Online education has become a critical mode of instruction in Chinese universities,particularly during and after the COVID-19 pandemic.This study examines information management in online education through a triadic f...Online education has become a critical mode of instruction in Chinese universities,particularly during and after the COVID-19 pandemic.This study examines information management in online education through a triadic framework encompassing classroom information management,teaching effectiveness management,and teaching information management.Drawing on in-depth interviews with administrators,teachers,and students,the findings reveal three primary challenges in online education:the absence of embodied information,the uncontrollable nature of online platforms,and nonverbal overload caused by Zoom fatigue.Teachers face difficulties maintaining presence and interaction due to limited feedback from students and risks associated with class recordings.Students experience increased psychological and physical fatigue due to the overlap of learning and living spaces.Recommendations to address these issues include enhancing teacher-student interaction to foster a sense of presence,improving the transparency of course information to align expectations,and adopting user-friendly teaching platforms with privacy safeguards.These insights aim to improve the security,effectiveness,and experience of online education in higher education institutions.展开更多
Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion.Nevertheless,when fusing highly conflicting evidence it may pro-duce counterintuitive outcomes.To address this iss...Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion.Nevertheless,when fusing highly conflicting evidence it may pro-duce counterintuitive outcomes.To address this issue,a fusion approach based on a newly defined belief exponential diver-gence and Deng entropy is proposed.First,a belief exponential divergence is proposed as the conflict measurement between evidences.Then,the credibility of each evidence is calculated.Afterwards,the Deng entropy is used to calculate information volume to determine the uncertainty of evidence.Then,the weight of evidence is calculated by integrating the credibility and uncertainty of each evidence.Ultimately,initial evidences are amended and fused using Dempster’s rule of combination.The effectiveness of this approach in addressing the fusion of three typical conflict paradoxes is demonstrated by arithmetic exam-ples.Additionally,the proposed approach is applied to aerial tar-get recognition and iris dataset-based classification to validate its efficacy.Results indicate that the proposed approach can enhance the accuracy of target recognition and effectively address the issue of fusing conflicting evidences.展开更多
An object learning and recognition system is implemented for humanoid robots to discover and memorize objects only by simple interactions with non-expert users. When the object is presented, the system makes use of th...An object learning and recognition system is implemented for humanoid robots to discover and memorize objects only by simple interactions with non-expert users. When the object is presented, the system makes use of the motion information over consecutive frames to extract object features and implements machine learning based on the bag of visual words approach. Instead of using a local feature descriptor only, the proposed system uses the co-occurring local features in order to increase feature discriminative power for both object model learning and inference stages. For different objects with different textures, a hybrid sampling strategy is considered. This hybrid approach minimizes the consumption of computation resources and helps achieving good performances demonstrated on a set of a dozen different daily objects.展开更多
For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for...For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.展开更多
This paper addresses the challenge of accurately and timely determining the position of a train,with specific consideration given to the integration of the global navigation satellite system(GNSS)and inertial navigati...This paper addresses the challenge of accurately and timely determining the position of a train,with specific consideration given to the integration of the global navigation satellite system(GNSS)and inertial navigation system(INS).To overcome the increasing errors in the INS during interruptions in GNSS signals,as well as the uncertainty associated with process and measurement noise,a deep learning-based method for train positioning is proposed.This method combines convolutional neural networks(CNN),long short-term memory(LSTM),and the invariant extended Kalman filter(IEKF)to enhance the perception of train positions.It effectively handles GNSS signal interruptions and mitigates the impact of noise.Experimental evaluation and comparisons with existing approaches are provided to illustrate the effectiveness and robustness of the proposed method.展开更多
In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for th...In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.展开更多
Purpose: Disseminating medical and health information is a mission of a public medical library. This paper describes a practice of a medical library in providing online access to health information for the general pub...Purpose: Disseminating medical and health information is a mission of a public medical library. This paper describes a practice of a medical library in providing online access to health information for the general public.Design/methodology/approach: A four-step workflow is developed to integrate and disseminate heterogeneous health information from medical associations. First, a raw data repository is developed to manage the original submissions from information providers.Second, each document in the raw data repository is represented in a standardized XML schema. Third, the medical terms are identified and manually annotated, enriching the semantics of health information. Lastly, all the semantically enriched XML documents are converted to HTMLs for online dissemination.Findings: A health information website, CHealth, was developed for Chinese speakers. It provides free online access for all without any login or IP constrains. CHealth is available at www.chealth.org.cn.Research limitations: The current workflow is time-consuming and labor-intensive due to the lack of information submission/exchange standard and commonly agreed-on consumer health terminology in Chinese.Originality/value: In this work, the target audience of the medical library has been extended from traditional academic/professional to the general public. Methodologies in library sciences have been combined with those in consumer health informatics in CHealth development.展开更多
