Three-phase four-wire shunt active power filter based on the SOGI filter and Lyapunov function for DC bus control

The three-phase four-wire shunt active power filter (SAPF) was developed to suppress the harmonic currents generated by nonlinear loads, and for the compensation of unbalanced nonlinear load currents, reactive power, and the harmonic neutral current. In this work, we consider instantaneous reactive power theory (PQ theory) for reference current identification based on the following two algorithms: the classic low-pass filter (LPF) and the second-order generalized integrator (SOGI) filter. Furthermore, since an important process in SAPF control is the regulation of the DC bus voltage at the capacitor, a new controller based on the Lyapunov function is also proposed. A complete simulation of the resultant active filtering system confirms its validity, which uses the SOGI filter to extract the reference currents from the distorted line currents, compared with the traditional PQ theory based on LPF. In addition, the simulation performed also demonstrates the superiority of the proposed approach, for DC bus voltage control based on the Lyapunov function, compared with the traditional proportional-integral (PI) controller. Both novel approaches contribute towards an improvement in the overall performance of the system, which consists of a small rise and settling time, a very low or nonexistent overshoot, and the minimization of the total harmonic distortion (THD).     

La0.6Sr0.4Co0.2Fe0.8O3-δ nanofiber cathode for intermediate-temperature solid oxide fuel cells by water-based sol-gel electrospinning: Synthesis and electrochemical behaviour

Water-based sol-gel electrospinning is employed to manufacture perovskite oxide La_0.6Sr_0.4Co_0.2Fe_0.8O_3-δ (LSCF) nanofiber cathodes for intermediate-temperature solid oxide fuel cells. LSCF fibrous scaffolds are synthesized through electrospinning of a sol-gel solution employing water as the only solvent. Morphological characterizations demonstrate that the LSCF fibers have highly crystalline structure with uniform elemental distribution. After heat treatment, the average fiber diameter is 250 nm and the porosity of the nanofiber tissue is 37.5 %. The heat treated LSCF nanofibers are applied directly onto a Ce_0.9Gd_0.1O_1.95 (CGO) electrolyte disk to form a symmetrical cell. Electrochemical characterization is carried out through electrochemical impedance spectroscopy (EIS) in the temperature range 550?°C–950?°C, and reproducibility of the electrochemical performance for a series of cells is demonstrated. At 650?°C, the average measured polarization resistance R_p is 1.0 Ω cm². Measured performance decay is 1 % during the first 33?h of operation at 750?°C, followed by an additional 0.7 % over the subsequent 70?h.     

Tough salami-inspired Cf/ZrB2 UHTCMCs produced by electrophoretic deposition

One of the biggest challenges of the materials science is the mutual exclusion of strength and toughness. This issue was minimized by mimicking the natural structural materials. To date, few efforts were done regarding materials that should be used in harsh environments. In this work we present novel continuous carbon fiber reinforced ultra-high-temperature ceramic matrix composites (UHTCMCs) for aerospace featuring optimized fiber/matrix interfaces and fibers distribution. The microstructures – produced by electrophoretic deposition of ZrB₂ on unidirectional carbon fibers followed by ZrB₂ infiltration and hot pressing – show a maximum flexural strength and fracture toughness of 330 MPa and 14 MPa m^1/2, respectively. Fracture surfaces are investigated to understand the mechanisms that affect strength and toughness. The EPD technique allows the achievement of a peculiar salami-inspired architecture alternating strong and weak interfaces.     

