Research on Jiangxi’s International Image Based on NOW Corpus

This paper retrieves and classifies reports on Jiangxi from 2010 to June 2024 in the NOW corpus,analyzing the status quo of Jiangxi’s international image portrayed by English news media through word frequency and collocates.It is found that international attention towards Jiangxi is closely related to the holding of the World Conference on VR Industry in Nanchang,and there are apparent regional disparities.With a severe labeling of its mining industry,Jiangxi’s international image lacks independence and distinctiveness,resulting in a relatively monotonous overall image.Based on this,relevant suggestions for further enhancing Jiangxi’s international image are proposed.

English Translation of Chinese Natural Ecological Discourse:A Corpus-based Case Study on Xi Jinping:The Governance of China

Amid the increasingly severe global natural eco-environment,it is necessary to build a natural ecological civilization by constructing an ecological civilization discourse.Against this background,this study compiles a corpus of natural ecological discourse in the English translation of Xi Jinping:The Governance of China(short for Xi).By using Wordsmith and AntConc,this study explores the linguistic features of the ecological discourse in English translation of Xi in the following dimensions,including high-frequency words,keywords,word collocations,concordance lines.This study aims to analyze the concepts and attitudes towards natural ecology,so as to provide certain valuable insights for the construction of China’s discourse on natural ecological civilization.The study found that the natural ecology discourse involving in the English translation of Xi turned out to be ecologically beneficial.

A Corpus Study of Acupuncture from the Perspective of Mass Media Based on the Corpus of Contemporary American English

Based on the Corpus of Contemporary American English(COCA),this study examines acupuncture from the perspective of mass media.Acupuncture has been circulating throughout the Chinese Cultural Circle since the Qin and Han Dynasties,and has since spread directly or indirectly to the rest of the world.The United States boasts the world’s second-largest acupuncture market,with its laws,regulations,industry growth,research,and education all positively influencing the development of acupuncture in other nations.This study uses COCA to analyze the form and content of acupuncture’s dissemination in eight different types of media.The findings show that acupuncture appears in COCA a total of 1,788 times,with the highest frequency in magazines,followed by blogs,and the lowest frequency in fiction.These findings reveal the popularity of acupuncture in mass media in the United States and provide empirical data and insights for the future dissemination and development of acupuncture in the United States.

The Application and Effect Evaluation of Corpus Linguistics in English Teaching

This paper explores the application and effect evaluation of corpus linguistics in English teaching.It first introduces the basic concepts and development history of corpus linguistics,then analyzes its connection with language teaching theories,discussing the advantages and challenges of using corpora in language teaching.Next,it delves into the methods and technologies for building and utilizing corpora,as well as their role in developing teaching resources.Lastly,within the framework and methods of teaching effect evaluation,specific application cases of corpus linguistics in teaching design are analyzed,and their effects are assessed.Recommendations for teaching improvement and future development directions are also proposed.

A Corpus-Based Behavioral Profile Study of the Near-Synonyms: Serious, Severe, Grave, Grievous

Using Corpus of Contemporary American English as the source data,this paper carries out a corpus-based behavioral profile study to investigate four near-synonymous adjectives(serious,severe,grave,and grievous),focusing on their register and the types of nouns they each modify.Although sharing core meaning,these adjectives exhibit variations in formality levels and usage patterns.The identification of fine-grained usage differences complements the current inadequacies in describing these adjectives.Furthermore,the study reaffirms the effectiveness of the corpus-based behavioral profile approach in examining synonym differences.

A Corpus-Based Study on Grammaticalization and Lexicalization in Medical English

Medical English,as a specialized form of English,has attracted considerable attention due to its unique linguistic features.Grammaticalization and lexicalization represent critical mechanisms in the evolution of language and are essential for understanding and mastering medical English.This study,drawing on the MEDLINE corpus,investigates the characteristics of grammaticalization and lexicalization in medical English from the perspective of cognitive linguistics,aiming to unveil its underlying cognitive foundations.The findings suggest that the processes of grammaticalization and lexicalization in medical English reflect cognitive mechanisms such as categorization,inferential processing,and pragmatic strategies.These insights provide novel approaches and methodologies for the teaching of medical English.

Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking and detecting the spread of fake news in Arabic becomes critical.Several artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake news.Thus,fake news in Arabic is identified by utilizing AI approaches.This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic corpus.The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.

