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IoT Based Smart Agriculture Monitoring, Automation and Intrusion Detection System

Journal of Physics: Conference Series 2021年第1期1962卷

作者： Ahmad Faisol Suhaimi Naimah Yaakob Sawsan Ali Saad Khairul Azami Sidek Mohamed Elobaid Elshaikh Alaa K.Y. Dafhalla Ong Bi Lynn Mahathir Almashor Faculty of Electronic Engineering technology Universiti Malaysia Perlis (UniMAP) Perlis Malaysia Advanced Computing Centre of Excellence (CoE) Universiti malaysia Perlis (UniMAP) Perlis Malaysia Department of Computer Engineering College of Computer Science and Engineering University of Ha'il Kingdom of Saudi Arabia Department of Electrical and Computer Engineering Kulliyyah of Engineering International Islamic University Malaysia (IIUM) Selangor Malaysia Data61 Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australia

Manual irrigation is still widely used in agricultural field using traditional drip and can watering. However, traditional irrigation systems are inefficient and inexact, leading to either insufficient or excessive watering. Moreover, it is difficult for farmers to predict suitable quantities at the appropriate time. Manual monitoring of the crop field may also lead to human error and is potentially risky for rural areas. Farmers may also not be aware of intrusions if they are not on location. Therefore, this project is designed to develop a smart monitoring and automated irrigation system to provide not only efficient water consumption based on specific conditions, but also enables real-time monitoring of the environment. Furthermore, this system prevents damage to plants and reduces the likelihood of plant theft. This system uses NodeMCU ESP32 as a microcontroller that collects environmental data such as humidity, temperature, soil moisture levels from sensors. The NodeMCU is integrated with a relay and RTC module to irrigate plants at specific times and is also equipped with a passive infrared sensor to detect intruders near the crop-field. Upon detection, an ESP32 camera is used to automatically capture the current conditions and farmers will be subsequently notified. Warnings are also sent to farmers upon detection of unwanted circumstances such as extreme temperature, which could prevent instances of open burning. The utility of the developed prototype is evident in the way it automatically irrigates the crop field without human intervention. Farmers may monitor and manually control the irrigation process using an attached Android application. Additionally, they may manually activate a buzzer warn off any potential malicious actors.

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Radio detection of an elusive millisecond pulsar in the Globular Cluster NGC 6397

arXiv

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arXiv 2022年

作者： Zhang, Lei Ridolfi, Alessandro Blumer, Harsha Freire, Paulo C.C. Manchester, Richard N. McLaughlin, Maura Kremer, Kyle Cameron, Andrew D. Zhang, Zhiyu Behrend, Jan Burgay, Marta Buchner, Sarah Champion, David J. Chen, Weiwei Dai, Shi Feng, Yi Fu, Xiaoting Guo, Meng Hobbs, George Keane, Evan F. Kramer, Michael Levin, Lina Li, Xiangdong Ni, Mengmeng Pan, Jingshan Padmanabh, Prajwal V. Possenti, Andrea Ransom, Scott M. Tsai, Chao-Wei Krishnan, Vivek Venkatraman Wang, Pei Zhang, Jie Zhi, Qijun Zhang, Yongkun Li, Di National Astronomical Observatories Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia SelargiusI-09047 Italy Department of Physics and Astronomy West Virginia University MorgantownWV26506 United States Max-Planck Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany CSIRO Space and Astronomy Australia Telescope National Facility PO Box 76 EppingNSW1710 Australia TAPIR California Institute of Technology PasadenaCA91125 United States The Observatories of the Carnegie Institution for Science PasadenaCA91101 United States Swinburne University of Technology Mail H74 PO Box 218 VIC3122 Australia School of Astronomy and Space Science Nanjing University Nanjing210023 China Ministry of Education Nanjing210093 China South African Radio Astronomy Observatory Fir Street Black River Park Cape Town7925 South Africa School of Science Western Sydney University Locked Bag 1797 PenrithNSW2751 Australia Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China National Supercomputing Center in Jinan Qilu University of Technology 28666 East Jingshi Road Licheng Distrist Jinan250103 China Jinan Institute of Supercomputing Technology 28666 East Jingshi Road Licheng Distrist Jinan250103 China School of Physics Trinity College Dublin College Green Dublin Ireland Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy The University of Manchester ManchesterM13 9PL United Kingdom HannoverD-30167 Germany MonserratoI-09042 Italy NRAO 520 Edgemont Road CharlottesvilleVA22903 United States College of Physics and Electronic Engineering Qilu Normal University 2 Wenbo Road Zhangqiu District Jinan250200 China Guizhou Provincial Key Laboratory of Radio

