Multiple metastatic infections in a hemodialysis patient with untunneled internal jugular catheter

We present an end‐stage renal disease patient on dialysis with fever. The primary source was right internal jugular vein catheter which had metastatic infections in the body probably via an arteriovenous communication in a cavity in left lung. Patient had right psoas muscle abscess and a left kidney abscess. An ¹⁸F‐fluorodeoxyglucose‐positron emission spectroscopy scan was done to find out left kidney abscess. A search of literature did not reveal many patients of psoas abscess secondary to infection of hemodialysis access.     

BAGEL: A non-ignorable missing value estimation method for mixed attribute datasets

Surveys are mainly conducted to obtain valuable information on some criteria from a specified population. But, the survey results often become biased due to non-response of the subjects under study for highly significant attributes. Such non-ignorable missingness need to be treated and the actual values should be retrieved. Many methods have already been proposed for handling missing values in either discrete or continuous attributes. But, there exists a large gap in handling non-ignorable missing values in datasets with mixed attributes. With the intent of addressing this gap, this paper proposes a methodology called as Bayesian Genetic Algorithm (BAGEL) with hybridized Bayesian and Genetic Algorithm principles. In BAGEL, the initial population is generated using Bayesian model and fitness values of the chromosomes are evaluated using Bayesian principles. BAGEL is implemented in real datasets for imputing both discrete and continuous missing values and the imputation accuracy is observed. The experimental results show the superior performance of BAGEL than other standard imputation techniques. Statistical tests conducted to validate the experimental results also prove that BAGEL outperforms at all missing rates from 5% to 50%.     

Fabrication of dense Ce0.9Mg0.1P2O7-PmOn composites by microwave heating for application as electrolyte in intermediate-temperature fuel cells

In the present work, we report a method of fabrication of dense 10 mol% Mg²⁺-doped cerium pyrophosphate-phosphate (Ce_0.9Mg_0.1P₂O₇-P_mO_n; CMP-P) composites by microwave heat-treatment of the preformed Ce_0.9Mg_0.1P₂O₇ substrates in the presence of phosphoric acid. The composite was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and electrochemical impedance spectroscopy (EIS). The microwave heating at 375 °C for 5 min resulted in the formation of dense CMP-P composites which retained most of the pyrophosphate phase. The electrical conductivity was extracted from the EIS data and for the CMP-P composite prepared by H₃PO₄ loading for 10 h and microwave heat-treatment for 5 min it was found to be >10^?2 S m^?1 in 100–250 °C range with a maximum of 0.062 S cm^?1 at 190 °C, which was significant for its application as electrolyte in intermediate temperature fuel cells.     

Detecting Streaming of Twitter Spam Using Hybrid Method

Twitter, the social network which evolving faster and regular usage by millions of people and who become addicted to it. So spam playing a major role for Twitter users to distract them and grab their attention over them. Spammers actually detailed like who send unwanted and irrelevant messages or websites and promote them to several users. To overcome the problem many researchers proposed some ideas using some machine learning algorithms to detect the spammers. In this research work, a new hybrid approach is proposed to detect the streaming of Twitter spam in a real-time using the combination of a Decision tree, Particle Swarm Optimization and Genetic algorithm. Twitter has given access to the researchers to get tweets from its Twitter-API for real-time streaming of tweet data which they can get direct access to public tweets. Here 600 million tweets are created by using URL based security tool and further some features are extracted for representation of tweets in real-time detection of spam. In addition, our research results are compared with other hybrid algorithms which a better detection rate is given by our proposed work.     

Genesis of enhanced photoactivity of CdS/Nix nanocomposites for visible-light-driven splitting of water

A series of CdS/Ni_x nanocomposite photocatalysts, containing ca. 0.6–15 wt% Ni, were synthesized using a one-step hydrothermal method and characterized for their crystallographic, morphological, interfacial, and optical properties. Rietveld refinement of powder XRD data revealed the coexistence of wurtzite (hexagonal) and zinc blende (cubic) phases of CdS in ratios dependent on Ni content. Only a fraction of Ni existed as a secondary phase of NiS while the majority occupied the lattice positions of hexagonal CdS. Whereas up to 10-fold enhancement in H₂ evolution compared with pure CdS was observed for samples containing ∼1.5–4.5 wt% Ni, samples with smaller or larger Ni content displayed poor activity for visible-light-induced splitting of water in presence of sulfide–sulphite ions as sacrificial electron donors. In contrary to recent findings, our study reveals that the enhanced CdS photoactivity is not a result of charge transfer between p-type NiS and n-type CdS, Ni-induced visible-region absorbance, or the coating of CdS particles by non-photoactive NiS. Instead, the preparation-dependent hexagonal/cubic CdS phase boundaries and particle morphology may play a crucial role. Additionally, certain Ni-doping-induced sub-bandgap shallow energy levels contribute to charge carrier separation.     

