Finite element modeling of environmental effects on rigid pavement deformation

In this study, finite element (FE)-based primary pavement response models are employed for investigating the early-age deformation characteristics of jointed plain concrete pavements (JPCP) under environmental effects. The FE-based ISLAB (two-and-one-half-dimensional) and EverFE (three-dimensional) software were used to conduct the response analysis. Sensitivity analyses of input parameters used in ISLAB and EverFE were conducted based on field and laboratory test data collected from instrumented pavements on highway US-34 near Burlington, Iowa. Based on the combination of input parameters and equivalent temperatures established from preliminary studies, FE analyses were performed and compared with the field measurements. Comparisons between field measured and computed deformations showed that both FE programs could produce reasonably accurate estimates of actual slab deformations due to environmental effects using the equivalent temperature difference concept.     

Effect of Intercritical Annealing Parameters on Dual Phase Behavior of Commercial Low-Alloyed Steels

 It is known that dual phase (DP) heat treatments and alloying elements have a strong effect on martensitic transformations and mechanical properties. In the present work, the effects of some intercritical annealing parameters (heating rate, soaking temperature, soaking time, and quench media) on the microstructure and mechanical properties of cold rolled DP steel were studied. The microstructure of specimens quenched after each annealing stage, was analyzed using optical microscopy. The tensile properties, determined for specimens submitted to complete annealing cycles, are influenced by the volume fractions of multi phases (originated from martensite, bainite and retained austenite), which depend on annealing processing parameters. The results obtained showed that the yield strength (YS) and the ultimate tensile strength (UTS) increase with the increasing intercritical temperature and cooling rate. This can be explained by higher martensite volume ratio with the increased volume fraction of austenite formed at the higher temperatures and cooling rates. The experimental data also showed that, for the annealing cycles carried out, higher UTS values than ~ 800 MPa could be obtained with the S3 steel grade.     

Identification of phenolic profiles,fatty acid compositions,antioxidant activities,and enzyme inhibition effects of seven wheat cultivars grown in Turkey: A phytochemical approach for their nutritional value

In the present article, seven wheat cultivars (Ahmetaga, Bezostaya, Dagdas-94, Ekiz, Karahan-99, Konya-2002, and Tosunbey) grown in Turkey were compared for their phytochemical composition, antioxidant, and enzyme inhibitory activities. Antioxidant capacities and enzyme inhibitory effects were investigated with colorimetric methods. Total phenolic content ranged from 40.71 to 86.34 mg of gallic acid equivalent/100 g wheat grain. Tosunbey (92 mg Trolox equivalent/100 g wheat grain) and Ahmetaga (114.56 mg Trolox equivalent/100 g wheat grain) cultivars exhibited strong 2,2 azino-bis (3-ethylbenzothiazloine-6-sulfonic acid) and 1,1-diphenyl-2-picrylhydrazyl free radical scavenging activities. As compared to other wheat cultivars, Tosunbey cultivar had remarkable both antioxidant and enzyme inhibitory effects with the highest level of phenolics. Ferulic acid, chlorogenic acid, and apigenin were the major phenolics in extracts tested. This study suggested that an increased intake of wheat derived products could represent an effective strategy for the management of oxidative stress related chronic and degenerative diseases such as Alzheimers and diabetes mellitus.     

A Novel Approach to Limit Chemical Deterioration of Gilthead Sea Bream (Sparus aurata) Fillets: Coating with Electrospun Nanofibers as Characterized by Molecular,Thermal, and Microstructural Properties

