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arXiv 2024年

作者： Akiyama, Kazunori Niinuma, Kotaro Hada, Kazuhiro Doi, Akihiro Hagiwara, Yoshiaki Higuchi, Aya E. Honma, Mareki Kawashima, Tomohisa Kolev, Dimitar Koyama, Shoko Masui, Sho Ohsuga, Ken Sano, Hidetoshi Takami, Hideki Tsunetoe, Yuh Uzawa, Yoshinori Akahori, Takuya Akiyama, Yuto Galison, Peter Hayashi, Takayuki J. Hirota, Tomoya Inoue, Makoto Iwata, Yuhei Johnson, Michael D. Kino, Motoki Kofuji, Yutaro Mizuno, Yosuke Moriyama, Kotaro Nagai, Hiroshi Nakamura, Kenta Notsu, Shota Ono, Fumie Oya, Yoko Oyama, Tomoaki Rana, Hannah Saida, Hiromi Saito, Ryo Saito, Yoshihiko Sasada, Mahito Sawada-Satoh, Satoko Takahashi, Mikiya M. Takamura, Mieko Tong, Edward Tsuji, Hiroyuki Yoshioka, Shogo Watanabe, Yoshimasa Massachusetts Institute of Technology Haystack Observatory WestfordMA01886 United States Mizusawa VLBI Observatory National Astronomical Observatory of Japan Iwate023-0861 Japan Black Hole Initiative Harvard University CambridgeMA02138 United States Graduate School of Sciences and Technology for Innovation Yamaguchi University Yamaguchi753-8512 Japan The Graduate School of Science Nagoya City University Aichi467-0001 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Kanagawa252-5210 Japan Department of Space and Astronautical Science The Graduate University for Advanced Studies SOKENDAI Kanagawa229-8510 Japan Natural Science Laboratory Toyo University Tokyo112-8606 Japan Division of Science School of Science and Engineering Tokyo Denki University Saitama350-0394 Japan Department of Astronomy Graduate School of Science The University of Tokyo Tokyo113-0033 Japan Institute for Cosmic Ray Research The University of Tokyo Chiba277-8582 Japan Space Communication Systems Laboratory Wireless Networks Research Center National Institute of Information and Communications Technology Tokyo184-8795 Japan Graduate School of Science and Technology Niigata University Niigata950-2181 Japan Advanced Technology Center National Astronomical Observatory of Japan Tokyo181-8588 Japan Center for Computational Sciences University of Tsukuba Ibaraki305-8577 Japan Faculty of Engineering Gifu University Gifu501-1193 Japan Harvard University CambridgeMA02138 United States Azabu Junior and Senior High School Tokyo106-0046 Japan Astronomical Science Program The Graduate University for Advanced Studies SOKENDAI Tokyo181-8588 Japan Institute of Astronomy and Astrophysics Academia Sinica Taipei10617 Taiwan Center for Astrophysics Harvard & Smithsonian CambridgeMA02138 United States Kogakuin University of Technology & Engineering Academic Support Center 2665-1 Nakano Hachioji Tokyo192-0015 Japan Tsung-Dao Lee Institut

The Black Hole Explorer (BHEX) is a next-generation space very long baseline interferometry (VLBI) mission concept that will extend the ground-based millimeter/submillimeter arrays into space. The mission, closely aligned with the science priorities of the Japanese VLBI community, involves an active engagement of this community in the development of the mission, resulting in the formation of the Black Hole Explorer Japan Consortium. Here we present the current Japanese vision for the mission, ranging from scientific objectives to instrumentation. The Consortium anticipates a wide range of scientific investigations, from diverse black hole physics and astrophysics studied through the primary VLBI mode, to the molecular universe explored via a potential single-dish observation mode in the previously unexplored 50-70 GHz band that would make BHEX the highest-sensitivity explorer ever of molecular oxygen. A potential major contribution for the onboard instrument involves supplying essential elements for its high-sensitivity dual-band receiving system, which includes a broadband 300 GHz SIS mixer and a space-certified multi-stage 4.5K cryocooler akin to those used in the Hitomi and XRISM satellites by the Japan Aerospace Exploration Agency. Additionally, the Consortium explores enhancing and supporting BHEX operations through the use of millimeter/submillimeter facilities developed by the National Astronomical Observatory of Japan, coupled with a network of laser communication stations operated by the National Institute of Information and Communication Technology. Copyright © 2024, The Authors. All rights reserved.

