Modifying the photocatalytic property of ZnO-based photoelectrodes by introducing MgFe2O4 nanoparticles

Journal of Materials Science: Materials in Electronics - Limited light absorption and acute charge recombination on the surface of ZnO nanostructure-based photoelectrodes are the basic challenges...     

FogMed: A Fog-Based Framework for Disease Prognosis Based Medical Sensor Data Streams

Recently, an increasing number of works start investigating the combination of fog computing and electronic health (ehealth) applications. However, there are still numerous unresolved issues worth to be explored. For instance, there is a lack of investigation on the disease prediction in fog environment and only limited studies show, how the Quality of Service (QoS) levels of fog services and the data stream mining techniques influence each other to improve the disease prediction performance (e.g., accuracy and time efficiency). To address these issues, we propose a fog-based framework for disease prediction based on Medical sensor data streams, named FogMed. This framework aims to improve the disease prediction accuracy by achieving two objectives: QoS guarantee of fog services and anomaly prediction of Medical data streams. We build a virtual FogMed environment and conduct comprehensive experiments on the public ECG dataset to validate the performance of FogMed. The experiment results show that it performs better than the cloud computing model for processing tasks with different complexities in terms of time efficiency.     

Fluorescence Enhancement Through Incorporation of Chromophores in Polymeric Nanoparticles

Incorporation of a pyrene chromophore on the surface of polymeric nanoparticles results in its fluorescence enhancement. Quantum yield and lifetime measurements suggest that the enrichment in fluorescent signal is due to the increase in the rigidity of the environment surrounding the fluorophores and that it is dependent on the particle size.     

Biohythane production from organic waste: Recent advancements,technical bottlenecks and prospects

The availability of fossil fuels is a major factor that determines the economy of a country. However, possible exhaustion of fossil fuel deposits as well as increased pollution, and other adverse effects on the environment has prompted us to search for alternative fuels. This resulted in the development of hythane, a blend of hydrogen with methane, at concentrations of 10%–30%. The breakdown of organic substrates using sequential dark fermentation (DF) and anaerobic digestion (AD) leads to biohythane production. The quality and quantity of biohythane can be improved by altering the following aspects: selection, development, and/or genetic engineering of suitable microbial consortium; the use of cheap, appropriate substrates; improved design of bioreactors; and the implementation of two-stage fermentation system. This review focusses on the mechanism of biohythane production and the different aspects involved in increasing both its production rate and quality. A comparative study has also been done to demonstrate the superiority of biohythane over other biofuels.     

Solids transport models comparison and fine‐tuning for horizontal,low concentration flow in single‐phase carrier fluid

We determined and fine‐tuned the solids transport models appropriate for predicting the single‐phase carrier fluid velocity to transport solid particles in conduits for horizontal, low concentration flow. A database with 538 experimental data points was compiled. A literature review was performed to determine the data ranges, forces, and mechanisms used to develop 44 models, and their velocity predictions were compared against the database using statistics. Using the dimensionless forms of the models and the data, the model parameters were adjusted to improve their accuracy and identify the dominant forces. At low concentrations: for liquid/solid flow from a bed of solids and gas/solid flow from the bottom of pipelines, the particle weight, and inertial and viscous forces dominate; for gas/solid flow from a bed of solids, the particle weight, and inertial, viscous, and adhesive forces play a role; and gaps exist in the data for large‐diameter pipes and high‐density gases. © 2013 American Institute of Chemical Engineers AIChE J, 60: 76–122, 2014     

Physicochemical Characteristics of Date Powder Produced in a Pilot-Scale Spray Dryer

