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Tools and Strategy for Distance Learning to Respond COVID-19 Pandemic in Indonesia

Ingenierie des Systemes d'Information 2020年第3期25卷 383-390页

作者： Utomo, Muhammad Nur Yasir Sudaryanto, Memet Saddhono, Kundharu Computer and Network Engineering Study Program Department of Electrical Engineering Politeknik Negeri Ujung Pandang Makassar90245 Indonesia Faculty of Teacher and Training Graduate School of Javanese Language and Literature Universitas Sebelas Maret Surakarta57126 Indonesia

Distance learning is a learning process that allows students and instructors are in different physical locations. During the COVID-19 pandemic, various countries including Indonesia chose the distance learning option to avoid the spread of the pandemic. However, the implementation of distance learning is not without problems. In Indonesia, the problems to implement distance learning are enormous both in terms of equitable distribution of infrastructure and costs for accessing the internet. To overcome these problems, research about best distance learning tools and its implementation strategy is really needed. Therefore, this research aimed to analyze the strategies and tools for implementing distance learning that reliable and cost-effective for the student at the same time. Based on research that has been conducted by analyzing questionnaire surveys of 256 students in Indonesia, this research recommends tools combination strategy consists of Google Classroom tool as a learning app, Whatsapp group as broadcast messaging and Zoom as a video conference media for distance learning. This research also recommended to maximize the e-learning/learning app features for theoretical lessons and consider video conference just in practical lessons so that the distance learning costs become cheaper for students. © 2020 International Information and engineering Technology Association. All rights reserved.

关键词： Students

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Electrostatic Properties and Ions Elimination Effect of Polyvinyl Chloride (PVC) Adhesive Tape Manufacturing

Electrostatic Properties and Ions Elimination Effect of Poly...

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International electrical engineering Congress (iEECON)

作者： Tatsanee Jenbanphue Komsan Hongesombut Kulsawasd Jitkajornwanich Siwapon Srisonphan Graduate program in safety engineering Kasetsart University Bangkok Thailand Department of Electrical Engineering Kasetsart University Bangkok Thailand Department of Computer Science King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9781728130767

ISBN: (纸本)9781728130774

Electrostatic discharges (ESD) can release large amounts of energy to products and humans, causing product failure and pain, respectively. In this research, we elucidate the electrostatic charge generation, and discharge phenomena in real polyvinyl chloride (PVC) based electrical tape factory, which is primarily operated by the roll to roll manufacturing process. We perform the electrostatic potential measurement to identify the critical process of charge generation and to eliminate static charge at the source in order to prevent incidents caused by electrostatic discharge to operators or staff in the winding process. We also perform a numerical simulation-based finite element method (FEM) that provides modeling of an induced electric field, allowing the electrostatic discharge. The maximum potential is ~20 kV measured at 25 mm, indicating that the meter was induced by the electric field approximately ~0.8 MV/m, corresponding to surface charges density is ~ 10 μC/m 2 . Therefore, we introduced the ionization emitter bar in this case because it is feasible to install to the current infrastructure of the tested factory. The ionization emitter appears to reduce the electrostatic charge deposited on the PVC surface ~ 30% of the original charges. Therefore, installing a charge-dissipation technology at a proper location or combining with other methods, the electrostatic charge generation can be minimized even in roll to roll process.

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A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest X-ray image

arXiv

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arXiv 2021年

作者： Hu, Zongsheng Yang, Zhenyu Lafata, Kyle J. Yin, Fang-Fang Wang, Chunhao Medical Physics Graduate Program Duke Kunshan University Jiangsu Kunshan215316 China Department of Radiation Oncology Duke University DurhamNC27710 United States Department of Radiology Duke University DurhamNC27710 United States Department of Electrical and Computer Engineering Duke University DurhamNC27710 United States