关键词： Radio astronomy

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Ruth Obe Brandt Kaufmann Kaelen Baird Sam Kadel Yasmin Soltani Mostafa Cham Matthias K. Gobbert Carlos A. Barajas Zhuoran Jiang Vijay R. Sharma Lei Ren Stephen W. Peterson Jerimy C. PoIf Dept. of Computer Science and of Software Engineering U. of Houston-Clear Lake USA Dept. of Mathematics and Statistics U. of San Francisco USA Dept. of Computer Science and of Mathematics Skidmore College USA Dept. of Computer Science and of Psychology Mount Holyoke College USA Dept. of Biomedical Engineering U. of Houston USA Dept. of Information Systems U. of Maryland Baltimore County Dept. of Mathematics and Statistics U. of Maryland Baltimore County Medical Physics Graduate Program Duke University USA Dept. of Radiation Oncology U. of Maryland School of Medicine USA Department of Radiation Oncology U. of Maryland School of Medicine USA Dept. of Physics U. of Cape Town South Africa H3D Inc. USA

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy.

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Ecological Footprint Model of Soekarno-Hatta International Airport from the Perspective of the Local Community

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IOP Conference Series: Earth and Environmental Science 2021年第1期794卷

作者： Wahyu Sardjono Haryoto Kusnoputranto Tri Edhi Budhi Soesilo Kristanto Gabriel A. Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Department of Environmental Health Faculty of Public Health University of Indonesia Indonesia Post Graduate Program School of Environmental Science University of Infonesia Jakarta Indonesia Environmental Engineering Department Faculty of Engineering University of Indonesia Jakarta Indonesia

Soekarno-Hatta International Airport is prone to various environmental problems. The problem of population growth has an impact on increasing economic activity which directly impacts the environment. In every production activity from the environmental side, it will bring up the carrying capacity of the environment and the carrying capacity of the environment or ecological footprint. The problem will be difficult if it turns out that the ecological footprint that occurs exceeds the capacity of the existing environmental carrying capacity, also for airport problems. This will result in the airport being unsustainable. This article will discuss the model of the airport environmental footprint that is currently occurring at Soekarno-Hatta International Airport by utilizing the opinions of respondents who live and have activities around the airport. The factor analysis method is used to process the respondent's data to obtain the factors that influence the environmental footprint and then through these findings a model can be developed which is expected to be able to abstract the current conditions. The research results show that the dominant environmental footprint at Soekarno-Hatta airport is overload airport activities impact, operational slot time optimization, and renewable of airport energy, which can then be built as an instrument to evaluate, build and develop airport control policies that are environmentally friendly and sustainable.

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System Dynamics Modeling for Petroleum Exploitation Using the 'Drifting Goals' Standard Model

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IOP Conference Series: Earth and Environmental Science 2021年第1期794卷

作者： Wahyu Sardjono Haryoto Kusnoputranto Tri Edhi Budhi Soesilo A. Kristanto information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Department of Environmental Health Faculty of Public Health University of Indonesia Indonesia Post Graduate Program School of Environmental Science University of Indonesia Jakarta Indonesia Environmental Engineering Department Faculty of Engineering University of Indonesia Jakarta Indonesia

In an oil drilling area, there are two oil drilling companies that have obtained permits to drill. As time goes by, the availability of existing petroleum decreases. To anticipate natural resources the government only prioritizes one company. Competition occurs between the two of them to explore petroleum from the ground so that they can drill. The purpose of this study is to provide an overview, how to make a system dynamics model related to the problem of oil drilling as an increasingly limited natural resource, by taking the case of the relationship between drilling companies in facing business competition. The method used is system dynamics with a standard model of mutual difficulties (drifting goals). The result obtained is a system dynamics model that can be used to demonstrate the behavior of a simulated petroleum drilling model to get an overview or condition of the limitations of petroleum and future availability.

