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Full-Coverage Mapping of Daily High-Resolution XCO2 across China from 2015 to 2020 by Deep Learning-Based Spatio-Temporal Fusion

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Li, Yi Yan, Jining Zhong, Liheng Bao, De Sun, Leigang Li, Guangyu China University of Geosciences School of Computer Science Wuhan430074 China Ministry of Education Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Wuhan430074 China Ant Group World Financial Center Beijing100020 China Hebei Academy of Sciences Institute of Geographical Sciences Shijiazhuang050011 China Hebei Technology Innovation Center for Geographic Information Application Shijiazhuang050011 China Environmental Development Center of the Ministry of Ecology and Environment China

Carbon neutrality has become a global priority, and high spatio-temporal resolution data on the column-average dry-air mole fraction carbon dioxide (XCO2) is essential for tracking progress and guiding policy adjustments. However, satellite-derived XCO2 exhibits significant temporal and spatial gaps due to influences such as orbital dynamics and cloud cover. Additionally, the low spatial resolution of CarbonTracker (CT) is insufficient to meet the current demands for fine-scale monitoring. Current XCO2 assessment methods often rely solely on single-pixel data, overlooking the spatio-temporal correlations. In this article, we introduce a deep learning-based spatio-temporal model (DSTM) that extracts features from multiple data sources related to atmospheric transport, carbon emissions, and carbon sinks, enabling fine-scale XCO2 assessments. Additionally, XCO2 data from the Orbiting Carbon Observatory-2 (OCO-2) and CT were fused at a 0.1° spatial resolution to generate training labels with broader coverage and more samples, serving as fitting labels. Our approach produced daily, full-coverage 0.1° resolution XCO2 maps for China from 2015 to 2020, and analyzed changes in XCO2 growth trends over this period. Numerical results show that our model outperforms traditional deep learning methods. Model validation using data from four ground-based observation sites of the Total Carbon Column Observing Network (TCCON) achieved an average R2 of 0.86 and an RMSE of 2.67 ppm. The extraction and fusion of spatio-temporal features from multiple data sources provide a novel approach for reconstructing missing XCO2 data. © 1980-2012 IEEE.

关键词： Atmospheric movements

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Advanced Approaches for Skin Cancer Identification: Training Transmission and Hummingbird Recovery

Advanced Approaches for Skin Cancer Identification: Training...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Himanshu Sharma Sonali Pawar M. B Marasulov Navdeep Kaur Ramy Riad Al-Fatlawy C. Karthikeyan Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Application & Information Technology IES University Bhopal M.P. India Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Institute of Polymer Chemistry and Physics Tashkent Uzbekistan Scientific Researcher Western Caspian University Baku Azerbaijan Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Science Engineering Karpagam Institute of Technology Coimbatore

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Because skin cancer is the most often emerging type of cancer, it affects millions each year. In the US alone, approximately 3.5 million people are diagnosed with skin cancer of some type each year! The success rate of surviving skin cancer drops drastically in the later stages. However spotting this form of cancer in the early stages is a complex and costly task. This paper identified a novel approach for thresholding in thermal image processing. Along with sparrow search algorithm, this new approach is used to automatic skin cancer Segmentation, detection and identification as well. Eight pre-trained CNN models+Five Unet for A's meta-heuristic optimzer with the Spasa optimizer to find optimum hyperparameters, and various combinations of five unets. The above is one of both dataset type from total five public sources used to build our data set. The AUNet with DenseNet201 abackbone construct obtains the finest classification on skin cancer segmentation and in 0.137, 94:75%, 92:65%,92:56%and,92:74%,96;20%.86:[30%;92' *5%'69:\|~* pie\'])28? The model applied has the best accuracy value calculated between CNN from Isic 2019 and 2020 Melanoma dataset is, using as a pre-trained MobileNet with an ***100k =98:27%. 99% The best-reported accuracy with a pre-trained model is obtained from CNN applied to Melanoma Classification dataset and the cumulative observed value of it using MobileNet for preprocessing, as follow: MobileNetV2 stable module public, 85:87% max agglomerate precision on applied network for skin disease image dataset Senior full stack developer at Software engineering Interpreter from French to Russian Conclusion Calculation and Recommendation on the Optimal Approach are Studied With 13 Relevant Studies.

