From the view of both objective and subjective factors,the indoor air quality(IAQ)evaluation was considered.Carbon dioxide(CO2)and formaldehyde(HCHO)were selected as the typical contaminants of indoor air,and the eval...From the view of both objective and subjective factors,the indoor air quality(IAQ)evaluation was considered.Carbon dioxide(CO2)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 CO2 and HCHO were given through analysis and calculation.The limits of CO2 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 CO2 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
Objective To explore the possible preventive mechanism of Hunan expert group recommended Chinese medicine prescription of No.2(Pre-No.2)against coronavirus disease 2019(COVID-19)by network pharmacology method.Methods ...Objective To explore the possible preventive mechanism of Hunan expert group recommended Chinese medicine prescription of No.2(Pre-No.2)against coronavirus disease 2019(COVID-19)by network pharmacology method.Methods The target proteins of effective components and active compounds in Pre-No.2 were screened by searching the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP).A component-target-disease interaction network of Pre-No.2 was constructed by Cytoscape 3.7.2,gene ontology(GO)analysis,and Kyoto encyclopedia of genes and genomes(KEGG)analysis of target protein pathway by DAVID.Results A total of 163 compounds and 278 target protein targets in Pre-No.2 were collected from the TCMSP database.Kaempferol,wogonin,7-methoxy-2-methyl isoflavone,formononetin,isorhamnetin,and licochalcone A were the most frequent targets in the regulatory network.GO enrichment analysis showed that Pre-No.2 regulated response to virus,viral processes,humoral immune responses,defense responses to virus and viral entry into host cells.KEGG enrichment analysis showed that the formula regulated the NF-κB signaling pathway,B cell receptor signaling pathway,viral carcinogenesis,T cell signaling pathway and FcγR-mediated phagocytosis signaling pathway.Conclusions Pre-No.2 may play a preventive role against COVID-19 through regulation of the Toll-like signaling,T cell signaling,B cell signaling and other signaling pathways.It may regulate the immune system to protect against anti-influenza virus.展开更多
Book 1: (Editor-in-Chief: Shi Yafeng; Published by Elsevier and Science Press Beijing in 2008, 539 pages) Glaciers and Related Environments in China Since the professional institution for glaciology attached to the Ch...Book 1: (Editor-in-Chief: Shi Yafeng; Published by Elsevier and Science Press Beijing in 2008, 539 pages) Glaciers and Related Environments in China Since the professional institution for glaciology attached to the Chinese Academy of Sciences was established in 1958, studies of glaciers in alpine regions, and of Quaternary glaciations throughout展开更多
Closer sino-African relations have encouraged more Chinese enterprises to invest in African countries.Statistics show that more than 2,000 Chinese enterprises had invested in the continent by 2012.
The response of rice to N fertilizer applicationhas shown that high rates of N application donot always ensure a proportional increase inyield due to high N losses. A model, ORYZA-0 was developed by ten Berge for desi...The response of rice to N fertilizer applicationhas shown that high rates of N application donot always ensure a proportional increase inyield due to high N losses. A model, ORYZA-0 was developed by ten Berge for designingoptimum N fertilizer management strategy inrice. We evaluated the performance ofORYZA-0 in Jinhua, Zhejiang Province. ORYZA-0 includes N uptakes, partition-ing of N among the organs, and utilization ofleaf N in converting solar energy to dry mat-ter. It can predict the amount and time of Nfertilizer application to achieve a maximumbiomass or yield combining with Price algo-rithm optimization procedure.展开更多
Current guidelines for treating asymptomatic common bile duct stones(CBDS)recommend stone removal,with endoscopic retrograde cholangiopan-creatography(ERCP)being the first treatment choice.When deciding on ERCP treatm...Current guidelines for treating asymptomatic common bile duct stones(CBDS)recommend stone removal,with endoscopic retrograde cholangiopan-creatography(ERCP)being the first treatment choice.When deciding on ERCP treatment for asymptomatic CBDS,the risk of ERCP-related complications and outcome of natural history of asymptomatic CBDS should be compared.The incidence rate of ERCP-related complications,particularly of post-ERCP pancreatitis for asymptomatic CBDS,was reportedly higher than that of symptomatic CBDS,increasing the risk of ERCP-related complications for asymptomatic CBDS compared with that previously reported for biliopancreatic diseases.Although studies have reported short-to middle-term outcomes of natural history of asymptomatic CBDS,its long-term natural history is not well known.Till date,there are no prospective studies that determined whether ERCP has a better outcome than no treatment in patients with asymptomatic CBDS or not.No randomized controlled trial has evaluated the risk of early and late ERCP-related complications vs the risk of biliary complications in the wait-and-see approach,suggesting that a change is needed in our perspective on endoscopic treatment for asymptomatic CBDS.Further studies examining long-term complication risks of ERCP and wait-and-see groups for asymptomatic CBDS are warranted to discuss whether routine endoscopic treatment for asymptomatic CBDS is justified or not.展开更多
