Dear Editor,Influenza viruses cause significant mortality and morbidity in humans.Vaccination is currently the most effective way to combat the virus(Perofsky and Nelson,2020).Unfortunately,the influenza virus frequen...Dear Editor,Influenza viruses cause significant mortality and morbidity in humans.Vaccination is currently the most effective way to combat the virus(Perofsky and Nelson,2020).Unfortunately,the influenza virus frequently changes its antigenicity through rapid mutations,leading to decreased vaccine efficacy or even failure.To improve vaccine effectiveness,it is necessary to monitor antigenic variation and update vaccine strains when significant antigenic variation occurs(Perofsky and Nelson,2020;Malik et al.,2024).展开更多
The application field of the Internet of Things(IoT)involves all aspects,and its application in the fields of industry,agriculture,environment,transportation,logistics,security and other infrastructure has effectively...The application field of the Internet of Things(IoT)involves all aspects,and its application in the fields of industry,agriculture,environment,transportation,logistics,security and other infrastructure has effectively promoted the intelligent development of these aspects.Although the IoT has gradually grown in recent years,there are still many problems that need to be overcome in terms of technology,management,cost,policy,and security.We need to constantly weigh the benefits of trusting IoT products and the risk of leaking private data.To avoid the leakage and loss of various user data,this paper developed a hybrid algorithm of kernel function and random perturbation method based on the algorithm of non-negative matrix factorization,which realizes personalized recommendation and solves the problem of user privacy data protection in the process of personalized recommendation.Compared to non-negative matrix factorization privacy-preserving algorithm,the new algorithm does not need to know the detailed information of the data,only need to know the connection between each data;and the new algorithm can process the data points with negative characteristics.Experiments show that the new algorithm can produce recommendation results with certain accuracy under the premise of preserving users’personal privacy.展开更多
The accurate selection of operational parameters is critical for ensuring the safety,efficiency,and automation of Tunnel Boring Machine(TBM)operations.This study proposes a similarity-based framework integrating model...The accurate selection of operational parameters is critical for ensuring the safety,efficiency,and automation of Tunnel Boring Machine(TBM)operations.This study proposes a similarity-based framework integrating model-based boring indexes(derived from rock fragmentation mechanisms)and Euclidean distance analysis to achieve real-time recommendations of TBM operational parameters.Key performance indicators-thrust(F),torque(T),and penetration(p)-were used to calculate three model-based boring indexes(a,b,k),which quantify dynamic rock fragmentation behavior.A dataset of 359 candidate samples,reflecting diverse geological conditions from the Yin-Chao water conveyance project in Inner Mongolia,China,was utilized to validate the framework.The system dynamically recommends parameters by matching real-time data with historical cases through standardized Euclidean distance,achieving high accuracy.Specifically,the mean absolute error(MAE)for rotation speed(n)was 0.10 r/min,corresponding to a mean absolute percentage error(MAPE)of 1.09%.For advance rate(v),the MAE was 3.4 mm/min,with a MAPE of 4.50%.The predicted thrust(F)and torque(T)values exhibited strong agreement with field measurements,with MAEs of 270 kN and 178 kN∙m,respectively.Field applications demonstrated a 30%reduction in parameter adjustment time compared to empirical methods.This work provides a robust solution for real-time TBM control,advancing intelligent tunneling in complex geological environments.展开更多
Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as ...Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph.To tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise.Specifically,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and items.Next,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view construction.This paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge graph.Finally,this paper introduces multi-task learning to mitigate the problem of weak supervisory signals.To validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM datasets.The results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.展开更多
With constant deepening of the reform and opening-up,national economic system has changed from planned economy to market economy,and rural survey and statistics remain in a difficult transition period. In this period,...With constant deepening of the reform and opening-up,national economic system has changed from planned economy to market economy,and rural survey and statistics remain in a difficult transition period. In this period,China needs transforming original statistical mode according to market economic system. All levels of government should report and submit a lot and increasing statistical information. Besides,in this period,townships,villages and counties are faced with old and new conflicts. These conflicts perplex implementation of rural statistics and survey and development of rural statistical undertaking,and also cause researches and thinking of reform of rural statistical and survey methods.展开更多
Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy,this paper introduces projec...Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy,this paper introduces project attribute fuzzy matrix,measures the project relevance through fuzzy clustering method,and classifies all project attributes.Then,the weight of the project relevance is introduced in the user similarity calculation,so that the nearest neighbor search is more accurate.In the prediction scoring section,considering the change of user interest with time,it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation,so that the newer evaluation information in the system has a relatively large weight.The experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality.展开更多
Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been ...Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.展开更多
CME is one of the important events in the sun-earth system as it can induce geomagnetic disturbance and an associated space environment effect.It is of special significance to predict whether CME will reach the Earth ...CME is one of the important events in the sun-earth system as it can induce geomagnetic disturbance and an associated space environment effect.It is of special significance to predict whether CME will reach the Earth and when it will arrive.In this paper,we firstly built a new multiple association list for 215 different events with 18 characteristics including CME features,eruption region coordinates and solar wind parameters.Based on the CME list,we designed a novel model based on the principle of the recommendation algorithm to predict the arrival time of CMEs.According to the two commonly used calculation methods in the recommendation system,cosine distance and Euclidean distance,a controlled trial was carried out respectively.Every feature has been found to have its own appropriate weight.The error analysis indicates the result using the Euclidean distance similarity is much better than that using cosine distance similarity.The mean absolute error and root mean square error of test data in the Euclidean distance are 11.78 and 13.77 h,close to the average level of other CME models issued in the CME scoreboard,which verifies the effectiveness of the recommendation algorithm.This work gives a new endeavor using the recommendation algorithm,and is expected to induce other applications in space weather prediction.展开更多
As ITU-R Recommendations is widely implemented for countries all over the world, the role and status of ITU-R Recommendations are increasingly prominent in the field of radio engineering. ITU and ITU-R Study Groups ar...As ITU-R Recommendations is widely implemented for countries all over the world, the role and status of ITU-R Recommendations are increasingly prominent in the field of radio engineering. ITU and ITU-R Study Groups are summarized. Furthermore, the operating mode of the third study group, and the input documents are interpreted in detail. Lastly, from both wireless system design and electromagnetic compatibility analysis perspective, all of 79 P-series Recommendations are analyzed and classified, and the main contents of each Recommendation are summarized. The above research promote P-series Recommendations are widely used in China.展开更多
A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared...A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.展开更多
From the view of both objective and subjective factors,the indoor air quality(IAQ)evaluation was considered.Carbon dioxide(CO_(2))and formaldehyde(HCHO)were selected as the typical contaminants of indoor air,and the e...From the view of both objective and subjective factors,the indoor air quality(IAQ)evaluation was considered.Carbon dioxide(CO_(2))and formaldehyde(HCHO)were selected as the typical contaminants of indoor air,and the evaluation method of logarithmic index was adopted as the evaluation means of IAQ.Then the recommended limits(RL)of typical contaminants CO_(2)and HCHO were given through analysis and calculation.The limits of CO_(2)and HCHO in Indoor Air Quality Standard of China or other existing standards probably correspond to the level of PD=25(%).The result shows that the existing standards fail to meet the requirement of the definition of"acceptable indoor air quality",that is to say,less than 20%of the people express dissatisfaction.When PD=20%,RL of CO_(2)and HCHO are 728×10-6 and 0.068×10-6 respectively,which are stricter than the limits in the existing standards.The method proposed in this paper is applicable to 13.1%≤PD≤86.7%.展开更多
基金upported by the Major Project of Guangzhou National Laboratory(GZNL2024A01002)National Key Plan for Scientific Research and Development of China(2022YFC2303802)+1 种基金National Natural Science Foundation of China(32170651&32370700)Hunan Provincial Natural Science Foundation of China(2024JJ2015).
