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Magnesium-incorporated biocomposite scaffolds:A novel frontier in bone tissue engineering

Journal of Magnesium and Alloys 2024年第6期12卷 2231-2248页

作者： Abinaya Shanmugavadivu Sundaravadhanan Lekhavadhani Sushma Babu Nivetha Suresh Nagarajan Selvamurugan Department of Biotechnology School of BioengineeringCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203Tamil NaduIndia

Nonunion represents a crucial challenge in orthopedic medicine,demanding innovative solutions beyond the scope of traditional bone grafting *** the various strategies available,magnesium(Mg)implants have been recognized for their biocompatibility and ***,their susceptibility to rapid corrosion and degradation has garnered notable research interest in bone tissue engineering(BTE),particularly in the development of Mg-incorporated biocomposite *** scaffolds gradually release Mg2+,which enhances immunomodulation,osteogenesis,and angiogenesis,thus facilitating effective bone *** review presents myriad fabrication techniques used to create Mg-incorporated biocomposite scaffolds,including electrospinning,three-dimensional printing,and sol-gel *** these advancements,the application of Mg-incorporated biocomposite scaffolds faces challenges such as controlling the degradation rate of Mg and ensuring mechanical *** limitations highlight the necessity for ongoing research aimed at refining fabrication techniques to better regulate the physicochemical and osteogenic properties of *** review provides insights into the potential of Mg-incorporated biocomposite scaffolds for BTE and the challenges that need to be addressed for their successful translation into clinical applications.

关键词： Magnesium Bone tissue engineering Immunomodulation Osteogenesis Angiogenesis

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An End-to-End Deep Learning QoS Prediction Model Based on Temporal Context and Feature Fusion

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IEEE Transactions on Services Computing 2025年第3期18卷 1232-1244页

作者： Zhang, Peiyun Fan, Jiajun Chen, Yutong Huang, Wenjun Zhu, Haibin Zhao, Qinglin Nanjing University of Information Science & Technology Engineering Research Center of Digital Forensics of Ministry of Education School of Computer Science Nanjing210044 China Nipissing University Department of Computer Science and Mathematics North BayONP1B 8L7 Canada Macau University of Science and Technology School of Computer Science and Engineering Taipa999078 China

Existing end-to-end quality of service (QoS) prediction methods based on deep learning often use one-hot encodings as features, which are input into neural networks. It is difficult for the networks to learn the information that is conducive to prediction. Aiming at the above problem, an end-to-end deep learning QoS prediction model based on a temporal context and feature fusion is proposed. In the proposed model, three blocks are designed for QoS prediction. Firstly, a user-service encoding conversion block is designed to convert the one-hot encodings of users and services into the latent features of users and services, which can make full use of the data in sparse matrices. Then a time feature extraction block is designed to extract time features based on the time-varying characteristics of QoS values. Finally, the time features are fused with the latent features of users and services to predict QoS values. The experimental results show that on existing datasets, the proposed model has better prediction accuracy than other advanced methods in response time and throughput. © 2025 IEEE.

关键词： Prediction models

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Hyperspectral Target Detection-Based 2-D-3-D Parallel Convolutional Neural Networks for Hyperspectral Image Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 17卷 9451-9469页

作者： Chen, Shih-Yu Hsu, Kai-Hsun Kuo, Tzu-Hsien National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Yunlin64002 Taiwan

This article presents a novel hyperspectral target detection (HTD) based two-dimensional (2-D)-three-dimensional (3-D) parallel convolutional neural network (HTD 2D-3D-PCNN) model, which integrates the HTD technique to achieve outstanding performance in hyperspectral image classification. The proposed model effectively leverages both spectral and spatial information through a dual-branch architecture in hyperspectral imaging. In the first branch, HTD is utilized to enhance the spectral features of targets of interest, while suppressing background. The resulting enhanced image is then inputted to a 2D-CNN, augmented with an additional deconvolution layer to highlight spatial characteristics. Concurrently, the second branch employs dimensionality reduction via principal component analysis, and a 3D-CNN is employed to capture both spectral and spatial attributes. Subsequently, the feature maps from both convolutional neural networks (CNNs) are combined and processed through fully connected layers for classification. To validate the effectiveness of the proposed HTD-2D-3D-PCNN, extensive experiments are conducted on five widely used hyperspectral public datasets (Indian Pines, Pavia University, Salinas Scene, Kennedy Space Center, and Botswana) with a consistent training sample ratio of either 10% or 5%. The results show that HTD-2D-3D-PCNN can achieve overall accuracy values of 98.41%, 99.85%, 99.92%, 99.82%, and 98.82% for the respective datasets, surpassing the performance of recent methodologies. © 2008-2012 IEEE.