A new collaborative filtered recommendation strategy oriented to trajectory data is proposed for communication bottlenecks and vulnerability in centralized system structure location services. In the strategy based on ...A new collaborative filtered recommendation strategy oriented to trajectory data is proposed for communication bottlenecks and vulnerability in centralized system structure location services. In the strategy based on distributed system architecture, individual user information profiles were established using daily trajectory information and neighboring user groups were established using density measure. Then the trajectory similarity and profile similarity were calculated to recommend appropriate location services using collaborative filtering recommendation method. The strategy was verified on real position data set. The proposed strategy provides higher quality location services to ensure the privacy of user position information.展开更多
With the rapid development of electric vehicles,the requirements for charging stations are getting higher and higher.In this study,we constructed a charging station topology network inNanjing through the Space-L metho...With the rapid development of electric vehicles,the requirements for charging stations are getting higher and higher.In this study,we constructed a charging station topology network inNanjing through the Space-L method,mapping charging stations as network nodes and constructing edges through road relationships.The experiment introduced five EV charging recommendation strategies(based on distance,number of fast charging piles,user preference,price,and overall rating)used to simulate disordered charging caused by different user preferences,and the impact of the networkdynamic robustness in case of node failure is exploredby simulating the load-capacity cascade failure model.In this paper,two important metrics for evaluating network robustness are selected:the relative size of the maximum connected subgraph and the network efficiency.The experimental results point out that in the price recommendation strategy,the network stability significantly decreases when the node failure ratio reaches 75.4%,while the fast charging quantity recommendation strategy significantly decreases when the node failure ratio is 62.3%.Therefore,the robustness of the charging station network is best under the price recommendation,while the network robustness is poor under the fast charging quantity recommendation.While the network robustness is poor under preference recommendation.Based on this finding,this study particularly emphasizes that in the process of improving the robustness of the charging station network,it is necessary to comprehensively consider the market demand and guide users to charge in an orderly manner by reasonably adjusting the price strategy.This strategy not only effectively prevents network stability problems that may result fromdisorderly charging behavior,but also enhances the ability of the charging network to resist node failure and improves the overall dynamic robustness of the network.展开更多
1.Introduction The COVID-19 pandemic is affecting the lives of the world population in various ways and has resulted in an unforeseen scale of disruption of activities across the globe.Its emergence has health and eco...1.Introduction The COVID-19 pandemic is affecting the lives of the world population in various ways and has resulted in an unforeseen scale of disruption of activities across the globe.Its emergence has health and economic implications that impact individuals,organizations and sovereign states which is inclusive of the stakeholders in a tax system.Thus,revenue authorities need to take actions to protect and ease the burden on its external and internal stakeholders.展开更多
Objective This study aimed to reexplore minimum iodine excretion and to build a dietary iodine recommendation for Chinese adults using the obligatory iodine loss hypothesis.Methods Data from 171 Chinese adults(19–21 ...Objective This study aimed to reexplore minimum iodine excretion and to build a dietary iodine recommendation for Chinese adults using the obligatory iodine loss hypothesis.Methods Data from 171 Chinese adults(19–21 years old)were collected and analyzed based on three balance studies in Shenzhen,Yinchuan,and Changzhi.The single exponential equation was accordingly used to simulate the trajectory of 24 h urinary iodine excretion as the low iodine experimental diets offered(iodine intake:11-26μg/day)and to further deduce the dietary reference intakes(DRIs)for iodine,including estimated average requirement(EAR)and recommended nutrient intake(RNI).Results The minimum iodine excretion was estimated as 57,58,and 51μg/day in three balance studies,respectively.Moreover,it was further suggested as 57,58,and 51μg/day for iodine EAR,and 80,81,and 71μg/day for iodine RNI or expressed as 1.42,1.41,and 1.20μg/(day·kg)of body weight.Conclusion The iodine DRIs for Chinese adults were established based on the obligatory iodine loss hypothesis,which provides scientific support for the amendment of nutrient requirements.展开更多
Azoospermia,defined as the absence of sperm in the ejaculate,is a well-documented consequence of exogenous testosterone(ET)and anabolic–androgenic steroid(AAS)use.These agents suppress the hypothalamic–pituitary–go...Azoospermia,defined as the absence of sperm in the ejaculate,is a well-documented consequence of exogenous testosterone(ET)and anabolic–androgenic steroid(AAS)use.These agents suppress the hypothalamic–pituitary–gonadal(HPG)axis,leading to reduced intratesticular testosterone levels and impaired spermatogenesis.This review examines the pathophysiological mechanisms underlying azoospermia and outlines therapeutic strategies for recovery.Azoospermia is categorized into pretesticular,testicular,and post-testicular types,with a focus on personalized treatment approaches based on the degree of HPG axis suppression and baseline testicular function.Key strategies include discontinuing ET and monitoring for spontaneous recovery,particularly in patients with shorter durations of ET use.For cases of persistent azoospermia,gonadotropins(human chorionic gonadotropin[hCG]and