Copper Ion Cementation in Presence of a Magnetic Field

The effect of an electromagnetic field (EMF) on the rate of copper(II) cementation from copper sulfate solutions on a rotating iron cylinder was investigated. The studied variables were cylinder rotation speed, magnetic field strength, and magnetic field direction. The application of EMF increased the rate of cementation in both parallel and perpendicular direction of the magnetic field where the latter proved to be more effective. The rate of mass transfer under an EMF was found to be more than doubled. The enhancement of copper recovery in presence of the EMF is due to the induced motion of Fe⁺ⁿ in the solution which is limited to a certain range of cylinder rotation speed. The power consumption for cementation of copper could be significantly reduced by utilizing EMF.     

Structurally Diverse Histone Deacetylase Photoreactive Probes: Design,Synthesis, and Photolabeling Studies in Live Cells and Tissue

Histone deacetylase (HDAC) activity is modulated in vivo by post-translational modifications and formation of multiprotein complexes. Novel chemical tools to study how these factors affect engagement of HDAC isoforms by HDAC inhibitors (HDACi) in cells and tissues are needed. In this study, a synthetic strategy to access chemically diverse photoreactive probes (PRPs) was developed and used to prepare seven novel HDAC PRPs 9 – 15 . The class I HDAC isoform engagement by PRPs was determined in biochemical assays and photolabeling experiments in live SET-2, HepG2, HuH7, and HEK293T cell lines and in mouse liver tissue. Unlike the HDAC protein abundance and biochemical activity against recombinant HDACs, the chemotype of the PRPs and the type of cells were key in defining the engagement of HDAC isoforms in live cells. Our findings suggest that engagement of HDAC isoforms by HDACi in vivo may be substantially modulated in a cell- and tissue-type-dependent manner.     

Full and reduced-order H∞ filtering of Takagi–Sugeno fuzzy time-varying delay systems: Input–output approach

This article addresses the issue of delay-dependent H_∞ filtering design for TakagiŮSugeno fuzzy time-varying delay systems using the input–output approach. A three-term approximation model has been used to transform the original system into two interconnected subsystems. Since the nonquadratic Lyapunov–Krasovskii functional requires to deal with the membership function's (MF) time derivative, upper-bound inequalities have been added to the obtained conditions. Based on the scaled small gain theorem, nonquadratic Lyapunov–Krasovskii functional approach and considering the bounds of the MF time-derivative, the H_∞ full- and reduced-order filters are designed and then formulated in terms of linear matrix inequalities. Finally, illustrative examples are presented to demonstrate the validity of the proposed methods.     

Performance Analysis of Novel Multi-band Monopole Antenna for Various Broadband Wireless Applications

Wireless Personal Communications - This study investigates a novel concept of using slot and Coplanar Waveguide fed antenna to obtain multiband operation. The compact antenna includes a star...     

Dearomatization of Diesel by Solvent Extraction: Influence of the Solvent Ratio and Temperature on Diesel Raffinate Properties

To comply with the stringent environmental regulations concerning the quality of fuels the production of ultra low sulfur fuels is obligatory. Consequently, the removal of aromatics from fuels has turned to be a serious issue. This is due to the fact that the presence of aromatics in fuel deters the ultra-low sulfur fuel production. Therefore the researcher’s interest has involved the dearomatization of fuels. As a result of the dearomatization, the quality of fuels improves tremendously. Here, solvent extraction was performed to dearomatize a feedstock sample with 20.1% aromatic and 166 ppm sulfur using acetonitrile. The extraction was performed at low temperature and ambient atmospheric pressure. The aromatic contents were determined via HPLC, while the ASTM methods were employed in other parameters determination. The results showed 72% minimum yield, 8.6% aromatic content, 58–64 cetane index, 73.2 ppm sulfur content, 5.4 viscosity, RI 1.4535, aniline point 82.15, specific gravity 0.824–0.812 with API 40.32–42.88 and flash point 70–78°C. The boiling range of the produced diesel fraction raffinate (172–373°C) that corresponds to C₈–C₂₄ cuts render it a potential candidate for other petrochemical applications.     

A Further Realization of a Flexible Metamaterial-Based Antenna on Nickel Oxide Polymerized Palm Fiber Substrates for RF Energy Harvesting

In this paper, we considered a distributed antenna system (DAS) to expand the coverage of VHF maritime communication systems and analyzed its performances. To reflect the geometrical distributed antenna port placements along the coastline, we considered a linear antenna layout. By deriving the lower bounds of the expected signal-to-noise ratio (SNR), we analyzed the SNR performances for both single-ship and multiple-ship transmission schemes. Next, based on the analytical results, we present design guidelines to implement the DAS based VHF maritime communication system. From the simulation results, the coverage expansion effect of the DAS was verified by the increased expected SNR. In addition, we confirmed that the system design parameters could be found from simple numerical calculations.

     

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.