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arXiv 2022年

作者： Lilly, Jeremy R. Capodaglio, Giacomo Petersen, Mark R. Brus, Steven R. Engwirda, Darren Higdon, Robert L. Oregon State University Department of Mathematics CorvallisOR97331-4605 United States Computational Physics and Methods Group Los Alamos National Laboratory NM87545 United States Fluid Dynamics and Solid Mechanics Group Los Alamos National Laboratory NM87545 United States Mathematics and Computer Science Division Argonne National Laboratory IL60439 United States

This paper presents the first scientific application of local time-stepping (LTS) schemes in the Model for Prediction Across Scales-Ocean (MPAS-O). We use LTS schemes in a single-layer, global ocean model that predicts the storm surge around the eastern coast of the United States during Hurricane Sandy. The variable-resolution meshes used are of unprecedentedly high resolution in MPAS-O, containing cells as small as 125 meters wide in Delaware Bay. It is shown that a particular, third-order LTS scheme (LTS3) produces sea-surface height (SSH) solutions that are of comparable quality to solutions produced by the classical four-stage, fourth-order Runge-Kutta method (RK4) with a uniform time step on the same meshes. Furthermore, LTS3 is up to 35% faster in the best cases, showing that LTS schemes are viable for use in MPAS-O with the added benefit of substantially less computational cost. The results of these performance experiments inform us of the requirements for efficient mesh design for LTS schemes. In particular, we see that for LTS to be efficient on a given mesh, it is important to have enough cells using the coarse time-step relative to those using the fine time-step, typically at least 1:5. © 2022, CC BY-SA.

关键词： Hurricanes

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Research on Data Extraction and Analysis of Software Defect in IoT Communication Software

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computers, Materials & Continua 2020年第11期65卷 1837-1854页

作者： Wenbin Bi Fang Yu Ning Cao Wei Huo Guangsheng Cao Xiuli Han Lili Sun Russell Higgs School of Computer and Software Dalian Neusoft University of InformationDalian116023China School of Information Engineering Qingdao Bin Hai UniversityQingdao266555China School of Internet of Things and Software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China Public Teaching Department Neuedu Software Talent Training SchoolQingdao266000China Public Teaching Department Qingdao Technical CollegeQingdao266555China School of Information Engineering Sanming UniversitySanming365004China School of Mathematics and Statistics University College DublinDublinIreland

Software defect feature selection has problems of feature space dimensionality reduction and large search *** research proposes a defect prediction feature selection framework based on improved shuffled frog leaping algorithm(ISFLA).Using the two-level structure of the framework and the improved hybrid leapfrog algorithm's own advantages,the feature values are sorted,and some features with high correlation are selected to avoid other heuristic algorithms in the defect prediction that are easy to produce local The case where the convergence rate of the optimal or parameter optimization process is relatively *** framework improves generalization of predictions of unknown data samples and enhances the ability to search for features related to learning *** the same time,this framework further reduces the dimension of the feature *** the contrast simulation experiment with other common defect prediction methods,we used the actual test data set to verify the framework for multiple iterations on Internet of Things(IoT)system *** experimental results show that the software defect prediction feature selection framework based on ISFLA is very effective in defect prediction of IoT communication *** framework can save the testing time of IoT communication software,effectively improve the performance of software defect prediction,and ensure the software quality.

关键词： Improved shuffled frog leaping algorithm defect prediction feature selection framework Internet of Things

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Brownian motion in a magneto Thermo-diffusion fluid flow over a semi-circular stretching surface

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Partial Differential Equations in Applied Mathematics 2024年 12卷

作者： Munjam, Shankar Rao Gopal, D. Kishan, N. Formanova, Shoira Karthik, K. Ahmad, Furqan Waqas, M. Gupta, Manish Khan, M. Ijaz School of Technology Woxsen University Telangana Hyderabad 502345 India Junior Lecturer in Mathematics Telangana Tribal Welfare Upgraded Residential Junior College for Boys Cheemanpally Telangana Nizamabad District 503165 India Department of Mathematics UCS Osmania University Telangana Hyderabad 500007 India Department of Chemistry and Its Teaching Methods Tashkent State Pedagogical University Tashkent Uzbekistan Department of Studies in Mathematics Davangere University Karnataka Davangere India Western Caspian University Baku Azerbaijan NUTECH School of Applied Sciences and Humanities National University of Technology Islamabad 44000 Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Division of Research and Development Lovely Professional University Phagwara India Department of Mechanical Engineering College of Engineering Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia

The current study explores the mass and heat transport analysis of a Casson liquid stream past a curved surface. The current model considers the effect of magnetic strength brought on by the strength of the applied uniform magnetic field. The significance of thermophoresis and Brownian motion are also taken into account using the Buongiorno nano-liquid model. The study of liquid flow over stretching sheets frequently addresses practical issues that have garnered significant attention from researchers due to their importance in various domains, including microfluidics, fibreglass production, manufacturing, transportation, metal extrusion, thermal insulation, glass production, paper manufacturing, and acoustic blasting. The governing partial differential equations (PDEs) are converted into ordinary differential equations (ODEs) using the similarity variables. These equations are numerically solved using the finite difference method (FDM). The concentration, temperature, and velocity graphs were produced by varying the different physical parameters. The upsurge in the magnetic parameter reduces the velocity profile. As the magnetic parameter increases, thermal and concentration profiles upsurge. The decrease in velocity profile can be seen as the Casson parameter rises. The intensification in values of thermophoretic parameter enhances the thermal and concentration profiles. The concentration and thermal profiles reduce as the curvature parameter upsurges. © 2024 The Author(s)

关键词： Casson fluid Curved stretching sheet Nanofluid Thermophoresis and Brownian motion

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Understanding the development of interest and self-efficacy in active-learning undergraduate physics courses

arXiv

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arXiv 2022年

作者： Dou, Remy Brewe, Eric Potvin, Geoff Zwolak, Justyna P. Hazari, Zahra Department of Teaching and Learning Florida International University MiamiFL United States STEM Transformation Institute Florida International University MiamiFL United States Department of Physics Drexel University PhiladelphiaPA United States Department of Physics Florida International University MiamiFL United States Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD United States

Modeling Instruction (MI), an active-learning introductory physics curriculum, has been shown to improve student academic success. Peer-to-peer interactions play a salient role in the MI classroom. Their impact on student interest and self-efficacy – preeminent constructs of various career theories – has not been thoroughly explored. Our examination of three undergraduate MI courses (N = 221) revealed a decrease in students’ physics self-efficacy, physics interest, and general science interest. We found a positive link from physics interest to self-efficacy, and a negative relationship between science interest and self-efficacy. We tested structural equation models confirming that student interactions make positive contributions to self-efficacy. This study frames students’ classroom interactions within broader career theory frameworks and suggests nuanced considerations regarding interest and self-efficacy constructs in the context of undergraduate active-learning science courses. Copyright © 2022, The Authors. All rights reserved.

关键词： Students

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Trends and Challenges of Sustainable Agriculture in the Iraqi Environment

Trends and Challenges of Sustainable Agriculture in the Iraq...

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Blockchain, Smart Healthcare and Emerging Technologies (SmartBlock4Health), IEEE International Conference on

作者： Hanan Hussein Al-asadi Maria Goga Haider Abdullah Ali Ramona Cristina Popa Awodele Oludele Faculty of Automatic Control and Computers University POLITEHNICA of Bucharest Bucharest Romania University of Technology-Iraq Baghdad Iraq UTCB Department for Training of Teaching Personnel Technical University of Civil Engineering of Bucharest Bucharest Romania Madenat Alelem University College Baghdad Iraq Faculty of Engineering in Foreign Languages University POLITEHNICA of Bucharest Bucharest Romania computer science department Babcock University Ogun State Nigeria

ISBN: (纸本)9781665460156

The use of Agriculture Intelligent Systems (AISs) in Iraq has become popular among farmers. The greenhouse is a solution for plant growth, which uses an effective microclimate to simulate seasons and to use alternative energy sources like solar, thermal, and photovoltaic systems for microclimate control. Greenhouses are considered a successful solution with developments and achievements in the technology of this field. However, problems like high costs, difficulties in installation and maintenance, or the need for some procedures should be considered. Because Iraq is close to the equator (3669 km), it receives sufficient solar power. It also has large areas available for the building of solar greenhouse systems. The farmers need an affordable and functional smart greenhouse to enable and encourage them to use the environment efficiently. We intend to develop low-cost, accurate, and easy-to-use greenhouse systems controlled by artificial intelligence algorithms. Farmers will be able to control the greenhouse by using simple commands. Machine learning algorithms provide real-time and effective analysis that could support farmers in decision-making and provide relevant data for researchers and policymakers. AISs algorithms can lead to a sustainable and productive agriculture system. The system can be used in Iraq with sufficient government support and subsidy.

