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Research on the Ecological Environment Evaluation of Technological Talents in the Shuangcheng Economic Circle of Chengdu Region 1

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International Conference on Data Analytics in Business and Marketing, ICDABM 2022

作者： Liu, Hong Yadav, Amit Hu, Li Qin Mehedi Hassan, Md. Mollick, Swarnali Talwar, Dinesh Department of Human Resource Management Chengdu University of Technology Chengdu China Darwin Australia Department of E-Commerce Chengdu Neusoft University Chengdu China Computer Science and Engineering North Western University Khulna Bangladesh Computer Science and Engineering Northern University of Business and Technology Khulna Bangladesh Department of Corporate and Digital Development Darwin Australia

ISBN: (数字)9789819953585

ISBN: (纸本)9789819953578

This paper takes 22 cities in Sichuan-Chongqing region as examples, builds a comprehensive evaluation index system for the ecological environment of scientific and technological talents on the basis of existing research, uses the mean square error weighting method to determine the weight of each index, and uses the comparative analysis method to analyze the Chengdu-Chongqing double-city economic circle. The current situation and shortcomings of the cities in the country proposed countermeasures and suggestions to improve the comprehensive ability of scientific and technological talents in the ecological environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mean square error

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Graph-based deep learning models in the prediction of early-stage Alzheimers 46

Graph-based deep learning models in the prediction of early-...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Thapaliya, Bishal Wu, Zundong Sapkota, Ram Ray, Bhaskar Suresh, Pranav Ghimire, Santosh Calhoun, Vince Liu, Jingyu Georgia State University Department of Computer Science Atlanta United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science United States Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta United States Tribhuvan University Department of Applied Sciences and Chemical Engineering Nepal

ISBN: (纸本)9798350371499

Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairment, and severe dementia. This study investigates the predictive potential of resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity (FC) in understanding Alzheimer's progression. Leveraging deep learning and graph-based models, we introduce two key contributions: 1) a comparative analysis of rs-fMRI time points and FC for Alzheimer's prediction. 2) an innovative graph transformer variant incorporating self-clustering for enhanced prediction accuracy. Experiments on the Alzheimer's Disease Neuroimaging Initiative dataset with 830 subjects reveal two notable conclusions. Firstly, rs-fMRI time points offer limited utility compared to functional network connectivity for transformer-based models, even when considering temporal information. Secondly, a clustering-based attention module proves effective for classifying brain networks in predicting Alzheimer's disease progression, providing valuable insights for future research and clinical applications. © 2024 IEEE.

关键词： Prediction models

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Primary User-Awareness-Based Energy-Efficient Duty-Cycle Scheme in Cognitive Radio Networks

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computers, Materials & Continua 2022年第3期70卷 5991-6005页

作者： Zilong Jin Chengbo Zhang Kan Yao Dun Cao Seokhoon Kim Yuanfeng Jin School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)Nanjing University of Information Science and Technology Nanjing210044China Jiangsu Institute of Economic and Information Technology Nanjing210003China School of Computer and Communications Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Rep.of Korea Department of Mathematics Yanbian UniversityYanji133002China

Cognitive radio devices can utilize the licensed channels in an opportunistic manner to solve the spectrum scarcity issue occurring in the unlicensed ***,these cognitive radio devices(secondary users)are greatly affected by the original users(primary users)of licensed *** users have to adjust operation parameters frequently to adapt to the dynamic network environment,which causes extra energy *** consumption can be reduced by predicting the future activity of primary ***,the traditional prediction-based algorithms require large historical data to achieve a satisfying precision accuracy which will consume a lot of time and memory ***,many of these schemes lack methods to deal with the very busy network *** this paper,one semi-supervised learning algorithm,i.e.,tri-training,has been employed to investigate the prediction of primary *** on the prediction results of tri-training,a duty-cycle mechanism and an intermediate node selection approach are proposed to improve the energy *** results show the effectiveness of the proposed algorithm.

