In recent years, much more attentions have been focused on siRNA-based gene therapy. This approach is based on PTGS (Post-transcriptional gene silencing). Due to some defects in viral vectors, non-viral vectors have been used to deliver nucleic acids into target cells. Although, transfection efficiency in non-viral vectors is less than viral ones, but safety of non-viral vectors is much more. Characteristic features of siRNA, such as high compatibility, application in low doses and its versatility make it suitable in the gene therapy field. However, some challenges, such as stimulating immune system and Off-target silencing will be remain. In this review article, we express bottlenecks existing in siRNA delivery into target cells. According to the information, with further development of siRNA delivery in the future, it could be a promising approach in treatment for a variety of genetic diseases.

In the current study, a production and distribution network (PDN) is formulated to deliver the products to both inland and outland customers in the least amount of time and optimize the total profit of the network, simultaneously. The proposed network is a multistage PDN with multi suppliers, multi producers, multi entrepots, multi retailers, and multi inland and outland customers with multitime period horizon with allowable shortage. A mixed integerprogramming model is designed to minimize total cost of the system and minimize total delivery lead time. We applied a novel heuristic method called selective firefly algorithm (SFA) in order to solve several sized, especially real-world, instances. Finally, the performance of the proposed algorithm is examined by solving several sized instances. The results indicated that the proposed algorithm is of higher performance.

The Probabilistic analysis could effectively apply the effects of uncertainty in the structural analysis, where fragility curves are a well-established technique for the Probabilistic evaluation of the structural performance. Notably, incremental dynamic analysis (IDA) is one of the most common analytical methods for obtaining fragility curves. In this study, the statistical and Probabilistic seismic performance of 3- and 9-story steel buildings are investigated under 22 pairs of far-fault records introduced in FEMA P695. The seismic performance of both uncontrolled and controlled buildings with LRB is studied using IDA. Then, a general mathematical equation corresponding to each structure will be determined for all damage states known as the Probabilistic seismic demand model (PSDM) of the structures. Using this equation, the collapse fragility curve of the structures will be determined for both uncontrolled and controlled structures with LRB. To evaluate the possible impact of different levels of seismic intensities on the performance of the isolated structure, the collapse fragility curves for three different levels of intensities of the benchmark records are presented. According to the collapse fragility curves, in addition to the effect of different levels of seismic intensity on the seismic performance of the structure, it is possible to see the positive effect of the LRB in reducing the probability of collapse. Also, the collapse margin ratio (CMR) in the 3- and 9-story buildings has increased by 100% and 81%, respectively, which indicates the better performance of the LRB isolators in low-rise structures.

Background: As any delay in the onset of milk production and ejection in the first few days after delivery can cause breast-feeding failure, this study has been done to determine factors that may affect the onset time of lactation in vaginal and cesarean section deliveries with and without labor pain (elective).Methods: 300 parturient mothers selected from two private and university hospitals were divided in three separate groups (each consisted of 100 cases) according to their delivery type [vaginal and cesarean section with and without pain (elective)], interfering factors like mothers' or newborns' illnesses were omitted, the needed information about filling of breasts and milk ejection was given to mothers before parturition, then a questionnaire was provided and offered to them, data collected and statistical analysis carried out.Results: There was a meaningful difference between average time of milk-ejection in groups with different types of delivery (p=0.001), but no difference was found between elective cesarean section and cesarean section with labor pain (p=0.741). We found that milk-ejection time has a strong correlation with type of delivery (r= 0.510) and also the time of taking the neonate back to mother (r = 0.256), and this correlation is statistically meaningful (p=0.000).Conclusion: Any kind of stress including cesarean section may postpone milk ejection by hormonal inhibition for a few days after delivery and this will result in newborn weight loss and failure of nursing by the mother. So, to support breast feeding in our country, the rate of cesarean section must be wisely diminished, vaginal delivery encouraged, and after delivery every newborn taken back to his or her mother as soon as possible for nursing.

This paper presents an analytical model based on mean value analysis (MVA) technique for a Probabilistic re-entrant line. The objective is to develop a solution method to determine the total cycle time of a Reflow Screening (RS) operation in a semiconductor assembly plant. The uniqueness of this operation is that it has to be borrowed from another department in order to perform the production screening task. Since the operation is being shared, there is a time limit to utilize it in a day. Screening of lots that cannot be completed within the given time has to be continued in the following days. The contributions of this paper is the development of a lot clustering method and factoring the limited time sharing condition and thus develop an analytical model. Comparison results were made using available real historical data. The proposed model provided operation managers with the total cycle time computation method and determining the appropriate cluster size to be loaded into the operation.

In this paper, a simultaneous lead time quotation and lot sizing problem in an assembly system is investigated. We address a production system with a product that has deterministic demand over a T-period planning horizon and is produced in lots because of the economy of scale. If a lot is completed before the demand period, inventory carrying cost is incurred. On shortages, a lead time is quoted to customers and a lead time quotation cost is incurred. Finally, if the order is delivered later than its due date period, a tardiness cost is charged. The components supply lead time is stochastic, which follows a discrete distribution. The problem is to decide on the lot size of products and components, supply and production starting periods and the due date of lots (to be quoted to customers) so that relevant costs are minimized. The objective function is the sum of the production, inventory carrying, lead time quotation and tardiness costs. We develop a genetic algorithm to solve the proposed model. An experimental framework is set up to test the efficiency of the proposed method, which turns out to rate high, both in terms of cost effectiveness and execution speed.