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A duplex adaptation mechanism in the personalized learning environment

JOURNAL OF COMPUTERS IN EDUCATION 2024年第4期11卷 1111-1131页

作者： Wongwatkit, Charoenchai Panjaburee, Patcharin Mae Fah Luang Univ Sch Informat Technol Chiang Rai Thailand Khon Kaen Univ Fac Educ Khon Kaen Thailand Mae Fah Luang Univ Ctr Excellence AI & Emerging Technol Sch Informat Technol Chiang Rai Thailand

The implementation of personalized learning environments presents a complex challenge in continuously analyzing the learning process and customizing instructional content for individual students. In response to this challenge, this study introduces a duplex adaptation mechanism. This innovative approach recommends learning content based on individual students' prior knowledge and concurrently adjusts learning activities according to their ongoing behavior. The mechanism was integrated within a personalized learning system focused on digital literacy. An empirical study, involving a sample of 336 secondary school students, was conducted to assess the impact of this approach on learning motivation and achievement. The findings reveal a significant enhancement in both student motivation and academic performance when compared to traditional methods. Most notably, the students expressed a marked preference for this duplex mechanism. This study provides a pivotal contribution to understanding the multi-dimensional benefits of such adaptive learning mechanisms and highlights the potential for its further application in personalized learning environments.

关键词： Adaptive learning environment Personalization Recommender system machine learning algorithm learning analytic

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Single-cell hdWGCNA reveals a novel diagnostic model and signature genes of macrophages associated with chronic obstructive pulmonary disease

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INFLAMMATION RESEARCH 2025年第1期74卷 1-19页

作者： Zhou, Xianqiang Meng, Yufeng Yang, Jie Wang, Hongtao Zhang, Yixin Jin, Zhengjie Feng, Cuiling Peking Univ Peoples Hosp Dept Tradit Chinese Med Beijing 100032 Peoples R China Peking Univ Inst Integrated Tradit Chinese & Western Med Beijing 100871 Peoples R China

Background Chronic obstructive pulmonary disease (COPD) is the leading cause of respiratory system-related mortality worldwide. Although COPD is associated with immune regulation, its underlying mechanisms remain unclear. MethodsCells from the single-cell RNA sequencing (scRNA-seq) datasets were subjected to clustering analysis and cell type identification to isolate immune cell subgroups specifically expressed in COPD. High-dimensional weighted gene co-expression network analysis (hdWGCNA) was used to identify hub genes related to the immune cell subpopulations. machine learning algorithms were applied to identify diagnostic genes in the immune cell subpopulations and construct clinical diagnostic models for COPD. In bulk RNA sequencing data, AUC curves were used to assess the stability of the diagnostic models in predicting COPD. Results Through 2 rounds of clustering analysis, the macrophage subgroups 1, 2, 7, 11, and 13 which specifically expressed in COPD (COPD_M phi) were identified. HdWGCNA analysis revealed a hub set of genes closely related to COPD_M phi from black, blue, yellow, and brown modules. Nonnegative Matrix Factorization (NMF) analysis separated the COPD samples into 2 clusters, with significant increases in the infiltration of Monocytic_lineage, Myeloid_dendritic_cells, and Neutrophils in cluster 1 (P < 0.001). Univariate logistic regression and LASSO regression analyses identified 11 feature genes associated with COPD_M phi, including CST3, LGALS3, CSTB, S100A10, CYBA, S100A11, ARPC3, FTH1, PFN1, MAN2B1, and RPL39. The RF and convolutional neural network (CNN) models constructed using these feature genes effectively distinguished between normal and COPD patients. Among them, S100A10, RPL39, and FTH1 exhibited differential expression between COPD patients and normal individuals and could serve as potential clinical diagnostic markers for COPD. ConclusionsThe study provides new insights into the immune mechanisms of COPD and lays the theoretical f

关键词： Chronic obstructive pulmonary disease (COPD) Macrophage Single-cell RNA sequencing (scRNA-seq) machine learning algorithm Convolutional neural network

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Research on 3D medical image surface reconstruction based on data mining and machine learning

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第8期37卷 4654-4669页

作者： Hua, Shanshan Liu, Qi Yin, Guanxiang Guan, Xiaohui Jiang, Nan Zhang, Yuejin East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Jiangxi Peoples R China Nanchang Univ Natl Engn Res Ctr Bioengn Drugs & Technol Nanchang Jiangxi Peoples R China

Three-dimensional (3D) medical images are prone to overlap, and there are some problems, such as low detection efficiency and inconsistent with the actual situation. Therefore, a 3D medical image surface reconstruction method based on data mining and machine learning is proposed. The 3D medical images were classified according to different ways, the information frame of 3D medical images was established and the surface overlapping information model of 3D images was given. Based on this information framework, the nonlinear function of overlapping area information of 3D medical images was constructed. The weight of the nonlinear function was used to calculate the input and output results of overlapping area information. Combined with the input mode of 3D medical image information, the error between the information output and the expected output was set. The nonlinear function weight of the overlapping area information of 3D medical images was modified by using the learning rate and the use time of the overlapping area information, and the influence factors of the overlapping information detection were obtained by increasing the situation terms, so as to complete the detection of the surface reconstruction information of 3D medical images. The experimental results show that the information detection results of the proposed method fit well with the actual situation, and the information detection efficiency is high.