关键词： Cognitive radio tri-training duty-cycle intermediate node energy efficiency

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SciMRC: Multi-perspective Scientific Machine Reading Comprehension 30

SciMRC: Multi-perspective Scientific Machine Reading Compreh...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Xiao Zheng, Heqi Nie, Yuxiang Huang, Heyan Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China State Grid Smart Grid Research Institute Co. Ltd. China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9782493814104

Scientific Machine Reading Comprehension (SMRC) aims to facilitate the understanding of scientific texts through human-machine interactions. While existing dataset has significantly contributed to this field, it predominantly focus on single-perspective question-answer pairs, thereby overlooking the inherent variation in comprehension levels among different readers. To address this limitation, we introduce a novel multi-perspective scientific machine reading comprehension dataset, SciMRC, which incorporates perspectives from beginners, students, and experts. Our dataset comprises 741 scientific papers and 6,057 question-answer pairs, with 3,306, 1,800, and 951 pairs corresponding to beginners, students, and experts respectively. Extensive experiments conducted on SciMRC using pre-trained models underscore the importance of considering diverse perspectives in SMRC and highlight the challenging nature of our scientific machine comprehension tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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Distributed Learning by Local Training ADMM 63

Distributed Learning by Local Training ADMM

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Ren, Xiaoxing Bastianello, Nicola Johansson, Karl H. Parisini, Thomas Kth Royal Institute of Technology 2School of Electrical Engineering and Computer Science and Digital Futures Stockholm Sweden Imperial College London Department of Electrical and Electronic Engineering London United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Trieste Italy

ISBN: (纸本)9798350316339

In this paper, we focus on distributed learning over peer-to-peer networks. In particular, we address the challenge of expensive communications (which arise when e.g. training neural networks), by proposing a novel local training algorithm, LTADMM. We extend the distributed ADMM enabling the agents to perform multiple local gradient steps per communication round (local training). We present a preliminary convergence analysis of the algorithm under a graph regularity assumption, and show how the use of local training does not compromise the accuracy of the learned model. We compare the algorithm with the state of the art for a classification task, and in different set-ups. The results are very promising showing a great performance of LT-ADMM, and paving the way for future important theoretical developments. © 2024 IEEE.

关键词： Peer to peer networks

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Identification of Key Genes as Potential Drug Targets for Gastric Cancer

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Tsinghua science and technology 2023年第4期28卷 649-664页

作者： Md.Tofazzal Hossain Md.Selim Reza Yin Peng Shengzhong Feng Yanjie Wei Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Statistics Bangabandhu Sheikh Mujibur Rahman Science and Technology UniversityGopalganj 8100Bangladesh Guangdong Provincial Key Laboratory for Genome Stability and Disease Prevention and Regional Immunity and Diseases Department of PathologyShenzhen UniversityShenzhen 518060China

Gastric cancer(GC)is one of the most common cancers and ranks the third in cancer mortality all over the *** goal of this study was to identify potential hub-genes,highlighting their functions,signaling pathways,and candidate drugs for the treatment of GC *** used publicly available next generation sequencing(NGS)data to identify differentially expressed(DE)*** top DE genes were mapped to STRING database to construct the protein-protein interaction(PPI)network and top hub genes were selected for further *** found a total of 1555 DE genes with 870 upregulated and 685 downregulated genes in *** selected the top 400(200 upregulated and 200 downregulated)genes to construct a PPI network and extracted the top 15 hub *** gene ontology(GO)term and kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analyses of the 15 hub genes exposed some important functions and signaling pathways that were significantly associated with GC *** survival analysis of the hub genes disclosed that the lower expressions of the three hub genes CDH2,COL4A1,and COL5A2 were associated with better survival of GC *** three genes might be the candidate biomarkers for the diagnosis and treatment of ***,we considered 3 key proteins(genomic biomarkers)(COL4A1,CDH2,and CO5A2)as the drug target proteins(receptors),performed their docking analysis with the 102 meta-drug agents,and found Everolimus,Docetaxel,Lanreotide,Venetoclax,Temsirolimus,and Nilotinib as the top ranked 6 candidate drugs with respect to our proposed target proteins for the treatment against GC ***,the proposed drugs might play vital role for the treatment against GC patients.