Coating of sea bream fillets with thymol loaded chitosan based electrospun nanofibers (TLCN) and chitosan based nanafibers (CN) has been presented a novel approach to delay chemical deterioration. We assessed CN and TLCN with respect of scanting of total volatile basic nitrogen (TVBN), trimethylamine (TMA), thiobarbituric acid (TBA) deterioration during cold storage condition. Electrospinning process was applied to obtain TLCN and CN. Both of nanofibers obtained from biopolymer and bioactive material were cylindrical, smooth, beadless. Thermal, molecular, zeta potential (ZP), and surface properties of the groups were investigated, revealing that CN indicated molecular interactions with thymol in nanofibers, reduce in physical properties of these structures, thermal decomposition (an alteration in mass of CN and TLCN at temperatures below 190 °C, corresponding to 20.53% and 19.97%, respectively) and also dispersion stabilities (ζ potential) of CN and TLCN were determined 33.68 ± 3.35 and 21.85 ± 1.96 mV, respectively. TVBN and TMA stability analyses demonstrated that CN and TLCN were both effective in delaying chemical deterioration of fish fillets, furthermore TLCN was more effective against chemical deterioration. TBA analyses results of fish fillets indicated that CN and TLCN delayed rancidity in fish meat as compared to control group samples. The presented study results suggested that coating of the sea bream fillets with CN and TLCN would be a promising approach to delay the chemical deterioration of fish fillets.     

Biohydrogen and 5-aminolevulinic acid production from waste barley by Rhodobacter sphaeroides O.U.001 in a biorefinery concept

The production of biohydrogen and 5-aminolevulinic acid (5-ALA) by Rhodobacter sphaeroides O.U.001 was investigated in a biorefinery concept. Waste barley was used as a substrate after acid hydrolysis. The hydrolysate was analyzed in terms of its total simple sugar, organic acid, ammonium, element and total phenol contents. Four different growth media having 5 g/L, 7 g/L, 9 g/L and 11 g/L sugar content were prepared using the waste barley hydrolysate to produce biohydrogen and 5-ALA. The increased sugar concentrations resulted in higher cell density and hydrogen accumulation. Accordingly, the highest cell density (OD₆₆₀: 1.78) and hydrogen production (0.4 L H₂/L culture) were observed in the 11 g/L sugar-containing medium. A 67.4 μM 5-ALA was produced upon vitamin B₁₂ and levulinic acid additions. These results showed that waste barley can be used as a substrate for R. sphaeroides for biohydrogen and 5-ALA production within a biorefinery concept.     

Screening of Possible In Vitro Neuroprotective,Skin Care,Antihyperglycemic, and Antioxidative Effects of Anchusa undulata L. subsp. hybrida (Ten.) Coutinho from Turkey and Its Fatty Acid Profile

The aim of this work was to determine antioxidant capacities, neuroprotective, skin care, antidiabetic effects, and fatty acid composition of Anchusa undulata subsp. hybrida. The antioxidant activity was screened by four different test systems including total antioxidant, antiradical, reducing power, and metal chelating activities. Neuroprotective potential was determined by anticholinesterase inhibitor assay. Tyrosinase inhibitory activity was tested to detect skin care effect. Antidiabetic effects were evaluated with α-amylase and α-glucosidase inhibitory assays. Inhibitory activities on acetycholinesterase, butyrylcholinesterase, tyrosinase, α-amylase, and α-glucosidase enzymes were observed as 2.238 and 1.239 μmol GALAEs/g, 0.339 mmol KAEs/g, 0.193, and 0.219 mmol ACEs/g extract, respectively. Amount of total phenolics and flavonoids were found as 80.34 μg GAEs/mg and 25.09 μg QEs/mg in the extract, respectively. Twenty-three fatty acids were found in the aerial parts, being oleic acid (24.30 g/100 g of total fatty acids) the most abundant, followed by linoleic (21.19 g/100 g of total fatty acids) and palmitic acids (17.50 g/100 g of total fatty acids).     

A novel comparative study for detection of Covid-19 on CT lung images using texture analysis,machine learning,and deep learning methods

