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Modeling diabetes progression with risk factors: A case study in China

Computers in Biology and Medicine 2025年 186卷 109643-109643页

作者： Yao, Shilu Shen, Yangling Xiong, Jianling Wei, Liuxia Zhao, Jiangyan Wang, Zhen Zhu, Guanghu School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Guilin Maternal and Child Health Hospital Guilin541001 China Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin541004 China

Background: Approximately 537 million adults worldwide have diabetes, more than 90 % of which is type 2 diabetes mellitus (T2DM). China has the largest number of people living with diabetes. Understanding the epidemiological mechanism can guide diabetes surveillance and control. Methods: Utilizing the most recent Global Burden of Disease 2021 (GBD2021) data on T2DM and risk factor exposure values, we developed a modelling framework that employs systems of difference equations to project the future burden of diabetes among the Chinese population. The model characterized the diabetes progression process from no diabetes, undiagnosed diabetes, and diagnosed diabetes with and without complications, in which genetic and lifestyle factors modulate the rate of development at these stages. We focused on the long-term dynamics of diabetes progression with the impacts of influence factors. We then fit the model to the longitudinal numbers of T2DM patients by Markov chain Monte Carlo (MCMC) algorithm. Results: The model with the influencing factors fitted an R2 of 98 % for the T2DM cases in China during the period 1990–2021. The incidence rate would keep increasing from 299.93/100,000 in 2022 to 421.58/100,000 in 2050 and 500.16/100,000 in 2080. The prevalence rates in 2040, 2060, and 2080 could be 14.62 %, 23.57 % and 34.83 %, respectively. The number of new cases was the most sensitive to high body mass index (BMI), followed by smoking and low physical activity. A 50 % reduction in single risk factor exposure would reduce new cases in 2022–2080 by 4.24 %, 2.52 %, and 1.12 %, respectively. Conclusions: This study provided a modelling framework to explore the mechanism of T2DM development, which allows to quantify the impacts of risk factors on T2DM progression. The results highlight the high burden of T2DM in China and emphasized the importance of lifestyle interventions. © 2024 Elsevier Ltd

关键词： Type 2 diabetes mellitus Risk factors Dynamic model Prediction

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Commutators of Bilinear Hardy Operators on Two Weighted Herz Spaces with Variable Exponents

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analysis in Theory and Applications 2021年第3期37卷 387-403页

作者： Shengrong Wang Jingshi Xu Guangxi Colleges and Universities Key Laboratory of Data Analysis and ComputationSchool of Mathematics and Computing ScienceGuilin University of Electronic TechnologyGuilinJiangxi 541004China

In this paper,we obtain the boundedness of bilinear commutators generated by the bilinear Hardy operator and BMO functions on products of two weighted Herz spaces.

关键词： Hardy operator commutator Muckenhoupt BMO variable exponent Herz space.

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Efficient multi-party privacy preserving federated k-means based on homomorphic encryption

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Information Sciences 2025年 717卷

作者： Tang, Zeng-Ao Duan, Xue-Feng Liang, Rong-Hua Ding, Yong Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guangxi Guilin541004 China School of Computer and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Guangxi Nanning530022 China

Nowadays, the collection and storage of data is increasingly decentralized, and the demand for data mining on distributed data is growing. Traditional k-means risks privacy leaks through direct data sharing. Existing privacy-preserving methods still expose intermediate cluster details during iterations. This paper introduces DTK-means (distributed privacy-preserving k-means) clustering algorithm, a distributed privacy-preserving k-means method addressing these issues. It involves multiple users and two non-colluding servers. The user locally computes centroids, encrypts them, and submits to the server. The two servers then aggregate these encrypted centroids into global cluster centroids with multiplicative perturbation, ensuring that neither the participants nor the servers have knowledge of the specific details. The scheme includes four algorithms for compute local centroids, data aggregation, compute global centroids and output final cluster centroids, implemented using Paillier homomorphic encryption. An extensive performance analysis is carried out to show that DTK-means ensures that intermediate data and private data are concealed from all parties involved. Participants can accurately perform k-means clustering using these hidden global centroids without any information loss. Furthermore, it can resist collusion attacks, even if one server colludes with all participants except one. Complexity analysis and numerical experiments show that our algorithm has good efficiency. © 2025 Elsevier Inc.

