A fault-tolerant cascaded switched-capacitor multilevel inverter for domestic applications in smart grids

Cascaded multilevel inverters (MLIs) generate an output voltage using series-connected power modules that employ standard configurations of low-voltage components. Each module may employ one or more switched capacitors to double or quadruple its input voltage. The higher number of switched capacitors and semiconductor switches in MLIs compared to conventional two-level inverters has led to concerns about overall system reliability. A fault-tolerant design can mitigate this reliability issue. If one part of the system fails, the MLI can continue its planned operation at a reduced level rather than the entire system failing, which makes the fault tolerance of the MLI particularly important. In this paper, a novel fault location technique is presented that leads to a significant reduction in fault location detection time based on the reliability priority of the components of the proposed fault-tolerant switched capacitor cascaded MLI (CSCMLI). The main contribution of this paper is to reduce the number of MLI switches under fault conditions while operating at lower levels. The fault-tolerant inverter requires fewer switches at higher reliability, and the comparison with similar MLIs shows a faster dynamic response of fault detection and reduced fault location detection time. The experimental results confirm the effectiveness of the presented methods applied in the CSCMLI. Also, all experimental data including processor code, schematic, PCB, and video of CSCMLI operation are attached. © 2013 IEEE

Agricultural Risk Management Using Fuzzy TOPSIS Analytical Hierarchy Process (AHP) and Failure Mode and Effects Analysis (FMEA)

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Agricultural Risk Management Using Fuzzy TOPSIS Analytical Hierarchy Process (AHP) and Failure Mode and Effects Analysis (FMEA)

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Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis

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The Effect of Fluphenazine and Thioridazine on Toxoplasma gondii In Vivo

Background: Toxoplasma gondii is the most common parasite causing latent cerebral infections in human. It has been shown that some anti-psychotic drugs are able to inhibit the proliferation of the parasite in in vitro study. There is very limited data regarding the inhibitory effect of anti-psychotics on Toxoplasma in in vivo. In this study, we evaluated anti-Toxoplasma activity of fluphenazine and thioridazine drugs on T. gondii in mice. Methods: Mice were divided into six groups: Control, sesame as vehicle, thioridazine 10 mg/kg, thioridazine 20 mg/kg, fluphenazine 0.06 mg/kg and fluphenazine 0.6 mg/kg. They were inoculated intraperitoneally with brain suspension containing tissue cysts of T. gondii Tehran strain. Two months after inoculation, the number of cysts in crushed smears of mice brain were counted microscopically and considered as an indicator of anti-Toxoplasma activity. This work has conducted in Qazvin, central Iran, 2014. Results: Our study showed that fluphenazine and thioridazine could not significantly inhibit the brain cystogenesis of T. gondii in mice. However, the number of brain cysts was less at higher dose compared to lower doses for both drugs. Conclusion: Further studies need to clear the mechanism of different structure of anti-psychotic drugs on activity of Toxoplasma

Assessing the Influence of Soil Quality on Rainfed Wheat Yield

Soil quality assessment based on crop yields and identification of key indicators of it can be used for better management of agricultural production. In the current research, the weighted additive soil quality index (SQIw), factor analysis (FA), and multiple linear regression (MLR) are used to assess the soil quality of rainfed winter wheat fields with two soil orders on 53.20 km2 of agricultural land in western Iran. A total of 18 soil quality indicators were determined for 100 soil samples (0–20 cm depth) from two soil orders (Inceptisols and Entisols). The soil properties measured were: pH, soil texture, organic carbon (OC), cation exchange capacity (CEC), electrical conductivity (EC), soil microbial respiration (SMR), carbonate calcium equivalent (CCE), soil porosity (SP), bulk density (BD), exchangeable sodium percentage (ESP), mean weight diameter (MWD), available potassium (AK), total nitrogen (TN), available phosphorus (AP), available Fe (AFe), available Zn (AZn), available Mn (AMn), and available Cu (ACu). Wheat grain yield for all of the 100 sampling sites was also gathered. The SQIw was calculated using two weighting methods (FA and MLR) and maps were created using a digital soil mapping framework. The soil indicators determined for the minimum data set (MDS) were AK, clay, CEC, AP, SMR, and sand. The correlation between the MLR weighting technique (SQIw-M) and the rainfed wheat yield (r = 0.62) was slightly larger than that the correlation of yield with the FA weighted technique (SQIw-F) (r = 0.58). Results showed that the means of both SQIw-M and SQIw-F and rainfed wheat yield for Inceptisols were higher than for Entisols, although these differences were not statistically significant. Both SQIw-M and SQIw-F showed that areas with Entisols had lower proportions of good soil quality grades (Grades I and II), and higher proportions of poor soil quality grades (Grades IV and V) compared to Inceptisols. Based on these results, soil type must be considered for soil quality assessment in future studies to maintain and enhance soil quality and sustainable production. The overall soil quality of the study region was of poor and moderate grades. To improve soil quality, it is therefore recommended that effective practices such as the implementation of scientifically integrated nutrient management involving the combined use of organic and inorganic fertilizers in rainfed wheat fields should be promoted.publishedVersio

Early Detection of the Advanced Persistent Threat Attack Using Performance Analysis of Deep Learning

One of the most common and critical destructive attacks on the victim system is the advanced persistent threat (APT)-attack. An APT attacker can achieve its hostile goal through obtaining information and gaining financial benefits from the infrastructure of a network. One of the solutions to detect a unanimous APT attack is using network traffic. Due to the nature of the APT attack in terms of being on the network for a long time and the fact that the system may crash due to the high traffic, it is difficult to detect this type of attack. Hence, in this study, machine learning methods of C5.0 decision tree, Bayesian network, and deep learning are used for the timely detection and classification of APT-attacks on the NSL-KDD dataset. Moreover, a 10-fold cross-validation method is used to experiment with these models. As a result, the accuracy (ACC) of the C5.0 decision tree, Bayesian network, and 6-layer deep learning models is obtained as 95.64%, 88.37%, and 98.85%, respectively. Also, in terms of the critical criterion of the false positive rate (FPR), the FPR value for the C5.0 decision tree, Bayesian network, and 6-layer deep learning models is obtained as 2.56, 10.47, and 1.13, respectively. Other criterions such as sensitivity, specificity, accuracy, false-negative rate, and F-measure are also investigated for the models, and the experimental results show that the deep learning model with automatic multi-layered extraction of features has the best performance for timely detection of an APT-attack comparing to other classification models.publishedVersio