Date fruit is a good source of bioactive compounds and natural sugar. It has the potential to be utilized as a substitute for added sugar. Although several forms of dates—such as fresh dates, date paste, and date syrup—are available in the market and used for different applications, free-flow date powder would be highly beneficial in improving shelf-life, ease of handling, and blendability with various foods prepared domestically and in industry. The objective of this study was to produce date powder in a pilot-scale spray dryer and determine its physicochemical qualities. Date powder was produced at eight processing conditions (2 carrier agents (maltodextrin (MD) and gum arabic (GA) × 2 inlet air temperature (150°C and 170°C) × 2 feedstock flow rates (25 ml/min and 40 ml/min)). To the carrier agent was added at 0.4 kg per 1.0 kg of date fruits (dry weight basis), and the feedstock to the spray dryer was prepared at 20% concentration. Date powder was obtained in all eight treatments. Color (L*a*b* values), moisture content, bulk density, wettability, solubility index, hygroscopicity, microstructure analysis (using scanning electron microscope (SEM)), and total phenolic compounds were determined for the spray-dried date powder. The physicochemical characteristics of date powder varied significantly with respect to the processing conditions. Although physical properties of date powder were significantly affected by the carrier agent, there was no difference in total phenolic compounds between date powders produced with MD and GA. An SEM study revealed that date powder produced with MD had smooth, regular-shaped spherical particles but with severe agglomeration. Date powder with GA had relatively smaller particles of irregular sphere with dented surfaces.     

Protection efficiencies of surface-active inhibitors in zinc-air batteries

Zinc (Zn) particles in alkaline electrolyte of a Zn-air battery (ZAB) are unstable and prone to corrosion. Zinc oxide (ZnO) generated on the surface of Zn particles affects the electrochemical reactions and reduces the battery efficiency. Thus, inhibiting the self-corrosion rate of Zn particles has become acritical issue for the development of these batteries. In this study, a research endeavor has been attempted by employing three types and concentrations of organic inhibitors in ZABs to constrain Zn anode corrosion. Significant analyses like polarization curve, constant current discharge, AC impedance, and dendrite growth are executed for in-depth understanding of the influences of these inhibitors. The experimental results reveal that the inhibiting efficiency of 10 wt% Sodium dodecyl benzene sulfonate surpassed polyethylene glycol 600 (PEG 600) and polysorbate 20 (Tween 20), with a maximum current density of 476.20 mA/cm² and voltage output of 1.4 V along with discharge capacitance of 10.31 Ah for 2 hours and 8 minutes. Zn anode surface analysis exposes significant dendrite growth and elemental Zn required for passivation suppression. Nevertheless, the results are also justified by Nyquist and Bode plots. Thus, the selected inhibitor will proficiently guarantee the enhanced performance and stability of the ZABs obtained and provide enormous opportunities for its applications.     

Unsupervised Quick Reduct Algorithm Using Rough Set Theory

Feature selection (FS) is a process to select features which are more informative.It is one of the important steps in knowledge discovery.The problem is that not all features are important.Some of the features may be redundant,and others may be irrelevant and noisy.The conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration.However,for many data mining applications,decision class labels are often unknown or incomplete,thus indicating the significance of unsupervised feature selection.However,in unsupervised learning,decision class labels are not provided.In this paper,we propose a new unsupervised quick reduct (QR) algorithm using rough set theory.The quality of the reduced data is measured by the classification performance and it is evaluated using WEKA classifier tool.The method is compared with existing supervised methods and the result demonstrates the efficiency of the proposed algorithm.     

Antihyperlipidemic effect of diosmin: A citrus flavonoid on lipid metabolism in experimental diabetic rats

The present study was hypothesized to evaluated the antihyperlipidemic effect of diosmin (DS) on lipid metabolism in experimental diabetic rats. Diabetes was induced male albino Wistar rats by intraperitoneal administration of streptozotocin (STZ) (45 mg/kg b.w.) 15 min after the ip administration of nicotinamide (NA) (110 mg/kg b.w.). DS were administered to diabetic rats intragastrically at 100 mg/kg b.w. for 45 days. The levels of plasma and tissue lipids (cholesterol, triglycerides) (TGs), free fatty acids (FFAs) and phospholipids (PLs), low density, very low-density lipoproteins (LDL and VLDL), and high-density-cholesterol (HDL-C) were measured. The activities of 3-hydroxy 3-methylglutaryl coenzyme A (HMG-CoA) reductase, lipoprotein lipase (LPL) and lecithin cholesterol acyl transferase (LCAT) were assayed. The levels of plasma and tissue lipids decreased with significant increase in HDL-C levels. The altered activities of lipid metabolic enzymes were restored to near normal. The present findings suggest that DS can potentially ameliorate lipid abnormalities in experimental diabetes.