Structure and Microwave Dielectric Behavior of A‐Site‐Doped Sr(1−1.5x)CexTiO3 Ceramics System

The ion valence state, phase composition, microstructure, and microwave dielectric properties of Sr_(1?1.5x)Ce_xTiO₃ (x = 0.1–0.67, SCT) ceramics were systematically investigated. Sr_(1?1.5x)Ce_xTiO₃ ceramics were produced with gradual structural evolution from a cubic to a tetragonal and turned to an orthorhombic structure in the range of 0.1 ≤ x ≤ 0.67. Above a critical Ce proportion (x = 0.4), microstructural changes and normal grain growth initially occurred. On the basis of chemical analysis results, the reduction of Ti⁴⁺ ions was hastened by tetravalent ions (Ce⁴⁺). By contrast, this reduction was inhibited by trivalent ions (Ce³⁺). The observed dielectric behavior was strongly influenced by phase composition, oxygen vacancies (), and defect dipoles, namely, () and (). Temperature stable ceramics sintered at 1350°C for 3 h in air yielded an intermediate value of dielectric constant (ε_r = 40), with the smallest reported value of temperature coefficient of resonant frequency (τ_f = +0.9 ppm/°C), and quality factor (Q × f = 5699 GHz) at x = 0.6.     

Synthesis,Characterization and Surface Properties of Amidosulfobetaine Surfactants Bearing Odd-Number Hydrophobic Tail

Three amidosulfobetaine surfactants were synthesized namely: 3-(N-pentadecanamidopropyl-N,N-dimethyl ammonium) propanesulfonate (2a); 3-(N-heptadecanamidopropyl-N,N-dimethyl ammonium) propanesulfonate (2b), and 3-(N-nonadecanamidopropyl-N,N-dimethyl ammonium) propanesulfonate (2c). These surfactants were prepared by direct amidation of commercially available fatty acids with 3-(dimethylamino)-1-propylamine and subsequent reaction with 1,3-propanesultone to obtain quaternary ammonium salts. The synthesized surfactants were characterized by IR, NMR and mass spectrometry. Thermogravimetric analysis (TGA) results showed that the synthesized surfactants have excellent thermal stability with no major thermal degradation below 300 °C. The critical micelle concentration (CMC) values of the surfactants 2a and 2b were found to be 2.2 × 10^?4 and 1.04 × 10^?4 mol/L, and the corresponding surface tension (γ_CMC) values were 33.14 and 34.89 mN m^?1, respectively. The surfactants exhibit excellent surface properties, which are comparable with conventional surfactants. The intrinsic viscosity of surfactant (2b) was studied at various temperatures and concentrations of multi-component brine solution. The plot of natural logarithm of relative viscosity versus surfactant concentration obtained from Higiro et al. model best fit the surfactant behavior. Due to good salt resistance, excellent surface properties and thermal stability, the synthesized surfactant has potential to be used in various oil field applications such as enhanced oil recovery, fracturing, acid diversion, and well stimulation.     

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach

Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F‐score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods.     

Carbon nanotubes‐filled thermoplastic polyurethane–urea and carboxylated acrylonitrile butadiene rubber blend nanocomposites

This article reports the preparation and characterization of multiwalled carbon nanotubes (MWCNTs)‐filled thermoplastic polyurethane–urea (TPUU) and carboxylated acrylonitrile butadiene rubber (XNBR) blend nanocomposites. The dispersion of the MWCNTs was carried out using a laboratory two roll mill. Three different loadings, that is, 1, 3, and 5 wt % of the MWCNTs were used. The electron microscopy image analysis proves that the MWCNTs are evenly dispersed along the shear flow direction. Through incorporation of the nanotubes in the blend, the tensile modulus was increased from 9.90 ± 0.5 to 45.30 ± 0.3 MPa, and the tensile strength at break was increased from 25.4 ± 2.5 to 33.0 ± 1.5 MPa. The wide angle X‐ray scattering result showed that the TPUU:XNBR blends were arranged in layered structures. These structures are formed through chemical reactions of ? NH group from urethane and urea with the carboxylic group on XNBR. Furthermore, even at a very low loading, the high degree of nanotubes dispersion results in a significant increase in the electrical percolation threshold. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40341.     

Lipid‐derived monomer and corresponding bio‐based nanocomposites

Fatty acid based monomer and corresponding hybrid polymer layered silicate nanocomposites have successfully been prepared by using in situ polymerizations. The hybrid materials were prepared by adding different ratios of nanoclay during free radical homopolymerization of 2‐(acryloyloxy)ethyl stearate (AOES) monomer and copolymerization of AOES with styrene. AOES monomer was synthesized by treating stearic acid with 2‐hydroxyethyl acrylate. The formation of AOES monomer, homopolymer and copolymer was confirmed by ¹H NMR spectroscopic analysis. Further analysis and characterization of the nanocomposites were carried out by XRD, transmission electron microscopy, AFM and attenuated total reflectance Fourier transform infrared spectroscopy. TGA of the polymer nanocomposites was also carried out to evaluate their thermal stability, while flammability tests were conducted to investigate the effect of layered silicate on flame retardancy. Nanofiller addition into the polymer matrix substantially improved the thermal properties and fire retardancy of the composites. © 2016 Society of Chemical Industry     

A human-assisted knowledge extraction method for machining operations

This paper deals with a human-assisted knowledge extraction method to extract “if…then…” rules from a small set of machining data. The presented method utilizes both probabilistic reasoning and fuzzy logical reasoning to benefit from the machining data and from the judgment and preference of a machinist. Using the extracted rules, one can determine the values of operational parameters (feed, cutting velocity, etc.) to ensure the desired machining performance (keep surface roughness within the stipulated range (e.g., moderate)). Applying the presented method in a real-life machining knowledge extraction situation and comparing it with the inductive learning based knowledge extraction method (i.e., ID3), the usefulness of the method is demonstrated. As the concept of manufacturing automation is shifting toward “how to support humans by computers”, the presented method provides some valuable hints to the developers of futuristic computer integrated manufacturing systems.     

Modeling and simulation of intracellular dynamics: choosing an appropriate framework

Systems biology is a reemerging paradigm which, among other things, focuses on mathematical modeling and simulation of biochemical reaction networks in intracellular processes. For most simulation tools and publications, they are usually characterized by either preferring stochastic simulation or rate equation models. The use of stochastic simulation is occasionally accompanied with arguments against rate equations. Motivated by these arguments, we discuss in this paper the relationship between these two forms of representation. Toward this end, we provide a novel compact derivation for the stochastic rate constant that forms the basis of the popular Gillespie algorithm. Comparing the mathematical basis of the two popular conceptual frameworks of generalized mass action models and the chemical master equation, we argue that some of the arguments that have been put forward are ignoring subtle differences and similarities that are important for answering the question in which conceptual framework one should investigate intracellular dynamics.