Enhancing Solubility and Dissolution of Celecoxib by Nanocrystals

Introduction: Celecoxib is a weakly acidic drug and has low aqueous solubility (3–7 μg/ml). Low solubility of drugs in water results in poor bioavailability because the solubility of a drug is an important factor in determining its absorption rate. According to the Noyes–Whitney equation, the saturation solubility and dissolution rate of poorly water soluble drugs can be enhanced by reducing the particle size, which increases the total surface area. Nanocrystals possess outstanding features enabling to overcome the solubility problems of poorly soluble drugs. The objective of this study was to investigate the dissolution behavior improving effects of differently sized nanocrystals of a poorly soluble model drug, Celecoxib. Methods and Results: Nanocrystals were prepared by antisolvent precipitation followed by high pressure homogenization (HPH) technique in the presence of varying percentage of SLS as a stabilizer (0.2 or 0.4%) and rate of homogenization (26500 or 12500 rpm). The obtained nanoparticles were analyzed in terms of particle size distribution, polydispersity index, saturation solubility, thermal behavior (DSC) and dissolution behavior. The particle size of nanosuspensions was between 140 and 532 nm with poly dispersibility index less than 0.5. That minimum of particle size relate to formulation which contained 0.4% stabilizer with rate of 26500 rpm. This formulation also revealed the highest saturation solubility (18.1 µg/ml) and dissolution efficiency compared to pure Celecoxib. The DSC results indicated the absence of any interactions between drug and stabilizer. These studies showed a decrease in crystalinity of Celecoxib. Conclusions: All microcrystals significantly (P<0.05) increased Celecoxib aqueous solubility and dissolution rate compared to plain drug. This result seemed to be due the significant particle size reduction and decreased drug crystallinity. Significant influence of increasing in rate of homogenizer on size reduction was observed.  As well as, high stabilizer concentration and rate of homogenizer had Significant influence on saturated solubility of Celecoxib compared to pure drug (P<0.05). DSC study showed that there is no change in the crystal structure of Celecoxib during the process and showed that nanocrystals exhibited decreased crystallinity.          &nbsp

A New Algorithm for Optimization of Fuzzy Decision Tree in Data Mining

Decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Classical crisp decision trees (DT) are widely applied to classification tasks. Nevertheless, there are still a lot of problems especially when dealing with numerical (continuous valued) attributes. Some of those problems can be solved using fuzzy decision trees (FDT). Over the years, additional methodologies have been investigated and proposed to deal with continuous or multi-valued data, and with missing or noisy features. Recently, with the growing popularity of fuzzy representation, a few researchers independently have proposed to utilize fuzzy representation in decision trees to deal with similar situations. Fuzzy representation bridges the gap between symbolic and non symbolic data by linking qualitative linguistic terms with quantitative data. In this paper, a new method of fuzzy decision trees is presented. This method proposed a new method for handling continuous valued attributes with user defined membership. The results of crisp and fuzzy decision trees are compared at the end

Modelling Integrated Multi-item Supplier Selection with Shipping Frequencies

There are many benefits for coordination of multiple suppliers when single supplier cannot satisfy buyer demands.  In addition, buyer needs to purchase multiple items in a real supply chain. So, a model that satisfies these requests has many advantages. We extend the existing approaches in the literature that assume all suppliers need to be put on a common replenishment cycle and each supplier delivers exactly once in a cycle. More specifically, inspired by approaches that perform well for the Economic Lot Scheduling Problem, we assume an integer number of times a supplier can ship available items in an overall replenishment cycle. Because of complexity issue, a new approach based on genetic algorithm is employed to solve the presented model. Results depict that new model is more beneficial and practical

Presenting an economic lot-sizing scheduling problem considering maximum permissible carbon emissions

Carbon emissions related to energy consumptions from the manufacturing industry have become a substantial part of environmental burdens. Carbon emissions related to energy consumptions from the manufacturing industry have become a substantial part of environmental burdens. This study presents carbon emission constraint into the economic lot scheduling problem to reduce carbon emissions. The aim of this research to satisfy customer demand for various items over the planning horizon, with an objective to minimize total costs, includes setup, production, rework and holding costs. In this problem, it is assumed that the production process is defective, and during the process some of the goods are produced with undesirable quality. Defective products can be sold using a rework process. This proposed model has been proven to be a nonlinear convex programming problem. Hence, the optimal solution of this proposed model can be obtained using the derivative method. Finally, a hypothetical example is solved to demonstrate the performance of the proposed exact solution algorithm

A Fuzzy Goal Programming Model for Efficient Portfolio Selection.

This paper considers a multi-objective portfolio selection problem imposed by gaining of portfolio, divided yield and risk control in an ambiguous investment environment, in which the return and risk are characterized by probabilistic numbers. Based on the theory of possibility, a new multi-objective portfolio optimization model with gaining of portfolio, divided yield and risk control is proposed and then the proposed model is solved as a fuzzy goal programming model to fulfill aspiration level of each objective. Furthermore, numerical example of efficient portfolio selection is provided to illustrate that proposed model is versatile enough to be applicable to various unexpected conditions. This paper considers a multi-objective portfolio selection problem imposed by gaining of portfolio, divided yield and risk control in an ambiguous investment environment, in which the return and risk are characterized by probabilistic numbers. Based on the theory of possibility, a new multi-objective portfolio optimization model with gaining of portfolio, divided yield and risk control is proposed and then the proposed model is solved as a fuzzy goal programming model to fulfill aspiration level of each objective. Furthermore, numerical example of efficient portfolio selection is provided to illustrate that proposed model is versatile enough to be applicable to various unexpected conditions

