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Rapid development of thermokarst lakes and driving factors along a highway in Northeast China (2000–2020)

research in Cold and Arid Regions 2025年

作者： Liang, JunHe Huang, Shuai Jin, XiaoYing Li, XiaoYing Li, AnYuan Yang, Lin Wang, WenHui Tang, JianJun Zhang, Ze Wang, YongPing Li, ShanZhen Li, XiangLong Zhai, JinBang Yang, Xue Wang, HongWei Lü, LanZhi He, RuiXia Şerban, Raul-David Jin, HuiJun School of Civil Engineering and Transportation School of Ecology Permafrost Institute and Ministry of Education Northeast-China Field Observational and Research Station of Permafrost Geo-environment Systems Northeast Forestry University Heilongjiang Harbin 150040 China Key Laboratory of Sustainable Forest Ecosystem Management (Ministry of Education) College of Forestry Northeast Forestry University Heilongjiang Harbin 150040 China Ministry of Natural Resources Field Observation and Research Station of Permafrost and Cold Regions Environment in the Da Xing'anling Mountains at Mo'he Northeast China Heilongjiang Resources Survey Institute of Heilongjiang Province Heilongjiang Harbin 150036 China National Key Laboratory of Cryosphere Science and Frozen Soils Engineering Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Gansu Lanzhou 730000 China National Key Laboratory of Arid Regions Ecological Safety and Sustainable Development Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Gansu Lanzhou 730000 China College of Agriculture Chifeng University Inner Mongolia Chifeng 024000 China School of Civil Engineering North Minzu University Ningxia Yinchuan 750030 China Faculty of Agricultural Environmental and Food Sciences Free University of Bozen-Bolzano Universitätsplatz 5 - Piazza Università 5 39100 Bozen-Bolzano 39100 Italy

In the northern Da Xing'anling Mountains of Northeast China, thermokarst lakes have undergone significant changes. These changes are particularly evident along the Highway X302, which stretches from Yakeshi in the south to Yituli'he in the north, across zones of discontinuous to patchy Xing'an permafrost (XAP). Thermokarst lakes are highly sensitive to both climate warming and human activities. However, changes in their areal extent and number over the past two decades remain unclear. To identify the drivers of thermokarst lake changes in the XAP region, this study analyzes the relationship among lake dynamics, climate change, and human activities. Rich data on lake changes were extracted using the modified normalized difference water index (MNDWI) from the Google Earth Engine (GEE) and verified against the Joint research Centre (JRC) global surface water datasets. The results show a net increase in both the number and areal extent of thermokarst lakes along the Highway X302 from 2000 to 2020. Larger lakes (greater than 0.01 km²) expanded in surface area. Meanwhile, smaller lakes (less than 0.01 km²) increased in number. A bell-shaped trend was observed, with an initial increase in lake area followed by a decline around 2013. Before 2013, climate factors such as precipitation, air temperature, and potential evapotranspiration (PET) strongly influenced lake changes. After 2013, annual precipitation became the dominant driver of lake expansion. Human activities also contributed to changes in permafrost thermal regimes, further influencing lake dynamics. These findings provide valuable insights into the management of thermokarst lakes and wetlands in the region, and offer a reference for mitigating frost-related hazards along the Highway X302. © 2025 Northwest institute of Eco-Environment and Resources, CAS

关键词： Boreal permafrost Google earth engine (GEE) Influencing factors Thermokarst lakes

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Transition metal ion ensembles in crystals as solid-state coherent spin-photon interfaces: The case of nickel in magnesium oxide

arXiv

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arXiv 2022年

作者： Poem, E. Gupta, S. Morris, I. Klink, K. Singh, L. Zhong, T. Becker, J.N. Firstenberg, O. Nicley, S.S. Department of Physics of Complex Systems Weizmann Institute of Science Rehovot7610001 Israel Prizker School of Molecular Engineering The University of Chicago ChicagoIL60637 United States Department of Physics and Astronomy Michigan State University 567 Wilson Rd. East LansingMI48824 United States Department of Electrical and Computer Engineering Michigan State University 428S. Shaw Ln. Rm. 2120 East LansingMI48824 United States Fraunhofer USA Inc. 1449 Engineering Research Ct. East LansingMI48824 United States