We report the discovery of a new 5.78 ms-period millisecond pulsar (MSP), PSR J1740−5340B (NGC 6397B), in an eclipsing binary system discovered with the Parkes radio telescope (now also known as Murriyanga), Australia, and confirmed with the MeerKAT radio telescope in South Africa. The measured orbital period, 1.97 days, is the longest among all eclipsing binaries in globular clusters (GCs) and consistent with that of the coincident X-ray source U18, previously suggested to be a ‘hidden MSP’. Our XMM-Newton observations during NGC 6397B’s radio quiescent epochs detected no X-ray flares. NGC 6397B is either a transitional MSP or an eclipsing binary in its initial stage of mass transfer after the companion star left the main sequence. The discovery of NGC 6397B potentially reveals a subgroup of extremely faint and heavily obscured binary pulsars, thus providing a plausible explanation to the apparent dearth of binary neutron stars in core-collapsed GCs as well as a critical constraint on the evolution of GCs. Copyright © 2022, The Authors. All rights reserved.

关键词： Mass transfer

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Cross-Lingual Ontology Enrichment Based on Multi-Agent Architecture

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Procedia Computer science 2018年 137卷 127-138页

作者： Mohamed Ali Said Fathalla Shimaa Ibrahim Mohamed Kholief Yasser Hassan Faculty of Science University of Alexandria Egypt Smart Data Analytics (SDA) University of Bonn Germany Institute of Graduate Studies and Research University of Alexandria Egypt College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport Alexandria Egypt

The proliferation of ontologies and multilingual data available on the Web has motivated many researchers to contribute to multilingual and cross-lingual ontology enrichment. Cross-lingual ontology enrichment greatly facilitates ontology learning from multilingual text/ontologies in order to support collaborative ontology engineering process. This article proposes a cross-lingual ontology enrichment (CLOE) approach based on a multi-agent architecture in order to enrich ontologies from a multilingual text or ontology. This has several advantages: 1) an ontology is used to enrich another one, written in a different natural language, and 2) several ontologies could be enriched at the same time using a single chunk of text ( Simultaneous Ontology Enrichment ). A prototype for the proposed approach has been implemented in order to enrich several ontologies using English, Arabic and German text. Evaluation results are promising and showing that CLOE performs well in comparison with four state-of-the-art approaches.

关键词： cross-lingual ontology enrichment multi-agent knowledge management ontology learning.

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Template bank for compact binary mergers in the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA

arXiv

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arXiv 2022年

作者： Sakon, Shio Tsukada, Leo Fong, Heather Kennington, James Niu, Wanting Hanna, Chad Adhicary, Shomik Baral, Pratyusava Baylor, Amanda Cannon, Kipp Caudill, Sarah Cousins, Bryce Creighton, Jolien D.E. Ewing, Becca George, Richard N. Godwin, Patrick Harada, Reiko Huang, Yun-Jing Huxford, Rachael Joshi, Prathamesh Kuwahara, Soichiro Li, Alvin K.Y. Magee, Ryan Meacher, Duncan Messick, Cody Morisaki, Soichiro Mukherjee, Debnandini Pace, Alex Posnansky, Cort Ray, Anarya Sachdev, Surabhi Singh, Divya Tapia, Ron Tsutsui, Takuya Ueno, Koh Viets, Aaron Wade, Leslie Wade, Madeline Wang, Jonathan Department of Physics The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Physics and Astronomy University of British Columbia VancouverBCV6T 1Z4 Canada RESCEU The University of Tokyo Tokyo113-0033 Japan Graduate School of Science The University of Tokyo Tokyo113-0033 Japan Department of Astronomy and Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Leonard E. Parker Center for Gravitation Cosmology and Astrophysics University of Wisconsin-Milwaukee MilwaukeeWI53201 United States Department of Physics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Data Science Research University of Massachusetts DartmouthMA02747 United States Department of Physics University of Illinois UrbanaIL61801 United States Center for Gravitational Physics University of Texas at Austin AustinTX78712 United States LIGO Laboratory California Institute of Technology PasadenaCA91125 United States MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan NASA Marshall Space Flight Center HuntsvilleAL35811 United States Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville HuntsvilleAL35899 United States School of Physics Georgia Institute of Technology AtlantaGA30332 United States Concordia University Wisconsin MequonWI53097 United States Department of Physics Hayes Hall Kenyon College GambierOH43022 United States Department of Physics University of Michigan Ann ArborMI48109 United States

