Fabrication of poly(ε‐caprolactone) (PCL)/poly(propylene carbonate) (PPC)/ethylene‐α‐octene block copolymer (OBC) triple shape memory blends with cycling performance by constructing a co‐continuous phase morphology

In this paper, a triple shape memory material was prepared by ultra‐simple melt blending from poly(ε‐caprolactone) (PCL), poly(propylene carbonate) (PPC) and ethylene‐α‐octene block copolymer (OBC). The obtained material possessed a co‐continuous phase morphology and presented an excellent triple shape memory effect (triple‐SME). Theoretical prediction demonstrated that a special continuous phase morphology could be constructed by adjusting the proportions of the blend. Moreover, the results indicated that a close relationship existed between the phase morphology and the triple‐SME of PCL/PPC/OBC. The sample with 35 vol% PPC content contributed to the formation of a continuous phase morphology and exhibited the optimal triple‐SME. Additionally, the sample PCL/PPC/OBC (32.5/35/32.5) showed outstanding structure and performance stability during cycle loading–unloading tests, which evidenced the prominent cycling shape memory property (nearly 100% shape fixing and recovery of temporary shape). Overall, this work could provide an efficient, convenient and recyclable method to obtain high‐performance shape memory materials. © 2020 Society of Chemical Industry     

Stabilisation of stochastic differential equations driven by G-Brownian motion via aperiodically intermittent control

ABSTRACT

As an emerging field, the G-Itô stochastic calculus plays an important role in describing the model uncertainty. Many interesting works have been done on stochastic differential equations driven by G-Brownian motion (G-SDEs). Among the theories and applications of G-SDEs, the stability is the vital important one. In this paper, we investigate the stabilisation for G-SDEs based on G-Lyapunov function and aperiodically adaptive intermittent controller. As an application, the sufficient conditions are established for the stabilisation of stochastic Cohen–Grossberg neural networks driven by G-Brownian motion (G-SCGNNs). Finally, an example is provided to illustrate the obtained results.     

Distributed privacy protection strategy for MEC enhanced wireless body area networks

With the rapid development and widespread application of Wireless Body Area Networks (WBANs), the traditional centralized system architecture cannot handle the massive data generated by the edge devices. Meanwhile, in order to ensure the security of physiological privacy data and the identity privacy of patients, this paper presents a privacy protection strategy for Mobile Edge Computing(MEC) enhanced WBANs, which leverages the blockchain-based decentralized MEC paradigm to support efficient transmission of privacy information with low latency, high reliability within a high-demand data security scenario. On this basis, the Merkle tree optimization model is designed to authenticate nodes and to verify the source of physiological data. Furthermore, a hybrid signature algorithm is devised to guarantee the node anonymity with unforgeability, data integrity and reduced delay. The security performance analysis and simulation results show that our proposed strategy not only reduces the delay, but also secures the privacy and transmission of sensitive WBANs data.     

Leveraging diverse knowledge sources through proactive behaviour: How companies can use inter‐organizational networks for business model innovation

The relevance of business model innovation (BMI) for strategic renewal and competitive advantage has been increasingly recognized by both strategy scholars and managers. In this paper, we explore how inter‐organizational networks and firms' proactiveness influence BMI. By examining Dutch design companies, we show that having a network of companies with different sizes has a positive effect on BMI. We also observe a positive effect of ties change. Finally, we show the importance of firms' proactiveness. Through the discussion of network elements and proactiveness as antecedents of BMI, we contribute to the business model and social network literature.     

Parallel spatial-temporal convolutional neural networks for anomaly detection and location in crowded scenes

Anomaly detection and location in crowded scenes have attracted a lot of attention in computer vision research community recently due to the increased applications of intelligent surveillance improve security in public. We propose a novel parallel spatial-temporal convolution neural networks model to detect and localize the abnormal behavior in video surveillance. Our approach contains two main steps. Firstly, considering the typical position of camera and the large number of background information, we introduce a novel spatial-temporal cuboid of interest detection method with varied-size cell structure and optical flow algorithm. Then, we use the parallel 3D convolution neural networks to describe the same behavior in different temporal-lengths. That step ensures that the most of behavior information in cuboids could be captured, also insures the reduction of information unrelated to the major behavior. The evaluation results on benchmark datasets show the superiority of our method compared to the state-of-the-art methods.     

