The Meaning Composition of Chinese Calligraphy Sign

This paper clarifies the composition of meaning in the art of Chinese calligraphy on the basis of American semiotician Charles Morris’s three divisions of sign,namely,“Syntactics,Semantics,Pragmatics”.The syntactical dimension of Chinese calligraphy sign is the artistic meaning of the form of Chinese character.“Semantical dimension”refers to semantic referents of linguistic signs and their literary significance,“pragmatical dimension”refers to the use and interpretation of calligraphy.The study of the three dimensions of meaning in Chinese calligraphy reveals a complex and unique composition of sign meaning.Chinese calligraphy deforms the structure and form of Chinese characters while retaining their semantic invariance.In this way,calligraphy acquires more practical semantic and symbolic meanings through the carrier of Chinese characters which are also linguistic symbols at the same time.Chinese calligraphy is dominated by the meaning of sign forms,taking into account the linguistic reference and the use of sign,and the three dimensions of meaning interact and promote each other,building a dynamic and multidimensional space of sign meaning.

Aspect-Level Sentiment Analysis Based on Deep Learning

In recent years,deep learning methods have developed rapidly and found application in many fields,including natural language processing.In the field of aspect-level sentiment analysis,deep learning methods can also greatly improve the performance of models.However,previous studies did not take into account the relationship between user feature extraction and contextual terms.To address this issue,we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method.To be specific,we design user comment feature extraction(UCFE)to distill salient features from users’historical comments and transform them into representative user feature vectors.Then,the aspect-sentence graph convolutional neural network(ASGCN)is used to incorporate innovative techniques for calculating adjacency matrices;meanwhile,ASGCN emphasizes capturing nuanced semantics within relationships among aspect words and syntactic dependency types.Afterward,three embedding methods are devised to embed the user feature vector into the ASGCN model.The empirical validations verify the effectiveness of these models,consistently surpassing conventional benchmarks and reaffirming the indispensable role of deep learning in advancing sentiment analysis methodologies.

A Comparative Analysis of Syntactic Complexity in Applied Linguistics Abstracts Written by Chinese Novice Writers and Native English Advanced Writers

The rhetorical structure of abstracts has been a widely discussed topic, as it can greatly enhance the abstract writing skills of second-language writers. This study aims to provide guidance on the syntactic features that L2 learners can employ, as well as suggest which features they should focus on in English academic writing. To achieve this, all samples were analyzed for rhetorical moves using Hyland’s five-rhetorical move model. Additionally, all sentences were evaluated for syntactic complexity, considering measures such as global, clausal and phrasal complexity. The findings reveal that expert writers exhibit a more balanced use of syntactic complexity across moves, effectively fulfilling the rhetorical objectives of abstracts. On the other hand, MA students tend to rely excessively on embedded structures and dependent clauses in an attempt to increase complexity. The implications of these findings for academic writing research, pedagogy, and assessment are thoroughly discussed.

Aspect-Level Sentiment Analysis Incorporating Semantic and Syntactic Information

Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.

Drop weight impact analysis of GFRP tubes with hollow glass particle-filled matrix

Protecting occupants or payloads in crashes and blasts is of utmost importance in both moving and immobile structures.One way of achieving this is by using a sacrificial energy absorber.Composite tubes have been studied as potential energy absorbers due to their ability to fail progressively under axial compression.In this study,the energy absorption capability of these tubes is enhanced by adding hollow glass particles to the matrix.Drop-weight tests are performed on composite tubes,and a digital image correlation(DIC)-based technique is used to capture their load-displacement behaviour.This eliminates the use of electronic data acquisition systems,load cells,and accelerometers.The load-displacement curves of the tubes are obtained from the DIC-based technique and examined to understand their crushing behaviour.Although the mean crush load shows a drop,an increase in crush length is noticed.The specific energy absorbed by the tubes improves with an increase in GMB volume fraction.The addition of 0.1,0.2,0.3 and 0.4 vol fractions of GMB results in the specific energy absorption increasing by6.6%,14.7%,24%and 36.6%,respectively,compared to neat glass fibre-epoxy tubes.Visual examination of the tubes and comparison with tubes subject to quasi-static compression is also performed.

Reading Levels of Leaflets of Common Over-the-Counter Drugs in Ghana

Health communication is critical component of effective health delivery. While there are studies on the readability Patient Information Leaflets (PILs) around the world, so far, there is only one Ghanaian study that explored the language of patient information leaflets for malaria. The present study explores the language of PILs of seven common ailments in Ghana. In all, 68 leaflets were purposely sampled for the study. Using SMOG and Flesch Kincaid grade level readability formulae, the researcher measured the readability of PILs and discovered that the readability of all the PILs was difficult to read except the dewormer leaflets that scored standard reading score. Further statistical analysis reveal that the lexical and syntactic complexity of the leaflets were not statistically different. Finally, an interview with 20 patients confirmed that the leaflets are difficult to read due to the complex words and long sentence structures. It is recommended that the PILs be revised to make the leaflets readable.