关键词： K-means clustering

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Density Clustering Algorithm Based on Nearest Neighbor Relationship and Relevant Space

SSRN

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SSRN 2024年

作者： Li, Xiangli Zhuang, Quan School of Mathematics and Computing Science Guilin University of Electronic Technology Guangxi541004 China Guangxi Colleges University Key Laboratory of Data Analysis and Computation Guangxi541004 China Center for Applied Mathematics of Guangxi Guangxi541004 China

Density-based clustering algorithms have the advantages of simplicity of thought and the ability to recognize clusters of arbitrary shapes as well as noise objects, and thus have been widely used in the fields of machine learning and data mining. However, traditional density-based clustering algorithms often have poor clustering performance when facing datasets with heterogeneous density and datasets with neighboring clusters. This is because the definition of density often relies solely on the computation of distance,so that sparse clusters are often incorrectly merged or ignored and the objects at the edge of adjacent clusters are incorrectly assigned. In order to solve the above problems, this paper proposes a density clustering algorithm based on nearest neighbor relationships and relevant spaces(NRDC). We introduce mutual nearest neighbors and reverse nearest neighbors into the algorithm,and also propose the concept of relevant space to define a new local density ,and propose a new core point identification method to identify the structure of clusters to form *** we propose a allocation strategy which based on relevant space for the remaining objects to obtain clustering results. Experimental results on 8 synthetic datasets and 12 real datasets on UCI show that the proposed algorithm NRDC exhibits good clustering performance. © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Non-Regular Pseudo-Differential Operators on Matrix Weighted Besov-Triebel-Lizorkin Spaces

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数学研究（英文） 2024年第1期57卷 84-100页

作者： Tengfei Bai Jingshi Xu School of Mathematics and Statistics Hainan Normal UniversityHaikouHainan 571158China Center for Applied Mathematics of Guangxi Guangxi Colleges and Universities Key Laboratory of Data Analysis and ComputationSchool of Mathematics and Computing ScienceGuilin University of Electronic TechnologyGuilin 54100China

In this paper we obtain the boundedness of non-regular pseudo-differential operators with symbols in Besov spaces on matrix-weighted Besov-Triebel-Lizorkin *** symbols include the classical Hörmander classes.

关键词： Besov space matrix weight Pseudo-differential operator Triebel-Lizorkin space

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Density Clustering Algorithm Based on Effective Neighbors and Multi-Stage Allocation Strategy

SSRN

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SSRN 2024年

作者： Zhuang, Quan Li, Xiangli School of Mathematics and Computing Science Guilin University of Electronic Technology Guangxi 541004 China Guangxi Colleges and University Key Laboratory of Data Analysis and Computation Guangxi 541004 China Center for Applied Mathematics of Guangxi Guangxi 541004 China

Density peak clustering (DPC) is an effective clustering method that is widely used in various *** idea of the density peak clustering is very simplistic, which can quickly select the cluster density peaks and the clustering process requires no iteration. However, DPC still has the following drawbacks:The calculation of the local density of the DPC algorithm is highly dependent on the calculation of the cut-off ***, the number of cluster centers is very challenging to determine and to select. Lastly, the straightforward allocation strategy makes the presence of domino effect, which greatly reduces the clustering performance of the DPC. To overcome the limitations presented above, we propose a density clustering algorithm based on effective neighbors and multistage allocation strategy(DCMS). We redeffne the density by introducing the concept of effective neighbors and identify the underlying structure of each *** addition, we combine the idea of outlier detection to divide the objects into different types of ***, we propose an multistage assignment strategy to generate initial clusters and assign the remaining points to obtain the ffnal clustering *** results on synthetic and UCI datasets validate that the proposed algorithm outperforms several famous clustering algorithms. © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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A Riemannian Inexact-Newton Method for Solving the Orthogonal INDSCAL Problem in Multidimensional Scaling