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Perception Analysis of Airport Noise on Environment, Sosial, and Economic at Halim Perdana Kusuma Airport

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IOP Conference Series: Earth and Environmental Science 2021年第1期794卷

作者： Wahyu Sardjono Haryoto Kusnoputranto Bagus Ferry Agrayanto Tri Edhi Budhi Soesilo Gabriel A. Kristanto Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Department of Environmental Health Faculty of Public Health University of Indonesia Indonesia Post Graduate Program School of Environmental Science University of Indonesia Jakarta Indonesia Environmental Engineering Department Faculty of Engineering University of Indonesia Jakarta Indonesia

The increase in aircraft noise is an inevitable impact of Halim Perdanakusuma Airport (HLP) commercial operations since 2014. The perception of noise risk from people living around airports is a factor that must be considered for formulating strategic, comprehensive airport noise control plans and policies, and participatory. The purpose of this study is to analyze the noise conditions and the perception of environmental noise risk to the perception of socioeconomic risks that live around the HLP Airport. The results of the WECPNL calculation in area of Jaticempaka and Makasar was ranged between 73.80—79.72. The environmental noise risk of the community in HLP Airport's vicinity was studied from knowledge aspects, perception aspects, and behavior aspects. Most of respondent were in the moderate category of environmental noise risk knowledge and environmental noise risk perception. While for environmental noise risk behavior aspects most respondent were in the low category. The social and economic risk perception for most respondent were in the moderate category. The conclusion of this research is that the community settlements in HLP Airport's vicinity at the distance of 300 m and 600 m does not meet the environmental noise standard for a residential area which is regulated at 55 dB(A). The ideal area for settlement use in the vicinity of HLP Airport is Makassar area which is located 600 m from HLP Airport's runway.

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Development of Eco-Airport Model to Measure Environmental Noise Footprint in Supporting Airport Sustainability

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IOP Conference Series: Earth and Environmental Science 2021年第1期794卷

作者： Wahyu Sardjono Haryoto Kusnoputranto Tri Edhi Budhi Soesilo Gabriel A. Kristanto Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Department of Environmental Health Faculty of Public Health University of Indonesia Indonesia Post Graduate Program School of Environmental Science University of Indonesia Jakarta Indonesia Environmental Engineering Department Faculty of Engineering University of Indonesia Jakarta Indonesia

Among a set of tools to emerge in recent years in environmental management, one appears to meet the criteria necessary to bridge this gap is the environmental footprint analysis based on biophysical reality of sustainable living, people must use natural renewable products and services not sooner than they are replacedand produces no waste faster than nature can absorb. The purpose of this study is to develop a model of noise environment footprint toward sustainability airport with the environmental assessment at Soekarno-Hatta International Airport. The results showed that the airport ecological footprint of the model can be built through the factors analysis that can give an idea of the influence of ecoairport. The conclusion of this study is to obtain the noise ecological footprint provides a method for the total impact of air transport to be assessed, the ecological footprint can be used as a communication and awareness tool to inform communities about the impact they make. It can lead to shifts to public transport for airport access, ecological footprint can be used by airports and airlines to identify operations where energy efficiency measures can be made and where resource consumption can be reduced

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Dynamic Multi-criteria Classifier Selection for Illegal Tapping Detection in Oil Pipelines

Dynamic Multi-criteria Classifier Selection for Illegal Tapp...

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International Joint Conference on Neural Networks (IJCNN)

作者： Victor Henrique Alves Ribeiro Pedro Henrique Domingues Paulo Rodrigo Cavalin Gilberto Reynoso-Meza Helon Vicente Hultmann Ayala Luís Fernando Alzuguir Azevedo Industrial and Systems Engineering Graduate Program Pontifícia Universidade Católica do Paraná Curitiba Brazil IBM Research Rio de Janeiro Brazil Department of Mechanical Engineering Pontifícia Universidade Católica do Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Illegal tapping of fuel pipelines has recently become one of the most relevant safety problems faced by the industry. Hundreds of illegal interventions have been reported around the world, causing a significant number of deaths, relevant impacts on the environment, and capital loss. Therefore, it is important to develop systems that are able to detect such scenarios at an early stage, enabling a fast counteract. To this end, machine learning algorithms can train models on available data for detecting future issues. Most recently, ensemble learning and dynamic classifier selection (DCS) techniques have been achieving promising results in supervised learning tasks. Such models are usually trained based on a single criterion. However, it is desirable to take into account both the number of false positives (FP) and false negatives (FN) for the illegal tapping detection task, since they are conflicting and both lead to financial losses and/or accidents. Therefore, this work proposes a novel DCS technique based on multiple criteria, namely overall local class-specific accuracy (OLCA), which employs multi-criteria decision making for dynamically selecting the best classifier for a new sample given the local true positive and negative ratios. A numerical experiment is conducted for assessing the generalization performance of the proposed method in an oil pipeline, with the goal of detecting illegal taping using pressure transient signals. Results show that OLCA is able to reduce the number of both FP and FN when dynamically selecting the classifiers of a baseline Random Forest ensemble.

