According to the relationship between load and response, the equivalent static wind load(ESWL) of a structure can be estimated by load-response correlation(LRC) method, which can be accurately used to estimate the bac...According to the relationship between load and response, the equivalent static wind load(ESWL) of a structure can be estimated by load-response correlation(LRC) method, which can be accurately used to estimate the background ESWL of a structure. The derivation of the classical expression of LRC formula is based on a specific command response at a critical position, and the ESWL distribution has only one form in this case. In this paper, a general expression of LRC formula is derived based on a specific command response at all positions. For the general expression, ESWLs can be expressed by load-response correlation coefficients, response-response correlation coefficients, RMS values of the fluctuating wind loads, and peak factor in the form of matrices. By comparing the expressions of LRC method, it was found that the classical expression was only one form of the general one. The general expression which introduces the response-response correlation coefficients provided more options for structural engineers to estimate ESWLs and offered further insights into the LRC method. Finally, a cable-stayed bridge, a rigid three span continuous girder bridge, and a suspension bridge were used to verify the correctness of the general expression of LRC method.展开更多
将2周龄雏鸡随机分成3组,试验组雏鸡颈部皮下注射0.l mL 107个/mL非复制型尿嘧啶营养缺陷型弓形虫(NRTUAs),感染对照组和空白对照组分别注射等剂量的RH虫株和PBS;于感染的第4天采集各组雏鸡的心脏、肝脏、脾脏、肺脏、肾脏、睾丸、法氏...将2周龄雏鸡随机分成3组,试验组雏鸡颈部皮下注射0.l mL 107个/mL非复制型尿嘧啶营养缺陷型弓形虫(NRTUAs),感染对照组和空白对照组分别注射等剂量的RH虫株和PBS;于感染的第4天采集各组雏鸡的心脏、肝脏、脾脏、肺脏、肾脏、睾丸、法氏囊、胸腺、脑、胸肌、腿肌组织,提取其DNA,采用绝对荧光定量PCR检测各器官组织的荷虫量;提取剩余脾脏组织的RNA,采用相对荧光定量PCR仪检测脾脏中IL-1β、IL-6、TNF-α、CCL26、STAT1、IRF1、IFN-γ、IL-10的转录水平。结果表明,雏鸡感染弓形虫的第4天,各器官组织不能完全清除NRTUAs,除肝脏、睾丸、腿肌和脑组织外,NRTUAs组的组织荷虫量均显著低于RH组的;NRTUAs刺激脾脏不会使免疫相关细胞因子的转录水平显著上调或下调,说明NRTUAs是致病性较低的虫株,不会刺激雏鸡脾脏引发强烈的先天性免疫反应,不会引起促炎、抑炎因子的过量产生。展开更多
The present paper aims at modeling suspended sediment load(SSL) using heuristic data driven methodologies, e.g. Gene Expression Programming(GEP) and Support Vector Machine(SVM) in three successive hydrometric stations...The present paper aims at modeling suspended sediment load(SSL) using heuristic data driven methodologies, e.g. Gene Expression Programming(GEP) and Support Vector Machine(SVM) in three successive hydrometric stations of Housatonic River in U.S. The simulations were carried out through local and cross-station data management scenarios to investigate the interrelations between the SSL values of upstream/downstream stations. The available scenarios were applied to predict SSL values using GEP to obtain the best models. Then, the best models were predicted by SVM approach and the obtained results were compared with those of GEP. The comparison of the results revealed that the SVM technique is more capable than the GEP for modeling the SSL through the both local and cross-station data management strategies. Besides, local application seems to be better than cross-station application for modeling SSL. Nevertheless, the cross-station application demonstrated to be a valid methodology for simulating SSL, which would be of interest for the stations with lack of observational data. Also, the prediction capability of conventional Sediment Rating Curve(SRC) method was compared with those of GEPand SVM techniques. The obtained results revealed the superiority of GEP and SVM-based models over the traditional SRC technique in the studied stations.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.51508107)the China Postdoctoral Science Foundation(Grant No.2016M590592)the Natural Science Foundation of Fujian Province(Grant No.2015J05098)。
文摘According to the relationship between load and response, the equivalent static wind load(ESWL) of a structure can be estimated by load-response correlation(LRC) method, which can be accurately used to estimate the background ESWL of a structure. The derivation of the classical expression of LRC formula is based on a specific command response at a critical position, and the ESWL distribution has only one form in this case. In this paper, a general expression of LRC formula is derived based on a specific command response at all positions. For the general expression, ESWLs can be expressed by load-response correlation coefficients, response-response correlation coefficients, RMS values of the fluctuating wind loads, and peak factor in the form of matrices. By comparing the expressions of LRC method, it was found that the classical expression was only one form of the general one. The general expression which introduces the response-response correlation coefficients provided more options for structural engineers to estimate ESWLs and offered further insights into the LRC method. Finally, a cable-stayed bridge, a rigid three span continuous girder bridge, and a suspension bridge were used to verify the correctness of the general expression of LRC method.
文摘将2周龄雏鸡随机分成3组,试验组雏鸡颈部皮下注射0.l mL 107个/mL非复制型尿嘧啶营养缺陷型弓形虫(NRTUAs),感染对照组和空白对照组分别注射等剂量的RH虫株和PBS;于感染的第4天采集各组雏鸡的心脏、肝脏、脾脏、肺脏、肾脏、睾丸、法氏囊、胸腺、脑、胸肌、腿肌组织,提取其DNA,采用绝对荧光定量PCR检测各器官组织的荷虫量;提取剩余脾脏组织的RNA,采用相对荧光定量PCR仪检测脾脏中IL-1β、IL-6、TNF-α、CCL26、STAT1、IRF1、IFN-γ、IL-10的转录水平。结果表明,雏鸡感染弓形虫的第4天,各器官组织不能完全清除NRTUAs,除肝脏、睾丸、腿肌和脑组织外,NRTUAs组的组织荷虫量均显著低于RH组的;NRTUAs刺激脾脏不会使免疫相关细胞因子的转录水平显著上调或下调,说明NRTUAs是致病性较低的虫株,不会刺激雏鸡脾脏引发强烈的先天性免疫反应,不会引起促炎、抑炎因子的过量产生。
文摘The present paper aims at modeling suspended sediment load(SSL) using heuristic data driven methodologies, e.g. Gene Expression Programming(GEP) and Support Vector Machine(SVM) in three successive hydrometric stations of Housatonic River in U.S. The simulations were carried out through local and cross-station data management scenarios to investigate the interrelations between the SSL values of upstream/downstream stations. The available scenarios were applied to predict SSL values using GEP to obtain the best models. Then, the best models were predicted by SVM approach and the obtained results were compared with those of GEP. The comparison of the results revealed that the SVM technique is more capable than the GEP for modeling the SSL through the both local and cross-station data management strategies. Besides, local application seems to be better than cross-station application for modeling SSL. Nevertheless, the cross-station application demonstrated to be a valid methodology for simulating SSL, which would be of interest for the stations with lack of observational data. Also, the prediction capability of conventional Sediment Rating Curve(SRC) method was compared with those of GEPand SVM techniques. The obtained results revealed the superiority of GEP and SVM-based models over the traditional SRC technique in the studied stations.
