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The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future (vol 398, pg 1619, 2021)

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LANCET 2021年第10317期398卷 2148-2148页

作者： Romanello, M. McGushin, A. Di Napoli, C. [a]Institute for Global Health University College London London UK [b]Institute for Sustainable Resources University College London London UK [c]UCL Energy Institute University College London London UK [d]Institute for Environmental Design and Engineering University College London London UK [e]Department of Geography University College London London UK [f]The Bartlett School of Sustainable Construction University College London London UK [g]Centre for Human Health and Performance University College London London UK [h]School of Agriculture Policy and Development University of Reading Reading UK [i]Department of Meteorology University of Reading Reading UK [j]The Walker Institute University of Reading Reading UK [k]Department of Health Sciences University of York York UK [l]Institute for Environment and Human Security United Nations University Bonn Germany [m]Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK [n]Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London UK [o]Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK [p]Department of Earth System Science Tsinghua University Beijing China [q]Department of Environment Climate Change and Health World Health Organization Geneva Switzerland [r]Institute for Environmental Sciences World Health Organization Geneva Switzerland [s]Centre for Climate Change and Social Transformations School of Psychology Cardiff University Cardiff UK [t]Yale Center on Climate Change and Health Yale University New Haven CT USA [u]School of Government University of Birmingham Birmingham UK [v]Economic analysis of Climate Impacts and Policy Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy [w]Natural Resources Institute University of Greenwich London UK [x]Department of Global Health University of Washington Seattle WA USA [y]Centre for He

Using machine learning to predict near-term mortality in cirrhosis patients hospitalized at the University of Virginia health system

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Using machine learning to predict near-term mortality in cir...

2018 systems and Information engineering Design Symposium, SIEDS 2018

作者： Harrison, Elizabeth Chang, Myron Hao, Yi Flower, Abigail School of Medicine and Data Science Institute University of Virginia United States Data Science Institute University of Virginia United States Department of Systems and Information Engineering University of Virginia United States

ISBN: (纸本)9781538663431

This study aimed to predict near-term mortality in patients hospitalized with cirrhosis at the University of Virginia Health System using two approaches - logistic regression and a long short-term memory neural network, with an emphasis on the latter. Using data from de-identified medical record entries for over 500 patient stays in the intensive care unit, a recurrent long short-term memory neural network was trained to predict whether a mortality would occur between 12 and 24 hours from the time of prediction. This was achieved by feeding the network a sequence of consecutive observations for each patient in the training set, thereby allowing the network to 'look back' and identify increasing mortality risk based on changes in vital signs and laboratory results over time. Performance of the neural network model was compared to that of a logistic regression model and the Chronic Liver Failure Consortium Acute Decompensation Score, a benchmark scoring method for mortality risk in cirrhosis patients. Although the primary model continues to need refinement, results indicate that the neural network and logistic regression models both outperform the Chronic Liver Failure Score. As more data are made available, further improvement in the performance of the neural network is anticipated. © 2018 IEEE.

