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Accurate method based on data filtering for quantitative multi-element analysis of soils using CF-LIBS
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作者 韩伟伟 孙对兄 +7 位作者 张国鼎 董光辉 崔小娜 申金成 王浩亮 张登红 董晨钟 苏茂根 《Plasma Science and Technology》 SCIE EI CAS CSCD 2024年第6期149-158,共10页
To obtain more stable spectral data for accurate quantitative analysis of multi-element,especially for the large-area in-situ elements detection of soils, we propose a method for a multielement quantitative analysis o... To obtain more stable spectral data for accurate quantitative analysis of multi-element,especially for the large-area in-situ elements detection of soils, we propose a method for a multielement quantitative analysis of soils using calibration-free laser-induced breakdown spectroscopy(CF-LIBS) based on data filtering. In this study, we analyze a standard soil sample doped with two heavy metal elements, Cu and Cd, with a specific focus on the line of Cu I324.75 nm for filtering the experimental data of multiple sample sets. Pre-and post-data filtering,the relative standard deviation for Cu decreased from 30% to 10%, The limits of detection(LOD)values for Cu and Cd decreased by 5% and 4%, respectively. Through CF-LIBS, a quantitative analysis was conducted to determine the relative content of elements in soils. Using Cu as a reference, the concentration of Cd was accurately calculated. The results show that post-data filtering, the average relative error of the Cd decreases from 11% to 5%, indicating the effectiveness of data filtering in improving the accuracy of quantitative analysis. Moreover, the content of Si, Fe and other elements can be accurately calculated using this method. To further correct the calculation, the results for Cd was used to provide a more precise calculation. This approach is of great importance for the large-area in-situ heavy metals and trace elements detection in soil, as well as for rapid and accurate quantitative analysis. 展开更多
关键词 laser-induced breakdown spectroscopy SOIL data filtering quantitative analysis multielement
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Quantitative Analysis of Seeing with Height and Time at Muztagh-Ata Site Based on ERA5 Database
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作者 Xiao-Qi Wu Cun-Ying Xiao +3 位作者 Ali Esamdin Jing Xu Ze-Wei Wang Luo Xiao 《Research in Astronomy and Astrophysics》 SCIE CAS CSCD 2024年第1期87-95,共9页
Seeing is an important index to evaluate the quality of an astronomical site.To estimate seeing at the Muztagh-Ata site with height and time quantitatively,the European Centre for Medium-Range Weather Forecasts reanal... Seeing is an important index to evaluate the quality of an astronomical site.To estimate seeing at the Muztagh-Ata site with height and time quantitatively,the European Centre for Medium-Range Weather Forecasts reanalysis database(ERA5)is used.Seeing calculated from ERA5 is compared consistently with the Differential Image Motion Monitor seeing at the height of 12 m.Results show that seeing decays exponentially with height at the Muztagh-Ata site.Seeing decays the fastest in fall in 2021 and most slowly with height in summer.The seeing condition is better in fall than in summer.The median value of seeing at 12 m is 0.89 arcsec,the maximum value is1.21 arcsec in August and the minimum is 0.66 arcsec in October.The median value of seeing at 12 m is 0.72arcsec in the nighttime and 1.08 arcsec in the daytime.Seeing is a combination of annual and about biannual variations with the same phase as temperature and wind speed indicating that seeing variation with time is influenced by temperature and wind speed.The Richardson number Ri is used to analyze the atmospheric stability and the variations of seeing are consistent with Ri between layers.These quantitative results can provide an important reference for a telescopic observation strategy. 展开更多
关键词 site testing atmospheric effects methods:data analysis telescopes EARTH
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Statistical Analysis of Abilities to Give Consent to Health Data Processing
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作者 Antonella Massari Biagio Solarino +5 位作者 Paola Perchinunno Angela Maria D’Uggento Marcello Benevento Viviana D’Addosio Vittoria Claudia De Nicolò Samuela L’Abbate 《Applied Mathematics》 2024年第8期508-542,共35页
The recent pandemic crisis has highlighted the importance of the availability and management of health data to respond quickly and effectively to health emergencies, while respecting the fundamental rights of every in... The recent pandemic crisis has highlighted the importance of the availability and management of health data to respond quickly and effectively to health emergencies, while respecting the fundamental rights of every individual. In this context, it is essential to find a balance between the protection of privacy and the safeguarding of public health, using tools that guarantee transparency and consent to the processing of data by the population. This work, starting from a pilot investigation conducted in the Polyclinic of Bari as part of the Horizon Europe Seeds project entitled “Multidisciplinary analysis of technological tracing models of contagion: the protection of rights in the management of health data”, has the objective of promoting greater patient awareness regarding the processing of their health data and the protection of privacy. The methodology used the PHICAT (Personal Health Information Competence Assessment Tool) as a tool and, through the administration of a questionnaire, the aim was to evaluate the patients’ ability to express their consent to the release and processing of health data. The results that emerged were analyzed in relation to the 4 domains in which the process is divided which allows evaluating the patients’ ability to express a conscious choice and, also, in relation to the socio-demographic and clinical characteristics of the patients themselves. This study can contribute to understanding patients’ ability to give their consent and improve information regarding the management of health data by increasing confidence in granting the use of their data for research and clinical management. 展开更多
