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Plant-Environment Interactions 2024年第2期5卷 e10136页

作者： Kearsley, Elizabeth Verbeeck, Hans Stoffelen, Piet Janssens, Steven B. Yakusu, Emmanuel Kasongo Kosmala, Margaret De Mil, Tom Bauters, Marijn Kitima, Elasi Ramanzani Ndiapo, José Mbifo Chuda, Adelard Lonema Richardson, Andrew D. Wingate, Lisa Ilondea, Bhély Angoboy Beeckman, Hans van den Bulcke, Jan Boeckx, Pascal Hufkens, Koen Computational and Applied Vegetation Ecology Lab Department of Environment Faculty of Bioscience Engineering Ghent University Gent Belgium BlueGreen Labs Melsele Belgium Meise Botanic Garden Meise Belgium Department of Biology Leuven Plant Institute KULeuven Leuven Belgium UGent-Woodlab (Laboratory of Wood Technology) Department of Environment Faculty of Bioscience Engineering Ghent University Gent Belgium Service of Wood Biology Royal Museum for Central Africa Tervuren Belgium Faculté de gestion des ressources naturelles renouvelables Université de Kisangani Kisangani Democratic Republic Congo Department of Organismic and Evolutionary Biology Harvard University Cambridge MA United States CIBO Technologies Cambridge MA United States Forest is Life TERRA Teaching and Research Centre Gembloux Agro Bio-Tech University of Liège Gembloux Belgium Isotope Bioscience Laboratory - ISOFYS Department of Green Chemistry and Technology Ghent University Gent Belgium Research Group of Plants and Ecosystems (PLECO) Department of Biology University of Antwerp Wilrijk Belgium Institut National pour l'Etude et la Recherche Agronomiques-INERA Yangambi Democratic Republic Congo Center for Ecosystem Science and Society Northern Arizona University Flagstaff AZ United States School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ United States INRAE UMR ISPA Villenave d'Ornon France Institut National pour l'Étude et la Recherche Agronomiques Kinshasa Democratic Republic Congo

Tropical forest phenology directly affects regional carbon cycles, but the relation between species-specific and whole-canopy phenology remains largely uncharacterized. We present a unique analysis of historical tropical tree phenology collected in the central Congo Basin, before large-scale impacts of human-induced climate change. Ground-based long-term (1937–1956) phenological observations of 140 tropical tree species are recovered, species-specific phenological patterns analyzed and related to historical meteorological records, and scaled to characterize stand-level canopy dynamics. High phenological variability within and across species and in climate–phenology relationships is observed. The onset of leaf phenophases in deciduous species was triggered by drought and light availability for a subset of species and showed a species-specific decoupling in time along a bi-modal seasonality. The majority of the species remain evergreen, although central African forests experience relatively low rainfall. Annually a maximum of 1.5% of the canopy is in leaf senescence or leaf turnover, with overall phenological variability dominated by a few deciduous species, while substantial variability is attributed to asynchronous events of large and/or abundant trees. Our results underscore the importance of accounting for constituent signals in canopy-wide scaling and the interpretation of remotely sensed phenology signals. © 2024 The Authors. Plant-Environment Interactions published by New Phytologist Foundation and John Wiley & Sons Ltd.

关键词： leaf senescence leaf turnover phenology scaling tropical forest

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Knowledge-routed visual question reasoning: Challenges for deep representation embedding

arXiv

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arXiv 2020年

作者： Cao, Qingxing Li, Bailin Liang, Xiaodan Wang, Keze Lin, Liang School of Intelligent Systems Engineering Sun Yat-sen University China DarkMatter AI Research China Department of statistics University of California Los AngelesCA90095 United States School of Computer Science and Engineering Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Engineering Research Center for Advanced Computing Engineering Software of Ministry of Education China