关键词： Image classification

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Multiple types of disease-associated RNAs identification for disease prognosis and therapy using heterogeneous graph learning

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science China(Information sciences) 2024年第8期67卷 341-342页

作者： Wenxiang ZHANG Hang WEI Wenjing ZHANG Hao WU Bin LIU School of Computer Science and Technology Beijing Institute of Technology School of Computer Science and Technology Xidian University Department of Teaching and Research Shenzhen University General Hospital Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical UltrasoundSchool of Biomedical Engineering Shenzhen University Medical School Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology

Identifying disease-associated RNAs is crucial in revealing the pathogenic mechanisms of diseases [1], and biologists have made notable progress in this field [2]. However, more effective computational methods are needed to provide reference disease-associated RNAs, reducing the manpower and material resources required for biological experiments.

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Hierarchical blockchain-based secure data storage in cloud using Merkle tree approach

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International Journal of Cloud Computing 2024年第6期13卷 605-626页

作者： Bruno, Judy Flavia Chelliah, Balika J. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India

An authenticated manager must reinforce huge applications and operating systems, keeping information in the cloud while resisting potentially unreliable service providers. This article explores the presence of multiple service providers based on the cloud to store and manage customer information. It has investigated the creation of an optimal system for shared cloud storage to facilitate flexible operations and information migration. This paper uses the Merkle tree approach to describe the experimental outcomes and designs of security systems provided by blockchain in cloud computing. We proposed that good authenticity and security be implemented with minimal significant degradation. The method was based on the position of the conscious Merkle tree that employs a three-tuple technique that has proven reliable in terms of authentication and encryption of security functions. The proposed system performance was measured by parameters such as retrieval time of information in blockchain technology, cost of storage, and average time with conventional information storage methods. The results indicate that the proposed blockchain technology solution has a response time almost 50% faster than conventional methods. Also, it claims that the storage cost would be around 20% lower. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Cloud storage

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Predicting Stock Prices Using Hybrid LSTM and ARIMA Model

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IAENG International Journal of Applied Mathematics 2024年第3期54卷 424-432页

作者： Ma, Chi Wu, Jie Hu, Hui Chen, YueNai Li, JingYan School of Computer Science and Engineering Huizhou University Guangdong Huizhou516007 China the School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China School of Mathematics and Statistics Huizhou University Guangdong Huizhou516007 China School of Computer Science and Engineering Huizhou University Guangdong Huizhou516007 China

With the continuous development of China's financial market and the gradual improvement of the financial system, investors are increasingly interested in participating in investments. At the same time, there is a strong demand for accurate and effective financial information services. In recent years, many researchers have begun to pay attention to the financial field and try to use a variety of artificial intelligence technologies to solve the problem of financial data prediction in this field. Aiming at the problem of stock trend prediction, we present a hybrid model based on Long Short-Term Memory (LSTM) and Auto Regressive Integrated Moving Average (ARIMA) approach to predict stock prices. Through sample training and experiments in multiple data sets and different market stages, it is shown that the proposed Mixture model has higher prediction accuracy and stability than the single deep learning model, ARIMA model after wavelet transform and the models in the references, and has a stronger market adaptability. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Long short-term memory

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Enhancing Multiclass Pneumonia Classification with Machine Learning and Textural Features

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Machine Graphics and Vision 2023年第3-4期32卷 83-106页

作者： Godbin, A. Beena Jasmine, S. Graceline School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The highly infectious and mutating COVID-19, known as the novel coronavirus, poses a substantial threat to both human health and the global economy. Detecting COVID-19 early presents a challenge due to its resemblance to pneumonia. However, distinguishing between the two is critical for saving lives. Chest X-rays, empowered by machine learning classifiers and ensembles, prove effective in identifying multiclass pneumonia in the lungs, leveraging textural characteristics such as GLCM and GLRLM. These textural features are instilled into the classifiers and ensembles within the domain of machine learning. This article explores the multiclass categorization of X-ray images across four categories: COVID-19-impacted, bacterial pneumonia-affected, viral pneumonia-affected, and normal lungs. The classification employs Random Forest, Support Vector Machine, K-Nearest Neighbor, LGBM, and XGBoost. Random Forest and LGBM achieve an impressive accuracy of 92.4% in identifying GLCM features. The network’s performance is evaluated based on accuracy, precision, sensitivity and F1-score. © 2023 Institute of Information technology, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： COVID-19