KERNEL: Enabler to build smart surrogates for online optimization and knowledge discovery

KERNEL – A novel parameter-free surrogate building algorithm using Adaptive Neuro Fuzzy Inference System (ANFIS) is presented to provide an intelligent and robust technology to optimally estimate the configuration of ANFIS along with Sobol-based fast sample size determination (SSD) methodology. The proposed algorithm is capable of fine-tuning the existing knowledge base about the physics of the process in terms of human experience. It also enables knowledge discovery through a multi-objective optimization problem (MOOP) solved by non-dominated sorting genetic algorithm, NSGA-II, thus presenting machine-invented physics of the process. Experimentally validated polymerization reaction network model is considered and ANFIS surrogates are built using KERNEL. Surrogate-based optimization was found to be nine times faster than conventional optimization using the time expensive model, thus enabling its online implementation. Comparison of ANFIS with Kriging is also included.     

A Novel Highly Sensitive Humidity Sensor Based on ZnO/SBA‐15 Hybrid Nanocomposite

This work deals with the study of hydrothermally synthesized zinc oxide (ZnO) loaded mesoporous SBA‐15 hybrid nanocomposite for relative humidity sensing (RH) at room temperature. The sensor exhibits an excellent ~5 orders impedance change along with excellent linearity, quick response time (17 s), rapid recovery time (18 s), negligible hysteresis (1.2%), good repeatability, and stability (1.8%) in 11%–98% RH range. In addition, complex impedance spectra of the sensor at different RHs were analyzed to understand the humidity sensing mechanism. Our study can open a new way for realizing ZnO/SBA‐15 hybrid nanocomposite for fabrication of high‐performance RH sensors.     

Natural Palm Olein Polyol as a Replacement for Polyether Polyols in Viscoelastic Polyurethane Foam

The impact of replacing three polyether polyols with different levels of a single palm olein‐based natural oil polyol (NOP) was systematically correlated with the changes in foaming reactivity, cell structure, physico‐mechanical properties, and morphology of viscoelastic (VE) foams. The data show that replacing the polyether polyols with the NOP slightly increased the rate of the foaming reactivity. Increasing the NOP content resulted in increased cell size and cells remained fully open. Increased NOP content contributed to higher load bearing properties of VE foam, which can be attributed to higher functionality of NOP compared to polyether polyols. Addition of the NOP slightly increased the resilience of the foams, however, the hysteresis which is the measure of energy absorption remained mostly unaffected. Age properties, characterized by dry and humid compression sets, were mostly unaffected by the replacement of the polyether polyol with the NOP. The addition of NOP did not impact the morphology of the VE foam polymer matrix, which appears to retain a low degree of hard and soft segment domain separation. Overall, the results demonstrate a feasibility that the NOP can be used to partially replace the polyether polyols in VE polyurethane foams without significant impact on the functional performance.     

Urethane‐forming reaction kinetics and catalysis of model palm olein polyols: Quantified impact of primary and secondary hydroxyls

Model palm olein natural oil polyols (NOPs) with varying ratios of primary to secondary hydroxyls were synthesized, characterized, and evaluated in reaction kinetics study with isocyanate in formation of polyurethanes. Reaction rate constants and activation energies associated with primary and secondary hydroxyls of NOPs were quantified. The kinetic study in toluene shows that the NOP containing primary hydroxyls have three times higher reaction rate constants in noncatalyzed reaction with 4,4′‐diphenylmethane diisocyanate (4,4′‐MDI) compared to the model NOP containing only secondary hydroxyls, which is associated with higher activation energy of secondary hydroxyls. However, the difference in reaction rate constants of primary and secondary hydroxyls in NOPs diminished in the reactions catalyzed with dibutyltin dilaurate. Bulk polymerization reaction confirms the kinetics results in toluene, showing that the model NOP containing primary hydroxyls reached gel time at a faster rate. Evaluation of elastomers from bulk polymerization shows low degree of phase separation of hard and soft segments for elastomers based on the model NOPs. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 42955.     

Highly selective sulfonated poly(vinylidene fluoride‐co‐hexafluoropropylene)/poly(ether sulfone) blend proton exchange membranes for direct methanol fuel cells

Proton exchange membranes (PEMs) based on blends of poly(ether sulfone) (PES) and sulfonated poly(vinylidene fluoride‐co‐hexafluoropropylene) (sPVdF‐co‐HFP) were prepared successfully. Fabricated blend membranes showed favorable PEM characteristics such as reduced methanol permeability, high selectivity, and improved mechanical integrity. Additionally, these membranes afford comparable proton conductivity, good oxidative stability, moderate ion exchange capacity, and reasonable water uptake. To appraise PEM performance, blend membranes were characterized using techniques such as Fourier transform infrared spectroscopy, AC impedance spectroscopy; atomic force microscopy, and thermogravimetry. Addition of hydrophobic PES confines the swelling of the PEM and increases the ultimate tensile strength of the membrane. Proton conductivities of the blend membranes are about 10^?3 S cm^?1. Methanol permeability of 1.22 × 10^?7cm² s^?1 exhibited by the sPVdF‐co‐HFP/PES10 blend membrane is much lower than that of Nafion‐117. AFM studies divulged that the sPVdF‐co‐HFP/PES blend membranes have nodule like structure, which confirms the presence of hydrophilic domain. The observed results demonstrated that the sPVdF‐co‐HFP/PES blend membranes have promise for possible usage as a PEM in direct methanol fuel cells. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43907.