Though beneficial for encouraging the Visual Question Answering (VQA) models to discover the underlying knowledge by exploiting the input-output correlation beyond image and text contexts, the existing knowledge VQA datasets are mostly annotated in a crowdsource way, e.g., collecting questions and external reasons from different users via the internet. In addition to the challenge of knowledge reasoning, how to deal with the annotator bias also remains unsolved, which often leads to superficial over-fitted correlations between questions and answers. To address this issue, we propose a novel dataset named Knowledge-Routed Visual Question Reasoning for VQA model evaluation. Considering that a desirable VQA model should correctly perceive the image context, understand the question, and incorporate its learned knowledge, our proposed dataset aims to cutoff the shortcut learning exploited by the current deep embedding models and push the research boundary of the knowledge-based visual question reasoning. Specifically, we generate the question-answer pair based on both the Visual Genome scene graph and an external knowledge base with controlled programs to disentangle the knowledge from other biases. The programs can select one or two triplets from the scene graph or knowledge base to push multi-step reasoning, avoid answer ambiguity, and balanced the answer distribution. In contrast to the existing VQA datasets, we further imply the following two major constraints on the programs to incorporate knowledge reasoning: i) multiple knowledge triplets can be related to the question, but only one knowledge relates to the image object. This can enforce the VQA model to correctly perceive the image instead of guessing the knowledge based on the given question solely;ii) all questions are based on different knowledge, but the candidate answers are the same for both the training and test sets. We make the testing knowledge unused during training to evaluate whether a model can unde

关键词： Knowledge based systems

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Precision monitoring of radial growth of trees and micro-climate at a Korean Fir (Abies koreana Wilson) forest at 10 minutes interval in 2016 on Mt. Hallasan National Park, Jeju Island, Korea

引用

Journal of Ecology and Environment 2019年第1期43卷 1页

作者： Kim, Eun-Shik Cho, Hong-Bum Heo, Daeyoung Kim, Nae-Soo Kim, Young-Sun Lee, Kyeseon Lee, Sung-Hoon Ryu, Jaehong Department of Forestry Environment and Systems Kookmin University Seoul 02707 South Korea Institute of Forest Science Kookmin University Seoul 02707 South Korea Grid and Cloud Computing Laboratory School of Computer Science Kookmin University Seoul 02707 South Korea Electronics and Telecommunications Research Institute 218 Gajeong Ro Yuseong Gu Daejeon 34129 South Korea

To understand the dynamics of radial growth of trees and micro-climate at a site of Korean fir (Abies koreana Wilson) forest on high-altitude area of Mt. Hallasan National Park, Jeju Island, Korea, high precision dendrometers were installed on the stems of Korean fir trees, and the sensors for measuring micro-climate of the forest at 10 minutes interval were also installed at the forest. Data from the sensors were sent to nodes, collected to a gateway wireless, and transmitted to a data server using mobile phone communication system. By analyzing the radial growth data for the trees during the growing season in 2016, we can estimate that the radial growth of Korean fir trees initiated in late April to early May and ceased in late August to early September, which indicates that period for the radial growth was about 4 months in 2016. It is interesting to observe that the daily ambient temperature and the daily soil temperature at the depth of 20 cm coincided with the values of about 10 °C when the radial growth of the trees initiated in 2016. When the radial growth ceased, the values of the ambient temperature went down below about 15 °C and 16 °C, respectively. While the ambient temperature and the soil temperature are evaluated to be the good indicators for the initiation and the cessation of radial growth, it becomes clear that radii of tree stems showed diurnal growth patterns affected by diurnal change of ambient temperature. In addition, the wetting and drying of the surface of the tree stems affected by precipitation became the additional factors that affect the expansion and shrinkage of the tree stems at the forest site. While it is interesting to note that the interrelationships among the micro-climatic factors at the forest site were well explained through this study, it should be recognized that the precision monitoring made possible with the application of high resolution sensors in the measurement of the radial increment combined with the observation of

关键词： Abies koreana Ecological platform Environmental factors Korean fir trees Micro-climate Precision dendrometer Radial growth Real-time monitoring

