics anxiety and effortful control. Data were analyzed by using 2 × 2 factorial design analysis of covariance. Results indicated that after the experiment students who were exposed to the situated learning model had significantly less mathematics anxiety than their counterparts who were in the control group with effect size .35. The implications of the study are discussed.