Relmβ亦称发现于炎症带2(found in inflammatory zone2,FIZZ2),是一种富含半胱氨酸的分泌蛋白。目前的研究表明,Relmβ在许多方面发挥着非常重要的作用,包括胰岛素抵抗,抵制肠腔寄生线虫的感染,预测胃肠道肿瘤的发生,促进小鼠结肠炎和...Relmβ亦称发现于炎症带2(found in inflammatory zone2,FIZZ2),是一种富含半胱氨酸的分泌蛋白。目前的研究表明,Relmβ在许多方面发挥着非常重要的作用,包括胰岛素抵抗,抵制肠腔寄生线虫的感染,预测胃肠道肿瘤的发生,促进小鼠结肠炎和回肠炎模型中的炎症反应,诱导肺部纤维化、缺氧肺血管的重建,促进气道重塑等。通过对Relmβ分子的结构、表达分布、生物学功能的研究进展进行归纳,为Relmβ分子在生物学和医学方面更深入的研究提供参考。展开更多
Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This stu...Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from19 asphalt pavements with different crude oil sources on a 2.038km long full-scale field accelerated pavement test track(Road Track Institute, RIOHTrack) in Tongzhou, Beijing. In addition,this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction(RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error(MSE), average mean absolute error(MAE), and a verage mean absolute percentage error(MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.展开更多
文摘Relmβ亦称发现于炎症带2(found in inflammatory zone2,FIZZ2),是一种富含半胱氨酸的分泌蛋白。目前的研究表明,Relmβ在许多方面发挥着非常重要的作用,包括胰岛素抵抗,抵制肠腔寄生线虫的感染,预测胃肠道肿瘤的发生,促进小鼠结肠炎和回肠炎模型中的炎症反应,诱导肺部纤维化、缺氧肺血管的重建,促进气道重塑等。通过对Relmβ分子的结构、表达分布、生物学功能的研究进展进行归纳,为Relmβ分子在生物学和医学方面更深入的研究提供参考。
基金supported by the Analytical Center for the Government of the Russian Federation (IGK 000000D730321P5Q0002) and Agreement Nos.(70-2021-00141)。
文摘Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from19 asphalt pavements with different crude oil sources on a 2.038km long full-scale field accelerated pavement test track(Road Track Institute, RIOHTrack) in Tongzhou, Beijing. In addition,this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction(RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error(MSE), average mean absolute error(MAE), and a verage mean absolute percentage error(MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.