Purpose: To develop a deep-learning model that integrates radiomics analysis for enhanced performance of COVID-19 and Non-COVID-19 pneumonia detection using chest X-ray image. Methods: Based on a pre-trained VGG-16 architecture, the deep-learning design consisted of a 5-Dense layer neural network with varying sizes, while the last three convolutional layers were set as free parameters for training. In radiomics analysis, a 2D sliding kernel was implemented to map the impulse response of radiomic features throughout the entire X-ray image;thus, each feature is rendered as a 2D map in the same dimension as X-ray image. For comparison purpose, a pilot model was trained using X-ray image only. Subsequently, 2 radiomic feature maps (RFMs) were selected based on cross-correlation analysis in reference to the pilot model saliency map results. The radiomics-boosted model was then trained based on the same deep-learning design using X-ray image plus the selected RFMs as input. Both models were developed using 812 chest X-ray images with 262/288/262 COVID-19/Non-COVID-19 pneumonia/healthy cases, and 649/163 cases were assigned as training-validation/independent test sets. For each model, 50 versions were trained with random assignments of training/validation cases following 7:1 ratio in the training-validation set. Sensitivity, specificity, accuracy, and ROC curves together with Area-Under-the-Curve (AUC) were compared between the two models. Results: The performance of detecting COVID-19 vs Non-COVID-19 pneumonia in the pilot model using X-ray image only were:1) sensitivity 0.90±0.07 vs 0.78±0.09, 2) specificity 0.94±0.04 vs 0.94±0.04, 3) accuracy 0.93±0.03 vs 0.89±0.03, and 4) ROC AUC 0.96±0.02 vs 0.92±0.04. Entropy and Short-Run-Emphasize were selected based on their high correlations with pilot model saliency map results. In the radiomics-boosted model, the corresponding results demonstrated significant improvements (p © 2021, CC BY-NC-ND.

关键词： Impulse response

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Hybrid dedicated and distributed coding in PMd/M1 provides separation and interaction of bilateral arm signals

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PLoS Computational Biology 2021年第11期17卷 e1009615页

作者： Dixon, Tanner C. Merrick, Christina M. Wallis, Joni D. Ivry, Richard B. Carmena, Jose M. UC Berkeley-UCSF Graduate Program in Bioengineering University of California-Berkeley BerkeleyCA United States Department of Psychology University of California-Berkeley BerkeleyCA United States Helen Wills Neuroscience Institute University of California-Berkeley BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California-Berkeley BerkeleyCA United States

Pronounced activity is observed in both hemispheres of the motor cortex during preparation and execution of unimanual movements. The organizational principles of bi-hemispheric signals and the functions they serve throughout motor planning remain unclear. Using an instructed-delay reaching task in monkeys, we identified two components in population responses spanning PMd and M1. A "dedicated"component, which segregated activity at the level of individual units, emerged in PMd during preparation. It was most prominent following movement when M1 became strongly engaged, and principally involved the contralateral hemisphere. In contrast to recent reports, these dedicated signals solely accounted for divergence of arm-specific neural subspaces. The other "distributed"component mixed signals for each arm within units, and the subspace containing it did not discriminate between arms at any stage. The statistics of the population response suggest two functional aspects of the cortical network: One that spans both hemispheres for supporting preparatory and ongoing processes, and another that is predominantly housed in the contralateral hemisphere and specifies unilateral output. © 2021 Dixon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Population statistics

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Sharma et al. Reply:

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Physical Review Letters 2022年第21期128卷 219602-219602页

作者： Keshav Sharma Nathalia L. Costa Yoong Ahm Kim Hiroyuki Muramatsu Newton M. Barbosa Neto Luiz G. P. Martins Jing Kong Alexandre Rocha Paschoal Paulo T. Araujo Department of Physics and Astronomy University of Alabama Tuscaloosa 35487 Alabama USA Department of Physics Federal University of Ceara Fortaleza 60455-760 Ceara Brazil Department of Polymer Engineering School of Polymer Science and Engineering and Alan G. MacDiarmid Energy Research Institute Chonnam National University 77 Yongbong-ro Buk-gu Gwangju 61186 Republic of Korea Research Institute for Supra-Materials Shinshu University 4-17-1 Wakasato 380-8553 Nagano Japan Institute of Natural Sciences Graduate Program in Physics Federal University of Para Belem 66075-110 Brazil Department of Physics Massachusetts Institute of Technology Cambridge 02139 Massachusetts USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge 02139 Massachusetts USA

