Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study...Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters （insensitive loss function e, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ） are 0.001, 0.5, and 2 000, respectively.展开更多
This paper presents modelling of a post-combustion CO2 capture process using bootstrap aggregated extreme learning machine （ELM）. ELM randomly assigns the weights between input and hidden layers and obtains the weig...This paper presents modelling of a post-combustion CO2 capture process using bootstrap aggregated extreme learning machine （ELM）. ELM randomly assigns the weights between input and hidden layers and obtains the weights between the hidden layer and output layer using regression type approach in one step. This feature allows an ELM model being developed very quickly. This paper proposes using principal component regression to obtain the weights between the hidden and output layers to address the collinearity issue among hidden neuron outputs. Due to the weights between input and hidden layers are randomly assigned, ELM models could have variations in performance. This paper proposes combining multiple ELM models to enhance model prediction accuracy and reliability. To predict the CO2 production rate and CO2 capture level, eight parameters in the process were utilized as model input variables： inlet gas flow rate, CO2 concentration in inlet flow gas, inlet gas temperature, inlet gas pressure, lean solvent flow rate, Jean solvent temperature, lean loading and reboiler duty. The bootstrap re-sampling of training data was applied for building each single ELM and then the individual ELMs are stacked, thereby enhancing the model accuracy and reliability. The bootstrap aggregated extreme learning machine can provide fast learning speed and good generalization performance, which will be used to optimize the CO2 capture process.展开更多
Interleukin-1β（IL-1β）is one of the first cytokines ever described.It has long been recognized to play an important role in mediating inflammation and orchestrating the physiological and behavioral adjustments that...Interleukin-1β（IL-1β）is one of the first cytokines ever described.It has long been recognized to play an important role in mediating inflammation and orchestrating the physiological and behavioral adjustments that occur during sickness. Recently,accumulating evidence has indicated that IL-1β also adversely affects cognitive function.Nevertheless,there are also some reports showing no effects or even beneficial effects of IL-1β on learning and memory.The relationship between IL-1β and cognitive impairment has not been clearly elucidated.Here we reviewed the evidence of both negative and positive effects of IL-1β on learning and memory,and the key factors that may affect the effects of IL-1β on learning and memory were discussed.展开更多
基金Projects(2007JT3018, 2008JT1013, 2009FJ4056) supported by the Key Project in Hunan Science and Technology Program, ChinaProject(20090161120014) supported by the New Teachers Sustentation Fund in Doctoral Program, Ministry of Education, China
文摘Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters （insensitive loss function e, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ） are 0.001, 0.5, and 2 000, respectively.
基金The work was supported by the EU through the project ＂Research and Development in Coal-fired Supercritical Power Plant with Post-combustion Carbon Capture using Process Systems Engineering techniques＂ （Project No. PIRSES-GA-2013-612230） and National Natural Science Foundation of China （61673236）.
文摘This paper presents modelling of a post-combustion CO2 capture process using bootstrap aggregated extreme learning machine （ELM）. ELM randomly assigns the weights between input and hidden layers and obtains the weights between the hidden layer and output layer using regression type approach in one step. This feature allows an ELM model being developed very quickly. This paper proposes using principal component regression to obtain the weights between the hidden and output layers to address the collinearity issue among hidden neuron outputs. Due to the weights between input and hidden layers are randomly assigned, ELM models could have variations in performance. This paper proposes combining multiple ELM models to enhance model prediction accuracy and reliability. To predict the CO2 production rate and CO2 capture level, eight parameters in the process were utilized as model input variables： inlet gas flow rate, CO2 concentration in inlet flow gas, inlet gas temperature, inlet gas pressure, lean solvent flow rate, Jean solvent temperature, lean loading and reboiler duty. The bootstrap re-sampling of training data was applied for building each single ELM and then the individual ELMs are stacked, thereby enhancing the model accuracy and reliability. The bootstrap aggregated extreme learning machine can provide fast learning speed and good generalization performance, which will be used to optimize the CO2 capture process.
基金supported by the grants from National Natural Science Foundation of China(No.30700213)National Basic Research Development Program of China(No.2007CB947804)
文摘Interleukin-1β（IL-1β）is one of the first cytokines ever described.It has long been recognized to play an important role in mediating inflammation and orchestrating the physiological and behavioral adjustments that occur during sickness. Recently,accumulating evidence has indicated that IL-1β also adversely affects cognitive function.Nevertheless,there are also some reports showing no effects or even beneficial effects of IL-1β on learning and memory.The relationship between IL-1β and cognitive impairment has not been clearly elucidated.Here we reviewed the evidence of both negative and positive effects of IL-1β on learning and memory,and the key factors that may affect the effects of IL-1β on learning and memory were discussed.
