A study of clinical psychological nursing research hotspots in China and variation trends based on word frequency analysis and visualization analysis

Objective: To analyze clinical psychological nursing research hotspots in China and variation trends in order to provide reference points on the current state of development of clinical psychological nursing and future research hotspots.Method: Clinical psychological nursing research literature sourced from Wanfang Data for the three periods of 2007-2009, 2010-2012, and 2013-2015 were selected as the research sample. A bibliographic co-occurrence analysis system(BICOMB software) was used to perform keyword word frequency analysis and generate a keyword co-occurrence matrix. In addition, Ucinet software's Netdraw tool was used to create visualized network diagrams.Results: A total of 27890 articles were retrieved, and word frequency analysis revealed that the highestfrequency keywords consisted of anxiety, depression, the elderly, expectant women, coronary heart disease, diabetes, breast cancer, perioperative period, quality of life, and psychological intervention.Research hotspot analysis revealed that consistent hotspots comprised anxiety, depression, health education, and perioperative period; expectant women became a hotspot during 2010-2012, and quality of life and efficacy became hotspots during 2013-2015.Conclusions: In addition to the care process, clinical psychological nursing research hotspots in China have increasingly included the effectiveness of psychological nursing and impact on patient quality of life. In addition, research hotspots have been influenced by the incidence of illnesses and people's health consciousness.

Quantum Particle Swarm Optimization with Deep Learning-Based Arabic Tweets Sentiment Analysis

Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the literature.The SA technique is specifically applied to the data collected from social media sites.The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process.In this background,the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets(QPSODL-SAAT).The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic.Initially,the data pre-processing is performed to convert the raw tweets into a useful format.Then,the word2vec model is applied to generate the feature vectors.The Bidirectional Gated Recurrent Unit(BiGRU)classifier is utilized to identify and classify the sentiments.Finally,the QPSO algorithm is exploited for the optimal finetuning of the hyperparameters involved in the BiGRU model.The proposed QPSODL-SAAT model was experimentally validated using the standard datasets.An extensive comparative analysis was conducted,and the proposed model achieved a maximum accuracy of 98.35%.The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches,such as the Surface Features(SF),Generic Embeddings(GE),Arabic Sentiment Embeddings constructed using the Hybrid(ASEH)model and the Bidirectional Encoder Representations from Transformers(BERT)model.

Improved Metaheuristics with Deep Learning Enabled Movie Review Sentiment Analysis

Sentiment Analysis(SA)of natural language text is not only a challenging process but also gains significance in various Natural Language Processing(NLP)applications.The SA is utilized in various applications,namely,education,to improve the learning and teaching processes,marketing strategies,customer trend predictions,and the stock market.Various researchers have applied lexicon-related approaches,Machine Learning(ML)techniques and so on to conduct the SA for multiple languages,for instance,English and Chinese.Due to the increased popularity of the Deep Learning models,the current study used diverse configuration settings of the Convolution Neural Network(CNN)model and conducted SA for Hindi movie reviews.The current study introduces an Effective Improved Metaheuristics with Deep Learning(DL)-Enabled Sentiment Analysis for Movie Reviews(IMDLSA-MR)model.The presented IMDLSA-MR technique initially applies different levels of pre-processing to convert the input data into a compatible format.Besides,the Term Frequency-Inverse Document Frequency(TF-IDF)model is exploited to generate the word vectors from the pre-processed data.The Deep Belief Network(DBN)model is utilized to analyse and classify the sentiments.Finally,the improved Jellyfish Search Optimization(IJSO)algorithm is utilized for optimal fine-tuning of the hyperparameters related to the DBN model,which shows the novelty of the work.Different experimental analyses were conducted to validate the better performance of the proposed IMDLSA-MR model.The comparative study outcomes highlighted the enhanced performance of the proposed IMDLSA-MR model over recent DL models with a maximum accuracy of 98.92%.