AIM: To determine whether online diffusion of the "Ten Warning Signs of Primary Immunodeficiency Diseases(PID)'' adheres to accepted scientific standards.METHODS: We analyzed how reproducible is online di...AIM: To determine whether online diffusion of the "Ten Warning Signs of Primary Immunodeficiency Diseases(PID)'' adheres to accepted scientific standards.METHODS: We analyzed how reproducible is online diffusion of a unique instrument, the "Ten Warning Signs of PID", created by the Jeffrey Modell Foundation(JMF),by Google-assisted searches among highly visited sites from professional, academic and scientific organizations;governmental agencies; and patient support/advocacy organizations. We examined the diffusion, consistency of use and adequate referencing of this instrument.Where applicable, variant versions of the instrument were examined for changes in factual content that would have practical impact on physicians or on patients and their families.RESULTS: Among the first 100 sites identified by Google search, 85 faithfully reproduced the JMF model, and correctly referenced to its source. By contrast, the other15 also referenced the JMF source but presented one or more changes in content relative to their purported model and therefore represent uncontrolled variants, of unknown origin. Discrepancies identified in the latter included changes in factual content of the original JMF list(C), as well as removal(R) and introduction(I) of novel signs(Table 2), all made without reference to any scientific publications that might account for the drastic changes in factual content. Factual changes include changes inthe number of infectious episodes considered necessary to raise suspicion of PID, as well as the inclusion of various medical conditions not mentioned in the original.Together, these changes will affect the way physicians use the instrument to consult or to inform patients,and the way patients and families think about the need for specialist consultation in view of a possible PID diagnosis.CONCLUSION: The retrieved adaptations and variants,which significantly depart from the original instrument,raise concerns about standards for scientific information provided online to physicians, patients and families.展开更多
<strong>Background:</strong> The potential for misinformation on usercontrolled Knowledge Exchange Social Websites (KESWs) is concerning since it can actively influence Internet users’ knowledge, attitude...<strong>Background:</strong> The potential for misinformation on usercontrolled Knowledge Exchange Social Websites (KESWs) is concerning since it can actively influence Internet users’ knowledge, attitudes, and behaviors related to childhood vaccinations. <strong>Objective:</strong> The present study examines the accuracy and predictors of health information posted to a Knowledge Exchange Social Website (KESW). <strong>Methods:</strong> A sample of 480 answers to childhood vaccination questions were retrieved and rated for accuracy. Multiple logistic regression modeling was used to examine whether answer characteristics (best answer, professional background, statistical information, source disclosure, online link, word count, vaccine stance, and tone) predict accuracy. <strong>Results:</strong> Overall, only 56.2% of the posted answers were rated as “accurate.” Accuracy varied by topics with between 52.8% - 64.3% being rated as accurate. When Yahoo Answers’ “best answers” were examined, only 49.2% rated as accurate compared to 57.7% of all other answers, a finding attributed to widespread nominations of vaccine misinformation as “best answers” for questions addressing the side effects of vaccines. For all other types of questions, “best answers” were more likely to be accurate. Regression modeling revealed that discussions of personal choices regarding childhood vaccinations predicted the accuracy of posted answers, with those who mentioned vaccinating their own children proving more likely to communicate accurate vaccine information, and those expressing vaccine hesitancy proving more likely to share factually inaccurate statements about vaccines. <strong>Conclusion:</strong> The high prevalence of misinformation on KESWs suggests that these websites may serve as a vector for spreading vaccine misperceptions. Further research is needed to assess the impact of various KESWs and to develop effective, coordinated responses by public health agencies.展开更多
The COVID-19 pandemic has brought significant challenges to higher education worldwide. Due to the COVID-19 pandemic, e-learning has begun to be widely used and applied in the teaching and learning processes. However,...The COVID-19 pandemic has brought significant challenges to higher education worldwide. Due to the COVID-19 pandemic, e-learning has begun to be widely used and applied in the teaching and learning processes. However, learning under technological circumstances has proven not always to be a proper solution in education. A highlight challenge, in this regard, is considered to be learning Mathematics online. While some support its positive impact, others greatly oppose it by arguing that neither teaching nor learning has proven successful. Thus, this study examines Kosovo selected universities to see the effectiveness of learning Mathematics online as a case study. Further, it compares the online and traditional learning methods and explores how teachers in higher education in Kosova Universities apply and integrate technology into learning mathematics. This study employed a methodology encompassing questionnaires for students. The results show that students are not overall satisfied with learning Mathematics online leading to the conclusion that online learning is not an effective educational method for learning Mathematics.展开更多