Pt–Ru nanoparticles anchored on poly(brilliant cresyl blue) as a new polymeric support: Application as an efficient electrocatalyst in methanol oxidation reaction

The glassy carbon electrode is modified by poly(brilliant cresyl blue) (PBCB) to be applied as a new green and efficient platform for Pt and Pt–Ru alloy nanoparticles deposition. Surface composition, morphology and catalytic activity of these modified electrodes towards methanol oxidation are assessed by applying X-ray diffraction, field emission scanning electron microscopy, cyclic voltammetry and electrochemical impedance spectroscopy techniques. The X-ray diffraction patterns reveal that the highly crystalline Pt and Pt–Ru alloy and RuO₂ nanoparticles with low crystallinity are deposited on the PBCB modified glassy carbon electrodes. The microscopic images indicate smaller size and better distribution of deposited nanoparticles on the surface of PBCB modified electrodes. Cyclic voltammetry and electrochemical impedance spectroscopy results reveal that PBCB supported Pt and Pt–Ru nanoparticles have better electrocatalytic performance and durability towards methanol oxidation rather than the unsupported nanoparticles. From the obtained results it can be concluded that the presence of PBCB not only improves the stability of nanoparticles on the surface, but also leads to the formation of smaller size and more uniform distribution of nanoparticles on the surface, which, in turn, cause the nanoparticles to provide a higher accessible surface area and more active centers for the oxidation of methanol. The results will be valuable in extending the applications of this polymer in surface modification steps and in developing promising catalyst supports to be applied in direct methanol fuel cells.     

Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.     

增效过磷酸钙的增效机制及其对玉米生长的影响

利用猪毛、风化煤和腐植酸钠的水解液为增效剂制备了一种增效过磷酸钙,探讨了增效过磷酸钙的增效机制及其对玉米生长的影响。结果表明,蒸馏水浸提条件下,增效过磷酸钙能减缓水溶磷的释放,具备较高的pH值缓冲性。与普通过磷酸钙相比,增效过磷酸钙处理的玉米生物量平均增加了41.9%,吸磷量和吸钙量分别增加了61.7%和27.8%,根系活力增加了24.3%。该研究结果对过磷酸钙生产的技术改进具有指导意义。     

Combination of nuclear magnetic resonance spectroscopy and nonlinear methods to analyze the copolymerization of phosphonic acid derivatives

We demonstrate in this study that the combination of modern inline monitoring methods [here: inline nuclear magnetic resonance (NMR)] with simulations gains more exact and profound kinetic results than previously used methods like linearization without that combination. The ¹H-NMR spectroscopic data (more than 100 data points) are used to construct the copolymerization diagram. The reactivity ratios are obtained applying the van Herks nonlinear least square method. The examination of the radical copolymerization of 2-hydroxyethyl methacrylate (HEMA) with (2-{[2-(ethoxycarbonyl)prop-2-en-1-yl]oxy}ethyl) phosphonic acid (ECPPA) as important adhesive monomer used in dentistry yields reactivity ratios of r_HEMA = 1.83; r_ECPPA = 0.42. The copolymerization diagram reflects nonideal, non-azeotropic copolymerization. The sequence distribution of the obtained by Monte Carlo simulation indicates the generation of statistical copolymers. As an important finding, it is demonstrated that the repeating units responsible for etching and adhesion are arranged over the whole polymer chain, which is necessary to achieve proper functionality. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48256.     

The Spatial LASSO With Applications to Unmixing Hyperspectral Biomedical Images

Hyperspectral imaging (HSI) is a spectroscopic method that uses densely sampled measurements along the electromagnetic spectrum to identify the unique molecular composition of an object. Traditionally HSI has been associated with remote sensing-type applications, but recently has found increased use in biomedicine, from investigations at the cellular to the tissue level. One of the main challenges in the analysis of HSI is estimating the proportions, also called abundance fractions of each of the molecular signatures. While there is great promise for HSI in the area of biomedicine, large variability in the measurements and artifacts related to the instrumentation has slow adoption into more widespread practice. In this article, we propose a novel regularization and variable selection method called the spatial LASSO (SPLASSO). The SPLASSO incorporates spatial information via a graph Laplacian-based penalty to help improve the model estimation process for multivariate response data. We show the strong performance of this approach on a benchmark HSI dataset with considerable improvement in predictive accuracy over the standard LASSO. Supplementary materials for this article are available online.