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Ruth Obe Brandt Kaufmann Kaelen Baird Sam Kadel Yasmin Soltani Mostafa Cham Matthias K. Gobbert Carlos A. Barajas Zhuoran Jiang Vijay R. Sharma Lei Ren Stephen W. Peterson Jerimy C. PoIf Dept. of Computer Science and of Software Engineering U. of Houston-Clear Lake USA Dept. of Mathematics and Statistics U. of San Francisco USA Dept. of Computer Science and of Mathematics Skidmore College USA Dept. of Computer Science and of Psychology Mount Holyoke College USA Dept. of Biomedical Engineering U. of Houston USA Dept. of Information Systems U. of Maryland Baltimore County Dept. of Mathematics and Statistics U. of Maryland Baltimore County Medical Physics Graduate Program Duke University USA Dept. of Radiation Oncology U. of Maryland School of Medicine USA Department of Radiation Oncology U. of Maryland School of Medicine USA Dept. of Physics U. of Cape Town South Africa H3D Inc. USA

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy.

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Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities

arXiv

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arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh The Graduate Degree Program of Robotics National Yang Ming Chiao Tung University Hsinchu Taiwan The Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Radar shows great potential for autonomous driving by accomplishing long-range sensing under diverse weather conditions. But radar is also a particularly challenging sensing modality due to the radar noises. Recent works have made enormous progress in classifying free and occupied spaces in radar images by leveraging lidar label supervision. However, there are still several unsolved issues. Firstly, the sensing distance of the results is limited by the sensing range of lidar. Secondly, the performance of the results is degenerated by lidar due to the physical sensing discrepancies between the two sensors. For example, some objects visible to lidar are invisible to radar, and some objects occluded in lidar scans are visible in radar images because of the radar's penetrating capability. These sensing differences cause false positive and penetrating capability degeneration, respectively. In this paper, we propose training data preprocessing and polar sliding window inference to solve the issues. The data preprocessing aims to reduce the effect caused by radar-invisible measurements in lidar scans. The polar sliding window inference aims to solve the limited sensing range issue by applying a near-range trained network to the long-range region. Instead of using common Cartesian representation, we propose to use polar representation to reduce the shape dissimilarity between long-range and near-range data. We find that extending a near-range trained network to long-range region inference in the polar space has 4.2 times better IoU than in Cartesian space. Besides, the polar sliding window inference can preserve the radar penetrating capability by changing the viewpoint of the inference region, which makes some occluded measurements seem non-occluded for a pretrained network. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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Angle-resolved photoemission spectroscopy with an in situ tunable magnetic field

arXiv

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arXiv 2023年

作者： Huang, Jianwei Yue, Ziqin Baydin, Andrey Zhu, Hanyu Nojiri, Hiroyuki Kono, Junichiro He, Yu Yi, Ming Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Smalley–Curl Institute Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Institute for Materials Research Tohoku University Katahira 2-1-1 Sendai980-8577 Japan Department of Applied Physics Yale University New HavenCT06511 United States