The Significance of Self-built Learners’ Written English Corpus

Researchers have reached an agreement that corpus exerts an important role in the English teaching and learning,and several learners’ corpus have been established.This paper aims to discuss the necessity and significance of establishing self-built learners’ written English corpus in the hope that teachers and learners will get to know it and use it effectively in their practice.

Corpus of Carbonate Platforms with Lexical Annotations for Named Entity Recognition

An obviously challenging problem in named entity recognition is the construction of the kind data set of entities.Although some research has been conducted on entity database construction,the majority of them are directed at Wikipedia or the minority at structured entities such as people,locations and organizational nouns in the news.This paper focuses on the identification of scientific entities in carbonate platforms in English literature,using the example of carbonate platforms in sedimentology.Firstly,based on the fact that the reasons for writing literature in key disciplines are likely to be provided by multidisciplinary experts,this paper designs a literature content extraction method that allows dealing with complex text structures.Secondly,based on the literature extraction content,we formalize the entity extraction task(lexicon and lexical-based entity extraction)for entity extraction.Furthermore,for testing the accuracy of entity extraction,three currently popular recognition methods are chosen to perform entity detection in this paper.Experiments show that the entity data set provided by the lexicon and lexical-based entity extraction method is of significant assistance for the named entity recognition task.This study presents a pilot study of entity extraction,which involves the use of a complex structure and specialized literature on carbonate platforms in English.

Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

Natural Language Processing(NLP)for the Arabic language has gained much significance in recent years.The most commonly-utilized NLP task is the‘Text Classification’process.Its main intention is to apply the Machine Learning(ML)approaches for automatically classifying the textual files into one or more pre-defined categories.In ML approaches,the first and foremost crucial step is identifying an appropriate large dataset to test and train the method.One of the trending ML techniques,i.e.,Deep Learning(DL)technique needs huge volumes of different types of datasets for training to yield the best outcomes.The current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machinebased Arabic Corpus Classification(DODHBM-ACC)model in this background.The presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding process.For Arabic text classification,the DHBM technique is utilized.This technique is a hybrid version of the Deep Boltzmann Machine(DBM)and Deep Belief Network(DBN).It has the advantage of learning the decisive intention of the classification process.To adjust the hyperparameters of the DHBM technique,the Dice Optimization Algorithm(DOA)is exploited in this study.The experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC model.The outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.

Multi-Versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis on Arabic Corpus

Sentiment analysis(SA)of the Arabic language becomes important despite scarce annotated corpora and confined sources.Arabic affect Analysis has become an active research zone nowadays.But still,the Arabic language lags behind adequate language sources for enabling the SA tasks.Thus,Arabic still faces challenges in natural language processing(NLP)tasks because of its structure complexities,history,and distinct cultures.It has gained lesser effort than the other languages.This paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis(MVODRL-AA)on Arabic Corpus.The presented MVODRL-AAmodelmajorly concentrates on identifying and classifying effects or emotions that occurred in the Arabic corpus.Firstly,the MVODRL-AA model follows data pre-processing and word embedding.Next,an n-gram model is utilized to generate word embeddings.A deep Q-learning network(DQLN)model is then exploited to identify and classify the effect on the Arabic corpus.At last,the MVO algorithm is used as a hyperparameter tuning approach to adjust the hyperparameters related to the DQLN model,showing the novelty of the work.A series of simulations were carried out to exhibit the promising performance of the MVODRL-AA model.The simulation outcomes illustrate the betterment of the MVODRL-AA method over the other approaches with an accuracy of 99.27%.

The correlation of miRNA expression and tumor mutational burden in uterine corpus endometrial carcinoma