关键词： 3D medical images data mining algorithm image surface reconstruction machine learning algorithm

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Circulating proteomic biomarkers for cerebral amyloid angiopathy screening and risk stratification

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ALZHEIMERS & DEMENTIA 2025年第3期21卷 e70044页

作者： Xu, Jiajie Su, Ya Sha, Yuhui Fu, Jiayu Yan, Tingmeng Xu, Feifan Tang, Han Ying, Yunqing Xia, Yiwei Dong, Qiang Gurol, M. Edip Ni, Jun Cheng, Xin Fudan Univ Huashan Hosp Natl Ctr Neurol Disorders Natl Clin Res Ctr Aging & MedDept Neurol 12 Wulumuqi Zhong Rd Shanghai 200040 Peoples R China Peking Union Med Coll & Chinese Acad Med Sci Peking Union Med Coll Hosp Dept Neurol Beijing Peoples R China Fudan Univ State Key Lab Med Neurobiol Shanghai Peoples R China Massachusetts Gen Hosp Dept Neurol J Philip Kistler Stroke Res Ctr Boston MA USA

INTRODUCTION: We systematically characterized plasma protein profiles in cerebral amyloid angiopathy (CAA) using proteomics and identified a hub protein panel for disease diagnosis and risk stratification. METHODS: A total of 146 patients with probable CAA and 128 community-dwelling controls were prospectively enrolled. Plasma samples underwent proteomic analysis, and the hub proteins were validated in two validation cohorts. machine learning algorithms were applied to construct and validate the performance of the circulating panel. RESULTS: We identified 166 differentially expressed proteins in patients with CAA. Six hub proteins were selected to form the circulating panel, demonstrating excellent performance to distinguish patients from controls in all cohorts. Additionally, the risk stratification system derived from the hub protein panel accurately identified patients at high risk for new-onset lobar intracerebral hemorrhage. DISCUSSION: Our findings revealed distinctive circulating protein signatures in CAA and established a validated hub protein panel aiding in CAA screening and risk stratification.

关键词： cerebral amyloid angiopathy disease screening machine learning algorithm plasma biomarker proteomic analysis risk stratification

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A Hybrid Model of Integrating Sentiment Analysis and Key Market Indicators for IPO Listing Trend Prediction

INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGE...

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INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGEMENT SCIENCES 2024年第4期9卷 902-913页

作者： Garg, Ashish Ghanshala, Kamal Kumar Sharma, Sachin Graph Era Deemed be Univ Dehradun Dept Comp Sci & Engn Dehra Dun Uttarakhand India

Initial Public Offerings (IPOs) provide great opportunities for companies to grow and expand, and they allow investors to invest their money wisely to get a decent Return on Investment (ROI) in the short term. Nevertheless, the intricate nature of the stock market is susceptible to several influences such as a company's financial statement, governmental regulations, and public sentiment, which hinders the attainment of a satisfactory ROI. This study aims to address this challenge by developing a model that combines public sentiment analysis and machine learning approaches to optimize the ROI for IPO trends. The study gives a novel approach that uses multiple different features associated with IPOs like the public opinion, grey market price (GMP), issue prices, lot size etc. and leverages the use of various machine learning techniques like Random Forest, Decision Tree, Naive Bayes, and K-Nearest Neighbour (KNN) to make well-informed investment recommendations. The testing results demonstrate that the Decision Tree method surpasses the other algorithms, with an accuracy rate of 82.3%. This discovery emphasizes the effectiveness of our method in forecasting the success of IPOs by utilizing a combination of sentiment analysis and crucial financial indicators.

