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Publisher Correction: Landscape-scale benefits of protected areas for tropical biodiversity

Nature 2024年第7996期625卷 E28页

作者： Jedediah F Brodie Jayasilan Mohd-Azlan Cheng Chen Oliver R Wearn Mairin C M Deith James G C Ball Eleanor M Slade David F R P Burslem Shu Woan Teoh Peter J Williams An Nguyen Jonathan H Moore Scott J Goetz Patrick Burns Patrick Jantz Christopher R Hakkenberg Zaneta M Kaszta Sam Cushman David Coomes Olga E Helmy Glen Reynolds Jon Paul Rodríguez Walter Jetz Matthew Scott Luskin Division of Biological Sciences University of Montana Missoula MT USA. jedediah.brodie@umontana.edu. Wildlife Biology Program University of Montana Missoula MT USA. jedediah.brodie@umontana.edu. Institute of Biodiversity and Environmental Conservation Universiti Malaysia Sarawak Kota Samarahan Malaysia. jedediah.brodie@umontana.edu. Institute of Biodiversity and Environmental Conservation Universiti Malaysia Sarawak Kota Samarahan Malaysia. Department of Forest Resources Management University of British Columbia Vancouver British Columbia Canada. Biodiversity Research Centre University of British Columbia Vancouver British Columbia Canada. Fauna and Flora International-Vietnam Programme Hanoi Vietnam. Institute for the Oceans and Fisheries University of British Columbia Vancouver British Columbia Canada. Department of Plant Sciences and Conservation Research Institute University of Cambridge Cambridge UK. Asian School of the Environment Nanyang Technological University Singapore Singapore. School of Biological Sciences University of Aberdeen Aberdeen UK. Wildlife Biology Program University of Montana Missoula MT USA. Division of Biological Sciences University of Montana Missoula MT USA. Leibniz Institute for Zoo and Wildlife Research Berlin Germany. School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen China. School of Environmental Sciences University of East Anglia Norwich UK. School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA. Department of Biological Sciences Northern Arizona University Flagstaff AZ USA. Wildlife Conservation Research Unit Department of Biology University of Oxford Oxford UK. School of Forestry Northern Arizona University Flagstaff AZ USA. Aldo Leopold Wilderness Research Institute United States Department of Agriculture Forest Service Rocky Mountain Research Station Missoula MT USA. The South East Asia Rainforest Research Partn

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Author Correction: Landscape-scale benefits of protected areas for tropical biodiversity

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Nature 2024年第8009期628卷 E5页

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Middle East and North African Health Informatics Association (MENAHIA): A New Opportunity for the Region

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Yearbook of Medical Informatics 2019年第1期28卷 292-296页

作者： Riyad Alshammari Najeeb Al-Shorbaji Zakiuddin Ahmed Zaid Al-Hamadan Abdulwahhab Alshammari Osama El Hassan Dari Alhuwail Salim Al Salmi Eiman Al-Jafar Hassan Ghazal College of Public Health and Health Informatics King Saud bin Abdulaziz University for Health Sciences Riyadh Saudi Arabia King Abdullah International Medical Research Center Riyadh Saudi Arabia Ministry of the National Guard- Health Affairs Riyadh Saudi Arabia President of the Saudi Health Informatics Association President of the eHealth Development Association of Jordan Program Director RAHAH King Saud University Project Director Riphah Institute of Health Improvement & Safety CEO Digital Care Pakistan Associate Professor Jordan University of Science and Technology College of Applied Medical Sciences King Saud bin Abdulaziz University for Health Sciences Riyadh Saudi Arabia Vice-Chair of Emirates Health Informatics Society Head of eHealth section at Dubai Health Authority Dubai United Arab Emirates Department of Information Science College of Computing Sciences and Engineering Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait Associate Deanship for Health Information Management Program Oman College of Health Sciences Muscat Sultanate of Oman Department of Health Informatics and Information Management Faculty of Allied Health Sciences Kuwait University Kuwait. National Center for Scientific and Technological Research Rabat and President of the Moroccan Society for Telemedicine and eHealth Rabat Morocco

