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A General Model for Large-Scale Paddy Rice Mapping by Combining Biological Characteristics, Deep Learning, and Multisource Remote Sensing Data

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 14705-14717页

作者： Zhenjie Liu Jialin Liu Yingyue Su Xiangming Xiao Jingwei Dong Luo Liu Hubei Key Laboratory of Intelligent Geo-Information Processing School of Computer Science China University of Geosciences Wuhan China Guangdong Province Key Laboratory for Agricultural Resources Utilization South China Agricultural University Guangzhou China Department of Microbiology and Plant Biology Center for Earth Observation and Modeling University of Oklahoma Norman OK USA Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Due to the influences combined with global climate change and human activity, paddy rice area and distribution have undergone dramatic changes. Currently, many approaches for paddy rice mapping rely on the prior knowledge of paddy rice phenology or require widely distributed ground samples of paddy rice, which are limited for large-scale applications. In this work, we propose a general paddy rice mapping (GPRM) model by combining biological characteristics, deep learning, and multisource remote sensing data. The proposed GPRM first utilizes the normalized difference vegetation index and land surface water index to acquire large-scale remote sensing dataset in key phenology periods of paddy rice, such as the transplanting period and peak vegetative growth period. Then, a general model using object-based deep neural networks is developed and trained by the remote sensing dataset and the ground reference data collected in one region (e.g., Guangdong Province), which can be directly applied for generating 10-m paddy rice maps in other regions with different climate conditions and complex cropping systems (e.g., Jiangxi Province and Heilongjiang Province). The results demonstrate that the GPRM can realize remarkable performance of paddy rice mapping in China. The overall accuracies are over 99%, and the user accuracy, producer accuracy, and Kappa coefficient vary from 0.77 to 0.93, 0.94 to 0.97, 0.9 to 0.95, respectively. Overall, the GPRM is has significant promise for large-scale paddy rice mapping with complex cropping systems, thus supporting global agricultural development strategies and food security.

关键词： Deep learning Climate change Earth Observing System Phenology Crops human activity recognition Terrain mapping

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Chances and Challenges of Chatgpt and Similar Models for Education in M&S 23

Chances and Challenges of Chatgpt and Similar Models for Edu...

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Proceedings of the Winter simulation Conference

作者： Andreas Tolk Philip Barry Margaret L. Loper Ghaith Rabadi William T. Scherer Levent Yilmaz The MITRE Corporation Modeling and Analysis Innovation Center Charlottesville USA L3Harris Corporation Mission Analysis Group Herndon USA Georgia Tech Research Institute Georgia Institute of Technology Atlanta USA University of Central Florida School of Modeling Simulation and Training Orlando USA University of Virgina Department of Systems and Information Engineering Charlottesville USA Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering Auburn USA

ISBN: (纸本)9798350369663

This position paper summarizes the inputs of a group of experts from academia and industry presenting their view on chances and challenges of using ChatGPT within modeling and simulation education. The experts also address the need to evaluate continuous education as well as education of faculty members to address scholastic challenges and opportunities while meeting the expectation of industry. Generally, the use of ChatGPT is encouraged, but it needs to be embedded into an updated curriculum with more emphasis on validity constraints, systems thinking, and ethics.

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Chances and Challenges of ChatGPT and Similar Models for Education in M&S

Chances and Challenges of ChatGPT and Similar Models for Edu...

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2023 Winter simulation Conference, WSC 2023

作者： Tolk, Andreas Barry, Philip Loper, Margaret L. Rabadi, Ghaith Scherer, William T. Yilmaz, Levent The Mitre Corporation Modeling and Analysis Innovation Center 1001 Research Park Blvd #200 CharlottesvilleVA22911 United States L3Harris Corporation Mission Analysis Group 2235 Monroe Street HerndonVA20171 United States Georgia Tech Research Institute Georgia Institute of Technology AtlantaGA30308 United States University of Central Florida School of Modeling Simulation and Training 3100 Technology Pkwy OrlandoFL32826 United States University of Virgina Department of Systems and Information Engineering CharlottesvilleVA22904 United States Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering AuburnAL36849 United States

ISBN: (纸本)9798350369663

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Chances and Challenges of ChatGPT and Similar Models for Education in M&S

Chances and Challenges of ChatGPT and Similar Models for Edu...

