Fabrication of ultrafast temperature-responsive nanofibrous hydrogel with superelasticity and its 'on–off' switchable drug releasing capacity

The temperature-responsive bulky hydrogel with fast response rate and satisfactory mechanical property has fascinating application potential in many aspects, such as the implantable macroscale controlled drug release carrier for post-surgical therapy; however, creating such a smart hydrogel was proven extremely challenging. Here a novel type of temperature-responsive bulky hydrogel with ultrafast response rate and super compressible elasticity was fabricated by the fibrous freeze-shaping technique using shortened temperature-responsive polymer based electrospun hollow nanofibers as building blocks, followed by heat treatment for endowing the hydrogel with high stability in water. Because the hydrogel has hierarchical porous structure and its constituent nanofibers have hollow structure, which are beneficial to diffusion of its embodied water during temperature-induced volume phase transition, its temperature-response time is less than 30 s. In addition, the hierarchical porous structure benefits dissipation of the compression stress exerted on the hydrogel. Fluorescein isothiocyanate (FITC)-dextran as a model biomacromolecular drug, was loaded into the shells of the hollow nanofibers during coaxial electrospinning, and the ultimately obtained nanofibrous hydrogel can release its loaded FITC-dextran in a 'on–off' switchable fashion in response to temperature alternation between 15 and 47°C. Cell cytotoxicity test results demonstrate that the temperature-responsive nanofibrous hydrogel is biocompatible.     

马尔可夫跳变线性系统最优控制的研究现状与进展

马尔可夫跳变线性系统（MJLS）是一种具有多个模态的随机系统，系统在各个模态之间的跳变转移由一组马尔可夫链来决定。MJLS模型因其在表示过程中可以产生突变而更能精确的描述实际工程应用中的系统。近年来，MJLS的最优控制问题成为了研究的热点，动态规划、极大值原理以及线性矩阵不等式等成为了解决此类问题的主流方法。本文对MJLS最优控制领域的研究现状进行了综述。分别对一般情况下、带有噪声的情况下、带有时滞的情况下以及某些特定情况下的MLJS最优控制问题的国内外研究现状进行论述。最后进行了总结并提出MJLS最优控制领域未来值得关注的研究方向。     

Profiling of knowledge organisation systems for the annotation of Linked Data cultural resources

The importance of cultural heritage for supporting the knowledge economy has promoted its digitisation and online publication. Many cultural heritage repositories have published millions of digitised items using semantic web technologies and Linked Data approaches. These repositories frequently use knowledge organisation systems to classify the resources, but the domain heterogeneity makes it difficult to know if they are the most suitable ones. This paper describes the process used to discover and profile the knowledge organisation systems used in the cultural heritage domain. Additionally, for those knowledge organisation systems with a thesaurus-like structure, a detailed quality analysis is performed. The results of this analysis can be used as a key factor for the selection of knowledge organisation systems in classification tasks.     

Nonlinear modal analysis via non‐parametric machine learning tools

Modal analysis is an important tool in the structural dynamics community; it is widely utilised to understand and investigate the dynamical characteristics of linear structures. Many methods have been proposed in recent years regarding the extension to nonlinear analysis, such as nonlinear normal modes or the method of normal forms, with the main objective being to formulate a mathematical model of a nonlinear dynamical structure based on observations of input/output data from the dynamical system. In fact, for the majority of structures where the effect of nonlinearity becomes significant, nonlinear modal analysis is a necessity. The objective of the current paper is to demonstrate a machine learning approach to output‐only nonlinear modal decomposition using kernel independent component analysis and locally linear‐embedding analysis. The key element is to demonstrate a pattern recognition approach which exploits the idea of independence of principal components from the linear theory by learning the nonlinear manifold between the variables. In this work, the importance of output‐only modal analysis via “blind source” separation tools is highlighted as the excitation input/force is not needed and the method can be implemented directly via experimental data signals without worrying about the presence or not of specific nonlinearities in the structure.     