follicle-stimulating hormone[FSH])and selective estrogen receptor modulators(SERMs),such as clomiphene citrate,are recommended,either alone or in combination.The global increase in exogenous testosterone use,including testosterone replacement therapy and AAS,underscores the need for improved management of associated azoospermia,which can be temporary or permanent depending on individual factors and the type of testosterone used.Additionally,the manuscript discusses preventive strategies,such as transitioning to short-acting testosterone formulations or incorporating low-dose hCG to preserve fertility during ET therapy.While guidelines for managing testosterone-related azoospermia remain limited,emerging research indicates the potential efficacy of hormonal stimulation therapies.However,there is a notable lack of well-structured,controlled,and long-term studies addressing the management of azoospermia related to exogenous testosterone use,highlighting the need for such studies to inform evidence-based recommendations.展开更多
A personalized outfit recommendation has emerged as a hot research topic in the fashion domain.However,existing recommendations do not fully exploit user style preferences.Typically,users prefer particular styles such...A personalized outfit recommendation has emerged as a hot research topic in the fashion domain.However,existing recommendations do not fully exploit user style preferences.Typically,users prefer particular styles such as casual and athletic styles,and consider attributes like color and texture when selecting outfits.To achieve personalized outfit recommendations in line with user style preferences,this paper proposes a personal style guided outfit recommendation with multi-modal fashion compatibility modeling,termed as PSGNet.Firstly,a style classifier is designed to categorize fashion images of various clothing types and attributes into distinct style categories.Secondly,a personal style prediction module extracts user style preferences by analyzing historical data.Then,to address the limitations of single-modal representations and enhance fashion compatibility,both fashion images and text data are leveraged to extract multi-modal features.Finally,PSGNet integrates these components through Bayesian personalized ranking(BPR)to unify the personal style and fashion compatibility,where the former is used as personal style features and guides the output of the personalized outfit recommendation tailored to the target user.Extensive experiments on large-scale datasets demonstrate that the proposed model is efficient on the personalized outfit recommendation.展开更多
The rapid development of short video platforms poses new challenges for traditional recommendation systems.Recommender systems typically depend on two types of user behavior feedback to construct user interest profile...The rapid development of short video platforms poses new challenges for traditional recommendation systems.Recommender systems typically depend on two types of user behavior feedback to construct user interest profiles:explicit feedback(interactive behavior),which significantly influences users’short-term interests,and implicit feedback(viewing time),which substantially affects their long-term interests.However,the previous model fails to distinguish between these two feedback methods,leading it to predict only the overall preferences of users based on extensive historical behavior sequences.Consequently,it cannot differentiate between users’long-term and shortterm interests,resulting in low accuracy in describing users’interest states and predicting the evolution of their interests.This paper introduces a video recommendationmodel calledCAT-MFRec(CrossAttention Transformer-Mixed Feedback Recommendation)designed to differentiate between explicit and implicit user feedback within the DIEN(Deep Interest Evolution Network)framework.This study emphasizes the separate learning of the two types of behavioral feedback,effectively integrating them through the cross-attention mechanism.Additionally,it leverages the long sequence dependence capabilities of Transformer technology to accurately construct user interest profiles and predict the evolution of user interests.Experimental results indicate that CAT-MF Rec significantly outperforms existing recommendation methods across various performance indicators.This advancement offers new theoretical and practical insights for the development of video recommendations,particularly in addressing complex and dynamic user behavior patterns.展开更多
In the Internet era,recommendation systems play a crucial role in helping users find relevant information from large datasets.Class imbalance is known to severely affect data quality,and therefore reduce the performan...In the Internet era,recommendation systems play a crucial role in helping users find relevant information from large datasets.Class imbalance is known to severely affect data quality,and therefore reduce the performance of recommendation systems.Due to the imbalance,machine learning algorithms tend to classify inputs into the positive(majority)class every time to achieve high prediction accuracy.Imbalance can be categorized such as by features and classes,but most studies consider only class imbalance.In this paper,we propose a recommendation system that can integrate multiple networks to adapt to a large number of imbalanced features and can deal with highly skewed and imbalanced datasets through a loss function.We propose a loss aware feature attention mechanism(LAFAM)to solve the issue of feature imbalance.The network incorporates an attention mechanism and uses multiple sub-networks to classify and learn features.For better results,the network can learn the weights of sub-networks and assign higher weights to important features.We propose suppression loss to address class imbalance,which favors negative loss by penalizing positive loss,and pays more attention to sample points near the decision boundary.Experiments on two large-scale datasets verify that the performance of the proposed system is greatly improved compared to baseline methods.展开更多
