Scanning near-field optical microscopy as a tool for the characterization of multimode interference devices

We report the scanning near-field optical microscopy (SNOM) characterization of a 4 x 4 multimode interference (MMI) device working at a wavelength of 1.55 microm and designed for astronomical signal recombination. A comprehensive analysis of the mapped propagating field is presented. We compare SNOM measurements with beam-propagation-method simulations and thus are able to determine the MMI structure's refractive-index contrast and show that the measured value is higher than the expected value. Further investigation allows us to demonstrate that good care must be taken with the refractive-index profile used in simulation when one deals with low-index contrast structures. We show evidence that a step-index contrast is not suitable for adequate simulation of our structure and present a model that permits good agreement between measured and simulated propagating fields.     

Microstructure of In-Situ composites

In this paper, the results of several years of research work by the authors on the microstructure of In-Situ composites are reviewed. Alloy systems investigated are the fibrous Al? Ni and cobalt-base superalloy Co? Cr? C, and the lamellar Al? Cu and Co? W. The effect of solidification variables and off-eutectic compositions on the micro-structure of Al? Ni Co? W and Al? Cu systems was studied. The influence of heat flow direction on the microstructure of Al? Ni was examined. Post solidification isothermal annealing was performed for Al? Cu and Co? Cr? C alloy and thermal cycling regimes were imposed on the Co? Cr? C alloy and the microstructure variations were examined. Computer simulation of directional, solidification to study the heat flow pattern in the metal and mould was also performed. It was found that low growth rates, high temperature gradients, eutectic composition and pure materials favor a planar solidification front and an aligned structure. Increasing the growth rate resulted in finer structures. The thermal stability of In-Situ composites was found to be reasonably good due to the strong interfacial bond between matrix and reinforcement in this class of composites.     

Poly[ethylene‐co‐(vinyl alcohol)]‐graft‐poly(ε‐caprolactone) Synthesis by Reactive Extrusion, 1 ‐ Structural and Kinetic Study

Chemical modification of EVOH in the molten state at 185 °C by a grafting from process of poly(ε‐caprolactone) in batch was studied. ¹H NMR was used to characterize the structure evolutions of PCL grafts. In addition to grafting reactions, dynamic covalent transesterification reactions between EVOH residual alcohols and the polyester grafts led to a redistribution of the PCL grafts length. up to 27 and SR up to 80% were obtained. Experiments made in a corotating mini twin‐screw extruder also confirmed these results. The effect of the alcohol to caprolactone ratio and catalyst concentration (SnOct₂) on kinetic evolution showed that few minutes were necessary to complete the polymerization. A kinetic model was proposed and adequate conditions for the synthesis by reactive extrusion were defined.

     

Effect of Reciproc blue,XP‐endo shaper,and WaveOne gold instruments on dentinal microcrack formation: A micro‐computed tomographic evaluation

The purpose of this study was to evaluate and compare the effect of Reciproc Blue (RPCB), XP‐endo Shaper (XPS), and WaveOne Gold (WOG) single‐files on dentinal microcrack formation using micro‐computed tomography (micro‐CT). Twenty‐four mesial roots (24 mesio‐buccal and 24 mesio‐lingual canals) of mandibular molar teeth were prepared using RPCB, XPS, and WOG files (n = 8/each group). The samples were scanned with micro‐CT in the pre‐ and post‐preparation. Then, before preparation and after preparation cross‐sectional images of the teeth were evaluated to detect the presence of microcracks. For each group, the number of microcracks was calculated as a percentage rate. The data were statistically analyzed using McNemar at 5% significance level (p < .05). Dentinal microcracks were observed in 25.99% (2,103 of 7,813), 31.99% (2,482 of 7,758), and 36.66% (2,836 of 7,731) of cross‐sectional images of the XPS, WOG, and RPCB groups, respectively. In all the groups, all the dentinal microcracks seen in the post‐treatment cross‐sectional images were present in the corresponding pre‐treatment images. Within the limitations of this study, the XPS, WOG, and RPCB files did not cause new dentinal microcrack formation or propagation of existing dentinal microcracks.     