We present general guidelines for finding solid-state systems that could serve as coherent electron spin – photon interfaces even at relatively high temperatures, where phonons are abundant but cooling is easier, and show that transition metal ions in various crystals could comply with these guidelines. As an illustrative example, we focus on divalent nickel ions in magnesium oxide. We perform electron spin resonance spectroscopy and polarization-sensitive magneto-optical fluorescence spectroscopy of a dense ensemble of these ions and find that (i) the ground-state electron spin stays coherent at liquid-helium temperatures for several microseconds, and (ii) there exists energetically well-isolated excited states which can couple to two ground state spin sub-levels via optical transitions of orthogonal polarizations. The latter implies that fast, coherent optical control over the electron spin is possible. We then propose schemes for optical initialization and control of the ground-state electron spin using polarized optical pulses, as well as two schemes for implementing a noise-free, broadband quantum-optical memory at near-telecom wavelengths in this material system. © 2022, CC BY.

关键词： Magnesia

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Simulation research on Aircraft Anti-collision Algorithm 4th

Simulation Research on Aircraft Anti-collision Algorithm

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4th International Conference on Human Centered Computing, HCC 2018

作者： Guan, Aijie Xiang, Wenhao Jiang, Suhuan Peng, Jun Xu, Jun Huang, He College of Computer Science and Technology Jilin University No. 2699 Qianjin Street Changchun City China Research Institute of China Shipbuilding Industrial Systems Engineering Beijing China

ISBN: (纸本)9783030151263

How to prevent collision problems on the aircraft need frequent detection for flight conflicts, therefore, it is necessary to design a good aircraft anti-collision algorithm. This paper analyzed the existing flight conflict detection algorithm paielli algorithm and carried out error analysis. On the basis of the original algorithm, this paper eliminated non-conflicting and coordinate processing, the error rate calculated by the improved algorithm was smaller. At the same time, the algorithm was verified by simulation and comparison with the conflict warning tools used at present, which proved that the new algorithm is more effective in detecting flight conflicts when the aircraft is turning. © 2019, Springer Nature Switzerland AG.

关键词： Aircraft

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An intelligent fuzzy-induced recommender system for cloud-based cultural communities

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International Journal of Web Based Communities 2019年第3期15卷 271-288页

作者： Ravi, Logesh Devarajan, Malathi Jeon, Gwanggil Bayat, Oguz Subramaniyaswamy, V. Department of Computer Science and Engineering Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai Tamilnadu India School of Computing SASTRA Deemed University Thanjavur India Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of School of Electronic Engineering Xidian University Xi'an China Department of Electrical Engineering Altinbas University Istanbul Turkey

The rapid development of communication technologies and web-based services generate a large amount of information. In recent years, recommender systems (RS) emerge as an effective mechanism to tackle the information overloading problems. By exploiting the cloud computing paradigm, RS discovers interesting new cultural items based on user preferences and interests. Recent investigations on RS reveal that employing social network data can yield enhanced personalised recommendations with better prediction accuracy. Since users tend to visit only conventional monuments, and many charming cultural items are hidden from them due to lack of awareness about the cultural sites. This article proposes a personalised recommendation model in the field of cultural heritage (CH) with the help of the cloud computing environment. The experimental results obtained demonstrate the improved performance of developed RS in the area of cultural heritage tourism services. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Recommender systems

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Noise Estimation and Type Identification in Natural Scene and Medical Images using Deep Learning Approaches

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Contrast Media & Molecular Imaging 2023年第1期2023卷

