检索结果-内蒙古大学图书馆

Research Square 2025年

作者： Batoure Bamana, Apollinaire Sokdou Bila Lamou, Yannick Fotsa-Mbogne, David Jaures Shafiee Kamalabad, Mahdi Department of Computer Engineering University Institute of Technology The University of Ngaoundere Cameroon Department of Robotic and Industrial Computing School of Chemical Engineering and Mineral Industries The University of Ngaoundere Cameroon Department of Mathematics and Computer Science Faculty of Science The University of Ngaoundere Cameroon Laboratory of Mathematics Computer Science and Applications Department of Mathematics and Computer Science National Higher School of Agro-Industrial Sciences The University of Ngaoundéré Cameroon Laboratory of Mathematics and Computer Science Department of Mathematics Statistics and Computer Science Faculty of Science The University of Bertoua Cameroon Department of Methodology and Statistics Faculty of Social and Behavioural Sciences Utrecht University Utrecht Netherlands Centre for Complex Systems Studies Utrecht University Utrecht Netherlands

Stationarity in time series is a key property for practical data analysis, inferences, and predictions particularly in biosciences. Stationarity can be either deterministic or stochastic. If a time series data is not stationary, it can be rendered stationary through detrending or differentiation techniques. Differentiation can be performed in either an integer or fractional form. This paper investigates non-stationarity in time series data, focusing specifically on stochastic trends characterised by a unit root. It hypothesises that fractional differentiation can enhance forecast accuracy for datasets with limited volume. For experiments, 24 series corresponding to weekly Malaria and Typhoid Fever from Adamawa Region (Cameroon) are analysed and forecasted. Each series comprises 156 observations covering January 2021 to December 2023. After collecting, checking and classifying the data, four forecasting models, Auto Regressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), Long Short-Term Memory (LSTM), and the proposed Fractional Differencing and LSTM (FD-LSTM), are implemented. The models' performances are evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of determination (R2) metrics, which inform further analysis and recommendations. Results reveal that ARFIMA outperformed the ARIMA by 93% in training and 100% in testing. Similarly, FD-LSTM achieved a 100% improvement over LSTM in training and testing. These findings underscore the value of achieving stationarity through fractional differentiation, enhancing model performance and providing a robust framework for forecasting in datasets with limited observations. © 2025, CC BY.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

AI-Powered Healthcare with Advanced Graph Convolutional Neural Networks and SVM for Diagnosis

AI-Powered Healthcare with Advanced Graph Convolutional Neur...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： A. Mahendran Sridhar Babu Nalla Lalit Mohan Trivedi S Mohankumar D.R Prince Williams Manne Renuka Department of Computer Science Sona College of Arts and Science Salem India Department of Computer Science and Engineering SR University Hyderabad India Department of Ash Mathematics Moradabad Institute of Technology Moradabad India Department of Computer Science and Engineering Erode Sengunthar Engineering College Perundurai India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Science- Sohar Sohar Sultanate of Oman Department of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Medical treatment costs are rising for many reasons. These include an increasing world population, an aging population, greater healthcare needs, more diseases, and inflation. With AI's emergence in navigation, voice recognition, image lesion segmentation, ride-sharing, and smartphone personal assistants, the healthcare industry has quickly adopted it. Initial preprocessing includes standardization, missing values, and discarding anomalies. Next, the model is trained using the obtained features. Spearman correlation, a rank-based nonparametric statistic, can quantify monotonic correlation strength and direction. Even with missing values, the Graph Convolutional Network (GCN) performs well in model training. The suggested model outperformed state-of-the-art alternatives with 93.48% accuracy. It outperformed TransLSTM and GraphSAGE on complex datasets. This research stresses the system's ability to improve healthcare forecast accuracy through preprocessing, feature extraction, and GCN model training. Results show that AI can improve healthcare analytics despite lacking data.

关键词： Training Support vector machines Correlation Accuracy Graph convolutional networks Medical services Feature extraction Integrated circuit modeling Artificial intelligence Stress

来源：评论

学校读者我要写书评

暂无评论

TWO-SCALE FINITE ELEMENT GREEN'S FUNCTION APPROXIMATIONS WITH APPLICATIONS TO ELECTROSTATIC POTENTIAL COMPUTATION

引用

Journal of Systems science & Complexity 2010年第1期23卷 177-193页

作者： Ying YANG Aihui ZHOU Department of Computational Science and Mathematics Guilin University of Electronic Technology Guilin 541004 China. The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China.

In this paper,a two-scale finite element approach is proposed and analyzed for approximationsof Green's function in *** approach is based on a two-scale finite elementspace defined,respectively,on the whole domain with size H and on some subdomain containing singularpoints with size h (h << H).It is shown that this two-scale discretization approach is very *** particular,the two-scale discretization approach is applied to solve Poisson-Boltzmann equationssuccessfully.

关键词： Error analysis finite element Green＇s function Poisson-Boltzmann equation two-scale.

