检索结果-内蒙古大学图书馆

Research Square 2021年

作者： Gao, Xiaoxiang Chen, Xiangjun Hu, Hongjie Wang, Xinyu Mu, Jing Lou, Zhiyuan Zhang, Ruiqi Shi, Keren Yue, Wentong Chen, Xue Lin, Muyang Qi, Baiyan Zhou, Sai Lu, Chengchangfeng Gu, Yue Ding, Hong Zhu, Yangzhi Huang, Hao Ma, Yuxiang Li, Mohan Mishra, Aditya Xu, Sheng Department of Nanoengineering University of California San Diego La Jolla CA92093 United States Materials Science and Engineering Program University of California San Diego La Jolla CA92093 United States Department of Electrical and Computer Engineering University of California San Diego La Jolla CA United States Department of Radiology School of Medicine University of California San Diego La Jolla CA92093 United States Department of Bioengineering University of California San Diego La Jolla CA92093 United States

Electronic patches, based on various mechanisms, allow continuous and noninvasive monitoring of biomolecules on the skin surface. However, to date, such devices have been unable to sense biomolecules in deep tissues, which have a stronger and faster correlation with the human physiological status. Here, we demonstrate a photoacoustic patch for three-dimensional (3D) mapping of hemoglobin in deep tissues. This photoacoustic patch integrates an array of ultrasonic transducers and vertical-cavity surface-emitting laser (VCSEL) diodes on a common soft substrate. The high-power VCSEL diodes can generate laser pulses that penetrate >2 cm into human tissues and activate hemoglobin molecules to generate acoustic waves, which can be collected by the transducers for 3D imaging of the hemoglobin with a sub-millimeter spatial resolution. Additionally, the photoacoustic signal amplitude and temperature have a linear relationship, which allows 3D mapping of core temperatures with high accuracy and fast response. The results of 3D core temperature mapping and long-term monitoring in ex-vivo and in-vivo studies are validated by commercial devices. With access to biomolecules in deep tissues, this technology adds unprecedented functions to wearable electronics and thus holds significant implications for various applications in both basic research and clinical practice. © 2021, CC BY.

关键词： Mapping

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Ultrafast coherence delocalization in real space simulated by polaritons

arXiv

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arXiv 2021年

作者： Xiang, Bo Yang, Zimo You, Yi-Zhuang Xiong, Wei Materials Science and Engineering Program UC San Diego San Diego92093 United States Department of Physics UC San Diego San Diego92093 United States Department of Chemistry and Biochemistry UC San Diego San Diego92093 United States Department of Electrical and Computer Engineering UC San Diego San Diego92093 United States

We investigated coherence delocalization on a coupled-cavity molecular polariton platform in time, frequency, and spatial domains, enabled by ultrafast two-dimensional infrared hyperspectral imaging. Unidirectional coherence delocalization (coherence prepared in one cavity transfer to another cavity) was observed in frequency and real spaces. This directionality was enabled by dissipation of delocalized photon from high-energy to low-energy modes, described by Lindblad dynamics. Further experiments showed that when coherences were directly prepared across cavities (superpositions between polaritons from different cavities), only energetically nearby polaritons could form coherences that survived the long-range environmental fluctuation. Together with the Lindblad dynamics, this result implied that coherences delocalized through a one-step mechanism where photons transferred from one cavity to another, shedding lights to coherence evolution in natural and artificial quantum systems. This work also demonstrated a way of combining photon and molecular modes to simulate coherence dynamics. © 2021, CC BY.

关键词： Photons

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Voltage Levels and Reactive Power in Distribution Networks with High Integration of Distributed Photovoltaic Generation

Voltage Levels and Reactive Power in Distribution Networks w...

