Multiparameter generalized universal characters and multiparameter generalized B-type universal characters

We construct the quantum fields presentation of the generalized universal character and the generalized B-type universal character,and by acting the quantum fields presentations to the constant 1,the generating functions are derived.Furthermore,we introduce two integrable systems known as the generalized UC(GUC)hierarchy and the generalized Btype UC(GBUC)hierarchy satisfied by the generalized universal character and the generalized B-type universal character,respectively.Based on infinite sequences of complex numbers,we further establish the multiparameter generalized universal character and the multiparameter generalized B-type universal character,which have been proved to be solutions of the GUC hierarchy and the GBUC hierarchy,respectively.

Characterization of Synonymous Codon Usage Bias in the Pseudorabies Virus US1 Gene

In the present study, we examined the codon usage bias between pseudorabies virus （PRV） US1 gene and the USl-like genes of 20 reference alphaherpesviruses. Comparative analysis showed noticeable disparities of the synonymous codon usage bias in the 21 alphaherpesviruses, indicated by codon adaptation index, effective number of codons （ENc） and GC3s value. The codon usage pattern of PRV US1 gene was phylogenetically conserved and similar to that of the USl-like genes of the genus Varicellovirus of alphaherpesvirus, with a strong bias towards the codons with C and G at the third codon position. Cluster analysis of codon usage pattern of PRV US1 gene with its reference alphaherpesviruses demonstrated that the codon usage bias of USl-like genes of 21 alphaherpesviruses had a very close relation with their gene functions. ENc-plot revealed that the genetic heterogeneity in PRV US1 gene and the 20 reference alphaherpesviruses was constrained by G＋C content, as well as the gene length. In addition, comparison of codon preferences in the US1 gene of PRV with those ofE. coli, yeast and human revealed that there were 50 codons showing distinct usage differences between PRV and yeast, 49 between PRV and human, but 48 between PRV and E. coil Although there were slightly fewer differences in codon usages between E.coli and PRV, the difference is unlikely to be statistically significant, and experimental studies are necessary to establish the most suitable expression system for PRV US1. In conclusion, these results may improve our understanding of the evolution, pathogenesis and functional studies of PRV, as well as contributing to the area of herpesvirus research or even studies with other viruses.

The Usage of Characters in the Article Mingxun of the Warring States Bamboo Manuscripts Collected by Tsinghua University

There are 93 groups of characters in the Chu bamboo slips of Mingxun that have different forms with traditional charac-ters used latter.The 93 groups of characters can be divided into 4 types.Among them Type 1,the interchangeable characters,ac-count for 72.0%in total.The statistics reveal that it was so common to use different forms of characters during the Warring States period and this is a characteristic of the Chu bamboo slips.Besides,the statistics emphasize the importance of phonic sign in the character.

Analysis of the Influence of New Usages on the State of Conservation of Historical Buildings through Microclimate Characterization

Recycling is an alternative for preserving historical heritage buildings. Through such process, buildings are occupied by new usages that assure they are not abandoned. However, conservation issues, like pathological manifestations by filamentous fungi, are common even in buildings receiving new uses. Thus, it is extremely important to know the microclimate behavior of these buildings and analyze if there are usages that favor the appearance of filamentous fungi or not. This study aims to verify if new usages modify the microclimate in historical buildings and if such possible change favors the appearance of pathological manifestations by filamentous fimgi in some specific use. To achieve the results, three buildings that have gone through recycling processes were chosen in the city of Pelotas, in Southernmost Brazil. The city possesses a large number of buildings with historical value and its climate conditions are propitious for appearance of filamentous fungi. For 12 months, microclimate was monitored, the environments modus operandi was characterized and there was a survey on the pathological manifestations by filamentous fungi in the three buildings with two different uses： administration offices and exhibition rooms. Conclusion was that usages that allow natural ventilation and lighting favor conservation, which is what happens in the ones used as administration offices. Besides that, it was noticed that the construction system also influences, since buildings with a high cellar that is being used did not present filamentous fungi, which also shows the influence of ascending humidity on the appearance of such pathological manifestations.

Optimised CNN Architectures for Handwritten Arabic Character Recognition

Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.The Arabic language includes 28 characters.Each character has up to four shapes according to its location in the word(at the beginning,middle,end,and isolated).This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters.The proposed architectures were derived from the popular CNN architectures,such as VGG,ResNet,and Inception,to make them applicable to recognizing character-size images.The experimental results on three well-known datasets showed that the proposed architectures significantly enhanced the recognition rate compared to the baseline models.The experiments showed that data augmentation improved the models’accuracies on all tested datasets.The proposed model outperformed most of the existing approaches.The best achieved results were 93.05%,98.30%,and 96.88%on the HIJJA,AHCD,and AIA9K datasets.

