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Numerical simulation of tsunami propagation with Finite Difference Method and Runge Kutta 4th Order Method (study case : south coast of Java island)

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Journal of Physics: Conference Series 2019年第1期1218卷

作者： Firdaus Priyatno Putra Shaffiani Nurul Fajar Prima Aditya Erna Apriliani Sadjidon Didik Khusnul Arif Department of Mathematics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya Indonesia 60111

Tsunamis are disasters that cause so much damage. In the history of the tsunami, Indonesia has been hit by tsunami several times, based on data from the National Disaster Management Agency (BNBP), as of 1629 - 2007, Indonesia was recorded 184 times affected by large and small tsunami disasters. Based on these data, it is very important for Indonesia to increase security against the tsunami disaster. Also, the tsunami in Indonesia occurred due to earthquakes, volcanic eruptions, and landslides in the sea. This study discussed about to the tsunami with a location located in the southern of Java, precisely in the area of Central Java, considering that in 2017 there was friction between the Indies and Eurasian plates which caused an earthquake measuring 6.9 magnitude, but did not cause a tsunami. In this study, a tsunami wave propagation simulation was carried out with the aim of knowing how long it would take the waves to the shoreline and how high the waves would be when they were on the shoreline. The model used is a tsunami model created by Imamura by solving differential equations using finite difference and Runge-Kutta 4th order. Based on the simulation results with the initial determination of a 5 m tsunami wave centered at a distance of 25 km from the shoreline, Tsunami waves with a height of 8.7 m will arrive at the shoreline at 140 seconds. With wave height is 8.7 m at the shoreline, the water will enter the land or can also be called the tsunami disaster. At that time, residents are expected to have been evacuated from the coastal area. In addition, based on numerical simulations that have been carried out, information is obtained that the computation time of the method is faster than the computation time of the Runge-Kutta method with differences in altitude results that are quite small and can be tolerated. This shows that the Finite Difference method can be applied to tsunami disaster mitigation software because it has a fairly fast computing time compared

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Integrating MOOCs with Traditional Classroom Instruction to Improve Teaching Effectiveness

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Integrating MOOCs with Traditional Classroom Instruction to ...

2017 2nd International Seminar on Education Innovation and Economic Management(SEIEM 2017)

作者： Xiaodong Wu Yuzhu Zeng Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

Massive Open Online Course(MOOC) is a new teaching mode appeared in recent years and its emergence has constituted some challenge to the traditional teaching. However, MOOCs teaching mode has both advantages and disadvantages. In this paper, both the merits and the demerits of MOOC education are analyzed. We point out that the MOOC education mode and the traditional face-to-face classroom instruction should be combined together so as to improve the teaching effectiveness in modern education. Finally, how to blend the MOOC mode and the traditional classroom teaching and further utilize the network platform to improve the teaching efficiency in several learning phases are examined.

关键词： MOOCs face-to-face teaching traditional teaching network teaching platform

The 2025 Roadmap to Ultrafast Dynamics: Frontiers of Theoretical and Computational Modelling

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arXiv 2025年

作者： Caruso, Fabio Sentef, Michael A. Attaccalite, Claudio Bonitz, Michael Draxl, Claudia De Giovannini, Umberto Eckstein, Martin Ernstorfer, Ralph Fechner, Michael Grüning, Myrta Hübener, Hannes Joost, Jan-Philip Juraschek, Dominik M. Karrasch, Christoph Kennes, Dante Marvin Latini, Simone Lu, I-Te Neufeld, Ofer Perfetto, Enrico Rettig, Laurenz Pela, Ronaldo Rodrigues Rubio, Angel Rudzinski, Joseph F. Ruggenthaler, Michael Sangalli, Davide Schüler, Michael Shallcross, Samuel Sharma, Sangeeta Stefanucci, Gianluca Werner, Philipp Institute of Theoretical Physics and Astrophysics University of Kiel Leibnizstrasse 15 Kiel24116 Germany Institute for Theoretical Physics Bremen Center for Computational Materials Science University of Bremen Bremen28359 Germany Luruper Chaussee 149 Hamburg22761 Germany CNRS Aix-Marseille Université Centre Interdisciplinaire de Nanoscience de Marseille UMR 7325 Campus de Luminy Marseille13288 cedex 9 France Physics Department CSMB Adlershof Humboldt-Universität zu Berlin Berlin12489 Germany Università degli Studi di Palermo Dipartimento di Fisica e Chimica—Emilio Segrè PalermoI-90123 Italy Institute for Theoretical Physics University of Hamburg Notkestraße 9-11 Hamburg22607 Germany The Hamburg Centre for Ultrafast Imaging Hamburg Germany Fritz Haber Institute Max Planck Society Faradayweg 4-6 Berlin14195 Germany Institut für Optik und Atomare Physik Technische Universität Berlin Straße des 17 Juni 135 Berlin10623 Germany Max Planck Institute for the Structure and Dynamics of Matter Hamburg Germany School of Mathematics and Physics Queen’s University Belfast University Road BelfastBT7 1NN United Kingdom Max Planck Institute for the Structure and Dynamics of Matter Center for Free Electron Laser Science Hamburg22761 Germany Department of Applied Physics and Science Education Eindhoven University of Technology Eindhoven Netherlands Technische Universität Braunschweig Institut für Mathematische Physik Mendelssohnstr. 3 Braunschweig38106 Germany Institut für Theorie der Statistischen Physik RWTH Aachen University JARA – Fundamentals of Future Information Technology Aachen52056 Germany Max Planck Institute for the Structure and Dynamics of Matter Center for Free Electron Laser Science Hamburg22761 Germany Department of Physics Technical University of Denmark Kgs Lyngby2800 Denmark Max Planck Institute for the Structure and Dynamics of Matter Center for Free-Electron Laser Science Luruper Chaussee 149 Hamburg22761 Germany Schulich Fac