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simulation Winter Conference

作者： Andreas Tolk Philip Barry Margaret L. Loper Ghaith Rabadi William T. Scherer Levent Yilmaz Modeling and Analysis Innovation Center The MITRE Corporation Charlottesville VA USA Mission Analysis Group L3Harris Corporation Herndon VA USA Georgia Tech Research Institute Georgia Institute of Technology Atlanta GA USA University of Central Florida School of Modeling Simulation and Training Orlando FL USA Department of Systems and Information Engineering University of Virgina Charlottesville VA USA Samuel Ginn College of Engineering Computer Science and Software Engineering Auburn University Auburn AL USA

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A Privacy-Preserving Access Control Protocol for Consumer Flying Vehicles in Smart City Applications

SSRN

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SSRN 2022年

作者： Mahmood, Khalid Saleem, Muhammad Asad Shamshad, Salman Ghaffar, Zahid Akram, Muhammad Wahid Conti, Mauro Das, Ashok Kumar National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Pakistan Department of Software Engineering The University of Lahore Lahore54590 Pakistan Department of Computer Science Barani Institute of Sciences Sahiwal57000 Pakistan Department of Mathematics University of Padua Padua35131 Italy Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500032 India Virginia Modeling Analysis and Simulation Center Old Dominion University SuffolkVA23435 United States

The Internet of Drones (IoD) offers supervised admittance to drones in a targeted fly zone as the byproduct of the Internet of Things (IoT). The term drone is the trendy alias for Intelligent Flying Vehicle (IFV). The contemporary sensing, processing, and connectivity services enriches the use of drones in a plethora of civilian and military applications. In these applications, consumers can acquire the real-time information directly from flying drones in a smart city environment. No doubt, this feature empowers the consumers, but it is deemed critical as direct access privilege can cause serious security threats. To prevent such threats, we propose an anonymous for consumer flying vehicles in smart city application. The proposed protocol utilizes a physically unclonable function to sustain the physical security of the flying vehicles. We ratify the security fortitude and persistence of our protocol through inclusive security analysis. Whereas, we demonstrate the performance evaluation under diverse performance metrics which shows that overall our devised protocol incurs 11 . 5771 ms to complete the authentication cycle. The careful performance comparison attests to the precedence of our protocol over peer protocols. © 2022, The Authors. All rights reserved.

关键词： Internet of things

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Optimizing Multi-Hop Quantum Communication using Bidirectional Quantum Teleportation Protocol

arXiv

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arXiv 2025年

作者： Ikken, N. Kumar, P. Slaoui, A. Kar, B. Laamara, R. Ahl Almousa, M. El-Latif, A.A. Abd LPHE-Modeling and Simulation Faculty of Sciences Mohammed V University Rabat Morocco Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Centre of Physics and Mathematics CPM Faculty of Sciences Mohammed V University in Rabat Rabat Morocco EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

In this paper, we introduce a new method for Bidirectional Quantum Teleportation called Bidirectional Quantum Teleportation using the Modified Dijkstra Algorithm and Quantum Walk (BQT-MDQW). This method uses different types of entangled states, such as the GHZ-Bell state, W-Bell state, and Cluster-Bell state, to improve quantum communication in multi-hop quantum wireless networks. We focus on the W-Bell state and compare the quantum Dijkstra algorithm with the classical Dijkstra method to see which one works better. We apply both versions to quantum and classical simulators, measuring their performance through fidelity, memory utilization, and throughput calculations. Our results show that the shortest path problem may be solved with significantly reduced computer complexity using the quantum Dijkstra algorithm based on quantum walks. The introduction of a quantum walk, which permits dynamic transitions between quantum channels and the effective exploration of quantum network states, is an important part of the protocol. Using the capacity of the quantum walk to adjust to changing quantum states, we also introduce a method for successfully identifying unitary matrices under varying quantum channels. The bidirectional teleportation structure of the protocol is designed to solve the multi-hop teleportation problem in quantum wireless networks. In addition, we present quantum Dijkstra’s algorithm, which uses quantum gates to significantly decrease computational complexity and solve the networking problem by building on the quantum walk framework. This method shows how quantum computing may be used to solve arbitrary optimization issues such as the shortest path problem. Finally, we present a novel multi-hop quantum teleportation system encompassing both unidirectional and bidirectional communication, as introduced in the quantum Dijkstra algorithm system. This system significantly enhances communication in quantum networks by enabling efficient and reliable information

关键词： Quantum channel

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CHANCES AND CHALLENGES OF CHATGPT AND SIMILAR MODELS FOR EDUCATION IN M&S

TechRxiv

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TechRxiv 2023年

作者： Scherer, William T. Tolk, Andreas Loper, Margaret L. Barry, Philip Rabadi, Ghaith Yilmaz, Levent The MITRE Corporation Modeling and Analysis Innovation Center 1001 Research Park Blvd #200 CharlottesvilleVA22911 United States Georgia Tech Research Institute Georgia Institute of Technology AtlantaGA30308 United States University of Virgina Department of Systems and Information Engineering CharlottesvilleVA22904 United States L3Harris Corporation Mission Analysis Group 2235 Monroe Street HerndonVA20171 United States University of Central Florida School of Modeling Simulation and Training 3100 Technology Pkwy OrlandoFL32826 United States Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering AuburnAL36849 United States

The position paper summarizes the inputs of a set of experts from academia and industry presenting their view on chances and challenges of using ChatGPT within the Modelling and simulation education. They also address the need to evaluate continuous education as well as education of faculty members to address scholastic challenges and opportunities while meeting the expectation of industry. © 2023, CC BY.