Reconfigurable topology synthesis for application-specific NoC on partially dynamically reconfigurable systems

In this paper, a four-stage method for synthesizing reconfigurable ASNoC topology is proposed for partially dynamically reconfigurable systems, where the topology is reconfigured dynamically at run-time along with the application's execution. Firstly, a simulated annealing based topology-aware integrated optimization framework is proposed to generate the proper schedule and floorplan of task modules. Secondly, based on the schedule and floorplan of task modules, an Integer Linear Programming (ILP)-based method and a heuristic method, are proposed to partition the communication requirements of the application into $T$ time intervals. Thirdly, we explore the proper positions of switches in the floorplan for global communications. Finally, considering the reconfiguration costs between adjacent time intervals, the routing path allocation problem is solved for time intervals in an iterative procedure to generate fine-grained dynamically reconfigurable ASNoC topologies. Experimental results show that, compared to the random partition of communication requirements, the proposed heuristic method and ILP-based method can achieve 5.4% and 10.0% power consumption improvement, respectively. And, the reconfigurable ASNoC can achieve 31.6% power consumption improvement when compared with static ASNoC.     

An unknown input multiobserver based on a discrete uncoupled multimodel for uncertain nonlinear systems: Experimental validation on a transesterification reactor

In the present paper, an unknown input multiobserver (UIMO) is designed for the state estimation of uncertain non-linear systems. A discrete decoupled state multimodel is exploited to describe the behavior of non-linear systems. A particular transformation of uncertainties to unknown inputs is considered. The LMI approach is used to establish the convergence conditions of the state estimation error. The efficiency of the proposed strategy is emphasized through an illustrative example and a real time application on a semi-batch reactor that lead to good performance in terms of robustness, convergence speed and precision.     

ATSP:时态网络可视化的自适应时间片划分方法

现有的时态网络可视化方法大多采用等量时间片来可视化网络的演变，不利于时态模式的快速挖掘和发现。为此，根据时态网络固有的特征提出自适应时间片划分方法（Adaptive Time Slice Partition method，ATSP）。在时态网络的两种表示方式（基于事件的表示方式和基于快照的表示方式）的基础上，构建了ATSP的基础模型，同时提出了一种改进模型用来描述事件间隔时间服从长尾分布的时态网络。为了实现时间片的不等量划分，针对探索任务的不同提出了基于时态模式的ATSP规则和基于中心节点的ATSP规则，并提出了实现算法--层次划分算法（Hierarchical Partition algorithm，HP）和增量划分算法（Incremental Partition algorithm，IP）。实验结果表明，ATSP方法比传统的时间片划分方法更能准确地表示网络的时态特征，且该方法应用于可视化时，能有效归纳并展示网络的特征，明显提高了视觉分析的效率。     

Source time reversal (STR) method for linear elasticity

We study the problem of source reconstruction for a linear elasticity problem applied to seismicity induced by mining. We assume the source is written as a variable separable function $\mathit{f}\left(\mathit{x}\right)g\left(t\right)$. We first present a simple proof a local decay result for elasticity in the case of homogeneous media. We then extend the source time reversal method, originally developed for acoustic waves, to an elastic system of waves. Additionally, we present a fast reconstruction implementation for large data sets. This is especially useful in the elastic case, in which the numerical cost is higher than in fluid acoustics. We complement this work with several 2D and 3D numerical experiments and an analysis of the results.     

When Does Communication Learning Need Hierarchical Multi-Agent Deep Reinforcement Learning

Abstract

Multi-agent systems need to communicate to coordinate a shared task. We show that a recurrent neural network (RNN) can learn a communication protocol for coordination, even if the actions to coordinate are performed steps after the communication phase. We show that a separation of tasks with different temporal scale is necessary for successful learning. We contribute a hierarchical deep reinforcement learning model for multi-agent systems that separates the communication and coordination task from the action picking through a hierarchical policy. We further on show, that a separation of concerns in communication is beneficial but not necessary. As a testbed, we propose the Dungeon Lever Game and we extend the Differentiable Inter-Agent Learning (DIAL) framework. We present and compare results from different model variations on the Dungeon Lever Game.