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arXiv 2020年

作者： Kazeminasab, Saber Akbari, Ali Jafari, Roozbeh Banks, M. Katherine Department of Electrical and Computer Engineering Texas A&M University College Station TX United States Department of Biomedical Engineering Texas A&M University College Station TX United States Departments of Biomedical Computer Science and Electrical and Computer Engineering Texas A&M University College Station TX United States College of Enginnering Texas A&M University College Station TX United States

— Leak detection and water quality monitoring are requirements and challenging tasks in Water Distribution Systems (WDS). In-line robots are designed for this aim. In our previous work, we designed an in-pipe robot [1]. In this research, we present the design of the central processor, characterize and control the robot based on the condition of operation in highly pressurized environment of pipelines with the presence of high-speed flow. To this aim, an extreme operation condition is simulated with computational fluid dynamics (CFD) and the spring mechanism is characterized to ensure sufficient stabilizing force during operation based on the extreme operating condition. Also, an end-to-end method is suggested for power considerations for our robot that calculates minimum battery capacity and operation duration in the extreme operating condition. Finally, we design a novel LQR-PID based controller based on the system’s auxiliary matrices that retains the robot’s stability inside pipeline against disturbances and uncertainties during operation. The ADAMS-MATLAB co-simulation of the robot-controller shows rotational velocity with -4°/sec and +3°/sec margin around x, y, and z axes while the system tracks different desired velocities in pipelines (i.e. 0.12m/s, 0.17m/s, and 0.35m/s). Also, experimental results for four iterations in a 14-inch diameter PVC pipe show that the controller brings initial values of stabilizing states to zero and oscillate around it with a margin of ±2° and the system tracks desired velocities of 0.1m/s, 0.2m/s, 0.3m/s, and 0.35m/s in which makes the robot dexterous in uncertain and highly disturbed environment of pipelines during operation. © 2020, CC BY.

关键词： Controllers

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Society for Cardiovascular Magnetic Resonance recommendations toward environmentally sustainable cardiovascular magnetic resonance

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Journal of Cardiovascular Magnetic Resonance 2025年第1期27卷 101840页

作者： Hanneman, Kate Picano, Eugenio Campbell-Washburn, Adrienne E Zhang, Qiang Browne, Lorna Kozor, Rebecca Battey, Thomas Omary, Reed Saldiva, Paulo Ng, Ming Rockall, Andrea Law, Meng Kim, Helen Lee, Yoo Jin Mills, Rebecca Ntusi, Ntobeko Bucciarelli-Ducci, Chiara Markl, Michael Department of Medical Imaging University of Toronto Toronto ON Canada University Clinical Center of Serbia Cardiology Division University of Belgrade Serbia Cardiovascular Branch Division of Intramural Research National Heart Lung and Blood Institute National Institutes of Health Bethesda MD United States RDM Division of Cardiovascular Medicine & NDPH Big Data Institute University of Oxford Oxford United Kingdom Dept of Radiology Division of Pediatric Radiology Children's Hospital Colorado University of Colorado School of Medicine United States University of Sydney and Royal North Shore Hospital Sydney Australia Department of Radiology and Medical Imaging University of Virginia Health System Charlottesville VA United States Departments of Radiology & Biomedical Engineering Vanderbilt University Nashville TN United States Department of Pathology University of Sao Paulo School of Medicine Sao Paulo Brazil Department of Diagnostic Radiology School of Clinical Medicine Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Dept of Surgery and Cancer Faculty of Medicine Imperial College London United Kingdom Departments of Neuroscience Electrical and Computer Systems Engineering Monash University Australia Department of Radiology University of Washington WA United States Department of Radiology and Biomedical Engineering UCSF San Francisco CA United States University of Oxford Centre for Clinical Magnetic Resonance Research Oxford United Kingdom Groote Schuur Hospital Department of Medicine University of Cape Town Cape Town South Africa Royal Brompton and Harefield Hospitals Guys’ & St Thomas NHS Trust London United Kingdom Department of Radiology Feinberg School of Medicine Northwestern University Chicago IL United States Greenwell Project Nashville TN United States Department of Radiology Alfred Health Melbourne Australia School of Biomedical Engineering and Imaging Sciences Faculty of Life Sciences and Medicine King's College U

