The kinetics of combined hot air-infrared thin layer drying of paddy was studied. The mechanical quality aspects of paddy kernels dried at different drying conditions were evaluated in terms of percentage of cracked kernels and also required failure force obtained from bending tests. The well-known Artificial Neural Network (ANN) modeling technique was applied to predict the drying time, variations in paddy moisture content, the percentages of cracked kernels and the values of required failure force of paddy, at different drying conditions. The best ANN topologies, transfer functions and training algorithms weredetermined for prediction of the mentioned parameters. In addition to the product quality aspects, the Specific Energy Consumption (SEC) was estimated for all drying conditions. The results indicated that application of a low intensity IR radiation (2000 W/m 2 ) together with lower values of inlet air temperature (30 °C) and moderate values of inlet air velocity (0.15 m/s) can effectively improve the final quality of paddy (as a heat-sensitive product) with a reasonable SEC.
Measuring the relative efficiency is one of the most important issues among hospitals in today's economy. These days, we hear that cost reduction is a necessity for survival of business owners and one primary to reduce the expenditures is to increase relative efficiency. The proposed study of this paper first uses output oriented data envelopment analysis (DEA) to measure the relative efficiencies of nine hospitals. The proposed model uses four types of employee namely specialists, physicians, technicians and other staffs as input parameters. The model also uses the number of surgeries, hospitalized and radiography as the outputs of the proposed model. Since the implementation of DEA leads us to have more than one single efficient unit, we implement supper efficiency technique to measure the relative efficiency of efficient units.
Traffic collisions are one of the most important challenges threatening the general health of the world. Iran’s crash statistics demonstrate that approximately 16,500 people lose their lives every year due to road collisions. According to the traffic police of Iran, heavy vehicles (including trailers, trucks, and panel trucks) contributed to 20.5% of the fatal road traffic collisions in the year 2013. This highlights the need for devoting special attention to heavy vehicle drivers to further explore their driving characteristics. In this research, the effect of heavy vehicle drivers’ behavior on at-fault collisions over three years has been investigated with an innovative approach of structural equation modeling (SEM) and Bayesian Network (BN). The database utilized in this research was collected using a questionnaire. For this purpose, 474 heavy vehicle drivers have been questioned in the Parviz Khan Border Market, located on the border of Iran and Iraq. The response rate of the survey was 80%. The participants answered the questions on Driver Behavior Questionnaire (DBQ) and a sleep assessing questionnaire named Global Dissatisfaction with Sleep (GSD). In this research, human factors affecting at-fault collisions of heavy vehicles were identified and their relationships with other variables were determined using the SEM approach. Then the descriptive model constructed by the SEM method was used as the basis of the BN, and the conditional probabilities of each node in the BN were calculated by the database collected by the field survey. SEM indicates that other attributes including GSD, mobile usage, daily fatigue, exposure, and education level have an indirect relation with heavy vehicle drivers’ at-fault collisions. According to the BN, if there is no information about the characteristics of a heavy vehicle driver, the driver will likely have at least one collision during the next three years with the probability of 0.17. Also, it was indicated that the minimum probability of the at-fault collision occurrence for a heavy vehicle is 0.08.
This article investigates the basis for pressure sensor application based on the magnetic shape memory effect in membranes. Von Karmans nonlinear terms are considered in strain–displacement relationships of thin films, and a new method is presented for solution of large deflections of thin films with arbitrary boundary condition. In this study, the equations of motion of magnetic shape memory alloys are extended. In pressurized membranes, the complex distribution of mechanical stress can cause the martensitic reorientation, which is the underlying mechanism for sensing applications in magnetic shape memory alloys. To examine the obtained model, the governing equations of magnetic shape memory alloys are solved for clamped thin films and results are compared with experimental data. Demonstrating good agreement with experimental data, the presented model can be used for analysis of magnetic shape memory alloy–based smart structures. In this article, the deflection profile of the uniformly loaded thin films is determined with different boundary conditions. Furthermore, the center deflection of magnetic shape memory alloy membrane under different magnetic bias field is simulated. The results of simulation can be used for designing a membrane-based pressure sensor using magnetic shape memory alloys.
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