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Total Factor Productivity (TFP) is a measure for assessing the productivity of a firm or organization. The level of productivity score of a firm shows that how the firm succeeds in optimizing the usage of sources and producing more outputs by certain level of inputs. Nowadays the productivity growth has a crucially important role in economics and firms competition. The industrial countries increase their outcomes not necessarily through more inputs but, by making growth in productivity. Some of the duties of executive organizations of Iran which is explained in the 4th program of development is to determine the productivity growth rate of the related products, organizing the programs, making some solutions for increasing the productivity such as that of GDP growth to be at least 31.3% and average growth of labor, capital and TFP to be at least 3.5%, 1%, 2.5% respectively. For achieving these targets, firstly, the productivity score should be calculated for every organization during previous periods of their activities. Then the effective factors of productivity growth should be determined and forecasted for the next period, to increase the productivity at least by 2.5%. In this paper we present a method based on productivity growth indexes and generalize inverse DEA. Using the proposed method, the productivity score of previous periods are calculated. Then the value of input and output changes for the next period is determined. This method is applied for a case study at National Iranian Oil Company

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The CD80 protein, a member of the super-family of immunoglobulin, is a transmembrane protein expressed on the surface of the antigen-presenting cells (APC). This protein has two receptors on the surface of T cells (CTLA-4 and CD28), due to the binding of this protein to these receptors, the inhibitory and stimulatory pathway in the T cells begin, respectively. Naturally, CD80 proteins tend to have more binding affinity to CTLA-4 than CD28, and this is a factor in the extinction of T cells in the immune system in order to prevent autoimmunity. The aim of the present study is to create a variant of the CD80 protein that has an increased binding affinity to CD28 to bind to this receptor more strongly and induce more simulate pathways than the wild type of this protein (primary CD80 protein) in T cells. To identify this variant, first, the ancestral sequence was mutated by R software at positions 31 and 92 with amino acids that play an important role in the formation of hydrogen bonds. The R software output sequences were modeled with the SWISS-MODEL server. Then, each output model was docked with the HADDOCK server, and finally, the electrostatic and van der Waals energies between the receptors and the ligands were calculated. Among all the built-in models, the mutated K31Y, R92F has the best electrostatic and van der Waals energies and has the ability to have a much better connection to its CD28 receptor compared to the ancestral type of CD80.

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Total Factor Productivity (TFP) index is the rate of transformation of total input into total output and Total Factor Productivity Growth (TFPG) is used to analyze productivity performance in multiple time periodso, Also, TFPG index is one of the major sources of economic development, thus understanding the factors affecting productivity is very important. The main purpose of productivity measurement during multiple periods is to identify areas of improvement in activities as well as to aid strategic decision making or continuous improvement. Therefore, information about effective factors on productivity changes is useful. This paper presents a general view to the productivity growth indexes and their qualities. Then, a new method for evaluating the productivity growth is proposed. This method will be constructing by the combination of DEA and Tornqvist productivity index to measure TFPG and its decompositions. This paper also evaluates productivity growth and finds out the factors affecting productivity which are critical for the National Iranian Oil Company (NIOC) on the years 1977-2000. The obtained results showed that growth in efficiency is dominant source of change in productivity at NIOC.

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