关键词： gastric cancer hub genes candidate genes molecular docking candidate drugs

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Combating Terrorism: Detecting and Analyzing Extremist Content on X (Formerly Twitter) 3

Combating Terrorism: Detecting and Analyzing Extremist Conte...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Mwendwa, Gideon Chouhan, Lokesh Etyang, Felix Mandela, Ngaira Gift, Uwineza School of Cyber Security and Digital Forensics National Forensic Sciences University Goa India Cochin University of Science and Technology School of Engineering Department of Computer Science Cochin India School of Cyber Security and Digital Forensics National Forensic Sciences University Gandhinagar India School of Cyber Security and Digital Forensics National Forensic Sciences University Jinja Uganda

ISBN: (纸本)9798331518592

This study investigates the efficacy of machine learning algorithms in detecting and evaluating extremist content on social media platforms, and we employ X, formerly known as Twitter. We therefore utilize Kaggle's dataset that contains 83,000 entries from both extremist and non-extremist posts to compare the performance of four machine learning models, namely Support Vector Machine (SVM), Random Forest, Gradient Boosting, and Naive Bayes. Moreover, we examine a Stacking Classifier that leverages the advantages of all four models. The models were assessed using accuracy, precision, recall, F1 Score, ROC-AUC Score, training duration, and prediction time. The findings of our study revealed that the Support Vector Machine (SVM) algorithm was the most accurate and resilient, whereas Naive Bayes showcased remarkable computing efficiency, rendering it well-suited for real-time detection. The Random Forest and Gradient Boosting algorithms offered a well-balanced approach. Notably, the novel Stacking Classifier outperformed individual models, demonstrating the strength of a hybrid methodology. Hence our study presents a thorough structure for improving the identification of extremist material on social media, thereby providing essential knowledge for creating efficient, immediate detection systems to combat online extremism successfully. © 2024 IEEE.

关键词： Tweets

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NOMA Cooperative Spectrum Sharing Average Capacity Improvement in 5G Network 3

NOMA Cooperative Spectrum Sharing Average Capacity Improveme...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Hassan, Mohamed Saeed, Rashid A. Mokhtar, Rania A. Hamid, Khalid Ali, Elmustafa Sayed Odeh, Nidhal Singh, Manwinder Khalifa, Othman O. Islam, Shayla Lovely Professional University Department of Wireless Communication Punjab Phagwara144001 India Taif University College of Computers and Information Technology Department of Computer Engineering P.O. Box 11099 Taif21944 Saudi Arabia University of Science & Technology Department of Communication Systems Engineering P.O. Box 00000 Omdurman Sudan Department of Electronics Engineering Khartoum Sudan University of Wollongong School of Electrical Computer and Telecommunications Engineering Australia International Islamic University Malaysia Gombak Malaysia Libyan Center for Engineering Resarch and Information Technology Department of Electrical and Computer Engineering Bani Waleed Libya Institute of Computer Science and Digital Innovation Ucsi University Malaysia Department of Computer Science Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350319897

One of the most effective approaches to satisfying the requirements of the next generation of wireless services is non-orthogonal multiple access (NOMA). Spectrum efficiency and average capacity are two aspects of wireless communication that have become increasingly important as the number of mobile services in use has increased. Using both traditional MIMO and Massive MIMO (M-MIMO), this study proposes two new methods for increasing the average capacity of a 5G network's downlink (DL) NOMA power domain (PD) in conjunction with a Cooperative Cognitive Radio Network (CCRN). The initial method involves NOMA users competing for available rivalry channels (R-CH) on the CCRN. The second technique creates a dedicated channel (D-CH) for NOMA users. The MATLAB program is utilized to evaluate the proposed methods in three scenarios with varying distances, power location coefficients, and transmission power. Successive interference cancellation (SIC) and unstable channel conditions are also considered while assessing the analysis of the proposed system under the assumption of Rayleigh fading. Using DL NOMA PD systems, The results reveal that the higher average capacity of best at transmit power DL NOMA with CCRN (dedicated channel) improved U4 average capacity performance by M-MIMO DL NOMA with CCRN (D-CH) improved by when compared with convention DL NOMA. Formulas in closed form were found for average capacity. The resulting formulas are verified using a Monte Carlo simulation. © 2023 IEEE.