This study aims to identify the current situation and problems of environmental information statement for major four home appliances （refrigerators, washing machines, air conditioners and television receivers） sold ...This study aims to identify the current situation and problems of environmental information statement for major four home appliances （refrigerators, washing machines, air conditioners and television receivers） sold at online stores in Japan, and then to suggest how to improve the situation, through a questionnaire survey conducted among businesses that operate online stores and online malls with multiple online stores. The findings of this study are summarized into the following two points： （l） It is found out that environmental information statement for the home appliances at online stores has four problems： （i） less information on ＂three Rs （reduce, reuse and recycle）＂ and ＂chemical substances＂ than the one on ＂energy conservation＂; （ii） cost for providing environmental information statement; （iii） issues associated with a label and mark placement; （iv） issues associated with energy conservation statement. （2） Improvements are suggested for each of the four problems listed above, and shown are （i） the effectiveness of, and need to promote, a label and mark placement; （ii） cost burden on buyers; （iii） need of active efforts made by businesses and of dissemination of legal regulations to businesses.展开更多
Online social network is increasingly showing a significant impact and role in many areas of social life. In the study of online social network related issues have become the consensus of the academic and industrial c...Online social network is increasingly showing a significant impact and role in many areas of social life. In the study of online social network related issues have become the consensus of the academic and industrial communities and the urgent need for. This paper mainly studies the problem of information dissemination in social network, the mode of communication, behavior, propagation paths and propagation characteristics are studied, and take the Tencent micro-blog as an example, based on the analysis of many examples, several main models and characteristics of information dissemination in social network platform.展开更多
Since its launch last year, the Chinese Government Public Information Online(CGPIO) platform's basic construction has developed rapidly. This paper describes the technology and service status of the platform, anal...Since its launch last year, the Chinese Government Public Information Online(CGPIO) platform's basic construction has developed rapidly. This paper describes the technology and service status of the platform, analyzes its problems, and details the future development of the alliance platform in the future.展开更多
For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors.In this paper,we adopt multi-source information...For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors.In this paper,we adopt multi-source information fusion technology to extract and fuse the features of cutting vibration signal,cutting force signal and acoustic emission signal in time domain,frequency domain and time-frequency domain,and downscale the sample features by Pearson correlation coefficient to construct a sample data set;then we propose a tool life prediction model based on CNN-SVM optimized by genetic algorithm(GA),which uses CNN convolutional neural network as the feature learner and SVM support vector machine as the trainer for regression prediction.The results show that the improved model in this paper can effectively predict the tool life with better generalization ability,faster network fitting,and 99.85%prediction accuracy.And compared with the BP model,CNN model,SVM model and CNN-SVM model,the performance of the coefficient of determination R2 metric improved by 4.88%,2.96%,2.53%and 1.34%,respectively.展开更多
“Cantonese Cuisine Master”project is an important policy proposed by China to inherit Cantonese Cuisine culture,promote employment,and achieve targeted poverty reduction and rural revitalization.Confronted with the ...“Cantonese Cuisine Master”project is an important policy proposed by China to inherit Cantonese Cuisine culture,promote employment,and achieve targeted poverty reduction and rural revitalization.Confronted with the demands of more diverse education,it is an essential opportunity and task for the education system to consider how to construct high-quality online courses and pursue higher-quality“Cantonese Cuisine Master”projects in line with the new era.This paper,based on the theory of instructional media and information processing theory,will further clarify the demand,dilemma,and developing strategy of online course construction for culinary majors,and explore its construction and practice with the example of“A Bite of Teochew Cuisine,”a Guangdong first-class course.展开更多
The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interest...The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.展开更多
