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Background: One of the main problems of dedicated IORT accelerators is to determine dosimetric characteristics of the electron beams. Monte Carlo simulation of IORT accelerator head and produced beam will be useful to improve the accuracy of beam dosimetry. Materials and Methods: Liac accelerator head was modeled using the BEAMnrcMonte Carlo simulation system. Phase-space files were generated at the bottom of the applicators. These phase-space files were used as an input source in DOSXYZnrc and BEAMDP codes for dose calculation and analysis of the characteristic of the electron beams in all applicators and energies. Results: The results of Monte Carlo calculations are in very close agreement with the measurements. There is a decrease in the peak of the initial spectrum when electrons come from the end of accelerator wave guide to the end of applicator. By decreasing the applicator diameter, the mean energy of electron beam decreased. Using applicators and increasing their size, X-ray contamination will increase. The percentage of X-ray contamination increases by applicator diameter. This is related to the increase of the mean energy of electron beams. Conclusion: Application of PMMA collimator leads to, although well below accepted level, the production of bremsstrahlung. The results of this study showed that special design of LIAC head accompanying by PMMA collimator system cause to produce an electron beam with an individual dosimetric characteristic making it a useful tool for intraoperative radiotherapy purposes.

Background: To eradicate a problem such as drug abuse, we need thorough knowledge of the problem and its epidemiological aspects. In response to increasing ecstasy abuse among youth, as noted by the increase in related health issues at emergency clinics, we performed this epidemiologic study on ecstasy use in Tehran.Methods: In this cross-sectional study, including 1,903 youth aged 15-25 years, at different coffee shops in Tehran, Iran, subjects filled out questionnaires to evaluate the prevalence of ecstasy use with regard to gender, age, family income, level of education, psychological state (Beck test), acute complications of ecstasy use, as well as manner and place of ecstasy use and reason for repeated ecstasy use. Information was gathered from September 2004 to January 2005, using simple nonprobable sampling.Results: The prevalence of ecstasy abuse among our study population was 18.5%, which meaningfully correlated with gender (male), education level (undergraduate degree), family income (high), drug abuse and Beck test score (high). The mean age of ecstasy abusers was 21.3 years (SD: 2.65), which wasn't statistically different than the study population as a whole and the portion that did not use ecstasy. Tablet was the most common form of ecstasy usage (97.1%). In this study population, 91.4% had heard of the ecstasy name, and 83.7% were familiar with its use. The most common site of usage was in parties (85.7%) and in group forms (84%). Of all the users, 30% stated the reason for repeat ecstasy use was their friends' insistence, 38.8% expressed emotional need, 37.7% had no reason, whereas 6.6% felt a physical need and 1.7% repeatedly used ecstasy to prevent withdrawal symptomsConclusion: Some studies have reported that drug abusing friends are the basic cause of drug abuse. In this study, the ecstasy users admitted that the influence of friends and emotional need were the causes of their repeat use. Remarkably, these subjects report withdrawal symptoms with ecstasy use. Most importantly, families have to consider the relationships and emotional needs of their children. The statistical difference between Beck test scores between ecstasy users and the non-users placed them in two clinically different psychological groups. The ecstasy users were in groups that definitely needed psychological consultation and non-users were in the slightly depressed group. These psychological aspect of drug abuse expressed here is an alarm.

Neuropeptide orexin mainly exists in neurons within and around the lateral hypothalamus and exhibits high affinity to orexin 1 and 2 receptors (OX1R and OX2R, respectively). Orexinergic neurons send their axons to the nucleus accumbens (NAc), which expresses OX1Rs. Previous studies have shown the involvement of orexins and their role in learning and memory processes in the dorsal raphe nucleus and hippocampus. However, no study has examined the effects of orexins in the NAc on memory. The present study examined the effect of the post-training and pre-probe trial intra-NAc administration of SB-33486-A (OX1R antagonist, 12 μg/0.5μl) and TCS-OX2-29 (OX2R antagonist, 10 μg/0.5 μl) on the consolidation and retrieval of memory in the Morris Water Maze (MWM) task. In experiment 1, rats were trained in the MWM and, immediately after every training, received bilateral injections of dimethyl sulfoxide (DMSO) (control group), SB-334867-A (SB), and TCS-OX2-29 (TCS) into the NAc. Experiment 2 was similar to experiment 1, except that the rats received DMSO, SB, and TCS 15 min before the probe test. Probe and visible tests were performed after the last training, and the distance moved, escape latency, and velocity were recorded. In experiment 3, rats trained in experiments 1 and 2, immediately after the probe test, were given the trials for visuomotor coordination assessment on the visible platform. According to the results, the injection of SB increased the distance moved and escape latency in the treated groups, compared to the control group, in the consolidation phase of spatial memory (P<0.05) but not in its retrieval phase (P>0.05). However, TCSOX2-29 had no effect. These results suggest that the inactivation of the NAc OX1Rs, but not OX2Rs, impairs the consolidation but not the retrieval of spatial memory in rats. [ABSTRACT FROM AUTHOR]