来源：评论

学校读者我要写书评

暂无评论

The OpenLAM Challenges

arXiv

引用

arXiv 2025年

作者： Peng, Anyang Liu, Xinzijian Guo, Ming-Yu Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China School of Chemistry Sun Yat-sen University Guangzhou510006 China

Inspired by the success of Large Language Models (LLMs), the development of Large Atom Models (LAMs) has gained significant momentum in scientific computation. Since 2022, the Deep Potential team has been actively pretraining LAMs and launched the OpenLAM Initiative to develop an open-source foundation model spanning the periodic table. A core objective is establishing comprehensive benchmarks for reliable LAM evaluation, addressing limitations in existing datasets. As a first step, the LAM Crystal Philately competition has collected over 19.8 million valid structures, including 1 million on the OpenLAM convex hull, driving advancements in generative modeling and materials science applications. Copyright © 2025, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multiresolution Scheme Based on WENO

引用

Communications in Nonlinear science and Numerical Simulation 1997年第3期2卷 45-50页

作者： Yongsheng LIAN & Ruquan WANG (institute of computational mathematics and Scientific/engineering computing,Academia Sinica) E mail:wrq@*** Institute of Computational Mathematics and Scientific/Engineering Computing Academia Sinica China

ＭｕｌｔｉｒｅｓｏｌｕｔｉｏｎＳｃｈｅｍｅＢａｓｅｄｏｎＷＥＮＯ１，２１ＴｈｅｐａｐｅｒｗａｓｒｅｃｅｉｖｅｄｏｎＡｕｇ．３０ｔｈ，１９９７２ＴｈｉｓｗｏｒｋｉｓｓｕｐｐｏｒｔｅｄｂｙｔｈｅＮａｔｉｏｎａｌＮａｔｕｒ... 详细信息

ＭｕｌｔｉｒｅｓｏｌｕｔｉｏｎＳｃｈｅｍｅＢａｓｅｄｏｎＷＥＮＯ１，２１ＴｈｅｐａｐｅｒｗａｓｒｅｃｅｉｖｅｄｏｎＡｕｇ．３０ｔｈ，１９９７２ＴｈｉｓｗｏｒｋｉｓｓｕｐｐｏｒｔｅｄｂｙｔｈｅＮａｔｉｏｎａｌＮａｔｕｒａｌＳｃｉｅｎｃｅＦｏｕｎｄａｔｉ...

关键词： Multiresolution Schemes ENO Scheme WENO Scheme

来源：评论

学校读者我要写书评

暂无评论

Survey on AI-Enabled Resource Management for 6G Heterogeneous Networks: Recent Research, Challenges, and Future Trends

引用

Computers, Materials and Continua 2025年第3期83卷 3585-3622页

作者： Hayder Faeq Alhashimi Mhd Nour Hindia Kaharudin Dimyati Effariza Binti Hanafi Feras Zen Alden Faizan Qamar Quang Ngoc Nguyen Centre of Advanced Communication Research and Innovation (ACRI) Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur 50603 Malaysia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi Mara Shah Alam 40450 Malaysia Centre for Cyber Security Faculty of Information Science and Technology (FTSM) Universiti Kebangsaan Malaysia (UKM) Bangi 43600 Malaysia Faculty of Science and Engineering Waseda University Tokyo 169-8555 Japan Faculty of Telecommunications Posts and Telecommunications Institute of Technology Hanoi 11518 Vietnam

The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks. Artificial Intelligence (AI) advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models, deep learning models, and hybrid models. Furthermore, intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods, which in turn improves the performance of 6G networks. Hence, 6G networks rely substantially on AI methods to manage resources. This paper comprehensively surveys the recent work of AI methods-based resource management for 6G networks. Firstly, the AI methods are categorized into Deep Learning (DL), Federated Learning (FL), Reinforcement Learning (RL), and Evolutionary Learning (EL). Then, we analyze the AI approaches according to optimization issues such as user association, channel allocation, power allocation, and mode selection. Thereafter, we provide appropriate solutions to the most significant problems with the existing approaches of AI-based resource management. Finally, various open issues and potential trends related to AI-based resource management applications are presented. In summary, this survey enables researchers to understand these advancements thoroughly and quickly identify remaining challenges that need further investigation.

关键词： Artificial intelligence (AI) resource management deep learning (DL) federated learning (FL) reinforcement learning (RL) evolutionary learning (EL)

来源：评论

学校读者我要写书评

暂无评论

Fractional coupled Halanay inequality and its applications

arXiv

引用

arXiv 2025年

作者： Van Thinh, La Tuan, Hoang The Wang, Dongling Yang, Yin Academy of Finance Le Van Hien Street Duc Thang Ward Bac Tu Liem District Hanoi Viet Nam Institute of Mathematics Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Hanoi10307 Viet Nam Hunan Key Laboratory for Computation and Simulation in Science and Engineering School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

