Effect of growth rate and lamellar spacing on microhardness in the directionally solidified Pb-Cd, Sn-Zn and Bi-Cd eutectic alloys

Lead-cadmium, Zinc-tin and Bismuth-cadmium of (99.99%) high purity eutectic alloys were melted in a graphite crucible under vacuum atmosphere. These eutectic alloys were directionally solidified upward with a constant temperature gradient G and different growth rates V in the Bridgman type directional solidification furnace. The lamellar spacings and microhardness H _V were measured from both transverse section and longitudinal section of the specimen. The variations of H _V with respect to V and have been determined by using the linear regression analysis method. H _V values increase with the increasing values of V and decrease with the increasing values. The Hall-petch type relationships obtained in this work have been compared with the previous works.     

Integrated optimal topology design and shape optimization using neural networks

In this paper, neural network- and feature-based approaches are introduced to overcome current shortcomings in the automated integration of topology design and shape optimization. The topology optimization results are reconstructed in terms of features, which consist of attributes required for automation and integration in subsequent applications. Features are defined as cost-efficient simple shapes for manufacturing. A neural network-based image-processing technique is presented to match the arbitrarily shaped holes inside the structure with predefined features. The effectiveness of the proposed approach in integrating topology design and shape optimization is demonstrated with several experimental examples.     

Antagonistic effect on Listeria monocytogenes and L. innocua of a bacteriocin-like metabolite produced by lactic acid bacteria isolated from sucuk

Two Lactobacilli and four Pediococci strains producing bacteriocin-like metabolities isolated from sucuk were tested with agar spot tests and well diffusion assays for their inhibitory activity against 16 Listeria strains, also isolated from sucuk. The production of organic acids and hydrogen peroxide limited, L. sake Lb 706 (used as a bacteriocin producer strain) and the isolated lactic acid bacteria (LAB) showed inhibitory activity against all of the Listeria strains, while L. sake Lb 706-A (used as a bacteriocin non-producer mutant) had the same effects against only two Listeria monocytogenes strains (51, 52) in agar spot tests. In the well diffusion assays, while L sake Lb 706 and four Pediococci isolates (413, 416, 419, 446) exhibited inhibitory activity against all of the Listeria strains tested, L. sake Lb 706-A and two of the Lactobacilli isolates (77, 116) showed no effect on the Listeria strains tested.     

Low-Power Tunable Analog Circuit Blocks Based on Nanoscale Double-Gate MOSFETs

We illustrate unique examples of low-power tunable analog circuits built using independently driven nanoscale DG-MOSFETs, where the top gate response is altered by application of a control voltage on the bottom gate. In particular, we provide examples for a single-ended CMOS amplifier pair, a Schmitt trigger circuit and a operational transconductance amplifier C filter, circuit blocks essential for low-noise high-performance integrated circuits for analog and mixed-signal applications. The topologies and biasing schemes explored here show how the nanoscale DG-MOSFETs may be used for efficient, tolerant and smaller circuits with tunable characteristics.     

模拟定向凝固生长速率对杆形Mn-Bi合金共晶间距的影响

为了深入了解定向凝固过程生长速率对杆形Mn-Bi合金共晶间距的影响，开展了试验和数值模拟研究工作，以期初步明确在地球上试验得到的杆形共晶间距和在外层空间(航天飞机上)同样试验条件下得到的杆形共晶间距发生变化这样一个事实的机理。在杆形MnBi共晶相和Bi基体生共前沿，用传盾方程计筑了Mn在合金液中的浓度场，得到了液、固两相间台式界面前沿Mn的等浓度曲线，进而依据Jackson-Hunt研究的理论模型计算出杆形共晶间距。试验和数值模拟的结果显示，规则生长时，随着Peclet数Λ的不断增加,杆形共晶间距逐渐减少，试验与模拟的结果吻合良好。Λ数还可在将来被用来模拟计算该合金凝固时非规则生长时的情况。     

New conjugated azomethine oligomers obtained from bis-(hydroxyphenyl)methylenediamine via oxidative polycondensation and their complexes with metals

A new conjugated aromatic oligo(azomethine) derivative was synthesized by oxidative polycondensation of 1,4-bis[(2-hydroxyphenyl)methylene]phenylenediamine (HPMPDA) by air, H₂O₂ and NaOCl oxidants in an aqueous alkaline medium. The structures of 1,4-bis[(2-hydroxyphenyl)methylene]phenylenediamine and oligo-1,4-bis[(2-hydroxyphenyl)methylene]phenylenediamine (OHPMPDA) were confirmed by FT-IR, UV–vis, ¹H NMR, ¹³C NMR and elemental analysis. The characterization was made by TGA–DTA, size exclusion chromatography (SEC), magnetic moment and solubility tests. The ¹H NMR and ¹³C NMR data showed that the polymerization was proceeded by C–C coupling according to ortho and para positions of –OH group of 1,4-bis[(2-hydroxyphenyl)methylene]phenylenediamine. Metal complex compounds of OHPMPDA were synthesized with metal salts of Fe, Co, Ni, Cu, Zn, Cd, Mn, Cr, Pb and Hg. Elemental analyses of oligomer–metal complexes suggested that the ratio of metal to oligomer is 1:1. Thermal stabilities of the oligomer–metal complexes were determined by thermogravimetric analyses (TGA). According to TG analyses, oligomer–metal complexes were fairly stable against temperature and thermal decomposition. Also, electrical conductivities of OHPMPDA and oligomer–metal complexes were measured by four-point technique. The results of this study showed that aromatic oligoazomethine and its metal complexes were an interesting class of conjugated compounds of which electronic structure and the other properties can be regulated over a wide range by using different oxidation reagents.     

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.     

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.