Objective To evaluate the utility of virtual reality system in quantitative comparison for cavernous sinus surgical approach. Methods Image data of CT and MRI scan performed in five adult cadaver heads was inputted in...Objective To evaluate the utility of virtual reality system in quantitative comparison for cavernous sinus surgical approach. Methods Image data of CT and MRI scan performed in five adult cadaver heads was inputted into the Destroscope virtual reality system to build 3-D model of cavernous sinus.展开更多
El Niño-Southern Oscillation(ENSO)can be currently predicted reasonably well six months and longer,but large biases and uncertainties remain in its real-time prediction.Various approaches have been taken to impro...El Niño-Southern Oscillation(ENSO)can be currently predicted reasonably well six months and longer,but large biases and uncertainties remain in its real-time prediction.Various approaches have been taken to improve understanding of ENSO processes,and different models for ENSO predictions have been developed,including linear statistical models based on principal oscillation pattern(POP)analyses,convolutional neural networks(CNNs),and so on.Here,we develop a novel hybrid model,named as POP-Net,by combining the POP analysis procedure with CNN-long short-term memory(LSTM)algorithm to predict the Niño-3.4 sea surface temperature(SST)index.ENSO predictions are compared with each other from the corresponding three models:POP model,CNN-LSTM model,and POP-Net,respectively.The POP-based pre-processing acts to enhance ENSO-related signals of interest while filtering unrelated noise.Consequently,an improved prediction is achieved in the POP-Net relative to others.The POP-Net shows a high-correlation skill for 17-month lead time prediction(correlation coefficients exceeding 0.5)during the 1994-2020 validation period.The POP-Net also alleviates the spring predictability barrier(SPB).It is concluded that value-added artificial neural networks for improved ENSO predictions are possible by including the process-oriented analyses to enhance signal representations.展开更多
The dominant property of building envelope fabric which contributes significantly to minimize electricity utilization in building is the thermo-physical properties. There is inadequate literature on representative pra...The dominant property of building envelope fabric which contributes significantly to minimize electricity utilization in building is the thermo-physical properties. There is inadequate literature on representative practical data of thermo-physical properties of the dominant building envelope components in Ghana. This study aims to use cost-effective approach to characterize the thermo-physical properties of only cement-based mortar and concrete blocks used in Ghana for building components specifically wall design. Mixed methods research design was employed to achieving the aim. A questionnaire survey was used among sampled building fabric components manufacturers to pick representative data on thermos-physical properties of their mortar and concrete blocks. Also, an experimental procedure employing a transient technique with a TCi Thermal Analyser was used to determine the thermo-physical properties of selected mortar and concrete blocks from Ghana in addition to designed parametric mortar and concrete blocks with varied ratios obtained from the survey were undertaken at University of Nottingham. From the study, a trend of decreasing thermal conductivity and thermal effusivity with corresponding decreasing sand content was observed with all the different sand types. The thermal conductivities of both mortar and concrete parametric blocks meet the range of expected standard values outlined in Chattered Institute of Building Services Engineers (CIBSE) Guide A. The major limitation of the work is the dimension of the sample size;which is not inconsistent with standard block size due to the experimental setup used. It is expected that, the characterization of the predominant cement-based building fabrics components will contribute to improved building performance analysis with significant savings in electricity utilization for space cooling.展开更多
文摘Objective To evaluate the utility of virtual reality system in quantitative comparison for cavernous sinus surgical approach. Methods Image data of CT and MRI scan performed in five adult cadaver heads was inputted into the Destroscope virtual reality system to build 3-D model of cavernous sinus.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA19060102)the National Natural Science Foundation of China[NSFCGrant Nos.41690122(41690120),and 42030410].
文摘El Niño-Southern Oscillation(ENSO)can be currently predicted reasonably well six months and longer,but large biases and uncertainties remain in its real-time prediction.Various approaches have been taken to improve understanding of ENSO processes,and different models for ENSO predictions have been developed,including linear statistical models based on principal oscillation pattern(POP)analyses,convolutional neural networks(CNNs),and so on.Here,we develop a novel hybrid model,named as POP-Net,by combining the POP analysis procedure with CNN-long short-term memory(LSTM)algorithm to predict the Niño-3.4 sea surface temperature(SST)index.ENSO predictions are compared with each other from the corresponding three models:POP model,CNN-LSTM model,and POP-Net,respectively.The POP-based pre-processing acts to enhance ENSO-related signals of interest while filtering unrelated noise.Consequently,an improved prediction is achieved in the POP-Net relative to others.The POP-Net shows a high-correlation skill for 17-month lead time prediction(correlation coefficients exceeding 0.5)during the 1994-2020 validation period.The POP-Net also alleviates the spring predictability barrier(SPB).It is concluded that value-added artificial neural networks for improved ENSO predictions are possible by including the process-oriented analyses to enhance signal representations.
文摘The dominant property of building envelope fabric which contributes significantly to minimize electricity utilization in building is the thermo-physical properties. There is inadequate literature on representative practical data of thermo-physical properties of the dominant building envelope components in Ghana. This study aims to use cost-effective approach to characterize the thermo-physical properties of only cement-based mortar and concrete blocks used in Ghana for building components specifically wall design. Mixed methods research design was employed to achieving the aim. A questionnaire survey was used among sampled building fabric components manufacturers to pick representative data on thermos-physical properties of their mortar and concrete blocks. Also, an experimental procedure employing a transient technique with a TCi Thermal Analyser was used to determine the thermo-physical properties of selected mortar and concrete blocks from Ghana in addition to designed parametric mortar and concrete blocks with varied ratios obtained from the survey were undertaken at University of Nottingham. From the study, a trend of decreasing thermal conductivity and thermal effusivity with corresponding decreasing sand content was observed with all the different sand types. The thermal conductivities of both mortar and concrete parametric blocks meet the range of expected standard values outlined in Chattered Institute of Building Services Engineers (CIBSE) Guide A. The major limitation of the work is the dimension of the sample size;which is not inconsistent with standard block size due to the experimental setup used. It is expected that, the characterization of the predominant cement-based building fabrics components will contribute to improved building performance analysis with significant savings in electricity utilization for space cooling.