关键词： Smart agriculture Machine learning algorithms Costs Green buildings Soil Control systems Agriculture

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A joint model for dropped pronoun recovery and conversational discourse parsing in Chinese conversational speech

arXiv

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arXiv 2021年

作者： Yang, Jingxuan Xu, Kerui Xu, Jun Li, Si Gao, Sheng Guo, Jun Xue, Nianwen Wen, Ji-Rong School of Artificial Intelligence Beijing University of Posts and Telecommunications China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China Department of Computer Science Brandeis University United States

In this paper, we present a neural model for joint dropped pronoun recovery (DPR) and conversational discourse parsing (CDP) in Chinese conversational speech. We show that DPR and CDP are closely related, and a joint model benefits both tasks. We refer to our model as DiscProReco, and it first encodes the tokens in each utterance in a conversation with a directed Graph Convolutional Network (GCN). The token states for an utterance are then aggregated to produce a single state for each utterance. The utterance states are then fed into a biaffine classifier to construct a conversational discourse graph. A second (multi-relational) GCN is then applied to the utterance states to produce a discourse relation-augmented representation for the utterances, which are then fused together with token states in each utterance as input to a dropped pronoun recovery layer. The joint model is trained and evaluated on a new Structure Parsing-enhanced Dropped Pronoun Recovery (SPDPR) dataset that we annotated with both two types of information. Experimental results on the SPDPR dataset and other benchmarks show that DiscProReco significantly outperforms the state-of-the-art baselines of both tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Recovery

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Production scheduling of open pit mine using sequential branch-and-cut and longest path algorithm: An application from an African copper mine

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Journal Europeen des Systemes Automatises 2020年第5期53卷 629-636页

作者： Joshi, Devendra Satpathy, Susanta Kumar University Teaching Department Chhattisgarh Swami Vivekanand Technical University Bhilai Chhattisgarh491107 India Department of Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Vadlamudi A.P.522213 India

Open pit mine production scheduling assigns mining blocks in different production periods for maximising profits after satisfying geotechnical and operational constraints. In this paper, two Open pit mine production scheduling models were applied in an African copper deposit. The first model is a traditional model with more tight resource constraints;the second model is a more robust model where resource constraints are relaxed by penalizing the objective function. Both the models were solved using two step algorithms: (a) year wise production scheduling using a sequential branch-and-cut algorithm;and (b) an iterative longest path algorithm to improve the solution generated from branch-and-cut. Results demonstrated that due to the tight constraints in Model 1, the optimizer was unable to generate a feasible solution after the first period, therefore the lower limit metal production constraint was eliminated to generate a feasible solution;however, Model 2 was able to generate a feasible solution for all periods. Results show that both the models generated nearly the same amount of ore, waste, metal content, and mine life. Model 2 generates relatively more net present value as compared to Model 1, whereas, the computational time required for solving the scheduling problem is relatively less for Model 1 than for Model 2. © 2020 Lavoisier. All rights reserved.

关键词： Integer programming

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What is STEM? Preservice elementary teachers’ conceptions of integrated STEM education

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teaching and Teacher Education 2025年 165卷

作者： Jeanna R. Wieselmann Deepika Menon Brynn C. Price Allison Johnson Sumreen Asim Sarah Haines Grace Morison Department of Teaching and Learning Southern Methodist University United States Department of Teaching Learning & Teacher Education and Center for Science Mathematics & Computer Education University of Nebraska – Lincoln United States SMU Libraries Southern Methodist University United States School of Education Indiana University Southeast United States Department of Biological Sciences Towson University United States

Teachers’ conceptions can relate to their teaching practices, but integrated science, technology, engineering, and mathematics (STEM) education does not have a consistent definition. This study included 627 preservice elementary preservice teachers across 11 different teacher preparation programs to identify common conceptions of integrated STEM education and how these conceptions shifted during science methods coursework. Findings from survey and interview data showed that participants commonly referred to the STEM acronym and surface-level integration across and beyond STEM disciplines. Key experiences that shaped their conceptions and preparedness to teach included planning and implementing STEM lessons and discussing quality integrated STEM instruction.