关键词： Forecasting

The XRISM /Resolve view of the Fe K region of Cyg X-3

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arXiv 2024年

作者： Audard, Marc Awaki, Hisamitsu Ballhausen, Ralf Bamba, Aya Behar, Ehud Boissay-Malaquin, Rozenn Brenneman, Laura Brown, Gregory V. Corrales, Lia Costantini, Elisa Cumbee, Renata Diaz Trigo, Maria Done, Chris Dotani, Tadayasu Ebisawa, Ken Eckart, Megan E. Eckert, Dominique Eguchi, Satoshi Enoto, Teruaki Ezoe, Yuichiro Foster, Adam Fujimoto, Ryuichi Fujita, Yutaka Fukazawa, Yasushi Fukushima, Kotaro Furuzawa, Akihiro Gallo, Luigi García, Javier A. Gu, Liyi Guainazzi, Matteo Hagino, Kouichi Hamaguchi, Kenji Hatsukade, Isamu Hayashi, Katsuhiro Hayashi, Takayuki Hell, Natalie Hodges-Kluck, Edmund Hornschemeier, Ann Ichinohe, Yuto Ishida, Manabu Ishikawa, Kumi Ishisaki, Yoshitaka Kaastra, Jelle Kallman, Timothy Kara, Erin Katsuda, Satoru Kanemaru, Yoshiaki Kelley, Richard Kilbourne, Caroline Kitamoto, Shunji Kobayashi, Shogo Kohmura, Takayoshi Kubota, Aya Leutenegger, Maurice Loewenstein, Michael Maeda, Yoshitomo Markevitch, Maxim Matsumoto, Hironori Matsushita, Kyoko McCammon, Dan McNamara, Brian Mernier, François Miller, Eric D. Miller, Jon M. Mitsuishi, Ikuyuki Mizumoto, Misaki Mizuno, Tsunefumi Mori, Koji Mukai, Koji Murakami, Hiroshi Mushotzky, Richard Nakajima, Hiroshi Nakazawa, Kazuhiro Ness, Jan-Uwe Nobukawa, Kumiko Nobukawa, Masayoshi Noda, Hirofumi Odaka, Hirokazu Ogawa, Shoji Ogorzalek, Anna Okajima, Takashi Ota, Naomi Paltani, Stephane Petre, Robert Plucinsky, Paul Porter, Frederick S. Pottschmidt, Katja Sato, Kosuke Sato, Toshiki Sawada, Makoto Seta, Hiromi Shidatsu, Megumi Simionescu, Aurora Smith, Randall Suzuki, Hiromasa Szymkowiak, Andrew Takahashi, Hiromitsu Takeo, Mai Tamagawa, Toru Tamura, Keisuke Tanaka, Takaaki Tanimoto, Atsushi Tashiro, Makoto Terada, Yukikatsu Terashima, Yuichi Tsuboi, Yohko Tsujimoto, Masahiro Tsunemi, Hiroshi Tsuru, Takeshi Uchida, Hiroyuki Uchida, Nagomi Uchida, Yuusuke Uchiyama, Hideki Ueda, Yoshihiro Uno, Shinichiro Vink, Jacco Watanabe, Shin Williams, Brian J. Yamada, Satoshi Yamada, Shinya Yamaguchi, Hiroya Yamaoka, Kazutaka Yamasaki, Noriko Yamauchi, Makoto Yamauchi, Shig Department of Astronomy University of Geneva VersoixCH-1290 Switzerland Department of Physics Ehime University Ehime790-8577 Japan Department of Astronomy University of Maryland College ParkMD20742 United States NASA Goddard Space Flight Center GreenbeltMD20771 United States GreenbeltMD20771 United States Department of Physics University of Tokyo Tokyo113-0033 Japan Department of Physics Technion Technion City Haifa3200003 Israel BaltimoreMD21250 United States Center for Astrophysics — Harvard-Smithsonian MA02138 United States Lawrence Livermore National Laboratory CA94550 United States Department of Astronomy University of Michigan MI48109 United States SRON Netherlands Institute for Space Research Leiden Netherlands ESO Karl-Schwarzschild-Strasse 2 Garching bei München85748 Germany Centre for Extragalactic Astronomy Department of Physics University of Durham South Road DurhamDH1 3LE United Kingdom Kanagawa252-5210 Japan Department of Economics Kumamoto Gakuen University Kumamoto862-8680 Japan Department of Physics Kyoto University Kyoto606-8502 Japan Department of Physics Tokyo Metropolitan University Tokyo192-0397 Japan Department of Physics Hiroshima University Hiroshima739-8526 Japan Department of Physics Fujita Health University Aichi470-1192 Japan Department of Astronomy and Physics Saint Mary’s University Nova ScotiaB3H 3C3 Canada California Institute of Technology PasadenaCA91125 United States Noordwijk2200 AG Netherlands Faculty of Engineering University of Miyazaki 1-1 Gakuen-Kibanadai-Nishi Miyazaki Miyazaki889-2192 Japan RIKEN Nishina Center Saitama351-0198 Japan Leiden Observatory University of Leiden P.O. Box 9513 LeidenNL-2300 RA Netherlands Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology MA02139 United States Department of Physics Saitama University Saitama338-8570 Japan Department of Physics Rikkyo University Tokyo171-8501 Japan Facult