This paper introduces a generalized fractional Halanay-type coupled inequality, which serves as a robust tool for characterizing the asymptotic stability of diverse time fractional functional differential equations, particularly those exhibiting Mittag-Leffler type stability. Our main tool is a sub-additive property of Mittag-Leffler function and its optimal asymptotic decay rate estimation. Our results further optimize and improve some existing results in the literature. We illustrate two significant applications of this fractional Halanay-type inequality. Firstly, by combining our results in this work with the positive representation method positive representation of delay differential systems, we establish an asymptotic stability criterion for a category of linear fractional coupled systems with bounded delays. This criterion extends beyond the traditional boundaries of positive system theory, offering a new perspective on stability analysis in this domain. Secondly, through energy estimation, we establish the contractility and dissipativity of a class of time fractional neutral functional differential equations. Our analysis reveals the typical long-term polynomial decay behavior inherent in time fractional evolutionary equations, thereby providing a solid theoretical foundation for subsequent numerical investigations. © 2025, CC BY.

关键词： Asymptotic stability

来源：评论

学校读者我要写书评

暂无评论

On the convergence rate of a parallel nonoverlapping domain decomposition method

引用

science China mathematics 2008年第8期51卷 1461-1478页

作者： QIN LiZhen SHI ZhongCi XU XueJun Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceBeijing 100190China

In recent years,a nonoverlapping domain decomposition iterative procedure,which is based on using Robin-type boundary conditions as information transmission conditions on the subdomain interfaces,has been developed and *** is known that the convergence rate of this method is 1-O(h),where h is mesh *** this paper,the convergence rate is improved to be 1-O(h1/2 H-1/2)sometime by choosing suitable parameter,where H is the subdomain *** examples are constructed to show that our convergence estimates are sharp,which means that the convergence rate cannot be better than 1-O(h1/2H-1/2)in a certain case no matter how parameter is chosen.

关键词： finite elements nonoverlapping domain decomposition convergence rate geometric optimal 34DK04

来源：评论

学校读者我要写书评

暂无评论

A new piecewise quadratic approximation approach for L_0 norm minimization problem

引用

science China mathematics 2019年第1期62卷 185-204页

作者： Qian Li Yanqin Bai Changjun Yu Ya-xiang Yuan Department of Mathematics Shanghai University Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of Sciences

In this paper, we consider the problem of finding sparse solutions for underdetermined systems of linear equations, which can be formulated as a class of L_0 norm minimization problem. By using the least absolute residual approximation, we propose a new piecewis, quadratic function to approximate the L_0 ***, we develop a piecewise quadratic approximation(PQA) model where the objective function is given by the summation of a smooth non-convex component and a non-smooth convex component. To solve the(PQA) model,we present an algorithm based on the idea of the iterative thresholding algorithm and derive the convergence and the convergence rate. Finally, we carry out a series of numerical experiments to demonstrate the performance of the proposed algorithm for(PQA). We also conduct a phase diagram analysis to further show the superiority of(PQA) over L_1 and L_(1/2) regularizations.

关键词： sparse optimization non-convex approximation iterative thresholding algorithm

来源：评论

学校读者我要写书评

暂无评论

An automatic approach to develop the fourth-order and L2-stable lattice Boltzmann model for diagonal-anisotropic diffusion equations

arXiv

引用

arXiv 2025年

作者： Chen, Ying Chai, Zhenhua Shi, Baochang School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China The State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

This paper discusses how to develop a high-order multiple-relaxation-time lattice Boltzmann (MRT-LB) model for the general d(≥ 1)-dimensional diagonal-anisotropic diffusion equation. Such an MRT-LB model considers the transformation matrix constructed in a natural way and the DdQ(2d2 + 1) [(2d2 + 1) discrete velocities in d-dimensional space] lattice structure. A key step in developing the high-order MRT-LB model is to determine the additional adjustable relaxation parameters and weight coefficients, which are used to eliminate the truncation errors at some certain orders of the MRT-LB model, while ensuring the stability of the MRT-LB model. In this work, we first present a unified MRT-LB model for the d-dimensional diagonal-anisotropic diffusion equation. Then, through the direct Taylor expansion, we analyze the macroscopic modified equations of the MRT-LB model up to fourth-order at the diffusive scaling, and further derive the conditions that ensure the MRT-LB model to be fourth-order consistent with the diagonal-anisotropic diffusion equation. In particular, when the diagonal-anisotropic diffusion equation is reduced to the isotropic type, we propose another MRT-LB model with the DdQ(2d+ 1) lattice structure [fewer discrete velocities than the DdQ(2d2 + 1) lattice structure], and the fourth-order conditions are similarly derived. Additionally, we also construct the fourth-order initialization scheme for the present LB method. After that, the condition which guarantees that the MRT-LB model can satisfy the stability structure is explicitly given, and we would like to point out that from a numerical perspective, once the stability structure is satisfied, the MRT-LB model must be L2 stable. In combination with the fourth-order consistent and L2 stability conditions, the relaxation parameters and weight coefficients of the MRT-LB model can be automatically given by a simple computer code. Finally, we perform numerical simulations of several benchmark problems, and f

关键词： Anisotropy

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：