On a volume flexible production policy in a family production context

A mathematical model for a volume flexible manufacturing system is developed in a family production context, assuming that there exists a dedicated production facility as well as a separate management unit for each of the items. The possibility of machine breakdowns resulting in idle times of the respective management units is taken into account. The production rates are treated as decision variables. It is also assumed that there is a limitation on the capital available for total production. An optimal production policy is derived with maximization of profit as the criterion of optimality. The results are illustrated with a numerical example. Sensitivity of the optimal solution to changes in the values of some key parameters is also studied

Optimal replenishment and sales team initiatives for pharmaceutical products – A mathematical model

AbstractThe paper addresses an inventory model of pharmaceutical products where the demand rate of the customers increases with the volume of the initiatives of the sales team. In this model, the deterioration of the product varies depending on on-hand inventory. The volume of sales team initiatives is a control variable. It is dependent on on-hand inventory and vice versa. The profit function of the farm is formulated by the trading of inventory costs, purchasing costs, losses due to deterioration and sales team initiative costs, considering inflation and the time value of the monetary cost and profit parameters. Finally, the profit function is maximized by a variation of the calculus method. A numerical example is given to justify our model

A cross-sectional study on iodine status among pregnant and non-pregnant women of Tripura: a North-Eastern state of India

Background: Due to excess metabolic demand of iodine in pregnancy, pregnant women and lactating mother and their neonates are most vulnerable of iodine deficiency disorder. Urinary iodine excretion is a good marker of recent dietary iodine intake. Thus, present study was conducted to assess the iodine status and median urinary iodine excretion (UIE μg/lit) among pregnant and non-pregnant women of Tripura.Methods: Tribal and Bengali pregnant and non-pregnant women from Bokafa and Jolaibari Block of South Tripura district were included in the study. Urinary iodine excretion was done using simple micro plate method. Salt iodine was estimated using iodometric titration. All the tests were performed at CNRT Lab, ICMR, India.Results: Total number of subjects included in this study was 1071. Total number of urine samples collected from pregnant and non-pregnant women was 538 and 533 respectively. Median value of UIE in pregnant and non-pregnant women of Tripura was 155.0µg/L and 130.0µg/L. In pregnant women percentage prevalence of severe (<20µg/L), moderate (20-49µg/L) and mild iodine deficiency (50-149µg/L) was found in 4.1%, 15.1% and 29.6% subjects. In case of non-pregnant women severe (<20µg/L), moderate (20-49µg/L) and mild iodine deficiency (50-99µg/L) was found in 0.6%, 9.6%, 27.8% subjects respectively. The overall prevalence of iodine deficiency was found in 48.8% pregnant women, compared to 38.0% non-pregnant subjects.Conclusions: Efforts towards universal salt iodization need to be stepped-up in Sub-Himalayan region (NE part of India) and pregnant and lactating mothers may be targeted with alternate iodine supplements (Colloidal Iodine)

Improving Performance of Classifiers using Rotational Feature Selection Scheme

The crucial points in machine learning research are that how to develop new classification methods with strong mathematic background and/or to improve the performance of existing methods. Over the past few decades, researches have been working on these issues. Here, we emphasis the second point by improving the performance of well-known supervised classifiers like Naive Bayesian, Decision Tree and k-Nearest Neighbor. For this purpose, recently developed rotational feature selection scheme is used before performing the classification task. It splits the training data set into different number of rotational non-overlapping subsets. Subsequently, principal component analysis is used for each subset and all the principal components are retained to create an informative set that preserve the diversity of the original training data. Thereafter, such informative set is used to train and test the classifiers. Finally, posterior probability is computed to get the classification results. The effectiveness of the rotational feature selection integrated classifiers is demonstrated quantitatively by comparing with aforementioned classifiers for 10 real-life data sets. Finally, statistical test has been conducted to show the superiority of the results

Integrated Classifier: A Tool for Microarray Analysis

Microarray technology has been developed and applied in different biological context, especially for the purpose of monitoring the expression levels of thousands of genes simultaneously. In this regard, analysis of such data requires sophisticated computational tools. Hence, we confined ourselves to propose a tool for the analysis of microarray data. For this purpose, a feature selection scheme is integrated with the classical supervised classifiers like Support Vector Machine, K-Nearest Neighbor, Decision Tree and Naive Bayes, separately to improve the classification performance, named as Integrated Classifiers. Here feature selection scheme generates bootstrap samples that are used to create diverse and informative features using Principal Component Analysis. Thereafter, such features are multiplied with the original data in order create training and testing data for the classifiers. Final classification results are obtained on test data by computing posterior probability. The performance of the proposed integrated classifiers with respect to their conventional classifiers is demonstrated on 12 microarray datasets. The results show that the integrated classifiers boost the performance up to 25.90% for a dataset, while the average performance gain is 9.74%, over the conventional classifiers. The superiority of the results has also been established through statistical significance test