The X-ray binary system Cygnus X-3 (4U 2030+40, V1521 Cyg) is luminous but enigmatic owing to the high intervening absorption. High-resolution X-ray spectroscopy uniquely probes the dynamics of the photoionized gas in the system. In this paper we report on an observation of Cyg X-3 with the XRISM/Resolve spectrometer which provides unprecedented spectral resolution and sensitivity in the 2–10 keV band. We detect multiple kinematic and ionization components in absorption and emission, whose superposition leads to complex line profiles, including strong P-Cygni profiles on resonance lines. The prominent Fe XXV Heα and Fe XXVI Lyα emission complexes are clearly resolved into their characteristic fine structure transitions. Self-consistent photoionization modeling allows us to disentangle the absorption and emission components and measure the Doppler velocity of these components as a function of binary orbital phase. We find a significantly higher velocity amplitude for the emission lines than for the absorption lines. The absorption lines generally appear blueshifted by ∼ − 500–600 km s−1. We show that the wind decomposes naturally into a relatively smooth and large scale component, perhaps originating with the background wind itself, plus a turbulent more dense structure located close to the compact object in its orbit. Copyright © 2024, The Authors. All rights reserved.

关键词： Photoionization

Deep learning detecting fraud in credit card transactions

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Deep learning detecting fraud in credit card transactions

2018 systems and Information engineering Design Symposium, SIEDS 2018

作者： Roy, Abhimanyu Sun, Jingyi Mahoney, Robert Alonzi, Loreto Adams, Stephen Beling, Peter Data Science Institute University of Virginia United States Library and Data Science Institute University of Virginia United States Systems and Information Engineering University of Virginia United States Department of Systems and Information Engineering University of Virginia United States

ISBN: (纸本)9781538663431

Credit card fraud resulted in the loss of $3 billion to North American financial institutions in 2017. The rise of digital payments systems such as Apple Pay, Android Pay, and Venmo has meant that loss due to fraudulent activity is expected to increase. Deep Learning presents a promising solution to the problem of credit card fraud detection by enabling institutions to make optimal use of their historic customer data as well as real-time transaction details that are recorded at the time of the transaction. In 2017, a study found that a Deep Learning approach provided comparable results to prevailing fraud detection methods such as Gradient Boosted Trees and Logistic Regression. However, Deep Learning encompasses a number of topologies. Additionally, the various parameters used to construct the model (e.g. the number of neurons in the hidden layer of a neural network) also influence its results. In this paper, we evaluate a subsection of Deep Learning topologies - from the general artificial neural network to topologies with built-in time and memory components such as Long Short-term memory - and different parameters with regard to their efficacy in fraud detection on a dataset of nearly 80 million credit card transactions that have been pre-labeled as fraudulent and legitimate. We utilize a high performance, distributed cloud computing environment to navigate past common fraud detection problems such as class imbalance and scalability. Our analysis provides a comprehensive guide to sensitivity analysis of model parameters with regard to performance in fraud detection. We also present a framework for parameter tuning of Deep Learning topologies for credit card fraud detection to enable financial institutions to reduce losses by preventing fraudulent activity. © 2018 IEEE.

关键词： Topology

Workflow to Facilitate the Detection of New Psychoactive Substances and Drugs of Abuse in Influent Urban Wastewater