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Overcoming the disconnect between energy system and climate modelling

arXiv

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arXiv 2022年

作者： Craig, Michael T. Wohland, Jan Stoop, Laurens P. Kies, Alexander Pickering, Bryn Bloomfield, Hannah C. Browell, Jethro De Felice, Matteo Dent, Chris J. Deroubaix, Adrien Frischmuth, Felix Gonzalez, Paula L.M. Grochowicz, Aleksander Gruber, Katharina Härtel, Philipp Kittel, Martin Kotzur, Leander Labuhn, Inga Lundquist, Julie K. Pflugradt, Noah van der Wiel, Karin Zeyringer, Marianne Brayshaw, David J. School for Environment and Sustainability University of Michigan United States Institute for Environmental Decisions ETH Zurich Switzerland Helmholtz-Zentrum Hereon Germany Information and Computing Science Copernicus Institute of Sustainable Development Utrecht University Netherlands TenneT TSO B.V. Arnhem Netherlands Royal Netherlands Meteorological Institute Netherlands Frankfurt Institute for Advanced Studies Germany Department of Electrical and Computer Engineering Aarhus University Denmark ETH Zürich Switzerland School of Geographical Sciences University of Bristol United Kingdom Department of Meteorology University of Reading United Kingdom University of Glasgow United Kingdom European Commission Joint Research Centre Netherlands School of Mathematics University of Edinburg United Kingdom The Alan Turing Institute United Kingdom Max Planck Institute Germany Fraunhofer Institute for Energy Economics and Energy System Technology Germany Department of Mathematics University of Oslo Norway University of Natural Resources and Life Sciences Austria German Institute for Economic Research Germany Institute of Energy and Climate Research Techno-economic Systems Analysis Germany University of Bremen Institute of Geography Germany Dept. of Atmospheric and Oceanic Science University of Colorado Boulder United States National Renewable Energy Laboratory United States Renewable and Sustainable Energy Institute United States Forschungszentrum Jülich Germany Department of Technology Systems University of Oslo Norway

Energy system models underpin decisions by energy system planners and operators. Energy system modelling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modelling has started to form that aims to better integrate energy system models with weather and climate models. Here, we evaluate the disconnects between the energy system and climate modelling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modelling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision making. © 2022, CC BY-NC-ND.

关键词： Decision making

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

arXiv

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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

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pymdp: A Python library for active inference in discrete state spaces

arXiv

引用

arXiv 2022年

作者： Heins, Conor Millidge, Beren Demekas, Daphne Klein, Brennan Friston, Karl Couzin, Iain D. Tschantz, Alexander Department of Collective Behaviour Max Planck Institute of Animal Behavior Konstanz78457 Germany Centre for the Advanced Study of Collective Behaviour Konstanz78457 Germany Department of Biology University of Konstanz Konstanz78457 Germany VERSES Research Lab Los AngelesCA United States MRC Brain Networks Dynamics Unit University of Oxford Oxford United Kingdom Department of Computing Imperial College London London United Kingdom Network Science Institute Northeastern University BostonMA United States Laboratory for the Modeling of Biological and Socio-Technical Systems Northeastern University Boston United States Wellcome Centre for Human Neuroimaging Queen Square Institute of Neurology University College London LondonWC1N 3AR United Kingdom Sussex AI Group Department of Informatics University of Sussex Brighton United Kingdom Sackler Centre for Consciousness Science University of Sussex Brighton United Kingdom

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Accurate RNA 3D structure prediction using a language model-based deep learning approach

arXiv

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arXiv 2022年

作者： Shen, Tao Hu, Zhihang Sun, Siqi Liu, Di Wong, Felix Wang, Jiuming Chen, Jiayang Wang, Yixuan Hong, Liang Xiao, Jin Zheng, Liangzhen Krishnamoorthi, Tejas King, Irwin Wang, Sheng Yin, Peng Collins, James J. Li, Yu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Zelixir Biotech Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA United States Department of Systems Biology Harvard Medical School BostonMA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States Synthetic Biology Center Massachusetts Institute of Technology CambridgeMA United States Integrated Biosciences Redwood CityCA United States OneAIM Ltd. Hong Kong Shanghai AI Laboratory Shanghai China The CUHK Shenzhen Research Institute Hi-Tech Park Shenzhen Nanshan China Shenzhen Institute of Advanced Technology Guangdong Shenzhen China School of Molecular Sciences Center for Molecular Design and Biomimetics the Biodesign Institute Arizona State University TempeAZ United States School of Computing and Augmented Intelligence Arizona State University TempeAZ United States