Novel Method to Deal with Interval Quadratic Equations via Sign-Variation Analysis

In this article, analytical results are obtained apparently for the first time in the literature, for the lower and upper bounds of the roots of quadratic equations when two or all three coefficients a, b, c constitute an interval, with a method called the sign-variation analysis. The results are compared with the parametrization technique offered by Elishakoff and Miglis, and with the solution yielded by minimization and maximization commands of the Maple software. Solutions for some interval word problems are also provided to edulcorate the methodology. This article only focuses on the real roots of those quadratic equations, complex solutions being beyond this investigation.

Mapping theme trends and knowledge structures for human neural stem cells:a quantitative and co-word biclustering analysis for the 2013-2018 period

Neural stem cells,which are capable of multi-potential differentiation and self-renewal,have recently been shown to have clinical potential for repairing central nervous system tissue damage.However,the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically.In this study,we retrieved 2742 articles from the PubMed database from 2013 to 2018 using "Neural Stem Cells" as the retrieval word.Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies.Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder.We identified 78 high-frequency Medical Subject Heading(MeSH)terms.A visual matrix was built with the repeated bisection method in gCLUTO software.A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software.The analyses demonstrated that in the 6-year period,hot topics were clustered into five categories.As suggested by the constructed strategic diagram,studies related to cytology and physiology were well-developed,whereas those related to neural stem cell applications,tissue engineering,metabolism and cell signaling,and neural stem cell pathology and virology remained immature.Neural stem cell therapy for stroke and Parkinson’s disease,the genetics of microRNAs and brain neoplasms,as well as neuroprotective agents,Zika virus,Notch receptor,neural crest and embryonic stem cells were identified as emerging hot spots.These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.

XGBRS Framework Integrated with Word2Vec Sentiment Analysis for Augmented Drug Recommendation

Machine Learning is revolutionizing the era day by day and the scope is no more limited to computer science as the advancements are evident in the field of healthcare.Disease diagnosis,personalized medicine,and Recommendation system(RS)are among the promising applications that are using Machine Learning(ML)at a higher level.A recommendation system helps inefficient decision-making and suggests personalized recommendations accordingly.Today people share their experiences through reviews and hence designing of recommendation system based on users’sentiments is a challenge.The recommendation system has gained significant attention in different fields but considering healthcare,little is being done from the perspective of drugs,disease,and medical recommendations.This study is engrossed in designing a recommendation system that is based on the fusion of sentiment analysis and radiant boosting.The polarity of the sentiments is analyzed through user reviews and the processed data is fed into the Extreme Gradient Boosting(XGBOOST)framework to generate the drug recommendation.To establish the applicability of the concept a comparative study is performed between the proposed approach and the existing approaches.

Function Words Analysis——A Reading Comprehension Aid for Chinese Engineers

Background Generally speaking. Chinese college graduates in the fifties and sixties took Russian as their second language, and those who graduated in the seventies had no second language to speak of. Now, in the years of our Open Door Policy, they find they have to learn some English and learn it quickly. They try to learn from radio and TV and many take English courses of 4 to 6 months, with varying degree of success. Their chief stumbling blocks

Co-word clustering analysis for nursing safety management research focuses by PubMed

目的：基于Pubmed数据库分析护理安全管理研究的现状及热点。方法：以“safety management”为主题词，检索2007年9月至2017年9月PubMed数据库中有关护理安全管理的文献，并使用Bicomb软件、SPSS20．0对主题词进行共词聚类分析。结果：共获得文献2353篇，提取高频主题词19个，占总频次的27．50％，通过对高频主题词词篇矩阵进行共词聚类，得到5个研究热点：护理安全文化的研究、团队协作促进护理安全、护理安全管理实践、护理人员工作场所暴力以及护理安全质量评价标准的相关研究。结论：对近10年护理安全管理研究热点的分析有助于了解该领域研究重点及发展趋势，为随后护理安全管理研究及实践提供参考。

The hot issues of studies in China on digital information resources: Based on co-word analysis

With the SPSS and the help of factor method and hierarchical clustered method,journal articles on digital information resources(DIR) from CNKI in the past ten years are analyzed with a co-word analytical method in this paper. The hot issues of studies on DIR and the relationship between those subjects are analyzed in this investigation as well.

