Effect of Iron sulfate as electrolyte additive on plasma electrolytic oxidation of aluminum alloy

This study has been carried out to investigate the incorporation of Iron(II) sulfate as an additive of electrolyte on formed AA1010 aluminum alloy, using plasma electrolytic oxidation method in silicate-based electrolytes containing Iron(II) sulfate. In order to fabricate nanocomposite coating, silicon nitride nanopowder was added to electrolyte. The effects of iron(II) sulfate additive on the voltage-time trend, microstructure, compositions, wear, and corrosion resistances of PEO coatings were investigated. In addition, current density and concentration of additive were studied as parameters that were effective on coating. Results showed that although FeSO₄ enters to the coating structure, but it does not develop a new phase. The corrosion and wear behavior of coated samples with FeSO₄ indicate an improvement as compared to those without additive.     

An assessment of k-ε turbulence models for gas distribution analysis

This paper presents the gas distribution analysis by injecting air fountain into the containment and simulations with the HYDRAGON code. Turbulence models of standard k-ε(SKE), re-normalization group k-ε(RNG) and a realizable k-ε(RLZ) are used to assess the effects on the gas distribution analysis during a severe accident in a nuclear power plant. By comparing with experimental data,the simulation results of the RNG and SKE turbulence models agree well with the experimental data on the prediction of dimensionless density distributions. The results illustrate that the turbulence model choice had a small effect on the simulation results, particularly the region near to the air fountain source.     

Big Data Analytics in Healthcare — A Systematic Literature Review and Roadmap for Practical Implementation

The advent of healthcare information management systems (HIMSs) continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale. Analysis of this big data allows for boundless potential outcomes for discovering knowledge. Big data analytics (BDA) in healthcare can, for instance, help determine causes of diseases, generate effective diagnoses, enhance QoS guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments, generate accurate predictions of readmissions, enhance clinical care, and pinpoint opportunities for cost savings. However, BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners. In this paper, we present a comprehensive roadmap to derive insights from BDA in the healthcare (patient care) domain, based on the results of a systematic literature review. We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on NoSQL databases. We also identify the limitations and challenges of these applications and justify the potential of NoSQL databases to address these challenges and further enhance BDA healthcare research. We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm. We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare. Finally, we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work. The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators, practitioners and professionals to successfully implement BDA initiatives in their organizations.      

Plasma Internal Inductance in the Presence of External Resonant Fields in IR-T1 Tokamak

In this paper we presented experimental investigation of effects of local limiter biasing (V_biasing = +200 v, V_biasing = +320 v) on the plasma parameters as plasma current, loop voltage, poloidal beta, plasma pressure, plasma energy, plasma resistance, plasma temperature, plasma displacement, Shafranov parameter and plasma internal inductance in IR-T1 tokamak. For these purposes, array of magnetic probes and also a diamagnetic loop have been used. The results show that applied biased voltage V_biasing = +200 v causes to decrease of about 40 % in plasma internal inductance. The plasma resistance and the plasma displacement have been decreased by V_biasing = +200 v. The main result of the application of V_biasing = +200 v is flatting the plasma parameters profiles. In other words, the addition of biasing voltage V_biasing = +200 v to plasma could be effective for improving the quality of tokamak plasma discharge by creating the steady state plasma. The plasma current, plasma pressure, plasma energy, plasma temperature and shift parameter have increased after the application of limiter biasing with V_biasing = +320 v but they decrease rapidly.     