科技英语翻译中的句法分析

科技英语作为独立文体，有它自身的词法和句法特征。科技英语词汇和术语往往在某一特定技术领域或专业内得以界定，在构词上有一定的专业脉络可循，每一专业几乎都有自己的词汇和术语系统，词法体现出较强的专业属性。科技英语的句法则带有一定的普遍性。不管何种专业，表...

Stylistic Characteristics of English Legal Texts

The characteristics of authority and standard in Legal English decide the significance of the translation about legal English texts. This thesis presents the stylistic features of legal English. It is hoped that translators would be able to understand legal English accurately.

Vague Language of Journalistic English in Syntactic Level

Vagueness is a natural character of language. Vagueness of journalistic English exists in lexical level and syntactic level as well. Lexical vagueness in journalistic English is usually resulted from the indefinite meaning of words, while in syntactic level, it may be resulted from the combined use of vague words or the employment of some sentence patterns. This thesis aims to help readers to understand journalistic English from the perspective of vague language.

Words for Fearful Symmetry:A Stylistic Analysis of The Tyger

It studies William Blake’s poem The Tyger on a basis of the theories of modern stylistics and discusses the meaning realization of the poem from the phonological features, the lexical and syntactic features, and the semantic features.

Stylistic Features of Syntax in English Journal Blogs

Blog, a new genre of internet communication, has its own unique syntactic features. With Halliday's stylistic analytical pattern, the writer analyzed the samples from the perspectives of linguistic description to reveal the sentence patterns used in the journal blogs. The results show that blog language displays many of the properties of both spoken and written language.

Oral Fossilization Phenomena of Vocational College Non-English Majors

This thesis sets out to classify the fossilized errors made by the subjects from lexical and syntactic levels respectively,through the empirical experiment and questionnaire.

General Review of Introduction to Government and Binding Theory

The book Introduction to Government and Binding Theory written by Liliane Haegeman has made a systematic introduction to the primary achievements of syntactic research during several decades before 1990s.GB Theory include seven important sub-theories:1) Theta Theory,2) X-bar Theory,3) Case Theory,4) Binding Theory,5) Control Theory,6) Government Theory and 7) Bounding Theory,which are applied together with some important principles to analyzing and illustrating syntactic structures.In doing that,sometimes only one of the sub-theories is enough and sometimes several sub-theories should be needed together.Although the theories proposed in this book are sometimes too limit and mechanical to make a full analyze and illustration about some syntactic phenomena,the book is still a dramatically useful introductory instrument which is helpful for the beginners to have fundamental understanding of basic syntactic theories and can lay good foundations for further study and research on syntactics.

Similarity measure of sedimentary successions and its application in inverse stratigraphic modeling

This paper presents a unique and formal method of quantifying the similarity or distance between sedimentary facies successions from measured sections in outcrop or drilled wells and demonstrates its first application in inverse stratigraphic modeling. A sedimentary facies succession is represented with a string of symbols, or facies codes in its natural vertical order, in which each symbol brings with it one attribute such as thickness for the facies. These strings are called attributed strings. A similarity measure is defined between the attributed strings based on a syntactic pattern-recognition technique. A dynamic programming algorithm is used to calculate the similarity. Inverse stratigraphic modeling aims to generate quantitative 3D facies models based on forward stratigraphic modeling that honors observed datasets. One of the key techniques in inverse stratigraphic modeling is how to quantify the similarity or distance between simulated and observed sedimentary facies successions at data locations in order for the forward model to condition the simulation results to the observed dataset such as measured sections or drilled wells. This quantification technique comparing sedimentary successions is demonstrated in the form of a cost function based on the defined distance in our inverse stratigraphic modeling implemented with forward modeling optimization.

Stylistic Analysis of Whitman'S Poem O Captain!My Captain!

The poem O Captain! My Captain! is a good poem written by the great American poet, Walt Whitman. It is a eulogy written on the death of Abraham Lincoln. In this poem the poet expresses his sadness at the death of the great leader and expresses his great love of his beloved leader. This paper is to have a stylistic analysis of the poem by observing its phonological, lexical, semantic and syntactic features.

Improved head-driven statistical models for natural language parsing

Head-driven statistical models for natural language parsing are the most representative lexicalized syntactic parsing models, but they only utilize semantic dependency between words, and do not incorporate other semantic information such as semantic collocation and semantic category. Some improvements on this distinctive parser are presented. Firstly, "valency" is an essential semantic feature of words. Once the valency of word is determined, the collocation of the word is clear, and the sentence structure can be directly derived. Thus, a syntactic parsing model combining valence structure with semantic dependency is purposed on the base of head-driven statistical syntactic parsing models. Secondly, semantic role labeling(SRL) is very necessary for deep natural language processing. An integrated parsing approach is proposed to integrate semantic parsing into the syntactic parsing process. Experiments are conducted for the refined statistical parser. The results show that 87.12% precision and 85.04% recall are obtained, and F measure is improved by 5.68% compared with the head-driven parsing model introduced by Collins.

