Background Genomic selection involves choosing as parents those elite individuals with the higher genomic estimated breeding values(GEBV)to accelerate the speed of genetic improvement in domestic animals.But after mul...Background Genomic selection involves choosing as parents those elite individuals with the higher genomic estimated breeding values(GEBV)to accelerate the speed of genetic improvement in domestic animals.But after multi-generation selection,the rate of inbreeding and the occurrence of homozygous harmful alleles might increase,which would reduce performance and genetic diversity.To mitigate the above problems,we can utilize genomic mating(GM)based upon optimal mate allocation to construct the best genotypic combinations in the next generation.In this study,we used stochastic simulation to investigate the impact of various factors on the efficiencies of GM to optimize pairing combinations after genomic selection of candidates in a pig population.These factors included:the algorithm used to derive inbreeding coefficients;the trait heritability(0.1,0.3 or 0.5);the kind of GM scheme(focused average GEBV or inbreeding);the approach for computing the genomic relationship matrix(by SNP or runs of homozygosity(ROH)).The outcomes were compared to three traditional mating schemes(random,positive assortative or negative assortative matings).In addition,the performance of the GM approach was tested on real datasets obtained from a Large White pig breeding population.Results Genomic mating outperforms other approaches in limiting the inbreeding accumulation for the same expected genetic gain.The use of ROH-based genealogical relatedness in GM achieved faster genetic gains than using relatedness based on individual SNPs.The GROH-based GM schemes with the maximum genetic gain resulted in 0.9%-2.6%higher rates of genetic gainΔG,and 13%-83.3%lowerΔF than positive assortative mating regardless of heritability.The rates of inbreeding were always the fastest with positive assortative mating.Results from a purebred Large White pig population,confirmed that GM with ROH-based GRM was more efficient than traditional mating schemes.Conclusion Compared with traditional mating schemes,genomic mating can not only achieve sustainable genetic progress but also effectively control the rates of inbreeding accumulation in the population.Our findings demonstrated that breeders should consider using genomic mating for genetic improvement of pigs.展开更多
Urgent care clinics and emergency departments around the world periodically suffer from extended wait times beyond patient expectations due to surges in patient flows.The delays arising from inadequate staffing levels...Urgent care clinics and emergency departments around the world periodically suffer from extended wait times beyond patient expectations due to surges in patient flows.The delays arising from inadequate staffing levels during these periods have been linked with adverse clinical outcomes.Previous research into forecasting patient flows has mostly used statistical techniques.These studies have also predominately focussed on short‐term forecasts,which have limited practicality for the resourcing of medical personnel.This study joins an emerging body of work which seeks to explore the potential of machine learning algorithms to generate accurate forecasts of patient presentations.Our research uses datasets covering 10 years from two large urgent care clinics to develop long‐term patient flow forecasts up to one quarter ahead using a range of state‐of‐the‐art algo-rithms.A distinctive feature of this study is the use of eXplainable Artificial Intelligence(XAI)tools like Shapely and LIME that enable an in‐depth analysis of the behaviour of the models,which would otherwise be uninterpretable.These analysis tools enabled us to explore the ability of the models to adapt to the volatility in patient demand during the COVID‐19 pandemic lockdowns and to identify the most impactful variables,resulting in valuable insights into their performance.The results showed that a novel combination of advanced univariate models like Prophet as well as gradient boosting,into an ensemble,delivered the most accurate and consistent solutions on average.This approach generated improvements in the range of 16%-30%over the existing in‐house methods for esti-mating the daily patient flows 90 days ahead.展开更多
Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the co...Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation,as it facilitates multiple new attack vectors to emerge effortlessly.As such,existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems.To address this problem,we designed a blended threat detection approach,considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence.We collectively refer to the convergence of different technology sectors as the internet of blended environment.The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder.An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02%detection accuracy.Furthermore,performance of the proposed approach was compared with various single model(autoencoder)-based network intrusion detection approaches:autoencoder,variational autoencoder,convolutional variational autoencoder,and long short-term memory variational autoencoder.The proposed model outperformed all compared models,demonstrating F1-score improvements of 4.99%,2.25%,1.92%,and 3.69%,respectively.展开更多