During the 2018 IMIA General Assembly (GA) held on Oct 2018 in Sri Lanka, a new organization, the Middle East and North African Health Informatics Association (MENAHIA) was established as a regional non-governmental organization to promote Health Informatics in the region. Health Informatics activities in applications, education, and research have been growing in the Middle East and North African region for many years. Over the past year, health informatics initiatives within the region focused on the education sector and the workforce. The need to enhance the education delivery and methods is essential in promoting Health Informatics in the region as well as establishing master programs in many countries within the region. This paper provides an overview of the variety of initiatives that are occurring in the region and are endorsed by MENAHIA.

关键词： Saudi Arabia Pakistan Jordan United Arab Emirates Morocco Kuwait Oman Bahrain Health Informatics the Middle East

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Multi-partition time aggregation for Markov Chains

Multi-partition time aggregation for Markov Chains

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IEEE Annual Conference on Decision and Control

作者： Edilson F. Arruda Marcelo D. Fragoso Fabricio O. Ourique Industrial Engineering Program Federal University of Rio de Janeiro Rio de Janeiro RJ Brazil Center for Systems and Control National Laboratory for Scientific Computing Petrópolis RJ Brazil Federal University of Santa Catarina Araranguá SC Brazil

Motivated by Markov decision processes, this paper introduces a form of embedding for Markov chains which is based on the partition of the state space into a manageable number of subsets, with the aim of enabling a decomposition algorithm for calculating long-term costs and probabilities. The decomposition enables the decision maker to derive the long term distribution by making use of evaluations in the domain of the partitions, which presents reduced cardinality with respect to the original state space and hence yields reduced computational effort.

关键词： Markov processes Trajectory Frequency modulation Aerospace electronics Partitioning algorithms Transient analysis Absorption

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A large-scale ENIGMA multisite replication study of brain age in depression

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Neuroimage: Reports 2022年第4期2卷 100149-100149页

作者： Han, Laura K.M. Dinga, Richard Leenings, Ramona Hahn, Tim Cole, James H. Aftanas, Lyubomir I. Amod, Alyssa R. Besteher, Bianca Colle, Romain Corruble, Emmanuelle Couvy-Duchesne, Baptiste Danilenko, Konstantin V. Fuentes-Claramonte, Paola Gonul, Ali Saffet Gotlib, Ian H. Goya-Maldonado, Roberto Groenewold, Nynke A. Hamilton, Paul Ichikawa, Naho Ipser, Jonathan C. Itai, Eri Koopowitz, Sheri-Michelle Li, Meng Okada, Go Okamoto, Yasumasa Churikova, Olga S. Osipov, Evgeny A. Penninx, Brenda W.J.H. Pomarol-Clotet, Edith Rodríguez-Cano, Elena Sacchet, Matthew D. Shinzato, Hotaka Sim, Kang Stein, Dan J. Uyar-Demir, Aslihan Veltman, Dick J. Schmaal, Lianne Centre for Youth Mental Health The University of Melbourne Parkville VIC Australia Orygen Parkville VIC Australia Department of Cognitive Science & Artificial Intelligence Tilburg University Tilburg Netherlands Institute for Translational Psychiatry University of Münster Münster Germany Faculty of Mathematics and Computer Science University of Münster Münster Germany Centre for Medical Image Computing Department of Computer Science University College London United Kingdom Dementia Research Centre Queen Square Institute of Neurology University College London United Kingdom Scientific Research Institute of Neuroscience and Medicine Novosibirsk Russian Federation Novosibirsk State University Novosibirsk Russian Federation Department of Psychiatry & Mental Health Neuroscience Institute University of Cape Town South Africa Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany Service Hospitalo-Universitaire de Psychiatrie de Bicêtre Mood Center Paris Saclay Assistance Publique-Hôpitaux de Paris Hôpitaux Universitaires Paris-Saclay Hôpital de Bicêtre Le Kremlin Bicêtre F-94275 France MOODS Team INSERM 1018 CESP (Centre de Recherche en Epidémiologie et Santé des Populations) Université Paris-Saclay Faculté de Médecine Paris-Saclay Le Kremlin Bicêtre F-94275 France Sorbonne Université Institut du Cerveau - Paris Brain Institute - ICM CNRS Inria Inserm AP-HP Hôpital de La Pitié Salpêtrière Paris F-75013 France Institute for Molecular Bioscience The University of Queensland St Lucia 4072 QLD Australia FIDMAG Hermanas Hospitalarias Research Foundation Barcelona Spain CIBERSAM Barcelona Spain SoCAT Lab Department of Psychiatry School of Medicine Ege University Izmir Turkey Department of Psychology Stanford University Stanford CA United States Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab) University Medical Center Göttingen Göttingen Germany Center for Social and Affective Neuroscience Department o