Research status and hotspots of economic evaluation in nursing by co-word clustering analysis

Objective:The aim of this study is to discover research status and hotspots of economic evaluation(EE)in nursing area using co-word cluster analysis.Methods:Medical Subject Heading(MeSH)term“cost–benefit analysis”was searched in PubMed and nursing journals were limited by the function of filter.The information of author,country,year,journal,and keywords of collected paper was extracted and exported to Bicomb 2.0 system,where high-frequency terms and other data could be further mined.SPSS 19.0 was used for cluster analysis to generate dendrogram.Results:In all,3,020 articles were found and 10,573 MeSH terms were detected;among them,1,909 were MeSH major topics and generated 42 high-frequency terms.The consequence of dendrogram showed seven clusters,representing seven research hotspots:skin administration,infection prevention,education program,nurse education and management,EE research,neoplasm patient,and extension of nurse function.Conclusions:Nursing EE research involved multiple aspects in nursing area,which is an important indicator for decision-making.Although the number of papers is increasing,the quality of study is not promising.Therefore,further study may be required to detect nurses’knowledge of economic analysis method and their attitude to apply it into nursing research.More nursing economics course could carry out in nursing school or hospitals.

2011—2015年国际应用行为分析热点研究——以《Journal of Applied Behavior Analysis》为例

为了探究应用行为分析在国外的研究成果和动态,利用共词分析的原理对2011—2015年JABA刊文的关键词进行了分析,结果显示近五年国外应用行为分析研究热点有自闭症谱系障碍、功能分析、区别性强化、口语行为、进食障碍等。对这些热点进行分类,探讨了各领域所处的研究地位,最后提出我国学界不仅要加强应用行为分析在特殊儿童问题行为管理和干预中的运用,也要把它推广到其他领域。

A Lightweight Sentiment Analysis Method

The emergence of big data leads to an increasing demand for data processing methods.As the most influential media for Chinese domestic movie ratings,Douban contains a huge amount of data and one can understand users'perspectives towards these movies by analyzing these data.In this article,we study movie's critics from the Douban website,perform sentiment analysis on the data obtained by crawling,and visualize the results with a word cloud.We propose a lightweight sentiment analysis method which is free from heavy training and visualize the results in a more conceivable way.

Uncertainty analysis of seawater intrusion forecasting

In order to describe the importance of uncertainty analysis in seawater intrusion forecasting and identify the main factors that might cause great differences in prediction results, we analyzed the influence of sea level rise, tidal effect, the seasonal variance of influx, and the annual variance of the pumping rate, as well as combinations of different parameters. The results show that the most important factors that might cause great differences in seawater intrusion distance are the variance of pumping rate and combinations of different parameters. The influence of sea level rise can be neglected in a short-time simulation （ten years, for instance）. Retardation of seawater intrusion caused by tidal effects is obviously important in aquifers near the coastline, but the influence decreases with distance away from the coastline and depth away from the seabed. The intrusion distance can reach a dynamic equilibrium with the application of the sine function for seasonal effects of influx. As a conclusion, we suggest that uncertainty analysis should be considered in seawater intrusion forecasting, if possible.

Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

The feedback collection and analysis has remained an important subject matter for long.The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis.However,the student expresses their feedback opinions on online social media sites,which need to be analyzed.This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews.Our technique computes the sentiment score of student feedback reviews and then applies a fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level.The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.

