Selectivity in Methanol Oxidation as Studied on Model Systems Involving Vanadium Oxides

Oxidation catalysts are modeled by oxide single crystals, thin oxide films, as well as supported oxide nanoparticles. We characterize the surface of those materials using a variety of surface sensitive techniques including scanning tunneling microscopy and spectroscopy, photoelectron spectroscopy, infrared spectroscopy, and thermal desorption spectroscopy. We find temperature dependent structural transformations from V₂O₅(001) to V₂O₃(0001) via V₆O₁₃(001). V₂O₃(0001) is found to be vanadyl terminated in an oxygen ambient and it loses the vanadyl termination after electron bombardment. It is shown that the concentration of vanadyl groups controls the selectivity of the methanol oxy-dehydrogenation towards formaldehyde. A proposal for the mechanism is made. The results on single crystalline thin films are compared with similar measurements on deposited vanadia nanoparticles. The experimental results are correlated with theoretical calculations and models.     

Transmission electron microscope study of a directionally solidified Cu-MgCu2 eutectic

The crystallography and the interface structure of a unidirectionally solidified Cu-MgCu₂ eutectic alloy have been examined by transmission electron microscopy. The microstructure of the eutectic was found to be lamellar and regularly interrupted by faults. The preference of the particular orientation relationship could not be explained by relative atomic densities of the planes comprising the interface. Based on the defect contrast observed and extinction distance calculations, it is suggested that the fine array of defects observed at the interface may be characterized as steps with step vectors parallel to or Dislocations were also observed at the interface but they were rarely regular.     

Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.

     

Effect of aging temperature and of retrogression treatment time on the microstructure and mechanical properties of alloy AA 7075

Alloy AA 7075-T6 is studied after retrogression and re-aging. The retrogression heat treatment is performed at various temperatures and hold times, and subsequent aging is performed at 130°C for 12 h. The microstructure and mechanical properties of the alloy are studied depending on the temperature and the hold time of the retrogression heat treatment. Electron microscopic studies are preformed and mechanical characteristics are determined in tensile and impact tests. The HRB microhardness is measured.     

Modification of γ-(Fe,Cr)3C pseudo-binary eutectic

The -(Fe, Cr)₃C pseudo-binary eutectic alloy with K, Ce, Sb additives was unidirectionally solidified in a Brigdman-type unit. The quasi-regular, lamellar eutectic carbide was changed into rods and bent blades by the modifiers under well-controlled conditions. At very slow growth, partial modification was common. At growth rates corresponding to a slightly cellular interface, a fully modified structure could be obtained. The modification behaviour as a function of the modifying element, its concentration and the growth rate is described and discussed.     

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.     

An efficient recommender system algorithm using trust data

The Journal of Supercomputing - Smart services are a concept that provides services to the citizens in an efficient manner. The online shopping and recommender system can play an important role for...     

Flexural behavior of stratified reinforced concrete: construction,testing, analysis,and design

Stratified concrete poses a promising alternative for construction. Its fresh and hardened properties have been studied at the material level; however, structural behavior in steel reinforced specimens has not been studied. This paper focuses on the flexural behavior of eight stratified reinforced concrete (SRC) specimens representing slices from a slab or non-bearing wall. Specimens with two stratified concrete designs and three steel ratios were tested and compared to estimates from a fiber element numerical model and rectangular stress-block design methods from ACI 318 and Eurocode 2. The results suggest that SRC has similar damage modes as ordinary reinforced concrete (ORC). The fiber element model accurately estimated the measured behavior, while ACI 318 and Eurocode 2 differed from the experimental results by <25%. These prediction accuracies are similar to those for ORC. Therefore, the flexural design of SRC can be done using both fiber element and rectangular stress-block approaches.     

Eco‐friendly synthesis of silver nanoparticles and its larvicidal property against fourth instar larvae of Aedes aegypti

In this study, larvicidal activity of silver nanoparticles (AgNPs) synthesised using apple extract against fourth instar larvae of Aedes aegypti was determined. As a result, the AgNPs showed moderate larvicidal effects against Ae. aegypti larvae (LC₅₀  = 15.76 ppm and LC₉₀  = 27.7 ppm). In addition, comparison of larvicidal activity performance of AgNPs at high concentration prepared using two different methods showed that Ae. aegypti larvae was fully eliminated within the duration of 2.5 h. From X‐ray diffraction, the AgNP crystallites were found to exhibit face centred cubic structure. The average size of these AgNPs as estimated by particle size distribution was in the range of 50–120 nm. The absorption maxima of the synthesised Ag showed characteristic Ag surface plasmon resonance peak. This green synthesis provides an economic, eco‐friendly and clean synthesis route to Ag.Inspec keywords: silver, nanofabrication, X‐ray diffraction, zoology, particle size, nanoparticles, biomedical materials, nanomedicineOther keywords: time 2.5 h, size 50 nm to 120 nm, silver nanoparticle, larvicidal property, instar larvae, Aedes aegypti, larvicidal effect, larvicidal activity performance, X‐ray diffraction, nanoparticle particle size distribution, absorption maxima, silver surface plasmon resonance peak