Salinity is a significant environmental factor that can affect the survival,metamorphosis,growth and feeding of Portunus trituberculatus.In order to analyze the key physiological characteristics of P.trituberculatus i...Salinity is a significant environmental factor that can affect the survival,metamorphosis,growth and feeding of Portunus trituberculatus.In order to analyze the key physiological characteristics of P.trituberculatus in response to short-term low salinity stress,the experiments of gradually decline and recovery as well as abrupt decline in salinity were carried out.The results showed that P.trituberculatus could survive in a certain low salinity range in the short term,and salinity 12 was the lowest tolerable salinity under the present experimental conditions.The change of the hemolymph osmotic pressure displayed significant positive correlations with water salinity,and the pressure was always higher than seawater osmotic pressure.Short-term low salinity stress changed the structure and morphology of gill tissue.The expansion of gill filament ends and epithelial cell shedding were conducive to osmotic adjustment.The activities of key ion transport enzymes such as Na^(+)-K^(+)-ATPase,carbonic anhydrase and V-ATPase also changed with the osmotic regulation,while Na^(+)-K^(+)-ATPase played a dominant role.In summary,as an osmotic adjustment species,P.trituberculatus rapidly adapt to the short-term low-salinity environment by osmotic adjustment in vivo,but salinity below salinity 12 is not conducive to its survival.Our result enriched the theoretical mechanism of osmotic regulation of P.trituberculatus,providing reference for the development of aquaculture technology of P.trituberculatus.展开更多
Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent t...Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July7th 2010.展开更多
为解决不同人员相同操作的个体差异以及同一人员不同时间相同操作差异的问题,提出一种基于混合专家系统(mixture of experts,MoE)和长短期记忆神经网络(long short-term memory,LSTM)的倒闸操作识别方法MoE-LSTM。基于MoE对LSTM进行集成...为解决不同人员相同操作的个体差异以及同一人员不同时间相同操作差异的问题,提出一种基于混合专家系统(mixture of experts,MoE)和长短期记忆神经网络(long short-term memory,LSTM)的倒闸操作识别方法MoE-LSTM。基于MoE对LSTM进行集成,学习不同来源数据的特征分布。采集加速度动作数据构建倒闸操作数据集,基于滑动窗口对动作序列进行切分;将动作序列输入到MoE-LSTM中,由不同LSTM独立学习不同动作的时序依赖;通过门控网络选择对当前输入分类较好的LSTM的输出作为动作识别结果。仿真结果表明:不同LSTM对来自不同时空的动作数据都有擅长分类的特征空间。展开更多
In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper intr...In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model.展开更多
Understanding the connection between brain and behavior in animals requires precise monitoring of their behaviors in three-dimensional(3-D)space.However,there is no available three-dimensional behavior capture system ...Understanding the connection between brain and behavior in animals requires precise monitoring of their behaviors in three-dimensional(3-D)space.However,there is no available three-dimensional behavior capture system that focuses on rodents.Here,we present MouseVenue3D,an automated and low-cost system for the efficient capture of 3-D skeleton trajectories in markerless rodents.We improved the most time-consuming step in 3-D behavior capturing by developing an automatic calibration module.Then,we validated this process in behavior recognition tasks,and showed that 3-D behavioral data achieved higher accuracy than 2-D data.Subsequently,MouseVenue3D was combined with fast high-resolution miniature two-photon microscopy for synchronous neural recording and behavioral tracking in the freely-moving mouse.Finally,we successfully decoded spontaneous neuronal activity from the 3-D behavior of mice.Our findings reveal that subtle,spontaneous behavior modules are strongly correlated with spontaneous neuronal activity patterns.展开更多
基金supported by the National Key R&D Pro-gram of China(No.2020YFD0900203)the China Agricul-ture Research System of MOF and MARAthe K.C.Wong Magna Fund in Ningbo University。
文摘Salinity is a significant environmental factor that can affect the survival,metamorphosis,growth and feeding of Portunus trituberculatus.In order to analyze the key physiological characteristics of P.trituberculatus in response to short-term low salinity stress,the experiments of gradually decline and recovery as well as abrupt decline in salinity were carried out.The results showed that P.trituberculatus could survive in a certain low salinity range in the short term,and salinity 12 was the lowest tolerable salinity under the present experimental conditions.The change of the hemolymph osmotic pressure displayed significant positive correlations with water salinity,and the pressure was always higher than seawater osmotic pressure.Short-term low salinity stress changed the structure and morphology of gill tissue.The expansion of gill filament ends and epithelial cell shedding were conducive to osmotic adjustment.The activities of key ion transport enzymes such as Na^(+)-K^(+)-ATPase,carbonic anhydrase and V-ATPase also changed with the osmotic regulation,while Na^(+)-K^(+)-ATPase played a dominant role.In summary,as an osmotic adjustment species,P.trituberculatus rapidly adapt to the short-term low-salinity environment by osmotic adjustment in vivo,but salinity below salinity 12 is not conducive to its survival.Our result enriched the theoretical mechanism of osmotic regulation of P.trituberculatus,providing reference for the development of aquaculture technology of P.trituberculatus.