关键词 PRIVACY Health data Consent Cluster analysis LOGIT
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Study of inter-well interference in shale gas reservoirs by a robust production data analysis method based on deconvolution
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作者 Wen-Chao Liu Cheng-Cheng Qiao +5 位作者 Ping Wang Wen-Song Huang Xiang-Wen Kong Yu-Ping Sun He-Dong Sun Yue-Peng Jia 《Petroleum Science》 SCIE EI CAS CSCD 2024年第4期2502-2519,共18页
In order to overcome the defects that the analysis of multi-well typical curves of shale gas reservoirs is rarely applied to engineering,this study proposes a robust production data analysis method based on deconvolut... In order to overcome the defects that the analysis of multi-well typical curves of shale gas reservoirs is rarely applied to engineering,this study proposes a robust production data analysis method based on deconvolution,which is used for multi-well inter-well interference research.In this study,a multi-well conceptual trilinear seepage model for multi-stage fractured horizontal wells was established,and its Laplace solutions under two different outer boundary conditions were obtained.Then,an improved pressure deconvolution algorithm was used to normalize the scattered production data.Furthermore,the typical curve fitting was carried out using the production data and the seepage model solution.Finally,some reservoir parameters and fracturing parameters were interpreted,and the intensity of inter-well interference was compared.The effectiveness of the method was verified by analyzing the production dynamic data of six shale gas wells in Duvernay area.The results showed that the fitting effect of typical curves was greatly improved due to the mutual restriction between deconvolution calculation parameter debugging and seepage model parameter debugging.Besides,by using the morphological characteristics of the log-log typical curves and the time corresponding to the intersection point of the log-log typical curves of two models under different outer boundary conditions,the strength of the interference between wells on the same well platform was well judged.This work can provide a reference for the optimization of well spacing and hydraulic fracturing measures for shale gas wells. 展开更多
关键词 Shale gas Inter-well interference DECONVOLUTION Production data analysis Typical curves Multi-stage fractured horizontal well
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Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data
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作者 SangSeok Lee HaeWon Moon Lee Sael 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第4期319-336,共18页
How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form... How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors.For example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long time.Such data,accumulated over time,is redundant and consumes a lot ofmemory in its raw form.We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks.To this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal data.Assuming that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save space.When modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker decomposition.The factor matrices and core tensor of the decomposed results can then be used for further data analysis.We compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker decomposition.We show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction error.We applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to 2022.We were able to model the spatial and temporal air quality trends.We were also able to verify unusual events,such as chronic ozone alerts and large fire events. 展开更多
关键词 Dynamic decomposition tucker tensor tensor factorization spatiotemporal data tensor analysis air quality
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Data-driven analysis of chemicals,proteins and pathways associated with peanut allergy:from molecular networking to biological interpretation
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作者 Emmanuel Kemmler Julian Braun +5 位作者 Florent Fauchère Sabine Dölle-Bierke Kirsten Beyer Robert Preissner Margitta Worm Priyanka Banerjee 《Food Science and Human Wellness》 SCIE CSCD 2024年第3期1322-1335,共14页