Medical data mining is considered as a new solution to the analysis of medical data and further expansion of the relative knowledge. Medical data mining has a high potential to discover the hidden patterns. Recently, some studies have been conducted on the relationship between the individuals' environmental surroundings and their exposure to diseases, thus clearly confirming the considerable impact of the quality of environmental indicators such as environmental pollutants on diseases. In this respect, the current study investigated the effect of the environmental conditions on the chance of diabetes based on medical data mining technique. Given that diabetes is a global threat to the human health, an Ensemble Classifier based on Genetic Algorithm (ECGA) method was designed to study the environmental conditions in diabetes. Decision tree, random forest, K-nearest neighbor, and naive were included in the designed ensemble classifier. It was found that ECGA was more accurate than the base classifier algorithms. For study purposes, three datasets were collected from different regions in Iran with different climatic conditions and it was found that environmental conditions had a significant impact on diabetes. [ABSTRACT FROM AUTHOR]

In electricity generation through photovoltaic cells, efficient inverters are required to inject the generated power into the grid. Among the inverters connected to the grid, current source inverters despite their advantages are used less than voltage source ones. Different circuits are presented for these converters. In this paper, several power circuit topologies of the current source inverters, which are an interface between solar panels and the grid, are reviewed. Also, the inverters are compared from the point of some indexes like efficiency, voltage transmission ratio, total harmonic distortion, leakage currents, and their reduction methods. The importance of these indexes is investigated too. Categorization is for full-bridge inverters and special structures groups. The first group includes the conventional inverter, 4-leg inverter, CH7 CSI, H7 CSI, three-mode, and other structures. The second group consists of inverters with special structures and is independent of the conventional CSI. The summary of the studies is presented in a table. [ABSTRACT FROM AUTHOR]

Magnetorheological elastomers (MREs) consist of micron-sized magnetizable particles embedded in a rubber matrix. Properties of these magneto-sensitive materials are changed reversibly upon application of external magnetic fields. They exhibit highly non-linear magneto-mechanical response which allows developing new devices and applications. However, the coupled magneto-mechanical behavior makes mathematical modeling of MREs quite complicated. So development of a reliable constitutive framework is essential for further understanding of this coupled behavior as well as simulation of the systems that utilize MREs. In this paper, a finite strain continuum model is developed for MREs where the effect of magnetization on material stiffness is directly introduced in the material shear modulus. It is shown that this approach simplifies the constitutive models and also perceives the magnetic saturation of MREs. Moreover, the coupled effects of magnetization, deformation and particle-chains orientation on the mechanical response are also taken into account in the introduced parameter. This reduces the number of material parameters, the required experimental tests for parameters identification and also simplifies the mathematical formulation of the developed constitutive equations which is beneficial for numerical formulations. A systematic two-step method is then introduced for material parameters identification which assures uniqueness of the parameters set. The predictive capabilities of the proposed model are examined via available mechanical and magneto-mechanical experimental data on both isotropic and anisotropic MRE samples at different configurations of magnetic field and loading with respect to the preferred direction of the samples. It is shown that the model can well predict the magneto-mechanical response of MREs at different deformation modes and magnetic fields. [ABSTRACT FROM AUTHOR]

An intelligent integrated approach of Jaya optimization algorithm and neuro-fuzzy network is introduced to model the natural convection in an open round cavity against the decision parameters. The decision parameters are the Rayleigh number (Ra) in range of 105 to 2 × 105 and ratio of the nonconductor barrier distance from the bottom of the cavity to the cavity diameter (H/D) in range of 0 to ∞. After that, the obtained hybrid model is applied to forecast the average Nusselt number in the cavity. In the next step, the experimentally obtained data by using a Mach–Zehnder interferometer is utilized to train the hybrid model. The accuracy of the hybrid model is evaluated through the calculation of average testing and checking errors. According to the obtained results, there is an optimum ratio (H/D = 0.5), in which the heat transfer is maximum. Moreover, it was found that the best hybrid model forecasts the natural convection in the cavity with the mean absolute percentage error of less than 0.75% and 1.40% for the testing and checking data, respectively. [ABSTRACT FROM AUTHOR]

The impedance network makes it possible to increase and decrease the voltage, which is not available in normal inverters (voltage and current sources).This paper presents a modified topology and modulation technique for a three-phase Modified Z-Source Neutral-Point Clamped (MZS-NPC) inverter. A modulation scheme for the proposed topology is designed based on maximum gain control method to achieve the maximum voltage gain by simple implementation and balancing the neutral point voltage of the dc link. In order to supply the desired voltage to the critical load in an islanded micro-grid, a closed-loop ac voltage controller is realized in fuel cell or photovoltaic applications based on the proposed inverter. The ability to reinforce and validity of topology operations and modulation techniques has been demonstrated by simulation. It should be noted that the simulations are implemented in MATLAB / Simulink software.