作者： G. Kavitha Chetana Prakash Majid Alhomrani N. Pradeep Abdulhakeem S. Alamri Piyush Kumar Pareek Musah Alhassan Department of Computer Science and Engineering Bapuji Institute of Engineering and Technology Davangere 577004 Karnataka Indiabietdvg.edu Department of Computer Science and Engineering University BDT College of Engineering Davangere 577004 Karnataka *** Department of Clinical Laboratories Sciences The Faculty of Applied Medical Sciences Taif University Taif Saudi Arabiatu.edu.sa Centre of Biomedical Sciences Research (CBSR) Deanship of Scientific Research Taif University Taif Saudi Arabiatu.edu.sa Department of Computer Science and Engineering and Head of IPR Cell Nitte Meenakshi Institute of Technology Bengaluru Indianmit.ac.in University for Development Studies Tamale Ghanauds.edu.gh

The image enhancement for the natural images is the vast field where the quality of the images degrades based on the capturing and processing methods employed by the capturing devices. Based on noise type and estimation of noise, filter need to be adopted for enhancing the quality of the image. In the same manner, the medical field also needs some filtering mechanism to reduce the noise and detection of the disease based on the clarity of the image captured; in accordance with it, the preprocessing steps play a vital role to reduce the burden on the radiologist to make the decision on presence of disease. Based on the estimated noise and its type, the filters are selected to delete the unwanted signals from the image. Hence, identifying noise types and denoising play an important role in image analysis. The proposed framework addresses the noise estimation and filtering process to obtain the enhanced images. This paper estimates and detects the noise types, namely Gaussian, motion artifacts, Poisson, salt-andpepper, and speckle noises. Noise is estimated by using discrete wavelet transformation (DWT). This separates the image into quadruple sub-bands. Noise and HH sub-band are high-frequency components. HH sub-band also has vertical edges. These vertical edges are removed by performing Hadamard operation on downsampled Sobel edge-detected image and HH sub-band. Using HH sub-band after removing vertical edges is considered for estimating the noise. The Rician energy equation is used to estimate the noise. This is given as input for Artificial Neural Network to improve the estimated noise level. For identifying noise type, CNN is used. After removing vertical edges, the HH sub-band is given to the CNN model for classification. The classification accuracy results of identifying noise type are 100% on natural images and 96.3% on medical images.

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A multi-hazard safety evaluation framework for a submerged bridge using machine learning model 13

A multi-hazard safety evaluation framework for a submerged b...

引用

13th International Conference on Applications of Statistics and Probability in Civil engineering, ICASP 2019

作者： Liao, Kuo-Wei Hoang, Nhat-Duc Chien, Fu-Sheng Department of Bioenvironmental Systems Engineering National Taiwan University 1 Section 4 Roosevelt Road Taipei Taiwan Faculty of Civil Engineering Institute of Research and Development Duy Tan University Viet Nam Department of Civil and Construction Engineering Taiwan

This study proposes a submerged bridge safety evaluation process against seismic and flood hazards. Due to the uncertainties in the scours, seismic hazard, and structural performance for a given seismic excitation are inevitable, reliability analysis is adopted. A machine-learning based scour risk curve, which is established by the multivariate adaptive regression splines (MARS) incorporated with firefly algorithm (FA), is built to reflect the flood hazard. The seismic hazard is measured using a code-based probabilistic seismic hazard curve. A series of nonlinear time-history analyses are performed to determine the structural performance under different peak-ground-acceleration values. Displacement ductility is used to measure the bridge performance under attacks of both hazards. The influence of the immersed water depth on a bridge's performance is investigated. A case study, in which the nonlinear behaviors in concrete (including core and cover areas), steel bar and soil are included in a bridge model, is conducted to illustrate the proposed methodology and the structural performances with added mass are investigated to show the submerged water effect. According to the results obtained, highly variability of seismic performances is observed and it is important to include the immersed water depth to capture the seismic capacity of a bridge if the submerged bridge depth is great. © 13th International Conference on Applications of Statistics and Probability in Civil engineering, ICASP 2019. All rights reserved.