关键词： Training Image segmentation Accuracy Thresholding (Imaging) Transfer learning Metaheuristics Melanoma Skin Telecommunication computing Convolutional neural networks

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Pinching Antennas: Principles, applications and Challenges

arXiv

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arXiv 2025年

作者： Yang, Zheng Wang, Ning Sun, Yanshi Ding, Zhiguo Schober, Robert Karagiannidis, George K. Wong, Vincent W.S. Dobre, Octavia A. Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou350117 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Khalifa University Abu Dhabi United Arab Emirates University of Manchester ManchesterM1 9BB United Kingdom Germany Group Electrical & Computer Engineering Dept. Aristotle University of Thessaloniki Thessaloniki54 124 Greece Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Department of Electrical and Computer Engineering Memorial University St. John’sNL Canada

Flexible-antenna systems, such as fluid antennas and movable antennas, have been recognized as key enabling technologies for sixth-generation (6G) wireless networks, as they can intelligently reconfigure the effective channel gains of the users and hence significantly improve their data transmission capabilities. However, existing flexible-antenna systems have been designed to combat small-scale fading in non-line-of-sight (NLoS) conditions. As a result, they lack the ability to establish line-of-sight links, which are typically 100 times stronger than NLoS links. In addition, existing flexible-antenna systems have limited flexibility, where adding/removing an antenna is not straightforward. This article introduces an innovative flexible-antenna system called pinching antennas, which are realized by applying small dielectric particles to waveguides. We first describe the basics of pinching-antenna systems and their ability to provide strong LoS links by deploying pinching antennas close to the users as well as their capability to scale up/down the antenna system. We then focus on communication scenarios with different numbers of waveguides and pinching antennas, where innovative approaches to implement multiple-input multiple-output and non-orthogonal multiple access are discussed. In addition, promising 6G-related applications of pinching antennas, including integrated sensing and communication and next-generation multiple access, are presented. Finally, important directions for future research, such as waveguide deployment and channel estimation, are highlighted. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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A novel augmented reality-based simulator for enhancing orthopedic surgical training

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computers in Biology and Medicine 2025年 185卷 109536-109536页

作者： Wu, Luohong Seibold, Matthias Cavalcanti, Nicola A. Hein, Jonas Gerth, Tatiana Lekar, Roni Hoch, Armando Vlachopoulos, Lazaros Grabner, Helmut Zingg, Patrick Farshad, Mazda Fürnstahl, Philipp Research in Orthopedic Computer Science Balgrist University Hospital University of Zurich Lengghalde 5 Zurich8008 Switzerland Department of Orthopaedics Balgrist University Hospital University of Zurich Forchstrasse 340 Zurich8008 Switzerland Computer Vision and Geometry Group ETH Zurich Ramistrasse 101 Zurich8092 Switzerland Institute of Computer Science School of Engineering Zurich University of Applied Sciences Obere Kirchgasse 2 Winterthur8401 Switzerland Institute of Data Analysis and Process Design School of Engineering Zurich University of Applied Sciences Technikumstrasse 81 Winterthur8400 Switzerland

Background: Total Hip Arthroplasty (THA) is a well-established and common orthopedic surgery. Due to the complexity involved in THA, orthopedic surgeons require rigorous training. However, the current gold standard, the tutor-guided and -evaluated apprenticeship model is time-consuming, costly, and poses risks to patients. There is a pressing need for additional training resources to enhance the efficiency and safety of the training process. In this work, we present a novel Augmented Reality (AR)-based simulator designed for THA that helps enable a new self-paced training and learning paradigm without the need for instructors. Methods: The simulator reduces the need for instructors by integrating an AR guidance module and an automated performance evaluation module. Three types of AR guidance were developed: Overlay, Virtual Twin, and Sectional Views. A feasibility study was conducted with five resident surgeons and two senior surgeons to compare these guidance methods quantitatively and qualitatively. The automated performance evaluation module was assessed against manual performance evaluation using Bland-Altman analysis with limits of agreement (LoA) and Mann–Whitney U tests. Results: The quantitative feasibility results indicate the efficacy of the developed AR guidance, characterized by mean transitional and rotational deviation errors below 3 mm and 3 degrees. Based on the qualitative results, we provide recommendations for efficient AR guidance designs. The Bland-Altman analysis results (0.22±1.32mm with LoA −2.37 to 2.81 mm for distance deviation, 0.94±2.41 degrees with LoA −3.78 to 5.66 degrees for yaw deviation, −0.34±1.30 degrees with LoA −2.90 to 2.22 degrees for pitch deviation) and p-values of Mann–Whitney U tests (0.64 for distance deviation, 0.12 for yaw deviation, 0.11 for pitch deviation) indicate no statistically significant differences between the automated and manual performance evaluation at a significance level of 0.05. Conclusion: This work sh