Matched-filtering gravitational wave search pipelines identify gravitational wave signals by computing correlations, i.e., signal-to-noise ratios, between gravitational wave detector data and gravitational wave template waveforms. Intrinsic parameters, the component masses and spins, of the gravitational wave waveforms are often stored in "template banks", and the construction of a densely populated template bank is essential for some gravitational wave search pipelines. This paper presents a template bank that is currently being used by the GstLAL-based compact binary search pipeline in the fourth observing run of the LIGO, Virgo, and KAGRA collaboration, and was generated with a new binary tree approach of placing templates, manifold. The template bank contains 1.8×106 sets of template parameters covering plausible neutron star and black hole systems up to a total mass of 400 Mo with component masses between 1-200 Mo and mass ratios between 1 and 20 under the assumption that each component object’s angular momentum is aligned with the orbital angular momentum. We validate the template bank generated with our new method, manifold, by comparing it with a template bank generated with the previously used stochastic template placement method. We show that both template banks have similar effectualness. The GstLAL search pipeline performs singular value decomposition (SVD) on the template banks to reduce the number of filters used. We describe a new grouping of waveforms that improves the computational efficiency of SVD by nearly 5 times as compared to previously reported SVD sorting schemes. Copyright © 2022, The Authors. All rights reserved.

关键词： Singular value decomposition

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Semi-supervised dictionary learning with label propagation for image classification

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Computational Visual Media 2017年第1期3卷 83-94页

作者： Lin Chen Meng Yang College of Computer Science and Software Engineering Shenzhen UniversityShenzhenChina School of Data and Computer Science Sun Yat-sen UniversityGuangzhouChina Key Laboratory of Machine Intelligence and Advanced Computing(Sun Yat-sen University) Ministry of EducationChina

Sparse coding and supervised dictionary learning have rapidly developed in recent years,and achieved impressive performance in image classification. However, there is usually a limited number of labeled training samples and a huge amount of unlabeled data in practical image classification,which degrades the discrimination of the learned dictionary. How to effectively utilize unlabeled training data and explore the information hidden in unlabeled data has drawn much attention of researchers. In this paper, we propose a novel discriminative semisupervised dictionary learning method using label propagation(SSD-LP). Specifically, we utilize a label propagation algorithm based on class-specific reconstruction errors to accurately estimate the identities of unlabeled training samples, and develop an algorithm for optimizing the discriminative dictionary and discriminative coding vectors *** experiments on face recognition, digit recognition, and texture classification demonstrate the effectiveness of the proposed method.

关键词： semi-supervised learning dictionary learning label propagation image classification

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Ultra-scalable spectral clustering and ensemble clustering

arXiv

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arXiv 2019年

作者： Huang, Dong Wang, Chang-Dong Wu, Jian-Sheng Lai, Jian-Huang Kwoh, Chee-Keong College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China School of Information Engineering Nanchang University Nanchang China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representative selection strategy and a fast approximation method for K-nearest representatives are proposed for the construction of a sparse affinity sub-matrix. By interpreting the sparse sub-matrix as a bipartite graph, the transfer cut is then utilized to efficiently partition the graph and obtain the clustering result. In U-SENC, multiple U-SPEC clusterers are further integrated into an ensemble clustering framework to enhance the robustness of U-SPEC while maintaining high efficiency. Based on the ensemble generation via multiple U-SEPC's, a new bipartite graph is constructed between objects and base clusters and then efficiently partitioned to achieve the consensus clustering result. It is noteworthy that both U-SPEC and U-SENC have nearly linear time and space complexity, and are capable of robustly and efficiently partitioning ten-million-level nonlinearly-separable datasets on a PC with 64GB memory. Experiments on various large-scale datasets have demonstrated the scalability and robustness of our algorithms. The MATLAB code and experimental data are available at https://***/publication/330760669. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

