We study the problem of reconstructing binary images from four projections data in a fuzzy environment. Given the uncertainly projections, we want to find a binary image that respects as best as possible these projections. We provide an iterative algorithm based on fuzzy Integer programming and linear membership functions.

Among various products available in agriculture, saffron plays a major role in contributing to Iran's gross domestic product and per capita income growth. Due to shortage of workforce and short duration of harvesting, areas under cultivation of saffron in Iran will be declining in coming years. Thus, proper planning for optimum use of workforce is one of the most important techniques to access efficient harvesting. In this regard, an Integer programming model is proposed to solve the problem in this paper. Number of working shift and working hours in each shift are among decision variables in the proposed model, which satisfy the objective function, i. e. minimizing the total cost of workforce, with constrains including number of working hours in each shift, speed of workforce, number of fields that should be harvested in each day and relationship between working hours of each worker and the cost allocated. To evaluate the proposed model, we employ the data collected from fields located in different areas of Qaen, South Khorasan province, Iran. By comparing the output of the proposed model to the real situation, the ability of the model is confirmed. Finally, concluding remarks and suggestions for future research are provided.

Data Envelopment Analysis (DEA) cannot provide adequate discrimination among efficient decision making units (DMUs). To discriminate these efficient DMUs is an interesting research subject. The purpose of this paper is to develop the mix Integer linear model which was proposed by Foroughi (Foroughi A.A. A new mixed Integer linear model for selecting the best decision making units in data envelopment analysis. Computers & Industrial Engineering 60 (2011) 550-554) to present new alternative mix Integer programming DEA (MIP-DEA) models which can be used to improve discrimination power of DEA and select the most BCC- efficient decision making unit (DMU). We will demonstrate that proposed model is able to select DMU throughout the real data sets.

β-sheet topology prediction is a major unresolved problem in modern computational biology. It is a challenging intermediate step toward the protein tertiary structure prediction. Different methods have been provided to deal with the problem of determining the β-sheet topology. Here, ab-initio probability-based methods called "BetaProbe1" and "BetaProbe2" are utilized to specify the β-sheet topology. In these methods, the stability and the frequency of β-strand pairwise interaction and β-sheet conformation are spotted. To predict more frequent interactions between β-strand pairs, besides pairwise alignment probability, the probability of occurring β-strand pairwise interaction is considered to compute the score of the interactions. Furthermore, to determine the β-strand pairwise alignment probability more accurately, a dynamic programming approach is utilized. In addition, the Integer programming optimization is combined with the probabilities of β-strand pairwise interactions to determine the β-sheet topology. Moreover, the β-sheet conformation probability is considered to give better chances to more observed conformations for selection. Experimental results show that BetaProbe1 and BetaProbe2 significantly outperform the most recent Integer programming-based method with respect to β-sheet topology prediction.