Effect of the filler morphology on the crystallization behavior and dielectric properties of the polyvinylidene fluoride-based composite

Ceramic/polymer composites can be chemically stable, mechanically strong, and flexible, which make them candidates for electric devices, such as pressure or temperature sensors, energy storage or harvesting devices, actuators, and so forth. Depending on the application, various electrical properties are of importance. Polymers usually have low dielectric permittivity, but increased dielectric permittivity can be achieved by the addition of the ceramic fillers with high dielectric constant. With the aim to enhance dielectric properties of the composite without loss of flexibility, 5 wt% of BaTiO₃-Fe₂O₃ powder was added into a polyvinylidene fluoride matrix. The powder was prepared by different synthesis conditions to produce core/shell structures. The effect of the phase composition and morphology of the BaTiO₃-Fe₂O₃ core/shell filler on the structure and lattice dynamics of the polymer composites was investigated. Based on the results of the thermal analysis, various parameters of ceramic/polymer composites were determined. Differences in the phase composition and morphology of the filler have an influence on the formation of various polyvinylidene fluoride allomorphs and the degree of crystallinity. Furthermore, the dielectric performances of pure polyvinylidene fluoride and the polymer/ceramic composites were measured.     

Mathematical modelling for predicting the growth of Pseudomonas spp. in poultry under variable temperature conditions

A dynamic growth model under variable temperature conditions was implemented and calibrated using raw data for microbial growth of Pseudomonas spp. in poultry under aerobic conditions. The primary model was implemented using measurement data under a set of fixed temperatures. The two primary models used for predicting the growth under constant temperature conditions were: Baranyi and modified Gompertz. For the Baranyi model the maximum specific growth rate and the lag phase at constant environmental conditions are expressed in exact form and it has been shown that in limit case when maximal cells concentration is much higher than the initial concentration the maximum specific growth rate is approximately equal to the specific growth rate. The model parameters are determined in a temperature range of 2-20 degrees C. As a secondary model the square root model was used for maximum specific growth rate in both models. In both models the main assumption, that the initial physiological state of the inoculum is constant and independent of the environmental parameters, is used, and a free parameter was implemented which was determined by minimizing the mean square error (MSE) relative to the measurement data. Two temperature profiles were used for calibration of the models on the initial conditions of the cells.     

Geometry optimization of a slender cantilever beam subjected to lateral buckling

The paper deals with the geometry optimization of a slender cantilever beam subjected to a concentrated force acting at the free end. The two-parametric mathematical model of lateral torsional buckling is based on the Bernoulli–Euler beam theory and is given in dimensionless form. The optimization procedure is performed using the optimal control theory and the relation between state and adjoint variables is presented. The boundary value problem derived from the optimization procedure is solved numerically and compared to solutions obtained via an alternative optimization approach called sequential approximate optimization.     

Sparse representation of precision matrices used in GMMs

The paper presents a novel precision matrix modeling technique for Gaussian Mixture Models (GMMs), which is based on the concept of sparse representation. Representation coefficients of each precision matrix (inverse covariance), as well as an accompanying overcomplete matrix dictionary, are learned by minimizing an appropriate functional, the first component of which corresponds to the sum of Kullback-Leibler (KL) divergences between the initial and the target GMM, and the second represents the sparse regularizer of the coefficients. Compared to the existing, alternative approaches for approximate GMM modeling, like popular subspace-based representation methods, the proposed model results in notably better trade-off between the representation error and the computational (memory) complexity. This is achieved under assumption that the training data in the recognition system utilizing GMM have an inherent sparseness property, which enables application of the proposed model and approximate representation using only one dictionary and a significantly smaller number of coefficients. Proposed model is experimentally compared with the Subspace Precision and Mean (SPAM) model, a state of the art instance of subspace-based representation models, using both the data from a real Automatic Speech Recognition (ASR) system, and specially designed sets of artificially created/synthetic data.     

Real-time PCA calculation for spectral imaging (using SIMD and GP-GPU)

This article presents two optimized implementations of the PCA algorithm, primarily targeted on spectral image analysis in real time. One of them utilizes the SSE instruction set of contemporary CPUs, and the other one runs on graphics processors, using the CUDA environment. The implementations are evaluated and compared with a multithreaded C implementation compiled by an optimizing compiler and the results show speed-ups of around 10?×?which allows for using PCA on RGB and spectral images in real time. The discussed implementations are made available in a dynamically linked library, including a MATLAB plug-in interface so that they can be used by the professional public.     

Hematite Core Nanoparticles with Carbon Shell: Potential for Environmentally Friendly Production from Iron Mining Sludge

Hematite nanoparticles with amorphous, yet relatively uniform carbon shell, were produced based exclusively on the waste sludge from the iron mine as the raw material. The procedure for acid digestion-based purification of the sludge with the full recovery of acid vapors and the remaining non-toxic rubble is described. Synthesis of the hematite nanoparticles was performed by the arrested precipitation method with cationic surfactant. The particles were thoroughly characterized and the potential of their economical production for the battery industry is indicated.     

Selectivity Determinants of RHO GTPase Binding to IQGAPs

IQ motif-containing GTPase-activating proteins (IQGAPs) modulate a wide range of cellular processes by acting as scaffolds and driving protein components into distinct signaling networks. Their functional states have been proposed to be controlled by members of the RHO family of GTPases, among other regulators. In this study, we show that IQGAP1 and IQGAP2 can associate with CDC42 and RAC1-like proteins but not with RIF, RHOD, or RHO-like proteins, including RHOA. This seems to be based on the distribution of charged surface residues, which varies significantly among RHO GTPases despite their high sequence homology. Although effector proteins bind first to the highly flexible switch regions of RHO GTPases, additional contacts outside are required for effector activation. Sequence alignment and structural, mutational, and competitive biochemical analyses revealed that RHO GTPases possess paralog-specific residues outside the two highly conserved switch regions that essentially determine the selectivity of RHO GTPase binding to IQGAPs. Amino acid substitution of these specific residues in RHOA to the corresponding residues in RAC1 resulted in RHOA association with IQGAP1. Thus, electrostatics most likely plays a decisive role in these interactions.