The exploration of ultrafast phenomena is a frontier of condensed matter research, where the interplay of theory, computation, and experiment is unveiling new opportunities for understanding and engineering quantum materials. With the advent of advanced experimental techniques and computational tools, it has become possible to probe and manipulate nonequilibrium processes at unprecedented temporal and spatial resolutions, providing insights into the dynamical behavior of matter under extreme conditions. These capabilities have the potential to revolutionize fields ranging from optoelectronics and quantum information to catalysis and energy storage. This Roadmap captures the collective progress and vision of leading researchers, addressing challenges and opportunities across key areas of ultrafast science. Contributions in this Roadmap span the development of ab initio methods for time-resolved spectroscopy, the dynamics of driven correlated systems, the engineering of materials in optical cavities, and the adoption of FAIR principles for data sharing and analysis. Together, these efforts highlight the interdisciplinary nature of ultrafast research and its reliance on cutting-edge methodologies, including quantum electrodynamical density-functional theory, correlated electronic structure methods, nonequilibrium Green’s function approaches, quantum and ab initio simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Time-resolved spectroscopy

Correction: On the usage of average Hausdorff distance for segmentation performance assessment: hidden error when used for ranking

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European radiology experimental 2022年第1期6卷 56页

作者： Orhun Utku Aydin Abdel Aziz Taha Adam Hilbert Ahmed A Khalil Ivana Galinovic Jochen B Fiebach Dietmar Frey Vince Istvan Madai CLAIM - Charité Lab for Artificial Intelligence in Medicine Charité Universitätsmedizin Berlin Berlin Germany. orhun-utku.aydin@charite.de. Research Studio Data Science Research Studios Austria Salzburg Austria. CLAIM - Charité Lab for Artificial Intelligence in Medicine Charité Universitätsmedizin Berlin Berlin Germany. Centre for Stroke Research Berlin Charité Universitätsmedizin Berlin Berlin Germany. Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany. Mind Brain Body Institute Berlin School of Mind and Brain Humboldt-Universität Berlin Berlin Germany. School of Computing and Digital Technology Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham UK.

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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U-Net-and-a-half: Convolutional network for biomedical image segmentation using multiple expert-driven annotations

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arXiv 2021年

作者： Zhang, Yichi Kers, Jesper Cassol, Clarissa A. Roelofs, Joris J. Idrees, Najia Farber, Alik Haroon, Samir Daly, Kevin P. Ganguli, Suvranu Chitalia, Vipul C. Kolachalama, Vijaya B. Section of Computational Biomedicine Department of Medicine Boston University School of Medicine BostonMA02118 United States Department of Pathology Academic Medical Center Meibergdreef 9 Amsterdam1105 AZ Netherlands Department of Pathology Leiden University Medical Center Albinusdreef 2 Leiden2333 ZA Netherlands Arkana Laboratories Little RockAR72211 United States Amsterdam Cardiovascular Sciences University of Amsterdam Netherlands Renal Section Department of Medicine Boston University School of Medicine BostonMA02118 United States Department of Surgery Boston University School of Medicine BostonMA02118 United States Department of Radiology Boston University School of Medicine BostonMA02118 United States Veterans Affairs Boston Healthcare System BostonMA02118 United States Department of Computer Science College of Arts and Sciences Boston University MA02215 United States University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Faculty of Computing and Data Sciences Boston University BostonMA02215 United States

