Ab initio characterization of Ti decorated SWCNT for hydrogen storage

Characterization has been performed on basis of several physicochemical parameters. The results indicate that the preferential adsorption is on Ti atom deposited on the top site of the (5,5) armchair SWCNT with energies (−0.44 and −0.71) eV for H₂ oriented parallel to the (x) and (y) axes respectively. The binding of H₂ is mostly dominated by the support-metal E (i)^S?Ti term. The role of the SWCNT is not restricted to support the metal. Significant reduction of the energy gap is observed when H₂ are anchored on the external surface of the SWCNT. The SWCNT?Ti?H₂(x) complex is the least reactive configuration with nucleophiles. The calculated parameters characterize H₂ that is oriented parallel to the (x)-[100] axis of the SWCNT to be the most suitable configuration for hydrogen storage based on the recommended adsorption energy range of DOE (−0.2 to −0.6) eV.     

Grafting of Methacrylic Acid and Other Vinyl Monomers Onto Cotton Fabric Using Ce (IV) Ion–Cellulose Thiocarbonate Redox System

Graft copolymerization of methacrylic acid (MAA) onto cotton fabric using tetravalent ceric ion (Ce^IV)–cellulose thiocarbonate redox system was investigated under different conditions including pH of the polymerization medium (1–4), ceric sulphate (CS) concentration (4–20 m mole/l), MAA concentration (1%–6%), polymerization time (1/4–2 h) and polymerization temperature (0–70°C). Results obtained indicated that the optimal conditions for MAA grafting onto cotton fabric using the said redox system consisted of: [CS], 20 m mole/l; [MAA], 4%; pH of the medium, 2; time, 2 h; temperature, 60 °C keeping a material-to-liquor ratio at 1:0. Applying optimized conditions to different monomers, namely, acrylic acid (AA), methacrylic acid (MAA), acrylamide (Aam), acrylonitrile (AN), butyl acrylate (BuA), methyl methacrylate (MMA), ethyl methacrylate (EMA) and glycidyl methacrylate (GMA) onto the same substrate, the rates of grafting followed the order:

Synthesis of Bi1.5Pb0.5Sr2Ca2Cu3Oy via sol-gel method using different acetate-derived precursors

Sol-gel method have been used to convert metal carbonates, oxides andnitrates into corresponding acetate precursors for gel formation.About 90% volume of high T _C superconducting phase (2223)has been obtained for oxide-carbonate system. This value iscomparable to that obtained from pure acetates (91% volume).However, the nitrates-'derived sample has a high-T _Cphase of 88% due to poor conversion of nitrates to acetates asobserved by the infrared spectra. The dominance of high-T _Cphase was observed in all systems as evidenced in the single steptransition of R-T curves. Hence the percentage of thehigh-T_C phase dictates the value of T _C(R = 0)as 104 K, 103 K and 99 K, for oxide-carbonate, pure metal acetatesand nitrates system respectively.     

Efficient Deep CNN Model for COVID-19 Classification

Coronavirus (COVID-19) infection was initially acknowledged as a global pandemic in Wuhan in China. World Health Organization (WHO) stated that the COVID-19 is an epidemic that causes a 3.4% death rate. Chest X-Ray (CXR) and Computerized Tomography (CT) screening of infected persons are essential in diagnosis applications. There are numerous ways to identify positive COVID-19 cases. One of the fundamental ways is radiology imaging through CXR, or CT images. The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality. Hence, automated classification techniques are required to facilitate the diagnosis process. Deep Learning (DL) is an effective tool that can be utilized for detection and classification this type of medical images. The deep Convolutional Neural Networks (CNNs) can learn and extract essential features from different medical image datasets. In this paper, a CNN architecture for automated COVID-19 detection from CXR and CT images is offered. Three activation functions as well as three optimizers are tested and compared for this task. The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it. The performance is tested and investigated on the CT and CXR datasets. Three activation functions: Tanh, Sigmoid, and ReLU are compared using a constant learning rate and different batch sizes. Different optimizers are studied with different batch sizes and a constant learning rate. Finally, a comparison between different combinations of activation functions and optimizers is presented, and the optimal configuration is determined. Hence, the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage. The proposed model achieves a classification accuracy of 91.67% on CXR image dataset, and a classification accuracy of 100% on CT dataset with training times of 58 min and 46 min on CXR and CT datasets, respectively. The best results are obtained using the ReLU activation function combined with the SGDM optimizer at a learning rate of 10⁻⁵ and a minibatch size of 16.     