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2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019

作者： Darui, Caroline B. F. Lucca, Tiago G. Abaide, Alzenira Da R. Da Veiga, Matheus F. Da Cruz, Matheus S. Knak Neto, Nelson Wüst, Lucas L. Figueiró, Iuri C. Federal University of Santa Maria Graduate Program in Electrical Engineering Santa Maria Brazil URI Santo Ângelo Department of Engineering and Computer Science Santo Ângelo Brazil

ISBN: (纸本)9781538695678

The Brazilian electrical system is undergoing many changes due to the exponential growth of photovoltaic systems connected to the grid (PV). This increase may affect power quality parameters, such as reduction of power factor and increase of voltage levels. This work aims to model and simulate a low voltage distribution network with high levels of integration of PV systems in order to verify how the voltage and reactive parameters behave in these cases. It was used new load curve models, the Monte Carlo method and the OpenDSS software. © 2019 IEEE.

关键词： Monte Carlo methods

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Machine Learning for Improved Current Density Reconstruction from 2D Vector Magnetic Images

arXiv

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arXiv 2024年

作者： Reed, Niko R. Bhutto, Danyal Turner, Matthew J. Daly, Declan M. Oliver, Sean M. Tang, Jiashen Olsson, Kevin S. Langellier, Nicholas Ku, Mark J.H. Rosen, Matthew S. Walsworth, Ronald L. Department of Physics University of Maryland College ParkMD United States Quantum Technology Center University of Maryland College ParkMD United States Joint Quantum Institute University of Maryland College ParkMD United States Department of Biomedical Engineering Boston University BostonMA United States A. A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Electrical and Computer Engineering University of Maryland College ParkMD United States The MITRE Corporation McLeanVA22102 United States Intelligence Community Postdoctoral Research Fellowship Program University of Maryland College ParkMD20742 United States Department of Physics Harvard University CambridgeMA United States Department of Materials Science and Engineering University of Delaware NewarkDE19716 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Harvard Medical School 25 Shattuck St. BostonMA United States

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high spatial frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional (2D) images of vector magnetic fields acquired by a quantum diamond microscope (QDM). Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower SNR data, significantly reducing the data collection time and permitting reconstructions of weaker and three-dimensional current sources. © 2024, CC BY.

关键词： Magnetic fields

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Data-driven sparse skin stimulation can convey social touch information to humans

arXiv

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arXiv 2021年

作者： Salvato, Mike Williams, Sophia R. Nunez, Cara M. Zhu, Xin Israr, Ali Lau, Frances Klumb, Keith Abnousi, Freddy Okamura, Allison M. Culbertson, Heather Department of Mechanical Engineering Stanford University StanfordCA94305 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States Departments of Bioengineering and Mechanical Engineering Stanford University StanfordCA94305 United States Department of Computer Science University of Southern California Los AngelesCA90089 United States Facebook Inc. 1 Hacker Way Menlo ParkCA94025 United States

During social interactions, people use auditory, visual, and haptic cues to convey their thoughts, emotions, and intentions. Due to weight, energy, and other hardware constraints, it is difficult to create devices that completely capture the complexity of human touch. Here we explore whether a sparse representation of human touch is sufficient to convey social touch signals. To test this we collected a dataset of social touch interactions using a soft wearable pressure sensor array, developed an algorithm to map recorded data to an array of actuators, then applied our algorithm to create signals that drive an array of normal indentation actuators placed on the arm. Using this wearable, low-resolution, low-force device, we find that users are able to distinguish the intended social meaning, and compare performance to results based on direct human touch. As online communication becomes more prevalent, such systems to convey haptic signals could allow for improved distant socializing and empathetic remote human-human interaction. Copyright © 2021, The Authors. All rights reserved.

