Values Of Independent Variables Research Articles

Experimental and Numerical Testing of an Induction Variable Air Volume (VAV) Controller with Two Damper Blades

A VAV (Variable Air Volume) air conditioning system without reheat of the air supplied is generally regarded as energy efficient. Re-heat can be avoided when induction VAV controllers are used instead of conventional controllers since primary airflow can be reduced to about 10–20% of its nominal value without a draught risk. The aim of the present tests was to determine the induction and efficiency characteristics of a compact square induction VAV controller with two damper blades and to find relationships between flow ratio and governing independent variables such as: inlet airflow, inlet pressure and terminating elements flow resistance. The aim was also to find a method of flow ratio interpolation for any values of independent variables within the range of tests. A controller of new construction with two damper blades was tested experimentally in a physical model using the tracer gas method and numerically using the CFD method. In order to approximate and interpolate the induced airflow for any values of governing independent variables, a set of relevant relationships was found. CFD calculation results can be approximated using a set of equations with the expanded uncertainty of 5 m3/h. This method can be implemented in the codes for VAV controller selection. The tested construction meets the most important requirement for an induction VAV controller i.e. for the lowest inlet airflow the amount of induced airflow is higher than the required minimum value (NV=2–3). When comparing efficiency characteristics of the tested model of induction VAV controller with test results of jet pumps, it can be concluded that the controller of the tested construction may reach similar efficiency and thus the new construction seems appropriate. There is a satisfactory agreement between CFD and measurement results. CFD method can be successfully used for optimization of the induction VAV controller construction.

Optimization of coagulant dosage using response surface methodology with central composite design

【The determining the appropriate dosage of coagulant is very important, because dosage of coagulant in the coagulation process for wastewater affects removing the amount of pollutants, cost, and producing sludge amount. Accordingly, in this study, in order to determine the optimal PAC dosage in the coagulation process, CCD (Central composite design) was used to proceed experimental design, and the quadratic regression models were constructed between independent variables (pH, influent turbidity, PAC dosage) and each response variable (Total coliform, E.coli, PSD (Particle size distribution) ( $10{\mu}m$ ), TP, $PO_4$ -P, and $COD_{cr}$ ) by the RSM (Response surface methodology). Also, Considering the various response variables, the optimum PAC dosage and range were derived. As a result, in order to maximize the removal rate of total coliform and E.coli, the values of independent variables are the pH 6-7, the influent turbidity 100-200 NTU, and the PAC dosage 0.07-0.09 ml/L. For maximizing the removal rate of TP, $PO_4$ -P, $COD_{cr}$ , and PSD( $10{\mu}m$ ), it is required for the pH 9, the influent turbidity 200-250 NTU, and the PAC dosage 0.05-0.065 ml/L. In the case of multiple independent variables, when the desirable removal rate for total coliform, E.coli, TP, and $PO_4$ -P is 90-100 % and that for $COD_{cr}$ and PSD( $10{\mu}m$ ) is 50-100 %, the required PAC dosage is 0.05-0.07 ml/L in the pH 9 and influent turbidity 200-250 NTU. Thus, if the influent turbidity is high, adjusting pH is more effective way in terms of cost since a small amount of PAC dosage is required.】

Robotics GMAW-weld Bead Geometry Modeling Using MATLAB Script Approach

This study is aimed at obtaining a relationship between the values of dependent and independent variables of robotic gas metal arc welding, using linear regression techniques. The welding parameters such as the arc current, stick out, arc voltage and welding speed are taken as independent variables and bead geometry namely bead width, bead penetration and bead reinforcement were dependent variables. In this study, the important factors on bead geometry are considered and the other parameters are held as constant. An extension of this model, namely multiple linear regressions, is used to represent the relationship between a dependent variable and several independent variables. Taguchi’s L₂₇, 3 level 4 parameter orthogonal array design of experiments, twenty seven samples were analyzed and the bead geometry values of the weld bead have been measured. Then, the relationship between the welding parameters is modeled and expressed in multiple linear regression models by a script approach with MATLAB. Each model is checked for its adequacy by using ANOVA. The proposed model is compared with the experiment values and it has positive correlation with maximum of 3.67% error. The results were confirmed by further experiments.