A novel low-latency DRAM based on the bitline-discharge rate

As well known by computer architects, the performance gap between the processor and the memory has been increasing over the years. This causes what is known as the memory wall. In order to alleviate the problem, a novel fast readout scheme is proposed in this article for the single-transistor single-capacitor dynamic random-access memory (1T-1C DRAM) cells. The proposed scheme works in the current domain in which the difference between the discharging rates of the bitline in the cases of ‘1’ and ‘0’ readings is detected. The proposed scheme is analysed quantitatively and compared with the conventional readout scheme. It is verified by simulation adopting the 45 nm CMOS Berkley predictive-technology model (BPTM) and shows 44 and 7.7% reductions in the average read-access and cycle times, respectively, as compared to the conventional readout scheme. It is also shown that the power is saved according to the proposed scheme if the probability of occurrence of ‘0’ storage exceeds 66.7%. This minimum value can be alleviated, however, at the expense of a smaller saving in the average read-access time. The impacts of process variations and technology scaling are also taken into account.     

On Demand Solid Texture Synthesis Using Deep 3D Networks

This paper describes a novel approach for on demand volumetric texture synthesis based on a deep learning framework that allows for the generation of high-quality three-dimensional (3D) data at interactive rates. Based on a few example images of textures, a generative network is trained to synthesize coherent portions of solid textures of arbitrary sizes that reproduce the visual characteristics of the examples along some directions. To cope with memory limitations and computation complexity that are inherent to both high resolution and 3D processing on the GPU, only 2D textures referred to as ‘slices’ are generated during the training stage. These synthetic textures are compared to exemplar images via a perceptual loss function based on a pre-trained deep network. The proposed network is very light (less than 100k parameters), therefore it only requires sustainable training (i.e. few hours) and is capable of very fast generation (around a second for 256³ voxels) on a single GPU. Integrated with a spatially seeded pseudo-random number generator (PRNG) the proposed generator network directly returns a color value given a set of 3D coordinates. The synthesized volumes have good visual results that are at least equivalent to the state-of-the-art patch-based approaches. They are naturally seamlessly tileable and can be fully generated in parallel.     

定向长钻孔整体水力压裂增透技术在赵固二矿的应用

针对赵固二矿煤层透气性低、钻孔有效影响半径小，实施定向长钻孔代替底板岩巷进行区域瓦斯治理期间钻孔工程量大、瓦斯抽采效果不理想的问题。结合煤层赋存特征及钻孔施工情况，采用定向长钻孔整体水力压裂增透技术，理论分析了合理坐封位置、压裂参数，完成200 m煤巷条带一次整体压裂，最大泵注压力24.3 MPa、累计注水量1 613 m³。并基于煤层全水分变化，考察确定了单个钻孔压裂影响范围达到巷道两帮30 m，有效改善了煤体储层特性，提高了煤层瓦斯抽采效率。在实现定向钻孔对预抽煤巷条带可靠控制的同时，最大程度降低了钻孔工程量、缩短了瓦斯治理周期，为实现矿区“以孔代巷”及高效安全开采提供了技术支撑。     

苯并噁嗪树脂的合成及在形状记忆聚合物中的应用研究进展

苯并噁嗪树脂作为一类新型的热固性树脂，具有分子设计性强、阻燃性能和耐腐蚀性能优异、固化时不需要强酸、无小分子放出等优点，在航空、建筑、电子等领域获得了广泛应用。本文主要介绍了苯并噁嗪单体的合成方法（溶剂法、无溶剂法和悬浮法）、降低苯并噁嗪开环聚合温度的方法（合成具有特殊基团的苯并噁嗪单体、添加催化剂）及苯并噁嗪树脂在形状记忆聚合物中的应用（与其他聚合物混合，纯苯并噁嗪化学改性），对苯并噁嗪形状记忆聚合物目前存在的问题进行了概述并对苯并噁嗪形状记忆聚合物的发展前景做出了展望。     

Neural network-based hybrid models developed for free radical polymerization of styrene

In the present work, the free radical polymerization of styrene is modeled by considering the phenomenology of the process (a simplified model, which does not include the diffusional effects, gel, and glass effects) in combination with an empirical model represented by an artificial neural network. Differential evolution (DE) algorithm, belonging to the class of evolutionary algorithms, is applied for developing the neural models in optimal forms. For improving the results—predicted conversion and molecular weights as function of time, temperature, and initiator concentration—different combinations between phenomenological model and neural network are tested; also, individual and stacked neural networks have been developed for the polymerization process. This methodology based on hybrid models, including neural networks aggregated in stacks, provides accurate results.