Modeling and prediction with a class of time delay dynamic neural networks

In this paper, we propose a time delay dynamic neural network (TDDNN) to track and predict a chaotic time series systems. The application of artificial neural networks to dynamical systems has been constrained by the non-dynamical nature of popular network architectures. Many of the drawbacks caused by the algebraic structures can be overcome with TDDNNs. TDDNNs have time delay elements in their states. This approach provides the natural properties of physical systems. The minimization of a quadratic performance index is considered for trajectory tracking applications. Gradient computations are presented based on adjoint sensitivity analysis. The computational complexity is significantly less than direct method, but it requires a backward integration capability. We used Levenberg–Marquardt parameter updating method.     

Effect of V,Nb, Ti and graphite additions on the hydrogen desorption temperature of magnesium hydride

In this study the effects of mechanical milling with 5 wt.% of additives (V, Nb, Ti and Graphite) on the hydrogen desorption temperature of the magnesium hydride (MgH₂) were studied. The powder mixtures were mechanically milled for 2 h. X-ray diffraction (XRD), scanning electron microscope (SEM), and optical microscope (OM) techniques were used for the structural and morphological characterization of powders. Differential scanning calorimeter (DSC) was used to investigate the effects of the mechanical milling with additives on the hydrogen desorption temperature of the magnesium hydride powder. DSC results show that the hydrogen desorption temperatures of mechanically milled MgH₂ with additives are depressed about ∼40–50 °C compared with that of as-received MgH₂. The particle size analysis results indicate that decrease of the particle size of powders leads to a decrease of the hydrogen desorption temperature. Moreover, increasing specific surface area can also contribute to a decrease on the hydrogen desorption temperature.     

Plane waves in electrically conducting and magnetizable viscoelastic isotropic solids subjected to a uniform magnetic field

The propagation of plane electromagneto-mechanical (EM M) waves in homogeneous, initially unstressed and isotropic, electrically conductive magnetoviscoelastic solids in a uniform primary magnetic field is investigated. Depending upon the direction of the applied magnetic field, several modes of the waves arise such as coupled mechanical (M) and electromagnetic (EM) waves. The phase velocities and the attenuations of the waves are obtained both analytically and numerically. Some interesting behavior of the phase velocities and the attenuations are, in particular, detected for certain frequencies, intensity and direction of the primary magnetic field, viscoelastic parameters and conductivity. For example, there are anomalous dispersions of the coupled modes of EM- and M-waves depending upon the intensity of the primary magnetic field, viscoelastic parameters and the conductivity of the material.     

Refuse-derived fuels as a renewable energy source in comparison to coal,rice husk,and sugarcane bagasse

The calorific potential of refuse-derived fuels (RDFs) was investigated with different coals, rice husk, and sugarcane bagasse. Carbon-sulfur analysis, gross calorific value (kJ/kg), and proximate analysis (%) were carried out. Total carbon of coal samples was found to be in the range from 62.65 to 79.19%, while RDF samples were ranged from 40.21 to 57.34% which were almost similar to rice husk (49.13%) and sugarcane bagasse (46.13%). Comparison of the total sulfur content of the coal (Duki) (10.52%) was very high as compared to RDF samples ranged from 0.17 to 0.46% and almost similar to rice husk (0.34%) and sugarcane bagasse (0.187%), while other coal samples ranged from 2.1 to 4.5%. The gross calorific value of the coal (Duki) (6,163 kJ/kg) was higher to other coal samples ranged from 4,935 to 4,972 kJ/kg, while found to be almost double to rise husk (3,518 kJ/kg), sugarcane bagasse (3,285 kJ/kg), and RDF samples (3,125–4,689 kJ/kg). The moisture content, volatile matter, and ash content were found higher in RDF 1 (42.14%), RFD 2 (66.55%), and coal (stone) (33.14%), respectively. Appropriate gross calorific value and very low sulfur content of the RDFs, especially RDF 2, appeared adequate to be used as a fuel with a lesser pollution potential and as an alternative fuel in mega cement industry of Pakistan.     

Purification and characterization of an alkaline pectin lyase produced by a newly isolated Brevibacillus borstelensis (P35) and its applications in fruit juice and oil extraction

An alkaline pectin lyase (PNL) (EC 4.2.2.10) secreted by Brevibacillus borstelensis P35 (GenBank Number: FJ417406) was purified using ammonium sulfate fractionation, anion exchange chromatography on DEAE-cellulose and gel filtration chromatography on Sephadex G-150. The pH and temperature optima of the enzyme were found to be 8.0 and 60 °C. The enzyme does not loose activity up to 60 °C if exposed for 1 h. The values of K _m and V _max of the enzyme were 0.625 mg/mL and 126.32 s^?1, respectively. The molecular weight was found to be 36 ± 01 kDa. The presence of 10 mM concentration of Ca²⁺, Cu²⁺, Mn²⁺, Mg²⁺, Zn²⁺, Hg²⁺, Fe²⁺ and EDTA, l-cystein, ascorbic acid significantly enhanced the PNL of the purified enzyme. In the course of the laboratory trials, it was demonstrated that PNL from B. borstelensis (P35) could be successfully applied to the production and clarification of fruit juice and oil extraction.     