Selecting appropriate tourist attractions to visit in real time is an important problem for travellers.Since recommenders proactively suggest items based on user preference,they are a promising solution for this probl...Selecting appropriate tourist attractions to visit in real time is an important problem for travellers.Since recommenders proactively suggest items based on user preference,they are a promising solution for this problem.Travellers visit tourist attractions sequentially by considering multiple attributes at the same time.Therefore,it is desirable to consider this when developing recommenders for tourist attractions.Using GRU4REC,we proposed RNN-based sequence-aware recommenders(RNN-SARs)that use multiple sequence datasets for training the recommended model,named multi-RNN-SARs.We proposed two types of multi-RNN-SARs-concatenate-RNN-SARs and parallel-RNN-SARs.In order to evaluate multi-RNN-SARs,we compared hit rate(HR)and mean reciprocal rank(MRR)of the item-based collaborative filtering recommender(item-CFR),RNN-SAR with the single-sequence dataset(basic-RNN-SAR),multi-RNN-SARs and the state-of-the-art SARs using a real-world travel dataset.Our research shows that multi-RNN-SARs have significantly higher performances compared to item-CFR.Not all multi-RNNSARs outperform basic-RNN-SAR but the best multi-RNN-SAR achieves comparable performance to that of the state-of-the-art algorithms.These results highlight the importance of using multiple sequence datasets in RNN-SARs and the importance of choosing appropriate sequence datasets and learning methods for implementing multi-RNN-SARs in practice.展开更多
This paper provides a comprehensive bibliometric exposition on deepfake research,exploring the intersection of artificial intelligence and deepfakes as well as international collaborations,prominent researchers,organi...This paper provides a comprehensive bibliometric exposition on deepfake research,exploring the intersection of artificial intelligence and deepfakes as well as international collaborations,prominent researchers,organizations,institutions,publications,and key themes.We performed a search on theWeb of Science(WoS)database,focusing on Artificial Intelligence and Deepfakes,and filtered the results across 21 research areas,yielding 1412 articles.Using VOSviewer visualization tool,we analyzed thisWoS data through keyword co-occurrence graphs,emphasizing on four prominent research themes.Compared with existing bibliometric papers on deepfakes,this paper proceeds to identify and discuss some of the highly cited papers within these themes:deepfake detection,feature extraction,face recognition,and forensics.The discussion highlights key challenges and advancements in deepfake research.Furthermore,this paper also discusses pressing issues surrounding deepfakes such as security,regulation,and datasets.We also provide an analysis of another exhaustive search on Scopus database focusing solely on Deepfakes(while not excluding AI)revealing deep learning as the predominant keyword,underscoring AI’s central role in deepfake research.This comprehensive analysis,encompassing over 500 keywords from 8790 articles,uncovered a wide range of methods,implications,applications,concerns,requirements,challenges,models,tools,datasets,and modalities related to deepfakes.Finally,a discussion on recommendations for policymakers,researchers,and other stakeholders is also provided.展开更多
This special issue of the Asian Journal of Andrology is fully dedicated to the thematic area of non-obstructive azoospermia(NOA),one of the most complex and challenging conditions in the realm of andrology,urology,and...This special issue of the Asian Journal of Andrology is fully dedicated to the thematic area of non-obstructive azoospermia(NOA),one of the most complex and challenging conditions in the realm of andrology,urology,and reproductive medicine.展开更多
A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by ana...A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by analyzing patterns in interactions and similarities between users,leveraging past behavior data to make personalized recommendations.Despite its popularity,collaborative filtering faces notable challenges,and one of them is the issue of grey-sheep users who have unusual tastes in the system.Surprisingly,existing research has not extensively explored outlier detection techniques to address the grey-sheep problem.To fill this research gap,this study conducts a comprehensive comparison of 12 outlier detectionmethods(such as LOF,ABOD,HBOS,etc.)and introduces innovative user representations aimed at improving the identification of outliers within recommender systems.More specifically,we proposed and examined three types of user representations:1)the distribution statistics of user-user similarities,where similarities were calculated based on users’rating vectors;2)the distribution statistics of user-user similarities,but with similarities derived from users represented by latent factors;and 3)latent-factor vector representations.Our experiments on the Movie Lens and Yahoo!Movie datasets demonstrate that user representations based on latent-factor vectors consistently facilitate the identification of more grey-sheep users when applying outlier detection methods.展开更多
文摘From the view of both objective and subjective factors,the indoor air quality(IAQ)evaluation was considered.Carbon dioxide(CO2)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 CO2 and HCHO were given through analysis and calculation.The limits of CO2 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 CO2 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%.
