EcoLens: visual analysis of ecological regions in urban contexts using traffic data

Journal of Visualization - The increasing availability of spatiotemporal data provides unprecedented opportunities for understanding the structure of an urban area in terms of people’s...     

VisAct: a visualization design system based on semantic actions

Current visualization design toolkits help users to explore data and create visualizations. However, most of these systems do not record the executed actions during the visualization construction process with semantic context. In this paper, we present VisAct, a visualization design system based on semantic actions, that helps average users to construct visualizations step by step. Our system contributes a set of action-based visualization components and a high-level grammar for semantic actions. VisAct also guides the visualization construction process and provides an action tracker for history management and data collection. We demonstrate the usability of VisAct by visualizations and a plugin application. Finally, we conduct a user study to evaluate the efficiency and effectiveness of our system.     

Recent advances in particle and droplet manipulation for lab-on-a-chip devices based on surface acoustic waves

Manipulation of microscale particles and fluid liquid droplets is an important task for lab-on-a-chip devices for numerous biological researches and applications, such as cell detection and tissue engineering. Particle manipulation techniques based on surface acoustic waves (SAWs) appear effective for lab-on-a-chip devices because they are non-invasive, compatible with soft lithography micromachining, have high energy density, and work for nearly any type of microscale particles. Here we review the most recent research and development of the past two years in SAW based particle and liquid droplet manipulation for lab-on-a-chip devices including particle focusing and separation, particle alignment and patterning, particle directing, and liquid droplet delivery.     

Accelerating Optimizing the Design of Carbon-based Electrocatalyst via Machine Learning

In this era of artificial intelligence, we urgently want to optimize the current material design methods to come up with a more efficient and more accurate closed-loop system. The approach requires at least three parts including high-throughput screening, automated synthesis platform, and machine learning algorithms. Fortunately, the techniques mentioned above have been substantial developed. We have introduced the common algorithms of machine learning. Then, several machine learning-based design of carbon-based electrocatalysts are discussed. We tried to illustrate the research norms involving machine learning. Besides, other paper structures and details have been also discussed.     

TrammelGraph: visual graph abstraction for comparison

Journal of Visualization - Network data, represented by graph-based structures, are used in a variety of applications such as social networks and disease complication networks. A crucial task in...     

基于改进Apriori的网络安全感知方法

针对网络安全态势评估过程中存在数据源单一、实时性不强、准确率不高的问题,提出一种基于改进关联规则算法(Apriori算法)的网络安全态势感知方法。通过对数据的分析,发现在网络中存在关于安全态势的关联规则;通过网络攻击影响熵值序列的分析,对关联规则进行分类为空间正常和异常空间,进而对关联规则进行聚类分析;根据聚类后的规则划分网络安全态势等级。将改进后的算法应用到网络安全态势感知当中,实验结果表明,该方法满足了网络安全危险预警和实时监控的要求。改进的算法用于安全态势感知是可行的、有效的。     

Advances in Photonic Devices Based on Optical Phase-Change Materials

Phase-change materials (PCMs) are important photonic materials that have the advantages of a rapid and reversible phase change, a great difference in the optical properties between the crystalline and amorphous states, scalability, and nonvolatility. With the constant development in the PCM platform and integration of multiple material platforms, more and more reconfigurable photonic devices and their dynamic regulation have been theoretically proposed and experimentally demonstrated, showing the great potential of PCMs in integrated photonic chips. Here, we review the recent developments in PCMs and discuss their potential for photonic devices. A universal overview of the mechanism of the phase transition and models of PCMs is presented. PCMs have injected new life into on-chip photonic integrated circuits, which generally contain an optical switch, an optical logical gate, and an optical modulator. Photonic neural networks based on PCMs are another interesting application of PCMs. Finally, the future development prospects and problems that need to be solved are discussed. PCMs are likely to have wide applications in future intelligent photonic systems.     

Exploring Core Microbiota Based on Characteristic Flavor Compounds in Different Fermentation Phases of Sufu

Sufu, a Chinese traditional fermented soybean product, has a characteristic foul smell but a pleasant taste. We determined the core functional microbiota and their metabolic mechanisms during sufu fermentation by examining relationships among bacteria, characteristic flavor compounds, and physicochemical factors. Flavor compounds in sufu were detected through headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry, and the microbial community structure was determined through high-throughput 16S rRNA sequencing. The results showed that the fermentation process of sufu could be divided into early and late stages. The early stage was critical for flavor development. Seven microbiota were screened based on their abundance, microbial relevance, and flavor production capacity. Five microbes were screened in the early stage: Pseudomonas, Tetragenococcus, Lysinibacillus, Pantoea, and Burkholderia–Caballeronia–Paraburkholderia. Three microbes were screened in the late stage: Exiguobacterium, Bacillus, and Pseudomonas. Their metabolic profiles were predicted. The results provided a reference for the selection of enriched bacterial genera in the fermentation process and controlling applicable process conditions to improve the flavor of sufu.