The objectives of this study were to estimate the genetic parameters and the breeding progress in a Landrace herd in China, and to predict the potential benefits by applying new breeding technology. Hereby, the perfor...The objectives of this study were to estimate the genetic parameters and the breeding progress in a Landrace herd in China, and to predict the potential benefits by applying new breeding technology. Hereby, the performance records from a Landrace swine herd in China, composing over 33 000 pigs born between 2001 and 2013, were collected on six economically important traits, i.e., average daily gain between 30-100 kg（ADG）, adjusted backfat thickness at 100 kg（BF）, adjusted days to 30 kg（D30）, adjusted days to 100 kg（D100）, number born alive（NBA）, and total number born（TNB）. The genetic parameters were estimated by restricted maximum likelihood via DMU, and realized genetic trends were analyzed. Based on the real population structure and genetic parameters obtained from this herd, the potential genetic trends by applying genomic selection（GS） were predicted via a computer simulation study. Results showed that the heritability estimates in this Landrace herd were 0.55（0.02）, 0.42（0.01）, and 0.12（0.01）, for BF, D100, and TNB, respectively. Favorable genetic trends were obtained for D100, BF, and TNB due to direct selection, for ADG and NBA due to indirect selection. Long-term selection against D100 did not improve D30, though they are highly genetically correlated（0.64）. Appling GS in such a swine herd, the genetic gain can be increased by 25%, or even larger for traits with low heritability or individuals without phenotypes before selection. It can be concluded that conventional breeding strategy was effective in the herd studied, while applying GS is promising and hence the road ahead in swine breeding.展开更多
The literature generally agrees that longer-horizon(over a month) predictions make more sense than short-horizon ones. However, it's an especially challenging task due to the lack of data(in unit of long horizon)a...The literature generally agrees that longer-horizon(over a month) predictions make more sense than short-horizon ones. However, it's an especially challenging task due to the lack of data(in unit of long horizon)and economic data have a low S/N ratio. We hypothesize that the stock trend is largely dictated by driving factors which are filtered by psychological factors and work on behavioral factors: representative indicators from these three aspects would be adequate in trend prediction. We then extend the Stepwise Regression Analysis(SRA)algorithm to constrained SRA(c SRA) to carry out a further feature selection and lag optimization. During modeling stage, we introduce the Deep Neural Network(DNN) model in stock prediction under the suspicion that economic interactions are too complex for shallow networks to capture. Our experiments indeed show that deep structures generally perform better than shallow ones. Instead of comparing to a kitchen sink model, where over-fitting can easily happen with a shortage of data, we turn around and use a model ensemble approach which indirectly demonstrates our proposed method is adequate.展开更多
The emission control of non-CO2greenhouse gases is conducive to slowing down global warming.It is also helpful in controlling environmental pollution,and beneficial in improving the local health benefits.This paper ai...The emission control of non-CO2greenhouse gases is conducive to slowing down global warming.It is also helpful in controlling environmental pollution,and beneficial in improving the local health benefits.This paper aims at six kinds of non-CO2greenhouse gases under United Nations Framework Convention on Climate Change,namely methane(CH4),nitrous oxide(N2O),hydrofluorocarbons(HFCs),perfluorocarbons(PFCs),sulfur hexafluoride(SF6),and nitrogen trifluoride(NF3).This paper analyzes the emission status and trend of China’s non-CO2greenhouse gases,and provides some technology selections for non-CO2emission reduction.Through strategic policy arrangements and appropriate technology choices,China can gain environmental protection and greenhouse gas control.展开更多
We consider the random field estimation problem with parametric trend in wireless sensor networks where the field can be described by unknown parameters to be estimated. Due to the limited resources, the network selec...We consider the random field estimation problem with parametric trend in wireless sensor networks where the field can be described by unknown parameters to be estimated. Due to the limited resources, the network selects only a subset of the sensors to perform the estimation task with a desired performance under the D-optimal criterion. We propose a greedy sampling scheme to select the sensor nodes according to the information gain of the sensors. A distributed algorithm is also developed by consensus-based incremental sensor node selection through information quality computation for and message exchange among neighboring sensors. Simulation results show the good performance of the proposed algorithms.展开更多
