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The Research on Disruptive technology Identification Based on Scientific and Technological Information Mining and Expert Consultation: A Case Study on the Energy Field 2nd

The Research on Disruptive Technology Identification Based o...

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2nd International Conference on Artificial Intelligence in China, ChinaAI 2020

作者： Lyu, Lucheng Wang, Xuezhao Chen, Wei Zhang, Xin Chen, Xiaoli Liu, Xiwen National Science Library Chinese Academy of Sciences Beijing100190 China Department of Library Information and Archives Management School of Economics and Management University of Chinese Academic of Sciences Beijing100190 China Wuhan Library Chinese Academy of Sciences Wuhan430071 China Hubei Key Laboratory of Big Data in Science and Technology Wuhan430071 China

ISBN: (纸本)9789811585982

Disruptive technology identification is of great significance to the development of the countries and enterprises. In this paper, both quantitative analysis and qualitative analysis are combined to propose a disruptive technology identification method based on scientific and technological information mining and expert consultation. In the part of the quantitative analysis, three kinds of scientific and technological information data are involved including papers, patents, and projects, and the word embedding model and clustering technology are applied to dimension reduction and aggregation the scientific and technological information which results in finding "seeds" or "sprouts" of disruptive technologies from the mass scientific and technological information. In the part of the expert consultation, the ranking of potential disruptive technologies is evaluated by an expert questionnaire based on a "three-dimensional index system of technology disruptive potential evaluation." The result of the energy field research is that 45 potential disruptive technologies are identified and their disruptive ranking is determined. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cluster analysis

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Deep Neural Network based Relation Extraction: An overview

arXiv

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arXiv 2021年

作者： Wang, Hailin Qin, Ke Zakari, Rufai Yusuf Lu, Guoming Yin, Jin Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

Knowledge is a formal way of understanding the world, providing a human-level cognition and intelligence for the next-generation artificial intelligence (AI). One of the representations of knowledge is semantic relations between entities. An effective way to automatically acquire this important knowledge, called Relation Extraction (RE), a sub-task of information extraction, plays a vital role in Natural Language Processing (NLP). Its purpose is to identify semantic relations between entities from natural language text. To date, there are several studies for RE in previous works, which have documented these techniques based on Deep Neural Networks (DNNs) become a prevailing technique in this research. Especially, the supervised and distant supervision methods based on DNNs are the most popular and reliable solutions for RE. This article 1) introduces some general concepts, and further 2) gives a comprehensive overview of DNNs in RE from two points of view: supervised RE, which attempts to improve the standard RE systems, and distant supervision RE, which adopts DNNs to design sentence encoder and de-noise method. We further 3) cover some novel methods and recent trends as well as discuss possible future research directions for this task. © 2021, CC0.

关键词： Deep neural networks

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Enhanced Adsorption of Copper Citrate Complex by Ferromanganese Oxides-Biochar from Water: Performance and Mechanism

SSRN

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SSRN 2024年

作者： Zhu, Ying Lei, Xiang Liu, Jianguo Li, Yiting Fan, Wenhong Key Laboratory of Environmental Pollution Control and Remediation Universities of Inner Mongolia Autonomous Region College of Resources and Environmental Engineering Inner Mongolia University of Technology Inner Mongolia Hohhot010051 China School of Materials Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing100191 China

Due to the stable structure of complex metals, their removal is more challenging compared to metal ions. Additionally, the removal mechanism of metal complexes is closely related to their structure. This study developed a low-cost and economically efficient ferromanganese oxide-modified biochar and explored the preparation conditions in detail. The surface of ferromanganese oxide-modified biochar was uniformly loaded with 100 nm nanoparticles, mainly comprising Mn3O4 and (FeO)0.099(MnO)0.901, and exhibited excellent copper citrate (CuCA) adsorption capacity. The removal rate of CuCA at an initial concentration of 10 mg/L by 1 g/L modified biochar reached 99.5% for Cu and 87.7% for TOC, with a copper adsorption capacity of 3.79 mg/L. The mechanism of using modified biochar to remove CuCA includes chemical adsorption at the bimetallic oxide active sites and oxygen-containing functional groups on the surface of the biochar, as well as physical adsorption due to biochar’s porous structure and other effects. The modified biochar shows good performance and stability in wide pH range and certain coexisting ions. Ferromanganese oxide-modified biochar own the advantage of efficient, economical and easy to preparation, which has significant potential for development in the removal of metal complexes in water. © 2024, The Authors. All rights reserved.