Development of deep learning systems for biomedical segmentation often requires access to expert-driven, manually annotated datasets. If more than a single expert is involved in the annotation of the same images, then the inter-expert agreement is not necessarily perfect, and no single expert's annotation can precisely capture the so-called ground truth of the regions of interest on all images. Also, it is not trivial to generate a reference estimate using annotations from multiple experts. Here we present a deep neural network, defined as U-Net-and-a-half, which can simultaneously learn from annotations performed by multiple experts on the same set of images. U-Net-and-a-half contains a convolutional encoder to generate features from the input images, multiple decoders that allow simultaneous learning from image masks obtained from annotations that were independently generated by multiple experts, and a shared lowdimensional feature space. To demonstrate the applicability of our framework, we used two distinct datasets from digital pathology and radiology, respectively. Specifically, we trained two separate models using pathologist-driven annotations of glomeruli on whole slide images of human kidney biopsies (n=10 patients), and radiologist-driven annotations of lumen crosssections of human arteriovenous fistulae obtained from intravascular ultrasound images (n=10 patients), respectively. The models based on U-Net-and-a-half exceeded the performance of the traditional U-Net models trained on single expert annotations alone, thus expanding the scope of multitask learning in the context of biomedical image *** Codes 68U10 © 2021, CC BY.

关键词： Deep neural networks

A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

Correction: An Artificial Therapist (Manage Your Life Online) to Support the Mental Health of Youth: Co-Design and Case Series

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JMIR human factors 2024年 11卷 e69628页

作者： Aimee-Rose Wrightson-Hester Georgia Anderson Joel Dunstan Peter M McEvoy Christopher J Sutton Bronwyn Myers Sarah Egan Sara Tai Melanie Johnston-Hollitt Wai Chen Tom Gedeon Warren Mansell Curtin enAble Institute Faculty of Health Sciences Curtin University Perth Australia. Discipline of Psychology School of Population Health Curtin University Perth Australia. School of Arts and Humanities Edith Cowan University Perth Australia. Mental Health Commission of Western Australia Perth Australia. Curtin Institute for Data Science Curtin University Perth Australia. Centre for Clinical Interventions North Metropolitan Health Service Nedlands Australia. Centre for Biostatistics School of Health Sciences The University of Manchester Manchester United Kingdom. Alcohol Tobacco and Other Drug Research Unit South African Medical Research Council Parow South Africa. Division of Addiction Psychiatry Department of Psychiatry and Mental Health University of Cape Town Cape Town South Africa. Department of Clinical Psychology School of Health Sciences The University of Manchester Manchester United Kingdom. Mental Health Service Fiona Stanley Hospital Perth Australia. Curtin Medical School Curtin University Perth Australia. Centre of Excellence in Medical Biotechnology Faculty of Medical Science Naresuan University Phitsanulok Thailand. Optus-Curtin Centre of Excellence in AI School of Electronic Engineering Computing and Mathematical Sciences Curtin University Perth Australia.

[This corrects the article DOI: 10.2196/46849.].

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Priority attachment: A comprehensive mechanism for generating networks

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arXiv 2018年

作者： Morzy, Mikolaj Kajdanowicz, Tomasz Kazienko, Przemyslaw Miebs, Grzegorz Rusin, Arkadiusz Institute of Computing Science Poznań University of Technology Poznań60-965 Poland ENGINE European Centre for Data Science Faculty of Computer Science and Management Wroclaw University of Science and Technology Wroclaw50-370 Poland

We claim that networks are created according to the priority attachment mechanism. We introduce a simple model, which uses the priority attachment to generate both synthetic and close to empirical networks. Priority attachment is a mechanism, which generalizes previously proposed mechanisms, such as small world creation or preferential attachment, but we also observe its presence in a range of real-world networks. In this paper, we show that by using priority attachment we can generate networks of very diverse topologies, as well as re-create empirical ones. An additional advantage of the priority attachment mechanism is an easy interpretation of the latent processes of network formation. We substantiate our claims by performing numerical experiments on both synthetic and empirical networks. The two main contributions of the paper are: the development of the priority attachment mechanism, and the design of the Priority Rank: a simple network generative model based on the priority attachment mechanism. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning