Aromatic products of 340 °C supercritical-toluene extraction of two Turkish lignites: an n.m.r. study

Fractions of Elbistan and Seyitomer (Turkish) lignites, extracted with supercritical toluene at 340 °C and 8 MPa, have been separated by solvent extraction and silica-gel chromatography. Analyses by n.m.r. and i.r. spectroscopies and other methods have been combined in structural-analysis schemes to yield information about the average molecule in aromatic extracts. Carbon aromaticities, f_a, derived from 22.63 MHz ¹H-decoupled pulse Fourier-transform (PFT) ¹³C-n.m.r. are more widely spread for Elbistan (0.34–0.56) than for Seyitomer (0.40–0.43), and are lower than for supercritical-gas (SCG) products from bituminous coals. ¹³C-n.m.r. also reveals the presence of aromatic ether-O in polar fractions. Narrow aromatic signals in 100 MHz ¹H-n.m.r. spectra suggest the presence of single-aromatic-ring average structures. In the hexane-soluble aromatics, 27% (Elbistan) and 29% (Seyitomer) of the available sites are substituted by alkyI groups, some of which are at least eight carbon atoms long; the hexane-soluble polar and asphaltene/asphaltol fractions contain fewer such groups.     

Colour and COD removal from textile effluent by coagulation and advanced oxidation processes

The aim of this study was to compare the performance of coagulation, Fenton's oxidation (Fe²⁺/H₂O₂) and ozonation for the removal of chemical oxygen demand (COD) and colour from biologically pretreated textile wastewater. FeSO₄ and FeCl₃ were used as coagulants at varying doses and varying colour removal efficiency was measured. For the Fenton process, COD and colour removal efficiencies were found to be 78% and 95% for the Fenton process, and to be 64% and 71% for the Fenton-like process (Fe³⁺/H₂O₂), respectively. Ozonation experiments were conducted at different initial pH values and fixed ozone doses. Ozonation resulted in 43% COD and 97% colour removal whereas these rates increased to 54% and 99% when 5 mg/l hydrogen peroxide was added to the wastewater before ozonation at the same dose. The operating costs of all proposed treatment systems were also evaluated in this study.     

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.     

Evolutionary design of neural network architectures: a review of three decades of research

We present a comprehensive review of the evolutionary design of neural network architectures. This work is motivated by the fact that the success of an Artificial Neural Network (ANN) highly depends on its architecture and among many approaches Evolutionary Computation, which is a set of global-search methods inspired by biological evolution has been proved to be an efficient approach for optimizing neural network structures. Initial attempts for automating architecture design by applying evolutionary approaches start in the late 1980s and have attracted significant interest until today. In this context, we examined the historical progress and analyzed all relevant scientific papers with a special emphasis on how evolutionary computation techniques were adopted and various encoding strategies proposed. We summarized key aspects of methodology, discussed common challenges, and investigated the works in chronological order by dividing the entire timeframe into three periods. The first period covers early works focusing on the optimization of simple ANN architectures with a variety of solutions proposed on chromosome representation. In the second period, the rise of more powerful methods and hybrid approaches were surveyed. In parallel with the recent advances, the last period covers the Deep Learning Era, in which research direction is shifted towards configuring advanced models of deep neural networks. Finally, we propose open problems for future research in the field of neural architecture search and provide insights for fully automated machine learning. Our aim is to provide a complete reference of works in this subject and guide researchers towards promising directions.

     

以第一性原理研究立方相CeO2的弹性、电子结构和光学性质

运用基于密度泛函理论(DFT)框架下的超软赝势平面波(USPP)方法,计算了立方相CeO2的几何结构、弹性性质、电子结构和光学性质,并采用HSE06杂化泛函矫正了带隙.所得晶格参数及体模量与先前文献报道数据基本吻合.计算出了二阶弹性常数及德拜温度值,并给出了能带结构、态密度、差分电荷密度的分布情况.最后,为了阐明CeO2的光学跃迁机制,计算并分析了其复介电常数、折射率、吸附光谱、反射光谱等光学性质.     

Eigenclassifiers for combining correlated classifiers

In practice, classifiers in an ensemble are not independent. This paper is the continuation of our previous work on ensemble subset selection [A. Ula?, M. Semerci, O.T. Y?ld?z, E. Alpayd?n, Incremental construction of classifier and discriminant ensembles, Information Sciences, 179 (9) (2009) 1298–1318] and has two parts: first, we investigate the effect of four factors on correlation: (i) algorithms used for training, (ii) hyperparameters of the algorithms, (iii) resampled training sets, (iv) input feature subsets. Simulations using 14 classifiers on 38 data sets indicate that hyperparameters and overlapping training sets have higher effect on positive correlation than features and algorithms. Second, we propose postprocessing before fusing using principal component analysis (PCA) to form uncorrelated eigenclassifiers from a set of correlated experts. Combining the information from all classifiers may be better than subset selection where some base classifiers are pruned before combination, because using all allows redundancy.