Background:The relationship between microRNA(miRNA)expression patterns and tumor mutation burden(TMB)in uterine corpus endometrial carcinoma(UCEC)was investigated in this study.Methods:The UCEC dataset from The Cancer Genome Atlas(TCGA)database was used to identify the miRNAs that differ in expression between high TMB and low TMB sample sets.The total sample sets were divided into a training set and a test set.TMB levels were predicted using miRNA-based signature classifiers developed by Lasso Cox regression.Test sets were used to validate the classifier.This study investigated the relationship between a miRNA-based signature classifier and three immune checkpoint molecules(programmed cell death protein 1[PD-1],programmed cell death ligand 1[PD-L1],cytotoxic T lymphocyte-associated antigen 4[CTLA-4]).For the miRNA-based signature classifier,functional enrichment analysis was performed on the miRNAs.An analysis of the relationship between PD-1,PD-L1,and CTLA-4 immune checkpoint genes was carried out using the miRNA-based signature classifier.Results:We identified 27 differentially expressed miRNAs in miRNA-base signature.For predicting the TMB level,27-miRNA-based signature classifiers had accuracies of 0.8689 in the training cohort,0.8276 in the test cohort,and 0.8524 in the total cohort.The correlation between the miRNA-based signature classifier and PD-1 was negative,while the correlation between PD-L1 and CTLA4 was positive.Based on the miRNA profiling described above,we validated the expression levels of 9 miRNAs in clinical samples by quantitative reverse transcription PCR(qRT-PCR).Four of them were highly expressed and many cancer-related and immune-associated biological processes were linked to these 27 miRNAs.Thus,the developed miRNA-based signature classifier was correlated with TMB levels that could also predict TMB levels in UCEC samples.Conclusion:In this study,we investigated the relationship between a miRNAbased signature classifier and TMB levels in Uterine Corpus Endometrial Carcinoma.Further,this is the first study to confirm their relationship in clinical samples,which may provide more evidence support for immunotherapy of endometrial cancer.

Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

The term‘corpus’refers to a huge volume of structured datasets containing machine-readable texts.Such texts are generated in a natural communicative setting.The explosion of social media permitted individuals to spread data with minimal examination and filters freely.Due to this,the old problem of fake news has resurfaced.It has become an important concern due to its negative impact on the community.To manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)techniques.To perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite effectively.Still,a huge effort is required from the human side to generate the labelled training data.The recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news detection.To unlock social media data,an automatic text classifier is highly helpful in the domain of NLP.The current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)approach.The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news.To attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding process.Besides,the QCLSTM model is utilized for classification.To boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the hyperparameters.The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset.The QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.

Research on Vocabulary Learning in English Classroom Teaching Based on Corpus

The difficulty of learning English is not only related to interest,but also related to the correctness of learning methods.Especially in English teaching,a comprehensive and in-depth mastery of vocabulary can improve the level of English language,learn English knowledge better,and improve the level of cross-cultural communication.The application of corpus in English classroom vocabulary teaching can provide more educational space for vocabulary teaching,enrich teaching methods,and at the same time,facilitate students to learn vocabulary and lay a foundation for learning English language.To this end,this article first describes the important role of corpus application in vocabulary learning in English classroom teaching.Secondly,it discusses the difficulties of vocabulary learning and the factors that affect the quality of learning.Finally,in order to enhance the learning effect of students and improve the teaching level,several learning strategies have been formulated to continuously highlight the practicality of the corpus.

Cytotoxic Lesions of the Corpus Callosum (CLOCCs) Associated with SARS-CoV-2 Infection in West Africa (Côte d’Ivoire)

Background: Cytotoxic lesions of the corpus callosum (CLOCCs) represent a collection of disparate conditions that can cause a signal change in the corpus callosum, usually involving the splenium. CLOCCs is present in a variety of disorders, such as cerebral infarction, bleeding, multiple sclerosis, acute disseminated encephalomyelitis, glioblastoma, lymphoma, metabolic diseases, and infections. Since 2020, World Health Organization (W.H.O) defined Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, as a pandemic. Numerous CLOCCs cases have been reported in adults in particular in Japan, in China, and recently in children in Turkey associated with SARS-CoV-2. We report the first case of CLOCCs diagnosed in West Africa (Côte d’Ivoire) in an adult associated with SARS-CoV-2. Case Report: A 60 year-old-woman with a medical history of high blood pressure and diabetes, presented to the emergency department with confusion without fever. Neurological examination was normal apart from temporospatial disorientation. Brain magnetic resonance imaging (MRI) showed abnormal signals in the splenium of the corpus callosum (SCC). Forty-eight hours (48 h) after admission, the patient experienced a fever (temperature: 385˚C), several episodes of hypoglycemia (capillary blood glycemia levels below 0.5 g/l) and a dry cough. Lung CT imaging showed typical features with ground-glass opacities. Oropharyngeal swab was positive for SARS-CoV-2 on reverse-transcriptase–polymerase-chain-reaction (RT-PCR) assay. The clinical course was favorable. One month after disease onset, a follow-up Brain MRI showed considerable regression of SCC abnormal signal. The multiple episodes of hypoglycemia and SARS-COV 2 infection were incriminated as the causal factors. Conclusion: The improvement of the technical platform in our context of work gives us the possibility to identify the etiological factors of this rare clinico-radiological entity.