基金funding support from the Scientific Research Fund of Hunan Administration of TCM(No.KYGG06,No.KYGG07)。
文摘Objective To explore the possible preventive mechanism of Hunan expert group recommended Chinese medicine prescription of No.2(Pre-No.2)against coronavirus disease 2019(COVID-19)by network pharmacology method.Methods The target proteins of effective components and active compounds in Pre-No.2 were screened by searching the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP).A component-target-disease interaction network of Pre-No.2 was constructed by Cytoscape 3.7.2,gene ontology(GO)analysis,and Kyoto encyclopedia of genes and genomes(KEGG)analysis of target protein pathway by DAVID.Results A total of 163 compounds and 278 target protein targets in Pre-No.2 were collected from the TCMSP database.Kaempferol,wogonin,7-methoxy-2-methyl isoflavone,formononetin,isorhamnetin,and licochalcone A were the most frequent targets in the regulatory network.GO enrichment analysis showed that Pre-No.2 regulated response to virus,viral processes,humoral immune responses,defense responses to virus and viral entry into host cells.KEGG enrichment analysis showed that the formula regulated the NF-κB signaling pathway,B cell receptor signaling pathway,viral carcinogenesis,T cell signaling pathway and FcγR-mediated phagocytosis signaling pathway.Conclusions Pre-No.2 may play a preventive role against COVID-19 through regulation of the Toll-like signaling,T cell signaling,B cell signaling and other signaling pathways.It may regulate the immune system to protect against anti-influenza virus.
文摘Book 1: (Editor-in-Chief: Shi Yafeng; Published by Elsevier and Science Press Beijing in 2008, 539 pages) Glaciers and Related Environments in China Since the professional institution for glaciology attached to the Chinese Academy of Sciences was established in 1958, studies of glaciers in alpine regions, and of Quaternary glaciations throughout
文摘Closer sino-African relations have encouraged more Chinese enterprises to invest in African countries.Statistics show that more than 2,000 Chinese enterprises had invested in the continent by 2012.
文摘The response of rice to N fertilizer applicationhas shown that high rates of N application donot always ensure a proportional increase inyield due to high N losses. A model, ORYZA-0 was developed by ten Berge for designingoptimum N fertilizer management strategy inrice. We evaluated the performance ofORYZA-0 in Jinhua, Zhejiang Province. ORYZA-0 includes N uptakes, partition-ing of N among the organs, and utilization ofleaf N in converting solar energy to dry mat-ter. It can predict the amount and time of Nfertilizer application to achieve a maximumbiomass or yield combining with Price algo-rithm optimization procedure.
文摘Current guidelines for treating asymptomatic common bile duct stones(CBDS)recommend stone removal,with endoscopic retrograde cholangiopan-creatography(ERCP)being the first treatment choice.When deciding on ERCP treatment for asymptomatic CBDS,the risk of ERCP-related complications and outcome of natural history of asymptomatic CBDS should be compared.The incidence rate of ERCP-related complications,particularly of post-ERCP pancreatitis for asymptomatic CBDS,was reportedly higher than that of symptomatic CBDS,increasing the risk of ERCP-related complications for asymptomatic CBDS compared with that previously reported for biliopancreatic diseases.Although studies have reported short-to middle-term outcomes of natural history of asymptomatic CBDS,its long-term natural history is not well known.Till date,there are no prospective studies that determined whether ERCP has a better outcome than no treatment in patients with asymptomatic CBDS or not.No randomized controlled trial has evaluated the risk of early and late ERCP-related complications vs the risk of biliary complications in the wait-and-see approach,suggesting that a change is needed in our perspective on endoscopic treatment for asymptomatic CBDS.Further studies examining long-term complication risks of ERCP and wait-and-see groups for asymptomatic CBDS are warranted to discuss whether routine endoscopic treatment for asymptomatic CBDS is justified or not.