The application of information and communications technology(ICT)in the education industry is becoming more and more extensive,and online education realized through ICT is developing in full swing.The influence of ICT...The application of information and communications technology(ICT)in the education industry is becoming more and more extensive,and online education realized through ICT is developing in full swing.The influence of ICT on online education consumer's choice behavior is the core issue of online education industry development research.The research on the interactive path and methods of information and online education consumer choice behavior is worth exploring and revealing.This study introduces the word-of-mouth factor as a new research variable under the framework of the Rational Choice Theory model(RCT)and the structural equation method to conduct empirical research and theoretical analysis to verify the validity of the hypothesis and model.The fifthGeneration mobile communication system(5 G)analyses the factors affecting online education consumer behavior choices based on the premise of ICT.Research on the path between ICT and choice behavior provides new ideas for online education consumer choice behavior research and ICT and content and provides a new scenario.This article is a cross-disciplinary research content in theory,and its innovation opens up a new path for the management of ICT research.The research results have innovative significance and value at both the theoretical and practical levels.展开更多
Fault diagnosis(FD)is essential for ensuring the reliable operation of chillers and preventing energy waste.Feature selection(FS)is a critical prerequisite for effective FD.However,current FS methods have two major ga...Fault diagnosis(FD)is essential for ensuring the reliable operation of chillers and preventing energy waste.Feature selection(FS)is a critical prerequisite for effective FD.However,current FS methods have two major gaps.First,most approaches rely on single-source ranking information(SSRI)to evaluate features individually,which results in non-robust outcomes across different models and datasets due to the one-sided nature of SSRI.Second,thermodynamic mechanism features are often overlooked,leading to incomplete initial feature libraries,making it challenging to select optimal features and achieve better diagnostic performance.To address these issues,a robust ensemble FS method based on multi-source ranking information(MSRI)is proposed.By employing an efficient strategy based on maximizing relevance while proper redundancy,the MSRI method fully leverages Mutual Information,Information Gain,Gain Ratio,Gini index,Chi-squared,and Relief-F from both qualitative and quantitative perspectives.Additionally,comprehensive consideration of thermodynamic mechanism features ensures a complete initial feature library.From a methodological standpoint,a general framework for constructing the MSRI-based FS method is provided.The proposed method is applied to chiller FD and tested across ten widely-used machine learning models.Thirteen optimized features are selected from the original set of forty-two,achieving an average diagnostic accuracy of 98.40%and an average F-measure above 94.94%,demonstrating the effectiveness and generalizability of the MSRI method.Compared to the SSRI approach,the MSRI method shows superior robustness,with the standard deviation of diagnostic accuracy reduced by 0.03 to 0.07 and an improvement in diagnostic accuracy ranging from 2.53%to 6.12%.Moreover,the MSRI method reduced computation time by 98.62%compared to wrapper methods,without sacrificing accuracy.展开更多
A thorough understanding of the information dissemination process in Online Social Networks (OSNs) is crucial for enhancing user behavior analysis. While recent studies usually focus on assessing the emotional intensi...A thorough understanding of the information dissemination process in Online Social Networks (OSNs) is crucial for enhancing user behavior analysis. While recent studies usually focus on assessing the emotional intensity of individual tweets or predicting their popularity, they frequently overlook how these tweets impact sentiment trends over time. The explosive and inflammatory nature of deliberate tweets is difficult to perceive by prediction or sentiment methods. To address this gap, we propose the multi-view Information Propagation State Awareness (IPSA) model, which aims to simultaneously assess and forecast both the popularity and sentiment strength throughout the information propagation process. Our approach begins by segmenting the information propagation into distinct time windows. Within each window, the IPSA model designs an encoder module to capture multi-view influence factors from structure, content, and time series data. Specifically, the encoder module includes a graph encoder layer based on graph attention networks to represent the backbone propagation structure formed by key nodes in the reply chain. Meanwhile, the sentiment encoder layer, utilizing an attention mechanism, extracts emotional factors present in the reply chain. Besides, we introduce a residual information prediction method that enhances the model’s precision in perceiving both popularity and sentiment intensity for each time window. Our comparative experiments, conducted on two datasets and benchmarked against State-of-the-Art (SOTA) methods, demonstrate that the IPSA model excels in predicting popularity and assessing future emotional trends in information propagation.展开更多
文摘Online education has become a critical mode of instruction in Chinese universities,particularly during and after the COVID-19 pandemic.This study examines information management in online education through a triadic framework encompassing classroom information management,teaching effectiveness management,and teaching information management.Drawing on in-depth interviews with administrators,teachers,and students,the findings reveal three primary challenges in online education:the absence of embodied information,the uncontrollable nature of online platforms,and nonverbal overload caused by Zoom fatigue.Teachers face difficulties maintaining presence and interaction due to limited feedback from students and risks associated with class recordings.Students experience increased psychological and physical fatigue due to the overlap of learning and living spaces.Recommendations to address these issues include enhancing teacher-student interaction to foster a sense of presence,improving the transparency of course information to align expectations,and adopting user-friendly teaching platforms with privacy safeguards.These insights aim to improve the security,effectiveness,and experience of online education in higher education institutions.
