Two dimensional representation of the Dirac equation in Non associative algebra

In this note a simple extension of the complex algebra to higher dimension is proposed. Using the postulated algebra a two dimensional Dirac equation is formulated and its solution is calculated. It is found that there is a sub-algebra where the associative nature can be recovered

Hybrid Coding Technique for Pulse Detection in an Optical Time Domain Reflectometer

The paper introduces a novel hybrid coding technique for improved pulse detection in an optical time domain reflectometer. The hybrid schemes combines Simplex codes with signal averaging to articulate a very sophisticated coding technique that considerably reduces the processing time to extract specified coding gains in comparison to the existing techniques. The paper quantifies the coding gain of the hybrid scheme mathematically and provide simulative results in direct agreement with the theoretical performance. Furthermore, the hybrid scheme has been tested on our self-developed OTDR

Working memory learning method and astrocytes number in different subfields of rat's Hippocampus

The aim of this study was evaluation of the astrocytes number in different subfields of rat's Hippocampus after spatial learning with usage of Morris Water Maze technique and working memory method. In this study, between 2005-2006 years in Pasteur institute of Iran-Tehran and histological department of Gorgan University with usage of Morris Water Maze and working memory technique, we used 14 male albino wistar rats. Seventh rats were in control group and 7 rats in working memory group. After histological preparation, the slides were stained with PTAH staining for showing the Astrocytes. Present results showed significant difference in astrocytes number in CA1, CA2 and CA3 areas of hippocampus between control and reference memory group. The number of astrocytes is increased in working memory group. Then we divided the hippocampus to three parts: Anterior, middle and posterior and with compare of different area (CA1, CA2 and CA3) of hippocampus, we found that the differences between Anterior-middle and Middle-Posterior of CA1 and CA2 area of hippocampus were significant, whereas the difference between Anterior-Posterior parts was not significant in CA1 and CA2 areas. In CA3 area, the difference between Anterior-Middle and Anterior-Posterior parts was significant, whereas the difference between middle and posterior parts was not significant. We concluded that the number of astrocytes increased due to spatial learning and working memory technique. © 2008 Science Publications

Decentralized Greedy-Based Algorithm for Smart Energy Management in Plug-in Electric Vehicle Energy Distribution Systems

Variations in electricity tariffs arising due to stochastic demand loads on the power grids have stimulated research in finding optimal charging/discharging scheduling solutions for electric vehicles (EVs). Most of the current EV scheduling solutions are either centralized, which suffer from low reliability and high complexity, while existing decentralized solutions do not facilitate the efficient scheduling of on-move EVs in large-scale networks considering a smart energy distribution system. Motivated by smart cities applications, we consider in this paper the optimal scheduling of EVs in a geographically large-scale smart energy distribution system where EVs have the flexibility of charging/discharging at spatially-deployed smart charging stations (CSs) operated by individual aggregators. In such a scenario, we define the social welfare maximization problem as the total profit of both supply and demand sides in the form of a mixed integer non-linear programming (MINLP) model. Due to the intractability, we then propose an online decentralized algorithm with low complexity which utilizes effective heuristics to forward each EV to the most profitable CS in a smart manner. Results of simulations on the IEEE 37 bus distribution network verify that the proposed algorithm improves the social welfare by about 30% on average with respect to an alternative scheduling strategy under the equal participation of EVs in charging and discharging operations. Considering the best-case performance where only EV profit maximization is concerned, our solution also achieves upto 20% improvement in flatting the final electricity load. Furthermore, the results reveal the existence of an optimal number of CSs and an optimal vehicle-to-grid penetration threshold for which the overall profit can be maximized. Our findings serve as guidelines for V2G system designers in smart city scenarios to plan a cost-effective strategy for large-scale EVs distributed energy management

Level of Serum Uric Acid in Pre-eclamptic and Normal Pregnant Women

Objective: The objective of study was to find out serum uric acid level in normal andpreeclamptic pregnant women of third trimester visiting outpatient department of obstetrics and gynecology of Bahawal Victoria Hospital, Bahawalpur. Methodology: It was a cross sectional descriptive study conducted form July 2018 to June 2019. All primigravida women of age 18-35 years in third trimester of singleton pregnancy attending in Obstetrics and Gynecology Outpatient Department of Bahawal Victoria Hospital in study duration were included in the study. Statistical analysis was performed by using SPSS version 14. Chi-square test was performed to find the statistical difference regarding uric acid distribution between groups and ‘p' value <0.05 was considered as a lowest level of significance. Results: Out of total 1212 women 84.6% were normal and 15.4% had preeclampsia. In our study out of 187 preeclamptic women, 63.6% had raised serum uric acid level and out of 268 normal pregnant women uric acid level was raised in only 39.5%. Results were found statistically significant. Conclusion: Results of our study suggest that serum uric acid level in pregnant women can be used as a useful and inexpensive marker in prediction of preeclampsia and preventive measures can be taken accordingly