关键词： Actuators

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Eigenvalue-based micromagnetic analysis of switching in spin-torque-driven structures

arXiv

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arXiv 2021年

作者： Lin, Zhuonan Volvach, I. Wang, X. Lomakin, V. Department of Electrical and Computer Engineering University of California San Diego La Jolla California92093 United States Center for Memory and Recording Research La Jolla California92093 United States Materials Science and Engineering Program University of California San Diego La Jolla California92093 United States

We present an eigenvalue-based approach for studying the magnetization dynamics in magnetic nanostructures driven by spintronic excitations, such as spin transfer torque and spin orbit torque. The approach represents the system dynamics in terms of normal oscillation modes (eigenstates) with corresponding complex eigenfrequencies. The dynamics is driven by a small number of active eigenstates and often considering just a single eigenstate is sufficient. We develop a perturbation theory that provides semi-analytical dynamic solutions by using eigenstates for the case in the absence of damping and spintronic excitations as a basis. The approach provides important insights into dynamics in such systems and allows solving several difficulties in their modeling, such as extracting the switching current in magnetic random access memories (MRAM) and understanding switching mechanisms. We show that the presented approach directly predicts the critical switching current, i.e., switching current for an infinite time. The approach also provides solutions for the switching dynamics allowing obtaining the switching current for a finite switching time, provided that the system symmetry is broken, e.g., by tilting the polarizer, so that switching by a finite pulse is possible. Copyright © 2021, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Question-Answering System Extracts Information on Injection Drug Use from Clinical Notes

arXiv

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arXiv 2023年

作者： Mahbub, Maria Goethert, Ian Danciu, Ioana Knight, Kathryn Srinivasan, Sudarshan Tamang, Suzanne Rozenberg-Ben-Dror, Karine Solares, Hugo Martins, Susana Trafton, Jodie Begoli, Edmon Peterson, Gregory D. Cyber Resilience and Intelligence Division Oak Ridge National Laboratory Oak RidgeTN United States Information Technology Services Division Oak Ridge National Laboratory Oak RidgeTN United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States Department of Biomedical Informatics Vanderbilt University NashvilleTN United States Program Evaluation and Resource Center Office of Mental Health and Suicide Prevention Department of Veterans Affairs United States Department of Medicine Stanford University School of Medicine StanfordCA United States Veterans Affairs Great Lakes Health Care System WestchesterIL United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville KnoxvilleTN United States

Background: Injection drug use (IDU) is a dangerous health behavior that increases mortality and morbidity. Identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients’ electronic health records (EHR) is difficult because there is no International Classification of Disease (ICD) code and the only place IDU information can be indicated is unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. Methods: To address this gap in clinical information, we design and demonstrate a question-answering (QA) framework to extract information on IDU from clinical notes. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing the QA model. We utilize 2323 clinical notes of 1145 patients sourced from the VA Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model’s ability to extract IDU-related information on temporally out-of-distribution data. Results: Here we show that for a strict match between gold-standard and predicted answers, the QA model achieves 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. Conclusions: Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care. © 2023, CC BY-NC-ND.

关键词： Data warehouses

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Analysing the effects of biofeedback using control-oriented formalisms: the case of gamified Kegel exercising

Analysing the effects of biofeedback using control-oriented ...

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European Control Conference (ECC)

作者： Steffi Knorn Damiano Varagnolo Nathalie Kask David M. Budgett Jennifer Kruger Poul M. F. Nielsen Ottovon-Guericke University Magdeburg Germany Department of Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Auckland Bioengineering Institute The University of Auckland New Zealand

ISBN: (数字)9783907144022

ISBN: (纸本)9781728188133

Biofeedback mechanisms affect the overall efficacy of the medical experience in different ways; while being often a suitable approach to improve and enable efficient treatments, they might also lead to unexpected, unplanned or even undesired effects. Motivated by these considerations, we discuss the problem of detecting whether specific designs of biofeedback mechanisms lead to unexpected, unplanned or undesired behaviors in the specific case of gamified pelvic floor muscles exercising. To solve the problem we use a control-theoretic framework, and ladder on the construction of a online estimator of the intentions of the patients in performing muscle activity. We propose thus to use a mixed model- and data-driven framework for which a parametric model is first opportunely identified and then used to forecast the behavior of the patient. This model, in conjunction with the data collected under the influence of biofeedback, is then used to investigate the resulting changes in patients' intentions in real life settings, and report quantitative assessments on the performance of the overall scheme in field tests.

