Self-oscillating control methods for the LCC current-output resonant converter

Abstract—A strategy for self-oscillating control of LCC current-output resonant converters, is presented, based on varying the phase-angle between the fundamental of the input voltage and current. Unlike other commonly employed control methodologies,the proposed technique is shown to provide a convenient, linear system input-output characteristic suitable for the design of regulators. The method is shown to have a similar effect as controlling the dc-link supply voltage, in terms of output-voltage/current control. The LCC converter variant is used as an application focus for demonstrating the presented techniques, with simulation and experimental measurements from a prototype converter being used to show the practical benefits. Third-order small and large-signal models are developed, and employed in the formulation of robust output-voltage and output-current control schemes. However, notably, the presented techniques are ultimately generic and readily applicable to other resonant converter variants

Design of an LCC current-output resonant converter for use as a constant current source

A methodology for the design of LCC resonant current-source converters, is presented. Unlike previous techniques, the resulting converter provides near constant steady-state output current over an extended load range when excited at the resonant frequency, through use of a self-oscillating controlle

Normalized analysis and design of LCC resonant converters

Abstract—A normalization of the LCC voltage-output resonant converter performance characteristics, in terms of the tank gain at resonance and the parallel-to-series-capacitor ratio, is presented. The resulting description is subsequently used for the derivation of a design procedure that incorporates the effects of diode losses and the finite charge/discharge time of the parallel capacitor. Unlike previously reported techniques, the resulting normalized behavior of the converter is used to identify design regions to facilitate a reduction in component electrical stresses, and the use of harmonics to transfer real power. Consideration of the use of preferred component values is also given. The underlying methodology is ultimately suitable for incorporation into a software suite for use as part of a rapid interactive design tool. Both simulation results and experimental measurements from a prototype converter are included to demonstrate the attributes of the proposed analysis and design methodologies

Analysis of CLL voltage-output resonant converters using describing functions

A new ac equivalent circuit for the CLL voltage output resonant converter is presented, that offers improved accuracy compared with traditional FMA-based techniques. By employing describing function techniques, the nonlinear interaction of the parallel inductor, rectifier and load is replaced by a complex impedance, thereby facilitating the use of ac equivalent circuit analysis methodologies. Moreover, both continuous and discontinuous rectifier-current operating conditions are addressed. A generic normalized analysis of the converter is also presented. To further aid the designer, error maps are used to demonstrate the boundaries for providing accurate behavioral predictions. A comparison of theoretical results with those from simulation studies and experimental measurements from a prototype converter, are also included as a means of clarifying the benefits of the proposed techniques

An Army of Me: Sockpuppets in Online Discussion Communities

In online discussion communities, users can interact and share information and opinions on a wide variety of topics. However, some users may create multiple identities, or sockpuppets, and engage in undesired behavior by deceiving others or manipulating discussions. In this work, we study sockpuppetry across nine discussion communities, and show that sockpuppets differ from ordinary users in terms of their posting behavior, linguistic traits, as well as social network structure. Sockpuppets tend to start fewer discussions, write shorter posts, use more personal pronouns such as "I", and have more clustered ego-networks. Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users. Our analysis suggests a taxonomy of deceptive behavior in discussion communities. Pairs of sockpuppets can vary in their deceptiveness, i.e., whether they pretend to be different users, or their supportiveness, i.e., if they support arguments of other sockpuppets controlled by the same user. We apply these findings to a series of prediction tasks, notably, to identify whether a pair of accounts belongs to the same underlying user or not. Altogether, this work presents a data-driven view of deception in online discussion communities and paves the way towards the automatic detection of sockpuppets.Comment: 26th International World Wide Web conference 2017 (WWW 2017

Hungry for Respect: Discrimination Among Adults Using Emergency Food Services

Objectives: We examined how adults using emergency food services report discrimination and how these reports may be associated with well-being. Methods: Data come from a survey (n=318) and from five focus groups of adults using emergency food services, conducted between 2003-2004. The survey included measures derived from the Everyday Discrimination Scale and the Centers for Epidemiologic Studies Depression Scale (CES-D). Focus groups were analyzed with content analysis. Results: The survey data suggest that everyday discrimination was associated with the CES-D, conditional on covariates. Focus group data are consistent with the survey results and suggest several avenues for future research, including how some individuals may forgo access to food and medications in order to protect their dignity in the face of discrimination. Conclusions: Qualitative and quantitative data converge into a similar theme - discrimination may be an important factor associated with well-being

