Synthesis,extraction, and adsorption properties of calix[4]arene‐poly(ethylene‐glycol) crosslinked polymer

Novel calix[4]arene‐poly(ethylene glycol) crosslinked polymer (CCP) has been synthesized by the polycondensation reaction between a p‐tert‐butylcalix[4]arene derivative and dihydroxyl capped poly(ethylene glycol) (DHPEG, M_n = 1000) catalyzed by neodymium tosylate. The hydrogel, consisted of 66.9% water and 33.1% CCP, can selectively extract aromatic organic molecules from aqueous solution according to the diameter of the guest molecules, which infers that the diameter of the calix[4]arene cavity is about 5.4 Å and the conformation of calix[4]arene units altered from cone conformation to 1,3‐alternate conformation during the polycondensation reaction. Furthermore, CCP can also adsorb naphthalene from gas phase, showing much higher capacity than active carbon does, which may have some potential applications in the field of separation and environment protection. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

CoGCN: Combining co‐attention with graph convolutional network for entity linking with knowledge graphs

Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co‐attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co‐attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context‐aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.     

Physical properties of transition to chaos in RF-driven Josephson junction analog

An analog circuit is used to investigate the behavior of the transition to chaos in the rf-driven Josephson junction under the variation of excitation amplitude and frequency with McCumber parameter >1. A series of transitions is observed, including a hysteretic jump, symmetry breaking, period doubling, and intermittency prior to chaos, and the transition boundaries are given in a state diagram in the vs. parameter space. Thus, the sequence of transitions to chaos can be inferred from this diagram. Based on these results, the harmonic balance method and Floquet theory are applied to study the instability of the transition series. It is suggested that all these transitions to chaos can be explained as parametric excitations. In addition, the transition boundaries in the state diagram can be satisfactorily computed from the conditions with a Floquet multiplier =±1.     

乙烯二聚制高纯丁烯－1技术经济评价

本文对生产高纯度丁烯－１的Ａｌｐｈａｂｎｔｏｌ工艺进行了技术经济评价，认为该工艺条件简单、反应条件温和，而且投资少，所以很适合事在发展中国家应用。     

Situational awareness architecture for smart grids developed in accordance with dispatcher’s thought process: a review

The operational environment of today’s smart grids is becoming more complicated than ever before. A number of factors, including renewable penetration, marketization, cyber security, and hazards of nature, bring challenges and even threats to control centers. New techniques are anticipated to help dispatchers become aware of the accurate situations as they manipulate and navigate the situations as quickly as possible. To address the issues, we first introduce the background for this topic as well as the emerging technical demands of situational awareness in the dispatcher’s environment. The general concepts and technical requirements of situational awareness are then summarized, aimed at offering an overview for readers to understand the state-of-the-art progress in this area. In addition, we discuss the importance of integrating the architecture of support tools in accordance with the dispatcher’s thought process, which in fact guides correct and swift reactions in real-time operations. Finally, the prospects for situational awareness architecture are investigated with the goal of presenting situational awareness modules in an advanced and visualized manner.     

互联网应建立全新的社会秩序管理体系

互联网诞生之初的作用是一个互联互通的通信工具。当时的主要管理工作都是基于安全目的,保证信息的＂可用性、完整性、保密性＂。经过20多年发展,互联网的作用、身份已经发生了巨大的变化,和现实社会密不可分,成为一个虚拟社会。而在管理互联网的理念、手段、工具等方面没有大的突破,还局限在信息安全范围内,言必谈＂安全＂。既然是＂社会＂,就必然有社会学的属性、需要用社会学的体系、手段、机制,才能管理好＂社会＂,解决社会学问题。事实也证明,用非社会学的手段来解决社会问题,是无解的!     

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic. Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air traffic flow management. Current researches mostly use traditional machine learning methods to extract features of weather scenes, and clustering algorithms to divide similar scenes. Inspired by the excellent performance of deep learning in image recognition, this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering (IDCEC), which uses the combination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image, retaining useful information to the greatest extent, and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area. Finally, terminal area of Guangzhou Airport is selected as the research object, the method proposed in this article is used to classify historical weather data in similar scenes, and the performance is compared with other state-of-the-art methods. The experimental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather; at the same time, compared with the actual flight volume in the Guangzhou terminal area, IDCEC's recognition results of similar weather scenes are consistent with the recognition of experts in the field.     

Label distribution learning for scene text detection

Recently, segmentation-based scene text detection has drawn a wide research interest due to its flexibility in describing scene text instance of arbitrary shapes such as curved texts. However, existing methods usually need complex post-processing stages to process ambiguous labels, i.e., the labels of the pixels near the text boundary, which may belong to the text or background. In this paper, we present a framework for segmentation-based scene text detection by learning from ambiguous labels. We use the label distribution learning method to process the label ambiguity of text annotation, which achieves a good performance without using additional post-processing stage. Experiments on benchmark datasets demonstrate that our method produces better results than state-of-the-art methods for segmentation-based scene text detection.