使用 X射线衍射技术判定 Si C单晶体的结构和极性

使用四圆衍射仪和双晶衍射技术 ,分析了 Si C体单晶的结构和极性 .Si C单晶体由化学气相淀积法获得 .六方 { 10 15 }极图证明了该单晶结构为 6 H型 .三轴晶衍射中的ω模式衍射强度的差异判定了该单晶的 Si终端面和 C终端面 ,即极性面。两个面的一、二、三级衍射强度的测量比值与经过散射因子修正后计算的结构振幅平方比值| F ( 0 0 0 L ) | 2 / | F( 0 0 0 L ) | 2 非常吻合 .因此 ,利用极性面的衍射强度差异 ,可以方便、严格地判断具有类似结构如 2 H{ 0 0 0 1}、4H { 0 0 0 1}及 3C- Si C{ 111}的极性     

基于TSCM模型的网络短文本情感挖掘

针对网络短文本情感挖掘问题,提出一种新的基于LDA和互联网短评行为理论的主题情感混合模型TSCM,TSCM模型中的整篇评论中每个句子的主题分布是不同的,TSCM产生词的流程是先确定词的情感极性,再确定词的主题,TSCM考虑了词与词之间的联系.真实数据集Movie与Amazon上的大量实验表明,与代表性算法JST、S-LDA、D-PLDA和SAS相比较,TSCM模型能对用户真实情感与讨论主题进行更加有效的分析建模.     

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users’ emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu language. Therefore, in this paper, we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’ emotions from the text. To accomplish this task, we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis. After that, we have preprocessed the data and selected dialogues with common emotions. Once the dataset is prepared, we have used different deep learning and machine learning techniques for the classification of emotion. We have tuned the algorithms according to the Urdu language datasets. The experimental evaluation has shown encouraging results with 67% accuracy for the Urdu dialogue datasets, more than 10, 000 dialogues are classified into five emotions i.e., joy, fear, anger, sadness, and neutral. We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.     

Lateral‐Polarity Structure of AlGaN Quantum Wells: A Promising Approach to Enhancing the Ultraviolet Luminescence

Aluminum‐gallium‐nitride alloys (Al _x Ga_{1– x} N, 0 ≤ x ≤ 1) can emit light covering the ultraviolet spectrum from 210 to 360 nm. However, these emitters have not fulfilled their full promise to replace the toxic and fragile mercury UV lamps due to their low efficiencies. This study demonstrates a promising approach to enhancing the luminescence efficiency of AlGaN multiple quantum wells (MQWs) via the introduction of a lateral‐polarity structure (LPS) comprising both III and N‐polar domains. The enhanced luminescence in LPS is attributed to the surface roughening, and compositional inhomogeneities in the N‐polar domain. The space‐resolved internal quantum efficiency (IQE) mapping shows a higher relative IQE in N‐polar domains and near inversion domain boundaries, providing strong evidence of enhanced radiative recombination efficiency in the LPS. These experimental observations are in good agreement with the theoretical calculations, where both lateral and vertical band diagrams are investigated. This work suggests that the introduction of the LPS in AlGaN‐based MQWs can provide unprecedented tunability in achieving higher luminescence performance in the development of solid state light sources.     

天然有机质对纳米碳管环境行为的影响研究进展

随着纳米技术的快速发展,大量的纳米碳管(CNTs)不可避免地释放到环境中。由于其较大的憎水性表面,CNTs与有机污染物和天然有机质(NOM)强烈地相互作用。综述了NOM存在下CNTs的环境行为,包括NOM对CNTs分散特性和吸附有机污染物特性的影响。着重论述了NOM的理化性质对CNTs分散的影响,"拉拉链"或"胶束包裹"是主要的分散机制。强调应该对不同分散机制下分散的CNTs与有机污染物的相互作用给予更多的关注,提出了目前在液相环境中直接测定CNTs表面积的新思路,并对今后的研究方向进行了展望。     

蓝宝石衬底上原子级光滑AlN外延层的MOCVD生长

在蓝宝石衬底表面无氮化、低Ⅴ/Ⅲ比的情况下,采用1200℃的衬底温度、5kPa反应室气压,用MOCVD方法在蓝宝石衬底上生长出了表面原子级光滑的AlN外延层.原子力显微镜测试表明其平均粗糙度为0.44nm,X射线衍射(0002)回摆曲线FWHM为166".实验结果和分析表明,极性和气相反应是影响AlN表面形貌的主要原因.以原子级光滑的AlN为模板生长出了高质量的高Al组分的n型AlGaN,证实了AlN模板具有较好的质量.     

基于情感分析的投资者情绪对房价波动的影响

了探究房地产市场投资者情绪对我国房价波动的影响机制,基于情感分析方法构建了我国房地产市场上的投资者情绪指数,验证了该指数的有效性.通过构建VAR模型,综合运用脉冲响应、方差分解分析等方法,定量探究了投资者情绪与我国房价波动之间的动态影响关系.结果表明:构建的投资者情绪指数与房价有着高度的相关性;投资者情绪的正向变动在短...     

基于主成分分析的房地产市场情绪指数构建 ——以重庆市为例

以构建房地产市场情绪指数为研究目标,爬取2019.07~2020.06 期间房地产舆情数据,基于情感词典及机器学习算法进行文本情感分析,构建舆情情感指数作为直接情绪指标;选择房地产开发投资额、新开工建筑面积、每月房屋成交建筑面积、楼面地价、个人住房贷款发放金额5 个指标作为间接情绪指标,结合文本情感分析技术得到的舆情情感指数,利用主成分分析方法构建复合市场情绪指数;与同期百度指数及冰山指数比较进行检验分析。结果表明,房地产市场情绪指数构建合理,可以反映房地产市场发展走势,为构建房地产市场情绪指数提供了一个合理方法。     

基于词向量的领域情感词典构建

针对现有领域情感词典在情感和语义表达等方面的不足,提出一种基于词向量的领域情感词典构建方法。利用25万篇新闻语料和10万余条酒店评论数据,训练得到word2vec模型;选择80个情感明显、内容丰富、词性多样化的情感词作为种子词集;利用TF-IDF值在词汇重要程度的度量作用,在酒店评论中获得9 860个领域候选情感词汇;通过计算候选情感词与种子词的词向量之间的语义相似度,将情感词映射到高维向量空间,实现了情感词的特征向量表示(Senti2vec)。将Senti2vec应用于情感词极性分类和文本情感分析任务中,试验结果表明,Senti2vec能实现情感词的语义表示和情感表示;基于特定领域语料的语义相似计算,使得提取的情感特征更具有领域特性,同时不受候选情感词集范围的约束。     

关于110kV CVT现场试验方法的探讨

电容式电压互感器(CVT),具备电磁式电压互感器的一切功能,与电磁式电压互感器相比,多了载波、绝缘强度高等优点。本文对110 kV电容式电压互感器的结构原理进行了简要介绍,主要对其试验中电容和介损、极性测量等测试的方法进行了探讨,并指出与现行测试方法的差异处,然后进行分析比较,为CVT的试验特别是中压端子无引出抽头的CVT试验提供了更多符合现场试验安全要求的方法。