Background: Several studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived “brain age gap” has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N = 2126 controls & N = 2675 cases;+1.08 years [SE 0.22], Cohen's d = 0.14, 95% CI: 0.08–0.20), in independent cohorts that were not part of the original study. Methods: A previously trained brain age model (***/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18–75 years) from 13 new cohorts collected from 20 different scanners. Meta-regressions were used to examine potential moderating effects of basic cohort characteristics (e.g., clinical and scan technical) on the brain age gap. Results: Our ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R² = 0.47, MAE = 7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohen's d = 0.15, 95% CI: 0.05–0.25) compared with controls, highly similar to our previous finding. Significant moderating effects of FreeSurfer version 6.0 (d = 0.41, p = 0.007) and Philips scanner vendor (d = 0.50, p < 0.0001) were found, leading to more positive effect size estimates. Conclusions: This study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. Thus, two large-s

关键词： Biological aging Brain age Depression ENIGMA consortium Replication study

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Fourier phase retrieval: Uniqueness and algorithms

arXiv

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arXiv 2017年

作者： Bendory, Tamir Beinerty, Robert Eldarz, Yonina C. Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Institute of Mathematics and Scientific Computing University of Graz Heinrichstraße 36 Graz8010 Austria Andrew and Erna Viterbi Faculty of Electrical Engineering Technion - Israel Institute of Technology Haifa Israel

The problem of recovering a signal from its phaseless Fourier transform measurements, called Fourier phase retrieval, arises in many applications in engineering and science. Fourier phase retrieval poses fundamental theoretical and algorithmic challenges. In general, there is no unique mapping between a one-dimensional signal and its Fourier magnitude and therefore the problem is ill-posed. Additionally, while almost all multidimensional signals are uniquely mapped to their Fourier magnitude, the performance of existing algorithms is generally not well-understood. In this chapter we survey methods to guarantee uniqueness in Fourier phase retrieval. We then present different algorithmic approaches to retrieve the signal in practice. We conclude by outlining some of the main open questions in this field. Copyright © 2017, The Authors. All rights reserved.

关键词： Fourier transforms

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Bullen’s parameter as a seismic observable for spin crossovers in the lower mantle

arXiv

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arXiv 2017年

作者： Valencia-Cardona, Juan J. Williams, Quentin Shukla, Gaurav Wentzcovitch, Renata M. Scientific Computing Program University of Minnesota MinneapolisMN United States Department of Earth and Planetary Sciences University of California Santa Cruz Santa CruzCA United States Department of Earth Ocean and Atmospheric Science Florida State University TallahasseeFL United States Department of Applied Physics and Applied Mathematics Columbia University New York CityNY United States Lamont-Doherty Earth Observatory Columbia University PalisadesNY United States

