Ultrastructure of Felis catus whole fetus (Fcwf-4) cell culture following infection with feline coronavirus

Feline coronavirus (FCoV) consists of two biotypes based on their growth in cell culture and their antigenicity. Infections with FCoV are highly prevalent in the cat population worldwide. In this study, Felis catus whole fetus (Fcwf-4)cell culture was infected with FCoV UPM11C/08. Virus multiplication in cell culture was monitored and examined under the transmission electron microscope. The virus particles revealed the characteristic morphology of feline FCoV represented by envelope viruses surrounded by peplomers. Virus attachment and entry into the cell occurred 15?h post-infection (pi), and the myriad of virus particles were observed both extracellularly and intracellularly after 48?h pi. Thereafter, intracellular virus particles were observed to be present in vacuoles or present freely in the cytoplasm.     

Numerical Modeling of the Performance of Thermal Interface Materials in the Form of Paste-Coated Sheets

The performance of thermal interface materials in the form of core sheets coated on both sides with a thermal paste is numerically modeled by finite-element analysis. The paste is polyol-ester-based carbon black paste and serves to improve the conformability. Good agreement is found between modeling and experimental results that involve copper proximate surfaces sandwiching the thermal interface material. The core sheets are copper, aluminum, indium, and flexible graphite. Flexible graphite (made from exfoliated graphite) is advantageous in its low elastic modulus, whereas copper and aluminum foils are advantageous in their high thermal conductivity. Indium is advantageous in its low elastic modulus compared with copper or aluminum and in its high thermal conductivity compared with flexible graphite. Among the four types of core sheet with identical thickness, coated indium foil gives the best performance for the range of foil thickness of 6 μm to 112 μm for the case of smooth (0.01 μm roughness) proximate surfaces and 117 μm to 320 μm for the case of rough (15 μm roughness) proximate surfaces. Aluminum foil gives the best performance for the thickness range of 112 μm to 2000 μm in the case of smooth proximate surfaces. For thicknesses below these ranges, flexible graphite performs the best. For thicknesses above these ranges, copper foil performs the best.     

Investigation on some physicochemical properties of guest-conjugated and -incorporated hybrid organic/inorganic linear-dendritic nanocarriers

Benzyl alcohol, as a model compound in conjugation, and 1-(2-pyridylazo)-2-naphtol (PAN), as a lipophilic dye molecule in encapsulation, were exploited using linear-dendritic ABA amphiphilic triblock copolymers, which is known as “hybrid macromolecules”, containing silicon atoms by two methods. In the first route, benzyl alcohol was attached to the Si-Cl peripheral groups of the hybrid in different generations to synthesize host-benzyl alcohol conjugates. In the second procedure, PAN as the guest molecule was incorporated into different generations of the synthesized hybrid. Binding capacity and incorporation content (IC) of different generations of the hybrid were investigated using conventional methods such as nuclear magnetic resonance and UV–vis spectroscopy. It was observed that the IC, hydrolytic behavior and the release rate from the prepared micellar structures can be tuned by either external parameters such as pH or internal parameters such as hydrophilic/lipophilic ratio by developing generations. Dynamic light scattering and transmission electron microscopy experiments depicted diameter of the prepared nanocarriers between 100 to 250 nm. The release of guest molecules from the carriers was evaluated at pH 1, 7.4 and 10. Briefly, the prepared micelles can play a role as carrier with tunable release rate without sacrificing their micellar stability.     

Change Point Estimation Methods for Control Chart Postsignal Diagnostics: A Literature Review

Control charts are the most popular monitoring tools used to distinguish between special (assignable) and common causes of variation and to detect any changes in processes. The time that a control chart gives an out‐of‐control signal is not the real time of change. The actual time of the change is called the change point. Knowing the real time of the change will help and simplify finding the assignable causes of the signal, which may be the result of a shift in the process mean or change in process variability. This article gives an overview of change point estimation in control charts, provides a classification scheme, and describes the research that has previously appeared in the literature. In addition, a gap analysis in this area provides direction for future research. Copyright © 2011 John Wiley & Sons, Ltd.     

What is the relationship between research funding and citation-based performance? A comparative analysis between critical disciplines

Scientometrics - The goal of the study here is to model and analyze the relation between research funding and citation-based performance in science to predict the diffusion of new scientific...     

A green route for biodiesel production from waste cooking oil over base heterogeneous catalyst

The present work describes the synthesis of porous BaSnO₃ by eco‐friendly sol‐gel method using albumin as a bio‐template agent, and its application as a solid base catalyst in biodiesel production from waste cooking oil. The physico‐chemical, textural, and morphological properties of the catalyst were evaluated by X‐ray diffraction (XRD), Brunauer‐Emmett‐Teller (BET), field emission scanning electron microscopy (FESEM), and temperature programmed desorption (TPD)–CO₂ techniques. The synthesized catalyst showed considerable stability, efficient catalytic activity, and negligible metal leaching. The satisfactory performance of the catalyst could be ascribed to the presence of basic sites of different strength on the surface of the catalyst. The catalyst produced maximum biodiesel yield of 96% at optimum reaction conditions of 90°C reaction temperature, methanol to oil molar ratio of 10:1, catalyst dosage of 6 wt%, and reaction time of 2 hours. Moreover, the catalyst showed substantial reusability up to five reaction cycles without any considerable decrease in transesterification activity.     

