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The development of reversible networks in elastomers provided unique inspiration for the design of advanced polymers with excellent properties. In this paper, we adopted an anionic melting method to introduce carboxylate groups into 1,2-polybutadiene (1,2-PB), using maleic anhydride as a modifier, and sodium hydride (NaH), calcium hydride (CaH2), and lithium aluminum hydride (LiAlH4) as metallization reagents. Na-Based, Ca-based, and Li/Al-based ionic bond networks were constructed in the covalently crosslinked 1,2-PB. The effects of the electronegativity and valence of the metal ions on the strength and reversible temperature of the ionic network were studied. Payne effect was shown by rheological tests, demonstrating the interactions between the ionic networks and rubber chains. The reforming temperature for these ionic networks was studied by stress-relaxation analysis, and shape memory experiments were performed based on these temperatures. This concept provides novel inspiration for the design of high-performance and temperature-adaptive elastomers.

Financial regulatory agencies are struggling to manage the systemic risks attributed to negative economic shocks. Preventive interventions are prominent to eliminate the risks and help to build a more resilient financial system. Although tremendous efforts have been made to measure multi-risk severity levels, understand the contagion behaviors and other risk management problems, there still lacks a theoretical framework revealing what and how regulatory intervention measurements can mitigate systemic risk. Here we demonstrate regshock, a practical visual analytical approach to support the exploration and evaluation of financial regulation measurements. We propose risk-island, an unprecedented risk-centered visualization algorithm to help uncover the risk patterns while preserving the topology of financial networks. We further propose regshock, a novel visual exploration and assessment approach based on the simulation-intervention-evaluation analysis loop, to provide a heuristic surgical intervention capability for systemic risk mitigation. We evaluate our approach through extensive case studies and expert reviews. To our knowledge, this is the first practical systemic method for the financial network intervention and risk mitigation problem; our validated approach potentially improves the risk management and control capabilities of financial experts., Comment: 9 pages, 14 figures

In the context of urbanization and the rapid growth of energy demand, understanding the spatial and temporal dynamics of urban energy use is crucial for identifying energy-saving potentials. In this demo, we present a visual analysis tool, VAP, that allows users to explore the dynamics of urban energy use at different spatial and temporal scales. In contrast to traditional statistical and machine learning methods, the visual analysis based tool focuses on analytical thinking, user interactions and answering business questions by examining different visual analysis views. In the demonstration, conference attendees will interact with VAP and learn its capabilities in discovering typical consumption patterns and spatio-temporal shift patterns from a real-world case study of electricity.

Understanding demand-side energy behaviour is critical for making efficiency responses for energy demand management. We worked closely with energy experts and identified the key elements of the energy demand problem including temporal and spatial demand and shifts in spatiotemporal demand. To our knowledge, no previous research has investigated the shifts in spatiotemporal demand. To fill this research gap, we propose a unified visual analytics approach to support exploratory demand analysis; we developed E3, a highly interactive tool that support users in making and verifying hypotheses through human-client-server interactions. A novel potential flow based approach was formalized to model shifts in energy demand and integrated into a server-side engine. Experts then evaluated and affirmed the usefulness of this approach through case studies of real-world electricity data. In the future, we will improve the modelling algorithm, enhance visualisation, and expand the process to support more forms of energy data., Comment: 5 Pages, with 1 page reference

Information visualization and visual analytics technology has attracted significant attention from the financial regulation community. In this research, we present regvis.net, a visual survey of regulatory visualization that allows researchers from both the computing and financial communities to review their literature of interest. We have collected and manually tagged more than 80 regulation visualization related publications. To the best of our knowledge, this is the first publication set tailored for regulatory visualization. We have provided a webpage (http://regvis.net) for interactive searches and filtering. Each publication is represented by a thumbnail of the representative system interface or key visualization chart, and users can conduct multi-condition screening explorations and fixed text searches., Comment: 2 pages. Refer to http://regvis.net

In the context of urbanization and the rapid growth of energy demand, understanding the spatial and temporal dynamics of urban energy use is crucial for identifying energy-saving potentials. In this demo, we present a visual analysis tool, VAP, that allows users to explore the dynamics of urban energy use at different spatial and temporal scales. In contrast to traditional statistical and machine learning methods, the visual analysis based tool focuses on analytical thinking, user interactions and answering business questions by examining different visual analysis views. In the demonstration, conference attendees will interact with VAP and learn its capabilities in discovering typical consumption patterns and spatio-temporal shift patterns from a real-world case study of electricity.

Feature recognition aids CAD model simplification in engineering analysis and machining path in manufacturing. In the domain of CAD model simplification, classic feature recognition approaches face two challenges: 1) insufficient performances; 2) engineering features are diverse, and no system can hard-code all possible features in advance.\ud A declarative approach allows engineers to specify new features without having to design algorithms to find them. However, naive translation of declarations leads to executable algorithms with high time complexity. Inspired by relational database management systems (RDBMS), I suppose that if there exists a way to turn a feature declaration into an SQL query that is further processed by a database engine interfaced to a CAD modeler, the optimizations can be utilized for “free”. \ud Testbeds are built to verify the idea. Initially, I devised a straightforward translator to turn feature declarations into queries. Experiments on SQLite show it gives a quasiquadratic performance for common features. Then it is extended with a new translator and PostgreSQL. In the updated version, I have made a significant breakthrough – my approach is the first to achieve linear time performance with respect to model size for common features, and acceptable times for real industrial models. I learn from the testbeds that PostgreSQL uses hash joins reduce the search space enable a fast feature finding.\ud Besides, I have further improved the performance by: (i) lazy evaluation, which can be used to reduce the workload on the CAD modeler, and (ii) predicate ordering, which reorders the query plan by taking into account the time needed to compute various geometric operations. Experimental results are presented to validate their benefits.