关键词： Scour

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MONAI: An open-source framework for deep learning in healthcare

arXiv

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arXiv 2022年

作者： Cardoso, M. Jorge Li, Wenqi Brown, Richard Ma, Nic Kerfoot, Eric Wang, Yiheng Murrey, Benjamin Myronenko, Andriy Zhao, Can Yang, Dong Nath, Vishwesh He, Yufan Xu, Ziyue Hatamizadeh, Ali Zhu, Wentao Liu, Yun Zheng, Mingxin Tang, Yucheng Yang, Isaac Zephyr, Michael Hashemian, Behrooz Alle, Sachidanand Darestani, Mohammad Zalbagi Budd, Charlie Modat, Marc Vercauteren, Tom Wang, Guotai Li, Yiwen Hu, Yipeng Fu, Yunguan Gorman, Benjamin Johnson, Hans Genereaux, Brad Erdal, Barbaros S. Gupta, Vikash Diaz-Pinto, Andres Dourson, Andre Maier-Hein, Lena Jaeger, Paul F. Baumgartner, Michael Kalpathy-Cramer, Jayashree Flores, Mona Kirby, Justin Cooper, Lee A.D. Roth, Holger R. Xu, Daguang Bericat, David Floca, Ralf Zhou, S. Kevin Shuaib, Haris Farahani, Keyvan Maier-Hein, Klaus H. Aylward, Stephen Dogra, Prerna Ourselin, Sebastien Feng, Andrew School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom NVIDIA Corporation Santa Clara and Bethesda United States School of Electrical and Computer Engineering Rice University Houston United States Department of Engineering Science University of Oxford Oxford United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Department of BioAI InstaDeep Ltd London United Kingdom Department of Electrical & Computer Engineering University of Iowa Iowa City United States Mayo Clinic Jacksonville United States Mars Incorporated United States Div. Intelligent Medical Systems German Cancer Research Center Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Medical Physics Guy’s & St Thomas’ NHS Foundation Trust London United Kingdom National Cancer Institute Bethesda United States Kitware Inc. Clifton Park United States Department of Ophthalmology University of Colorado Aurora United States Frederick National Laboratory for Cancer Research Frederick United States Department of Pathology Northwestern University Chicago United States

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare. © 2022, CC BY.

关键词： Clinical research

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An ensemble machine learning model for the prediction of danger zones: Towards a global counter-terrorism

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Soft Computing Letters 2021年 3卷

作者： Olabanjo, Olusola A. Aribisala, Benjamin S. Mazzara, Manuel Wusu, Ashiribo S. Department of Computer Science Lagos State University Lagos Nigeria Department of Mathematics Morgan State University MD United States Brain Research Imaging Centre University of Edinburgh Edinburgh United Kingdom Institute of Software Development and Engineering Innopolis University Innopolis Russia Department of Mathematics Lagos State University Lagos Nigeria

Terrorism can be described as the use of violence against persons or properties to intimidate or coerce a government or its citizens to some certain political or social objectives. It is a global problem which has led to loss of lives and properties and known to have negative impacts on tourism and global economy. Terrorism has also been associated with high level of insecurity and most nations of the world are interested in any research efforts that can reduce its menace. Most of the research efforts on terrorism have focused on measures to fight terrorism or how to reduce the activities of terrorists but there are limited efforts on terrorism prediction. The aim of this work is to develop an ensemble machine learning model which combines Support Vector Machine and K-Nearest Neighbor for prediction of continents susceptible to terrorism. Data was obtained from Global Terrorism Database and data preprocessing included data cleaning and dimensionality reduction. Two feature selection techniques, Chi-squared, Information Gain and a hybrid of both were applied to the dataset before modeling. Ensemble machine learning models were then constructed and applied on the selected features. Chi-squared, Information Gain and the hybrid-based features produced an accuracy of 94.17%, 97.34% and 97.81% respectively at predicting danger zones with respective sensitivity scores of 82.3%, 88.7% and 92.2% and specificity scores of 98%, 90.5% and 99.67% respectively. These imply that the hybrid-based selected features produced the best results among the feature selection techniques at predicting terrorism locations. Our results show that ensemble machine learning model can accurately predict terrorism locations. © 2021 The Author(s)

