Gas Metal Arc Welding is a process in which the source of heat is an arc format between consumable metal electrode and the work piece with an externally supplied gaseous shield of gas either inert such as argon, helium. This experimental study aims at optimizing various Gas Metal Arc welding parameters including welding voltage, welding current, welding speed and nozzle to plate distance (NPD) by developing a mathematical model for sound weld deposit area of a mild steel specimen. Factorialdesignapproach has been applied for finding the relationship between the various process parameters and weld deposit area. The study revealed that the welding voltage and NPD varies directly with weld deposit area and inverse relationship is found between welding current and speed with weld deposit area.
and further utilized for the development of nanoparticles. Three square factorialdesignapproach was used for the optimization of nanoparticles. The effect of process variables on the solubility and particle size was identified. The optimum concentration of TMC was 3.5 mg/ml. The ultrasonication was proven to be a simple and efficient technology for reducing the particle size. The positive value of zeta potential was obtained by DLS study. DSC and PXRD study confirmed the amorphous nature of drug particles. The saturation solubility study revealed 3.9 times solubility enhancement compared to the pure telmisartan. In vitro studies revealed that nanoparticles were capable to increase the dissolution rate of the drug. Furthermore, the results of the accelerated stability study showed its solubility, and the particle size retained within the range, as well, the amorphous state of nanoparticles was not altered. This study concluded that the developed nanoparticles could enhance the solubility and dissolution rate of telmisartan. Future works shall consist of the oral bioavailability study of telmisartan nanoparticles.
The effect of various process parameters like welding current, torch height, welding speed and plasma gas flow rate on front melting width, back melting width and weld reinforcement of plasma arc welding on aluminum alloy is investigated by using factorialdesignapproach. Variable polarity plasma arc welding is used for welding aluminum alloy. Trail experiments are conducted and the limits of the input process parameters are decided. Two levels and four input process parameters are chosen and experiments are conducted as per typical design matrix considering full factorialdesign. Total sixteen experiments are conducted and output responses are measured. The coefficients are calculated by using regression analysis and the mathematical models are constructed. By using the mathematical models the main and interaction effect of various process parameters on weld quality is studied.
A 2 3 full factorialdesign using Design Expert version 9. 6. 0. 2 (Stat-Ease Inc., and Minnepolis, MN) with three central points and 2 replicates was employed for modeling and analysis of problem. The design predicted the optimized critical formulation variables based on their effect on the response of an interest.  The independent variables or factors were concentration of gold salt (X 1 ), concentration of drug (X 2 ) and reaction time (X 3 ). The values
In the present study, a framework of ANN model had been developed for the prediction of fatigue behavior. The presented work is focus on the techniques to obtain the best ANN architecture for the specific application using factorialdesignapproach. The performance of fatigue prediction through the combination of ANN and heart rate as the recognition feature show promising results, as the success rate for all configurations of factors is above 80%, except for the case when traingd and purelin activation function was used together. In addition, the application of factorialdesign shows satisfactory results on the investigation of the influences or effects of experimental parameters. The conducted analyses provide a useful guidance and play an important role in optimizing the ANN parameters. In this case of study, the training algorithm has the strongest effect on the ANN success rate, followed by the neuron activation function. The highest success rate (94.7%) achieved when Levenberg- Marquardt is used as training algorithm together with sigmoid activation function, regardless of the amount of hidden neurons (either 3 or 5).
The ATAFUTI study is the first double-blind, placebo- controlled, factorial randomised trial to investigate a traditional herbal medicinal product as an alternative treatment for UTIs in women, with the aim of reducing symptoms and reducing antibiotic consumption in pri- mary care. The open, pragmatic arm of the trial also builds on existing research into symptom relief of un- complicated UTI with ibuprofen, whereby two thirds of women (n = 248) with mild to moderate symptoms re- covered without the need for antibiotics . It will pro- vide clinical evidence on the efficacy and safety of using ibuprofen and a traditional herbal medicine uva-ursi, ei- ther singularly or in combination for the relief of the dis- tressing symptoms of UTI in women, one of the most common reasons for antibiotic prescription. The NSAID has known anti-inflammatory properties, and uva-ursi has demonstrated antibacterial properties against E. coli both in vitro as well as in urine samples of healthy vol- unteers .