Elastic anomalies produced by the spin crossover in ferropericlase have been documented by both first principles calculations and high pressure-temperature experiments. The predicted signature of this spin crossover in the lower mantle is, however, subtle and difficult to geophysically observe within the mantle. Indeed, global seismic anomalies associated with spin transitions have not yet been recognized in seismologic studies of the deep mantle. A sensitive seismic parameter is needed to determine the presence and amplitude of such a spin crossover signature. The effects of spin crossovers on Bullen’s parameter, η, are assessed here for a range of compositions, thermal profiles, and lateral variations in temperature within the lower mantle. Velocity anomalies associated with the spin crossover in ferropericlase span a depth range near 1,000 km for typical mantle temperatures. Positive excursions of Bullen’s parameter with a maximum amplitude of ∼ 0.03 are calculated to be present over a broad depth range within the mid-to-deep lower mantle: these are largest for peridotitic and harzburgitic compositions. These excursions are highest in amplitude for model lower mantles with large lateral thermal variations, and with cold downwellings having longer lateral length-scales relative to hot upwellings. We conclude that predicted deviations in Bullen’s parameter due to the spin crossover in ferropericlase for geophysically relevant compositions may be sufficiently large to resolve in accurate seismic inversions of this parameter, and could shed light on both the lateral variations in temperature at depth within the lower mantle, and the amount of ferropericlase at depth. Copyright © 2017, The Authors. All rights reserved.

关键词： Seismology

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High-order Discretization of a Gyrokinetic Vlasov Model in Edge Plasma Geometry

arXiv

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arXiv 2017年

作者： Dorr, Milo R. Colella, Phillip Dorf, Mikhail A. Ghosh, Debojyoti Hittinger, Jeffrey A.F. Schwartz, Peter O. Center for Applied Scientific Computing Lawrence Livermore National Laboratory 7000 East Avenue L-561 LivermoreCA94550 United States Fusion Energy Program Lawrence Livermore National Laboratory 7000 East Avenue L-630 LivermoreCA94550 United States Applied Numerical Algorithms Group Lawrence Berkeley National Laboratory One Cyclotron Road Mail Stop 50A-1148 BerkeleyCA94720 United States

We present a high-order spatial discretization of a continuum gyrokinetic Vlasov model in axisymmetric tokamak edge plasma geometries. Such models describe the phase space advection of plasma species distribution functions in the absence of collisions. The gyrokinetic model is posed in a four-dimensional phase space, upon which a grid is imposed when discretized. To mitigate the computational cost associated with high-dimensional grids, we employ a high-order discretization to reduce the grid size needed to achieve a given level of accuracy relative to lower-order methods. Strong anisotropy induced by the magnetic field motivates the use of mapped coordinate grids aligned with magnetic flux surfaces. The natural partitioning of the edge geometry by the separatrix between the closed and open field line regions leads to the consideration of multiple mapped blocks, in what is known as a mapped multiblock (MMB) approach. We describe the specialization of a more general formalism that we have developed for the construction of high-order, finite-volume discretizations on MMB grids, yielding the accurate evaluation of the gyrokinetic Vlasov operator, the metric factors resulting from the MMB coordinate mappings, and the interaction of blocks at adjacent boundaries. Our conservative formulation of the gyrokinetic Vlasov model incorporates the fact that the phase space velocity has zero divergence, which must be preserved discretely to avoid truncation error accumulation. We describe an approach for the discrete evaluation of the gyrokinetic phase space velocity that preserves the divergence-free property to machine precision. A distinguishing feature of an edge geometry is the X point, where the poloidal field component vanishes. The inability to construct fully flux-surface aligned MMB coordinate systems that are smooth up to and through the X point requires the relaxation of alignment in the vicinity of this point. We therefore describe an approach for the generation of s

关键词： Finite volume method

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Searching for Gravitational Waves from Cosmological Phase Transitions with the NANOGrav 12.5-Year Dataset

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Physical Review Letters 2021年第25期127卷 251302-251302页

