Determinants of consumers’ continuance intention to use social recommender systems: A self-regulation perspective

Social recommender systems have transformed how consumers seek product- or service-related information. Based on Bagozzi's self-regulation framework, this study examines the determinants of consumers' continuance intention to use social recommender systems. We found that appraisal factors (perceived information quality and perceived system quality) are strong predictors of emotional reaction (recommendation satisfaction and trust in recommendation). Particularly, perceived information quality has a significant effect on both recommendation satisfaction and trust in recommendation. However, the positive effect of perceived system quality is only on recommendation satisfaction. Both recommendation satisfaction and trust in recommendation positively affect consumers' continuance intention to use social recommender systems.     

Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework

As one of the representative unsupervised data augmentation methods, generative adversarial networks (GANs) have the potential to solve the problem of insufficient samples in fault diagnosis of rotating machinery. However, the existing unsupervised GANs are usually incapable of simultaneously generating multi-mode fault samples and have some shortcomings such as mode collapse and gradient vanishing. To overcome these deficiencies, a supervised model called modified auxiliary classifier GAN (MACGAN) designed with new framework is proposed in this paper. Firstly, a new ACGAN framework is developed by adding an independent classifier to improve the compatibility between the classification and discrimination. Secondly, the Wasserstein distance is introduced in the new loss functions to overcome mode collapse and gradient vanishing. Finally, to achieve stable training, a spectral normalization is used to replace the weight clipping to constrain the weight parameters of discriminator. The proposed method is applied to fault diagnosis of bearing and gear. Compared with the existing GANs, the proposed method can more efficiently generate multi-mode fault samples with higher qualities, which can be used to assist the training of deep learning-based fault diagnosis models with high accuracy and good stability.     

Interfacial ensemble effect of copper nanoparticles and nickel metal-organic framework on promoting hydrogen generation

Ammonia borane is a promising chemical hydrogen storage material to the hydrogen economy in the future. However, efficient and inexpensive catalyst for realizing this process remains challenging. Herein, the interfaces of copper nanoparticles (NPs) and nickel metal-organic framework (MOF) with different Cu/Ni ratios (Cu@Ni_x-MOF) have been constructed by Cu NPs in situ growth in Ni-MOF via a facile one-step solvothermal method. The Cu@Ni_x-MOF composites show much higher catalytic activity than their monometal counterparts and the optimized Cu@Ni₆-MOF catalyst achieves a turnover frequency of 69.1 min^?1 for ammonia borane hydrolysis, superior to most reported Ni-based catalysts. Density functional theory simulation and in situ Raman spectroscopy analysis propose that this remarkable enhanced activity of Ni-based catalysts is induced by a synergetic effect of Cu–Ni²⁺ dual active sites at their interfaces. This study shows that the strategy of dual active sites hold great promise to achieve efficient non-noble metal catalysts toward heterogeneous catalysis.     

Highly active Ni/Co-metal organic framework bifunctional electrocatalyst for water splitting reaction

Rational design of electrocatalycally active materials with excellent performance for renewable energy conversion is of great interest. We have developed a nanosheet array of Ni/Co metal-organic framework (MOF) grown on CoO modified Ni foam (CoO/NF) substrate via the solvothermal process. The high surface area and low resistance of Ni/Co-MOF@CoO/NF (NC@CoO/NF) catalyst contribute to efficient water splitting. We have prepared a series of NC-n/CoO/NF (n = 1–4) catalysts to optimize the molar ratio of the Co atom in Ni MOF-74. Among them, NC-2@CoO/NF shows an excellent electrochemical performance in alkaline medium, i.e., low overpotential of 290 and 139 mV for oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), respectively. For a two-electrode system with NC-2@CoO/NF, a low cell voltage of 1.54 V at 10 mA cm^?2 has been obtained for overall water splitting which is much smaller than that with commercial Ir/C– Pt/C pair. This excellent performance can be attributed to the synergistic effects of Ni/Co-MOF and CoO/NF. In addition, the as-prepared NC-2@CoO/NF exhibits excellent long-term stability. The computational simulation also supports experimental results.     

“数字信号处理”课程的几何视角

“数字信号处理”的经典课程体系以“系统”为中心，以离散时间傅立叶变换、z变换和离散傅立叶变换为分析工具，以滤波器设计为目的，注重定理和公式推导及其性质分析，很难让学生建立一个形象的、完整统一的知识体系框架。本文从几何视角将 “系统”化和“代数”化的经典课程体系重构为面向“信号”的“几何”知识体系，从而提升学生对本质问题的“洞察力”，帮助学生建立系统的知识体系。     

Porphyrin polymers and organic frameworks

The versatility of porphyrins as optoelectronic or catalytic units makes them attractive elements for inclusion in functional materials. Polymers that include porphyrins have been created with a wide variety of structures and used in a wide range of applications. This review covers recent developments in the synthesis, characterization and applications of polymeric materials in which porphyrins are key components of the repeat units, including the rapidly growing area of metal–organic frameworks and related covalent organic frameworks. © 2015 Society of Chemical Industry     

基于单机容错服务器的控制系统网络架构设计

针对目前企业控制系统网络架构中存在的控制站通讯接口负荷大、易造成数据拥堵及双机热备冗余服务器有出现冗余故障的隐患等问题,设计了基于单机容错服务器技术的组合式控制系统网络架构。实践表明,基于单机容错服务器技术的组合式控制系统网络架构运行稳定可靠,具有一定的推广价值。     

Adaptive–interpolative binarization with stroke preservation for restoration of faint characters in degraded documents

A novel technique for binarization with stroke preservation of faint characters in degraded documents is proposed. It works in a multi-scale framework with an adaptive–interpolative thresholding technique. Instead of computing a global threshold value, it computes the local threshold values for a small set of grid points by observing the intensity pattern of the pixels lying in the concerned grid cells. Estimated thresholds are used, in turn, to compute the threshold values of all the remaining pixels using a fast-yet-efficient interpolation procedure. To handle noises in degraded images, this grid-based adaptive thresholding is applied in successively reducing scales to obtain the near-optimal binarization as a set of connected components. After a post-processing meant for stroke preservation with these connected components, we get the final output. Exhaustive experimentation and comparison with other existing methods have been successfully carried out with benchmark datasets and also with our own datasets.