关键词： Sentiment analysis IPO machine learning algorithm GMP Decision tree Issue prices

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An Active Control Method for a Lower Limb Rehabilitation Robot with Human Motion Intention Recognition

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SENSORS 2025年第3期25卷 713-713页

作者： Song, Zhuangqun Zhao, Peng Wu, Xueji Yang, Rong Gao, Xueshan Beibu Gulf Univ Coll Mech & Marine Engn Qinzhou 535011 Peoples R China Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China Minist Civil Affairs Key Lab Intelligent Control & Rehabil Technol Beijing 100176 Peoples R China

This study presents a method for the active control of a follow-up lower extremity exoskeleton rehabilitation robot (LEERR) based on human motion intention recognition. Initially, to effectively support body weight and compensate for the vertical movement of the human center of mass, a vision-driven follow-and-track control strategy is proposed. Subsequently, an algorithm for recognizing human motion intentions based on machine learning is proposed for human-robot collaboration tasks. A muscle-machine interface is constructed using a bi-directional long short-term memory (BiLSTM) network, which decodes multichannel surface electromyography (sEMG) signals into flexion and extension angles of the hip and knee joints in the sagittal plane. The hyperparameters of the BiLSTM network are optimized using the quantum-behaved particle swarm optimization (QPSO) algorithm, resulting in a QPSO-BiLSTM hybrid model that enables continuous real-time estimation of human motion intentions. Further, to address the uncertain nonlinear dynamics of the wearer-exoskeleton robot system, a dual radial basis function neural network adaptive sliding mode Controller (DRBFNNASMC) is designed to generate control torques, thereby enabling the precise tracking of motion trajectories generated by the muscle-machine interface. Experimental results indicate that the follow-up-assisted frame can accurately track human motion trajectories. The QPSO-BiLSTM network outperforms traditional BiLSTM and PSO-BiLSTM networks in predicting continuous lower limb motion, while the DRBFNNASMC controller demonstrates superior gait tracking performance compared to the fuzzy compensated adaptive sliding mode control (FCASMC) algorithm and the traditional proportional-integral-derivative (PID) control algorithm.

关键词： human motion intention recognition follow-up lower extremity exoskeleton rehabilitation robot machine learning algorithm intelligent optimization algorithm dual radial basis function neural network adaptive sliding mode controller

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Research progress on the detection of circulating tumor cells by in vivo flow cytometry

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OPTICS AND LASERS IN ENGINEERING 2025年 186卷

作者： Zhang, Yuanhao Ge, Jiaming Liu, Zhaowei Yao, Jingwen Li, Gang Li, Zheng Liu, Ming Zhao, Jing Tianjin Univ Tradit Chinese Med Coll Pharmaceut Engn Tradit Chinese Med Tianjin 301617 Peoples R China Tianjin Univ Tradit Chinese Med Tianjin Key Lab Intelligent TCM Diag & Treatment T Tianjin 301617 Peoples R China Tianjin Univ State Key Lab Precis Measurement Technol & Instrum Tianjin 300072 Peoples R China Tianjin Univ Tradit Chinese Med Tianjin Key Lab Intelligent & Green Pharmaceut Tra Tianjin 301617 Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Inst Biomed Engn Tianjin 300192 Peoples R China

In vivo flow cytometry (IVFC) represents a novel technology for the dynamic, real-time, and quantitative detection of circulating tumor cells (CTCs) in vivo in animal models. Its increasing attention in recent years can be attributed to the fact that it can be performed in vivo under noninvasive conditions. As a result of the increasing convergence of disciplines, a number of new IVFCs have been developed based on fluorescence imaging, photoacoustic, photothermal, and diffuse reflection technologies. These have enabled the detection of a wider range of tumor cells, and have significant implications for the study of tumor development, metastasis, and therapeutic intervention. This review begins with an overview of the effectiveness of IVFC for the detection of CTCs, both nationally and internationally. It then compares and analyzes the basic principles, system design, advantages and disadvantages of different types of IVFC, and also introduces the optimization of the optical pathway, fluorescent labelling methods and data processing of IVFC. Finally, it discusses the challenges and development trends of IVFC in practical applications. The aim is to help researchers gain a deeper understanding of the development and prospects of IVFC.

关键词： Circulating tumor cells In vivo flow cytometry Non-invasive detection machine learning algorithm

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Assessment of the effects of characterization methods selection on the landslide susceptibility: a comparison between logistic regression (LR), naive bayes (NB) and radial basis function network (RBF Network)

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2025年第3期84卷 1-34页

作者： Shang, Hui Su, Lixiang Liu, Yang Tsangaratos, Paraskevas Ilia, Ioanna Chen, Wei Cui, Shaobo Duan, Zhao Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Peoples R China Shaanxi Min Dev Ind & Trade Co Ltd Xian Peoples R China Natl Tech Univ Athens Sch Min & Met Engn Dept Geol Sci Lab Engn Geol & Hydrogeol Zografou Campus Athens 15780 Greece