Peanut allergy is majorly related to severe food induced allergic reactions.Several food including cow's milk,hen's eggs,soy,wheat,peanuts,tree nuts(walnuts,hazelnuts,almonds,cashews,pecans and pistachios),fis... Peanut allergy is majorly related to severe food induced allergic reactions.Several food including cow's milk,hen's eggs,soy,wheat,peanuts,tree nuts(walnuts,hazelnuts,almonds,cashews,pecans and pistachios),fish and shellfish are responsible for more than 90%of food allergies.Here,we provide promising insights using a large-scale data-driven analysis,comparing the mechanistic feature and biological relevance of different ingredients presents in peanuts,tree nuts(walnuts,almonds,cashews,pecans and pistachios)and soybean.Additionally,we have analysed the chemical compositions of peanuts in different processed form raw,boiled and dry-roasted.Using the data-driven approach we are able to generate new hypotheses to explain why nuclear receptors like the peroxisome proliferator-activated receptors(PPARs)and its isoform and their interaction with dietary lipids may have significant effect on allergic response.The results obtained from this study will direct future experimeantal and clinical studies to understand the role of dietary lipids and PPARisoforms to exert pro-inflammatory or anti-inflammatory functions on cells of the innate immunity and influence antigen presentation to the cells of the adaptive immunity. 展开更多
关键词 Allergy informatics Knowledge-graph data analysis Food allergy Peroxisome proliferator-activated receptors Fatty acids
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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data
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作者 Jiufang Chen Kechen Liu +4 位作者 Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第11期2220-2235,共16页
High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation lear... High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable requirements.However, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational efficiency.Hence, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices. 展开更多
关键词 data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)
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A Robust Framework for Multimodal Sentiment Analysis with Noisy Labels Generated from Distributed Data Annotation
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作者 Kai Jiang Bin Cao Jing Fan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第6期2965-2984,共20页
Multimodal sentiment analysis utilizes multimodal data such as text,facial expressions and voice to detect people’s attitudes.With the advent of distributed data collection and annotation,we can easily obtain and sha... Multimodal sentiment analysis utilizes multimodal data such as text,facial expressions and voice to detect people’s attitudes.With the advent of distributed data collection and annotation,we can easily obtain and share such multimodal data.However,due to professional discrepancies among annotators and lax quality control,noisy labels might be introduced.Recent research suggests that deep neural networks(DNNs)will overfit noisy labels,leading to the poor performance of the DNNs.To address this challenging problem,we present a Multimodal Robust Meta Learning framework(MRML)for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities simultaneously.Specifically,we propose a two-layer fusion net to deeply fuse different modalities and improve the quality of the multimodal data features for label correction and network training.Besides,a multiple meta-learner(label corrector)strategy is proposed to enhance the label correction approach and prevent models from overfitting to noisy labels.We conducted experiments on three popular multimodal datasets to verify the superiority of ourmethod by comparing it with four baselines. 展开更多
关键词 Distributed data collection multimodal sentiment analysis meta learning learn with noisy labels
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Enhancing Data Analysis and Automation: Integrating Python with Microsoft Excel for Non-Programmers
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作者 Osama Magdy Ali Mohamed Breik +2 位作者 Tarek Aly Atef Tayh Nour El-Din Raslan Mervat Gheith 《Journal of Software Engineering and Applications》 2024年第6期530-540,共11页
Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision... Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision-making across diverse domains. Conversely, Python is indispensable for professional programming due to its versatility, readability, extensive libraries, and robust community support. It enables efficient development, advanced data analysis, data mining, and automation, catering to diverse industries and applications. However, one primary issue when using Microsoft Excel with Python libraries is compatibility and interoperability. While Excel is a widely used tool for data storage and analysis, it may not seamlessly integrate with Python libraries, leading to challenges in reading and writing data, especially in complex or large datasets. Additionally, manipulating Excel files with Python may not always preserve formatting or formulas accurately, potentially affecting data integrity. Moreover, dependency on Excel’s graphical user interface (GUI) for automation can limit scalability and reproducibility compared to Python’s scripting capabilities. This paper covers the integration solution of empowering non-programmers to leverage Python’s capabilities within the familiar Excel environment. This enables users to perform advanced data analysis and automation tasks without requiring extensive programming knowledge. Based on Soliciting feedback from non-programmers who have tested the integration solution, the case study shows how the solution evaluates the ease of implementation, performance, and compatibility of Python with Excel versions. 展开更多
