The reversible and click nature of Diels−Alder (DA) reactions has made them ideal candidates to design materials with nonconventional properties. Most commonly, the reversibility of DA is utilized for designing thermosets that can be liquefied for reprocessing and self-healing, yet the dynamic equilibrium nature has been largely neglected. In this work, shape memory polymers (SMP) containing DA moieties in the networks were synthesized. In addition to its remoldability at the liquid state at sufficiently high temperatures (above 110°C), we show uniquely and surprisingly that such a network can undergo plastic deformation in its solid state at intermediate temperatures (60−100°C) by taking advantage of its dynamic equilibrium for network topological rearrangement. The liquid state remoldability and solid state plasticity represent two distinct yet complementary mechanisms to manipulate the permanent shape of an SMP, leading to unprecedented versatility that can benefit a variety of applications in the future.T he ability of shape memory polymer (SMP) to fix a temporary shape and recover to its permanent shape has shown unique advantages toward various applications, including aerospace structures and biomedical devices. 1−6 Expanding the material properties beyond the traditional shape memory effect can drastically open up new opportunities. On this front, much of the recent progress is centered on the temporary shape fixing (e.g., multishape memory effect 7 ) and the reversibility of the polymer shape memory effect. 8−10 In contrast, the attention on the permanent shapes has been largely nonexistent beyond the simple distinction between thermoset SMP and thermoplastic SMP, with the latter remoldable for permanent shape resetting. This is in sharp contrast to the requirement of many real world device applications that often demand sophistication in manipulating the permanent geometry.Plasticity in thermoset SMP allows covalent bond exchange in the network, consequently, permanent shape reconfiguration without melting through the network topological rearrangement. 2−5,11−16 Thus, the permanent shape can be reconfigured repetitively without using mold, more importantly, this offers a new way to access extremely sophisticated permanent shapes that cannot be made otherwise. 2 However, the types of permanent shapes accessible via plastic deformation are limited to the solid state deformation of the prior permanent shape. In this work, we reveal that the permanent shape of an SMP network containing Diels−Alder (DA) adducts can be redefined by both liquid molding and solid state plasticity in two different temperature ranges by taking advantage of their dynamic equilibrium nature.The click and reversible nature of DA reactions has historically attracted much attention for designing nonconventional thermosets. 17−22 Typically, the network material consisting of furan/maleimide adducts is heated to a high temperature (above 110°C) to trigger the reverse Diels−Alder reaction (retro-DA) such that it can reach a liquid state t...

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As XML becomes ubiquitous, the efficient retrieval of XML data becomes critical. Research to improve query response time has been largely concentrated on indexing paths, and optimizing XML queries. An orthogonal approach is to discover frequent XML query patterns and cache their results to improve the performance of XML management systems. In this paper, we present an efficient algorithm called FastXMiner, to discover frequent XML query patterns. We develop theorems to prove that only a small subset of the generated candidate patterns needs to undergo expensive tree containment tests. In addition, we demonstrate how the frequent query patterns can be used to improve caching performance. Experiments results show that FastXMiner is efficient and scalable, and caching the results of frequent patterns significantly improves the query response time.

Biomedical relation extraction can automatically extract high-quality biomedical relations from biomedical texts, which is a vital step for the mining of biomedical knowledge hidden in the literature. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two major neural network models for biomedical relation extraction. Neural network-based methods for biomedical relation extraction typically focus on the sentence sequence and employ RNNs or CNNs to learn the latent features from sentence sequences separately. However, RNNs and CNNs have their own advantages for biomedical relation extraction. Combining RNNs and CNNs may improve biomedical relation extraction. In this paper, we present a hybrid model for the extraction of biomedical relations that combines RNNs and CNNs. First, the shortest dependency path (SDP) is generated based on the dependency graph of the candidate sentence. To make full use of the SDP, we divide the SDP into a dependency word sequence and a relation sequence. Then, RNNs and CNNs are employed to automatically learn the features from the sentence sequence and the dependency sequences, respectively. Finally, the output features of the RNNs and CNNs are combined to detect and extract biomedical relations. We evaluate our hybrid model using five public (protein-protein interaction) PPI corpora and a (drug-drug interaction) DDI corpus. The experimental results suggest that the advantages of RNNs and CNNs in biomedical relation extraction are complementary. Combining RNNs and CNNs can effectively boost biomedical relation extraction performance.

Suicide ideation expressed in social media has an impact on language usage. Many at-risk individuals use social forum platforms to discuss their problems or get access to information on similar tasks. The key objective of our study is to present ongoing work on automatic recognition of suicidal posts. We address the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media. For such purpose, we employ an LSTM-CNN combined model to evaluate and compare to other classification models. Our experiment shows the combined neural network architecture with word embedding techniques can achieve the best relevance classification results. Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.

Solid-state plasticity by dynamic covalent bond exchange in a shape-memory polymer network bestows a permanent shape reconfiguration ability. Spatio-selective control of thermally induced plasticity may further extend the capabilities of materials into unexplored domains. However, this is difficult to achieve because of the lack of spatio-control in typical polymer network synthesis. Metal-ligand interactions possess the high strength of covalent bonds while maintaining the dynamic reversibility of supramolecular bonds. Metallosupramolecular shape-memory polymer networks were designed and prepared, which demonstrated solid-state plasticity. The metallo-coordination bonds within these networks permit facile tuning of the plasticity behavior across a wide temperature range, simply by changing the metal ion. By controlling the diffusion of two different metal ions during preparation of a polymer film, a plasticity behavior with a spatial gradient was achieved, providing a unique shape-morphing versatility with potential in shape-memory devices.

Learners in a massive open online course often express feelings, exchange ideas and seek help by posting questions in discussion forums. Due to the very high learner-to-instructor ratios, it is unrealistic to expect instructors to adequately track the forums, find all of the issues that need resolution and understand their urgency and sentiment. In this paper, considering the biases among different courses, we propose a transfer learning framework based on a convolutional neural network and a long short-term memory model, called ConvL, to automatically identify whether a post expresses confusion, determine the urgency and classify the polarity of the sentiment. First, we learn the feature representation for each word by considering the local contextual feature via the convolution operation. Second, we learn the post representation from the features extracted through the convolution operation via the LSTM model, which considers the long-term temporal semantic relationships of features. Third, we investigate the possibility of transferring parameters from a model trained on one course to another course and the subsequent fine-tuning. Experiments on three real-world MOOC courses confirm the effectiveness of our framework. This work suggests that our model can potentially significantly increase the effectiveness of monitoring MOOC forums in real time.

In this paper, we present a novel method to couple Smoothed Particle Hydrodynamics (SPH) and nonlinear FEM to animate the interaction of fluids and deformable solids in real time. To accurately model the coupling, we generate proxy particles over the boundary of deformable solids to facilitate the interaction with fluid particles, and develop an efficient method to distribute the coupling forces of proxy particles to FEM nodal points. Specifically, we employ the Total Lagrangian Explicit Dynamics (TLED) finite element algorithm for nonlinear FEM because of many of its attractive properties such as supporting massive parallelism, avoiding dynamic update of stiffness matrix computation, and efficient solver. Based on a predictor-corrector scheme for both velocity and position, different normal and tangential conditions can be realized even for shell-like thin solids. Our coupling method is entirely implemented on modern GPUs using CUDA. We demonstrate the advantage of our two-way coupling method in computer animation via various virtual scenarios.