Electron–Electron Interactions in Sb-Doped SnO<Subscript>2</Subscript> Thin Films

Electrical conductivity and Hall-effect measurements on undoped and Sb-doped SnO₂ thin films prepared by the sol–gel technique were carried out as a function of temperature (55 K to 300 K). Structural characterizations of the films were performed by atomic force microscopy (AFM) and x-ray diffraction (XRD). A doping-induced metal–insulator transition (MIT) was observed. On the metallic side of the transition, the experimental data were interpreted in terms of electron–electron interactions (EEI). The existence of EEI was confirmed by excellent agreement between theoretical and experimental data. The experimental data on the insulator side of the transition were analyzed in terms of variable-range hopping (VRH) conduction. A complete set of parameters describing the properties of the localized electrons, including hopping energy, hopping distance, and the value of the density of states at the Fermi level, was determined.     

A Study on Magnetoresistivity, Activation Energy, Irreversibility and Upper Critical Field of Slightly Mn Added Bi-2223 Superconductor Ceramics

This study discusses the effect of Mn addition on the superconducting and physical properties in Bi_1.8Pb_0.4Sr₂Mn _x Ca_2.2Cu_3.0O _y bulk superconductors with x=0,0.03,0.06,0.15,0.3, and 0.6 by means of the magnetoresistivity measurements. The magnetoresistivity of the samples prepared using the standard solid-state reaction method was measured for different values of the applied magnetic field strengths. The superconducting and physical properties of the samples such as the zero resistivity transition temperatures (T _c ), irreversibility fields (?? ₀ H _irr ), and upper critical fields (?? ₀ H _c2) were deduced from the magnetoresistivity curves. Moreover, thermally activated flux creep model was studied for activation energy (U ₀) values of the samples. According to the results of the measurements, not only were the T _c and U ₀ values of the samples found to decrease significantly but the ?? ₀ H _irr and ?? ₀ H _c2 values were also observed to reduce with the increase in the Mn addition, indicating that the doping degrades the physical and superconducting properties of the samples.     

Analysis of Indentation Size Effect on Mechanical Properties of Cu-Diffused Bulk MgB2 Superconductor Using Experimental and Different Theoretical Models

This study indicates the change of the electrical, microstructural, physical, mechanical and superconducting properties of Cu-diffused bulk MgB₂ superconductors by means of scanning electron microscopy (SEM), X-ray diffraction analysis (XRD), microhardness and dc resistivity measurements. The samples are prepared at different annealing temperatures in the range from 650 to 850 ^°C. Electrical and superconducting properties of samples are estimated from the dc electrical resistivity measurements. Moreover, microhardness measurements are performed to investigate the mechanical properties. Further, phase composition, grain sizes and lattice parameters are determined from the XRD measurements. At the same time, the surface morphology and grain connectivity of the samples are examined by SEM investigations. The measurements conducted demonstrate that both the Cu diffusion into the MgB₂ system and the increment in the diffusion-annealing temperature increase the critical transition temperatures. Similarly, microstructure and grain size improve while the voids and porosity decrease with the increase of the diffusion-annealing temperature. In addition, the experimental results of the microhardness measurements are investigated using the Meyer’s law, PSR (proportional specimen resistance), modified PRS (MPSR), elastic-plastic deformation model (EPD) and Hays–Kendall (HK) approach. The obtained microhardness values of the samples decrease with the increase of the diffusion-annealing temperature up to 850 ^°C. The Hays–Kendall approach is found to be the most successful model describing the mechanical properties of the samples studied in this work.     

Free Vibration Analysis and Design of an Adhesively Bonded Corner Joint with Double Support

This study carries out the three dimensional free vibration analysis of an adhesively bonded corner joint and investigates the effect of an additional horizontal support to the adhesive corner joint with single support on the first ten natural frequencies and mode shapes. In the presence of a horizontal support the effects of the vertical support length, the adhesive thickness, the plate thickness, and the joint length on the natural frequencies and modal strain energies of the adhesive joint were also investigated using the back-propagation Artificial Neural Network (ANN) method and the finite element method. The natural frequencies and modal strain energies increased with increasing plate thickness, whereas an adverse effect was observed for increasing joint length. Both horizontal and vertical support lengths exhibited similar effects but the adhesive thickness had a negligible effect. The plate thickness and the joint length are dominant geometrical parameters in comparison with both horizontal and vertical support lengths. The proposed ANN models were combined with the Genetic Algorithm in order to determine the optimal corner joint in which the maximum natural frequency and minimum elastic modal strain energy are achieved for each natural frequency and mode shape of the adhesive corner joint and the optimal dimensions were given versus one geometrical parameter.     

Rheology,particle-size distribution,and stability of low-fat mayonnaise produced via double emulsions

In this study, the effects of the double emulsification method on the rheological properties, particle size, and stability of low-fat mayonnaise were studied. Different water-phase-to-oil ratios (2:8 and 4:6) of primary emulsions and different stabilizer types (sodium caseinate, xanthan gum, and lecithin-whey protein concentrate) were used to produce double-emulsified mayonnaise. As a control sample, mayonnaise was prepared conventionally. Sodium caseinate was found to be the most efficient stabilizer. In the presence of sodium caseinate, the stability and apparent viscosity of double-emulsified mayonnaise increased but their particle sizes decreased. It was found that flow behavior of double-emulsified and conventionally prepared mayonnaise could be described by the power law model. The double-emulsified mayonnaise samples were not different from the control samples in terms of stability and particle size. In addition, using the double emulsion method, it was possible to reduce the oil content of mayonnaise to 36.6%.     