Authentication of Vehicles and Road Side Units in Intelligent Transportation System

Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents, economically damaging traffic jams, hijacking, motivating to wrong routes, and financial losses for businesses and governments. Smart and autonomous vehicles are connected wirelessly, which are more attracted for attackers due to the open nature of wireless communication. One of the problems is the rogue attack, in which the attacker pretends to be a legitimate user or access point by utilizing fake identity. To figure out the problem of a rogue attack, we propose a reinforcement learning algorithm to identify rogue nodes by exploiting the channel state information of the communication link. We consider the communication link between vehicle-tovehicle, and vehicle-to-infrastructure. We evaluate the performance of our proposed technique by measuring the rogue attack probability, false alarm rate (FAR), misdetection rate (MDR), and utility function of a receiver based on the test threshold values of reinforcement learning algorithm. The results show that the FAR and MDR are decreased significantly by selecting an appropriate threshold value in order to improve the receiver’s utility.     

A comparative study of existing metrics for 3D-mesh segmentation evaluation

In this paper, we present an extensive experimental comparison of existing similarity metrics addressing the quality assessment problem of mesh segmentation. We introduce a new metric, named the 3D Normalized Probabilistic Rand Index (3D-NPRI), which outperforms the others in terms of properties and discriminative power. This comparative study includes a subjective experiment with human observers and is based on a corpus of manually segmented models. This corpus is an improved version of our previous one (Benhabiles et al. in IEEE International Conference on Shape Modeling and Application (SMI), 2009). It is composed of a set of 3D-mesh models grouped in different classes associated with several manual ground-truth segmentations. Finally the 3D-NPRI is applied to evaluate six recent segmentation algorithms using our corpus and the Chen et al.’s (ACM Trans. Graph. (SIGGRAPH), 28(3), 2009) corpus.     

Dynamic evolution control for synchronous buck DC–DC converter: Theory,model and simulation

This paper proposes a new control technique for synchronous buck DC–DC converter. Theory, design and implementation of the proposed control technique are provided. A new approach for converter controller synthesis based on dynamic evolution control theory is presented. In order to synthesize the converter controller, this method uses a simple analysis of nonlinear equation models of the converter. The synthesis process is simple and requires a quite low bandwidth for the controller. Therefore, this control method is suitable for digital control implementation. As an illustrative example, the synthesis of synchronous buck DC–DC converter controller is discussed in detail. The model of the synchronous buck DC–DC converter system was implemented using SimPowerSystems toolbox of MATLAB-SIMULINK. Performance of the proposed dynamic evolution control under step load change and step input voltage condition was investigated. Simulation results confirm that the proposed control method is superior to traditional PI based controller because of fast transient response and good disturbance rejection.     

Radiation,Crystallization, and Physical Properties of Cadmium Borate Glasses

Silicon - The melt-quenching method has been used to fabricate Na2B4O7 - CdO glass system. The XRD diffractometer procedure was used to check the status of these samples. Inter-ionic distance (Ri)...     

Wild Bootstrap Tests

In this article, we have derived tests based on the wild bootstrap that allow checking for differences between two irregular signals observed with additive noise where the latter consists of independent but not necessarily identically distributed random variables. The signals are first denoised by kernel estimates and then compared by looking at the integrated squared difference. The bound between accepting and rejecting the hypothesis of equal signals are determined by the wild bootstrap and numerically calculated by Monte Carlo simulation. The test is applied to all pairwise rows and columns of two images, which results in an algorithm that allows detection of defects and additional information on their location and shape surface inspection problems. The idea and theory of the test may be straightforwardly extended to the direct comparison of two images. This is computationally less expensive than doing all the row- and columnwise tests, but it provides less information.