Delivery of health care, including medical imaging, generates substantial global greenhouse gas emissions. The cardiovascular magnetic resonance (CMR) community has an opportunity to decrease our carbon footprint, mitigate the effects of the climate crisis, and develop resiliency to current and future impacts of climate change. The goal of this document is to review and recommend actions and strategies to allow for CMR operation with improved sustainability, including efficient CMR protocols and CMR imaging workflow strategies for reducing greenhouse gas emissions, energy, and waste, and to decrease reliance on finite resources, including helium and waterbody contamination by gadolinium-based contrast agents. The article also highlights the potential of artificial intelligence and new hardware concepts, such as low-helium and low-field CMR, in achieving these aims. Specific actions include powering down magnetic resonance imaging scanners overnight and when not in use, reducing low-value CMR, and implementing efficient, non-contrast, and abbreviated CMR protocols when feasible. Data on estimated energy and greenhouse gas savings are provided where it is available, and areas of future research are highlighted. © 2025 The Authors

关键词： Climate change Energy consumption Greenhouse gas emission Planetary health Sustainability

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Theoretical modeling of tunable vibrations of three-dimensional serpentine structures for simultaneous measurement of adherent cell mass and modulus

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MRS Bulletin 2020年

作者： Zhao, Jianzhong Li, Weican Guo, Xingming Wang, Heling Rogers, John A. Huang, Yonggang Shanghai Institute of Applied Mathematics and Mechanics Shanghai Key Laboratory of Mechanics in Energy Engineering School of Mechanics and Engineering Science Shanghai University China Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Northwestern University Illinois United States School of Engineering Brown University Rhode Island United States Shanghai Institute of Applied Mathematics and Mechanics Shanghai Key Laboratory of Mechanics in Energy Engineering School of Mechanics and Engineering Science Shanghai University China Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Northwestern University Illinois United States Departments of Materials Science and Engineering Biomedical Engineering Chemistry Mechanical Engineering Electrical Engineering and Computer Science and Neurological Surgery Northwestern University Illinois United States Querrey-Simpson Institute for Bioelectronics Northwestern University Illinois United States Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Northwestern University Illinois United States Querrey-Simpson Institute for Bioelectronics Northwestern University Illinois United States

Vibration-based methods can be used effectively to characterize the physical properties of biological materials, with an increasing interest focused on the mechanics of individual, living cells. Real-time measurements of cell properties, such as mass and Young's modulus, can yield important insights into many aspects of cell growth and metabolism as well as the interaction of cells with external stimuli (e.g., drugs). Vibrational test structures designed for the study of such cell properties often use fixed configurations and operational modes, with associated limitations in determining multiple characteristics of the cell, simultaneously. Recent development of mechanics-guided techniques for deterministic assembly of three-dimensional (3D) microstructures provides a route to vibrational frameworks that offer tunable configurations, vibration modes, and resonant frequencies. Here we propose a method that exploits such tunable vibrational structures to simultaneously determine the mass and modulus of a single adherent cell, or of other biological materials or small-scale living systems (e.g., organoids), through theoretical modeling and finite element analysis. The idea involves a 3D architecture that supports two different vibrational structures and can be converted from one to the other through application of strain to an elastomeric substrate. Specifically, tailored designs for serpentine ribbons in these systems enable a decoupling of the dependence of the resonant frequencies of the two structures to the cell mass and modulus, with an associated ability to measure these two properties accurately and independently. These same concepts can be scaled to apply to various types of cells, as well as to organoids (3D clusters of cells) and other biological materials with small geometries, across a range of values of mass and modulus. This method could serve as the foundation for microelectromechanical systems capable of monitoring mass and modulus in real time for use

