Optimizing the Temperature of Hot outlet Air of Vortex Tube using Taguchi Method.

AbstractVortex tube produces hot and cold streams from inlet pressurized gas. Though mainly used for spot cooling purposes, it may also be used for heating/pre-heating applications. In this work, the effect of - inlet air pressure, hot tube length, hot tube internal diameter, orifice diameter, and nozzle diameter, on hot-outlet air temperature is analyzed. Taguchi's parameter design approach is used to optimize the response. Above parameters are considered at three levels each. L-27 Orthogonal Array is used for experimentation with two replicates. From the ANOVA table, all the parameters considered are found to be statistically significant. Relevant graphs are drawn; optimum response value at optimal factor levels is predicted. Through confirmatory test, experimental results are validated

Preliminary Screening of Antibacterial Compounds from Palar River Basin Flora

Considering the significance of phytochemicals as antimicrobial agents, attempt was made in the present study, to categorize several rare plant species present in and around Palar river basin and to assess their antimicrobial activity. The densities of the green cover of the Palar river basin flora were assessed by the Google Earth software. Totally 28 plants were identified and classified into 17 families according to binomial classification system. Plant extracts were prepared from leaves of all collected plants by using methanol and chloroform. Thus, the crude methanol and chloroform extracts of 28 plant species were subjected to preliminary screening against 6 strains of human bacterial pathogen using the dick diffusion method at 500 µg/disc concentrations. The results indicated that 21 different plant species exhibited activity against one or more of the bacteria while four species, viz., Ammania baccifera, Plectranthus sp., Vitex trifolia and Vitex negundo showed activity against all test organisms. The plants containing bioactive metabolites demonstrated stronger anti-microbial properties stressing the need for further investigations using fractionated extracts and purified chemical components

Anti-Adjacency Matrices of Certain Graphs Derived from Some Graph Operations

If we go through the literature, one can find many matrices that are derived for a given simple graph. The one among them is the anti-adjacency matrix which is given as follows; The anti-adjacency matrix of a simple undirected graph $G$ with vertex set   $V (G) \,= \,\{\,v_1,\,v_2,\\ \dots, v_n\}$   is an $n \times n$ matrix $B(G) = (b_{ij} )$, where $b_{ij} = 0$ if there exists an edge between $v_i$ and $v_j$ and $1$ otherwise. In this paper, we try to bring out an expression, which establishes a connection between the anti-adjacency matrices of the two graphs $G_1$ and $G_2$ and the   anti-adjacency matrix of their tensor product, $G_1 \otimes G_2$. In addition, an expression for the anti-adjacency matrix of the disjunction of two graphs, $G_1\lor G_2$, is obtained in a similar way. Finally, we obtain an expression for the anti-adjacency matrix for the generalized tensor product and generalized disjunction of two graphs.  Adjacency and anti-adjacency matrices are square matrices that are used to represent a finite graph in graph theory and computer science. The matrix elements show whether a pair of vertices in the graph are adjacent or not

Optimization and analysis of dry sliding wear behaviour of N-B4C/MOS2 unreinforced AA2219 nano hybrid composites using response surface methodology

The effect of heat treatment on nano-size B4C particle reinforced hybrid composites is discussed in this paper. For this, hybrid reinforced AA2219 composites with 2% by weight nano B4C and 2% by weight MoS2 particulates were fabricated using a two-stage stir casting process, and the specimens were heat treated to assess their influence on wear behavior. Experiments were carried out to study the wear behavior by varying important factors such as aging temperature, load, and sliding distance. Response Surface Methodology (RSM) designed by Box-Behnken was used to identify the critical variables influencing wear rate and optimize wear behavior. To comprehend the wear mechanisms involved, an analysis of the worn surface was presented. Based on the analysis, a regression equation with a predictability of 97.2% was developed for the response to obtain the optimum wear rate. The following order effectively captures the relative importance of the various factors determining the alloy's wear resistance: sliding distance, load, and aging temperature. When compared to load and sliding distance, heat treatments via artificial aging in the temperature range of 200-240 °C have no significant effect on the wear resistance of hybrid AA2219 composites reinforced with n-B4C and MoS2 particulates. However, when a temperature range of 200-240 °C is considered, composites exhibit better wear resistance at the aging temperature of 240 °C with ice quenching

Study of serum cortisol levels in complicated and uncomplicated Plasmodium vivax malaria patients

Background: Malaria results in pathological changes in various body organs, as the parasite invade and multiply in circulating red blood cells. Despite of advances in diagnostic and treatment modalities, worldwide incidences of malaria are significant. Current study was conducted to investigate serum cortisol level changes as a promising biomarker for risk prediction in malaria and to study adrenal insufficiency in malaria patients.Methods: Current investigation was a prospective observational study, conducted on complicated and uncomplicated Plasmodium vivax malaria patients. Serum cortisol levels in patients were investigated through immunoassay using direct chemiluminescent technology and were statistically correlated with Plasmodium vivax malaria infection.Results: Results of present investigation revealed that on day 1 there was significant difference in mean serum cortisol levels between the Plasmodium vivax malaria patients and control group and cortisol levels were significantly higher in complicated Plasmodium vivax malaria patients compared to uncomplicated cases on day 1 and 7. Cortisol levels were observed to be normal on day 1 and 7 in uncomplicated malaria cases and in patients with bleeding manifestations, renal failure and jaundice. In 10 out of 15 cases of cerebral malaria, significant increase in serum cortisol levels were observed on day 1, while on day 7 levels were normal in all 15 cases.Conclusions: Rise in serum cortisol level had a positive correlation with temperature and thus can be useful to predict the severity of disease in Plasmodium vivax malaria patients. No cortisol insufficiency was observed in during active and convalescent stages of illness

Performance analysis of cooperative sugar factories in north-eastern Karnataka

The study was attempted to measure the economic performance of cooperative sugar factories in terms of total costs and returns, capacity utilization, physical and financial indicators and ratio analysis of the factories. In this study the three cooperative sugar factories are taken into consideration and the Compound Annual Growth Rate (CAGR) for all the physical and financial indicators are worked out wherein the results suggested that a significant variation in the total cost and returns, capacity utilization and both physical and financial indicators over years within the three sugar factories was found. Further, the study revealed enough evidence about the financial ratios, which in turn showed the economic potentiality of the respective sugar factories. For the better performance of the factories an efficient planning and automation well before the start of the season is necessary and the government should come forward to help the farmers in making the cane bill payment at an early stage by the factories, by extending the financial assistance

Application of Soft Computing for the Prediction of Warpage of Plastic Injection

This paper deals with the development of accurate warpage prediction model for plastic injection molded parts using softcomputing tools namely, artificial neural networks and support vector machines. For training, validating and testing of thewarpage model, a number of MoldFlow (FE) analyses have been carried out using Taguchi’s orthogonal array in the designof experimental technique by considering the process parameters such as mold temperature, melt temperature, packing pressure,packing time and cooling time. The warpage values were found by analyses which were done by MoldFlow PlasticInsight (MPI) 5.0 software. The artificial neural network model and support vector machine regression model have beendeveloped using conjugate gradient learning algorithm and ANOVA kernel function respectively. The adequacy of the developedmodels is verified by using coefficient of determination. To judge the ability and efficiency of the models to predictthe warpage values absolute relative error has been used. The finite element results show, artificial neural network modelpredicts with high accuracy compared with support vector machine model