Predicting Shear Capacity of RC Beams Strengthened with NSM FRP Using Neural Networks

This research aims to predict the shear capacity of NSM FRP beams using the neural network method. The study investigates the key considerations and the necessary analysis for this prediction. NSM FRP beams are reinforced concrete beams that are strengthened with near-surface mounted (NSM) fiber-reinforced polymer (FRP) composites. Accurately predicting their shear capacity is important for ensuring their safety and reliability in real-world applications. The neural network method is a machine learning approach that is increasingly used in engineering analysis and design. The study explores how this method can be used to predict the shear capacity of NSM FRP beams and what factors should be taken into account in this analysis. The research also discusses the analytical approach required for this prediction, highlighting the necessary steps for obtaining accurate results. Overall, this study provides valuable insights into the use of the neural network method for predicting the shear capacity of NSM FRP beams. The findings can help inform future research and practical applications in the field of structural engineering, contributing to the development of safer and more reliable structures

Machine Learning-Based Prediction of Compressive Performance in Circular Concrete Columns Confined with FRP

This article presents a comprehensive investigation, focusing on the prediction and formulation of the design equation of compressive strength of circular concrete columns confined with Fiber Reinforced Polymer (FRP) using advanced machine learning models. Through an extensive analysis of 170 experimental data specimens, the study examines the effects of six key parameters, including concrete cylinder diameter, concrete cylinder-FRP thickness, compressive strength of concrete without FRP, initial compressive strain of concrete without FRP, elastic modulus and tensile strength of FRP, on the compressive strength of the circular concrete columns confined with FRP. The predictive model and design equation of compressive strength is developed using a machine learning technique, specifically the artificial neural networks (ANN) model. The results demonstrates strong correlations between the compressive strength of the circular concrete columns confined with FRP and certain factors, such as the compressive strength of the concrete and compressive strain of the concrete column without FRP, elastic modulus of FRP, and tensile strength of FRP. The ANN model specifically developed using Neural Designer, exhibits superior predictive accuracy compared to other constitutive models, showcasing its potential for practical implementation. The study's findings contribute valuable insights into accurately predicting the compressive performance of circular concrete columns confined with FRP, which can aid in optimizing and designing civil engineering structures for enhanced performance and efficiency

Pitman estimator for the parameter and reliability function of the exponential distribution

The easiest and most essential distribution in reliability studies is the exponential distribution. An important issue in this area is to seek for a good estimator to estimate the parameter and reliability function of this distribution. So in this article, The Pitman method is used to derive an estimator for the parameter and reliability function of the exponential distribution, then a comparison was made with the usual methods such as maximum likelihood estimator and Bayes estimator through simulation studies with different sizes of samples. The results showed that the Pitman estimator has a good performance for the parameter of exponential distribution compared with other methods by relying on the statistical measure Mean square error. Also it showed that the Bayes estimator has a good performance compared with the other methods in estimating the reliability function by relying on the statistical measure integral mean square error

Fulminant hepatic failure in pregnant women: acute fatty liver or acute viral hepatitis?

Background: Hepatitis E virus, which is endemic in our region, can cause severe liver dysfunction in pregnant women and this can be clinically confused with acute fatty liver of pregnancy. Methods: We studied the clinical and laboratory data as well as the maternal and fetal outcomes of 12 pregnant women presenting with fulminant hepatic failure in order to determine the etiology of the disease. The clinical diagnoses were subsequently correlated with serologic assays for acute HEV infection. All patients were severely ill with deep jaundice, grade 3-4 encephalopathy and abnormal prothrombin times. Results: A clinical diagnosis of acute viral hepatitis was made in nine patients and of acute fatty liver in the other three cases. IgM and IgG antibodies confirmed acute viral hepatitis E in six of the nine patients while one had acute hepatitis A infection. HEV IgM and IgG antibodies were, however, also positive in two of the three patients thought to have acute fatty liver. Maternal and fetal mortality were 16.6% and 50%, respectively.CONCLUSIONS: We conclude that hepatitis E is the usual cause of acute liver failure in our pregnant women and that clinical and laboratory features do not permit accurate distinction between acute HEV infection and acute fatty liver of pregnancy. The prognosis in patients with acute HEV infection is much better than in other groups with severe liver failure (mortality 16% vs 68%)