基金supported by the National Natural Science Foundation of China(61903305,62073267)the Fundamental Research Funds for the Central Universities(HXGJXM202214).
文摘Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion.Nevertheless,when fusing highly conflicting evidence it may pro-duce counterintuitive outcomes.To address this issue,a fusion approach based on a newly defined belief exponential diver-gence and Deng entropy is proposed.First,a belief exponential divergence is proposed as the conflict measurement between evidences.Then,the credibility of each evidence is calculated.Afterwards,the Deng entropy is used to calculate information volume to determine the uncertainty of evidence.Then,the weight of evidence is calculated by integrating the credibility and uncertainty of each evidence.Ultimately,initial evidences are amended and fused using Dempster’s rule of combination.The effectiveness of this approach in addressing the fusion of three typical conflict paradoxes is demonstrated by arithmetic exam-ples.Additionally,the proposed approach is applied to aerial tar-get recognition and iris dataset-based classification to validate its efficacy.Results indicate that the proposed approach can enhance the accuracy of target recognition and effectively address the issue of fusing conflicting evidences.
基金The National Natural Science Foundation of China(No.60672094,60971098)
文摘An object learning and recognition system is implemented for humanoid robots to discover and memorize objects only by simple interactions with non-expert users. When the object is presented, the system makes use of the motion information over consecutive frames to extract object features and implements machine learning based on the bag of visual words approach. Instead of using a local feature descriptor only, the proposed system uses the co-occurring local features in order to increase feature discriminative power for both object model learning and inference stages. For different objects with different textures, a hybrid sampling strategy is considered. This hybrid approach minimizes the consumption of computation resources and helps achieving good performances demonstrated on a set of a dozen different daily objects.
基金supported by the National Natural Science Foundation of China under Grant 51722406,52074340,and 51874335the Shandong Provincial Natural Science Foundation under Grant JQ201808+5 种基金The Fundamental Research Funds for the Central Universities under Grant 18CX02097Athe Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-008the Science and Technology Support Plan for Youth Innovation of University in Shandong Province under Grant 2019KJH002the National Research Council of Science and Technology Major Project of China under Grant 2016ZX05025001-006111 Project under Grant B08028Sinopec Science and Technology Project under Grant P20050-1
文摘For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.
基金supported by the National Natural Science Foundation of China(Nos.61925302,62273027)the Beijing Natural Science Foundation(L211021).
文摘This paper addresses the challenge of accurately and timely determining the position of a train,with specific consideration given to the integration of the global navigation satellite system(GNSS)and inertial navigation system(INS).To overcome the increasing errors in the INS during interruptions in GNSS signals,as well as the uncertainty associated with process and measurement noise,a deep learning-based method for train positioning is proposed.This method combines convolutional neural networks(CNN),long short-term memory(LSTM),and the invariant extended Kalman filter(IEKF)to enhance the perception of train positions.It effectively handles GNSS signal interruptions and mitigates the impact of noise.Experimental evaluation and comparisons with existing approaches are provided to illustrate the effectiveness and robustness of the proposed method.
基金supported by the Science and Technology Project of State Grid Shandong Electric Power Company?“Research on the Data-Driven Method for Energy Internet”?(Project No.2018A-100)。
文摘In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.