关键词 PYTHON End-User Approach Microsoft Excel data analysis Integration SPREADSHEET PROGRAMMING data Visualization
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Performance Analysis and Optimization of Energy Harvesting Modulation for Multi-User Integrated Data and Energy Transfer
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作者 Yizhe Zhao Yanliang Wu +1 位作者 Jie Hu Kun Yang 《China Communications》 SCIE CSCD 2024年第1期148-162,共15页
Integrated data and energy transfer(IDET)enables the electromagnetic waves to transmit wireless energy at the same time of data delivery for lowpower devices.In this paper,an energy harvesting modulation(EHM)assisted ... Integrated data and energy transfer(IDET)enables the electromagnetic waves to transmit wireless energy at the same time of data delivery for lowpower devices.In this paper,an energy harvesting modulation(EHM)assisted multi-user IDET system is studied,where all the received signals at the users are exploited for energy harvesting without the degradation of wireless data transfer(WDT)performance.The joint IDET performance is then analysed theoretically by conceiving a practical time-dependent wireless channel.With the aid of the AO based algorithm,the average effective data rate among users are maximized by ensuring the BER and the wireless energy transfer(WET)performance.Simulation results validate and evaluate the IDET performance of the EHM assisted system,which also demonstrates that the optimal number of user clusters and IDET time slots should be allocated,in order to improve the WET and WDT performance. 展开更多
关键词 energy harvesting modulation(EHM) integrated data and energy transfer(IDET) performance analysis wireless data transfer(WDT) wireless energy transfer(WET)
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Analysis of Urban Agglomeration Network Structure Based on Baidu Migration Data: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Urban Agglomeration
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作者 XIA Yuan WANG Bin 《Journal of Landscape Research》 2024年第4期47-50,共4页
The inter-city linkage heat data provided by Baidu Migration is employed as a characterization of inter-city linkages in order to facilitate the study of the network linkage characteristics and hierarchical structure ... The inter-city linkage heat data provided by Baidu Migration is employed as a characterization of inter-city linkages in order to facilitate the study of the network linkage characteristics and hierarchical structure of urban agglomeration in the Greater Bay Area through the use of social network analysis method.This is the inaugural application of big data based on location services in the study of urban agglomeration network structure,which represents a novel research perspective on this topic.The study reveals that the density of network linkages in the Greater Bay Area urban agglomeration has reached 100%,indicating a mature network-like spatial structure.This structure has given rise to three distinct communities:Shenzhen-Dongguan-Huizhou,Guangzhou-Foshan-Zhaoqing,and Zhuhai-Zhongshan-Jiangmen.Additionally,cities within the Greater Bay Area urban agglomeration play different roles,suggesting that varying development strategies may be necessary to achieve staggered development.The study demonstrates that large datasets represented by LBS can offer novel insights and methodologies for the examination of urban agglomeration network structures,contingent on the appropriate mining and processing of the data. 展开更多
关键词 Baidu migration data Social network analysis Urban agglomeration network structure Greater Bay Area urban agglomeration
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Comparison of R and Excel in the Field of Data Analysis
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作者 Jue Wang 《Journal of Electronic Research and Application》 2024年第3期178-184,共7页
This research paper compares Excel and R language for data analysis and concludes that R language is more suitable for complex data analysis tasks.R language’s open-source nature makes it accessible to everyone,and i... This research paper compares Excel and R language for data analysis and concludes that R language is more suitable for complex data analysis tasks.R language’s open-source nature makes it accessible to everyone,and its powerful data management and analysis tools make it suitable for handling complex data analysis tasks.It is also highly customizable,allowing users to create custom functions and packages to meet their specific needs.Additionally,R language provides high reproducibility,making it easy to replicate and verify research results,and it has excellent collaboration capabilities,enabling multiple users to work on the same project simultaneously.These advantages make R language a more suitable choice for complex data analysis tasks,particularly in scientific research and business applications.The findings of this study will help people understand that R is not just a language that can handle more data than Excel and demonstrate that r is essential to the field of data analysis.At the same time,it will also help users and organizations make informed decisions regarding their data analysis needs and software preferences. 展开更多
关键词 EXCEL R language data analysis Open source COMPARE data management Advantages Disadvantages FUNCTION