Accurate prediction of RNA three-dimensional (3D) structure remains an unsolved challenge. Determining RNA 3D structures is crucial for understanding their functions and informing RNA-targeting drug development and synthetic biology design. The structural flexibility of RNA, which leads to scarcity of experimentally determined data, complicates computational prediction efforts. Here, we present RhoFold+, an RNA language model-based deep learning method that accurately predicts 3D structures of single-chain RNAs from sequences. By integrating an RNA language model pre-trained on ∼23.7 million RNA sequences and leveraging techniques to address data scarcity, RhoFold+ offers a fully automated end-to-end pipeline for RNA 3D structure prediction. Retrospective evaluations on RNA-Puzzles and CASP15 natural RNA targets demonstrate RhoFold+’s superiority over existing methods, including human expert groups. Its efficacy and generalizability are further validated through cross-family and cross-type assessments, as well as time-censored benchmarks. Additionally, RhoFold+ predicts RNA secondary structures and inter-helical angles, providing empirically verifiable features that broaden its applicability to RNA structure and function studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Practical Secure Two-Party EdDSA Signature Generation with Key Protection and Applications in Cryptocurrency

Practical Secure Two-Party EdDSA Signature Generation with K...

引用

IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Qi Feng Debiao He Min Luo Zengxiang Li Kim-Kwang Raymond Choo Key Laboratory of Aerospace Information Security and Trusted Computing School of Cyber Science and Engineering Wuhan University Ministry of Education Wuhan China Digital Research Institute ENN Group Beijing China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio TX USA

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

In cryptocurrency and blockchain-based distributed ledgers, transfer of money (digital coins) can be presented as a transaction. Due to the irreversibility nature of blockchain transactions, a single fraudulent use of private key (used to sign transactions) could have significant consequences (e.g. financial loss). Key protection alone is not adequate in protecting cryptocurrencies, and threshold signature is a viable method to avoid fraudulent key usage or key theft. In this paper, we focus on the Edwards-curve digital security algorithm (EdDSA), which has been applied in several cryptocurrencies (e.g. Cardano, Zcash, and Decred) and design the first efficient two-party EdDSA signing protocol. Unlike standard secret sharing, a valid signature is generated using an interactive protocol without the original key ever being exposed. We mathematically prove the security of our proposed protocol. Findings from the performance evalation of the protocol show that it achieves good performance for curve Ed25519, with a single signing operation in the malicious setting taking approximately 3.32 ms between two devices.

关键词： Performance evaluation Privacy Protocols Distributed ledger Conferences Cryptocurrency Standards

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An orthogonalization-free parallelizable framework for all-electron calculations in density functional theory

arXiv

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arXiv 2020年

作者： Bin, G.A.O. Guanghui, H.U. Kuang, Yang Xin, L.I.U. ICTEAM Institute UCLouvain Louvain-la-Neuve Belgium Department of Mathematics University of Macau Macao SAR China Zhuhai UM Science & Technology Research Institute Guangdong Province China Department of Mathematics National University of Singapore Singapore State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences China

