MPSIAC is currently known as an appropriate method to measure sediment of Watershed basins of the country while there has not been any SENSITIVITY ANALYSIS so far for this method. In this study, required data for MPSIAC model were gathered from six basins; Amame-Kamarkhani, Kand-Golandok, Tang Kenesht (from two different references), Nojian (from three different references), Pegahe sorkh katvand (from one reference) and Ivanaki (authors’ research). Eleven SENSITIVITY analyses were conducted and the amount of sediment was calculated using the sum of nine factors. Each input parameter was increased or decreased by 20% using a computer program in Visual Basic in Excel. Then SENSITIVITY of the model for the parameters was analyzed. The less sediment has the basin, the less SENSITIVITY has the input parameters in the model. MPSIAC model has twenty input variables that resulted in nine main factors. Erosion parameter (R) was calculated by adding nine main factors and the quantity of sediment was calculated by an exponential function. By evaluation of nine main factors, it was concluded that land use and Gully development were the most sensitive factors. As a result, based on the area and SENSITIVITY of main factors, more investments must be done on the most sensitive factor to reduce soil erosion. In the assessment of nine main factors occurred errors are reduced due to adding operation.Regarding all items, each factor that has more input quantity has the highest SENSITIVITY. As a result, if score of each factor grows more than six, more attention must be paid to score assignation. If occurred errors in assessment of nine factors did not neutralize each other and have additive or decreasing on the R, by addition of 20% to R, it showed that this factor was the most sensitive factor to run the model. If R is equal to 60 and 20% related to the error occurred in calculation of it, 54% error occurs in estimating the amount of sediment and SENSITIVITY reaches to 2.7. This evaluation indicated that MPSIAC model for evaluation of basins with the amount of sediment more than 2.2 ton/hectare must be used more preciously because the model is so sensitive in this status and possible error may get over 50%.

In this paper additive perturbations of all data in the Charnes-Cooper-Rhodes (CCR) model preserving efficiency of an efficient Decision Making Unit (DMU) are considered first. Sufficient conditions for an efficient DMU to preserve its efficiency after additive perturbations of all data are given. These result are applied to 10 bank branches of a commercial bank of iran using 3 inputs and producing 2 outputs are considered. For each DMU, efficient according to the additive model, a region of efficiency is obtained. In that region inputs and outputs of the corresponding efficient DMU can be changed and its efficiency is preserved.

SIRMOD model is more suitable tools for evaluation of irrigation systems that must be tested and evaluated before use in practice. SENSITIVITY of SIRMOD to input parameters in three solution methods of Saint- Venant equation was evaluated with ±25% of input parameters variation. SENSITIVITY ANALYSIS results was shown, model is more sensitive to input parameters in advance phase and runoff volume than other parameters (Slope, roughness coefficient and furrow geometry). Model in infiltrated water volume is more sensitive to infiltration equation coefficient and inflow respectively, than other input parameters. SIRMOD isn't sensitive to solution methods of Saint-Venant equation. SENSITIVITY of model is variable with soil type. The percent of input parameters variation isn't effective in SENSITIVITY order. Therefore, the parameters must be measured with maximum accuracy consist on inflow and soil infiltration coefficients.

In this paper, we introduce systems consisting of several production units, each of which include several subunits working in parallel. Meanwhile, each subunit is working independently. The input and output of each production unit are the sums of the inputs and outputs of its subunits, respectively. We consider each of these subunits as an independent decision making unit (DMU) and create the production possibility set (PPS) produced by these DMUs, in which the frontier points are considered as efficient DMUs. Then we introduce models for obtaining the efficiency of the production subunits. Using super-efficiency models, we categorize all efficient subunits into different efficiency classes. Then we follow by presenting the SENSITIVITY ANALYSIS and stability problem for efficient subunits, including extreme efficient and non-extreme efficient subunits, assuming simultaneous perturbations in all inputs and outputs of subunits such that the efficiency of the subunit under evaluation declines while the efficiencies of other subunits improve.

METRIC (Mapping Evapotranspiration at High Resolution with Internalized Calibration) is known as an appropriate surface energy balance model for the estimation of the spatial distribution of evapotranspiration (ET) in semi-arid regions. Based on lysimeter measurements, METRIC has shown ET estimates of 10% on a sub-field scale on a daily basis. There is a need to identify how the model is sensitive to the input parameters. Therefore, the most influential parameters in the algorithm can be identified and the model can be further improved. SENSITIVITY ANALYSIS at three levels of vegetation cover shows that METRIC is highly sensitive to dT, surface temperature, net radiation, sensible heat flux, surface albedo, soil heat flux, and air temperature. It is also moderately sensitive to friction velocity, aerodynamic resistance to heat transfer, surface emissivity and less sensitive to leaf area index, soil adjusted vegetation index, wind speed (except wind speed at low level of vegetation cover), and roughness length for momentum (except Zom<0.1). A two-factor ANALYSIS of the algorithm’s primary inputs showed that the pair albedo-surface temperature is the most and the normalized vegetation index-soil adjusted vegetation index or normalized vegetation index-leaf area index is the least effective pair in this model. In order to improve the accuracy of METRIC, this study suggests upgrading the equations for the above-mentioned effective variables.

Importance measures are well-known and important tools which are widely used in risk-informed decision making. Their outstanding traditional definitions have made them useful in many applications related to risk and reliability aspects of different systems. These perfect traditional definitions help researchers to find the most important components in a system, and consequently, to detect and obviate weaknesses in system structure and operations. Generally, these measures are based on fault tree technique. Although fault tree is a powerful tool to study risk, reliability, and structural characteristics of systems, Bayesian networks have indicated explicit advantages over it in modeling and ANALYSIS abilities. Classical fault tree is not suitable in ANALYSIS of large systems that include aspects such as: common cause failure, redundant failure, uncertainty, and some kind of complex dependencies such as sequentially dependent failures, while these aspects are not negligible in large modern systems anymore. So, the perfect definitions of importance measures are restricted to limitations of fault tree. Bayesian networks, on the other hand, have become a widely used method in different kinds of statistical problems, including fault diagnosis, reliability and safety assessment, and updating safety systems failure probabilities. In addition, Bayesian networks due to their modeling and analytical abilities, are capable of accommodating the mentioned aspects easily and straightforwardly. In this paper, we extend the traditional definitions of importance measures to Bayesian networks resulting in more capable importance measures in terms of modeling and ANALYSIS. The importance measures that are extended to Bayesian networks in this research are the most important and widely used ones that some of them are used in famous methods named probabilistic safety assessment. The extended importance measures are: Risk achievement worth, Risk reduction worth, Fussell-Vesely importance measure, Birnbaum importance measure, and Differential importance measure. The results of implementing the new achievements on a real-world case study prove the desired effectiveness.

Design SENSITIVITY ANALYSIS is a necessary task for optimization of structures. Methods of SENSITIVITY ANALYSIS for linear systems have been developed and well documented in the literature; however there are a few such research works for nonlinear systems. Nonlinear SENSITIVITY ANALYSIS of structures under seismic loading is very complicated. This paper presents an analytical SENSITIVITY technique for Reinforcement Concrete Moment Resisting Frames (RCMRF) that accounts for both material and geometric nonlinearity under pushover ANALYSIS. The results of proposed method are compared with the results of finite difference method. A three-story, two bays moment frame example is used to illustrate the efficiency of the method. This technique can be very useful and efficient for optimal performance-based design of RC buildings.