Effects of sphere size on the microstructure and mechanical properties of ductile iron–steel hollow sphere syntactic foams

The effects of sphere size on the microstructural and mechanical properties of ductile iron–steel hollow sphere（DI–SHS） syntactic foams were investigated in this study. The SHSs were manufactured by fluidized-bed coating via the Fe-based commercial powder–binder suspension onto expanded polystyrene spheres（EPSs）. Afterwards, the DI–SHS syntactic foams were produced via a sand-mold casting process. The microstructures of specimens were investigated by optical microscopy, scanning electron microscopy（SEM）, and energy-dispersive X-ray spectroscopy（EDS）. The microscopic evaluations of specimens reveal distinct regions composed of the DI matrix, SHS shells, and compatible interface. As a result, the microstructures and graphite morphologies of the DI matrix depend on sphere size. When the sphere size decreases, the area fractions of cementite and graphite phases are observed to increase and decrease, respectively. Compression tests were subsequently conducted at ambient temperature on the DI–SHS syntactic foams. The results reveal that the compression behavior of the syntactic foams is enhanced with increasing sphere size. Furthermore, the compressed specimens demonstrate that microcracks start and grow from the interface region.

Effects of particle size,bimodality and heat treatment on mechanical properties of pumice reinforced aluminum syntactic foams produced by cold chamber die casting

In recent years,metal matrix syntactic foams(MMSFs)have become highly attractive owing to their unique physical,microstructural and mechanical features.Due to their promising potential for different industrial areas like automotive,aviation,and defense,these advanced engineering materials can also be evaluated as serious alternatives to particle reinforced metallic composites and conventional metallic foams.Differently from previously reported laboratory scaled techniques in the literature,this experimental effort focuses on the feasibility of MMSF manufacturing via a fully automated and industrial-based cold chamber die casting technique.Accordingly,1-2 mm,2-4 mm,and bimodal(50vol.%)natural-based pumice filled aluminum syntactic foams were manufactured utilizing a purpose-made casting machine.Physical,macroscopic,and microscopic examinations show that all of the fabricated samples display perfect matrix/filler harmony.Average density levels of fabricated syntactic foams range between 1.50 and 1.80 g·cm^(-3) depending upon the pumice particles size interval.To assess mechanical responses,quasi-static compression tests were performed.Furthermore,half of the foam samples were subjected to heat treatment to explore possible influences of aging on the compressive features and damage modes.Results indicate that although the heat treatment enhances the compressive strength,plateau stress,and energy absorption properties of the fabricated foams,it changes damage mode of the samples by causing brittle dominant deformation.

University Learning and Anti-Plagiarism Back-End Services

Plagiarism refers to the use of other people’s ideas and information without acknowledging the source.In this research,anti-plagiarism software was designed especially for the university and its campuses to identify plagiarized text in students’written assignments and laboratory reports.The proposed framework collected original documents to identify plagiarized text using natural language processing.Our research proposes a method to detect plagiarism by applying the core concept of text,which is semantic associations of words and their syntactic composition.Information on the browser was obtained through Request application programming interface by name Url.AbsoluteUri,and it is stored in a centralized Microsoft database Server.A total of 55,001 data samples were collected from 2015 to 2019.Furthermore,we assimilated data from a university website,specifically from the psau.edu.sa network,and arranged the data into students’categories.Furthermore,we extracted words from source documents and student documents using the WordNet library.On a benchmark dataset consisting of 785 plagiarized text and 4,716 original text data,a significant accuracy of 90.2%was achieved.Therefore,the proposed framework demonstrated better performance than the other available tools.Many students mentioned that working on assignments using the framework was suitable because they were able to work on the assignments in harmony,as per their timeframe and from different network locations.The framework also recommends procedures that can be used to avoid plagiarism.

Complex human activities recognition using interval temporal syntactic model

A novel method based on interval temporal syntactic model was proposed to recognize human activities in video flow. The method is composed of two parts: feature extract and activities recognition. Trajectory shape descriptor, speeded up robust features(SURF) and histograms of optical flow(HOF) were proposed to represent human activities, which provide more exhaustive information to describe human activities on shape, structure and motion. In the process of recognition, a probabilistic latent semantic analysis model(PLSA) was used to recognize sample activities at the first step. Then, an interval temporal syntactic model, which combines the syntactic model with the interval algebra to model the temporal dependencies of activities explicitly, was introduced to recognize the complex activities with a time relationship. Experiments results show the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases for the recognition of complex activities.