文摘A new collaborative filtered recommendation strategy oriented to trajectory data is proposed for communication bottlenecks and vulnerability in centralized system structure location services. In the strategy based on distributed system architecture, individual user information profiles were established using daily trajectory information and neighboring user groups were established using density measure. Then the trajectory similarity and profile similarity were calculated to recommend appropriate location services using collaborative filtering recommendation method. The strategy was verified on real position data set. The proposed strategy provides higher quality location services to ensure the privacy of user position information.
基金supported by the Jiangsu Science and Technology Think Tank Program(Youth)Project(JSKX24085)the Jiangsu Provincial College Students Innovation and Entrepreneurship Training Plan Project(202311276097Y).
文摘With the rapid development of electric vehicles,the requirements for charging stations are getting higher and higher.In this study,we constructed a charging station topology network inNanjing through the Space-L method,mapping charging stations as network nodes and constructing edges through road relationships.The experiment introduced five EV charging recommendation strategies(based on distance,number of fast charging piles,user preference,price,and overall rating)used to simulate disordered charging caused by different user preferences,and the impact of the networkdynamic robustness in case of node failure is exploredby simulating the load-capacity cascade failure model.In this paper,two important metrics for evaluating network robustness are selected:the relative size of the maximum connected subgraph and the network efficiency.The experimental results point out that in the price recommendation strategy,the network stability significantly decreases when the node failure ratio reaches 75.4%,while the fast charging quantity recommendation strategy significantly decreases when the node failure ratio is 62.3%.Therefore,the robustness of the charging station network is best under the price recommendation,while the network robustness is poor under the fast charging quantity recommendation.While the network robustness is poor under preference recommendation.Based on this finding,this study particularly emphasizes that in the process of improving the robustness of the charging station network,it is necessary to comprehensively consider the market demand and guide users to charge in an orderly manner by reasonably adjusting the price strategy.This strategy not only effectively prevents network stability problems that may result fromdisorderly charging behavior,but also enhances the ability of the charging network to resist node failure and improves the overall dynamic robustness of the network.
文摘1.Introduction The COVID-19 pandemic is affecting the lives of the world population in various ways and has resulted in an unforeseen scale of disruption of activities across the globe.Its emergence has health and economic implications that impact individuals,organizations and sovereign states which is inclusive of the stakeholders in a tax system.Thus,revenue authorities need to take actions to protect and ease the burden on its external and internal stakeholders.
基金supported by the National Natural Science Foundation of China(Grant No.81872624)Fundamental Research Program of Shanxi Province(Grant No.202403021211139).
文摘Objective This study aimed to reexplore minimum iodine excretion and to build a dietary iodine recommendation for Chinese adults using the obligatory iodine loss hypothesis.Methods Data from 171 Chinese adults(19–21 years old)were collected and analyzed based on three balance studies in Shenzhen,Yinchuan,and Changzhi.The single exponential equation was accordingly used to simulate the trajectory of 24 h urinary iodine excretion as the low iodine experimental diets offered(iodine intake:11-26μg/day)and to further deduce the dietary reference intakes(DRIs)for iodine,including estimated average requirement(EAR)and recommended nutrient intake(RNI).Results The minimum iodine excretion was estimated as 57,58,and 51μg/day in three balance studies,respectively.Moreover,it was further suggested as 57,58,and 51μg/day for iodine EAR,and 80,81,and 71μg/day for iodine RNI or expressed as 1.42,1.41,and 1.20μg/(day·kg)of body weight.Conclusion The iodine DRIs for Chinese adults were established based on the obligatory iodine loss hypothesis,which provides scientific support for the amendment of nutrient requirements.