关键词： Serpentine

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On sparse identification of complex dynamical systems: a study on discovering influential reactions in chemical reaction networks

arXiv

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arXiv 2020年

作者： Harirchi, Farshad Kim, Doohyun Khalil, Omar Liu, Sijia Elvati, Paolo Baranwal, Mayank Hero, Alfred Violi, Angela Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109–2125 United States Department of Mechanical Engineering University of Michigan Ann ArborMI48109–2125 United States Departments of Chemical Engineering Biomedical Engineering Macromolecular Science and Engineering Biophysics Program University of Michigan Ann ArborMI48109–2125 United States

A wide variety of real life complex networks are prohibitively large for modeling, analysis and control. Understanding the structure and dynamics of such networks entails creating a smaller representative network that preserves its relevant topological and dynamical properties. While modern machine learning methods have enabled identification of governing laws for complex dynamical systems, their inability to produce white-box models with sufficient physical interpretation renders such methods undesirable to domain experts. In this paper, we introduce a hybrid black-box, white-box approach for the sparse identification of the governing laws for complex, highly coupled dynamical systems with particular emphasis on finding the influential reactions in chemical reaction networks for combustion applications, using a data-driven sparse-learning technique. The proposed approach identifies a set of influential reactions using species concentrations and reaction rates, with minimal computational cost without requiring additional data or simulations. The new approach is applied to analyze the combustion chemistry of H2 and C3H8 in a constant-volume homogeneous reactor. The influential reactions determined by the sparse-learning method are consistent with the current kinetics knowledge of chemical mechanisms. Additionally, we show that a reduced version of the parent mechanism can be generated as a combination of the significantly reduced influential reactions identified at different times and conditions and that for both H2 and C3H8 fuel, the reduced mechanisms perform closely to the parent mechanisms as a function of the ignition delay time over a wide range of conditions. Our results demonstrate the potential of the sparse-learning approach as an effective and efficient tool for dynamical system analysis and reduction. The uniqueness of this approach as applied to combustion systems lies in the ability to identify influential reactions in specified conditions and times dur

关键词： Learning systems

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Unravelling the origins of anomalous diffusion: From molecules to migrating storks

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Physical Review Research 2022年第3期4卷 033055-033055页

作者： Ohad Vilk Erez Aghion Tal Avgar Carsten Beta Oliver Nagel Adal Sabri Raphael Sarfati Daniel K. Schwartz Matthias Weiss Diego Krapf Ran Nathan Ralf Metzler Michael Assaf Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem 91904 Israel Movement Ecology Lab Department of Ecology Evolution and Behavior Alexander Silberman Institute of Life Sciences Faculty of Science The Hebrew University of Jerusalem Jerusalem 91904 Israel Departments of Physics and Chemistry University of Massachusetts Boston Massachusetts 02125 USA Wildlife Space-Use Ecology Lab Department of Wildland Resources and Ecology Center Utah State University Logan Utah 84332 USA Institute of Physics and Astronomy University of Potsdam Potsdam 14476 Germany Experimental Physics I University of Bayreuth D-95440 Bayreuth Germany Department of Chemical and Biological Engineering University of Colorado Boulder Boulder Colorado 80309 USA Department of Electrical and Computer Engineering and School of Biomedical Engineering Colorado State University Fort Collins Colorado 80523 USA Asia Pacific Centre for Theoretical Physics Pohang 37673 Republic of Korea

Anomalous diffusion or, more generally, anomalous transport, with nonlinear dependence of the mean-squared displacement on the measurement time, is ubiquitous in nature. It has been observed in processes ranging from microscopic movement of molecules to macroscopic, large-scale paths of migrating birds. Using data from multiple empirical systems, spanning 12 orders of magnitude in length and 8 orders of magnitude in time, we employ a method to detect the individual underlying origins of anomalous diffusion and transport in the data. This method decomposes anomalous transport into three primary effects: long-range correlations (“Joseph effect”), fat-tailed probability density of increments (“Noah effect”), and nonstationarity (“Moses effect”). We show that such a decomposition of real-life data allows us to infer nontrivial behavioral predictions and to resolve open questions in the fields of single-particle tracking in living cells and movement ecology.