2 2 factorialdesign. Ritonavir tablets were prepared by direct compression method and were evaluated for drug content, hardness, friability, disintegration time and dissolution rate characteristics.All the ritonavir tablets prepared fulfilled the official (IP 2010) requirements with regard to drug content, hardness, friability and disintegration time specified for uncoated tablets. Much variations were observed in the disintegration and dissolution characteristics of the ritonavir tablets prepared due to formulation variables. The disintegration times were in the range 30 sec to 6 min 15 sec with various tablets. Ritonavir tablets (Rb) which are prepared employing βCD in 1:0.5 ratio of drug: βCD and Soluplus at 2 % of drug content gave very rapid dissolution of ritonavir than others. These tablets (Rb) gave 99.50 % dissolution in 15 min. The increasing order of dissolution rate (K 1 ) observed with various
For optimization of valsartan tablets as per 2 3 factorialdesign the βCD, Primojel and PVP K 30 are considered as the three factors. The two levels of the factor A (Primojel) are 2% and 30% of drug content; the two levels of the factor B (βCD) are 1:1 and 1:5 ratio of drug: βCD and the two levels of factor C (PVP) are 0 and 2% of drug content. Eight valsartan tablet formulations employing selected combinations of the three factors i.e. Primojel , βCD and PVP K 30 as per 2 3 factorialdesign were formulated and prepared by direct compression method.
Modeling & Simulation (M&S) has proved to be a day-to-day highly indispensable tool for complex systems design, management and monitoring. Therefore, the proposed research study aims to develop a simulation model to recreate the complexity of a medium-sized Mediterranean seaport and analyse the performance evolution of such system with particular reference to the ship turnaround time. After the input data analysis, simulation model development, verification and validation, a design of experiments (according to a 24 factorial experimental design) was carried out in order to evaluate how some critical factors (i.e. inter-arrival times, loading/unloading times, number of cars and trucks to be unloaded/loaded) may affect the seaport’s performance. To this end an analysis of variance is performed and an analytical input-output meta-model was created to evaluate the system’s performance.
We present new statistical models for jointly labeling multiple sequences and apply them to the combined task of part- of-speech tagging and noun phrase chunk- ing. The model is based on the Factorial Hidden Markov Model (FHMM) with dis- tributed hidden states representing part- of-speech and noun phrase sequences. We demonstrate that this joint labeling ap- proach, by enabling information sharing between tagging/chunking subtasks, out- performs the traditional method of tag- ging and chunking in succession. Fur- ther, we extend this into a novel model, Switching FHMM, to allow for explicit modeling of cross-sequence dependencies based on linguistic knowledge. We report tagging/chunking accuracies for varying dataset sizes and show that our approach is relatively robust to data sparsity.
Design Expert® (10.0.0.1) program was used for statistical design of present NS formulation. The statistical software was utilized to acquire information not only about critical values required to achieve the desired response but also to study the possible interactions of the selected independent variables on the dependent variables. A design with 4 centre points and 4 points for lack of fit was used to obtain a robust model. Independent variables such as the amount of ethyl cellulose (X 1 ) and % PVA (X 2 ) were used at low, medium and high levels as shown in Table 1. This design has provided 13 runs to study the effect of independent variables on two dependent variables namely particle size (Y 1 ) and % entrapment efficiency (EE, Y 2 ). The amount of RS, volume of organic phase (10 ml), stirring speed (1600 rpm) and stirring time (90 min) were kept constant throughout the optimization process. For this design, statistical significance was set at 0.05 for the determination of probability values. The interaction between independent and dependent variables were graphically shown in contour plots and 3D surface response plots. The reliability of model was confirmed by check point analysis.