基金supported by the National Key Technology R&D Program of China (Grant No.:2013BAI06B01)
文摘Purpose: Disseminating medical and health information is a mission of a public medical library. This paper describes a practice of a medical library in providing online access to health information for the general public.Design/methodology/approach: A four-step workflow is developed to integrate and disseminate heterogeneous health information from medical associations. First, a raw data repository is developed to manage the original submissions from information providers.Second, each document in the raw data repository is represented in a standardized XML schema. Third, the medical terms are identified and manually annotated, enriching the semantics of health information. Lastly, all the semantically enriched XML documents are converted to HTMLs for online dissemination.Findings: A health information website, CHealth, was developed for Chinese speakers. It provides free online access for all without any login or IP constrains. CHealth is available at www.chealth.org.cn.Research limitations: The current workflow is time-consuming and labor-intensive due to the lack of information submission/exchange standard and commonly agreed-on consumer health terminology in Chinese.Originality/value: In this work, the target audience of the medical library has been extended from traditional academic/professional to the general public. Methodologies in library sciences have been combined with those in consumer health informatics in CHealth development.
文摘AIM: To determine whether online diffusion of the "Ten Warning Signs of Primary Immunodeficiency Diseases(PID)'' adheres to accepted scientific standards.METHODS: We analyzed how reproducible is online diffusion of a unique instrument, the "Ten Warning Signs of PID", created by the Jeffrey Modell Foundation(JMF),by Google-assisted searches among highly visited sites from professional, academic and scientific organizations;governmental agencies; and patient support/advocacy organizations. We examined the diffusion, consistency of use and adequate referencing of this instrument.Where applicable, variant versions of the instrument were examined for changes in factual content that would have practical impact on physicians or on patients and their families.RESULTS: Among the first 100 sites identified by Google search, 85 faithfully reproduced the JMF model, and correctly referenced to its source. By contrast, the other15 also referenced the JMF source but presented one or more changes in content relative to their purported model and therefore represent uncontrolled variants, of unknown origin. Discrepancies identified in the latter included changes in factual content of the original JMF list(C), as well as removal(R) and introduction(I) of novel signs(Table 2), all made without reference to any scientific publications that might account for the drastic changes in factual content. Factual changes include changes inthe number of infectious episodes considered necessary to raise suspicion of PID, as well as the inclusion of various medical conditions not mentioned in the original.Together, these changes will affect the way physicians use the instrument to consult or to inform patients,and the way patients and families think about the need for specialist consultation in view of a possible PID diagnosis.CONCLUSION: The retrieved adaptations and variants,which significantly depart from the original instrument,raise concerns about standards for scientific information provided online to physicians, patients and families.
文摘<strong>Background:</strong> The potential for misinformation on usercontrolled Knowledge Exchange Social Websites (KESWs) is concerning since it can actively influence Internet users’ knowledge, attitudes, and behaviors related to childhood vaccinations. <strong>Objective:</strong> The present study examines the accuracy and predictors of health information posted to a Knowledge Exchange Social Website (KESW). <strong>Methods:</strong> A sample of 480 answers to childhood vaccination questions were retrieved and rated for accuracy. Multiple logistic regression modeling was used to examine whether answer characteristics (best answer, professional background, statistical information, source disclosure, online link, word count, vaccine stance, and tone) predict accuracy. <strong>Results:</strong> Overall, only 56.2% of the posted answers were rated as “accurate.” Accuracy varied by topics with between 52.8% - 64.3% being rated as accurate. When Yahoo Answers’ “best answers” were examined, only 49.2% rated as accurate compared to 57.7% of all other answers, a finding attributed to widespread nominations of vaccine misinformation as “best answers” for questions addressing the side effects of vaccines. For all other types of questions, “best answers” were more likely to be accurate. Regression modeling revealed that discussions of personal choices regarding childhood vaccinations predicted the accuracy of posted answers, with those who mentioned vaccinating their own children proving more likely to communicate accurate vaccine information, and those expressing vaccine hesitancy proving more likely to share factually inaccurate statements about vaccines. <strong>Conclusion:</strong> The high prevalence of misinformation on KESWs suggests that these websites may serve as a vector for spreading vaccine misperceptions. Further research is needed to assess the impact of various KESWs and to develop effective, coordinated responses by public health agencies.