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SSRN

SSRN 2024年

作者： Bade, Richard van Herwerden, Denice Rousis, Nikolaos Adhikari, Sangeet Allen, Darren Baduel, Christine Bijlsma, Lubertus Boogaerts, Tim Burgard, Dan Chappell, Andrew Driver, Erin M. Sodre, Fernando Fabriz Fatta-Kassinos, Despo Gracia-Lor, Emma Gracia-Marín, Elisa Halden, Rolf U. Heath, Ester Jaunay, Emma Krotulski, Alex Lai, Foon Yin Löve, Arndís Sue Ching O’Brien, Jake W. Oh, Jeong-Eun Pasin, Daniel Castro, Marco Pineda Psichoudaki, Magda Salgueiro-Gonzalez, Noelia Gomes, Cezar Silvino Subedi, Bikram Thomas, Kevin V. Thomaidis, Nikolaos Wang, Degao Yargeau, Viviane Samanipour, Saer Mueller, Jochen The University of Queensland 20 Cornwall Street WoolloongabbaQLD4102 Australia University of Amsterdam Netherlands School of Sustainable Engineering and Built Environment Arizona State University TempeAZ85281 United States Biodesign Center for Environmental Health Engineering Biodesign Institute Arizona State University 1001 S. McAllister Ave. TempeAZ85281 United States Royal Brisbane and Women's Hospital HerstonQLD4029 Australia Grenoble France Environmental and Public Health Analytical Chemistry Research Institute for Pesticides and Water University Jaume I Avda Sos Baynat s/n CastellónE-12071 Spain Toxicological Centre Department of Pharmaceutical Sciences University of Antwerp Wilrijk2610 Belgium Department of Chemistry and Biochemistry University of Puget Sound TacomaWA98416 United States Christchurch Science Centre 27 Creyke Road Ilam Christchurch8041 New Zealand Institute of Chemistry University of Brasília DF Brasília70910-000 Brazil Nireas-International Water Research Centre Department of Civil and Environmental Engineering University of Cyprus P.O. Box 20537 Nicosia1678 Cyprus Department of Analytical Chemistry Faculty of Chemistry Complutense University of Madrid Avenida Complutense s/n Madrid28040 Spain OneWaterOneHealth Arizona State University Foundation 1001 S. McAllister Avenue TempeAZ85287-8101 United States Jožef Stefan Institute International Postgraduate School Jožef Stefan Jamova 39 Ljubljana1000 Slovenia Health and Biomedical Innovation UniSA: Clinical and Health Sciences University of South Australia AdelaideSA5001 Australia Center for Forensic Science Research and Education Fredric Rieders Family Foundation Willow GrovePA19090 United States UppsalaSE-75007 Sweden University of Iceland Department of Pharmacology and Toxicology Hofsvallagata 53 Reykjavik107 Iceland University of Iceland Faculty of Pharmaceutical Sciences Hofsvallagata 53 Reykjavik107 Iceland Department of Civil and Envir

The complexity around the dynamic markets for new psychoactive substances (NPS) forces researchers to develop and apply innovative analytical strategies to detect and identify them in influent urban wastewater. In this work a comprehensive suspect screening workflow following liquid chromatography – high resolution mass spectrometry analysis was established utilising the open-source InSpectra data processing platform and the HighResNPS library. In total, 278 urban influent wastewater samples from 47 sites in 16 countries were collected to investigate the presence of NPS and other drugs of abuse. A total of 50 compounds were detected in samples from at least one site. Most compounds found were prescription drugs such as gabapentin (detection frequency 79%), codeine (40%) and pregabalin (15%). However, cocaine was the most found illicit drug (83%), in all countries where samples were collected apart from the Republic of Korea and China. Eight NPS were also identified with this protocol: 3-methylmethcathinone 11%), eutylone (6%), etizolam (2%), 3-chloromethcathinone (4%), mitragynine (6%), phenibut (2%), 25I-NBOH (2%) and trimethoxyamphetamine (2%). The latter three have not previously been reported in municipal wastewater samples. The workflow employed allowed the prioritisation of features to be further investigated, reducing processing time and gaining in confidence in their identification. © 2024, The Authors. All rights reserved.

关键词： data handling

An extreme test case for planet formation: a close-in Neptune orbiting an ultracool star