关键词： STEM integration Preservice teacher preparation Teacher conceptions Preservice elementary teachers

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Evaluation of OpenAI o1: Opportunities and Challenges of AGI

arXiv

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arXiv 2024年

作者： Zhong, Tianyang Liu, Zhengliang Pan, Yi Zhang, Yutong Zhou, Yifan Liang, Shizhe Wu, Zihao Lyu, Yanjun Shu, Peng Yu, Xiaowei Cao, Chao Jiang, Hanqi Chen, Hanxu Li, Yiwei Chen, Junhao Hu, Huawen Liu, Yihen Zhao, Huaqin Xu, Shaochen Dai, Haixing Zhao, Lin Zhang, Ruidong Zhao, Wei Yang, Zhenyuan Chen, Jingyuan Wang, Peilong Ruan, Wei Wang, Hui Zhao, Huan Zhang, Jing Ren, Yiming Qin, Shihuan Chen, Tong Li, Jiaxi Zidan, Arif Hassan Jahin, Afrar Chen, Minheng Xia, Sichen Holmes, Jason Zhuang, Yan Wang, Jiaqi Xu, Bochen Xia, Weiran Yu, Jichao Tang, Kaibo Yang, Yaxuan Sun, Bolun Yang, Tao Lu, Guoyu Wang, Xianqiao Chai, Lilong Li, He Lu, Jin Sun, Lichao Zhang, Xin Ge, Bao Hu, Xintao Zhang, Lian Zhou, Hua Zhang, Lu Zhang, Shu Liu, Ninghao Jiang, Bei Kong, Linglong Xiang, Zhen Ren, Yudan Liu, Jun Jiang, Xi Bao, Yu Zhang, Wei Li, Xiang Li, Gang Liu, Wei Shen, Dinggang Sikora, Andrea Zhai, Xiaoming Zhu, Dajiang Zhang, Tuo Liu, Tianming Department of Mathematical and Statistical Sciences University of Alberta Edmonton Canada School of Computing University of Georgia GA United States Institute of Medical Research Northwestern Polytechnical University Xi'An China College of Arts and Sciences University of Georgia Athens United States Institute of Plant Breeding Genetics & Genomics University of Georgia AthensGA United States Department of Computer Science and Engineering University of Texas ArlingtonTX United States The Lamar Dodd School of Art University of Georgia GA United States School of Computer Science Northwestern Polytechnical University Xi'An China School of Automation Northwestern Polytechnical University Xi'An China University of California Los AngelesCA United States Department of Radiology The Second Xiangya Hospital Central South University Changsha China Clinical Research Center for Medical Imaging in Hunan Province Changsha China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Guanghua School of Management Peking University Beijing China Department of Radiation Oncology Mayo Clinic PhoenixAZ United States Second Language Acquisition and Teaching University of Arizona TucsonAZ United States School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China The First Hospital of Hebei Medical University Hebei Shijiazhuang China School of Computer and Cyber Sciences Augusta University AugustaGA United States School of Physics and Information Technology Shaanxi Normal University Xi'An China School of Future Technology South China University of Technology Guangzhou China Department of Radiology and BRIC University of North Carolina Chapel HillNC United States Department of Educational Psychology University of Georgia AthensGA United States Johns Hopkins University BaltimoreMD United States School of Architecture Tsinghua Universit

This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences. Through rigorous testing, o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance in areas ranging from coding challenges to scientific reasoning and from language processing to creative problem-solving. Key findings include: • 83.3% success rate in solving complex competitive programming problems, surpassing many human experts. • Superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models. • 100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions. • Advanced natural language inference capabilities across general and specialized domains like medicine. • Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis. • Remarkable proficiency in anthropology and geology, demonstrating deep understanding and reasoning in these specialized fields. • Strong capabilities in quantitative investing. O1 has comprehensive financial knowledge and statistical modeling skills. • Effective performance in social media analysis, including sentiment analysis and emotion recognition. The model excelled particularly in tasks requiring intricate reasoning and knowledge integration across various fields. While some limitations were observed, including occasional errors on simpler problems and challenges with certain highly specialized concepts, the overall results indicate significant progress towards artificial general intelligence. This evaluation not only highlights o1-preview's current strengths and limitations but also identifies crucial areas for future development, including multi-modal integratio

关键词： Emotion Recognition

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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