The Covid-19 virus outbreak that emerged in China at the end of 2019 caused a huge and devastating effect worldwide. In patients with severe symptoms of the disease, pneumonia develops due to Covid-19 virus. This causes intense involvement and damage in lungs. Although the emergence of the disease occurred a short time ago, many literature studies have been carried out in which these effects of the disease on the lungs were revealed by the help of lung CT imaging. In this study, 1.396 lung CT images in total (386 Covid-19 and 1.010 Non-Covid-19) were subjected to automatic classification. In this study, Convolutional Neural Network (CNN), one of the deep learning methods, was used which suggested automatic classification of CT images of lungs for early diagnosis of Covid-19 disease. In addition, k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) was used to compare the classification successes of deep learning with machine learning. Within the scope of the study, a 23-layer CNN architecture was designed and used as a classifier. Also, training and testing processes were performed for Alexnet and Mobilenetv2 CNN architectures as well. The classification results were also calculated for the case of increasing the number of images used in training for the first 23-layer CNN architecture by 5, 10, and 20 times using data augmentation methods. To reveal the effect of the change in the number of images in the training and test clusters on the results, two different training and testing processes, 2-fold and 10-fold cross-validation, were performed and the results of the study were calculated. As a result, thanks to these detailed calculations performed within the scope of the study, a comprehensive comparison of the success of the texture analysis method, machine learning, and deep learning methods in Covid-19 classification from CT images was made. The highest mean sensitivity, specificity, accuracy, F-1 score, and AUC values obtained as a result of the study were 0,9197, 0,9891, 0,9473, 0,9058, 0,9888; respectively for 2-fold cross-validation, and they were 0,9404, 0,9901, 0,9599, 0,9284, 0,9903; respectively for 10-fold cross-validation.

     

Discovering Structured Variations Via Template Matching

Understanding patterns of variation from raw measurement data remains a central goal of shape analysis. Such an understanding reveals which elements are repeated, or how elements can be derived as structured variations from a common base element. We investigate this problem in the context of 3D acquisitions of buildings. Utilizing a set of template models, we discover geometric similarities across a set of building elements. Each template is equipped with a deformation model that defines variations of a base geometry. Central to our algorithm is a simultaneous template matching and deformation analysis that detects patterns across building elements by extracting similarities in the deformation modes of their matching templates. We demonstrate that such an analysis can successfully detect structured variations even for noisy and incomplete data.     

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern,dual tree complex wavelet transform and convolutional neural networks

In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN). In the first training-test data set used in the study, there were 230 X-ray images, of which 150 were Covid-19 and 80 were non-Covid-19, while in the second training-test data set there were 476 X-ray images, of which 150 were Covid-19 and 326 were non-Covid-19. Thus, classification results have been provided for two data sets, containing predominantly Covid-19 images and predominantly non-Covid-19 images, respectively. In the study, a 23-layer CNN architecture and a 54-layer CNN architecture were developed. Within the scope of the study, the results were obtained using chest X-ray images directly in the training-test procedures and the sub-band images obtained by applying dual tree complex wavelet transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying local binary pattern (LBP) to the chest X-ray images. Within the scope of the study, four new result generation pipeline algorithms having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in this study, the training sessions were carried out using the k-fold cross validation method. Here the k value was chosen as 23 for the first and second training-test data sets. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy, F-1 score, and area under the receiver operating characteristic curve (AUC) for the first training-test data set were 0,9947, 0,9800, 0,9843, 0,9881 and 0,9990 respectively; while for the second training-test data set, they were 0,9920, 0,9939, 0,9891, 0,9828 and 0,9991; respectively. Within the scope of the study, finally, all the images were combined and the training and testing processes were repeated for a total of 556 X-ray images comprising 150 Covid-19 images and 406 non-Covid-19 images, by applying 2-fold cross. In this context, the average highest values of sensitivity, specificity, accuracy, F-1 score, and AUC for this last training-test data set were found to be 0,9760, 1,0000, 0,9906, 0,9823 and 0,9997; respectively.

     

Hydrogenolysis and hydrocracking of the carbon-oxygen bond. 2. Thermal cleavage of the carbon-oxygen bond in guaiacol

Low-rank coals and their precursors contain, in addition to aromatic hydroxy groups, aromatic methoxy groups. In the present work a model compound, guaiacol, is used for the study of the behaviour of the carbon-oxygen bonds under thermolytic conditions. The thermolysis of guaiacol is studied in tetralin, naphthalene and without solvent under hydrogen or nitrogen pressure at 578–618 K. The compound is homolytically converted by first-order kinetics. The major product is pyrocatechol. Phenol, o-cresol, methyl catechols and methyl guaiacols are also formed. When tetralin is present it reacts in a molar ratio of 1:4 with guaiacol to form naphthalene. The source of hydrogen when tetralin is not present is guaiacol itself because molecular hydrogen does not participate in the reaction. The kinetics and reaction mechanism are discussed.