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Improving the generalizability of convolutional neural network-based segmentation on CMR images

arXiv

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arXiv 2019年

作者： Chen, Chen Bai, Wenjia Davies, Rhodri H. Bhuva, Anish N. Manisty, Charlotte Moon, James C. Aung, Nay Lee, Aaron M. Sanghvi, Mihir M. Fung, Kenneth Paiva, Jose Miguel Petersen, Steffen E. Lukaschuk, Elena Piechnik, Stefan K. Neubauer, Stefan Rueckert, Daniel Biomedical Image Analysis Group Department of Computing Imperial College London London United Kingdom Data Science Institute Imperial College London London United Kingdom Department of Medicine Imperial College London London United Kingdom Institutes for Cardiovascular Science University College London London United Kingdom Department of Cardiovascular Imaging Barts Heart Centre St Bartholomew's Hospital London United Kingdom NIHR Biomedical Research Centre at Barts Queen Mary University of London London United Kingdom NIHR BRC Oxford Division of Cardiovascular Medicine Radcliffe Department of Medicine University of Oxford Oxford United Kingdom

Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g. same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium and 0.82 for the right ventricle on the ACDC dataset;and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task. Copyright © 2019, The Authors. All rights reserved.

关键词： Image segmentation

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VC-TWJoin: A Stream Join Algorithm Based on Variable Update Cycle Time Window

VC-TWJoin: A Stream Join Algorithm Based on Variable Update ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yimu Ji Shangdong Liu Lili Lu Xianbo Lang Haichang Yao Ruchuan Wang Institute of High Performance Computing and Big Data Processing Nanjing University of Posts and Telecommunications School of Computer Science Nanjing College of Information and Technology School of Computer Science Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781538614839

Stream join is one of the key operations for real-time stream data query and calculation. In light of changeable velocity of stream data, traditional static stream join methods are not so adaptive that stream data computing performance will be affected. Based on the large quantity and constantly changing velocity of stream data, by considering traditional stream join algorithm, this paper proposes an optimized algorithm for variable update cycle stream based on time window (VC-TWJoin, Variable Cycle Time Window Join). For unsteady stream calculated in stream join, the optimal update cycle will be calculated to reduce the response time of stream join and improve join efficiency and real-time capability. Both theoretical analysis and experiments demonstrate that the algorithm is better than traditional join algorithms in terms of real-time capability, join response time and throughput.

关键词： Microsoft Windows Real-time systems data models Semantics Computer science Telecommunications Wireless sensor networks

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The Wide-field Spectroscopic Telescope (WST) science White Paper