关键词： Support vector machines

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Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort (vol 54, pg 134, 2022)

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NATURE GENETICS 2024年第3期56卷 554-554页

作者： Qin, Youwen Havulinna, Aki S. Liu, Yang Jousilahti, Pekka Ritchie, Scott C. Tokolyi, Alex Sanders, Jon G. Valsta, Liisa Brozynska, Marta Zhu, Qiyun Tripathi, Anupriya Vazquez-Baeza, Yoshiki Loomba, Rohit Cheng, Susan Jain, Mohit Niiranen, Teemu Lahti, Leo Knight, Rob Salomaa, Veikko Inouye, Michael Meric, Guillaume Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia School of BioSciences The University of Melbourne Melbourne Victoria Australia Department of Clinical Pathology Melbourne Medical School The University of Melbourne Melbourne Victoria Australia Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK British Heart Foundation Centre of Research Excellence University of Cambridge Cambridge UK British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK Health Data Research UK Cambridge Wellcome Genome Campus & University of Cambridge Cambridge UK The Alan Turing Institute London UK Department of Infectious Diseases Central Clinical School Monash University Melbourne Victoria Australia Department of Public Health and Welfare Finnish Institute for Health and Welfare Helsinki Finland Institute for Molecular Medicine Finland FIMM-HiLIFE Helsinki Finland Department of Medicine Turku University Hospital and University of Turku Turku Finland Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Ecology and Evolutionary Biology Cornell University Ithaca NY USA Cornell Institute for Host-Microbe Interaction and Disease Cornell University Ithaca NY USA Department of Pediatrics School of Medicine University of California San Diego La Jolla CA USA Division of Biological Sciences University of California San Diego La Jolla CA USA Center for Microbiome Innovation University of California San Diego La Jolla CA USA Department of Computer Science & Engineering Jacobs School of Engineering University of California San Diego La Jolla CA USA NAFLD Research Center Department of Medicine University of California San Diego La Jolla CA USA Smidt Heart Institute Cedars-Sinai Medical Center Los Angeles CA USA Department of Computing Univer

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A systematic review of artificial intelligence applications in education: Emerging trends and challenges

Decision Analytics Journal

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Decision Analytics Journal 2025年 15卷

作者： Matos, Tomás Santos, Walter Zdravevski, Eftim Coelho, Paulo Jorge Pires, Ivan Miguel Madeira, Filipe Escola Superior de Gestão e Tecnologia Polytechnic University of Santarém 2001-904 Portugal Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal Faculty of Computer Science and Engineering University Ss Cyril and Methodius Skopje 1000 North Macedonia School of Technology and Management Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal Digital Literacy and Social Inclusion Research Hub from CIAC Polytechnic University of Santarém 2001-904 Portugal

The academic world is becoming increasingly interested in the applications of Artificial Intelligence technology in education. A systematic review examines AI applications in education, focusing on their effectiveness, challenges, and implications. A comprehensive analysis of studies published between 2011 and 2024 encompassed 45 research articles from major databases, such as PubMed Central, IEEE Xplore, Elsevier, Springer, MDPI, ACM, and PMC. The findings highlight the predominant use of generative AI tools like ChatGPT (30%), followed by other advanced technologies, such as GPT-4, machine learning, and virtual reality. research across global regions, particularly in Canada (18%), the United States (12%), and China (8%), highlights the multifaceted applications of AI in enhancing personalized learning, fostering critical thinking, and supporting professional education. Tools such as ChatGPT have demonstrated strong performance in theoretical knowledge delivery and medical education, while augmented and virtual reality excels in practical skill development. Despite these advances, challenges such as data privacy concerns, algorithmic bias, and the need for specialized educator training remain critical. © 2025 The Author(s)

关键词： Adaptive learning Artificial intelligence ChatGPT Educational technology Machine learning Systematic review

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