文摘The COVID-19 pandemic has brought significant challenges to higher education worldwide. Due to the COVID-19 pandemic, e-learning has begun to be widely used and applied in the teaching and learning processes. However, learning under technological circumstances has proven not always to be a proper solution in education. A highlight challenge, in this regard, is considered to be learning Mathematics online. While some support its positive impact, others greatly oppose it by arguing that neither teaching nor learning has proven successful. Thus, this study examines Kosovo selected universities to see the effectiveness of learning Mathematics online as a case study. Further, it compares the online and traditional learning methods and explores how teachers in higher education in Kosova Universities apply and integrate technology into learning mathematics. This study employed a methodology encompassing questionnaires for students. The results show that students are not overall satisfied with learning Mathematics online leading to the conclusion that online learning is not an effective educational method for learning Mathematics.
文摘This study aims to identify the current situation and problems of environmental information statement for major four home appliances （refrigerators, washing machines, air conditioners and television receivers） sold at online stores in Japan, and then to suggest how to improve the situation, through a questionnaire survey conducted among businesses that operate online stores and online malls with multiple online stores. The findings of this study are summarized into the following two points： （l） It is found out that environmental information statement for the home appliances at online stores has four problems： （i） less information on ＂three Rs （reduce, reuse and recycle）＂ and ＂chemical substances＂ than the one on ＂energy conservation＂; （ii） cost for providing environmental information statement; （iii） issues associated with a label and mark placement; （iv） issues associated with energy conservation statement. （2） Improvements are suggested for each of the four problems listed above, and shown are （i） the effectiveness of, and need to promote, a label and mark placement; （ii） cost burden on buyers; （iii） need of active efforts made by businesses and of dissemination of legal regulations to businesses.
文摘Online social network is increasingly showing a significant impact and role in many areas of social life. In the study of online social network related issues have become the consensus of the academic and industrial communities and the urgent need for. This paper mainly studies the problem of information dissemination in social network, the mode of communication, behavior, propagation paths and propagation characteristics are studied, and take the Tencent micro-blog as an example, based on the analysis of many examples, several main models and characteristics of information dissemination in social network platform.
文摘Since its launch last year, the Chinese Government Public Information Online(CGPIO) platform's basic construction has developed rapidly. This paper describes the technology and service status of the platform, analyzes its problems, and details the future development of the alliance platform in the future.
基金financed with the means of Basic Scientific Research Youth Program of Education Department of Liaoning Province,No.LJKQZ2021185Yingkou Enterprise and Doctor Innovation Program (QB-2021-05).
文摘For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors.In this paper,we adopt multi-source information fusion technology to extract and fuse the features of cutting vibration signal,cutting force signal and acoustic emission signal in time domain,frequency domain and time-frequency domain,and downscale the sample features by Pearson correlation coefficient to construct a sample data set;then we propose a tool life prediction model based on CNN-SVM optimized by genetic algorithm(GA),which uses CNN convolutional neural network as the feature learner and SVM support vector machine as the trainer for regression prediction.The results show that the improved model in this paper can effectively predict the tool life with better generalization ability,faster network fitting,and 99.85%prediction accuracy.And compared with the BP model,CNN model,SVM model and CNN-SVM model,the performance of the coefficient of determination R2 metric improved by 4.88%,2.96%,2.53%and 1.34%,respectively.
基金The research result of“A Bite of Teochew Cuisine”of Guangdong Quality Project(Open Online Course)“The Creation of Excellent Science Popularization Works for Chinese Molecular Cooking Micro-course”of Guangdong Science and Technology Program(Project No.:2019A141405059).
文摘“Cantonese Cuisine Master”project is an important policy proposed by China to inherit Cantonese Cuisine culture,promote employment,and achieve targeted poverty reduction and rural revitalization.Confronted with the demands of more diverse education,it is an essential opportunity and task for the education system to consider how to construct high-quality online courses and pursue higher-quality“Cantonese Cuisine Master”projects in line with the new era.This paper,based on the theory of instructional media and information processing theory,will further clarify the demand,dilemma,and developing strategy of online course construction for culinary majors,and explore its construction and practice with the example of“A Bite of Teochew Cuisine,”a Guangdong first-class course.
文摘The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.
