CuxNi1-xO nanostructures and their nanocomposites with reduced graphene oxide: Synthesis,characterization, and photocatalytic applications

NiO nanostructure was synthesized using a simple co-precipitation method and was embedded on reduced graphene oxide surface via ultrasonication. Structural investigations were made through X-ray diffraction (XRD) and functional groups were confirmed by Fourier transform infrared spectroscopy (FTIR). XRD analysis revealed the grain size reduction with doping. Fourier transform infrared spectroscopy confirmed the presence of metal-oxygen bond in pristine and doped NiO nanostructure as well as the presence of carbon containing groups. Scanning electron microscopy (SEM) indicated that the particle size decreased when NiO nanostructure was doped with copper. BET surface area was found to increase almost up to 43 m²/g for Cu doped NiO nanostructure/rGO composite. Current-voltage measurements were performed using two probe method. UV–Visible spectroscopic profiles showed the blue and red shift for Cu doped NiO nanostructure and Cu doped NiO Nanostructure/rGO composite respectively. Rate constant for Cu doped NiO nanostructure/rGO composite found to increase 4.4 times than pristine NiO nanostructure.     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

Molecular structure and granule morphology of native and heat‐moisture‐treated pinhão starch

Pinhão seed is an unconventional source of starch and the pines grow up in native forests of southern Latin America. In this study, pinhão starch was adjusted at 15, 20 and 25% moisture content and heated to 100, 110 and 120 °C for 1 h. A decrease in λ max (starch/iodine complex) was observed as a result of increase in temperature and moisture content of HMT. The ratio of crystalline to amorphous phase in pinhão starch was determined via Fourier transform infra red by taking 1045/1022 band ratio. A decrease in crystallinity occurred as a result of HMT. Polarised light microscopy indicated a loss of birefringence of starch granules under 120 °C at 25% moisture content. Granule size distribution was further confirmed via scanning electron microscopy which showed the HMT effects. These results increased the understanding on molecular and structural properties of HMT pinhão starch and broadened its food and nonfood industrial applications.     

Electrophoretic deposition of graphene oxide on carbon brush as bioanode for microbial fuel cell operated with real wastewater

Microbial fuel cell (MFC) is a promising technology for simultaneous wastewater treatment and energy harvesting. The properties of the anode material play a critical role in the performance of the MFC. In this study, graphene oxide was prepared by a modified hummer's method. A thin layer of graphene oxide was incorporated on the carbon brush using an electrophoretic technique. The deoxygenated graphene oxide formed on the surface of the carbon brush (RGO-CB) was investigated as a bio-anode in MFC operated with real wastewater. The performance of the MFC using the RGO-CB was compared with that using plain carbon brush anode (PCB). Results showed that electrophoretic deposition of graphene oxide on the surface of carbon brush significantly enhanced the performance of the MFC, where the power density increased more than 10 times (from 33 mWm^?2 to 381 mWm^?2). Although the COD removal was nearly similar for the two MFCs, i.e., with PCB and RGO-CB; the columbic efficiency significantly increased in the case of RGO-CB anode. The improved performance in the case of the modified electrode was related to the role of the graphene in improving the electron transfer from the microorganism to the anode surface, as confirmed from the electrochemical impedance spectroscopy measurements.     

ANT-colony based disjoint set assortment in wireless sensor networks

Wireless Networks - Wireless sensor network (WSN) consists of small sized devices containing different sensors to monitor physical, environmental and medical conditions during surveillance of...     

A fog-based security framework for intelligent traffic light control system

Multimedia Tools and Applications - The world is facing many problems including that of traffic congestion. To highlight the issue of traffic congestion worldwide specially in urban areas and to...     

Role of Nd-Ni on structural,spectral and dielectric properties of strontium-barium based nano-sized X-type ferrites

The influence of neodymium and nickel substitution on structural and dielectric parameters was investigated in strontium-barium X-type hexagonal ferrites having composition SrBaCu_2?xNi_xNd_yFe_28?yO₄₆ (x = 0, 0.2, 0.4, 0.6, 0.8, 1 and y = 0, 0.02, 0.04, 0.06, 0.08, 0.1). Sol-gel method was employed for synthesizing these hexagonal ferrites. The XRD plots of all studied materials which were annealed at 1250 °C show single phase characteristics. Lattice parameter ‘c’ increased as a consequence of larger radius of rare earth ion (Nd³⁺) as compared to (Fe³⁺), while lattice parameter ‘a’ showed very small variation. The cell volume was obtained in the range 2508.32–2523.75 (ų). The inclusion of Nd-Ni also affected X-ray density, bulk density and porosity. The FTIR spectroscopy indicated the particular absorption peaks of hexagonal ferrites and it was performed in the range of 500–700 cm^?1. On account of Nd-Ni doping, the dielectric constant, dielectric loss and AC-conductivity showed decreasing trend. The occupancy of Nd³⁺ ions at octahedral site impedes the valence alternation of Fe³⁺; therefore there was decrease in dielectric permittivity. Ac conductivity has been decreased from 9.14 to 6.49 (Ω cm)^?1 at frequency of 2.7 GHz. The Cole-Cole plots of synthesized materials noticeably revealed grain boundary contribution. The appearance of single semi-circle in impedance Cole-Cole graphs confirms the exceptional role of grain boundaries in the conduction process. The considerably lower dielectric parameters of investigated nano X-type ferrites propose their feasibility for high-frequency applications (phase shifters, dielectric resonators, stealth technology etc).     

A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG

Frontiers of Information Technology & Electronic Engineering - Reconstruction of a 12-lead electrocardiogram (ECG) from a serial 3-lead ECG has been researched in the past to satisfy the need...