A Corpus-Based Study on the Translators’Styles in the English Versions of The True Story of Ah-Q

The True Story of Ah-Q written by Lu Xun has been translated into forty different languages,making great contributions to the external dissemination of Chinese culture.This paper adopts the corpus stylistics method to analyze the translators’styles presented by William A.Lyell and Julia Lovell’s translations of Ah-Q.Based on the self-built comparable corpus of the two translations,the paper investigates and analyzes them from the lexical and sentence level.In terms of vocabulary,the translations are statistically analyzed from the standardized type-token ratio,word frequency,word length and vocabulary density;at the sentence level,the article studies the average sentence lengths and sentence complexity.The first major finding is that Lovell’s vocabulary is richer than that of Lyell,and Lovell’s average word length is longer.Also,the average sentence length of both translations exceeds the original text,and Lovell has the highest average sentence length.Secondly,Lovell prefers to use simple sentences while Lyell prefers complex sentences.Nevertheless,both of them strive to make Lu Xun’s works understandable to foreign readers,thus understanding Chinese culture better.

Literature Review on the Renovation Project of Self-Built Residential Buildings in the South Under the Low-Carbon Background

With the continuous exploration and research of low-carbon buildings in China,combined with foreign theories and practices,there are a few basic theoretical and practical projects for low-carbon construction in major cities of China.However,throughout the entire academic research,there is still no comprehensive and clear guidance for the green and low-carbon research and transformation of rural self-built houses.Therefore,the existing development of low-carbon technology for rural self-built houses has been summarized,and some prospects for the future development of rural self-built houses have been summarized.It is hoped that this will have positive help for future rural low-carbon transformation.

Translator Stylistics:A Corpus Analysis of Two English Translations of Traditional Chinese Medicine Classic Da Sheng Bian

Da Sheng Bian,a significant work on obstetrics and gynecology that emerged in the early Qing Dynasty,was initially published as“A Treatise on Midwifery”in 1842 by William Lockhart,a British missionary to China.In 1894,John G.Kerr,an American missionary,translated the text as“The Tat Shang Pin”.This paper conducts a comparative study of the two English translations using a self-constructed English-Chinese parallel corpus of Da Sheng Bian.The study explores the translation styles of the two translators by examining the token-types ratio and frequency at the lexical level,mean sentence length at the syntactic level,and the use of conjunctions at the discourse level.The observed differences in translation styles between the two translations are analyzed in relation to the translators’backgrounds and translation strategies.

Risk Prediction Model for Surgical Treatment of Ruptured Corpus Luteum in the Ovary

Objective: To explore the related factors of surgical treatment of patients with corpus luteum rupture and establish a risk prediction model of surgical treatment of corpus luteum rupture. Methods: 222 patients with corpus luteum rupture treated in Jingzhou First People’s Hospital from January 2015 to March 2022 were analyzed retrospectively, including 45 cases of surgery and 177 cases of conservative treatment. The training set and validation set were randomly assigned according to 7:3. We collected the basic information, laboratory and ultrasonic examination data of 222 patients. Logistic regression analysis was used to determine the independent risk factors and combined predictors of surgical treatment of corpus luteum rupture. The risk prediction model was established and the nomogram was drawn. The discrimination and calibration of the prediction model were verified and evaluated by receiver operating characteristic (ROC) curve, calibration curve and Hosmer-Lemeshow goodness of fit test;Decision curve analysis (DCA) was used to evaluate the clinical effectiveness of the prediction model. Results: Univariate logistic regression showed that whole abdominal pain (OR: 2.314, 95% CI: 1.090 - 4.912), abdominal muscle tension (OR: 2.379, 95% CI: 1.112 - 5.089), adnexal mass ≥ 4 cm (OR: 3.926, 95% CI: 1.771 - 8.266), hemoglobin Conclusion: The nomogram prediction model containing three predictive variables (hemoglobin, depth of pelvic effusion under ultrasound and cervical lifting pain) can be used to predict the risk of surgical treatment in patients with corpus luteum rupture.

基于Corpus库的词语相似度计算方法

构建了一个语义关联库,称为Corpus库,该库使用词语空间和关系空间结构化地存储了词语和其上下文之间的统计信息,并通过阅读大量的预料数据来训练其相关数据。详细介绍了Corpus库的训练方法,并对训练过程中出现的大量关系提出了裁剪方案。在此基础上,通过构建词语的上下文关系向量提出了一种词语相似度算法。实验证明这是一种有效的对词语相似度进行计算的方法。