study, the fermentative production, optimization of medium composition using Plackett-Burman Design, FactorialDesign of process variables using Response Surface Methodology (RSM) and its corresponding analysis of variance by novel isolate Candida parapsilosis BKR1 were reported. The Plackett- Burman screening design is applied for identifying the most significant nutrient stimulating the xylitol production. Face Centred central composite design (FC-CCD) and Box-Behnken design (BBD) were applied to determine the optimum levels of each significant nutrient and process variable, respectively. MATERIALS AND METHODS
Design of experiments (DOE) is a powerful approach for discovering a set of process (or design) variables which are most important to the process and then determine at what levels these variables must be kept to optimize the response (or quality characteristic) of interest. This paper presents two catapult experiments which can be easily taught to engineers and managers in organizations to train for design of experiments. The results of this experiment have been taken from a real live catapult experiment performed by a group of engineers in a company during the training program on DOE. The first experiment was conducted to separate out the key factors (or variables) from the trivial and the second experiment was carried out using the key factors to understand the nature of interactions among the key factors. The results of the experiment were analysed using simple but powerful graphical tools for rapid and easier understanding of the results to engineers with limited statistical competency. Copyright 2002 John Wiley & Sons, Ltd.
The study employs a cluster randomized design. The anal- ysis will be carried out using multi-level, hierarchical, logistic regression models, with the trial arm being a phy- sician-level variable, and the prescribing (or screening) outcome a patient-level variable. These models account for clustering of patients within practices, and the conse- quent lack of independence of prescribing outcomes within a given physician's place of practice . No place- of-practice characteristics, aside from the arm of the trial and baseline adherence to guidelines, will be entered into the model in the primary analysis. With so many physi- cians randomized to each arm, we anticipate that meas- ured and unmeasured physician factors that might affect the outcome will be equally distributed between the arms. Moreover, including more place-of-practice characteristics in the model would reduce the statistical power to detect the primary outcome.
Floating oral drug delivery system is retained in the stomach and are useful for drugs that are poorly soluble or unstable in intestinal fluids. Nevirapine are the suitable drug candidates for the formulation of floating sustained drug delivery system. Nevirapine is a non-peptic, protease inhibitor antiretroviral drug used in the treatment of the human immune deficiency virus. Floating tablet of Nevirapine is prepared with a view to attain early onset of action and giving prolonged release of Nevirapine by enhancing gastric retention time. A 3 7 full factorialdesign by Design Expert software
Dimensional accuracy of wax specimens used as a sacrificed specimen in investment casting may be influenced by some input parameters such as injection temperature, holding time, and injection pressure. In this paper, the effects of each input parameters are studied. It is concluded that the injection pressure is the most effective parameter on dimensional errors of wax specimens created by the metallic mold. The Taguchi procedure based on fractional factorialdesign method in the design of experiments shows that the optimum values of input parameters to achieve the best dimensional accuracy may be 80 o C for injection temperature,
A five level, four factor full factorialdesign matrix based on the central composite rotatable design technique was used for the development of mathematical models to predict the pitting corrosion rate of AISI 304L Austenitic stainless sheets welded by pulsed current micro plasma arc welding process. From the contour plots, it was observed that peak current was the most dominating parameter which affected pitting corrosion rate compared to other parameters. According to the surface plots, minimum obtained pitting corrosion rate was 0.64569 mm/Year for the input parameter combination
The survey was conducted on students and instructors in the graphic design college, the primary data collection methods included individual interviews with the instructors as well as the experiences of one of the researchers who participated in team teaching this course. The subjects participating in the interviews consisted of two primary faculty instructor and eight assistant instructors who were teaching blended courses in the college during the same semester. A statistician was engaged to ensure processing of data was done properly satisfying the requirements of the research project. Data were keyed in into a statistical package system and processed to obtain the results. The interviews aimed to know how instructors experienced in teaching the blended course including their perceptions about virtual classes, the strategies employed, and the challenges facing participants within the blended context. In our survey questions, the researcher’s experiences in this course influenced the initial list of questions for the interviews as well as analyses of the data collected. In terms of the data is a mix of the students’ experiences in this study, a course evaluation survey and the course instructors’ critique assessment reports all were utilized to interpret the data collected and uncover any differences between the instructors’ comments and other data sources.