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arXiv 2023年

作者： Stefánsson, Guðmundur Mahadevan, Suvrath Miguel, Yamila Robertson, Paul Delamer, Megan Kanodia, Shubham Cañas, Caleb Winn, Joshua Ninan, Joe R., Terrien R., Holcomb E., Ford B., Zawadzki B.P., Bowler C., Bender W., Cochran S., Diddams M., Endl C., Fredrick S., Halverson F., Hearty G.J., Hill A., Lin A., Metcalf A., Monson L., Ramsey A., Roy C., Schwab J., Wright G., Zeimann Princeton University Department of Astrophysical Sciences United States Department of Astronomy & Astrophysics The Pennsylvania State University United States Center for Exoplanets and Habitable Worlds The Pennsylvania State University United States ETH Zurich Institute for Particle Physics & Astrophysics Switzerland Leiden Observatory University of Leiden Netherlands SRON Netherlands Institute for Space Research Netherlands Department of Physics & Astronomy University of California Irvine United States Earth and Planets Laboratory Carnegie Institution for Science United States NASA Goddard Space Flight Center United States Department of Astronomy & Astrophysics Tata Institute of Fundamental Research India Carleton College United States Center for Astrostatistics The Pennsylvania State University United States Institute for Computational & Data Sciences The Pennsylvania State University United States Department of Astronomy The University of Texas at Austin AustinTX78712 United States Steward Observatory The University of Arizona United States Center for Planetary Systems Habitability and McDonald Observatory UT Austin United States Electrical Computer and Energy Engineering University of Colorado United States National Institute of Standards & Technology United States Department of Physics University of Colorado United States Jet Propulsion Laboratory California Institute of Technology United States McDonald Observatory UT Austin United States Space Vehicles Directorate Air Force Research Laboratory United States Space Telescope Science Institute United States Department of Physics and Astronomy Johns Hopkins University United States School of Mathematical and Physical Sciences Macquarie University Australia Penn State Extraterrestrial Intelligence Center The Pennsylvania State University United States Hobby-Eberly Telescope University of Texas Austin AustinTX78712 United States

In current theories of planet formation, close-orbiting planets as massive as Neptune are expected to be very rare around low-mass stars. We report the discovery of a Neptune-mass planet orbiting the 'ultracool' star LHS 3154, which is nine times less massive than the Sun. The planet's orbital period is 3.7 days and its minimum mass is 13.2 Earth masses, giving it the largest known planet-to-star mass ratio among short-period planets ( Copyright © 2023, The Authors. All rights reserved.

关键词： Planets

The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

Cocreating the Visualization of Digital Mobility Outcomes: Delphi-Type Process With Patients

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JMIR Formative Research 2025年 9卷 e68782页

作者： Lumsdon, Jack Wilson, Cameron Alcock, Lisa Becker, Clemens Benvenuti, Francesco Bonci, Tecla van den Brande, Koen Brittain, Gavin Brown, Philip Buckley, Ellen Caruso, Marco Caulfield, Brian Cereatti, Andrea Delgado-Ortiz, Laura Del Din, Silvia Evers, Jordi Garcia-Aymerich, Judith Gaßner, Heiko Gur Arieh, Tova Hansen, Clint Hausdorff, Jeffrey M. Hiden, Hugo Hume, Emily Kirk, Cameron Maetzler, Walter Megaritis, Dimitrios Rochester, Lynn Scott, Kirsty Sharrack, Basil Sutton, Norman Vereijken, Beatrix Vogiatzis, Ioannis Yarnall, Alison Keogh, Alison Cantu, Alma Population Health Sciences Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne United Kingdom School of Clinical Medicine Department of Public Health and Primary Care University of Cambridge Cambridge United Kingdom Translational and Clinical Research Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne United Kingdom NIHR Newcastle Biomedical Research Centre Newcastle University The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle upon Tyne United Kingdom Robert Bosch Society for Medical Research Stuttgart Germany Digital Geriatrics Unit Medical Centre University of Heidelberg Heidelberg Germany Mobilise-D Patient and Public Advisory Group Newcastle Upon Tyne United Kingdom School of Mechanical Aerospace and Civil Engineering University of Sheffield Sheffield United Kingdom Insigneo Institute for Silico Medicine University of Sheffield Sheffield United Kingdom Department of Neuroscience Sheffield NIHR Translational Neuroscience BRC Sheffield Teaching Hospitals NHS Foundation Trust Sheffield United Kingdom Sheffield Institute for Translational Neuroscience University of Sheffield Sheffield United Kingdom The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne United Kingdom Division of Clinical Medicine University of Sheffield Sheffield United Kingdom Department of Electronics and Telecommunications Politecnico di Torino Turin Italy Insights Centre Data Analytics University College Dublin Dublin Ireland School of Public Health Physiotherapy and Sports Science University of College Dublin Dublin Ireland Barcelona Institute for Global Health Barcelona Spain Department of Medicine and Life Sciences Pompeu Fabra University Barcelona Spain Center for Biomedical Research Madrid Spain McRoberts BV The Hague Netherlands Department of Molecular Neurology University Hospital Erlangen Erlangen Germany Fraunhofer Institute for Integrated Circuits IIS Erlangen Germany Departmen