文摘Azoospermia,defined as the absence of sperm in the ejaculate,is a well-documented consequence of exogenous testosterone(ET)and anabolic–androgenic steroid(AAS)use.These agents suppress the hypothalamic–pituitary–gonadal(HPG)axis,leading to reduced intratesticular testosterone levels and impaired spermatogenesis.This review examines the pathophysiological mechanisms underlying azoospermia and outlines therapeutic strategies for recovery.Azoospermia is categorized into pretesticular,testicular,and post-testicular types,with a focus on personalized treatment approaches based on the degree of HPG axis suppression and baseline testicular function.Key strategies include discontinuing ET and monitoring for spontaneous recovery,particularly in patients with shorter durations of ET use.For cases of persistent azoospermia,gonadotropins(human chorionic gonadotropin[hCG]and follicle-stimulating hormone[FSH])and selective estrogen receptor modulators(SERMs),such as clomiphene citrate,are recommended,either alone or in combination.The global increase in exogenous testosterone use,including testosterone replacement therapy and AAS,underscores the need for improved management of associated azoospermia,which can be temporary or permanent depending on individual factors and the type of testosterone used.Additionally,the manuscript discusses preventive strategies,such as transitioning to short-acting testosterone formulations or incorporating low-dose hCG to preserve fertility during ET therapy.While guidelines for managing testosterone-related azoospermia remain limited,emerging research indicates the potential efficacy of hormonal stimulation therapies.However,there is a notable lack of well-structured,controlled,and long-term studies addressing the management of azoospermia related to exogenous testosterone use,highlighting the need for such studies to inform evidence-based recommendations.
基金Shanghai Frontier Science Research Center for Modern Textiles,Donghua University,ChinaOpen Project of Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment,Zhengzhou University of Light Industry,China(No.IM202303)National Key Research and Development Program of China(No.2019YFB1706300)。
文摘A personalized outfit recommendation has emerged as a hot research topic in the fashion domain.However,existing recommendations do not fully exploit user style preferences.Typically,users prefer particular styles such as casual and athletic styles,and consider attributes like color and texture when selecting outfits.To achieve personalized outfit recommendations in line with user style preferences,this paper proposes a personal style guided outfit recommendation with multi-modal fashion compatibility modeling,termed as PSGNet.Firstly,a style classifier is designed to categorize fashion images of various clothing types and attributes into distinct style categories.Secondly,a personal style prediction module extracts user style preferences by analyzing historical data.Then,to address the limitations of single-modal representations and enhance fashion compatibility,both fashion images and text data are leveraged to extract multi-modal features.Finally,PSGNet integrates these components through Bayesian personalized ranking(BPR)to unify the personal style and fashion compatibility,where the former is used as personal style features and guides the output of the personalized outfit recommendation tailored to the target user.Extensive experiments on large-scale datasets demonstrate that the proposed model is efficient on the personalized outfit recommendation.
基金supported by National Natural Science Foundation of China(62072416)Key Research and Development Special Project of Henan Province(221111210500)Key TechnologiesR&DProgram of Henan rovince(232102211053,242102211071).
文摘The rapid development of short video platforms poses new challenges for traditional recommendation systems.Recommender systems typically depend on two types of user behavior feedback to construct user interest profiles:explicit feedback(interactive behavior),which significantly influences users’short-term interests,and implicit feedback(viewing time),which substantially affects their long-term interests.However,the previous model fails to distinguish between these two feedback methods,leading it to predict only the overall preferences of users based on extensive historical behavior sequences.Consequently,it cannot differentiate between users’long-term and shortterm interests,resulting in low accuracy in describing users’interest states and predicting the evolution of their interests.This paper introduces a video recommendationmodel calledCAT-MFRec(CrossAttention Transformer-Mixed Feedback Recommendation)designed to differentiate between explicit and implicit user feedback within the DIEN(Deep Interest Evolution Network)framework.This study emphasizes the separate learning of the two types of behavioral feedback,effectively integrating them through the cross-attention mechanism.Additionally,it leverages the long sequence dependence capabilities of Transformer technology to accurately construct user interest profiles and predict the evolution of user interests.Experimental results indicate that CAT-MF Rec significantly outperforms existing recommendation methods across various performance indicators.This advancement offers new theoretical and practical insights for the development of video recommendations,particularly in addressing complex and dynamic user behavior patterns.
基金supported by the National Key Research and Development Program of China(Grant numbers:2021YFF0901705,2021YFF0901700)the State Key Laboratory of Media Convergence and Communication,Communication University of China+1 种基金the Fundamental Research Funds for the Central Universitiesthe High-Quality and Cutting-Edge Disciplines Construction Project for Universities in Beijing(Internet Information,Communication University of China).