Angle-resolved photoemission spectroscopy (ARPES) is a powerful tool for probing the momentum-resolved single-particle spectral function of materials. Historically, in situ magnetic fields have been carefully avoided as they are detrimental to the control of photoelectron trajectory during the photoelectron detection process. However, magnetic field is an important experimental knob for both probing and tuning symmetry-breaking phases and electronic topology in quantum materials. In this paper, we introduce an easily implementable method for realizing an in situ tunable magnetic field at the sample position in an ARPES experiment and analyze magnetic field induced artifacts in ARPES data. Specifically, we identified and quantified three distinct extrinsic effects of a magnetic field: Constant energy contour rotation, emission angle contraction, and momentum broadening. We examined these effects in three prototypical quantum materials, i.e., a topological insulator (Bi2Se3), an iron-based superconductor (LiFeAs), and a cuprate superconductor (Pb-Bi2Sr2CuO6+x), and demonstrate the feasibility of ARPES measurements in the presence of a controllable magnetic field. Our studies lay the foundation for the future development of the technique and interpretation of ARPES measurements of field-tunable quantum phases. Copyright © 2023, The Authors. All rights reserved.

关键词： Photoelectrons

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Repositioned Drugs for COVID-19-the Impact on Multiple Organs

引用

SN comprehensive clinical medicine 2021年第7期3卷 1484-1501页

作者： Christina Chan Sean T Foster Kayla G Chan Matthew J Cacace Shay L Ladd Caleb T Sandum Paul T Wright Brett Volmert Weiyang Yang Aitor Aguirre Wen Li Neil T Wright Department of Chemical Engineering and Materials Sciences Michigan State University 428 S. Shaw Lane Room 2100 EB East Lansing MI 48824 USA. Department of Biochemistry and Molecular Biology Michigan State University East Lansing MI USA. Department of Biomedical Engineering Michigan State University East Lansing MI USA. Institute for Quantitative Health Science and Engineering Michigan State University East Lansing MI USA. Integrative Neuroscience Program Binghamton University Binghamton NY USA. Department of Mechanical Engineering The Pennsylvania State University University Park PA USA. Department of Mechanical Engineering Virginia Polytechnic Institute and State University Blacksburg VA USA. Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA. Department of Mechanical Engineering Michigan State University East Lansing MI USA.

This review summarizes published findings of the beneficial and harmful effects on the heart, lungs, immune system, kidney, liver, and central nervous system of 47 drugs that have been proposed to treat COVID-19. Many of the repurposed drugs were chosen for their benefits to the pulmonary system, as well as immunosuppressive and anti-inflammatory effects. However, these drugs have mixed effects on the heart, liver, kidney, and central nervous system. Drug treatments are critical in the fight against COVID-19, along with vaccines and public health protocols. Drug treatments are particularly needed as variants of the SARS-Cov-2 virus emerge with some mutations that could diminish the efficacy of the vaccines. Patients with comorbidities are more likely to require hospitalization and greater interventions. The combination of treating severe COVID-19 symptoms in the presence of comorbidities underscores the importance of understanding the effects of potential COVID-19 treatments on other organs.

关键词： COVID-19 Central nervous system Drug repositioning Heart Immune system Kidneys Liver Lungs

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Nanoplasmonic imaging of extracellular exosomes on undercut arrayed gold nanodisks

Nanoplasmonic imaging of extracellular exosomes on undercut ...

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Optical Sensors, Sensors 2020 - Part of Optical Sensors and Sensing Congress 2020

作者： Kafle, Bijesh Misbah, Ibrahim Gunawardhana, Lkadds Lin, Steven H. Shih, Wei-Chuan Department of Electrical and Computer Engineering University of Houston 4800 Calhoun Rd HoustonTX77204 United States Program of Materials Science and Engineering University of Houston 4800 Calhoun Rd HoustonTX77204 United States Department of chemistry University of Houston 4800 Calhoun Rd HoustonTX77204 United States Department of Biomedical Engineering University of Houston 4800 Calhoun Rd HoustonTX77204 United States Department of Radiation Oncology M.D. Anderson Cancer Center 1515 Holcombe Blvd. HoustonTX77030 United States

We present nanoplasmonic imaging of exosome using antibody-functionalized undercut arrayed gold nanodisks. We show a nanoplasmonic shift map that allows the detection and quantitation of exosome binding. © OSA 2020.

ISBN: (纸本)9781557528209

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