Sentiment Analysis of Investor Opinions on Twitter

The rapid growth of social networks has produced an unprecedented amount of user-generated data, which provides an excellent opportunity for text mining. Sentiment analysis, an important part of text mining, attempts to learn about the authors’ opinion on a text through its content and structure. Such information is particularly valuable for determining the overall opinion of a large number of people. Examples of the usefulness of this are predicting box office sales or stock prices. One of the most accessible sources of user-generated data is Twitter, which makes the majority of its user data freely available through its data access API. In this study we seek to predict a sentiment value for stock related tweets on Twitter, and demonstrate a correlation between this sentiment and the movement of a company’s stock price in a real time streaming environment. Both n-gram and “word2vec” textual representation techniques are used alongside a random forest classification algorithm to predict the sentiment of tweets. These values are then evaluated for correlation between stock prices and Twitter sentiment for that each company. There are significant correlations between price and sentiment for several individual companies. Some companies such as Microsoft and Walmart show strong positive correlation, while others such as Goldman Sachs and Cisco Systems show strong negative correlation. This suggests that consumer facing companies are affected differently than other companies. Overall this appears to be a promising field for future research.

Chebyshev Fitting Way and Error Analysis for Propeller Atlas across Four Quadrants

A Chebyshev fitting way for a propeller atlas across four quadrants is discussed. As an example, Chebyshev polynomialfitting results and its error analysis are given. Because it’s difficult generally to get a propeller atlas across four quadrants,a wayis used to construct an alternative with higher accuracy based on the properties. As an application example, an alternative forthe propeller property of a Deep Submergence Vebicle across four quadrants is given practically and a simulation model of

The Response Time Analysis of Digital Broadcasting System

Digital broadcasting system has become a high-light of research on computer application. To respond to the changes of the playbill in the broadcasting system in real time, the response time of the system must be studied. There is scarcely the research on this area currently. The influence factors in the response time are analyzed; the model on the response time of the system service is built; how the influence factors affect the response time of the system service is validated; and four improvement measures are proposed to minimize the response time of system service.

Cloning and Sequence Analysis of a Cysteine Proteinase Inhibitor Gene from Seedless Litchi

[Objective] This study aimed to clone and analyze the cysteine proteinase inhibitor gene from seedless litchi. [Method] According to the EST se- quence of cysteine proteinase inhibitor in constructed SSH snhtraetive library of seedless litchi abortion, nucleotide sequence of the cysteine proteinase inhibitor gene was obtained by using RACE technology and analyzed by using bioinformatics software. [ Result ] A cysteine protease inhibitor gene was obtained with the sequence of 635 bp containing a 321 bp open reading frame. It was predicted that the erlcoded protein contained 106 amino acids with conserved domain of cysteine proteinase inhibitor and had relatively high homology with the cysteine proteinase inhibitor gene of several species, [ Conclusion] This study laid the foundation for further ex- ploring the physiological functions of this cysteine proteinase inhibitor gene in plants.

Modelling Animal Activity as Curves: An Approach Using Wavelet-Based Functional Data Analysis

Temporal activity patterns in animals emerge from complex interactions between choices made by organisms as responses to biotic interactions and challenges posed by external factors. Temporal activity pattern is an inherently continuous process, even being recorded as a time series. The discreteness of the data set is clearly due to data-acquisition limitations rather than a true underlying discrete nature of the phenomenon itself. Therefore, curves are a natural representation for high-frequency data. Here, we fully model temporal activity data as curves integrating wavelets and functional data analysis, allowing for testing hypotheses based on curves rather than on scalar and vector-valued data. Temporal activity data were obtained experimentally for males and females of a small-bodied marsupial and modelled as wavelets with independent and identically distributed errors and dependent errors. The null hypothesis of no difference in temporal activity pattern between male and female curves was tested with functional analysis of variance (FANOVA). The null hypothesis was rejected by FANOVA and we discussed the differences in temporal activity pattern curves between males and females in terms of ecological and life-history attributes of the reference species. We also performed numerical analysis that shed light on the regularity properties of the wavelet bases used and the thresholding parameters.