Meta-heuristic Algorithms for an Integrated Production-Distribution Planning Problem in a Multi-Objective Supply Chain

In today's globalization, an effective integration of production and distribution plans into a unified framework is crucial for attaining competitive advantage. This paper addresses an integrated multi-product and multi-time period production/distribution planning problem for a two-echelon supply chain subject to the real-world variables and constraints. It is assumed that all transportations are outsourced to third-party logistics providers and all-unit quantity discounts in transportation costs are taken into consideration. The problem has been formulated as a multi-objective mixed-integer linear programming model which attempts to simultaneously minimize total delivery time and total transportation costs. Due to the complexity of the considered problem, genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are developed within the LP-metric method and desirability function framework for solving the real-sized problems in reasonable computational time. As the performance of meta-heuristic algorithms is significantly influenced by calibrating their parameters, Taguchi methodology has been used to tune the parameters of the developed algorithms. Finally, the efficiency and applicability of the proposed model and solution methodologies are demonstrated through several problems in different size

A multi-objective evolutionary approach for integrated production-distribution planning problem in a supply chain network

Integrated production-distribution planning (PDP) is one of the most important approaches in supply chain networks. We consider a supply chain network (SCN) to consist of multi suppliers, plants, distribution centers (DCs), and retailers. A bi-objective mixed integer linear programming model for integrating production-distribution designed here aim to simultaneously minimize total net costs in supply chain and transfer time of products for retailers. From different terms of evolutionary computations, this paper proposes a Pareto-based meta-heuristic algorithm called multi-objective simulated annealing (MOSA) to solve the problem. To validate the results obtained, a popular algorithm namely non-dominated sorting genetic algorithm (NSGA-II) is utilized as well. Since the solution-quality of proposed meta-heuristic algorithm severely depends on their parameters, the Taguchi method is utilized to calibrate the parameters of the proposed algorithm. Finally, in order to prove the validity of the proposed model, a numerical example is solved and conclusions are discussed

Developing a Method for Increasing Accuracy and Precision in Measurement System Analysis: A Fuzzy Approach

Measurement systems analysis (MSA) has been applied in different aspect of industrial assessments to evaluate various types of quantitative and qualitative measures. Qualification of a measurement system depends on two important features: accuracy and precision. Since the capability of each quality system is severely related to the capability of its measurement system, the weakness of the two mentioned features can reduce the reliance on the qualitative decisions. Consequently, since in the literature fuzzy MSA is not considered as an independent study, in this paper, a fuzzy method is developed for increasing method accuracy and precision by encountering the impreciseness of some measures of MSA. To do so, bias, capability, and gauge repeatability and reproducibility (GR&R) indices are considered as triangular fuzzy numbers. The application of the proposed method is illustrated through a case study taken from an automotive parts industry. All rights reserved

Evaluation of Possible Effects of Hyoscine in Xylazine-Induced Fetal Death in Pregnant Rats

Although xylazine is widely used in domestic animals as a sedative, analgesic, and muscle relaxant, its side effects on the uterus prevent its utilization in pregnant animals or in embryo transfer. Although the effects of xylazine on increasing uterine contractions have been confirmed, no reliable report of fetal death due to xylazine administration has been published. Hyoscine is an anticholinergic medication that has antimuscarinic and antispasmodic effects in the uterine tissue of pregnant cattle during in vitro studies, therefore, we investigated if administration of xylazine in the last third of pregnancy could increase fetal death and if hyoscine could prevent its adverse effects. Twenty adult female rats, after mating with four adult male rats and confirming pregnancy, were randomly divided into two equal control and treatment groups. On the 18th day of pregnancy, the number of fetuses per rat was determined using ultrasonography. Rats in the treatment group received hyoscine (1 mg/kg, intraperitoneally) for 3 days. Subsequently, all rats were administered xylazine (10 mg/kg, intraperitoneally) for 3 days. On the 21st day of pregnancy, the number of living and dead fetuses was counted after laparotomy. Also, the weight and dimensions of the fetuses were measured. The results showed that although more fetuses lost their lives in the treatment group compared to the control group, the statistical difference in the percentage of fetal mortality in the two groups was not significant (p 0.05). In addition, the comparison of the mean weight, body length, and body width of living and dead fetuses in both groups showed that there was no statistically significant difference between these groups (p 0.05). It could be concluded that maternal xylazine intake in rats could cause about 18-25% of fetal mortality. However, the use of hyoscine to prevent fetal death induced by xylazine is not recommended

A New Fuzzy Method for Assessing Six Sigma Measures

Six-Sigma has some measures which measure performance characteristics related to a process. In most of the traditional methods, exact estimation is used to assess these measures and to utilize them in practice. In this paper, to estimate some of these measures, including Defects per Million Opportunities (DPMO), Defects per Opportunity (DPO), Defects per unit (DPU) and Yield, a new algorithm based on Buckley's estimation approach is introduced. The algorithm uses a family of confidence intervals to estimate the mentioned measures. The final results of introduced algorithm for different measures are triangular shaped fuzzy numbers. Finally, since DPMO, as one of the most useful measures in Six-Sigma, should be consistent with costumer need, this paper introduces a new fuzzy method to check this consistency. The method compares estimated DPMO with fuzzy customer need. Numerical examples are given to show the performance of the method. All rights reserve