Indirect and direct estimation of forest biomass change using forest inventory and airborne laser scanning data

Remote sensing-based change estimation typically takes two forms. Indirect estimation entails constructing models of the relationship between the response variable of interest and remotely sensed auxiliary variables at two times and then estimating change as the differences in the model predictions for the two times. Direct estimation entails constructing models of change directly using observations of change in the response and the remotely sensed auxiliary variables for two dates. The direct method is generally preferred, although few statistically rigorous comparisons have been reported. This study focused on statistically rigorous, indirect and direct estimation of biomass change using forest inventory and airborne laser scanning (ALS) data for a Norwegian study area. Three sets of statistical estimators were used: simple random sampling estimators, indirect model-assisted regression estimators, and direct model-assisted regression estimators. In addition, three modeling approaches were used to support the direct model-assisted estimators. The study produced four relevant findings. First, use of the ALS auxiliary information greatly increased the precision of change estimates, regardless of whether indirect or direct methods were used. Second, contrary to previously reported results, the indirect method produced greater precision for the study area mean than the traditional direct method. Third, the direct method that used models whose predictor variables were selected in pairs but with separate coefficient estimates and models whose predictor variables were selected without regard to pairing produced the greatest precision. Finally, greater emphasis should be placed on the effects of model extrapolations for values of independent variables in the population that are beyond the range of the variables in the sample.

Optimization of the canola oil based vitamin E nanoemulsions stabilized by food grade mixed surfactants using response surface methodology

The objective of the present study was to prepare canola oil based vitamin E nanoemulsions by using food grade mixed surfactants (Tween:80 and lecithin; 3:1) to replace some concentration of nonionic surfactants (Tween 80) with natural surfactant (soya lecithin) and to optimize their preparation conditions. RBD (Refined, Bleached and Deodorized) canola oil and vitamin E acetate were used in water/vitamin E/oil/surfactant system due to their nutritional benefits and oxidative stability, respectively. Response surface methodology (RSM) was used to optimize the preparation conditions. The effects of homogenization pressure (75–155MPa), oil concentrations (4–12% w/w), surfactant concentrations (3–11% w/w) and vitamin E acetate contents (0.4–1.2% w/w) on the particle size and emulsion stability were studied. RSM analysis has shown that the experimental data could be fitted well into second-order polynomial model with the coefficient of determinations of 0.9464 and 0.9278 for particle size and emulsion stability, respectively. The optimum values of independent variables were 135MPa homogenization pressure, 6.18% oil contents, 6.39% surfactant concentration and 1% vitamin E acetate concentration. The optimized response values for particle size and emulsion stability were 150.10nm and 0.338, respectively. Whereas, the experimental values for particle size and nanoemulsion stability were 156.13±2.3nm and 0.328±0.015, respectively.

Motivations for a Custom Apparatus for Sonophoresis Experimentation

This paper discusses the motivations and initial design specifications relating to a custom experimental apparatus for sonophoresis experimentation. Sonophoresis experiments usually involve inserting a cylindrical transducer into a glass Franz diffusion cell. The motivations for a custom apparatus are: the hypothesis that the pressure amplitude that is applied to the current system by the transducer is not equal to the pressure amplitude at a point on the skin surface due to acoustic reflections in the system (this is supported by preliminary qualitative results from a numerical model under development); the ultrasound induced heating within Franz diffusion cells that often requires researchers to change experimental fluids mid experiment; and the high standard deviations in published results. Some proposed design specifications are: to maximize the uniformity of the ultrasound field; to allow for the control of temperature in all mediums present; to allow for independent variable values at the skin surface to be measured or accurately estimated; to maximize the accuracy of the measured dependent variable/s; to minimize the effects of any extraneous variables; and to minimize standard deviation between repetitions (maximize repeatability). Numerical modelling and 3D printing are both expected to be vital to the development of this apparatus. This proposed apparatus is intended to improve the validity and repeatability of in vitro sonophoresis experiments.

