Kharazmi University

About: Kharazmi University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Membrane & Supply chain. The organization has 3395 authors who have published 5321 publications receiving 45645 citations. The organization is also known as: Tarbiat Moallem University of Tehran & Teacher Training University.

Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks

Abstract: Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of ASD. Thus far, the childhood diagnosis of ASD has been done based on clinical interviews and behavioral observations. There is a significant need to reduce the use of traditional diagnostic techniques and to diagnose this disorder in the right time and before the manifestation of behavioral symptoms. The purpose of this study is to present the intelligent model to diagnose ASD in young children based on resting-state functional magnetic resonance imaging (rs-fMRI) data using convolutional neural networks (CNNs). CNNs, which are by far one of the most powerful deep learning algorithms, are mainly trained using datasets with large numbers of samples. However, obtaining comprehensive datasets such as ImageNet and achieving acceptable results in medical imaging domain have become challenges. In order to overcome these two challenges, the two methods of “combining classifiers,” both dynamic (mixture of experts) and static (simple ‌Bayes) approaches, and “transfer learning” were used in this analysis. In addition, since diagnosis of ASD will be much more effective at an early age, samples ranging in age from 5 to 10 years from global Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets were used in this research. The accuracy, sensitivity, and specificity of presented model outperform the results of previous studies conducted on ABIDE I dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7273, sensitivity = 0.712, specificity = 0.7348). Furthermore, acceptable classification results were obtained from ABIDE II dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7, sensitivity = 0.582, specificity = 0.804) and the combination of ABIDE I and ABIDE II datasets (the best results obtained from Adam optimization technique: accuracy = 0.7045, sensitivity = 0.679, specificity = 0.7421). We can conclude that the proposed architecture can be considered as an efficient tool for diagnosis of ASD in young children. From another perspective, this proposed method can be applied to analyzing rs-fMRI data related to brain dysfunctions.

Detection of Helicobacter pylori with a nanobiosensor based on fluorescence resonance energy transfer using CdTe quantum dots

Abstract: We report on a method for the sensitive determination of Helicobacter that is based on fluorescence resonance energy transfer using two oligonucleotide probes labeled with CdTe quantum dots (QDs) and 5-carboxytetramethylrhodamine (Tamra) respectively. QDs labeled with an amino-modified first oligonucleotide, and a Tamra-labeled second oligonucleotide were added to the DNA targets upon which hybridization occurred. The resulting assembly brings the Tamra fluorophore (the acceptor) and the QDs (the donor) into close proximity and causes fluorescence resonance energy transfer (FRET) to occur upon photoexcitation of the donor. In the absence of target DNA, on the other hand, the probes are not ligated, and no emission by the Tamra fluorophore is produced due to the lack of FRET. The feasibility of the method was demonstrated by the detection of a synthetic 210-mer nucleotide derived from Helicobacter on a nanomolar level. This homogeneous DNA detection scheme is simple, rapid and efficient, does not require excessive washing and separation steps, and is likely to be useful for the construction of a nanobiosensor for Helicobacter species.

Cryptanalysis and improvement of an efficient mutual authentication RFID scheme based on elliptic curve cryptography

Abstract: Radio frequency identification (RFID) is a wireless technology for automatic identification and data capture. Security and privacy issues in the RFID systems have attracted much attention. Many approaches have been proposed to achieve the security and privacy goals. One of these approaches is RFID authentication protocols by which a server and tags can authorize each other through an intracity process. Recently, Chou proposed a RFID authentication protocol based on elliptic curve cryptography. However, this paper demonstrates that the Chou's protocol does not satisfy tag privacy, forward privacy and authentication, and server authentication. Based on these security and privacy problems, we also show that Chou's protocol is defenseless to impersonation attacks, tag cloning attacks and location tracking attacks. Therefore, we propose a more secure and efficient scheme, which does not only cover all the security flaws and weaknesses of related previous protocols, but also provides more functionality. We prove the security of the proposed improved protocol in the random oracle model.

Evaluation of groundwater quality and assessment of scaling potential and corrosiveness of water samples in Kadkan aquifer, Khorasan-e-Razavi Province, Iran

Abstract: The chemical analysis of 129 groundwater samples in the Kadkan area, Khorasan-e-Razavi Province, NE of Iran was evaluated to determine the hydrochemical processes, assessment of groundwater quality for irrigation purposes, corrosiveness, and scaling potential of the groundwater Accordingly, the suitability of groundwater for irrigation was evaluated based on the sodium adsorption ratio, residual sodium carbonate, sodium percent, salinity hazard, and US Salinity Laboratory hazard diagram Based on the electrical conductivity and sodium adsorption ratio, the dominant classes are C3–S1, C3–S2, C2–S1, and C4–S2 According to the Wilcox plot, about 50 % of the samples fall in the “Excellent to Good” and “Good to Permissible” classes Besides, the Langelier saturation index, Ryznar stability index (RSI), Larson–Skold index, and Puckorius scaling index were evaluated for assessing the corrosiveness and scaling potential of the groundwater Corrosiveness and scaling indices stated that the majority of samples are classified into “Aggressive” and “Very Aggressive” category In addition, chloride and sulfate interfere in 90 % of the samples Assessment of hydrochemical characteristics indicates Na-Mg-Cl as the predominant hydrochemical type Spatial distribution of hydrochemical parameters indicates that hydrochemical processes are influenced by geology and hydrogeology of Kadkan aquifer The Gibbs plots gave an indication that groundwater chemistry in this area may have acquired the chemistry mainly from evaporation and mineral precipitation Grouping the samples based on Q-mode hierarchical cluster analysis helped to more separation of similar samples The R-mode HCA grouped analyzed parameters into two groups based on similarity of hydrochemical characteristics As a result, the samples collected in northern and southern parts of the study area show the best quality (ie, lowest salinity) for some purposes such as irrigation and drinking

Decomposition algorithms for the integrated process planning and scheduling problem

Abstract: There are several algorithms to solve the integrated process planning and scheduling (IPPS) problem (i.e., flexible job shop scheduling with process plan flexibility) in the literature. All the existing algorithms for IPPS are heuristic-based search methods and no research has investigated the use of exact solution methods for this problem. We develop several decomposition approaches based on the logic-based Benders decomposition (LBBD) algorithm. Our LBBD algorithm allows us to partition the decision variables in the IPPS problem into two models, master-problem and sub-problem. The master-problem determines process plan and operation-machine assignment, while the sub-problem optimizes sequencing and scheduling decisions. To achieve faster convergence, we develop two relaxations for the optimal makespan objective function and incorporate them into the master-problem. We analyze the performance and further enhance the algorithm with two ideas, a Benders optimality cut based on the critical path and a faster heuristic way to solve the sub-problem. 16 standard benchmark instances available in the literature are solved to evaluate and compare the performances of our algorithms with those of the state-of-the-art methods in the literature. The proposed algorithm either results in the optimal solution or improves the best-known solutions in all the existing instances, demonstrating its superiority to the existing state-of-the-art methods in literature.