文摘Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July7th 2010.
文摘为解决不同人员相同操作的个体差异以及同一人员不同时间相同操作差异的问题,提出一种基于混合专家系统(mixture of experts,MoE)和长短期记忆神经网络(long short-term memory,LSTM)的倒闸操作识别方法MoE-LSTM。基于MoE对LSTM进行集成,学习不同来源数据的特征分布。采集加速度动作数据构建倒闸操作数据集,基于滑动窗口对动作序列进行切分;将动作序列输入到MoE-LSTM中,由不同LSTM独立学习不同动作的时序依赖;通过门控网络选择对当前输入分类较好的LSTM的输出作为动作识别结果。仿真结果表明:不同LSTM对来自不同时空的动作数据都有擅长分类的特征空间。
文摘In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model.
基金the Key Area R&D Program of Guangdong Province,China(2018B030338001 and 2018B030331001)the National Key R&D Program of China(2018YFA0701403)+11 种基金the National Natural Science Foundation of China(31500861,31630031,91732304,and 31930047)Chang Jiang Scholars Program,the International Big Science Program Cultivating Project of the Chinese Academy of Science(CAS172644KYS820170004)the Strategic Priority Research Program of the CAS(XDB32030100)the Youth Innovation Promotion Association of the CAS(2017413)the CAS Key Laboratory of Brain Connectome and Manipulation(2019DP173024)Shenzhen Government Basic Research Grants(JCYJ20170411140807570,JCYJ20170413164535041)the Science,Technology and Innovation Commission of Shenzhen Municipality(JCYJ20160429185235132)a Helmholtz-CAS Joint Research grant(GJHZ1508)Guangdong Provincial Key Laboratory of Brain Connectome and Behavior(2017B030301017)the Ten Thousand Talent Program,the Guangdong Special Support Program,Key Laboratory of Shenzhen Institute of Advanced Technology(2019DP173024)the Shenzhen Key Science and Technology Infrastructure Planning Project(ZDKJ20190204002).
文摘Understanding the connection between brain and behavior in animals requires precise monitoring of their behaviors in three-dimensional(3-D)space.However,there is no available three-dimensional behavior capture system that focuses on rodents.Here,we present MouseVenue3D,an automated and low-cost system for the efficient capture of 3-D skeleton trajectories in markerless rodents.We improved the most time-consuming step in 3-D behavior capturing by developing an automatic calibration module.Then,we validated this process in behavior recognition tasks,and showed that 3-D behavioral data achieved higher accuracy than 2-D data.Subsequently,MouseVenue3D was combined with fast high-resolution miniature two-photon microscopy for synchronous neural recording and behavioral tracking in the freely-moving mouse.Finally,we successfully decoded spontaneous neuronal activity from the 3-D behavior of mice.Our findings reveal that subtle,spontaneous behavior modules are strongly correlated with spontaneous neuronal activity patterns.