Single 5HTR2A-1438 A/G nucleotide polymorphism affects performance in a metacontrast masking task: Implications for vulnerability testing and neuromodulation of pyramidal cells

It is known that 5HTR2A (rs6311) receptors have high concentration in the cortical layer-5 pyramidal neurons and that these receptors play an important role in the modulation of neurocognitive functions. For example, layer-5 pyramidal neurons mediate cellular level integrative interaction of primary sensory afferent signals and top-down signals exerting contextual modulatory influence. It is also known that genetic variability of 5HTR2A is implicated in individual differences in mental processes. Interestingly, serotonin selectively enhances the asynchronous type of glutamate release when modulating the activity of cortical layer-5 pyramidal neurons, with a post-stimulation delay of this effect at about 50ms. There are not many behavioral tasks capable of tapping that small temporal intervals in terms of change of the values of independent variables leading to an observable change in the subjects’ behavior. However, in the metacontrast masking vision task stimulus onset asynchronies between target and mask critical for the change in the expression of the masking effect correspond to this small temporal value. Thus we hypothesized that genetic variability in 5HTR2A (rs6311) is likely to be associated with different behavioral effects of metacontrast masking and more specifically, that because A allele carriers typically demonstrate greater promoter activity, target-to-mask asynchrony variations should have stronger impact on the masking effect especially with this group of subjects. We obtained support for this hypothesis, but only when target and mask shapes were mutually incongruent.

Sequential learning versus no learning in Bayesian regression models

We consider two regression models: linear and logistic. The dependent variable is observed periodically and in each period a Bayesian formulation is used to generate updated forecasts of the dependent variable as new data is observed. One would expect that including new data in the Bayesian updates results in improved forecasts over not including the new data. Our findings indicate that this is not always true. We show there exists a subset of the independent variable space that we call the “region of no learning.” If the independent variable values for a given period in the future are in this region, then the forecast does not change with any new data. Moreover, if the independent variable values are in a neighborhood of the region of no learning, then there may be little benefit to wait for the new data and update the forecast. We propose a statistical approach to characterize this neighborhood which we call the “region of little learning.” Our results provide insights into the trade-offs that exist in situations when the decision maker has an incentive to make an early decision based on an early forecast versus waiting to make a later decision based on an updated forecast. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 532–548, 2014

Inflation and Market Uncertainty in South Africa

AbstractComparison of the movements in the VIX index, the rand – dollar exchange rate and South African CPI inflation reveals a striking resemblance between them, raising the question as to whether or not there is an empirical relationship among them. The aim of this paper is to determine whether or not changes in market uncertainty, as reflected in the VIX index, influence South African inflation. Given that the VIX index reflects market uncertainty, its impact on the inflation rate may differ between times of heightened uncertainty and normality, thus suggesting the presence of multiple regimes. To cater for this possibility, the analysis first uses the general‐to‐specific procedure (including squared and cubed values of dependent and independent variables) with impulse indicator saturation dummies to look for non‐linear behaviour in the form of statistically significant squared and cubed variables and clustered periods of outlier dummies that might reflect an alternative regime. Finding such periods, the analysis next uses a Markov‐switching model to model this non‐linear behaviour explicitly. The results show that market volatility as measured by the VIX indeed explains South African inflation. Moreover, as shown by the second regime of the Markov‐switching model, when market volatility is elevated, its influence on inflation also increases.