Fuzzy-modeling with Particle Swarm Optimization for enhancing the production of biodiesel from Microalga

Biodiesel is one of the promising energy sources that could replace petroleum oil in the near future. Microalgae is occupying a distinguished position among the promising sources for biodiesel production. Enhancement of the lipids production during the pretreatment is a key factor for the biodiesel production. High-pressure homogenizer is a better pretreatment procedure to enhance the lipid extraction from microalgae. In this research, a robust model of biodiesel system using fuzzy logic is built based on the experimental data for biodiesel system. Then, Particle Swarm Optimization (PSO) optimizer is applied for determining the best operating parameters of biodiesel system. The decision variables used in the optimization process are; pressure, number of passes, and reaction time that maximizes the percentage of recovery lipids of biodiesel. A comparison study was carried out between the optimized results thought PSO algorithm and those obtained by the experimental results and the optimized results through the Response Surface Methodology (RMS). Results demonstrated that using the proposed optimization methodology is significantly better than RSM, a nearly 78.7% increase in lipids extraction could be achieved according to the current model.     

Arabic sign language intelligent translator

ABSTRACT

Arabic sign language (ArSL) is method of communication between deaf communities in Arab countries; therefore, the development of systemsthat can recognize the gestures provides a means for the Deaf to easily integrate into society. In this research we implemented a computational structurefor an intelligent interpreter that automatically recognizes the isolated dynamic gestures. The proposed system recognizes and translates gesturesperformed with one or both hands. It comprises five subsystems, building dataset, video processing, feature extraction, mapping between ArSL and Arabictext, and text generation. To apply the system, 100-signs of ArSL was used, which was applied on 1500 video files. It's were divided into five classes:alphabet, numbers, "prepositions, pronouns and question words", Arabic life expressions, and "nouns and verbs". The evaluation indicated that thesystem automatically recognizes and translates isolated dynamic ArSL gestures by highly accurate manner. The results showed that the system accuracy is 95.8%.     

Reviewing the transport domain: an evolutionary bibliometrics and network analysis

Tracing the trajectory of scientific fields has been recognized by informaticians, nonetheless, little effort has been dedicated to understanding the evolution of the fast-moving research field of transport, quantitatively and qualitatively. This paper identifies intellectual turning points and emerging trends in the area of transport. Using bibliometric methods, co-keyword networks, journal co-citation networks, highly cited categories, and country and institute networks are detected, visualized and discussed. To conduct this analysis, all publications (35,712) in 23 top journals in the field of transport are extracted from the Institute for Scientific Information (Web of Science). The output of this article could be a valuable source for academics and practitioners working in the field of transport planning and those who work in the areas having a strong relationship with transport issues including mathematicians, economics, operation research, management and geography.     

Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space. Meanwhile,the Recursive Feature Elimination algorithm (RFE) was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features. The features are fed into the various classification algorithms, namely, Support Vector Machine (SVM), K Nearest Neighbours (KNN), Decision Tree, Random Forest, and Multilayer Perceptron. All algorithms achieved superior results. The Random Forest algorithm achieved the best performance amongst the algorithms; it reached an overall accuracy of 99%. This algorithm classified stroke cases with Precision, Recall and F1 score of 98%, 100% and 99%, respectively. In the second dataset, the MRI image dataset was evaluated by using the AlexNet model and AlexNet + SVM hybrid technique. The hybrid model AlexNet + SVM performed is better than the AlexNet model; it reached accuracy, sensitivity, specificity and Area Under the Curve (AUC) of 99.9%, 100%, 99.80% and 99.86%, respectively.     

Investigation of the iron doping on the structural,optical, and magnetic properties of Fe-doped ZnO nanoparticles synthesized by sol–gel method

Journal of Materials Science: Materials in Electronics - In this work, the compatibility of zinc oxide for spintronic applications motivated the development of single-phase Fe/ZnO nanostructures by...