关键词： Biological control systems Games Muscles Pressure sensors Fatigue Pressure measurement Force

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Predicting 1-year mortality of acute kidney injury: A risk model using electronic health records

Predicting 1-year mortality of acute kidney injury: A risk m...

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International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Huang, Chao-Jung Wu, Brian Li, Xiaodong Han, Yongxia Zhang, Yaqi Hao, Shiying Ling, Xuefeng B. National Taiwan University-Stanford Joint Program Office of AI in Biotechnology Ministry of Science and Technology Joint Research Center for Artificial Intelligence Technology and All Vista Healthcare Taipei Taiwan Clinical Research University of California San Diego San DiegoCA92093 United States School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Electrical Power Engineering South China University of Technology Guangzhou China Department of Surgery Stanford University StanfordCA94305 United States Department of Cardiothoracic Surgery Stanford University StanfordCA94305 United States Clinical and Translational Research Program Betty Irene Moore Children's Heart Center Lucile Packard Children's Hospital Palo AltoCA94305 United States

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Acute kidney injury (AKI) is associated with increased morbidity and mortality in intensive care units (ICU). The sudden episode of kidney failure may lead to end-stage renal disease (ESRD) or deaths, and has been related to significantly increasing costs of ICU admissions and treatments. Early prediction of AKI inpatient mortality will help decision-making, and benefit resource allocation in ICU. Therefore, it is crucial to develop an early warning system for AKI prediction. We aimed to create a more comprehensive predictive model for 1-year AKI mortality. A cohort of 2,247 patients with AKI was assembled, of which the in-hospital mortality was 36.67%. Longitudinal data of each patient were collected from the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. An interpretable XGBoost risk model was developed and validated by 10-fold cross validation. Model predictors included 11 routinely collected AKI-related laboratory measurements, 8 complications of AKI, and demographic data. An artificial neural network (ANN) model was also developed in parallel for comparison. The XGBoost model demonstrated an area under the receiver-operating characteristic curve (AUC) of 0.83, which was superior to ANN (AUC = 0.79). Our model was able to predict mortality of AKI in ICU with high accuracy. Our model can predict 1-year AKI mortality. Furthermore, it had great potential for identifying at-risk patients in ICU. These findings indicated that our approach might offer opportunities for better resource utilization and better administration of AKI. © 2021 SPIE.

关键词： Neural networks

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Observation of current-induced switching in non-collinear antiferromagnetic IrMn3 by differential voltage measurements

arXiv

引用

arXiv 2021年

作者： Arpaci, Sevdenur Lopez-Dominguez, Victor Shi, Jiacheng Sánchez-Tejerina, Luis Garesci, Francesca Wang, Chulin Yan, Xueting Sangwan, Vinod K. Grayson, Matthew Hersam, Mark C. Finocchio, Giovanni Amiri, Pedram Khalili Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina Messina98166 Italy Department of Engineering University of Messina Messina98166 Italy Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States

There is accelerating interest in developing memory devices using antiferromagnetic (AFM) materials, motivated by the possibility for electrically controlling AFM order via spin-orbit torques, and its read-out via magnetoresistive effects. Recent studies have shown, however, that high current densities create non-magnetic contributions to resistive switching signals in AFM/heavy metal (AFM/HM) bilayers, complicating their interpretation. Here we introduce an experimental protocol to unambiguously distinguish current-induced magnetic and nonmagnetic switching signals in AFM/HM structures, and demonstrate it in IrMn3/Pt devices. A six-terminal double-cross device is constructed, with an IrMn3 pillar placed on one cross. The differential voltage is measured between the two crosses with and without IrMn3 after each switching attempt. For a wide range of current densities, reversible switching is observed only when write currents pass through the cross with the IrMn3 pillar, eliminating any possibility of non-magnetic switching artifacts. Micromagnetic simulations support our findings, indicating a complex domain-mediated switching process. © 2021, CC BY.

关键词： Binary alloys

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