关键词： modeling languages

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Outcomes from a Workshop on a National center for Quantum Education

arXiv

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arXiv 2024年

作者： Barnes, Edwin Bennett, Michael B. Boltasseva, Alexandra Borish, Victoria Brown, Bennett Carr, Lincoln D. Ceballos, Russell R. Dukes, Faith Easton, Emily W. Economou, Sophia E. Edwards, E.E. Finkelstein, Noah D. Fracchiolla, C. Franklin, Diana Freericks, J.K. Goss, Valerie Hannum, Mark Holincheck, Nancy Kelly, Angela M. Lanes, Olivia Lewandowski, H.J. Matsler, Karen Jo Mercurio, Emily Montaño, Inès Murdock, Maajida Peltz, Kiera Perron, Justin K. Richardson, Christopher J.K. Rosenberg, Jessica L. Ross, Richard S. Ryu, Minjung Samuel, Raymond E. Schrode, Nicole Schwamberger, Susan Searles, Thomas A. Singh, Chandralekha Tingle, Alexandra Zwickl, Benjamin M. Department of Physics Virginia Tech Center for Quantum Information Science and Engineering Virginia Tech 850 West Campus Drive BlacksburgVA24061 United States Q-SEnSE NSF QLCI University of Colorado 440 UCB BoulderCO80301 United States Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University 610 Purdue Mall West LafayetteIN47907 United States Quantum Science Center A National Quantum Information Science Research Center of The U.S. Department of Energy Oak Ridge National Laboratory 1 Bethel Valley Road Oak RidgeTN37830 United States JILA National Institute of Standards and Technology the University of Colorado BoulderCO80309 United States Department of Physics University of Colorado 390 UCB BoulderCO80309 United States QuSTEAM 510 Ronalds St. Iowa CityIA52245 United States Quantum Engineering Program Department of Physics Colorado School of Mines 1523 Illinois St. GoldenCO80401 United States QuSTEAM Non-Profit Wilbur Wright College City Colleges of Chicago 4300 N Narragansett Ave ChicagoIL60634 United States Government and Community Relations Office - K-12 Programs Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA United States Chicago Quantum Exchange 5235 S. Harper Court ChicagoIL60615 United States Department of Physics Virginia Tech 850 West Campus Drive BlacksburgVA24060 United States Department of Electrical and Computer Engineering Duke Quantum Center Duke University Duke University 130 Hudson Hall DurhamNC27708 United States American Physical Society 555 12th St. NW WashingtonDC20004 United States Computer Science University of Chicago 5730 S. Ellis Ave. ChicagoIL60637 United States Department of Physics Georgetown University 552 Reiss Science Building WashingtonDC20057 United States Chemistry Physics and Engineering Studies Chicago State University 9501 S. King Drive WSC 309 ChicagoIL606

In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science. © 2024, CC BY.

关键词： Students

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Experiments in Routing Vehicles for Municipal Services

Experiments in Routing Vehicles for Municipal Services

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International Conference on High Performance Computing and simulation

作者： Imran Mahmood Sahar Idwan Junaid Ahmed Zubairi Izzeddin Matar Center for Research in Modeling & Simulation School of Electrical Engineering and Computer Science National University of Sciences and Technology CS and Engineering Department School of Engineering American U of Ras Al Khaimah Ras al Khaimah UAE and Comp Sci & Apps Dept Hashemite University Computer and Information Sciences Department College of Liberal Arts and Sciences State University of New York at Fredonia Faculty of Information Technology Software Engineering Department University of Petra

ISBN: (纸本)9781538678800

In this paper, route planning of waste collection trucks using R&R (Ruin and Recreate) approach is explored. We assume the trucks are guided by the central depot in selecting the optimal route for waste collection. Heuristic algorithms are simulated to find the optimal routes for the waste management fleet. Through the use of smart dumpsters that can communicate the current level of waste using sensors and communication modules, we aim to reduce the number of trucks used, the total time taken and total distance traveled by the fleet in a day. Our work is based on variations of CVRPTW (Capacitated Vehicle Routing Problem with Time Window Constraint). The central management system selects the dumpsters, based on their waste levels, in descending order, and dispatches appropriate number of trucks, with path assignments using Ruin and Recreate (R&R) approach of the VRPTW strategy. The municipal authority saves transit time, fuel cost and service time by using our approach, through a simulation of smart waste collection.

关键词： Smart Dumpster Capacitated Vehicle Routing Problem with Time Window Constraint Ruin & Recreate Search heuristic Waste management simulation

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