Landslides are natural disasters that are difficult to control without continuous monitoring. Xiji County is located in the southern mountainous area of Ningxia Hui Autonomous Region, where geological and ecological conditions are complex and the number and extent of landslides hinder local economic development. To address this, a comprehensive landslide inventory was created, comprising 529 historical landslides and an equal number of non-landslide points. Thorough analysis of these datasets ensured an unbiased assessment. The data was randomly divided into training (70%) and validation (30%) sets. Using 15 spatial datasets, including elevation, slope, curvature, distance to various features, rainfall, land use, lithology, and maximum ground acceleration, a system for landslide susceptibility evaluation was established with 12 influential indices. The frequency ratio method was applied to analyze the relationship between landslides and each index. Three evaluation models (LR, NB, and RBF Network) were built, utilizing different landslide characterization methods (landslide point and landslide polygon), resulting in six result maps for landslide susceptibility evaluation. Statistical analysis of frequency ratios in susceptibility class intervals ensured model rationality. The NB model based on landslide polygons showed optimal performance with high success rate (AUC = 0.965), prediction rate (AUC = 0.886), consistency (FRA = 0.873). This methodology and landslide susceptibility map provide decision-making support for researchers and local governments in mitigating future geological hazards.

关键词： Susceptibility assessment Landslide characterization method machine learning algorithm Xiji county Raster cells

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Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms

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CATENA 2022年 216卷

作者： de Mello, Danilo Cesar Ferreira, Tiago Osorio Veloso, Gustavo Vieira de Lana, Marcos Guedes de Oliveira Mello, Fellipe Alcantara Di Loreto Di Raimo, Luis Augusto Goncalves Reynaud Schaefer, Carlos Ernesto Francelino, Marcio Rocha Fernandes-Filho, Elpidio Inacio Dematte, Jose A. M. Univ Fed Vicosa Dept Soil Sci Vicosa MG Brazil Univ Sao Paulo Luiz de Queiroz Coll Agr Dept Soil Sci Av Padua Dias 11CP 9 BR-13418900 Piracicaba SP Brazil

Pedogenetic processes such as ferralitization and argilluviation control various soil attributes. Understanding the intensities of pedogenesis can provide answers for several fields of environmental studies, including soil science and the geosciences. Recently, new geotechnologies such as geophysics applied to soil science and machine learning algorithms have proven to be a potential tool in pedosphere studies. In this research, we performed component principal analyses and determined the ideal number of clusters based on geophysical soil data and satellite images. Then, we used the ideal number of clusters, and ferralitization and argilluviation indices, as input data in five modeling (prediction and spatialization) algorithms to infer different ferralitization and argilluviation intensities in soils formed from the same soil parent material. The results showed that avNNet had the best model performance for modeling the clusters showing that the ideal number of clusters was three. The variables which contributed the most to the modeling were the solar diffuse radiation, topographic wetness index, and digital elevation model. The model's specificity was greater than its sensitivity. The areas over diabase and Nitisols in the east of the study area presented greater ferralitization rates than diabase and Nitisols over western areas. On the other hand, the areas over siltite and Lixisols in the east presented greater argilluviation rates than metamorphosed siltite/siltite and Lixisols over western areas. The relief and topographic position strongly affected the evaluated pedogenetic processes, since they controlled the hydric dynamics in the area. The geophysical variables were related to soil attributes and pedogenesis, which contributed to modeling and clusterization processes.

关键词： Argilluviation Ferralitization Soil attributes Geophysical characterization machine learning algorithm Clustering

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Data-driven fault diagnosis approaches for industrial equipment: A review

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EXPERT SYSTEMS 2024年第2期41卷 e13360-e13360页

作者： Sahu, Atma Ram Palei, Sanjay Kumar Mishra, Aishwarya Godavari Inst Engn & Technol A Dept Min Engn Rajamahendravaram Andhra Pradesh India Indian Inst Technol BHU Dept Min Engn Varanasi 221005 Uttar Pradesh India

Undetected and unpredicted faults in heavy industrial machines/equipment can lead to unwanted failures. Therefore, prediction of faults puts paramount importance on maintaining the reliability and availability of capital-intensive equipment. Due to large number of interconnected and interdependent mechanical and electrical components in the machines, fault analysis becomes a complex and challenging task. Under these circumstances, data-driven fault diagnosis (DDFD) is one of the most powerful, reliable and cost-effective artificial intelligence tools to detect, isolate, identify and classify the occurrence of faults. This article aims to make a comprehensive literature survey of various DDFD approaches used for analysing faults in industrial machines/equipment;and summarizing the strengths, limitations, and possible applications of existing fault diagnosis methods. Analysing and synthesizing 190 research works conducted on DDFD in last two decades revealed three types of DDFD approaches: supervised-learning, semi-supervised-learning and unsupervised-learning-based fault diagnosis. The supervised-learning is predominantly applied for fault diagnosis contributing to 82% of research works. Therefore, this article puts special emphasis on two supervised-learning-based approaches for fault diagnosis: (i) classification-based artificial neural network approach, and (ii) inference-based Bayesian network approach. Finally, these fault diagnosis approaches have been briefly discussed with effectiveness of the models and their possible inclusion in future industrial applications.

关键词： artificial intelligence data-driven approach fault diagnosis industrial equipment machine learning algorithm

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