文摘In the Internet era,recommendation systems play a crucial role in helping users find relevant information from large datasets.Class imbalance is known to severely affect data quality,and therefore reduce the performance of recommendation systems.Due to the imbalance,machine learning algorithms tend to classify inputs into the positive(majority)class every time to achieve high prediction accuracy.Imbalance can be categorized such as by features and classes,but most studies consider only class imbalance.In this paper,we propose a recommendation system that can integrate multiple networks to adapt to a large number of imbalanced features and can deal with highly skewed and imbalanced datasets through a loss function.We propose a loss aware feature attention mechanism(LAFAM)to solve the issue of feature imbalance.The network incorporates an attention mechanism and uses multiple sub-networks to classify and learn features.For better results,the network can learn the weights of sub-networks and assign higher weights to important features.We propose suppression loss to address class imbalance,which favors negative loss by penalizing positive loss,and pays more attention to sample points near the decision boundary.Experiments on two large-scale datasets verify that the performance of the proposed system is greatly improved compared to baseline methods.
文摘Selecting appropriate tourist attractions to visit in real time is an important problem for travellers.Since recommenders proactively suggest items based on user preference,they are a promising solution for this problem.Travellers visit tourist attractions sequentially by considering multiple attributes at the same time.Therefore,it is desirable to consider this when developing recommenders for tourist attractions.Using GRU4REC,we proposed RNN-based sequence-aware recommenders(RNN-SARs)that use multiple sequence datasets for training the recommended model,named multi-RNN-SARs.We proposed two types of multi-RNN-SARs-concatenate-RNN-SARs and parallel-RNN-SARs.In order to evaluate multi-RNN-SARs,we compared hit rate(HR)and mean reciprocal rank(MRR)of the item-based collaborative filtering recommender(item-CFR),RNN-SAR with the single-sequence dataset(basic-RNN-SAR),multi-RNN-SARs and the state-of-the-art SARs using a real-world travel dataset.Our research shows that multi-RNN-SARs have significantly higher performances compared to item-CFR.Not all multi-RNNSARs outperform basic-RNN-SAR but the best multi-RNN-SAR achieves comparable performance to that of the state-of-the-art algorithms.These results highlight the importance of using multiple sequence datasets in RNN-SARs and the importance of choosing appropriate sequence datasets and learning methods for implementing multi-RNN-SARs in practice.
文摘This paper provides a comprehensive bibliometric exposition on deepfake research,exploring the intersection of artificial intelligence and deepfakes as well as international collaborations,prominent researchers,organizations,institutions,publications,and key themes.We performed a search on theWeb of Science(WoS)database,focusing on Artificial Intelligence and Deepfakes,and filtered the results across 21 research areas,yielding 1412 articles.Using VOSviewer visualization tool,we analyzed thisWoS data through keyword co-occurrence graphs,emphasizing on four prominent research themes.Compared with existing bibliometric papers on deepfakes,this paper proceeds to identify and discuss some of the highly cited papers within these themes:deepfake detection,feature extraction,face recognition,and forensics.The discussion highlights key challenges and advancements in deepfake research.Furthermore,this paper also discusses pressing issues surrounding deepfakes such as security,regulation,and datasets.We also provide an analysis of another exhaustive search on Scopus database focusing solely on Deepfakes(while not excluding AI)revealing deep learning as the predominant keyword,underscoring AI’s central role in deepfake research.This comprehensive analysis,encompassing over 500 keywords from 8790 articles,uncovered a wide range of methods,implications,applications,concerns,requirements,challenges,models,tools,datasets,and modalities related to deepfakes.Finally,a discussion on recommendations for policymakers,researchers,and other stakeholders is also provided.
文摘This special issue of the Asian Journal of Andrology is fully dedicated to the thematic area of non-obstructive azoospermia(NOA),one of the most complex and challenging conditions in the realm of andrology,urology,and reproductive medicine.
文摘A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by analyzing patterns in interactions and similarities between users,leveraging past behavior data to make personalized recommendations.Despite its popularity,collaborative filtering faces notable challenges,and one of them is the issue of grey-sheep users who have unusual tastes in the system.Surprisingly,existing research has not extensively explored outlier detection techniques to address the grey-sheep problem.To fill this research gap,this study conducts a comprehensive comparison of 12 outlier detectionmethods(such as LOF,ABOD,HBOS,etc.)and introduces innovative user representations aimed at improving the identification of outliers within recommender systems.More specifically,we proposed and examined three types of user representations:1)the distribution statistics of user-user similarities,where similarities were calculated based on users’rating vectors;2)the distribution statistics of user-user similarities,but with similarities derived from users represented by latent factors;and 3)latent-factor vector representations.Our experiments on the Movie Lens and Yahoo!Movie datasets demonstrate that user representations based on latent-factor vectors consistently facilitate the identification of more grey-sheep users when applying outlier detection methods.
