A Linearized Branch Flow Model Considering Line Shunts for Radial Distribution Systems and Its Application in Volt/VAr Control

When urban distribution systems are gradually modernized, the overhead lines are replaced by underground cables, whose shunt admittances can not be ignored. Traditional power flow（PF） model with π equivalent circuit shows non-convexity and long computing time, and most recently proposed linear PF models assume zero shunt elements. All of them are not suitable for fast calculation and optimization problems of modern distribution systems with non-negligible line shunts. Therefore, this paper proposes a linearized branch flow model considering line shunt（LBFS）. The strength of LBFS lies in maintaining the linear structure and the convex nature after appropriately modeling the π equivalent circuit for network equipment like transformers. Simulation results show that the calculation accuracy in nodal voltage and branch current magnitudes is improved by considering shunt admittances. We show the application scope of LBFS by controlling the network voltages through a two-stage stochastic Volt/VAr control（VVC） problem with the uncertain active power output from renewable energy sources（RESs）. Since LBFS results in a linear VVC program, the global solution is guaranteed. Case study exhibits that VVC framework can optimally dispatch the discrete control devices, viz. substation transformers and shunt capacitors, and also optimize the decision rules for real-time reactive power control of RES. Moreover, the computing efficiency is significantly improved compared with that of traditional VVC methods.     

Study of thermal aspects in open and greenhouse ponds

In this study, thermal aspects, e.g. convective heat transfer coefficients were evaluated and modelled in open and greenhouse pond systems (natural convection). Values of the constants C and n were determined by linear regression analysis from the experimental data. It was observed that the ranges of the convective heat transfer coefficient were strongly dependent upon D/H (Diameter / Height) ratio of the container. It was also observed that the uninsulated container 5 (D/H=5.240) and the insulated container 1 (D/H=0.196) indicate minimum and maximum convective heat transfer coefficient in winter and summer months, respectively. Predicted and experimental values of convective heat transfer coefficients, verified in terms of root mean square of percent deviation, correlation coefficient and error bars (±SD), exhibit fair agreement. The model has been validated for the composite climate of New Delhi. Copyright © 2006 John Wiley & Sons, Ltd.     

Counterdoping of MOS channel (CDC)-a new technique of improvingsuppression of latching in insulated gate bipolar transistors

A novel technique of improving suppression of latching in insulated-gate bipolar transistors (IGBTs) is proposed and experimentally verified. By counterdoping the channel of the DMOS cell, the doping of the p-base can be increased up to a factor of two. Dynamic latching improvement of 40-80%, corresponding to the p-base doping increase, has been obtained. The degradation in forward blocking voltage was observed when the counterdoping dosage exceeds about 2×10¹² cm^-2 for 600-V devices     

The effect of substrate doping on the performance of anode-shortedn-channel lateral insulated-gate bipolar transistors

The performance of n-channel lateral-insulated-gate bipolar transistors (n-LIGBTs) with anode shorts on p^- epi/p⁺ substrates is compared to that of anode-shorted n-LIGBTs on p^- substrates, as well as to that of conventional n-LIGBTs on either substrate. It is shown that both forward-voltage drop and turn-off time are better for anode-shorted devices fabricated on p^- epi/p⁺ substrate than for those on p^- substrates, due to a larger percentage component of vertical bipolar current and a lower collector resistance. Forward-voltage drops of 3.05 and 3.3 V at 133 A/cm² and turn-off times of 400 and 750 ns have been measured for devices on p^- epi/p⁺ and p ^- substrates respectively. All the LIGBTs showed current limiting at two to four times the ON-state conduction current during dynamic switching     

Decursinol Angelate Mitigates Sepsis Induced by Methicillin-Resistant Staphylococcus aureus Infection by Modulating the Inflammatory Responses of Macrophages

The herbal plant Angelica gigas (A. gigas) has been used in traditional medicine in East Asian countries, and its chemical components are reported to have many pharmacological effects. In this study, we showed that a bioactive ingredient of A. gigas modulates the functional activity of macrophages and investigated its effect on inflammation using a sepsis model. Among 12 different compounds derived from A. gigas, decursinol angelate (DA) was identified as the most effective in suppressing the induction of TNF-α and IL-6 in murine macrophages. When mice were infected with a lethal dose of methicillin-resistant Staphylococcus aureus (MRSA), DA treatment improved the mortality and bacteremia, and attenuated the cytokine storm, which was associated with decreased CD38⁺ macrophage populations in the blood and liver. In vitro studies revealed that DA inhibited the functional activation of macrophages in the expression of pro-inflammatory mediators in response to microbial infection, while promoting the bacterial killing ability with an increased production of reactive oxygen species. Mechanistically, DA treatment attenuated the NF-κB and Akt signaling pathways. Intriguingly, ectopic expression of an active mutant of IKK2 released the inhibition of TNF-α production by the DA treatment, whereas the inhibition of Akt resulted in enhanced ROS production. Taken together, our experimental evidence demonstrated that DA modulates the functional activities of pro-inflammatory macrophages and that DA could be a potential therapeutic agent in the management of sepsis.     

