A mini review on two-dimensional nanomaterial assembly

Two-dimensional (2D) nanomaterials have attracted a great deal of attention since the discovery of graphene in 2004, due to their intriguing physicochemical properties and wide-ranging applications in catalysis, energy-related devices, electronics and optoelectronics. To maximize the potential of 2D nanomaterials for their technological applications, controlled assembly of 2D nanobulding blocks into integrated systems is critically needed. This mini review summarizes the reported strategies of 2D materials-based assembly into integrated functional nanostructures, from in-situ assembly method to post-synthesis assembly. The applications of 2D assembled integrated structures are also covered, especially in the areas of energy, electronics and sensing, and we conclude with discussion on the remaining challenges and potential directions in this emerging field.

     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

PWR堆芯燃料组件非线性梁模型研究

为描述燃料组件的非线性特性，采用有限元方法建立燃料组件横向梁模型，并引入迟滞模型，用于模拟燃料组件横向变形时的非线性效应。计算结果表明:该非线性梁模型能够更准确地获得组件的弯曲变形和受力，有助于开展燃料组件事故分析。      

Influence of Sodium Microalloying on Rolling Contact Endurance and Mechanical Properties of Hot-Deformed Powder Steels

Russian Journal of Non-Ferrous Metals - Rolling bearing rings comprise a large reserve for expanding powder metallurgy production. This reserve is being incompletely implemented. The hot forging of...     

MPE: a mobility pattern embedding model for predicting next locations

World Wide Web - The wide spread use of positioning and photographing devices gives rise to a deluge of traffic trajectory data (e.g., vehicle passage records and taxi trajectory data), with each...     

铝合金熔炼铸造能源与资源节约

在铝材生产中,熔炼、铸造工序是能源与资源消耗多的工序,因此,节约资源、降低烧损与能耗有着重要意义。降低能耗还可以相应地减少温室气体与有害、有毒物质排放,对环保有利。中国当前铝合金熔炼铸造能源与资源节约的主要措施:调整加工用锭坯供应结构,大幅提高电解铝厂供应的锭坯,由2013年供应的42%提高到"十三五"末的65%-70%;加快走出去的力度,到国外去办铝厂,将在国外生产的铝合金锭坯运回来,争取2020年出口的铝材与铝制品全部以进口锭坯加工,这是最大的资源与能源节约;加强废铝回收与有效再生利用;加强创新,大力采用新装备、新技术与新工艺;加强管理,精心操作,管理中还有较大的能源与资源节约潜力。