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Jamming-Resilient Consensus for Wireless Blockchain Networks

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Tsinghua science and technology 2025年第1期30卷 262-278页

作者： Yifei Zou Meng Hou Li Yang Minghui Xu Libing Wu Dongxiao Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266237China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China

As the device complexity keeps increasing,the blockchain networks have been celebrated as the cornerstone of numerous prominent platforms owing to their ability to provide distributed and immutable ledgers and data-driven autonomous *** distributed consensus algorithm is the core component that directly dictates the performance and properties of blockchain ***,the inherent characteristics of the shared wireless medium,such as fading,interference,and openness,pose significant challenges to achieving consensus within these networks,especially in the presence of malicious jamming *** cope with the severe consensus problem,in this paper,we present a distributed jamming-resilient consensus algorithm for blockchain networks in wireless environments,where the adversary can jam the communication channel by injecting jamming *** on a non-binary slight jamming model,we propose a distributed four-stage algorithm to achieve consensus in the wireless blockchain network,including leader election,leader broadcast,leader aggregation,and leader announcement *** high probability,we prove that our jamming-resilient algorithm can ensure the validity,agreement,termination,and total order properties of consensus with the time complexity of O(n).Both theoretical analyses and empirical simulations are conducted to verify the consistency and efficiency of our algorithm.

关键词： consensus in blockchain jamming attacks distributed algorithm

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On the bitmap compression for joint coding and data hiding of AMBTC compressed images

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Multimedia Tools and Applications 2024年第35期83卷 82131-82148页

作者： Hong, Wien Su, Guan-Zhong Chen, Tung-Shou Chen, Jeanne Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung City Taiwan

The compressed code of Absolute Moment Block Truncation Coding (AMBTC) consists of quantized values (QVs) and bitmaps. The QVs exhibit greater predictability, and the bitmaps themselves carry more randomness. While existing joint coding and data hiding based methods have focused on compressing the QVs, none of them have addressed the coding of bitmaps. Furthermore, evidence also reveals that the sub-divided bitmaps exhibit a highly uneven pattern distribution. Therefore, we propose an efficient method to compress the bitmaps by representing sub-divided bitmaps as decimal digits. This exploits the varying frequency of certain digits, allowing Huffman encoding for shorter codewords to represent frequently occurring digits. Moreover, we have observed that less frequent digits, which require longer codewords, tend to appear among blocks with smaller difference of QVs. As a solution, we employ an adaptive approach to directly record the original bitmap when the difference is below a specific threshold. Otherwise, we employ Huffman encoding to reduce the code length. The experimental results demonstrate the effectiveness of our approach in reducing the storage space required for Lena’s bitmaps by 7.36%. Moreover, the reduction in bitrate is more pronounced when the test image exhibits a smooth texture. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Textures

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Centralized RANSAC-Based Point Cloud Registration With Fast Convergence and High Accuracy

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 17卷 5431-5442页

作者： Chung, Kuo-Liang Chang, Wei-Tai National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106335 Taiwan

For point cloud registration, the purpose of this article is to propose a novel centralized random sample consensus (RANSAC) (C-RANSAC) registration with fast convergence and high accuracy. In our algorithm, the novel contributions are, first, the proposal of a scale histogram-based outlier removal to delete outliers from the initial line vector set L for constructing a reduced line vector set Lred;second, the handshake cooperation between the host RANSAC (H-RANSAC) only working on L and the local RANSAC (LCL-RANSAC) only working on Lred;third, in each handshake process, after receiving the global registration solution and the global iteration number xH from H-RANSAC, LCLRANSAC uses the received global solution as the initial solution of the modified TEASER++ (M-TEASER++) method to calculate its first local registration solution. If the first local registration solution satisfies the global iteration number inheritance condition, LCL-RANSAC directly sends the accumulated iteration number, xH + 1, and the first local solution back to H-RANSAC;otherwise, LCL-RANSAC iteratively refines its local solution using the M-TEASER++ method, and then sends the resultant local solution and the required local iteration number xLCL to H-RANSAC for updating the global solution, the global iteration number to xH := xH + xLCL, and the global confidence level. Due to |Lred| |L| and employing the global iteration number inheritance condition test into our algorithm, we have conducted extensive experiments on testing point cloud pairs to show the registration accuracy and execution time merits of our algorithm relative to the state-of-theart methods. © 2008-2012 IEEE.

关键词： Iterative methods

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