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Application of Bayesian Analysis Based on Neural Network and Deep Learning in Data Visualization
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作者 Jiying Yang Qi Long +1 位作者 Xiaoyun Zhu Yuan Yang 《Journal of Electronic Research and Application》 2024年第4期88-93,共6页
This study aims to explore the application of Bayesian analysis based on neural networks and deep learning in data visualization.The research background is that with the increasing amount and complexity of data,tradit... This study aims to explore the application of Bayesian analysis based on neural networks and deep learning in data visualization.The research background is that with the increasing amount and complexity of data,traditional data analysis methods have been unable to meet the needs.Research methods include building neural networks and deep learning models,optimizing and improving them through Bayesian analysis,and applying them to the visualization of large-scale data sets.The results show that the neural network combined with Bayesian analysis and deep learning method can effectively improve the accuracy and efficiency of data visualization,and enhance the intuitiveness and depth of data interpretation.The significance of the research is that it provides a new solution for data visualization in the big data environment and helps to further promote the development and application of data science. 展开更多
关键词 Neural network Deep learning Bayesian analysis data visualization Big data environment
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The use of concept mapping in data analysis:a phenomenology study of family members'experiences in taking care of people with cancer
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作者 Merry Andriani Titan Ligita 《Frontiers of Nursing》 2024年第4期365-372,共8页
Objective:To explain the use of concept mapping in a study about family members'experiences in taking care of people with cancer.Methods:This study used a phenomenological study design.In this study,we describe th... Objective:To explain the use of concept mapping in a study about family members'experiences in taking care of people with cancer.Methods:This study used a phenomenological study design.In this study,we describe the analytical process of using concept mapping in our phenomenological studies about family members'experiences in taking care of people with cancer.Results:We developed several concept maps that aided us in analyzing our collected data from the interviews.Conclusions:The use of concept mapping is suggested to researchers who intend to analyze their data in any qualitative studies,including those using a phenomenological design,because it is a time-efficient way of dealing with large amounts of qualitative data during the analytical process. 展开更多
关键词 CANCER concept mapping data analysis FAMILY PHENOMENOLOGY
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Exploration of University English Teachers’Acceptance and Willingness to Use Learning Management System Data Analysis Tools
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作者 Xiaochao Yao 《Journal of Contemporary Educational Research》 2024年第9期120-128,共9页
This study investigates university English teachers’acceptance and willingness to use learning management system(LMS)data analysis tools in their teaching practices.The research employs a mixed-method approach,combin... This study investigates university English teachers’acceptance and willingness to use learning management system(LMS)data analysis tools in their teaching practices.The research employs a mixed-method approach,combining quantitative surveys and qualitative interviews to understand teachers’perceptions and attitudes,and the factors influencing their adoption of LMS data analysis tools.The findings reveal that perceived usefulness,perceived ease of use,technical literacy,organizational support,and data privacy concerns significantly impact teachers’willingness to use these tools.Based on these insights,the study offers practical recommendations for educational institutions to enhance the effective adoption of LMS data analysis tools in English language teaching. 展开更多
关键词 Learning management system data analysis tools Technology acceptance University English teachers Educational technology data privacy concerns
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Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning
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作者 Abhishek Bajpai Harshita Verma Anita Yadav 《Data Science and Management》 2024年第3期189-196,共8页
The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and healthcare.To address the limitations im... The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and healthcare.To address the limitations imposed by inadequate resources,energy,and network scalability,this type of network relies heavily on data aggregation and clustering algorithms.Although various conventional studies have aimed to enhance the lifespan of a network through robust systems,they do not always provide optimal efficiency for real-time applications.This paper presents an approach based on state-of-the-art machine-learning methods.In this study,we employed a novel approach that combines an extended version of principal component analysis(PCA)and a reinforcement learning algorithm to achieve efficient clustering and data reduction.The primary objectives of this study are to enhance the service life of a network,reduce energy usage,and improve data aggregation efficiency.We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring.Our proposed approach(PQL)was compared to previous studies that utilized adaptive Q-learning(AQL)and regional energy-aware clustering(REAC).Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network. 展开更多