关键词： Adsorption

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A Power Balance Control Strategy of Wireless Sensor Network with Collaborating Heuristic 16th

A Power Balance Control Strategy of Wireless Sensor Network ...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2020

作者： Yu, Jie Dao, Thi-Kien Ngo, Truong-Giang Nguyen, Trong-The College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Department of Information Technology Haiphong University of Management and Technology Haiphong18000 Viet Nam

ISBN: (纸本)9789813367562

In order to effort a strong enough signal, nodes in wireless sensor networks (WSN) have to increase their transmission power that continues to maintain the transmission power. However, a vicious circle is iterated that causes a decline in the overall network performance, low utility, and network life cycle shorten. This paper presents a solution to the power balance control strategy for WSN with collaborating heuristic. A distance between the weight factors and obtained nodes interference value is used to establish a useful interference model for enhancing the signal-to-interference-noise ratio (SINR). The utility function of the nodes residual energy and transmission rate is modeled by apply heuristic strategy. The optimal transmission power is obtained after several iterations of the heuristic algorithm. Simulation results show that the proposed approach can prolong the network life cycle and achieve higher network utility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Heuristic algorithms

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Predictive analysis of COVID-19 occurrence and vaccination impacts across the 50 US states

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Computers in Biology and Medicine 2025年 185卷 109493-109493页

作者： Rayguru, Chinmayee Husnayain, Atina Chiu, Hua-Sheng Sumazin, Pavel Su, Emily Chia-Yu Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Public Health Monash University Indonesia Department of Pediatrics Baylor College of Medicine Texas Children's Hospital Cancer Center HoustonTX United States Institute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan

Objective: This study aimed to outline a machine learning model to assess the effectiveness of vaccination in COVID-19 confirmed cases and fatalities. The proposed model was evaluated using external validation to ensure optimal protection of vaccinated populations, distinguishing between males and females. Methods: The data from the Centers for Disease Control and Prevention (CDC) in the US, collected between 2021 and 2023, were preprocessed through merging and imputation. A deep learning long short-term memory (LSTM) model was developed to analyze the effectiveness of vaccination in predicting COVID-19 cases and fatalities. The model, which was validated internally and externally, examined the impact of vaccination according to sex. The performance was assessed against current state-of-the-art models, with the LSTM model exhibiting lower root mean square error (RMSE) values. Results: We performed intra-, inter-, and external-validation analyses. First, one- and two-dose vaccinations significantly reduced the number of COVID-19 cases and mortality in highly affected states. Second, in the inter-model analysis, the LSTM outperformed the autoregressive integrated moving average (ARIMA) model in predicting cases and deaths, yielding superior results for Texas, California, and Florida. Third, with external validation, our LSTM model effectively predicted vaccination impacts regardless of sex. Conclusions: Our study demonstrates the effectiveness of COVID-19 vaccination, showing that full vaccination significantly reduced the number of confirmed cases and deaths, influencing future public health policies. © 2024 Elsevier Ltd

关键词： COVID-19

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Label Incorporated Graph Neural Networks for Text Classification

Label Incorporated Graph Neural Networks for Text Classifica...

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International Conference on Pattern Recognition

作者： Yuan Xint Linli Xu Junliang Guo Jiquan Li Xin Sheng Yuanyuan Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China IFLYTEK Co. Ltd. Information Science Center University of Science and Technology of China

Graph Neural Networks (GNNs) have achieved great success on graph-structured data, and their applications on traditional data structures such as natural language processing and semi-supervised text classification have been extensively explored in recent years. While previous works only consider the text information while building the graph, heterogeneous information such as labels is ignored. In this paper, we consider incorporating the label information while building the graph by adding text-label-text paths, through which the supervision information will propagate among the graph more directly. Specifically, we treat labels as nodes in the graph which also contains text and word nodes, and then connect labels with texts belonging to that label. Through graph convolutions, label embeddings are jointly learned with text embeddings in the same latent semantic space. The newly incorporated label nodes will facilitate learning more accurate text embeddings by introducing the label information, and thus benefit the downstream text classification tasks. Extensive results on several benchmark datasets show that the proposed framework outperforms baseline methods by a significant margin.

关键词： Text categorization Buildings Semantics Benchmark testing data structures Graph neural networks Natural language processing

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Integrated medical resource consumption stratification in hospitalized patients:an Auto Triage Management model based on accurate risk,cost and length of stay prediction

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science China(Life sciences) 2022年第5期65卷 988-999页

作者： Qin Zhong Zongren Li Wenjun Wang Lei Zhang Kunlun He Medical Big Data Research Center Chinese PLA General HospitalBeijing 100853China Medical Artificial Intelligence Research Center Chinese PLA General HospitalBeijing 100853China Bio-engineering Research Center Chinese PLA General HospitalBeijing 100039China Department of Cardiology Fourth Medical Center of Chinese PLA General HospitalBeijing 100048China Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine Chinese PLA General HospitalBeijing 100048China China-Australia Joint Research Center for Infectious Diseases School of Public HealthXi’an Jiaotong University Health Science CenterXi’an 710061China

Triage management plays important roles in hospitalized patients for disease severity stratification and medical burden *** progression risks have been extensively researched for numbers of diseases,other crucial indicators that reflect patients’economic and time costs have not been systematically *** address the problems,we developed an automatic deep learning based Auto Triage Management(ATM)Framework capable of accurately modelling patients’disease progression risk and health economic *** on them,we can first discover the relationship between disease progression and medical system cost,find potential features that can more precisely aid patient triage in resource allocation,and allow treatment plan searching that has cured *** ATM in COVID-19,we built a joint model to predict patients’risk,the total length of stay(Lo S)and cost when at-admission,and remaining Lo S and cost at a given hospitalized time point,with C-index0.930 and 0.869 for risk prediction,mean absolute error(MAE)of 5.61 and 5.90 days for total Lo S prediction in internal and external validation data.