Paired Comparison and Theory Development: Considerations for Case Selection

ABSTRACTDespite the widespread use of paired comparisons, we lack clear guidance about how to use this research strategy in practice, particularly in case selection. The literature tends to assume that cases are systematically selected from a known population, a major assumption for many topics of interest to political scientists. This article speaks to this gap. It describes three distinct logics of paired comparison relevant to theory development, presents a simple way of considering and comparing them, and explores how this approach can inform more intentional research design, with particular attention to low information settings where substantial research is needed to ascertain the values of independent or dependent variables. The discussion underscores inter alia the need to be aware and explicit about the implications of case selection for the ability to test and build theory, and the need to reconsider the well-cited “rule” of not selecting on the dependent variable.

Modelling of a Turning Process Using the Gravitational Search Algorithm

This paper proposes the modelling of a turning process using a gravitational search algorithm (GSA). GSA is an optimization algorithm based on Newton's law of universal gravitation and mass interactions. In order to sufficiently describe the turning process, at least three independent variables are required: cutting speed, feed-rate, and cutting depth. Independent variables have impacts on dependent variables, which were in our case cutting force, surface roughness, and tool-life. The values of independent and dependent variables obtained by measurements serve as a knowledge database for feeding the GSA optimization process. During our research the GSA was used for optimizing the numerical coefficients of predefined polynomial models for describing the observed output variables. The accuracies of the obtained prediction models were proved by means of a testing data set that was excluded from the training data. The research showed that the obtained results were comparable with the other optimization algorithms such as particle swarm optimization (PSO). However, the optimization time required for GSA optimization was, in certain cases, significantly shorter. (Received in December 2012, accepted in August 2013. This paper was with the authors 2 months for 2 revisions.)

Design Optimization and Evaluation of Gastric Floating Matrix Tablet of Glipizide

Purpose: To formulate an optimized gastric floating drug delivery system (GFDDS) containing glipizide with carbomers and cellulosic polymers.Method: Central composite design (CCD) was employed in formulating the GFDDS using hydroxypropyl methylcellulose K4M (HPMC K4M) (A) and Carbopol 934P (CP934P) (B), as independent variables. Floating lag time (FLT), total floating time (TFT) and time required to release 50 % of the drug (T50) were selected as dependent variables. The dissolution data obtained were fitted to various release models and the floating profiles of the formulations analyzed.Results: HPMC K4M loading clearly enhanced floating properties while CP934P showed negative effect on floating properties but was helpful in controlling drug release. The quadratic mathematical model developed was used to predict optimum formulations. The computer optimization process, contour plots and response surface plots predicted the concentration of independent variables A and B to be 47.32 and 8.4 mg, respectively, for maximum TFT and T50 at the same time for least FLT. Predicted concentration of independent variables showed the same results experimentally, with -0.75 - 1.47 percentage errors.Conclusion: CCD demonstrated the role of the derived equations, contour plots and response surface plots in predicting the values of independent variables for the preparation and optimization of glipizide gastric floating matrix tablet.Keywords: Effervescent, Floating tablet, Design of Experiment, Release kinetics, Central composite design, Optimization.

The Effects of Classifying CRM Sources on the Asphalt Cement Modification for Paving Roads

The improvement of Basrah asphalt cement that used in hot mix pavement can be achieved by using four influencing variables, which are the value of temperature needed for mixing, time of mixing, the amount of wasted car tire rubber (WCR) and the amount of wasted truck tire rubber (WTR). The main virtue of this study is to know the affecting of classifying the CRM sources, and how that will enhance the properties of tire rubber modified asphalt binder. The improvement is estimated through using the ranges (150-180 oC) of temperature, (20-60) minute of mixing time, (10-20) gm of WCR and (0-4) gm of WTR. The using of two different types of waste tires had shown clearly the affecting on the results of responses studied. The effect of independent variables was studied by using Box-Wilson technique of experimental design. Moreover, the regression coefficients for responses models equation and the optimum conditions were estimated by using STATISTICA software. The results had shown that the optimum values of the independent variables to submit the best responses of penetration test and softening point test was at temperature (180oC), mixing time (60 minute), WCR (20 gm), and WTR (4gm) and then found new mathematical models to estimate these responses at any values of independent variables. The Marshall stability result of the modified asphalt mixes was higher than of the unmodified asphalt mixes.

