20 research outputs found
Modeling of parallel power MOSFETs in steady-state
In high-power applications, multiple power MOSFETs are connected in parallel
and treated as a single switch in order to handle much larger total currents.
In this paper, a parallel power MOSFETs model from the turnoff state until they
reach their steady state is introduced. The model represents the relationship
between each power MOSFET's gate voltage and the current distribution among
them. The study's key purpose is to use the model for dealing with the
asymmetry in sharing current and power loss between these semiconductor devices
during the steady state region.Comment: 10 pages, 7 figures, The 2023 INTERNATIONAL SYMPOSIUM ON ADVANCED
ENGINEERING (ISAE2023
Synthesis and redetermination of the crystal structure of salicyl-aldehyde <i>N</i>(4)-morpholino-thio-semi-carbazone.
The structure of the title compound (systematic name: N-{[(2-hy-droxy-phen-yl)methyl-idene]amino}-morpholine-4-carbo-thio-amide), C12H15N3O2S, was prev-iously determined (Koo et al., 1977 ▸) using multiple-film equi-inclination Weissenberg data, but has been redetermined with higher precision to explore its conformation and the hydrogen-bonding patterns and supra-molecular inter-actions. The mol-ecular structure shows intra-molecular O-H⋯N and C-H⋯S inter-actions. The configuration of the C=N bond is E. The mol-ecule is slightly twisted about the central N-N bond. The best planes through the phenyl ring and the morpholino ring make an angle of 43.44 (17)°. In the crystal, the mol-ecules are connected into chains by N-H⋯O and C-H⋯O hydrogen bonds, which combine to generate sheets lying parallel to (002). The most prominent contribution to the surface contacts are H⋯H contacts (51.6%), as concluded from a Hirshfeld surface analysis
A modern purification by accelerated solvent extraction and centrifugal partition chromatography and biological evaluation of capsaicin from Capsicum chinense
A special alkaloid compound known as capsaicin, which can only be found in the fruit of the Capsicum plant, was isolated and tested for its anti-inflammatory activity. The purpose of this work is to establish a simple and quick approach for capsaicin purification utilizing centrifugal partition chromatography (CPC) as well as an effective method - accelerated solvent extraction (ASE), for extracting capsaicin from Capsicum chinense. After purification, capsaicin was validated by HPLC-DAD at 281 nm to be > 90% purity. The in vivo anti-inflammatory activity of the isolated capsaicin was also investigated, and the IC50 value of the capsaicin was determined to be 57.61 µg/mL. The current work emphasizes how an ASE and CPC system may combine to extract high-purity capsaicin from Capsicum chinense, which have the anti-inflammatory activity, as we evaluated in the experiment
Non-Cooperative Energy Efficient Power Allocation Game in D2D Communication: A Multi-Agent Deep Reinforcement Learning Approach
NEURODEGENERATION CAUSED BY TRIMETHYLTIN VIA INHIBITION OF TROPOMYOSIN-RECEPTOR-KINASE B AND PHOSPHOINOSITIDE 3-KINASE/PROTEIN KINASE B SIGNALING CASCADE
Trimethyltin (TMT, 2.4mg/kg, i.p) can trigger neuronal
damage by inhibiting Tropomyosin receptor kinase B (TrkB
receptor) following by phosphoinositide 3-kinase (PI3K)/protein
kinase B or Akt signaling cascade. We examined hippocampal
changes in TrkA/B phosphorylation on Tyr490/Tyr516 of TMTtreated
mice in a time-dependent manner. Phosphorylated PI3K
(Tyr508), phosphorylated 3-phosphoinositide-dependent protein
kinase 1 (PDK1, Ser241) and phosphorylated Akt (Ser473) were
changed following by TMT injury (from 3 hours until 7 days after
injury). Treatment with 7,8-dihydroxyflavone (7,8-DHF), a
specific agonist of TrkB, significantly attenuated the TMT-caused
inhibition of phospho-TrkB, thereby increased in expressions of
phospho-PI3K, phospho-PDK1 and phospho-Akt in TMT-treated
mice, simultaneously 7,8-DHF showed a neuroprotective effect in
observation of nuclear chromatic clumping by cresyl violet- and
terminal deoxynucleotidyltransferase-mediated dUTP-biotin nick
end labeling- (TUNEL) staining in the hippocampal dentate gyrus
(DG) of TMT-treated mice, as compared to saline-treated group.