基金supported by the Earmarked Fund for the China Agriculture Research System(CARS-36)the National High-Tech R&D Program of China(863 Program, 2011AA100304)+3 种基金the National Key Technology R&D Program of China(2011BAD28B01)the National Natural Science Foundation of China(31200925)the Guangdong Provincial Department of S&T,China(2011A020102003)the Pearl River S&T Nova Program of Guangzhou,China (201506010027)
文摘The objectives of this study were to estimate the genetic parameters and the breeding progress in a Landrace herd in China, and to predict the potential benefits by applying new breeding technology. Hereby, the performance records from a Landrace swine herd in China, composing over 33 000 pigs born between 2001 and 2013, were collected on six economically important traits, i.e., average daily gain between 30-100 kg（ADG）, adjusted backfat thickness at 100 kg（BF）, adjusted days to 30 kg（D30）, adjusted days to 100 kg（D100）, number born alive（NBA）, and total number born（TNB）. The genetic parameters were estimated by restricted maximum likelihood via DMU, and realized genetic trends were analyzed. Based on the real population structure and genetic parameters obtained from this herd, the potential genetic trends by applying genomic selection（GS） were predicted via a computer simulation study. Results showed that the heritability estimates in this Landrace herd were 0.55（0.02）, 0.42（0.01）, and 0.12（0.01）, for BF, D100, and TNB, respectively. Favorable genetic trends were obtained for D100, BF, and TNB due to direct selection, for ADG and NBA due to indirect selection. Long-term selection against D100 did not improve D30, though they are highly genetically correlated（0.64）. Appling GS in such a swine herd, the genetic gain can be increased by 25%, or even larger for traits with low heritability or individuals without phenotypes before selection. It can be concluded that conventional breeding strategy was effective in the herd studied, while applying GS is promising and hence the road ahead in swine breeding.
基金the National Natural Science Foundation of China(Nos.11501355 and 71571116)the Project of Knowledge Innovation Program of Shanghai Municipal Education Commission(No.15ZZ090)+2 种基金the 59th China Postdoctoral Sciences Foundation Funded Project(No.2016M591640)the Humanities and Social Sciences Research Project of Ministry of Education(No.15YJA790039)the National Social Science Foundation of China(No.15ZDA058)
文摘The literature generally agrees that longer-horizon(over a month) predictions make more sense than short-horizon ones. However, it's an especially challenging task due to the lack of data(in unit of long horizon)and economic data have a low S/N ratio. We hypothesize that the stock trend is largely dictated by driving factors which are filtered by psychological factors and work on behavioral factors: representative indicators from these three aspects would be adequate in trend prediction. We then extend the Stepwise Regression Analysis(SRA)algorithm to constrained SRA(c SRA) to carry out a further feature selection and lag optimization. During modeling stage, we introduce the Deep Neural Network(DNN) model in stock prediction under the suspicion that economic interactions are too complex for shallow networks to capture. Our experiments indeed show that deep structures generally perform better than shallow ones. Instead of comparing to a kitchen sink model, where over-fitting can easily happen with a shortage of data, we turn around and use a model ensemble approach which indirectly demonstrates our proposed method is adequate.
基金supported by the Policy Study on China’s Low-carbon Industrial Development(No.201312)
文摘The emission control of non-CO2greenhouse gases is conducive to slowing down global warming.It is also helpful in controlling environmental pollution,and beneficial in improving the local health benefits.This paper aims at six kinds of non-CO2greenhouse gases under United Nations Framework Convention on Climate Change,namely methane(CH4),nitrous oxide(N2O),hydrofluorocarbons(HFCs),perfluorocarbons(PFCs),sulfur hexafluoride(SF6),and nitrogen trifluoride(NF3).This paper analyzes the emission status and trend of China’s non-CO2greenhouse gases,and provides some technology selections for non-CO2emission reduction.Through strategic policy arrangements and appropriate technology choices,China can gain environmental protection and greenhouse gas control.
基金supported by the National Natural Science Foundation of China-Key Program (No. 61032001),the National Natural Science Foundation of China (No. 60828006)
文摘We consider the random field estimation problem with parametric trend in wireless sensor networks where the field can be described by unknown parameters to be estimated. Due to the limited resources, the network selects only a subset of the sensors to perform the estimation task with a desired performance under the D-optimal criterion. We propose a greedy sampling scheme to select the sensor nodes according to the information gain of the sensors. A distributed algorithm is also developed by consensus-based incremental sensor node selection through information quality computation for and message exchange among neighboring sensors. Simulation results show the good performance of the proposed algorithms.