关键词： Pipelines Leak detection Oils Decision making Feature extraction Biological system modeling Task analysis

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Optomechanical dissipative solitons (vol 600, pg 75, 2021)

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NATURE 2022年第7896期602卷 E24-E24页

作者： Zhang, Jing Peng, Bo Kim, Seunghwi Monifi, Faraz Jiang, Xuefeng Li, Yihang Yu, Peng Liu, Lianqing Liu, Yu-xi Alu, Andrea Yang, Lan Department of Electrical and Systems Engineering Washington University St. Louis MO USA Department of Automation Tsinghua University Beijing P. R. China Photonics Initiative Advanced Science Research Center City University of New York New York NY USA Physics Program Graduate Center City University of New York New York NY USA State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang P. R. China Institute of Microelectronics Tsinghua University Beijing P. R. China

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Direct Comparison of RT-ddPCR and Targeted Amplicon Sequencing for SARS-CoV-2 Mutation Monitoring in Wastewater

SSRN

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SSRN 2022年

作者： Lou, Esther G. Sapoval, Nicolae McCall, Camille Bauhs, Lauren Carlson-Stadler, Russell Kalvapalle, Prashant Lai, Yanlai Palmer, Kyle Penn, Ryker Rich, Whitney Wolken, Madeline Ensor, Katherine B. Hopkins, Loren Treangen, Todd J. Stadler, Lauren B. Department of Civil and Environmental Engineering Rice University 6100 Main Street MS 519 HoustonTX77005 United States Department of Computer Science Rice University 6100 Main Street HoustonTX77005 United States Systems Synthetic and Physical Biology Graduate Program Rice University 6100 Main Street HoustonTX77005 United States Houston Health Department 8000 N. Stadium Dr. HoustonTX77054 United States Department of Statistics Rice University 6100 Main Street MS 138 HoustonTX77005 United States

Over the course of the COVID-19 pandemic, variants of SARS-CoV-2 have emerged that are more contagious and more likely to cause breakthrough infections. Targeted amplicon sequencing approach is a gold standard for identification and analysis of variants. However, when applied to environmental samples such as wastewater, it remains unclear how sensitive this method is for detecting variant-associated mutations in environmental samples. Here we directly compare a targeted amplicon sequencing approach (using ARTIC v3;hereafter referred to as sequencing) with RT-ddPCR quantification for the detection of five mutations that are characteristic of variants of concern (VoCs) in wastewater samples. In total, 547 wastewater samples were analyzed using both methods in parallel. When we observed positive mutation detections by RT-ddPCR, 42.6% of the detection events were missed by sequencing, due to negative detection or the limited read coverage at the mutation position. Further, when sequencing reported negative or depth-limited mutation detections, 26.7% of those events were instead positive detections by RT-ddPCR, highlighting the relatively poor sensitivity of sequencing. No or weak associations were observed between quantitative measurements of target mutations determined by RT-ddPCR and sequencing. These findings caution the use of quantitative measurements of SARS-CoV-2 variants in wastewater samples determined solely based on sequencing. © 2022, The Authors. All rights reserved.

关键词： SARS

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A Machine Learning Method for Vehicle Classification by Inductive Waveform Analysis ⁎

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IFAC-PapersOnLine 2020年第2期53卷 13928-13932页

作者： Bruno R. Vasconcellos Marcelo Rudek Marcelo de Souza Pontifícia Universidade Católica do Paraná - PUCPR Control and Automation Egineering Curitiba PR 80215-901 Pontifícia Universidade Católica do Paraná - PUCPR Industrial and Systems Engineering Graduate Program Curitiba PR 80215-901 Fiscaltech Curitiba PR 81290-270

The classification of vehicles is a matter of great importance for traffic control and management, helping with traffic surveillance as well as in statistical data collection. Among the several vehicular classification techniques, the most popular uses inductive loop sensors, because they achieve high accuracy rate at low cost. This paper proposes 5 different vehicle classification models by inductive waveform analysis: KNN, SVC, Decision Tree, Random Forest, and Voting Classifier. A brief introduction to the mathematical basis of these models and the main forms of vehicle detection are also presented. The obtained results reached an accuracy of 94% and showed how inductive waveform analysis is still a valid option for vehicle classification.

关键词： Machine learning Intelligent transportation systems Information processing Decision support

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