关键词： 5G mobile communication systems

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Magnesium alloys in tumor treatment:Current research status, challenges and future prospects

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Journal of Magnesium and Alloys 2023年第10期11卷 3399-3426页

作者： Yuchien Hsu Yupu Lu Siyi Wang Yufeng Zheng Dandan Xia Yunsong Liu Department of Prosthodontics Peking University School and Hospital of StomatologyBeijing100081China National Center for Stomatology National Clinical Research Center for Oral DiseasesNational Engineering Research Center of Oral Biomaterials and Digital Medical DevicesBeijing Key Laboratory of Digital StomatologyResearch Center of Engineering and Technology for Computerized Dentistry Ministry of HealthNMPA Key Laboratory for Dental MaterialsBeijing100081China Department of Dental Materials Peking University School and Hospital of StomatologyBeijing100081China Department of Prosthodontics Shanghai Ninth People’s HospitalShanghai Jiao Tong University School of MedicineCollege of StomatologyShanghai Jiao Tong UniversityNational Center for StomatologyNational Clinical Research Center for Oral DiseasesShanghai Key Laboratory of StomatologyShanghai Research Institute of StomatologyChina Department of Materials Science and Engineering College of EngineeringPeking UniversityBeijing 100871China

Cancer is a major threat to human life worldwide. Traditional cancer treatments, such as chemotherapy and surgery, have major limitations and can cause irreversible damage to normal tissues while killing the cancer cells. Magnesium(Mg) alloys are widely reported novel potential biomedical materials with acceptable mechanical properties and good osteogenic and angiogenic properties. In this review, we summarize the Mg alloys for antitumor applications, including pure Mg and Mg alloys(Mg-Ag, Mg-Gd, Mg-Li-Zn, Mg-Ca-Sr-Zn, et al.) fabricated by casting and extruding, selective laser melting methods. Mg alloys can exhibit antitumor effect on bone tumor, breast cancer, and liver tumor,etal. What's more, after tumor tissue is eliminated, Mg alloys prevent tumor recurrence, fill tissue defects and promote tissue *** antitumor effects of Mg alloys are mainly due to their degradation products. Overall, Mg alloys show great potential in tumor treatments due to the dual function of antitumor and tissue regeneration.

关键词： Antitumor Cancer Magnesium alloys Mechanism Tissue regeneration

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Robust Watermarking of Screen-Photography Based on JND

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computers, Materials & Continua 2022年第6期71卷 4819-4833页

作者： Siyu Gu JinHan Xingming Sun Yi Cao Nanjing University of Information Science&Technology Nanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing201144China Department of Electrical and Computer Engineering University of Windsor401 Sunset AvenueWindsorONN9B 3P4Canada

With the popularity of smartphones,it is often easy to maliciously leak important information by taking pictures of the *** watermarking that can resist screen photography can achieve the protection of *** the screen photo process can cause some irreversible distortion,the currently available screen photo watermarks do not consider the image content well and the visual quality is not very ***,this paper proposes a new screen-photography robust *** terms of embedding region selection,the intensity-based Scale-invariant feature transform(SIFT)algorithm used for the construction of feature regions based on the density of feature points,which can make it more focused on the key content of the image;in terms of embedding strength,the Just noticeable difference(JND)model is applied to limit the intensity of the watermark embedding according to the luminance and texture of the picture to balance robustness and invisibility;after embedding watermark,the coefficients in the neighborhood are again adjusted with optimal constraints to improve the accuracy of watermark *** experiments,it is shown that the method we proposed can improve the correct rate of watermark extraction,the quality of the visual aspect of the watermarked picture is also improved.

关键词： Screen photography robust watermarking SIFT JND

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