Erysipelothrix rhusiopathiae bacteremia after dog bite

A case of erysipeloid with bacteraemia caused by Erysipelothrix rhusiopathiae (ER) in a previously healthy 41-year old man is presented. The bacterium was probably introduced by the bite of a dog. He was treated successfully with penicillin V. The ER bacteraemia occurred without complications of endocarditis.     

THE EFFECT OF PRECIPITATION PARAMETERS ON PREPARATION OF LITHIUM FLUORIDE (LIF) NANO-POWDER

Lithium fluoride powder (LiF) is a white powder with a density of 2.64 gr/cm³ and a melting point of 848°C. This powder has several applications such as flux, glaze, soldering, and aluminum melting process, but one of the most important uses of this powder is its application in dosimetry. The commercial powders currently used for this purpose have average sizes of 5 to 10 micrometers; the objective of this research is to produce LiF powder with nano-metric particle size. In this study, the reaction of LiOH + HF → LiF + H₂O has been selected from among several reactions that were able to produce LiF powder, and some precipitation parameters such as temperature, time, agitation type, and supersaturation degree have been controlled. The morphology, phase analysis, and particle size distribution of the resulting powders were analyzed by SEM, XRD, and LPSA. Finally, lithium fluoride nano-powder was synthesized at a temperature of 25°C, pH about 2-3, reaction time less than 1 s, and agitation by ultrasonic bath.     

Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network

Rainfall forecasting plays many important role in water resources studies such as river training works and design of flood warning systems. Recent advancement in artificial intelligence and in particular techniques aimed at converting input to output for highly nonlinear, non-convex and dimensionalized processes such as rainfall field, provide an alternative approach for developing rainfall forecasting model. Artificial neural networks (ANNs), which perform a nonlinear mapping between inputs and outputs, are such a technique. Current literatures on artificial neural networks show that the selection of network architecture and its efficient training procedure are major obstacles for their daily usage. In this paper, feed-forward type networks will be developed to simulate the rainfall field and a so-called back propagation (BP) algorithm coupled with genetic algorithm (GA) will be used to train and optimize the networks. The technique will be implemented to forecast rainfall for a number of times using rainfall hyetograph of recording rain gauges in the Upper Parramatta catchment in the western suburbs of Sydney, Australia. Results of the study showed the structuring of ANN network with the input parameter selection, when coupled with GA, performed better compared to similar work of using ANN alone.     

Seeing the woods for the trees: the problem of information inefficiency and information overload on operator performance

One of the recurring questions in designing dynamic control environments is whether providing more information leads to better operational decisions. The idea of having every piece of information is increasingly tempting (and in safety critical domains often mandatory) but has become a potential obstacle for designers and operators. The present research study examined this challenge of appropriate information design and usability within a railway control setting. A laboratory study was conducted to investigate the presentation of different levels of information (taken from data processing framework, Dadashi et al. in Ergonomics 57(3):387–402, 2014) and the association with, and potential prediction of, the performance of a human operator when completing a cognitively demanding problem-solving scenario within railways. Results indicated that presenting users only with information corresponding to their cognitive task, and in the absence of other, non task-relevant information, improves the performance of their problem-solving/alarm handling. Knowing the key features of interest to various agents (machine or human) and using the data processing framework to guide the optimal level of information required by each of these agents could potentially lead to safer and more usable designs.     

Copper (I) ion stabilized on fe3o4‐core ethylated branched polyethyleneimine‐shell as magnetically recyclable catalyst for ATRP reaction

Branched polyethyleneimine (bPEI) was used to modify the surface of Fe₃O₄ nanoparticles coated with silica layer, and then, it was treated with ethyl iodide to prepare Fe₃O₄@SiO₂@Ethylated‐bPEI. In the next step, the yolk–shell structure was gained by selectively etching the SiO₂ middle layer. Finally, copper(I) was introduced to the yolk–shell Fe₃O₄@Ethylated‐bPEI and the activity of the catalyst was evaluated for atom transfer radical polymerization (ATRP) of styrene, led to obtain the well‐defined polymer with relatively low polydispersity. The toxicity of the residual copper in the polymer product was a limiting issue for applicability of ATRP reactions especially for biological purposes. In this report, the copper content in the polymer was reduced to the excellent value of 1.1 ppm. Moreover, the magnetic isolation, recyclability, and remove the need for an external ligand were other advantages of the synthesized catalyst which makes it suitable for employing in ATRP reactions. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42337.     

A combinatorial approach to evaluation of monomer conversion and particle size distribution in vinyl chloride emulsion polymerization

This work is targeted to study emulsion polymerization of vinyl chloride monomer (VCM) using experimental and mathematical methods. To fulfill this goal, a computer code was developed on the basis of zero–one population balance by which the effects of initiator and emulsifier concentration on the evolution of VCM conversion were investigated in the course of polymerization. The model was also trained to capture the coagulation of the particles. This enabled to adopt a reliable way of evaluating the particle size distribution (PSD). In particular, the rates of homogeneous and micellar nucleation mechanisms were simulated and reasonably predicted alterations in the PSD and the number of polymer particles under the influence of aforementioned parameters. The results from modeling were satisfactorily consistent with the experimental outputs and obviously visualized the impact of initiator and surfactant concentration on the PSD of the prepared PVC latexes.     