关键词： Augmented reality

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IoT and AI Convergence: Automatic and Autonomous Viewpoint

IoT and AI Convergence: Automatic and Autonomous Viewpoint

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： S.A. Shoyusupov Sandeep Bisht Krishna Kant Dixit Sourav Muntather Almusawi Jaishankar Bhatt Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" National Research University Tashkent Uzbekistan School of Management Graphic Era Hill University Bhimtal Uttarakhand India Department of Electrical Engineering GLA University Mathura India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Computers Techniques Engineering College Of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department. of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

While various business, industrial, healthcare and economic sectors are deploying the world wide web of things during this current generation of internet evolution. IoT and Intelligence (AI) helps in boosting performance across a wide range of everyday apps with main focus on automation systems for robotics, transportation & commercial/industrial applications. The aim of this essay, is to take a deep dive in both current and future scenarios where the combination between IoT and AI could make possible applications that would significantly improve our way of life. The introductory part of the article provides some background to those who are not familiar with IoT and AI: definition on the "synergy between two magnificent fields", a case study for understanding what brings about an increase in 'IoT enabled by AI' focusing on new analytics toolkit, definitions i.e. Industrial IoT vs Internet About Robotic Things or Industrial Robotics lloT glossary terms etc. This article provides a summary of some case studies followed by few sample use cases of IoT and AI assisted robot & automated manufacturing.

关键词： Privacy Automation Supply chain management Service robots Transportation Telecommunication computing Security Web sites Artificial intelligence Industrial Internet of Things

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Contemporary Cybersecurity Framework for Industrial Cloud Deployment

Contemporary Cybersecurity Framework for Industrial Cloud De...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： I. Kabulov C. Karthikeyan Prashant Chourasiya Sandeep Singh Myasar Mundher Adnan Arti Badhoutiya Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" National Research University Tashkent Uzbekistan Scientific Researcher Western Caspian University Baku Azerbaijan Department of Computer Science Engineering Karpagam Institute of Technology Coimbatore Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electrical Engineering GLA University Mathura India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

In this paper, we provide an adoption approach for cloud computing tailored to corporate environments. The foundation of the CCAF multi-layered security is the development and integration of three critical security technologies: identity management, encryption, and firewall. These technologies are built on top of advancements in Enterprise File Sync and Exchange technologies. In this article, we provide our objectives, pertinent literature, and points of view about the security architecture. We have extensively examined important technologies and conducted experiments to show the robustness of the CCAF multilayered security. In penetration testing, the CCAF multi-layered security successfully identified and eliminated 99.95% of viruses and trojans, and it maintained an average blocking rate of 85% or higher for 100 hours in a row. The detection and blocking times for each trojan and virus were less than 0.012 seconds. Real data safety is ensured by the comprehensive multi-layered CCAF security, which offers effective protection against SQL injection. Remarkably, the CCAF security system has a 100% false warning rate. Every single outcome of the CCAF test has F-measures greater than 99.75%. This research demonstrates how simple it is to combine the complicated security of CCAF with real services, rules, and business procedures. Verified research contributions suggest that multi-layered security with CCAF may enhance the volume, velocity, and validity of cloud-based Big Data applications.