基金supported by National Social Science Fund Youth Project“Research on the Group Behavior of‘Post-95’College Students Based on Complex Networks”of China(Project Number:17CKS047)。
文摘The application of information and communications technology(ICT)in the education industry is becoming more and more extensive,and online education realized through ICT is developing in full swing.The influence of ICT on online education consumer's choice behavior is the core issue of online education industry development research.The research on the interactive path and methods of information and online education consumer choice behavior is worth exploring and revealing.This study introduces the word-of-mouth factor as a new research variable under the framework of the Rational Choice Theory model(RCT)and the structural equation method to conduct empirical research and theoretical analysis to verify the validity of the hypothesis and model.The fifthGeneration mobile communication system(5 G)analyses the factors affecting online education consumer behavior choices based on the premise of ICT.Research on the path between ICT and choice behavior provides new ideas for online education consumer choice behavior research and ICT and content and provides a new scenario.This article is a cross-disciplinary research content in theory,and its innovation opens up a new path for the management of ICT research.The research results have innovative significance and value at both the theoretical and practical levels.
基金the National Natural Science Foundation of China(No.52478087)China Postdoctoral Science Foundation(No.2024M750799,No.2024T170238)+4 种基金China Scholarship Council(No.202308410494)Zhongyuan Outstanding Youth Talent Program(No.2022 Year)Youth Scientist Project in Henan Province(No.225200810087)the Program for Science&Technology Innovation Talents in Universities of Henan Province(No.22HASTIT025)the Program for Innovative Research Team(in Science and Technology)in University of Henan Province(No.22IRTSTHN006).
文摘Fault diagnosis(FD)is essential for ensuring the reliable operation of chillers and preventing energy waste.Feature selection(FS)is a critical prerequisite for effective FD.However,current FS methods have two major gaps.First,most approaches rely on single-source ranking information(SSRI)to evaluate features individually,which results in non-robust outcomes across different models and datasets due to the one-sided nature of SSRI.Second,thermodynamic mechanism features are often overlooked,leading to incomplete initial feature libraries,making it challenging to select optimal features and achieve better diagnostic performance.To address these issues,a robust ensemble FS method based on multi-source ranking information(MSRI)is proposed.By employing an efficient strategy based on maximizing relevance while proper redundancy,the MSRI method fully leverages Mutual Information,Information Gain,Gain Ratio,Gini index,Chi-squared,and Relief-F from both qualitative and quantitative perspectives.Additionally,comprehensive consideration of thermodynamic mechanism features ensures a complete initial feature library.From a methodological standpoint,a general framework for constructing the MSRI-based FS method is provided.The proposed method is applied to chiller FD and tested across ten widely-used machine learning models.Thirteen optimized features are selected from the original set of forty-two,achieving an average diagnostic accuracy of 98.40%and an average F-measure above 94.94%,demonstrating the effectiveness and generalizability of the MSRI method.Compared to the SSRI approach,the MSRI method shows superior robustness,with the standard deviation of diagnostic accuracy reduced by 0.03 to 0.07 and an improvement in diagnostic accuracy ranging from 2.53%to 6.12%.Moreover,the MSRI method reduced computation time by 98.62%compared to wrapper methods,without sacrificing accuracy.
基金supported by the National Natural Science Foundation of China(No.62302150)China Postdoctoral Science Foundation(No.2024M750731)Jiangsu Province Higher Education Basic Science(Natural Science)Foundation(No.24KJB520014).
文摘A thorough understanding of the information dissemination process in Online Social Networks (OSNs) is crucial for enhancing user behavior analysis. While recent studies usually focus on assessing the emotional intensity of individual tweets or predicting their popularity, they frequently overlook how these tweets impact sentiment trends over time. The explosive and inflammatory nature of deliberate tweets is difficult to perceive by prediction or sentiment methods. To address this gap, we propose the multi-view Information Propagation State Awareness (IPSA) model, which aims to simultaneously assess and forecast both the popularity and sentiment strength throughout the information propagation process. Our approach begins by segmenting the information propagation into distinct time windows. Within each window, the IPSA model designs an encoder module to capture multi-view influence factors from structure, content, and time series data. Specifically, the encoder module includes a graph encoder layer based on graph attention networks to represent the backbone propagation structure formed by key nodes in the reply chain. Meanwhile, the sentiment encoder layer, utilizing an attention mechanism, extracts emotional factors present in the reply chain. Besides, we introduce a residual information prediction method that enhances the model’s precision in perceiving both popularity and sentiment intensity for each time window. Our comparative experiments, conducted on two datasets and benchmarked against State-of-the-Art (SOTA) methods, demonstrate that the IPSA model excels in predicting popularity and assessing future emotional trends in information propagation.