Pure and mixed gas permeation study of silica incorporated polyurethane-urea membrane modified by MOCA chain extender

Polyurethane-urea (PUU) nanocomposite membranes have been prepared using various loadings of silica (SiO₂) nanoparticles. A Novel PU was fabricated by a two-step bulk polymerization technique based on polycaprolactone (PCL), hexamethylene diisocyanate (HDI), and diamine chain extender, 4,4-methylenebis(2-chloroaniline) (MOCA). The FTIR spectra indicated that the extent of phase separation reduces with increasing SiO₂ content. The presence of crystal regions in the soft and hard segments was confirmed by DSC and XRD analyses. The obtained results illustrated a decrement in the gases' permeation in the presence of SiO₂ particles. By increasing the filler content up to 15 wt% and pressure of 8 bar, the gas permeation value of the CO₂, O₂, and N₂ decreased 36%, 54%, and 59%, respectively. However, the permselectivity of the CO₂/N₂ and O₂/N₂ increased considerably, 55% and 13% respectively. On the contrary, by raising the temperature, a dramatic augmentation in the permeability of all gases with a simultaneous reduction in the selectivity values of both gas pairs was revealed. Increasing the pressure led to a decrease in the permeability values of all membranes for O₂ and N₂, whereas the permeability for CO₂ increased with the pressure. Nevertheless, the selectivity values for the pair of gases increased (at a pressure of 10 bar, 1.66 and 1.17 times the neat PU for CO₂/N₂ and O₂/N₂, respectively). Furthermore, the permeability of the CO₂, O₂, and N₂ for the mixed gases was smaller than for pure ones at the same gas upstream pressure. Nonetheless, like the pure gas, the selectivity of both pair gases increased.     

Review of snow water equivalent retrieval methods using spaceborne passive microwave radiometry

ABSTRACT

The physical properties of a snowpack strongly influence the emissions from the substratum, making snow property retrievals feasible by means of the surface brightness temperature observed by passive microwave sensors. Depending on the spatial resolution observed, time series records of daily snow coverage and critical snowpack properties such as snow depth (SD) and snow water equivalent (SWE) could be helpful in applications ranging from modelling snow variations for water resources management in a catchment to global climatologic studies. However, the challenge of including spaceborne SWE products in operational hydrological and hydroclimate modelling applications is very demanding with limited uptake by these systems, mostly attributed to insufficient SWE estimation accuracy. The root causes of this challenge include the coarse spatial resolution of passive microwave (PM) observations that observe highly aggregated snowpack properties at the spaceborne scale, and inadequacies during the retrieval process caused by uncertainties with the forward emission modelling of snow and challenges to find robust parameterizations of the models. While the spatial resolution problem is largely in the realm of engineering design and constrained by physical restrictions, a better understanding of developed and adopted retrieval methodologies can provide the clarity needed to enhance our knowledge in this field. In this paper, we review snow depth and SWE retrieval methods using PM observations, taking only dry snow retrieval processes into consideration. Snow properties using PM observations can be modelled by purely empirical relations based on underlying physical processes, and SD and SWE can be estimated by regression-based approaches. Snow property retrievals have been refined gradually throughout four decades use of PM observations in tandem with better understanding of physical processes, inclusion of better snowpack parameterizations, improved uncertainty analysis frameworks, and applying better inversion algorithms. Studying available methods, we conclude that snowpack parameterization is key to accurate retrieval. By improving retrieval algorithm architectures to better capture dynamic snowpack evolution processes, SWE estimates are likely to improve. We conclude that this challenge can be addressed by coupling emission models and land surface models or integrating weather-driven snowpack evolution into emission models and performing inversion in a Bayesian framework.     

SOLUTION OF POPULATION BALANCE EQUATIONS IN EMULSION POLYMERIZATION USING METHOD OF MOMENTS

In this work, the method of moments is used for solution of population balance equations appearing in modeling of emulsion polymerization (EP). The zero-one model without coagulation effect and the pseudo-bulk model including coagulation effect are investigated as two common approaches for modeling EP processes. The fixed quadrature method is used to close the set of moment equations, and the maximum entropy approach is applied to reconstruct the particle size distribution from a finite number of its moments. Comparing the results with those obtained by the high-precision finite volume technique indicates that, despite the low computational load of the moment method, it has an acceptable accuracy. These features support use of the moment technique for other applications such as on-line control or optimization in particulate processes.     

Solubility of Ketoconazole in Ethanol + Water Mixtures at Various Temperatures

Ketoconazole is an important drug with low water solubility. Its low aqueous solubility could be increased using various methods such as by the addition of a pharmaceutical cosolvent. The solubility of ketoconazole in ethanol + water mixtures at 293.2, 298.2, 303.2, and 308.2 K were determined, and the results showed that the addition of ethanol increased the solubility and the maximum value was obtained at 80% of ethanol. The generated data were mathematically represented using the Jouyban–Acree model within an acceptable accuracy.