关键词： Cloud computing computer viruses SQL injection Encryption Telecommunication computing Security Trojan horses Synchronization Viruses (medical) Penetration testing

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Cartpole Problem with PDL and GP Using Multi-objective Fitness Functions Differing in a Priori Knowledge 1

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14th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2021

作者： Shannon, Peter David Nehaniv, Chrystopher L. Phon-Amnuaisuk, Somnuk Adaptive Systems Research Group Centre for Computer Science & Informatics Research University of Hertfordshire Hatfield United Kingdom Laksmana College of Business Bandar Seri Begawan Brunei Department of Systems Design Engineering University of Waterloo WaterlooON Canada Department of Electrical & Computer Engineering University of Waterloo WaterlooON Canada School of Computing and Informatics Universiti of Teknologi Brunei Bandar Seri Begawan Brunei

ISBN: (数字)9783030802530

ISBN: (纸本)9783030802523

We present a study looking at the effect of a priori domain knowledge on an EA fitness function. Our experiment has two aims: (1) applying an existing NSGA-II framework for GP with PDL to the cartpole problem—applying GP & PDL to cartpole and a purely behavioral problem for the first time—and (2) contrasting two multi-objective fitness functions: one with high and the other with low a priori domain knowledge. In our experiment we created two populations with an EA, varying in the number of objectives use for the fitness function, 2 objective criteria to represent low a priori knowledge and 3 to represent high. With fitness functions tailored to find specifically prescribed solutions we expect greater discriminating power and more feedback to an evolutionary process. This comes at the cost of excluding some unexpected solutions from the evolutionary process and placing a greater burden on the designer. We address the question: how large is the disadvantage for the low a priori fitness function in a worst-case scenario, where innovative solutions will not enhance performance. This question is interesting because we would prefer to guide EA with simple, easy to create and understand, objective criteria rather than complex and highly specific criteria. Understanding any associated penalty for using simple, easy to create fitness functions, is crucial in assessing how much effort and should be put into designing objective criteria. © 2021, Springer Nature Switzerland AG.

关键词： Domain Knowledge

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Stochastic Resource Allocation for Semantic Communication-aided Virtual Transportation Networks in the Metaverse

arXiv

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arXiv 2022年

作者： Ng, Wei Chong Du, Hongyang Lim, Wei Yang Bryan Xiong, Zehui Niyato, Dusit Miao, Chunyan Alibaba Group China Alibaba-NTU Joint Research Institute Singapore Energy Research Institute NTU Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore University of Technology and Design Singapore

The physical-virtual world synchronization to develop the Metaverse will require a massive transmission and exchange of data. In this paper, we introduce semantic communication for the development of virtual transportation networks in the Metaverse. Leveraging the perception capabilities of edge devices, virtual service providers (VSPs) can subscribe to their preferred edge devices to receive the semantic data of interest. However, the demands of the VSPs are highly dependent on the users that they are serving. To address the resource allocation problem amid stochastic user demand, we propose a stochastic semantic transmission scheme (SSTS) based on two-stage stochastic integer programming. Using real data captured by edge devices we deploy in Singapore, the simulation results show that SSTS can minimize the transmission cost of the VSPs while accounting for the users’ demand uncertainties. © 2022, CC BY.

关键词： Resource allocation

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shuo Zhao Qiliang Gu Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets.

关键词： Deep learning Computational modeling Semantics Neural networks Feature extraction Natural language processing Multilingual Convolutional neural networks Cybernetics Overfitting

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A Novel Text Matching Model Based on Multilayer Coding and Feature Enhancement

A Novel Text Matching Model Based on Multilayer Coding and F...

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International Conference on computer Supported Cooperative Work in design

作者： Yu Zhang Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the rapid development of Natural Language Processing (NLP), text matching has become the basis of many downstream tasks in NLP, and the study of text matching is of great research significance for solving tasks such as question and answer and information retrieval in NLP. Most of the current text matching methods tend to have problems such as mismatch of grammatical structures and insufficient interaction information in sentences. In order to solve the problems of insufficient interaction information and lack of features and ability to capture keyword and sequence information in text matching, this paper proposes a text matching method based on multi-layer coding and soft attention mechanism. The method first embeds the text and sends it to a gating module for processing, then sends the processed result to a module containing a combination of multilayer coding and soft attention mechanism for further operations such as multiple alignment, and finally sends it to a classifier containing a three-layer fully-connected network for predicting whether the input text pairs match or not. The two modules proposed in this paper are practically feasible, and comparison and ablation experiments have been conducted on the publicly available datasets LCQMC dataset and BQ dataset, and the experimental results show that the two modules improve the accuracy of text matching by 2.44% and 1.02%, respectively.

关键词： Attention mechanisms Accuracy Computational modeling Impedance matching Nonhomogeneous media Information retrieval Encoding

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