Labeled data is widely used in various classification tasks.However,there is a huge challenge that labels are often added artificially.Wrong labels added by malicious users will affect the training effect of the model...Labeled data is widely used in various classification tasks.However,there is a huge challenge that labels are often added artificially.Wrong labels added by malicious users will affect the training effect of the model.The unreliability of labeled data has hindered the research.In order to solve the above problems,we propose a framework of Label Noise Filtering and Missing Label Supplement(LNFS).And we take location labels in Location-Based Social Networks(LBSN)as an example to implement our framework.For the problem of label noise filtering,we first use FastText to transform the restaurant's labels into vectors,and then based on the assumption that the label most similar to all other labels in the location is most representative.We use cosine similarity to judge and select the most representative label.For the problem of label missing,we use simple common word similarity to judge the similarity of users'comments,and then use the label of the similar restaurant to supplement the missing labels.To optimize the performance of the model,we introduce game theory into our model to simulate the game between the malicious users and the model to improve the reliability of the model.Finally,a case study is given to illustrate the effectiveness and reliability of LNFS.展开更多
Chuño production is a kind of ancient method of potato preservation that has been used to the present day.In this study,physicochemical property and nutrition quality of white chuño(WC),black chuño,and ...Chuño production is a kind of ancient method of potato preservation that has been used to the present day.In this study,physicochemical property and nutrition quality of white chuño(WC),black chuño,and dehydrated potato flour prepared by hot air drying(AD)and freezing drying were analyzed and compared.The results revealed that the average particle size of the starch in WC is almost 10 times of the dehydrated potato flour by AD treatment according to the laser particle size meter.During the dehydration of WC,water-soluble minerals(K+,Mg2+),proteins,ascorbic acid,etc.were partly lost while Ca2+content increased dramatically.In addition,WC showed the lowest antioxidant capacity among the four different kinds of dehydrated potato products.The polyphenol oxidase activity of WC,black chuño and AD were between 0.62–12.2 U/g fresh weight,which indicated that the color will be stable when chuño was used as staple food ingredient in the subsequent process.Therefore,as a potato processed food,chuño displayed great potential for promotion in the cold and poor rural areas of the northern China.展开更多
基金funded by the Natural Science Foundations of China(No.32172702)National Key Research and Development Program of China(2021YFD1301101)Agricultural Science and Technology Innovation Program(ASTIP-IAS02)。
文摘Background Genomic selection involves choosing as parents those elite individuals with the higher genomic estimated breeding values(GEBV)to accelerate the speed of genetic improvement in domestic animals.But after multi-generation selection,the rate of inbreeding and the occurrence of homozygous harmful alleles might increase,which would reduce performance and genetic diversity.To mitigate the above problems,we can utilize genomic mating(GM)based upon optimal mate allocation to construct the best genotypic combinations in the next generation.In this study,we used stochastic simulation to investigate the impact of various factors on the efficiencies of GM to optimize pairing combinations after genomic selection of candidates in a pig population.These factors included:the algorithm used to derive inbreeding coefficients;the trait heritability(0.1,0.3 or 0.5);the kind of GM scheme(focused average GEBV or inbreeding);the approach for computing the genomic relationship matrix(by SNP or runs of homozygosity(ROH)).The outcomes were compared to three traditional mating schemes(random,positive assortative or negative assortative matings).In addition,the performance of the GM approach was tested on real datasets obtained from a Large White pig breeding population.Results Genomic mating outperforms other approaches in limiting the inbreeding accumulation for the same expected genetic gain.The use of ROH-based genealogical relatedness in GM achieved faster genetic gains than using relatedness based on individual SNPs.The GROH-based GM schemes with the maximum genetic gain resulted in 0.9%-2.6%higher rates of genetic gainΔG,and 13%-83.3%lowerΔF than positive assortative mating regardless of heritability.The rates of inbreeding were always the fastest with positive assortative mating.Results from a purebred Large White pig population,confirmed that GM with ROH-based GRM was more efficient than traditional mating schemes.Conclusion Compared with traditional mating schemes,genomic mating can not only achieve sustainable genetic progress but also effectively control the rates of inbreeding accumulation in the population.Our findings demonstrated that breeders should consider using genomic mating for genetic improvement of pigs.