关键词： Animal behavior Anomalous diffusion Biological movement Chemical Physics & Physical Chemistry Continuous-time random walk Ecology & evolution Random walks Transport phenomena Quantum dots Imaging & optical processing Scaling methods Single molecule techniques Time series analysis

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Prostate cancer histopathology with label-free multispectral deep UV microscopy quantifies phenotypes of tumor grade and aggressiveness

Research Square

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Research Square 2021年

作者： Soltani, Soheil Ojaghi, Ashkan Qiao, Hui Kaza, Nischita Li, Xinyang Dai, Qionghai Osunkoya, Adeboye O. Robles, Francisco E. Wallace H. Coulter Dept. of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30332 United States Department of Automation Tsinghua University Beijing100084 China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30313 United States Departments of Pathology and Urology Emory University School of Medicine AtlantaGA30322 United States Winship Cancer Institute of Emory University AtlantaGA30322 United States

Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. To help address this problem, we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease. First, we find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that differentiates critical structures of thin tissue sections with subcellular spatial resolution, including nuclei, cytoplasm, stroma, basal cells, nerves, and inflammation. Further, we show that this phenotypical continuum can be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. Lastly, we adapt a two-part Cycle-consistent Generative Adversarial Network to translate the label-free deep-UV images into virtual hematoxylin and eosin (H&E) stained images. Agreement between the virtual H&E images and the gold standard H&E-stained tissue sections is evaluated by a panel of pathologists who find that the two modalities are in excellent agreement. This work has significant implications towards improving our ability to objectively quantify prostate cancer grade and aggressiveness, thus improving the management and clinical outcomes of prostate cancer patients. This same approach can also be applied broadly in other tumor types to achieve low-cost, stain-free, quantitative histopathological analysis. © 2021, CC BY.

关键词： Tumors

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Prostate cancer histopathology using label-free multispectral deep-UV microscopy quantifies phenotypes of tumor aggressiveness and enables multiple diagnostic virtual stains

Research Square

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Research Square 2021年

Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. Here we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease, thus providing a new tool to help address this important challenge. We find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that provides unique structural insight (i.e., molecular maps or "optical stains") of thin tissue sections with subcellular (nanoscale) resolution. We show that this phenotypical continuum can also be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. In addition to providing several novel "optical stains" with contrast for disease, we also adapt a two-part Cycle-consistent Generative Adversarial Network to translate the label-free deep-UV images into virtual hematoxylin and eosin (H&E) stained images, thus providing multiple stains (including the gold-standard H&E) from the same unlabeled specimen. Agreement between the virtual H&E images and the H&E-stained tissue sections is evaluated by a panel of pathologists who find that the two modalities are in excellent agreement. This work has significant implications towards improving our ability to objectively quantify prostate cancer grade and aggressiveness, thus improving the management and clinical outcomes of prostate cancer patients. This same approach can also be applied broadly in other tumor types to achieve low-cost, stain-free, quantitative histopathological analysis. © 2021, CC BY.

关键词： Grading

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iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry

arXiv

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arXiv 2020年

作者： Fu, Wanyi Sharma, Shobhit Abadi, Ehsan Iliopoulos, Alexandros-Stavros Wang, Qi Lo, Joseph Y. Sun, Xiaobai Segars, William P. Samei, Ehsan Department of Electrical and Computer Engineering Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Department of Physics Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Department of Computer Science Duke University DurhamNC27708 United States Department of Radiology Fourth Clinical Hospital of Hebei Medical University Heibei050011 China Departments of Electrical and Computer Engineering Biomedical Engineering Medical Physics Graduate Program Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Departments of Biomedical Engineering Medical Physics Graduate Program and Radiology Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Carl E. Ravin Advanced Imaging Laboratories Medical Physics Graduate Program Departments of Radiology Electrical and Computer Engineering Biomedical Engineering and Physics Duke University DurhamNC27705 United States

Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or "digital-twins (DT)" using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. Method: From patient CT images, iPhantom segments selected anchor organs (e.g. liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs challenging to segment (e.g. intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting full-patient phantoms are used to assess organ doses during routine CT exams. Result: iPhantom was validated on both the XCAT (n=50) and an independent clinical (n=10) dataset with similar accuracy. iPhantom precisely predicted all organ locations with good accuracy of Dice Similarity Coefficients (DSC) >0.6 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed Copyright © 2020, The Authors. All rights reserved.

关键词： Phantoms

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Corrections to "iPhantom: A Framework for Automated Creation of Individualized Computational Phantoms and its Application to CT Organ Dosimetry"

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IEEE journal of biomedical and health informatics 2022年第1期26卷 478页

作者： Wanyi Fu Shobhit Sharma Ehsan Abadi Alexandros-Stavros Iliopoulos Qi Wang Joseph Y Lo Xiaobai Sun William P Segars Ehsan Samei Department of Electrical and Computer Engineering and Carl E. Ravin Advanced Imaging Laboratories Duke University Durham NC USA Department of Physics and Carl E. Ravin Advanced Imaging Laboratories Duke University Durham NC USA Department of Computer Science Duke University Durham NC USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Department of Radiology the Fourth Clinical Hospital of Hebei Medical University Heibei China Departments of Electrical and Computer Engineering Biomedical Engineering Medical Physics Graduate Program and Carl E. Ravin Advanced Imaging Laboratories Duke University Durham NC USA Medical Physics Graduate Program and Radiology and the Carl E. Ravin Advanced Imaging Laboratories Departments of Biomedical Engineering Duke University Durham NC USA Carl E. Ravin Advanced Imaging Laboratories Medical Physics Graduate Program Department of Radiology Electrical and Computer Engineering Biomedical Engineering and Physics Duke University Durham NC USA

In [1], the dose estimation accuracy using the alternative baseline method under modulated tube current was not correctly calculated due to an unintentional simulation error.

关键词： Phantoms Image segmentation Computed tomography Image registration

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Tracking and Failure Analysis of Explanted Neuromodulation Devices

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Neuromodulation : journal of the International Neuromodulation Society 2025年第4期28卷 700-702页

作者： Tess L Veuthey Deborah Duricka Dario J Englot David A Provenzano David M Dickerson Sanket Dhruva Christopher Miranda Loftus Nina Meizu-Nwaba David Prescott McMullen Richard B North Pierre D'Haese Peter E Konrad Lawrence Poree Department of Neurology and Neurological Sciences Stanford Medicine Stanford CA USA. Washington Wyoming Alaska Montana and Idaho School of Medical Education Anchorage AK USA. Departments of Neurological Surgery & Electrical Engineering Neurology Radiology & Radiological Sciences Computer Science and Biomedical Engineering Vanderbilt Health Nashville TN USA. Pain Diagnostics and Interventional Care Pittsburgh PA USA. Anesthesia Pain Management Services NorthShore University Health System Chicago IL USA. Department of Internal Medicine University for California San Francisco San Francisco CA USA. Office of Neurological and Physical Medicine Devices OHT5 OPEQ CDRH FDA Silver Springs MD USA. Department of Neurosurgery Anesthesiology and Critical Care Medicine (ret.) Johns Hopkins University School of Medicine Baltimore MD USA. Rockefeller Neuroscience Institute West Virginia University Morgantown WV USA. Dept of Neurosurgery West Virginia University Morgantown WV USA. Department of Anesthesia and Perioperative Care Division of Pain Medicine University of California San Francisco CA USA. Electronic address: Lawrence.Poree@UCSF.edu.

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