All-electron calculations play an important role in density functional theory, in which improving computational efficiency is one of the most needed and challenging tasks. In the model formulations, both nonlinear eigenvalue problem and total energy minimization problem pursue orthogonal solutions. Most existing algorithms for solving these two models invoke orthogonalization process either explicitly or implicitly in each iteration. Their efficiency suffers from this process in view of its cubic complexity and low parallel scalability in terms of the number of electrons for large scale systems. To break through this bottleneck, we propose an orthogonalization-free algorithm framework based on the total energy minimization problem. It is shown that the desired orthogonality can be gradually achieved without invoking orthogonalization in each iteration. Moreover, this framework fully consists of Basic Linear Algebra Subprograms (BLAS) operations and thus can be naturally parallelized. The global convergence of the proposed algorithm is established. We also present a precondition technique which can dramatically accelerate the convergence of the algorithm. The numerical experiments on all-electron calculations show the efficiency and high scalability of the proposed algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Density functional theory

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The FAST HI 21-cm absorption blind survey. II. – Statistic Exploration for Associated and Intervening systems

arXiv

引用

arXiv 2024年

作者： Hu, Wenkai Wang, Yougang Li, Yichao Pen, Ue-Li Wang, Jie Jing, Yingjie Zhu, Ming Zhang, Xin Yang, Wenxiu Xu, Yidong Xu, Chen Chen, Jingze Zheng, Zheng Li, Di Chen, Xuelei Chen, Qingze Wu, Fengquan Department of Physics and Astronomy University of the Western Cape Robert Sobukwe Road Bellville7535 South Africa Australia National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Key Laboratory of Radio Astronomy and Technology Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing100049 China College of Sciences Northeastern University Shenyang110819 China Institute of Astronomy and Astrophysics Academia Sinica Astronomy-Mathematics Building No. 1 Sec. 4 Roosevelt Road Taipei10617 Taiwan Canadian Institute for Theoretical Astrophysics University of Toronto 60 Saint George Street TorontoONM5S 3H8 Canada Canadian Institute for Advanced Research 180 Dundas St West TorontoONM5G 1Z8 Canada Dunlap Institute for Astronomy and Astrophysics University of Toronto 50 St George Street TorontoONM5S 3H4 Canada Perimeter Institute of Theoretical Physics 31 Caroline Street North WaterlooONN2L 2Y5 Canada Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Department of Astronomy Tsinghua Univerisity 30 Shuangqing Road Beijing100084 China Guizhou Radio Astronomical Observatory Guizhou University Guiyang550000 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Northeastern University Shenyang110819 China

We present an extragalactic HI 21-cm absorption lines catalog from a blind search at z ≤ 0.35, using drift-scan data collected in 1325.6 hours by the ongoing Commensal Radio Astronomy FasT Survey (CRAFTS) and FAST All Sky HI Survey (FASHI), which spans a sky area of 6072.0 deg2 and covers 84533 radio sources with a flux density greater than 12 mJy. 14 previously identified HI absorbers and 20 newly discovered HI absorbers were detected, comprising 15 associated systems, 10 intervening systems, and 9 systems with undetermined classifications. Through spectral stacking, the mean peak optical path, mean velocity-integrated optical path, mean FWHM and mean HI column density are measured to be 0.47 and 0.30;27.19 and 4.36 km s−1;42.61 and 9.33 km s−1;0.49 and 0.08 Ts× 1020cm−2K−1, for the associated and intervening samples, respectively. Statistical analysis also reveals that associated systems tend to be hosted by red (g − r >0.7) galaxies at lower redshifts, whereas galaxies hosting intervening HI absorption are typically found at higher redshifts and are of a bluer (g−r ≤0.7) type. A noticeable difference is observed in the positions of foregrounds, backgrounds of intervening systems, and high-redshift and low-redshift associated systems on the WISE color-color diagram. All identified foreground sources in our sample have W1-W2 magnitudes below 0.8, suggesting no Active Galactic Nuclei (AGN). In contrast, backgrounds of intervening systems tend to have W1-W2 magnitudes above 0.8, indicating AGN presence. For associated absorption, most low-redshift (z ≤0.5) systems show W1-W2 values below 0.8, while higher-redshift associated absorption (z >0.5) displays a broader range of W1-W2 values. Copyright © 2024, The Authors. All rights reserved.

关键词： Moon

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