关键词 Wireless sensor network Principal component analysis(PCA) Reinforcement learning data aggregation
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Telomerase-related advances in hepatocellular carcinoma:A bibliometric and visual analysis 被引量:2
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作者 Hai-Yang Li Lin-Lin Zheng +9 位作者 Nan Hu Zhi-Hao Wang Chang-Cheng Tao Ya-Ru Wang Yue Liu Zulihumaer Aizimuaji Hong-Wei Wang Rui-Qi Zheng Ting Xiao Wei-Qi Rong 《World Journal of Gastroenterology》 SCIE CAS 2024年第9期1224-1236,共13页
BACKGROUND As a critical early event in hepatocellular carcinogenesis,telomerase activation might be a promising and critical biomarker for hepatocellular carcinoma(HCC)patients,and its function in the genesis and tre... BACKGROUND As a critical early event in hepatocellular carcinogenesis,telomerase activation might be a promising and critical biomarker for hepatocellular carcinoma(HCC)patients,and its function in the genesis and treatment of HCC has gained much attention over the past two decades.AIM To perform a bibliometric analysis to systematically assess the current state of research on HCC-related telomerase.METHODS The Web of Science Core Collection and PubMed were systematically searched to retrieve publications pertaining to HCC/telomerase limited to“articles”and“reviews”published in English.A total of 873 relevant publications related to HCC and telomerase were identified.We employed the Bibliometrix package in R to extract and analyze the fundamental information of the publications,such as the trends in the publications,citation counts,most prolific or influential writers,and most popular journals;to screen for keywords occurring at high frequency;and to draw collaboration and cluster analysis charts on the basis of coauthorship and co-occurrences.VOSviewer was utilized to compile and visualize the bibliometric data.RESULTS A surge of 51 publications on HCC/telomerase research occurred in 2016,the most productive year from 1996 to 2023,accompanied by the peak citation count recorded in 2016.Up to December 2023,35226 citations were made to all publications,an average of 46.6 citations to each paper.The United States received the most citations(n=13531),followed by China(n=7427)and Japan(n=5754).In terms of national cooperation,China presented the highest centrality,its strongest bonds being to the United States and Japan.Among the 20 academic institutions with the most publications,ten came from China and the rest of Asia,though the University of Paris Cité,Public Assistance-Hospitals of Paris,and the National Institute of Health and Medical Research(INSERM)were the most prolific.As for individual contributions,Hisatomi H,Kaneko S,and Ide T were the three most prolific authors.Kaneko S ranked first by H-index,G-index,and overall publication count,while Zucman-Rossi J ranked first in citation count.The five most popular journals were the World Journal of Gastroenterology,Hepatology,Journal of Hepatology,Oncotarget,and Oncogene,while Nature Genetics,Hepatology,and Nature Reviews Disease Primers had the most citations.We extracted 2293 keywords from the publications,120 of which appeared more than ten times.The most frequent were HCC,telomerase and human telomerase reverse transcriptase(hTERT).Keywords such as mutational landscape,TERT promoter mutations,landscape,risk,and prognosis were among the most common issues in this field in the last three years and may be topics for research in the coming years.CONCLUSION Our bibliometric analysis provides a comprehensive overview of HCC/telomerase research and insights into promising upcoming research. 展开更多
关键词 TELOMERASE Bibliometric analysis Telomerase reverse transcriptase PROGNOSIS treatment Hepatocellular carcinoma
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Global trends and hotspots of treatment for nonalcoholic fatty liver disease:A bibliometric and visualization analysis(2010-2023) 被引量:4
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作者 Jin-Jin Dai Ya-Fei Zhang Zhen-Hua Zhang 《World Journal of Gastroenterology》 SCIE CAS 2023年第37期5339-5360,共22页
BACKGROUND Nonalcoholic fatty liver disease(NAFLD)is chronic,with its progression leading to liver fibrosis and end-stage cirrhosis.Although NAFLD is increasingly common,no treatment guideline has been established.Man... BACKGROUND Nonalcoholic fatty liver disease(NAFLD)is chronic,with its progression leading to liver fibrosis and end-stage cirrhosis.Although NAFLD is increasingly common,no treatment guideline has been established.Many mechanistic studies and drug trials have been conducted for new drug development to treat NAFLD.An up-to-date overview on the knowledge structure of NAFLD through bibliometrics,focusing on research hotspots,is necessary to reveal the rational and timely directions of development in this field.AIM To research the latest literature and determine the current trends in treatment for NAFLD.METHODS Publications related to treatment for NAFLD were searched on the Web of Science Core Collection database,from 2010 to 2023.VOSviewers,CiteSpace,and R package“bibliometrix”were used to conduct this bibliometric analysis.The key information was extracted,and the results