Background: Recent technological advances in wearable devices offer new potential for measuring mobility in real-world contexts. Mobilise-D has validated digital mobility outcomes to provide novel outcomes and end points in clinical research of 4 different long-term health conditions (Parkinson disease, multiple sclerosis, chronic obstructive pulmonary disease, and proximal femoral fracture). These outcomes also provide unique information that is important to patients;however, there is limited literature that explores the optimal methods to achieve this, such as the best way to visualize patients’ data. Objective: This study aimed to identify meaningful outcomes for each condition and how to best visualize them from the perspective of end users. Methods: Using a Delphi-type protocol with patients as subject matter experts, we gathered iterative feedback on the cocreation of visualizations through 3 rounds of questionnaires. An open-ended questionnaire was used in round 1 to understand what aspects of mobility were most influenced by their health condition. These responses were mapped onto relevant digital mobility outcomes and walking experiences and then prioritized for visualization. Using patient responses, we worked alongside researchers, clinicians, and a patient advisory group to develop visualizations that depicted a week of mobility data. During rounds 2 and 3, participants rated usefulness and ease of understanding on a 5-point Likert scale and provided unstructured feedback in comment boxes for each visualization. Visualizations were refined using the feedback from round 2 before receiving further feedback in round 3. Results: Participation varied across rounds 1 to 3 (n=48, n=79, and n=78, respectively). Round 1 identified important outcomes and contexts for each health condition, such as walking speed and stride length for people with Parkinson disease or multiple sclerosis and number of steps for people with chronic obstructive pulmonary disease or prox

关键词： cocreation data visualization digital mobility outcomes mobility wearable devices

Robust optical frequency transfer in a noisy urban fiber network

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arXiv 2021年

作者： Zhang, Xiang Hu, Liang Deng, Xue Zang, Qi Liu, Jie Wang, Dongjie Liu, Tao Dong, Ruifang Zhang, Shougang National Time Service Center Chinese Academy of Sciences Xi’an710600 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Time and Frequency Standards Chinese Academy of Sciences Xi’an710600 China State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Institute for Advanced Communication and Data Science Shanghai Key Laboratory of Navigation and Location-Based Services Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

Optical fibers have been recognized as one of the most promising host material for high phase coherence optical frequency transfer over thousands of kilometers. In the pioneering work, the active phase noise cancellation (ANC) technique has been widely used for suppressing the fiber phase noise introduced by the environmental perturbations, in which an ideal phase detector with high resolution and unlimited detection range is needed to extract the fiber phase noise, in particular for noisy fiber links. We demonstrate the passive phase noise cancellation (PNC) technique without the need of phase detector could be preferable for noisy fiber links. To avoid the effect of the radio frequency (RF) from the time base at the local site in the conventional active or passive phase noise cancellation techniques, here we introduce a fiber-pigtailed acousto-optic modulator (AOM) with two diffraction order outputs (0 and +1 order) with properly allocating the AOM-driving frequencies allowing to cancel the time base effect. Using this technique, we demonstrate transfer of coherent light through a 260 km noisy urban fiber link. The results show the effect of the RF reference can be successfully removed. After being passively compensated, we demonstrate a fractional frequency instability of 4.9 × 10−14 at the integration time of 1 s and scales down to 10−20 level at 10,000 s in terms of modified Allan deviation over the 260 km noisy urban fiber link. The frequency uncertainty of the retrieved light after transferring through this noise-compensated fiber link relative to that of the input light achieves (0.41 ± 4.7) × 10−18. The proposed technique opens a way to a broad distribution of an ultrastable frequency reference with high coherence without any effects coming from the RF reference and enables a wide range of applications beyond metrology over fiber networks. © 2021, CC BY.

关键词： Natural frequencies