Dinitrochlorobenzene test in patients with chronic nephritis

The effect of passive immunization with hetero and isoimmune antiovary antiserum on the fertilization of mouse, rat, and hamster eggs was determined. Fertilization of the eggs could be inhibited by a single ip injection of respective antiovary antiserum obtained from rabbits. The antiovary antisera absorbed with the ovary did not prevent fertilization but antisera absorbed with the liver and kidney inhibited fertilization was not inhibited by a single ip injection of antiovary antiserum obtained from rats and mice, respectively. Fertilization in vitro of mouse eggs was low in the presence of control serum but was significantly reduced in the presence of antiserum (p less than .001).     

The application of homotopy perturbation method for MHD flows of UCM fluids above porous stretching sheets

In this study, by means of homotopy perturbation method (HPM) an approximate analytical solution of the magnetohydrodynamic (MHD) boundary layer flow of an upper-convected Maxwell (UCM) fluid over a porous stretching sheet is obtained. The main feature of the HPM is that it deforms a difficult problem into a set of problems which are easier to solve. HPM produces analytical expressions for the solution of nonlinear differential equations. The obtained analytic solution is in the form of an infinite power series. In this work, the analytical solution obtained by using only two terms from HPM solution. The results reveal that the proposed method is very effective and simple and can be applied to other nonlinear problems. Also it is shown that this method coincides with homotopy analysis method (HAM) for the studied problem.     

Automatic diagnosis of ureteral stone and degree of hydronephrosis with proposed convolutional neural network,RelieF, and gradient-weighted class activation mapping based deep hybrid model

Urinary system stone disease is a common disease group all over the world. Ureteral stones constitute 20% of all urinary system stones. Ureteral stones are important because they can cause hydronephrosis and related renal parenchymal damage in the kidneys. In the study, a hybrid model was developed to detect hydronephrosis and ureteral stones from kidney images. In the developed model, heat maps of the original images were obtained by using gradient-weighted class activation mapping (Grad-CAM) technology. Then, feature maps were extracted from both the original and heatmap datasets using the Efficientnetb0 architecture. Extracted feature maps were concatenated using a multimodal fusion technique. In this way, different features of an image are obtained. This has a positive effect on the performance of the model. The Relief dimension reduction technique was used to eliminate unnecessary features in the obtained feature map so that the proposed model can work faster and more effectively. Finally, the optimized feature map is classified in the support vector machine (SVM) classifier. To compare the performance of the proposed hybrid model, results were obtained with 8 state-of-the-art models accepted in the literature. Among these models, the highest accuracy value was achieved in the Efficientnetb0 architecture with 67.98%, whereas the accuracy of the proposed hybrid model was 91.1%. This value indicates that the proposed model can be used for HUN diagnosis.     

End-to-End Data-Flow Parallelism for Throughput Optimization in High-Speed Networks

The increase in the data produced by large-scale scientific applications necessitates innovative solutions for efficient transfer of data. Although the current optical networking technology reached theoretical speeds of 100?Gbps, applications still suffer from inefficient transport protocols and bottlenecks on the end-systems (e.g. disk, CPU, NIC). High-performance systems provide us with parallel disks, processors and network interfaces. However the lack of orchestration of these end-system resources with the available network capacity results in underutilization of the network bandwidth. In this study, a model and two algorithms that use ‘end-to-end data-flow parallelism’ to optimize the use of network and end-system resources are proposed. This is achieved by using multiple parallel streams over the network; and multiple parallel disks and CPUs at the end systems. Our model predicts the optimal number of streams and disk/CPU stripes that maximizes the data transfer speed for any setting. Our algorithms use GridFTP parallel samplings and calculate the optimal level of parallelism based on our prediction model. The experiments conducted by using actual GridFTP transfers show that the predictions performed by our model and algorithms provide close-to-optimal performances with negligible overhead and use minimal number of resources. The end-to-end data transfer throughput is improved dramatically in existence of end-system bottlenecks compared to the non-optimized transfers.     

Dynamic path planning of mobile robots with improved genetic algorithm

In this study, a new mutation operator is proposed for the genetic algorithm (GA) and applied to the path planning problem of mobile robots in dynamic environments. Path planning for a mobile robot finds a feasible path from a starting node to a target node in an environment with obstacles. GA has been widely used to generate an optimal path by taking advantage of its strong optimization ability. While conventional random mutation operator in simple GA or some other improved mutation operators can cause infeasible paths, the proposed mutation operator does not and avoids premature convergence. In order to demonstrate the success of the proposed method, it is applied to two different dynamic environments and compared with previous improved GA studies in the literature. A GA with the proposed mutation operator finds the optimal path far too many times and converges more rapidly than the other methods do.