Research Square

引用

Research Square 2024年

作者： Zhou, Xue-Lin Li, Chao-Qian Li, Jiao-Fen School of Mathematics and Statistics Yunan University Kunming650000 China School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541004 China Guilin541004 China

The well-known individual differences scaling (INDSCAL) model is intended for simultaneous metric multidimensional scaling (MDS) of several doubly centered matrices of squared dissimilarities. In this work, the problem of fitting the orthogonal INDSCAL model to the data is reformulated and studied as a matrix optimization problem on the product manifold of orthonormal and diagonal matrices. A Riemannian inexact Newton method is proposed to address the underlying problem, with the global and quadratic convergence of the proposed method established under some mild assumptions. Furthermore, the positive definiteness condition of the Riemannian Hessian of the objective function at a solution is derived. Some numerical experiments are provided to illustrate the efficiency of the proposed method. © 2024, CC BY.

关键词： Newton-Raphson method

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A Medical Pre-diagnosis Scheme Based on Neural Network and Inner Product Function Encryption

A Medical Pre-diagnosis Scheme Based on Neural Network and I...

引用

Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Xu Cui Hao Liu Min Tang Yihong Ma School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin China Center for Applied Mathematics of Guangxi (GUET) Guilin China

Artificial neural networks are widely used in various fields, such as intelligent road networks, Internet of Things, and smart medical systems due to their ability to process large amounts of data in parallel, store information in a distributed manner, and self-organize and self-learn. Cloud computing technology has further expanded the development of neural network applications. However, user data often contains sensitive information, and once the data management right is transferred to the cloud, it faces serious security and privacy issues. In the medical field, privacy-preserving implementation of classification algorithms is crucial for ensuring the privacy of electronic medical diagnosis services. Current privacy-preserving medical pre-diagnosis schemes based on homomorphic encryption impose a significant computational and communication burden on users and servers. This paper proposes an efficient privacy-preserving medical pre-diagnosis scheme based on neural networks and inner product function encryption that protects user privacy during pre-diagnosis while having small computational and communication overheads.

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The Commutators of n-dimensional Rough Fractional Hardy Operators on Two Weighted Grand Herz-Morrey Spaces with Variable Exponents

arXiv

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arXiv 2025年

作者： Wang, Shengrong Guo, Pengfei Xu, Jingshi School of Mathematics and Statistics Hainan Normal University Haikou571158 China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin541004 China

In this paper, we obtain the boundedness of mth order commutators generated by the n-dimensional fractional Hardy operator with rough kernel and its adjoint operator with BMO functions on two weighted grand Herz-Morrey spaces with variable exponents. Replacing Lipschitz functions with BMO functions the corresponding result is also given. Copyright © 2025, The Authors. All rights reserved.

关键词： Mathematical operators

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The Weighted Grand Herz-Morrey-Lizorkin-Triebel Spaces with Variable Exponents

arXiv

引用

arXiv 2025年

作者： Wang, Shengrong Guo, Pengfei Xu, Jingshi School of Mathematics and Statistics Hainan Normal University Haikou571158 China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin541004 China

Let a vector-valued sublinear operator satisfy the size condition and be bounded on weighted Lebesgue spaces with variable exponent. Then we obtain its boundedness on weighted grand Herz-Morrey spaces with variable exponents. Next we introduce weighted grand Herz-Morrey-Triebel-Lizorkin spaces with variable exponents and provide their equivalent quasi-norms via maximal functions. Copyright © 2025, The Authors. All rights reserved.

关键词： Vector spaces

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