Hydrogen treated TiO2 nanoparticles onto FTO glass as photoanode for dye-sensitized solar cells with remarkably enhanced performance

Hydrogen treatment is a facile and efficient approach for the enhancement in the functioning of TiO₂ nanoparticles for dye-sensitized solar cells (DSSC). In this work, TiO₂ nanoparticles have been synthesized in the hydrogen environment followed by the deposition onto FTO glass substrates with various film thickness as photoanodes for DSSC. The synthesized hydrogen treated TiO₂ nanoparticles based photoanodes have showed significantly improved photocurrent in the resulting fabricated devices. SEM and TEM analyses have confirmed the particle size and morphology of TiO₂ nanoparticles at various magnifications. The crystalline structure and phase identification were studied by XRD analysis and Raman spectroscopic measurements. The UV–Vis spectroscopy analysis was carried out to find the response of samples for ultraviolet and visible light. The current-voltage measurements have confirmed the improvement of photocurrent that is principally due to improved photo-activity of hydrogen treated TiO₂ nanoparticles. Moreover, hydrogen treated TiO₂ nanoparticles-based photoanode with the film thickness of 11.65 μm has remarkably enhanced power conversion efficiency of 6.05% in DSSCs. The ability of highly photoactive hydrogen treated TiO₂ nanoparticles will provide the new openings in different fields that include photo-electrochemical water splitting and in many other applications.     

Influence of fat replacers on chemical composition, proteolysis, texture profiles, meltability and sensory properties of low-fat Kashar cheese

Changes in chemical composition, proteolysis, lipolysis, texture, melting and sensory properties of low-fat Kashar cheese made with three different fat replacers (Simplesse D-100, Avicel Plus CM 2159 or beta-glucan) were investigated throughout ripening. The low-fat cheeses made with fat replacers were compared with full- and low-fat counterparts as controls. Reduction of fat caused increases in moisture and protein contents and decreases in moisture-in-non fat substance and yield values in low-fat cheeses. The use of fat replacers in the manufacture of low-fat Kashar cheese increased water binding capacity and improved overall quality of the cheeses. Use of fat replacer in low-fat cheese making has enhanced cheese proteolysis. All samples underwent lipolysis during ripening and low-fat cheeses with fat replacers had higher level of total free fatty acid than full- or low-fat control cheeses. Texture attributes and meltability significantly increased with addition of fat replacers. Sensory scores showed that the full-fat cheese was awarded best in all stages of ripening and low-fat variant of Kashar cheeses have inferior quality. However, fat replacers except beta-glucan improved the appearance, texture and flavour attributes of low-fat cheeses. When the fat replacers are compared, the low-fat cheese with Avicel Plus CM 2159 was highly acceptable and had sensory attributes closest to full-fat Kashar cheese.     

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.

     

Pre-treatment process of B<Subscript>4</Subscript>C particles to improve incorporation into molten AA2014 alloy

The properties of particle-reinforced aluminum alloy composites depend on the microstructure and evolved uniformity distribution of particles in the matrix, wettability of particles by the melt and chemical reactions between particles and matrix. The wettability of B₄C particles by the molten aluminum alloys is generally poor below 1100 °C making their incorporation difficult. In this study, in order to improve incorporation of the B₄C particles by AA2014 alloy melt a novel pre-treatment process was presented and B₄C-2014 Al composites containing B₄C particles up to 12 vol.% were manufactured by using stir casting. SEM observations have shown that uniformity distribution of B₄C in the matrix was satisfactory and there was no reaction at the particle-matrix interface due to low processing temperature. However clustering of B₄C particles was observed in relatively high particle-containing composites, which is commonly associated with the casting route.     

Discharge Characteristics of Polydisperse Powders through Conical Hoppers. Part 1: Predictions for Fine, Granular, Free Flowing Powders

Discharge characteristics of fine polydisperse granular powders of equal-solid density and near-spherical particle shape through a conical hopper were investigated by measuring solid discharge rates of powders. Effects of orifice size of hopper and size distribution of powders on discharge rate were determined by means of experiments conducted for six different sizes of hopper orifice and three different powder types under gravity flow conditions. A new effective mean diameter characterizing polydisperse powders is first introduced and determined from the particle size versus weight fraction distribution of a powder as the size corresponding to 50% cumulative weight fraction. This effective mean diameter was efficiently used in two modified forms of the Beverloo equation to predict discharge rates of polydisperse powders through hopper orifices.