作者： Zaven Arzoumanian Paul T. Baker Harsha Blumer Bence Bécsy Adam Brazier Paul R. Brook Sarah Burke-Spolaor Maria Charisi Shami Chatterjee Siyuan Chen James M. Cordes Neil J. Cornish Fronefield Crawford H. Thankful Cromartie Megan E. DeCesar Paul B. Demorest Timothy Dolch Justin A. Ellis Elizabeth C. Ferrara William Fiore Emmanuel Fonseca Nathan Garver-Daniels Peter A. Gentile Deborah C. Good Jeffrey S. Hazboun A. Miguel Holgado Kristina Islo Ross J. Jennings Megan L. Jones Andrew R. Kaiser David L. Kaplan Luke Zoltan Kelley Joey Shapiro Key Nima Laal Michael T. Lam T. Joseph W. Lazio Vincent S. H. Lee Duncan R. Lorimer Jing Luo Ryan S. Lynch Dustin R. Madison Maura A. McLaughlin Chiara M. F. Mingarelli Andrea Mitridate Cherry Ng David J. Nice Timothy T. Pennucci Nihan S. Pol Scott M. Ransom Paul S. Ray Brent J. Shapiro-Albert Xavier Siemens Joseph Simon Renée Spiewak Ingrid H. Stairs Daniel R. Stinebring Kevin Stovall Jerry P. Sun Joseph K. Swiggum Stephen R. Taylor Jacob E. Turner Michele Vallisneri Sarah J. Vigeland Caitlin A. Witt Kathryn M. Zurek X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 Greenbelt Maryland 20771 USA Department of Physics and Astronomy Widener University One University Place Chester Pennsylvania 19013 USA Department of Physics and Astronomy West Virginia University P.O. Box 6315 Morgantown West Virginia 26506 USA Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building Morgantown West Virginia 26505 USA Department of Physics Montana State University Bozeman Montana 59717 USA Cornell Center for Astrophysics and Planetary Science and Department of Astronomy Cornell University Ithaca New York 14853 USA Cornell Center for Advanced Computing Cornell University Ithaca New York 14853 USA CIFAR Azrieli Global Scholars program CIFAR Toronto Canada Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place Nashville Tennessee 37235 USA Station de Radioastronomie de Nancay Observatoire de Paris Universite PSL CNRS Universite d’Orleans 18330 Nancay France FEMTO-ST Institut de recherche Department of Time and Frequency UBFC and CNRS ENSMM 25030 Besancon France Laboratoire de Physique et Chimie de l’Environment et de l’Espace LPC2E UMR7328 Universite d’Orleans CNRS 45071 Orleans France Department of Physics and Astronomy Franklin & Marshall College P.O. Box 3003 Lancaster Pennsylvania 17604 USA Department of Physics Lafayette College Easton Pennsylvania 18042 USA George Mason University Fairfax Virginia 22030 USA National Radio Astronomy Observatory 1003 Lopezville Road Socorro New Mexico 87801 USA Department of Physics Hillsdale College 33 E. College Street Hillsdale Michigan 49242 USA Eureka Scientific Inc. 2452 Delmer Street Suite 100 Oakland California 94602-3017 Infinia ML 202 Rigsbee Avenue Durham North Carolina 27701 USA Department of Astronomy University of Maryland College Park Maryland 20742 USA Center for Research and Exploration in Space Science and Technolog

We search for a first-order phase transition gravitational wave signal in 45 pulsars from the NANOGrav 12.5-year dataset. We find that the data can be modeled in terms of a strong first order phase transition taking place at temperatures below the electroweak scale. However, we do not observe any strong preference for a phase-transition interpretation of the signal over the standard astrophysical interpretation in terms of supermassive black hole mergers; but we expect to gain additional discriminating power with future datasets, improving the signal to noise ratio and extending the sensitivity window to lower frequencies. An interesting open question is how well gravitational wave observatories could separate such signals.

关键词： First order phase transitions Gravitational wave detection Gravitational wave sources Gravitational waves

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