arXiv

引用

arXiv 2024年

作者： Mainieri, Vincenzo Anderson, Richard I. Brinchmann, Jarle Cimatti, Andrea Ellis, Richard Hill, Vanessa Kneib, Jean-Paul McLeod, Anna Opitom, Cyrielle Roth, Martin M. Sáez, Paula Sánchez Smiljanic, Rodolfo Tolstoy, Eline Bacon, Roland Randich, Sofia Pasquini, Luca Barsanti, Stefania Bryant, Julia Bryson, Ian Casali, Mark Colless, Matthew Couch, Warrick Danielski, Camilla Dierickx, Philippe Driver, Simon Fontana, Adriano Garilli, Bianca de Jong, Roelof S. Lee, David Lehnert, Matt Magrini, Laura Montet, Ben Sanchez-Janssen, Ruben Pandey-Pommier, Mamta Sargent, Mark Schipani, Pietro Steinmetz, Matthias Travouillon, Tony Tresse, Laurence Vernet, Joël Yeche, Christophe Ziegler, Bodo Audard, Marc Arévalo, Patricia Bayo Aran, Amelia M. Bisero, Sofia Bisogni, Susanna Blondin, Stéphane Bonito, Rosaria Branchesi, Marica Catelan, Márcio D'Ammando, Filippo Dainotti, Maria G. De Cia, Annalisa Dhawan, Suhail Dupletsa, Ulyana Hazra, Nandini Ilić, Dragana Ivanov, Valentin D. Izzo, Luca Kovačević, Andjelka B. Kuncarayakti, Hanindyo Kwon, Yuna La Forgia, Fiorangela Lazzarin, Monica Lira, Paulina Loffredo, Eleonora Maguire, Kate Majidi, Fatemeh Z. Marconi, Marcella McGee, Sean Migliorini, Alessandra Minniti, Dante Nicholl, Matt Onori, Francesca Pala, Anna F. Panda, Swayamtrupta Pawlak, Michal Pessi, Priscila J. Popović, Luka Č. Smith, Graham P. Vergani, Susanna D. Vietri, Giustina Ye, Quanzhi Zafar, Tayyaba Matsuno, Tadafumi Kordopatis, Georges Annibali, Francesca Bayo, Amelia Bellazzini, Michele Beltran, Maria Teresa Berni, Leda Bianchi, Simone Biazzo, Katia Bland-Hawthorn, Joss Boffin, Henri M.J. Bono, Giuseppe Bowman, Dominic Braga, Vittorio F. Bragaglia, Angela Brucalassi, Anna Busà, Innocenza Casali, Giada Casasola, Viviana Castro, Norberto Cavallo, Lorenzo Chiappini, Cristina Colzi, Laura Damiani, Francesco da Silva, Ronaldo D'Orazi, Valentina Escorza, Ana Fabrizio, Michele Fiorentino, Giuliana Fontani, Francesco François, Patrick Galindo-Guil, Francisco J. Galli, Daniele Garcia-Rojas, Jorge Guarcello, Mario Giuseppe Helmi, Am European Southern Observatory Karl-Schwarzschild-Strasse 2 Garching bei München85748 Germany Switzerland Instituto de Astrofísica e Ciências do Espaço Universidade do Porto Portugal Dipartimento di Fisica e Astronomia "Augusto Righi" Alma Mater Studiorum Università di Bologna Italy Department of Physics and Astronomy University College London United Kingdom Observatoire de la Côte d'Azur Université Côte d'Azur CNRS Laboratoire Lagrange France Centre for Extragalactic Astronomy Department of Physics Durham University United Kingdom Institute for Computational Cosmology Department of Physics University of Durham United Kingdom Institute for Astronomy University of Edinburgh United Kingdom Germany Nicolaus Copernicus Astronomical Center Polish Academy of Sciences Poland Kapteyn Astronomical Institute University of Groningen Netherlands France INAF - Osservatorio Astrofisico di Arcetri Italy Stockholm University Sweden INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Italy Universidad de Valparaíso Chile University of Geneva Switzerland Research School of Astronomy and Astrophysics Australian National University CanberraACT2611 Australia Australia Instituto de Astrofísica de Canarias Spain Universidad de La Laguna Spain SISSA - Scuola Internazionale Superiore di Studi Avanzati Italy University of Florence Italy INAF - Osservatorio Astronomico di Roma Italy GEPI Observatoire de Paris Universite PSL CNRS France INAF Istituto di Astrofisica Spaziale e Fisica cosmica Milano Italy Sydney Institute for Astronomy School of Physics The University of Sydney Australia Aix Marseille Univ CNRS CNES LAM France INAF - Osservatorio Astronomico di Palermo Italy University of Rome Tor Vergata Italy Newcastle University United Kingdom Italy UK Astronomy Technology Ctr. Royal Observatory Edinburgh United Kingdom INAF OACT Italy Dipartimento di Fisica Università degli Studi di Torino Italy Istituto Nazionale di Fisica Nucleare INFN Sezione di

The WST is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys and this white paper summarises the initial concept as well as the corresponding science cases. It will provide an end-to-end service from the preparation of surveys to the delivery of high-quality calibrated science data product. Uniquely, it will have the ability to simultaneously deliver fibre-fed multi-object spectroscopy over a large field of view (MOS) and integral field imaging spectroscopy of a central field of 3 × 3 arcmin2 (IFS). WST will fill a crucial gap in the astronomical landscape and will allow full exploitation of major current and upcoming ground and space-based facilities. WST will address outstanding scientific questions in the areas of cosmology;galaxy assembly, evolution, and enrichment, including our own Milky Way;origin of stars and planets;time domain and multi-messenger astrophysics. WST features a 12 meter aperture telescope that covers the UV to the H band and operates in seeing limited conditions. Its field of view (FoV) of 3.1 deg2 will be the largest of any telescope equipped with a MOS. In particular, its FoV is 2.5 times larger than PFS@Subaru and a remarkable 22 times larger than MOONS@VLT. In addition, the WST has a substantial aperture, twice as large (100 square metres) as its 8 meter class competitors. A central circular area with a diameter of 13 arcmin is available for simultaneous IFS observations. Within this area, the IFS with a FoV of 3 × 3 arcmin2 (9 times larger than MUSE@VLT) can be utilised simultaneously with the MOS. In addition, the IFS can be used to mosaic a larger area within the 13 arcmin circle (an area larger than the entire FoV of the ELT) while the MOS positioner can perform multiple configurations. The WST operational model will also implement target of opportunity (ToO) operations at both the telescope and MOS fibre levels to support the time domain science cases. The demand for a facility dedicated to spect

关键词： Telescopes

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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