文摘Dear Editor,Influenza viruses cause significant mortality and morbidity in humans.Vaccination is currently the most effective way to combat the virus(Perofsky and Nelson,2020).Unfortunately,the influenza virus frequently changes its antigenicity through rapid mutations,leading to decreased vaccine efficacy or even failure.To improve vaccine effectiveness,it is necessary to monitor antigenic variation and update vaccine strains when significant antigenic variation occurs(Perofsky and Nelson,2020;Malik et al.,2024).
基金the National Natural Science Foundation of Chinaunder Grant No.61772280by the China Special Fund for Meteorological Research in the Public Interestunder Grant GYHY201306070by the Jiangsu Province Innovation and Entrepreneurship TrainingProgram for College Students under Grant No.201910300122Y.
文摘The application field of the Internet of Things(IoT)involves all aspects,and its application in the fields of industry,agriculture,environment,transportation,logistics,security and other infrastructure has effectively promoted the intelligent development of these aspects.Although the IoT has gradually grown in recent years,there are still many problems that need to be overcome in terms of technology,management,cost,policy,and security.We need to constantly weigh the benefits of trusting IoT products and the risk of leaking private data.To avoid the leakage and loss of various user data,this paper developed a hybrid algorithm of kernel function and random perturbation method based on the algorithm of non-negative matrix factorization,which realizes personalized recommendation and solves the problem of user privacy data protection in the process of personalized recommendation.Compared to non-negative matrix factorization privacy-preserving algorithm,the new algorithm does not need to know the detailed information of the data,only need to know the connection between each data;and the new algorithm can process the data points with negative characteristics.Experiments show that the new algorithm can produce recommendation results with certain accuracy under the premise of preserving users’personal privacy.
基金supported by the National Key R&D Program of China(2022YFE0200400).
文摘The accurate selection of operational parameters is critical for ensuring the safety,efficiency,and automation of Tunnel Boring Machine(TBM)operations.This study proposes a similarity-based framework integrating model-based boring indexes(derived from rock fragmentation mechanisms)and Euclidean distance analysis to achieve real-time recommendations of TBM operational parameters.Key performance indicators-thrust(F),torque(T),and penetration(p)-were used to calculate three model-based boring indexes(a,b,k),which quantify dynamic rock fragmentation behavior.A dataset of 359 candidate samples,reflecting diverse geological conditions from the Yin-Chao water conveyance project in Inner Mongolia,China,was utilized to validate the framework.The system dynamically recommends parameters by matching real-time data with historical cases through standardized Euclidean distance,achieving high accuracy.Specifically,the mean absolute error(MAE)for rotation speed(n)was 0.10 r/min,corresponding to a mean absolute percentage error(MAPE)of 1.09%.For advance rate(v),the MAE was 3.4 mm/min,with a MAPE of 4.50%.The predicted thrust(F)and torque(T)values exhibited strong agreement with field measurements,with MAEs of 270 kN and 178 kN∙m,respectively.Field applications demonstrated a 30%reduction in parameter adjustment time compared to empirical methods.This work provides a robust solution for real-time TBM control,advancing intelligent tunneling in complex geological environments.
基金supported by the Natural Science Foundation of Ningxia Province(No.2023AAC03316)the Ningxia Hui Autonomous Region Education Department Higher Edu-cation Key Scientific Research Project(No.NYG2022051)the North Minzu University Graduate Innovation Project(YCX23146).
文摘Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph.To tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise.Specifically,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and items.Next,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view construction.This paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge graph.Finally,this paper introduces multi-task learning to mitigate the problem of weak supervisory signals.To validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM datasets.The results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.