Experimental Modeling and Simulation of Supercritical Fluid Extraction of Moringa oleifera Seed Oil by Carbon Dioxide

Moringa oleifera seed is an important source of high oleic acid in vegetable oil. In the present work, supercritical extraction of Moringa seed oil has been carried out to study the influence of operating temperature, pressure, particle size, carbon dioxide flow rate, and co-solvent addition by performing experiments in the range of 333–373?K, 20–40?MPa, 0.50–1.00?mm, 0.83?×?10^?4–2.50?×?10^?4?kg/s, and 0–10% ethanol. The extraction data have been successfully modeled by extended Lack’s model and fitting parameters are optimized by the Box global optimization technique. The results showed that pressure has a significant effect followed by temperature, co-solvent, solvent flow rate, and particle size.     

Knoevenagel condensation reaction over acid–base bifunctional nanocrystalline CexZr1−xO2 solid solutions

Highly thermally stable three-dimensional spongelike mesoporous Ce_xZr_1?xO₂ solid solutions consisting of nanometer size particles with different Ce/Zr compositions were synthesized by a modified sol–gel procedure using a triethanolamine/water mixture as a solvent to be used in liquid Knoevenagel condensation reaction. These materials were investigated in detail by means of X-ray diffraction (XRD), Raman spectroscopy, chemical analysis, X-ray photoelectron spectroscopy (XPS), high-resolution transmission electron microscopy (HRTEM), UV–Vis spectroscopy, Fourier transform infrared spectroscopy (FT-IR) and adsorption microcalorimetry. The XRD, HRTEM and XPS studies proved the presence of nanocrystalline Ce_xZr_1?xO₂ solid solutions. These solid solutions showed excellent chemoselectivity in the classical Knoevenagel reaction. The large pore sizes (around 10 nm) highlight the possibility of using Ce_xZr_1?xO₂ as a support material for versatile catalytic systems. The results obtained from NH₃ and SO₂ adsorption microcalorimetry experiments successfully demonstrated the incorporation of ZrO₂ into the CeO₂ lattice resulting in both acidic and basic surface sites in a mixed oxide matrix.     

A genetic algorithm-based rule extraction system

Individual classifiers predict unknown objects. Although, these are usually domain specific, and lack the property of scaling up prediction while handling data sets with huge size and high-dimensionality or imbalance class distribution. This article introduces an accuracy-based learning system called DTGA (decision tree and genetic algorithm) that aims to improve prediction accuracy over any classification problem irrespective to domain, size, dimensionality and class distribution. More specifically, the proposed system consists of two rule inducing phases. In the first phase, a base classifier, C4.5 (a decision tree based rule inducer) is used to produce rules from training data set, whereas GA (genetic algorithm) in the next phase refines them with the aim to provide more accurate and high-performance rules for prediction. The system has been compared with competent non-GA based systems: neural network, Naïve Bayes, rule-based classifier using rough set theory and C4.5 (i.e., the base classifier of DTGA), on a number of benchmark datasets collected from UCI (University of California at Irvine) machine learning repository. Empirical results demonstrate that the proposed hybrid approach provides marked improvement in a number of cases.     

Comparative Proteomic Profiling of Secreted Extracellular Vesicles from Breast Fibroadenoma and Malignant Lesions: A Pilot Study

Extracellular vesicles (EVs) shuttle proteins, RNA, DNA, and lipids crucial for cell-to-cell communication. Recent findings have highlighted that EVs, by virtue of their cargo, may also contribute to breast cancer (BC) growth and metastatic dissemination. Indeed, EVs are gaining great interest as non-invasive cancer biomarkers. However, little is known about the biological and physical properties of EVs from malignant BC lesions, and even less is understood about EVs from non-malignant lesions, such as breast fibroadenoma (FAD), which are clinically managed using conservative approaches. Thus, for this pilot study, we attempted to purify and explore the proteomic profiles of EVs from benign breast lesions, HER2+ BCs, triple–negative BCs (TNBCs), and continuous BC cell lines (i.e., BT-549, MCF–10A, and MDA-MB-231), combining experimental and semi-quantitative approaches. Of note, proteome-wide analyses showed 49 common proteins across EVs harvested from FAD, HER2+ BCs, TNBCs, and model BC lines. This is the first feasibility study evaluating the physicochemical composition and proteome of EVs from benign breast cells and primary and immortalized BC cells. Our preliminary results hold promise for possible implications in precision medicine for BC.     

Structural,Electrical, and Optical Behavior of Strontium Bismuth Titanate Ceramic

In this report, we present the structural, electrical, and optical study of layer-structured SrBi₄Ti₄O₁₅ (SBT) ceramic prepared by solid-state reaction route. The X-ray diffraction and Rietveld refinement studies show a single-phase orthorhombic structure with space group A₂₁am. The scanning electron micrograph shows plate-like grains. The various Raman peaks originated due to the TiO₆ octahedron confirm the orthorhombic structure. The temperature-dependent dielectric study shows a normal ferroelectric phase transition with a transition temperature at 813 K (540 °C). Impedance studies show a non-Debye-type relaxation and relaxation frequency shift to higher side with increase in temperature. The Nyquist plot shows overlapping semicircles which results the existence of both for grain and grain boundary effect in SBT ceramic. The frequency-dependent AC conductivity at different temperatures indicates that the conduction process is thermally activated and the spectra follow the universal power law. The variation of DC conductivity confirms that the SBT ceramic exhibits negative temperature coefficient of resistance behavior. The Ferroelectric behavior is studied by hysteresis loop. The optical band gap is found to be 2.93 eV from the UV–Visible spectroscopy study. The room-temperature photoluminescence study shows a strong red emission.