关键词： patient triage AutoML electronic medical records

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Environmental impacts assessment of ncm cathode material production of power lithium-ion batteries 20th

Environmental impacts assessment of ncm cathode material pro...

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20th Chinese Materials Conference, CMC 2019

作者： Jiao, Yulong Gao, Feng Xie, Jun College of Materials Science and Engineering Beijing University of Technology No.100 Ping Le Yuan Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology China

ISBN: (纸本)9783035715668

The nickel cobalt manganese ternary (NCM) cathode material is one of the important parts of power lithium battery. The NCM cathode material production process including the Li2CO3 preparation, precursor preparation and synthesis process of cathode materials is resource-and energy-intensive, thus it leads to relatively severe environment pollution. In this article, life cycle assessment of NCM cathode material production using coprecipitation method in China was conducted based on ReCiPe method. The environmental impacts of the main processes were calculated. The main factors and sources causing environmental pressure were identified. The results indicated that the precursor production contributed the most to environmental impacts in the NCM cathode material production. The energy consumption of the precursor production was 612MJ, accounting for 94.6% of the total energy consumption of NCM production. The environmental impacts of the CoSO4 production and NiSO4 production were the main contributors to that of the precursor production. The primary cobalt and nickel resources in China with lower grade and difficult mining led to the high energy consumption of these two kinds of raw materials. Therefore, the development and utilization of the secondary cobalt and nickel metals will be a great potential to reduce environmental impacts of NCM production. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Cobalt

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MFF-AMD: Multivariate Feature Fusion for Android Malware Detection 17th

MFF-AMD: Multivariate Feature Fusion for Android Malware Det...

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17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Xu, Guangquan Feng, Meiqi Jiao, Litao Liu, Jian Dai, Hong-Ning Wang, Ding Panaousis, Emmanouil Zheng, Xi College of Intelligence and Computing Tianjin University Tianjin China Big Data School Qingdao Huanghai University Qingdao China Faculty of Information Technology Macau University of Science and Technology Macau SAR Taipa China School of EECS Peking University Beijing China University of Greenwich London United Kingdom Department of Computing Macquarie University Sydney Australia

ISBN: (纸本)9783030926342

Researchers have turned their focus on leveraging either dynamic or static features extracted from applications to train AI algorithms to identify malware precisely. However, the adversarial techniques have been continuously evolving and meanwhile, the code structure and application function have been designed in complex format. This makes Android malware detection more challenging than before. Most of the existing detection methods may not work well on recent malware samples. In this paper, we aim at enhancing the detection accuracy of Android malware through machine learning techniques via the design and development of our system called MFF-AMD. In our system, we first extract various features through static and dynamic analysis and obtain a multiscale comprehensive feature set. Then, to achieve high classification performance, we introduce the Relief algorithm to fuse the features, and design four weight distribution algorithms to fuse base classifiers. Finally, we set the threshold to guide MFF-AMD to perform static or hybrid analysis on the malware samples. Our experiments performed on more than 25,000 applications from the recent five-year dataset demonstrate that MFF-AMD can effectively detect malware with high accuracy. © 2021, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Android malware

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Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning

arXiv

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arXiv 2022年

作者： Sim, Sunghyun Liu, Ling Bae, Hyerim Industrial Management & Big Data Engineering Major Division of Industrial Convergence System Engineering Dongeui University 176 Eomgwang No Gaya Dong 24 Busanjin Gu Busan47340 Korea Republic of The College of Computing Georgia Institute of Technology 266 Ferst Drive AtlantaGA30332-0765 United States Industrial Data Science & Engineering Department of Industrial Engineering Pusan National University Pusan National University 30-Jan-jeon Dong Geum-Jeong Gu Busan609-753 Korea Republic of

Process mining is a methodology for derivation and analysis of processes models based on the event log. When process mining is employed to analyze business processes, the process discovery step, the conformance checking step, and the enhancements step are repeated. If a user wants to analyze a process from multiple perspectives (such as activity perspectives, originator perspectives, and time perspectives), the above procedure, inconveniently, has to be repeated over and over again. Although past studies involving process mining have applied detailed stepwise methodologies, no attempt has been made to incorporate and optimize multi-perspective process mining procedures. This paper contributes to developing solution approach to this problem. First, we propose an automatic discovery framework of a multi-perspective process model based on deep Q-Learning. Our Dual Experience Replay with Experience Distribution (DERED) approach can automatically perform process model discovery steps, conformance check steps, and enhancements steps. Second, we propose a new method that further optimizes the experience replay (ER) method, one of the key algorithms of deep Q-learning, to improve the learning performance of reinforcement learning agents. Finally, we validate our approach using six real world event datasets collected in port logistics, steel manufacturing, finance, IT, and government administration. We show that our DERED approach can provide users with multi-perspective, high-quality process models that can be employed more conveniently for multi-perspective process mining. © 2022, CC BY.

关键词： Reinforcement learning

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