A comparative study of loss functions for bayesian control in mixture models

Berliner (1987) discussed the issue of controlling the output (response) towards the specified value by choosing the values for independent variables in a regression mixture model, taking it as a Bayesian Decision Problem. The quantification of the potential loss was done with the help of quadratic loss function, which was a symmetric loss function. We have tried to quantify this loss with the help of Precautionary Loss Function and Modified Squared Error Loss Function, in linear Scheffe mixture model and comparison is established between these loss function. Results are improved as compared to Berliner (1987).

Analisis Kebijakan Dividen Terhadap Nilai Intrinsik dan Nilai Pasar Perusahaan

Dividend policy is basically a company’s decision in determining how much net income wil be distributed to shareholders as dividend and how much will be retained. Dividend policy is able to influence intrinsic and market value of a company. In this study, the proxy of dividend policy is Dividend Pay-out (DPO) as independent variable and intrinsic value as dependent variable with indicator that is multiplication result of Earning Multiplier (EM) and Company Market Value obtained from stock price data of LQ45 at closing price. The purpose of this study is to identify the relationship between variables, the influence of independent variable to dependent variable, and finally to measure how significant the influence of variable X to variable Y will be, dividend policy to intrinsic and market value of the company. This case study was conducted at PT Aneka Tambang Tbk. and PT Timah Tbk., both are mining company.
 The results of this study are: 1) Dividend policy proxied by DPO has significant influence and relationship to the intrinsic value of PT Aneka tambang Tbk. But, at PT Timah Tbk., dividend policy to intrinsic value is not significant with alpha as high as 193.064, it’s due to the dividend policy does not influence the intrinsic value, but the intrinsic value is more influenced by EPS. 2) Dividend policy proxied by DPO has connection and fairly significant influence with the company’s market value at PT Aneka Tambang Tbk. But, at PT Tima Tbk., dividend policy to the company’s market value is not significant with alpha as high as 54.859, it’s due to the dividend policy does not influence the company’s market value, but the intrinsic value is influenced dominantly by the company’s market value. The result of the research shows that the dividend policy at PT Aneka Tambang Tbk has impact on intrinsic and market value better than the dividend policy has impact on intrinsic and market value at PT Timah Tbk.
 
 Keywords: dividend policy; intrinsic value; market value

Optimization of Polymeric Nano Drug Delivery System Using 3.2 Full Factorial Design

This study investigated the utility of a 3(2) factorial design and optimization process for nanoparticle suspension prepared by two different polymers Eudragit(®) RS 100 and Eudragit (®) RL 100 respectively. Total 18 formulations (9 formulations with each polymer) were prepared by solvent displacement technique. In these designs, two factors namely polymer weight (X1) and Aq. phase volume (X2) were evaluated each at three levels and experimental trials were performed at all nine possible combinations. Polymer weight (X1) and aqueous phase volume (X2) were selected as independent variables and particle size (Y1), % entrapment (Y2), drug release at 12(th) hrs. (Y3) are chosen as depended variables. In case of 3(2) factorial design, a full-model polynomial equation was established by subjecting the transformed values of independent variables to multiple regression analysis, and contour plots were drawn using the equation. The derived polynomial equations for particle size and % drug entrapment were verified by check point formulation. The result showed a wide variation in the responses for both of the polymer. For RS 100 polymer, particle size was 112-350 nm, entrapment: 26-72%, drug release was 42- 89% at 12(th) hrs. and for RL 100 polymer, particle size was 114-390 nm, entrapment: 30-72%, drug release was 50-90% at 12(th) hrs. The application of factorial design yielded a statistically systematic approach for the formulation and optimization of nanoparticles with desired particle size and high entrapment efficiency and release profile. The results of the optimized formulations showed particle size 225 nm, 63% drug entrapment and 83% drug release for RS 100 polymer and particle size was 148 nm, drug entrapment was 57%, drug release was 80% for RL 100 polymer.