Dynamics of chromosome organization in a minimal bacterial cell

Computational models of cells cannot be considered complete unless they include the most fundamental process of life, the replication and inheritance of genetic material. By creating a computational framework to model systems of replicating bacterial chromosomes as polymers at 10 bp resolution with Brownian dynamics, we investigate changes in chromosome organization during replication and extend the applicability of an existing whole-cell model (WCM) for a genetically minimal bacterium, JCVI-syn3A, to the entire cell-cycle. To achieve cell-scale chromosome structures that are realistic, we model the chromosome as a self-avoiding homopolymer with bending and torsional stiffnesses that capture the essential mechanical properties of dsDNA in Syn3A. In addition, the conformations of the circular DNA must avoid overlapping with ribosomes identitied in cryo-electron tomograms. While Syn3A lacks the complex regulatory systems known to orchestrate chromosome segregation in other bacteria, its minimized genome retains essential loop-extruding structural maintenance of chromosomes (SMC) protein complexes (SMC-scpAB) and topoisomerases. Through implementing the effects of these proteins in our simulations of replicating chromosomes, we find that they alone are sufficient for simultaneous chromosome segregation across all generations within nested theta structures. This supports previous studies suggesting loop-extrusion serves as a near-universal mechanism for chromosome organization within bacterial and eukaryotic cells. Furthermore, we analyze ribosome diffusion under the influence of the chromosome and calculate in silico chromosome contact maps that capture inter-daughter interactions. Finally, we present a methodology to map the polymer model of the chromosome to a Martini coarse-grained representation to prepare molecular dynamics models of entire Syn3A cells, which serves as an ultimate means of validation for cell states predicted by the WCM. </p

Chemical characterization of fine particulate matter emitted by peat fires in Central Kalimantan, Indonesia, during the 2015 El Niño

Fine particulate matter (PM2:5) was collected in situ from peat smoke during the 2015 El Niño peat fire episode in Central Kalimantan, Indonesia. Twenty-one PM samples were collected from 18 peat fire plumes that were primarily smoldering with modified combustion efficiency (MCE) values of 0.725-0.833. PM emissions were determined and chemically characterized for elemental carbon (EC), organic carbon (OC), water-soluble OC, water-soluble ions, metals, and organic species. Fuel-based PM2:5 mass emission factors (EFs) ranged from 6.0 to 29.6 g kg1 with an average of 17:36:0 g kg1. EC was detected only in 15 plumes and comprised 1% of PM mass. Together, OC (72 %), EC (1 %), water-soluble ions (1 %), and metal oxides (0.1 %) comprised 7411% of gravimetrically measured PM mass. Assuming that the remaining mass is due to elements that form organic matter (OM; i.e., elements O, H, N) an OM-to-OC conversion factor of 1.26 was estimated by linear regression. Overall, chemical speciation revealed the following characteristics of peat-burning emissions: high OC mass fractions (72%), primarily water-insoluble OC (8411 %C), low EC mass fractions (1 %), vanillic to syringic acid ratios of 1.9, and relatively high n-alkane contributions to OC (6.2 %C) with a carbon preference index of 1.2-1.6. Comparison to laboratory studies of peat combustion revealed similarities in the relative composition of PM but greater differences in the absolute EF values. The EFs developed herein, combined with estimates of the mass of peat burned, are used to estimate that 3.2-11 Tg of PM2:5 was emitted to atmosphere during the 2015 El Niño peatland fire event in Indonesia. Combined with gas-phase measurements of CO2, CO, CH4, and volatile organic carbon from Stockwell et al. (2016), it is determined that OC and EC accounted for 2.1 and 0.04% of total carbon emissions, respectively. These in situ EFs can be used to improve the accuracy of the representation of Indonesian peat burning in emission inventories and receptor-based models

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level