基金Supported by Project of Business Management Cultivation Discipline in Commerce Department of Rongchang Campus,Southwest University
文摘With constant deepening of the reform and opening-up,national economic system has changed from planned economy to market economy,and rural survey and statistics remain in a difficult transition period. In this period,China needs transforming original statistical mode according to market economic system. All levels of government should report and submit a lot and increasing statistical information. Besides,in this period,townships,villages and counties are faced with old and new conflicts. These conflicts perplex implementation of rural statistics and survey and development of rural statistical undertaking,and also cause researches and thinking of reform of rural statistical and survey methods.
基金supported by the National Natural Science Foundation of China(61772196,61472136)the Hunan Provincial Focus Social Science Fund(2016ZDB006)+2 种基金Hunan Provincial Social Science Achievement Review Committee results appraisal identification project(Xiang social assessment 2016JD05)Key Project of Hunan Provincial Social Science Achievement Review Committee(XSP 19ZD1005)the financial support provided by the Key Laboratory of Hunan Province for New Retail Virtual Reality Technology(2017TP1026).
文摘Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy,this paper introduces project attribute fuzzy matrix,measures the project relevance through fuzzy clustering method,and classifies all project attributes.Then,the weight of the project relevance is introduced in the user similarity calculation,so that the nearest neighbor search is more accurate.In the prediction scoring section,considering the change of user interest with time,it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation,so that the newer evaluation information in the system has a relatively large weight.The experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality.
基金This work was supported by the Kyonggi University Research Grant 2022.
文摘Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.
基金supported by a NASA Heliophysics vip Investigator Grantsupported by the National Natural Science Foundation of China (Grant Nos.12071166 and 42074224)。
文摘CME is one of the important events in the sun-earth system as it can induce geomagnetic disturbance and an associated space environment effect.It is of special significance to predict whether CME will reach the Earth and when it will arrive.In this paper,we firstly built a new multiple association list for 215 different events with 18 characteristics including CME features,eruption region coordinates and solar wind parameters.Based on the CME list,we designed a novel model based on the principle of the recommendation algorithm to predict the arrival time of CMEs.According to the two commonly used calculation methods in the recommendation system,cosine distance and Euclidean distance,a controlled trial was carried out respectively.Every feature has been found to have its own appropriate weight.The error analysis indicates the result using the Euclidean distance similarity is much better than that using cosine distance similarity.The mean absolute error and root mean square error of test data in the Euclidean distance are 11.78 and 13.77 h,close to the average level of other CME models issued in the CME scoreboard,which verifies the effectiveness of the recommendation algorithm.This work gives a new endeavor using the recommendation algorithm,and is expected to induce other applications in space weather prediction.
文摘As ITU-R Recommendations is widely implemented for countries all over the world, the role and status of ITU-R Recommendations are increasingly prominent in the field of radio engineering. ITU and ITU-R Study Groups are summarized. Furthermore, the operating mode of the third study group, and the input documents are interpreted in detail. Lastly, from both wireless system design and electromagnetic compatibility analysis perspective, all of 79 P-series Recommendations are analyzed and classified, and the main contents of each Recommendation are summarized. The above research promote P-series Recommendations are widely used in China.
基金supporting by grant fund under the Strategic Scholarships for Frontier Research Network for the PhD Program Thai Doctoral degree
文摘A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.
文摘From the view of both objective and subjective factors,the indoor air quality(IAQ)evaluation was considered.Carbon dioxide(CO_(2))and formaldehyde(HCHO)were selected as the typical contaminants of indoor air,and the evaluation method of logarithmic index was adopted as the evaluation means of IAQ.Then the recommended limits(RL)of typical contaminants CO_(2)and HCHO were given through analysis and calculation.The limits of CO_(2)and HCHO in Indoor Air Quality Standard of China or other existing standards probably correspond to the level of PD=25(%).The result shows that the existing standards fail to meet the requirement of the definition of"acceptable indoor air quality",that is to say,less than 20%of the people express dissatisfaction.When PD=20%,RL of CO_(2)and HCHO are 728×10-6 and 0.068×10-6 respectively,which are stricter than the limits in the existing standards.The method proposed in this paper is applicable to 13.1%≤PD≤86.7%.