Rigorous correlations for predicting the solubility of H2S in methylimidazolium-based ionic liquids

Two general and simple models, a group contribution correlation (model I) and an empirical relation (model II), were proposed to predict the solubility of H₂S in methylimidazolium based ionic liquids (ILs) over wide range of temperatures (303.15-363.15 K) and pressures (60.8-2016.8 kPa). The constants of the suggested functionality relations were found via the Nelder-Mead simplex algorithm. Both correlations were trained with 407 data points of H₂S solubility in 9 methylimidazolium based ILs and tested through 121 H₂S solubility data points of 3 different methylimidazolium based ILs to ensure generality. A comprehensive statistical evaluation showed that both suggested correlations are vigorous and have satisfactory error trends. The dataset was subjected to a statistical outlier diagnostic test and the validity of the database was confirmed. In addition, the sensitivity analysis revealed that the experimental data and both models have the same responses toward pressure and temperature, which indicates the reliability of the proposed correlations.     

A new method for grafting functional groups onto mesoporous silica: an electrochemical approach

A novel method for the modification of mesoporous silica, MCM-41, using an electrochemical approach has been developed, and the process was monitored by cyclic voltammetery and spectrometric methods. The method was applied to the modification of mesoporous silica with new functional groups which are not accessible by conventional methods. Malononitrile-functionalized MCM-41 mesoporous silica was characterized by low-angle X-ray diffraction, Fourier transform infrared spectroscopy, mass spectrometry, thermal analysis, elemental analysis, high resolution transmission electron microscopy, and surface area measurement (S _BET). In addition, the application of malononitrile-functionalized MCM-41 as a sorbent for gold ions was demonstrated.     

One-Dimensional Quantum Compass Model in an Out-plane Magnetic Field

The one-dimensional quantum compass model (1D-QCM) in an out-plane magnetic field is investigated by numerical and analytical methods. The presence of the 1D-QCM in an area of the ground-state magnetic phase diagram where the odd couplings are antiferromagnetic and larger than the even couplings is studied. Using pseudo spin ladder operators indicates that the 1D-QCM in an out-plane magnetic field can be mapped to the one-dimensional Ising model in a transverse magnetic field (ITF). Analytical investigation of the effective ITF Hamiltonian predicts the occurrence of two quantum phase transitions with increasing magnetic field. Also, the critical fields are obtained from this method, which are completely in agreement with the results of the numerical experiment based on the Lanczos method. This numerical method accurately determined the behavior of the energy gap, magnetization and string order parameter, and therefore it is a very useful method for indicating the ground-state phases of the system.     

Modeling the effect of non-linear process parameters on the prediction of hydrogen production by steam reforming of bio-oil and glycerol using artificial neural network

Biomass-derived substrates such as bio-oil and glycerol are gaining wide acceptability as feedstocks to produce hydrogen using a steam reforming process. The wide acceptability can be attributed to a huge amount of glycerol and bio-oil obtained as by-products of biodiesel production and pyrolysis processes. Several parameters have been reported to affect the production of hydrogen by biomass steam reforming. This study investigates the effect of non-linear process parameters on the prediction of hydrogen production by biomass (bio-oil and glycerol) steam reforming using artificial neural network (ANN) modeling technique. Twenty different multilayer ANN model architectures were tested using datasets obtained from the bio-oil and glycerol steam reforming. Two algorithms namely Levenberg-Marquardt and Bayesian regularization were employed for the training of the ANNs. An optimized network configuration consisting of 3 input layer 14 hidden neurons, 1 output layer, and 3 input layer, 5 hidden neurons, and 1 output layer were obtained for the Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by bio-oil steam reforming. While an optimized network configuration consisting of 5 input nodes, 9 hidden neurons, 1 output node, and 5 input nodes, 8 hidden neurons, and 1 output node were obtained for Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by glycerol steam reforming. Based on the optimized network, the predicted hydrogen production from the bio-oil and glycerol steam agreed with the actual values with the coefficient of determination (R²) > 0.9. A low mean square error of 3.024 × 10⁻²⁴ and 6.22 × 10⁻¹⁵ for the optimized for Levenberg-Marquardt and Bayesian regularization-trained ANN, respectively. The neural network analyses of the two processes showed that reaction temperature and glycerol-to-water molar ratio were the most relevant factors that influenced the production of hydrogen by bio-oil and glycerol steam reforming, respectively. This study has demonstrated the robustness of the ANN as a technique for investigating the effect of non-linear process parameters on hydrogen production by bio-oil and glycerol steam reforming.