This finding suggests that inhibition of TrkB receptor followed by
PI3K/Akt cascade may play a part in the molecular mechanism
by which TMT caused neurodegeneration in mice
Real-Time Energy Harvesting Aided Scheduling in UAV-Assisted D2D Networks Relying on Deep Reinforcement Learning
Real-time energy harvesting aided scheduling in UAV-assisted D2D networks relying on deep reinforcement learning
Unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) communications can be deployed flexibly thanks to UAVs’ agility. By exploiting the direct D2D interaction supported by UAVs, both the user experience and network performance can be substantially enhanced at public events. However, the continuous moving of D2D users, limited energy and flying time of UAVs are impediments to their applications in real-time. To tackle this issue, we propose a novel model based on deep reinforcement learning in order to find the optimal solution for the energy-harvesting time scheduling in UAV-assisted D2D communications. To make the system model more realistic, we assume that the UAV flies around a central point, the D2D users move continuously with random walk model and the channel state information encountered during each time slot is randomly time-variant. Our numerical results demonstrate that the proposed schemes outperform the existing solutions. The associated energy efficiency game can be solved in less than one millisecond by an off-the-shelf processor using trained neural networks. Hence our deep reinforcement learning techniques are capable of solving real-time resource allocation problems in UAV-assisted wireless networks
sj-docx-1-aph-10.1177_10105395231169084 – Supplemental material for Infrastructure and Software Skills in the Expanded Program on Immunization at Commune Health Centers in Central Vietnam: A Cross-Sectional Mixed-Methods Study
Supplemental material, sj-docx-1-aph-10.1177_10105395231169084 for Infrastructure and Software Skills in the Expanded Program on Immunization at Commune Health Centers in Central Vietnam: A Cross-Sectional Mixed-Methods Study by Nguyen Khoi Nguyen, Phan Thanh Long, Tran Binh Thang, Le Dinh Duong, Nguyen Minh Tu, Dang Thi Anh Thu and Nguyen Thanh Gia in Asia Pacific Journal of Public Health</p
RGB-D to CAD retrieval with objectNN dataset
The goal of this track is to study and evaluate the performance of 3D object retrieval algorithms using RGB-D data. This is inspired from the practical need to pair an object acquired from a consumer-grade depth camera to CAD models available in public datasets on the Internet. To support the study, we propose ObjectNN, a new dataset with well segmented and annotated RGB-D objects from SceneNN [HPN*16] and CAD models from ShapeNet [CFG*15]. The evaluation results show that the RGB-D to CAD retrieval problem, while being challenging to solve due to partial and noisy 3D reconstruction, can be addressed to a good extent using deep learning techniques, particularly, convolutional neural networks trained by multi-view and 3D geometry. The best method in this track scores 82% in accuracy
SHREC\u2717: RgB-D to CAD Retrieval With ObjectNN Dataset
© 2017 The Eurographics Association. The goal of this track is to study and evaluate the performance of 3D object retrieval algorithms using RGB-D data. This is inspired from the practical need to pair an object acquired from a consumer-grade depth camera to CAD models available in public datasets on the Internet. To support the study, we propose ObjectNN, a new dataset with well segmented and annotated RGB-D objects from SceneNN [HPN∗16] and CAD models from ShapeNet [CFG∗15]. The evaluation results show that the RGB-D to CAD retrieval problem, while being challenging to solve due to partial and noisy 3D reconstruction, can be addressed to a good extent using deep learning techniques, particularly, convolutional neural networks trained by multi-view and 3D geometry. The best method in this track scores 82% in accuracy