文摘Urgent care clinics and emergency departments around the world periodically suffer from extended wait times beyond patient expectations due to surges in patient flows.The delays arising from inadequate staffing levels during these periods have been linked with adverse clinical outcomes.Previous research into forecasting patient flows has mostly used statistical techniques.These studies have also predominately focussed on short‐term forecasts,which have limited practicality for the resourcing of medical personnel.This study joins an emerging body of work which seeks to explore the potential of machine learning algorithms to generate accurate forecasts of patient presentations.Our research uses datasets covering 10 years from two large urgent care clinics to develop long‐term patient flow forecasts up to one quarter ahead using a range of state‐of‐the‐art algo-rithms.A distinctive feature of this study is the use of eXplainable Artificial Intelligence(XAI)tools like Shapely and LIME that enable an in‐depth analysis of the behaviour of the models,which would otherwise be uninterpretable.These analysis tools enabled us to explore the ability of the models to adapt to the volatility in patient demand during the COVID‐19 pandemic lockdowns and to identify the most impactful variables,resulting in valuable insights into their performance.The results showed that a novel combination of advanced univariate models like Prophet as well as gradient boosting,into an ensemble,delivered the most accurate and consistent solutions on average.This approach generated improvements in the range of 16%-30%over the existing in‐house methods for esti-mating the daily patient flows 90 days ahead.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2021R1A2C2011391)was supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2021-0-01806Development of security by design and security management technology in smart factory).
文摘Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation,as it facilitates multiple new attack vectors to emerge effortlessly.As such,existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems.To address this problem,we designed a blended threat detection approach,considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence.We collectively refer to the convergence of different technology sectors as the internet of blended environment.The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder.An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02%detection accuracy.Furthermore,performance of the proposed approach was compared with various single model(autoencoder)-based network intrusion detection approaches:autoencoder,variational autoencoder,convolutional variational autoencoder,and long short-term memory variational autoencoder.The proposed model outperformed all compared models,demonstrating F1-score improvements of 4.99%,2.25%,1.92%,and 3.69%,respectively.
基金supported by the National Natural Science Foundation of China(No.61872219)the Natural Science Foundation of Shandong Province(ZR2019MF001).
文摘Labeled data is widely used in various classification tasks.However,there is a huge challenge that labels are often added artificially.Wrong labels added by malicious users will affect the training effect of the model.The unreliability of labeled data has hindered the research.In order to solve the above problems,we propose a framework of Label Noise Filtering and Missing Label Supplement(LNFS).And we take location labels in Location-Based Social Networks(LBSN)as an example to implement our framework.For the problem of label noise filtering,we first use FastText to transform the restaurant's labels into vectors,and then based on the assumption that the label most similar to all other labels in the location is most representative.We use cosine similarity to judge and select the most representative label.For the problem of label missing,we use simple common word similarity to judge the similarity of users'comments,and then use the label of the similar restaurant to supplement the missing labels.To optimize the performance of the model,we introduce game theory into our model to simulate the game between the malicious users and the model to improve the reliability of the model.Finally,a case study is given to illustrate the effectiveness and reliability of LNFS.
基金supported by the earmarked fund for CARS(Grant No.CARS-09)High Technology Industrialization of Sccience and Technology Cooperation Between Jilin Province and Chinese Academy of Sciences(Grant No.2021SYHZ0005)Key Research and Development Plan of Ningxia Hui Autonomous Region(Grant No.2020BBF03018).
文摘Chuño production is a kind of ancient method of potato preservation that has been used to the present day.In this study,physicochemical property and nutrition quality of white chuño(WC),black chuño,and dehydrated potato flour prepared by hot air drying(AD)and freezing drying were analyzed and compared.The results revealed that the average particle size of the starch in WC is almost 10 times of the dehydrated potato flour by AD treatment according to the laser particle size meter.During the dehydration of WC,water-soluble minerals(K+,Mg2+),proteins,ascorbic acid,etc.were partly lost while Ca2+content increased dramatically.In addition,WC showed the lowest antioxidant capacity among the four different kinds of dehydrated potato products.The polyphenol oxidase activity of WC,black chuño and AD were between 0.62–12.2 U/g fresh weight,which indicated that the color will be stable when chuño was used as staple food ingredient in the subsequent process.Therefore,as a potato processed food,chuño displayed great potential for promotion in the cold and poor rural areas of the northern China.
基金This research is partially supported by the Key Teachers Foundation of Chongqing Uni-versity (No2003018)the Key Teachers Foundation of Universities in Chongqing (No20020126)