of the cluster analysis were based on network data for generating and investigating maps for country,institution,journal,and author.Historiography analysis,bursts and cluster analysis,cooccurrence analysis,and trend topic revealed the knowledge structure and research hotspots in this field.GraphPad Prism 9.5.1.733 and Microsoft Office Excel 2019 were used for data analysis and visualization.RESULTS In total,10829 articles from 120 countries(led by China and the United States)and 8785 institutions were included.The number of publications related to treatment for NAFLD increased annually.While China produced the most publications,the United States was the most cited country,and the United Kingdom collaborated the most from an international standpoint.The University of California-San Diego,Shanghai Jiao Tong University,and Shanghai University of Traditional Chinese Medicine produced the most publications of all the research institutions.The International Journal of Molecular Sciences was the most frequent journal out of the 1523 total journals,and Hepatology was the most cited and co-cited journal.Sanyal AJ was the most cited author,the most co-cited author was Younossi ZM,and the most influential author was Loomba R.The most studied topics included the epidemiology and mechanism of NAFLD,the development of accurate diagnosis,the precise management of patients with NAFLD,and the associated metabolic comorbidities.The major cluster topics were“emerging drug,”“glucagon-like peptide-1 receptor agonist,”“metabolic dysfunction-associated fatty liver disease,”“gut microbiota,”and“glucose metabolism.”CONCLUSION The bibliometric study identified recent research frontiers and hot directions,which can provide a valuable reference for scholars researching treatments for NAFLD. 展开更多
关键词 BIBLIOMETRICS treatment Therapy Nonalcoholic fatty liver disease Metabolic dysfunction-associated fatty liver disease Historiography analysis
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Explainable Neural Network for Sensitivity Analysis of Lithium-ion Battery Smart Production
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作者 Kailong Liu Qiao Peng +2 位作者 Yuhang Liu Naxin Cui Chenghui Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第9期1944-1953,共10页
Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery performance.As battery production is complicated with strongly coupled intermediate and control par... Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery performance.As battery production is complicated with strongly coupled intermediate and control parameters,an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently required.This paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven modelling.To be specific,an explainable neural network named generalized additive model with structured interaction(GAM-SI)is designed to predict two key battery properties,including electrode mass loading and porosity,while the effects of four early production terms on manufactured batteries are explained and analysed.The experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating stages.In addition,the importance ratio ranking,global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural network.Due to the merits of interpretability,the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior,further benefitting smart battery production. 展开更多
关键词 Battery management battery manufacturing data science explainable artificial intelligence sensitivity analysis
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Observation and Analysis of VLF Nocturnal Multimode Interference Phenomenon based on Waveguide Mode Theory
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作者 Sai Yang You-Tian Niu +5 位作者 Zhe Wang Xiu-Kun Zhao Bei Li Yu-Ling Ding Ge-Ge Zhao An-Qi Zhang 《Research in Astronomy and Astrophysics》 SCIE CAS CSCD 2024年第1期78-86,共9页
Very low frequency(VLF)signals are propagated between the ground-ionosphere.Multimode interference will cause the phase to show oscillatory changes with distance while propagating at night,leading to abnormalities in ... Very low frequency(VLF)signals are propagated between the ground-ionosphere.Multimode interference will cause the phase to show oscillatory changes with distance while propagating at night,leading to abnormalities in the received VLF signal.This study uses the VLF signal received in Qingdao City,Shandong Province,from the Russian Alpha navigation system to explore the multimode interference problem of VLF signal propagation.The characteristics of the effect of multimode interference phenomena on the phase are analyzed according to the variation of the phase of the VLF signal.However,the phase of VLF signals will also be affected by the X-ray and energetic particles that are released during the eruption of solar flares,therefore the two phenomena are studied in this work.It is concluded that the X-ray will not affect the phase of VLF signals at night,but the energetic particles will affect the phase change,and the influence of energetic particles should be excluded in the study of multimode interference phenomena.Using VLF signals for navigation positioning in degraded or unavailable GPS conditions is of great practical significance for VLF navigation systems as it can avoid the influence of multimode interference and improve positioning accuracy. 展开更多
关键词 WAVES methods:data analysis Sun:flares Sun:X-rays GAMMA-RAYS
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