Optimization of isolation of cellulose from orange peel using sodium hydroxide and chelating agents

Response surface methodology was used to optimize cellulose recovery from orange peel using sodium hydroxide (NaOH) as isolation reagent, and to minimize its ash content using ethylenediaminetetraacetic acid (EDTA) as chelating agent. The independent variables were NaOH charge, EDTA charge and cooking time. Other two constant parameters were cooking temperature (98°C) and liquid-to-solid ratio (7.5). The dependent variables were cellulose yield and ash content. A second-order polynomial model was used for plotting response surfaces and for determining optimum cooking conditions. The analysis of coefficient values for independent variables in the regression equation showed that NaOH and EDTA charges were major factors influencing the cellulose yield and ash content, respectively. Optimum conditions were defined by: NaOH charge 38.2%, EDTA charge 9.56%, and cooking time 317min. The predicted cellulose yield was 24.06% and ash content 0.69%. A good agreement between the experimental values and the predicted was observed.

Rebuilding Critical Values of Durbin-Watson Test by Sufficient Statistic in the Multivariate Regression and First-Order Autogressive Error Model with Intercept

This paper establishes a Durbin-Watson test statistic with sufficiency and rebuilds the probability table for hypothesis testing in the multivariate regression model with intercept because the Durbin-Watson test which has numerous problems first established in a multiple regression model with first-order autoregressive error by Durbin and Watson (1950). The paper shows that the independent variables, hypothesized autocorrelation coefficient of error and sample sizes have the influence on the shapes and coefficients of probability distribution and critical values in the Durbin-Watson test. First, the multi-collinearity causes the distortion on probability distribution. Second, the changes of independent variable values represent different shapes of probability distribution and critical values in the Durbin-Watson test. Third, the sample sizes induce in the effect of law of large number and asymptotic normality, so there is no gray area in the Durbin-Watson test table when the samples are large enough. Even in the case of small samples there is no gray area.

Rebuilding Critical Values of Durbin-Watson Test in the Multivariate Regression and First-Order Autogressive Error Model Without Intercept

This paper revalidates the Durbin-Watson test statistic and rebuilds the probability table for hypothesis testing in the multivariate regression model without intercept because the Durbin-Watson test which has numerous problems. The paper shows that the independent variables, hypothesized autocorrelation coefficient of error and sample sizes have the influence on the shapes and coefficients of probability distribution and critical values in the Durbin-Watson test. First, the multi-collinearity causes the distortion on probability distribution. Second, the changes of independent variable values represent different shapes of probability distribution and critical values in the Durbin-Watson test. Third, the sample sizes induce in the effect of law of large number and asymptotic normality, so there is no gray area in the Durbin-Watson test table when the samples are large enough. Even in the case of small samples there is no gray area. The limitation of the paper is the Durbin-Watson test statistic has no sufficient property.

Rebuilding Critical Values of Durbin-Watson Test by Sufficient Statistic in the Multivariate Regression and First-Order Autogressive Error Model Without Intercept

This paper establishes a Durbin-Watson test statistic with sufficiency and rebuilds the probability table for hypothesis testing in the multivariate regression model without intercept because the Durbin-Watson test which has numerous problems first established in a multiple regression model with first-order autoregressive error by Durbin and Watson (1950). The paper shows that the independent variables, hypothesized autocorrelation coefficient of error and sample sizes have the influence on the shapes and coefficients of probability distribution and critical values in the Durbin-Watson test. First, the multi-collinearity causes the distortion on probability distribution. Second, the changes of independent variable values represent different shapes of probability distribution and critical values in the Durbin-Watson test. Third, the sample sizes induce in the effect of law of large number and asymptotic normality, so there is no gray area in the Durbin-Watson test table when the samples are large enough. Even in the case of small samples there is no gray area.