目的 RELMβ(Resistin-like molecule beta)又称为FIZZ2(found in inflammatory zone 2),是近年来发现的参与多种慢性炎性疾病的炎症因子,在炎症反应上游及下游均发挥了重要作用。目前关于RELMβ与呼吸系统及胃肠道相关疾病的研究报道较...目的 RELMβ(Resistin-like molecule beta)又称为FIZZ2(found in inflammatory zone 2),是近年来发现的参与多种慢性炎性疾病的炎症因子,在炎症反应上游及下游均发挥了重要作用。目前关于RELMβ与呼吸系统及胃肠道相关疾病的研究报道较多,且在其他疾病中的作用也陆续见诸报道,RELMβ已成为研究的热点,具有广阔的研究前景。本文就RELMβ的生理作用和其在炎症反应中的作用机制进行综述,旨为今后研究提供参考。展开更多
20世纪90年代由世界多个国家的地震学家围绕“地震可否预测”问题进行国际讨论后,人们开始思考适用于地震预测研究的规则应该有哪些,尤其是地震学家针对地震预测研究中所采取的途径和工作思路开始发生了变化。2007年开始的“区域地震似...20世纪90年代由世界多个国家的地震学家围绕“地震可否预测”问题进行国际讨论后,人们开始思考适用于地震预测研究的规则应该有哪些,尤其是地震学家针对地震预测研究中所采取的途径和工作思路开始发生了变化。2007年开始的“区域地震似然模型”(Regional Earthquake Likelihood Models,RELM)工作组和由此进一步而来的“地震可预测性国际合作研究”(Collaboratory for the Study of Earthquake Predictability,CSEP)计划开始之后,一大批地震预测模型和与评估其预测效能有关的统计检验方法加入进来,在设立相同的预测规则和使用统一的数据来源下,通过全球设立不同测试中心的方式,共同参与到对地震可预测性问题的系统研究中来。当前,CSEP计划已由开始的1.0阶段发展至2.0阶段,为使读者了解与这几项国际合作研究相关的工作主旨和发展历程,本文总结了与CSEP工作1.0阶段相关的工作理念和工作成果以及存在的问题,以期为下一步工作的开展提供参考。展开更多
In communication channel estimation,the Least Square(LS)technique has long been a widely accepted and commonly used principle.This is because the simple calculation method is compared with other channel estimation met...In communication channel estimation,the Least Square(LS)technique has long been a widely accepted and commonly used principle.This is because the simple calculation method is compared with other channel estimation methods.The Minimum Mean Squares Error(MMSE),which is developed later,is devised as the next step because the goal is to reduce the error rate in the communication system from the conventional LS technique which still has a higher error rate.These channel estimations are very important to modern communication systems,especially massive MIMO.Evaluating the massive MIMO channel is one of the most researched and debated topics today.This is essential in technology to overcome traditional performance barriers.The better the channel estimation,the more accurate it is.This paper investigated machine learning(ML)for channel estimation.ML channel estimations based on the Extreme Learning Machine(ELMx)group are also implemented.These estimations,known as the ELMx group,include Regularized Extreme Learning Machine(RELM)and Outlier Robust Extreme Learning Machine(ORELM).Then,it was compared with LS and MMSE.The simulation results reveal that the ELMx group outperforms LS and MMSE in channel capacity and bit error rate.Additionally,this paper has proven complexity for verified computational times.The RELM method is less time consuming and has low complexity which is suitable for future use in large MIMO systems.展开更多
The current situation and difficulties of the structural system reliability analysis are mentioned. Then on the basis of Monte Carlo method and computer simulation, a new analysis method reduced expanding load method ...The current situation and difficulties of the structural system reliability analysis are mentioned. Then on the basis of Monte Carlo method and computer simulation, a new analysis method reduced expanding load method (RELM) is presented, which can be used to solve structural reliability problems effectively and conveniently. In this method, the uncertainties of loads, structural material properties and dimensions can be fully considered. If the statistic parameters of stochastic variables are known, by using this method, the probability of failure can be estimated rather accurately. In contrast with traditional approaches,RELM method gives a much better understanding of structural failure frequency and its reliability index β is more meaningful.To illustrate this new idea, a specific example is given.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
文摘目的 RELMβ(Resistin-like molecule beta)又称为FIZZ2(found in inflammatory zone 2),是近年来发现的参与多种慢性炎性疾病的炎症因子,在炎症反应上游及下游均发挥了重要作用。目前关于RELMβ与呼吸系统及胃肠道相关疾病的研究报道较多,且在其他疾病中的作用也陆续见诸报道,RELMβ已成为研究的热点,具有广阔的研究前景。本文就RELMβ的生理作用和其在炎症反应中的作用机制进行综述,旨为今后研究提供参考。
文摘20世纪90年代由世界多个国家的地震学家围绕“地震可否预测”问题进行国际讨论后,人们开始思考适用于地震预测研究的规则应该有哪些,尤其是地震学家针对地震预测研究中所采取的途径和工作思路开始发生了变化。2007年开始的“区域地震似然模型”(Regional Earthquake Likelihood Models,RELM)工作组和由此进一步而来的“地震可预测性国际合作研究”(Collaboratory for the Study of Earthquake Predictability,CSEP)计划开始之后,一大批地震预测模型和与评估其预测效能有关的统计检验方法加入进来,在设立相同的预测规则和使用统一的数据来源下,通过全球设立不同测试中心的方式,共同参与到对地震可预测性问题的系统研究中来。当前,CSEP计划已由开始的1.0阶段发展至2.0阶段,为使读者了解与这几项国际合作研究相关的工作主旨和发展历程,本文总结了与CSEP工作1.0阶段相关的工作理念和工作成果以及存在的问题,以期为下一步工作的开展提供参考。
文摘In communication channel estimation,the Least Square(LS)technique has long been a widely accepted and commonly used principle.This is because the simple calculation method is compared with other channel estimation methods.The Minimum Mean Squares Error(MMSE),which is developed later,is devised as the next step because the goal is to reduce the error rate in the communication system from the conventional LS technique which still has a higher error rate.These channel estimations are very important to modern communication systems,especially massive MIMO.Evaluating the massive MIMO channel is one of the most researched and debated topics today.This is essential in technology to overcome traditional performance barriers.The better the channel estimation,the more accurate it is.This paper investigated machine learning(ML)for channel estimation.ML channel estimations based on the Extreme Learning Machine(ELMx)group are also implemented.These estimations,known as the ELMx group,include Regularized Extreme Learning Machine(RELM)and Outlier Robust Extreme Learning Machine(ORELM).Then,it was compared with LS and MMSE.The simulation results reveal that the ELMx group outperforms LS and MMSE in channel capacity and bit error rate.Additionally,this paper has proven complexity for verified computational times.The RELM method is less time consuming and has low complexity which is suitable for future use in large MIMO systems.
文摘The current situation and difficulties of the structural system reliability analysis are mentioned. Then on the basis of Monte Carlo method and computer simulation, a new analysis method reduced expanding load method (RELM) is presented, which can be used to solve structural reliability problems effectively and conveniently. In this method, the uncertainties of loads, structural material properties and dimensions can be fully considered. If the statistic parameters of stochastic variables are known, by using this method, the probability of failure can be estimated rather accurately. In contrast with traditional approaches,RELM method gives a much better understanding of structural failure frequency and its reliability index β is more meaningful.To illustrate this new idea, a specific example is given.
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.
