文本情感分析方法研究综述

文本情感分析是自然语言处理领域的一个重要分支,广泛应用于舆情分析和内容推荐等方面,是近年来的研究热点.根据使用的不同方法,将其划分为基于情感词典的情感分析方法、基于传统机器学习的情感分析方法、基于深度学习的情感分析方法.通过对这三种方法进行对比,分析其研究成果,并对不同方法的优缺点进行归纳总结,介绍相关数据集和评价指标...     

树脂催化重汽油烷基化硫转移的动力学研究

采用树脂催化剂,研究重汽油中噻吩类硫化物的烷基化硫转移性能,分析反应前后硫分布的变化,并对反应动力学进行研究。结果表明,在反应温度110℃和反应时间60 min条件下,烷基化硫转移率大于90%,其反应动力学为一级反应速率方程,反应活化能为20.32 kJ·mol~(-1),指前因子为1 364 h~(-1),噻吩类硫化物烷基化反应的动力学方程可表示为:r=1364exp(-2.445×10~3/T)c_T。     

Parallelization of entanglement-resistant multi-prover interactive proofs

Multi-prover interactive proof systems are said to be entanglement-resistant if the soundness holds even when provers are allowed to share an arbitrary quantum state before the interaction starts. This letter proves that every entanglement-resistant multi-prover interactive proof system can be parallelized to two rounds without ruining its entanglement resistance at the expense of adding one prover.     

A novel unified correlation model using ensemble support vector regression for prediction of flooding velocity in randomly packed towers

Traditional empirical correlations and models have found insufficient to predict the flooding velocity accurately mainly because there are many kinds of random packings which exhibit different characteristics. In this work, a novel data-driven modeling method, i.e. ensemble least squares support vector regression (ELSSVR), is proposed to construct a unified correlation for prediction of the flooding velocity for packed towers with random packings. The flooding data are first clustered into several classes by the fuzzy c-means clustering algorithm. Then, several single LSSVR models can be trained using each sub-class of samples to capture the special characteristics. Moreover, a weighted least squares approach is adopted to integrate these single LSSVR models. Consequently, the ELSSVR model can extract the feature information of flooding data effectively and improve the prediction performance. The proposed ELSSVR method is applied to construct a unified correlation for prediction of the flooding velocity in randomly packed towers. The obtained results for several kinds of random packings demonstrate that the ELSSVR-based correlation can obtain better prediction performance, compared with the traditional semi-empirical correlations and artificial neural networks-based models. Finally, a database containing the modeling information of flooding velocity in randomly packed towers of China is provided for academic research.     

Towards a sales assistant using a product knowledge graph

The phenomenal growth in the consumer electronics industry over the last decade is mainly due to rapid advances in integration technologies. The market is ever evolving with new products made available on a frequent basis. However, typically, the average consumer tends to have insufficient knowledge of the features and functions of an electronic device. Although most manufacturers provide product specifications, they are often difficult for consumers to understand and it is hard for customers to grasp the details of a product. This study introduces the concept of a product knowledge graph, which is constructed by transforming a set of data from a product management system and external data. This study overcomes the technical barriers for customers with the use of knowledge graph technologies. To construct large-scale product knowledge, an ontology model is designed and a platform for knowledge extraction and transformation is also developed. Then, a sales assistant called God of Sales is demonstrated. This study provides a novel approach to explaining product features and functions in a comprehensive manner with semantic recommendations.     

Synthesis and electrochemical properties of nickel oxide/carbon nanofiber composites

The electrospinning of polyacrylonitrile (PAN) with a polyaniline and graphene sol–gel mixture produced uniform, smooth fibers with an average diameter of 0.3 μm. These electrospun fibers were stabilized for 2 h at 200 °C and then carbonized at 800 °C for 5 h. Composites were prepared by depositing Ni(OH)₂ on the carbon nanofibers (CNFs) and calcining them at different temperatures. The composites were characterized with X-ray diffraction (XRD), transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The effect of the calcination temperatures on the electrochemical properties was studied using cyclic voltammetry and electrochemical impedance spectroscopy. The specific capacitance (SC) was found to be highest (738 F g⁻¹) at a calcination temperature of 400 °C. The charge transfer resistance (R_p) decreased as the calcination temperature was increased. However, the electrical double layer capacitance (EDLC) increased with an increase in the calcination temperature. The EDLC increased from 0.144 F g⁻¹ at a calcination temperature of 100 °C to 485 F g⁻¹ at a calcination temperature of 500 °C.     

Facile fabrication of a binary NiCo phosphide with hierarchical architecture for efficient hydrogen evolution reactions

Exploring and designing efficient non-noble catalysts formed by element doping and nanostructure modification for the hydrogen evolution reaction (HER) is of critical importance with respect to sustainable resources. Herein, we have prepared a three-dimensional binary NiCo phosphide with hierarchical architecture (HA) composed of NiCoP nanosheets and nanowires grown on carbon cloth (CC) via a facile hydrothermal method followed by oxidation and phosphorization. Due to its unique hierarchical nanostructure, the NiCoP HA/CC electrocatalyst exhibits excellent performance and good working stability for the HER in both acidic and alkaline conditions. The obtained NiCoP HA/CC shows excellent HER activity with a low potential of 74 and 89 mV at 10 mA cm⁻², a small Tafel slope of 77.2 and 99.8 mV dec⁻¹ and long-term stability up to 24 h in acidic and alkaline electrolyte, respectively. NiCoP HA/CC, a non-noble metal material, is a promising electrocatalyst to replace noble metal-based electrocatalysts for the HER.     

Two-level defect-correction Oseen iterative stabilized finite element method for the stationary conduction–convection equations

In this paper, a two-level defect-correction Oseen iterative finite element method is presented for the stationary conduction–convection equations based on local Gauss integration. The method combines the defect-correction method, the two-level strategy, and the locally stabilized method. The stability and convergence of the proposed method are deduced. Finally, numerical examples verify the theoretical results of the proposed algorithm and show that it is highly efficient and reliable for the considered problem.     

The supported CeO2/Co3O4–MnO2/CeO2 catalyst on activated carbon prepared by a successive-loading approach with superior catalytic activity and selectivity for CO preferential oxidation in H2-rich stream

The supported CeO₂/Co₃O₄–MnO₂/CeO₂ catalyst on activated carbon (AC) prepared by a successive loading approach to support ceria, cobalt-manganese oxide and ceria on activated carbon exhibits superior catalytic activity and selectivity to Co₃O₄–MnO₂–CeO₂/AC prepared by a one-step loading for CO preferential oxidation in H₂-rich stream, although the same loading of Co, Mn and Ce was used, which illustrates that the addition of ceria doesn't always enhance catalytic performance in CO PROX reaction, and appreciate supporting method is essential. The superior catalytic activity and selectivity of developed catalyst can be ascribed to high reducibility, well dispersion, unique porous structure, and strong interaction between Co₃O₄–MnO₂ and CeO₂.