61 research outputs found
Output Voltage Response Improvement and Ripple Reduction Control for Input-parallel Output-parallel High-Power DC Supply
A three-phase isolated AC-DC-DC power supply is widely used in the industrial
field due to its attractive features such as high-power density, modularity for
easy expansion and electrical isolation. In high-power application scenarios,
it can be realized by multiple AC-DC-DC modules with Input-Parallel
Output-Parallel (IPOP) mode. However, it has the problems of slow output
voltage response and large ripple in some special applications, such as
electrophoresis and electroplating. This paper investigates an improved
Adaptive Linear Active Disturbance Rejection Control (A-LADRC) with flexible
adjustment capability of the bandwidth parameter value for the high-power DC
supply to improve the output voltage response speed. To reduce the DC supply
ripple, a control strategy is designed for a single module to adaptively adjust
the duty cycle compensation according to the output feedback value. When
multiple modules are connected in parallel, a Hierarchical Delay Current
Sharing Control (HDCSC) strategy for centralized controllers is proposed to
make the peaks and valleys of different modules offset each other. Finally, the
proposed method is verified by designing a 42V/12000A high-power DC supply, and
the results demonstrate that the proposed method is effective in improving the
system output voltage response speed and reducing the voltage ripple, which has
significant practical engineering application value.Comment: Accepted by IEEE Transactions on Power Electronic
Virtual histological staining of unlabeled autopsy tissue
Histological examination is a crucial step in an autopsy; however, the
traditional histochemical staining of post-mortem samples faces multiple
challenges, including the inferior staining quality due to autolysis caused by
delayed fixation of cadaver tissue, as well as the resource-intensive nature of
chemical staining procedures covering large tissue areas, which demand
substantial labor, cost, and time. These challenges can become more pronounced
during global health crises when the availability of histopathology services is
limited, resulting in further delays in tissue fixation and more severe
staining artifacts. Here, we report the first demonstration of virtual staining
of autopsy tissue and show that a trained neural network can rapidly transform
autofluorescence images of label-free autopsy tissue sections into brightfield
equivalent images that match hematoxylin and eosin (H&E) stained versions of
the same samples, eliminating autolysis-induced severe staining artifacts
inherent in traditional histochemical staining of autopsied tissue. Our virtual
H&E model was trained using >0.7 TB of image data and a data-efficient
collaboration scheme that integrates the virtual staining network with an image
registration network. The trained model effectively accentuated nuclear,
cytoplasmic and extracellular features in new autopsy tissue samples that
experienced severe autolysis, such as COVID-19 samples never seen before, where
the traditional histochemical staining failed to provide consistent staining
quality. This virtual autopsy staining technique can also be extended to
necrotic tissue, and can rapidly and cost-effectively generate artifact-free
H&E stains despite severe autolysis and cell death, also reducing labor, cost
and infrastructure requirements associated with the standard histochemical
staining.Comment: 24 Pages, 7 Figure
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data
Search for dark matter produced in association with bottom or top quarks in âs = 13 TeV pp collisions with the ATLAS detector
A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fbâ1 of protonâproton collision data recorded by the ATLAS experiment at âs = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements
Efficient highâfidelity deep convolutional generative adversarial network model for received signal strength reconstruction in indoor environments
Abstract With the rapid development of wireless communication systems, particularly in the era of 5G and the Internet of Things, deploying wireless communication networks in indoor environments has become crucial. Indoor infrastructure deployment necessitates innovative approaches for efficiently and accurately obtaining received signal strength (RSS) maps. However, traditional methods for acquiring RSS maps, such as empirical and deterministic models, are limited by significant inaccuracies and high computational demands. Empirical models often fail to capture the complex dynamics of indoor environments, resulting in deviations from actual signal behaviours. On the other hand, deterministic models, while more accurate, are computationally intensive due to their reliance on detailed physical modelling of wave propagation. This study introduces a machine learning approach based on deep convolutional generative adversarial networks (DCGAN) aimed at reconstructing indoor RSS maps with minimal RSS measurements. By leveraging DCGAN's generative and adversarial training capabilities, the method not only surpasses traditional interpolation methods in efficiency and precision but also offers new possibilities for the rapid deployment and optimization of wireless communication systems in indoor environments
Comparative Chemical Composition of the Essential Oils from Hedyotis Diffusa WILLD and Hedyotis Corymbosa Lam by GC-MS
Introduction: Hedyotis diffusa Willd. (Baihuasheshecao) is an ingredient of herbal commonly consumed in China for cancer treatment and health maintenance. In the market, this ingredient is frequently adulterated by the related species Hedyotis corymbosa Lam. Methods: The objective of this comparative research is to study the chemical composition of the essential oils from Hedyotis diffusa WILLD and Hedyotis corymbosa Lam by GC-MS. Result: In total, 43 components were identified in Hedyotis diffusa Willd. The identified components comprised 11 alcohols, 7 alkenes, 5 ketones, 7 aldehydes, 6 acid substances, 2 esters, 5 other substances. The major components in the Hedyotis diffusa WILLD oil were Hexadecanoic acid (48.89%) followed by Pentadecanoic acid (6.11%), D-Limonene (5.74%) and fatty acid were the most abundant components in Hedyotis diffusa WILLD. 32 components were identified in Hedyotis corymbosa (L.) Lam. The identified components comprised 2 alcohols, 8 alkenes, 4 ketones, 1 aldehyde, 6 acid substances, 6 esters, 5 other substances. The major components in the Hedyotis corymbosa (L.) Lam oil were Hexadecanoic acid (64.93%) followed by Linolenic acid (7.62%), Linoleic acid (3.73%). Borneol, 2-Carene epoxide, cis-Anethol, three compounds were identiïŹed exclusively in Hedyotis diffusa Willd. Conclusion: This study showed that the chemical composition of the essential oils from Hedyotis diffusa WILLD and Hedyotis corymbosa Lam GC-MS were different. Borneol, 2-Carene epoxide, cis-Anethol, three compounds were identiïŹed exclusively in Hedyotis diffusa Willd. This study is signiïŹcant for better quality control of this common herbal ingredient.</p
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