Weldability of titanium and nickel with alloy filler addition and different electrodes tip shapes by using micro spot brazing method

The weldability of titanium and nickel using micro spot brazing technology was successfully investigated. Brazing was performed using a 4-kA welding current, 2-V voltage, 60-N load, and a 25–50-ms welding time. An alloy filler metal, specifically a 0.1-mm thick foil with a composition of 71Ag-28Cu-1Mg, was sandwiched between Ti and Ni. The three electrode copper tip shapes that were investigated in this study were circular-, rectangular-, and ring-shaped tips. The weldability of the joint was evaluated in terms of the fracture load, weld size, shear strength, and micro hardness. A high joint strength was obtained in the case of the 71Ag-28Cu-1Mg filler when a rectangular electrode tip face was used.     

Wheat gluten: high molecular weight glutenin subunits--structure, genetics, and relation to dough elasticity

ABSTRACT:  Gluten proteins, representing the major protein fraction of the starchy endosperm, are predominantly responsible for the unique position of wheat amongst cereals. These form a continuous proteinaceous matrix in the cells of the mature dry grain and form a continuous viscoelastic network during the mixing process of dough development. These viscoelastic properties underline the utilization of wheat to prepare bread and other wheat flour based foodstuffs. One group of gluten proteins is glutenin, which consists of high molecular weight (HMW) and low molecular weight (LMW) subunits. The HMW glutenin subunits (HMW-GS) are particularly important for determining dough elasticity. The common wheat possesses 3 to 5 HMW subunits encoded at the Glu-1 loci on the long arms of group 1 chromosomes (1A, 1B, and 1D). The presence of certain HMW subunits is positively correlated with good bread-making quality. Glutamine-rich repetitive sequences that comprise the central part of the HMW subunits are actually responsible for the elastic properties due to extensive arrays of interchain hydrogen bonds. Genetic engineering can be used to manipulate the amount and composition of the HMW subunits, leading to either increased dough strength or more drastic changes in gluten structure and properties.     

Detection of microscopic glaucoma through fundus images using deep transfer learning approach

Glaucoma disease in humans can lead to blindness if it progresses to the point where it affects the oculus' optic nerve head. It is not easily detected since there are no symptoms, but it can be detected using tonometry, ophthalmoscopy, and perimeter. However, advances in artificial intelligence approaches have permitted machine learning techniques to diagnose at an early stage. Numerous methods have been proposed using Machine Learning to diagnose glaucoma with different data sets and techniques but these are complex methods. Although, medical imaging instruments are used as glaucoma screening methods, fundus imaging specifically is the most used screening technique for glaucoma detection. This study presents a novel DenseNet and DarkNet combination to classify normal and glaucoma affected fundus image. These frameworks have been trained and tested on three data sets of high-resolution fundus (HRF), RIM 1, and ACRIMA. A total of 658 images have been used for healthy eyes and 612 images for glaucoma-affected eyes classification. It has also been observed that the fusion of DenseNet and DarkNet outperforms the two CNN networks and achieved 99.7% accuracy, 98.9% sensitivity, 100% specificity for the HRF database. In contrast, for the RIM1 database, 89.3% accuracy, 93.3% sensitivity, 88.46% specificity has been attained. Moreover, for the ACRIMA database, 99% accuracy, 100% sensitivity, 99% specificity has been achieved. Therefore, the proposed method is robust and efficient with less computational time and complexity compared to the literature available.     

Multifractal analysis of Mg-doped ZnO thin films deposited by sol–gel spin coating method

A multifractal analysis has been performed on the 3D (three-dimensional) surface microtexture of magnesium-doped zinc oxide (ZnO:Mg) thin films with doping concentration of 0, 2, 4, and 5%. Thin films were deposited onto the glass substrates via the sol–gel spin coating method. The effect of magnesium doping, on the crystal structure, morphology, and band gap for ZnO:Mg thin films has been analyzed by X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), and UV–Vis spectroscopy. It has been observed that the surface of ZnO thin films is multifractal in nature. However, multifractality and complexity observed to decrease with increasing content of Mg in ZnO thin films due to formation of islands on the surface in accordance with Volmer–Weber growth mechanism. The investigations revealed that crystallinity, microtexture, morphology, and optical properties of the thin films can be tuned by controlling the Mg content within the ZnO lattice. In particular, their optical band gap energies were 3.27, 3.31, 3.34, and 3.33 eV at 0, 2, 4, and 5%, respectively. The prepared thin films of ZnO:Mg with tuned characteristics would have promising applications in optoelectronic devices.     

Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging

Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains. The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms.     

Operation of quantum-dot semiconductor optical amplifiers under nonuniform current injection

The influence of nonuniform current injection along the active region, on the linear operation of a quantum-dot semiconductor optical amplifier (QD-SOA) is investigated. For this purpose, we have utilized some functions to generate various nonuniform current injection profiles. These profiles have been considered in our numerical calculations, where the rate equation model is employed to construct different characteristics of the QD-SOA. We have found that the gain, as well as the crosstalk, of a QD-SOA is closely associated to the variance of the carrier density along the cavity. Simulation results show that nonuniform current injection can be used as a technique for gain enhancement as well as crosstalk suppression.     

Peer to peer trade in HTM5 meta model for agent oriented cloud robotic systems

Cloud computing is a methodology and not a technology. Adaptation of cloud computing services for robotic applications is relatively straightforward while adaptation of underlying ideas will require a new design attitude. Cloud computing is a cost-effective and dynamic business model. Currently cloud robotics is understood as a client server methodology which enables robots utilize resources and services placed at centralized servers. These cloud servers treat robots as any other client computer offering them platform, infrastructure, process or algorithm as a service. HTM5 is an OMG MDA based multi-view meta-model for agent oriented development of cloud robotic systems. HTM5 encourages design of peer-to-peer service ecosystems based on an open registry and matchmaking mechanism. In peer-to-peer cloud robotics, a robot can trade its hardware, software and functional resources as a service to other robots in the ecosystem. The peer-to-peer trade in such systems may be driven by contracts and relationships between its member agents. This article discusses trade-view model of HTM5 methodology and its use in developing a cloud robotic ecosystem that implements peer-to-peer, contract based economy. The article also presents a case study with experiments that implement distributed artificial intelligence and peer-to-peer service oriented trade on simulated and real robot colonies.     

Molecular beam epitaxial growth and characterization of catalyst-free InN/InxGa1-xN core/shell nanowire heterostructures on Si(111) substrates

We report on the achievement of, for the first time, InN/InGaN core/shell nanowire heterostructures, which are grown directly on Si(111) substrates by plasma-assisted molecular beam epitaxy. The crystalline quality of the heterostructures is confirmed by transmission electron microscopy, and the elemental mapping through energy dispersive x-ray spectrometry further reveals the presence of an InGaN shell covering the sidewall and top regions of the InN core. The optical characterizations reveal two emission peaks centered at ～1685?nm and 1845?nm at 5?K, which are related to the emission from the InGaN shell and InN core, respectively. The InN/InGaN core/shell nanoscale heterostructures exhibit a very high internal quantum efficiency of ～62% at room temperature, which is attributed to the strong carrier confinement provided by the InGaN shell as well as the nearly intrinsic InN core.     

Modeling river water quality parameters using modified adaptive neuro fuzzy inference system

Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm(EA) is a new technique for improving the performance of artificial intelligence models such as the adaptive neuro fuzzy inference system(ANFIS) and artificial neural networks(ANN). Attempts have been made to make the models more suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization(PSO)and ant colony optimization for continuous domains(ACO_R) in estimating water quality parameters at three stations along the Zayandehrood River, in Iran. The ANFIS-PSO and ANFIS-ACO_R methods were also compared with the classic ANFIS method, which uses least squares and gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity(EC), total dissolved solids(TDS), the sodium adsorption ratio(SAR), carbonate hardness(CH), and total hardness(TH). Correlation analysis was performed using SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with ANFIS-ACO_R. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.