Storage and permeation of hydrogen molecule,atom and ions (H+ and H−) through silicon carbide nanotube; a DFT approach

The barriers for the encapsulation and decapsulation of hydrogen ions (cationic hydrogen and hydride), atom, and molecule through silicon carbide nanotube are thoroughly studied. DFT method is selected to measure the kinetic barriers for the passage of hydrogen atom, ions and molecule through nanotube via scanning potential energy surface. The kinetic barriers for the passage (encapsulation and decapsulation) of hydrogen are very important to understand the mechanism of hydrogen storage and release. The barriers for the permeation of H, H⁺ and H^? across SiC nanosheet are lower compared to hydrogen molecule (H₂). The exohedral and endohedral adsorption of hydrogen ions (cation and anion), atom and exohedral hydrogen molecule on silicon carbide are exothermic in nature. Whereas the encapsulation of hydrogen molecule in silicon carbide is endothermic. Electronic properties are analyzed through measurement of energy gap between highest occupied and lowest unoccupied molecular orbitals gap (G_H-L) and the density of state (DOS) spectra. The G_H-L analysis reveals that endohedral complexes have more pronounced effect on electronic properties compared to exohedral complexes. The SiC nanotube has highly favorable properties for storage and release of hydrogen ions, and atom.     

Selection of optimal denoising-based regularization hyper-parameters for performance improvement in a sensor validation model

Multilayered auto-associative neural architectures have widely been used in empirical sensor modeling. Typically, such empirical sensor models are used in sensor calibration and fault monitoring systems. However, simultaneous optimization of related performance metrics, i.e., auto-sensitivity, cross-sensitivity, and fault-detectability, is not a trivial task. Learning procedures for parametric and other relevant non-parametric empirical models are sensitive to optimization and regularization methods. Therefore, there is a need for active learning strategies that can better exploit the underlying statistical structure among input sensors and are simple to regularize and fine-tune. To this end, we investigated the greedy layer-wise learning strategy and denoising-based regularization procedure for sensor model optimization. We further explored the effects of denoising-based regularization hyper-parameters such as noise-type and noise-level on sensor model performance and suggested optimal settings through rigorous experimentation. A visualization procedure was introduced to obtain insight into the internal semantics of the learned model. These visualizations allowed us to suggest an implicit noise-generating process for efficient regularization in higher-order layers. We found that the greedy-learning procedure improved the overall robustness of the sensor model. To keep experimentation unbiased and immune to noise-related artifacts in real sensors, the sensor data were sampled from simulators of a nuclear steam supply system of a pressurized water reactor and a Tennessee Eastman chemical process. Finally, we compared the performance of an optimally regularized sensor model with auto-associative neural network, auto-associative kernel regression, and fuzzy similarity-based sensor models.     

Synthesis and characterization of novel curcumin based polyurethanes varying diisocyanates structure

In this research work, novel polyurethanes (PUs) based on blends of curcumin/1,4-butane diol (BDO) by varying the structure of diisocyanates were prepared following step growth polymerization. Structural study of blends and various diisocyanates based PU through Fourier Transform Infrared (FTIR) spectroscopy confirmed the incorporation of curcumin into the backbone of the PU. The scanning electron microscopic (SEM) study confirmed the well dispersion of incorporated curcumin and homogeneity of surface of synthesized samples. The SEM results also indicated that surface morphology of synthesized samples much dependent on diisocynates structure. Moreover SEM images inferred that phase separation is more pronounced in aromatic diisocyanate based PU. The anti-bacterial and anti-fungal tests were performed against different strains in order to determine the biocompatibility of the curcumin based PU. The antimicrobial activity results revealed that the material having aromatic diisocyanate are more biocompatible than the aliphatic diisocyanates in the PU structure. On the whole, this work is actually a step towards the generation of novel biocompatible materials preferably useful for biomedical applications.     

Exploration of connectivity with SEM: An fMRI study of resting state

In this era, the neuroscientists get attention to find a network of brain which is activated in resting state of mind. It is challenging for any analytical technique to find out the connectivity among Brodmann areas of brain in resting state. To find network, a study was conducted in which brains are scan by functional magnetic resonance imaging of four healthy right handed individuals. Exploratory factor analysis was used to explore that which Brodmann areas are related to each other and then structural equation model has applied on explored factors (lobes) to find a network of activated brain in resting state. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 264–269, 2016     

Measuring research performance of Iraqi universities using Scopus data

Scientometrics - This study investigates the research performance of the Iraqi public and private universities using the Scopus citation database. The investigation consists of three stages. The...