Effect of Co on Solidification Characteristics and Microstructural Transformation of Non-equilibrium Solidified Cu-Ni Alloys

Non-equilibrium solidification structures of Cu55Ni45 and Cu55Ni43Co2 alloys were prepared by the molten glass purification cycle superheating method.The variation of the recalescence phenomenon with the degree of undercooling in the rapid solidification process was investigated using an infrared thermometer.The addition of the Co element affected the evolution of the recalescence phenomenon in Cu-Ni alloys.The images of the solid-liquid interface migration during the rapid solidification of supercooled melts were captured by using a high-speed camera.The solidification rate of Cu-Ni alloys,with the addition of Co elements,was explored.Finally,the grain refinement structure with low supercooling was characterised using electron backscatter diffraction(EBSD).The effect of Co on the microstructural evolution during nonequilibrium solidification of Cu-Ni alloys under conditions of small supercooling is investigated by comparing the microstructures of Cu55Ni45 and Cu55Ni43Co2 alloys.The experimental results show that the addition of a small amount of Co weakens the recalescence behaviour of the Cu55Ni45 alloy and significantly reduces the thermal strain in the rapid solidification phase.In the rapid solidification phase,the thermal strain is greatly reduced,and there is a significant increase in the characteristic undercooling degree.Furthermore,the addition of Co and the reduction of Cu not only result in a lower solidification rate of the alloy,but also contribute to the homogenisation of the grain size.

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow 3.0.The goal is to contribute to the preservation and understanding of historical texts,showcasing the potential of modern deep learning methods in archaeological research.Our research culminates in several key findings and scientific contributions.We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context.We also created and annotated an extensive dataset of Palmyrene inscriptions,a crucial resource for further research in the field.The dataset serves for training and evaluating the segmentation models.We employ comparative evaluation metrics to quantitatively assess the segmentation results,ensuring the reliability and reproducibility of our findings and we present custom visualization tools for predicted segmentation masks.Our study advances the state of the art in semi-automatic reading of Palmyrene inscriptions and establishes a benchmark for future research.The availability of the Palmyrene dataset and the insights into algorithm performance contribute to the broader understanding of historical text analysis.

A Method for Detecting and Recognizing Yi Character Based on Deep Learning

Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition,we present a deep learning-based approach for Yi character detection and recognition.In the detection stage,an improved Differentiable Binarization Network(DBNet)framework is introduced to detect Yi characters,in which the Omni-dimensional Dynamic Convolution(ODConv)is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features,thereby improving the accuracy of Yi character detection.Then,the feature pyramid network fusion module is used to further extract Yi character image features,improving target recognition at different scales.Further,the previously generated feature map is passed through a head network to produce two maps:a probability map and an adaptive threshold map of the same size as the original map.These maps are then subjected to a differentiable binarization process,resulting in an approximate binarization map.This map helps to identify the boundaries of the text boxes.Finally,the text detection box is generated after the post-processing stage.In the recognition stage,an improved lightweight MobileNetV3 framework is used to recognize the detect character regions,where the original Squeeze-and-Excitation(SE)block is replaced by the efficient Shuffle Attention(SA)that integrates spatial and channel attention,improving the accuracy of Yi characters recognition.Meanwhile,the use of depth separable convolution and reversible residual structure can reduce the number of parameters and computation of the model,so that the model can better understand the contextual information and improve the accuracy of text recognition.The experimental results illustrate that the proposed method achieves good results in detecting and recognizing Yi characters,with detection and recognition accuracy rates of 97.5%and 96.8%,respectively.And also,we have compared the detection and recognition algorithms proposed in this paper with other typical algorithms.In these comparisons,the proposed model achieves better detection and recognition results with a certain reliability.

Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters Recognition Model

Handwritten character recognition becomes one of the challenging research matters.More studies were presented for recognizing letters of various languages.The availability of Arabic handwritten characters databases was confined.Almost a quarter of a billion people worldwide write and speak Arabic.More historical books and files indicate a vital data set for many Arab nationswritten in Arabic.Recently,Arabic handwritten character recognition(AHCR)has grabbed the attention and has become a difficult topic for pattern recognition and computer vision(CV).Therefore,this study develops fireworks optimizationwith the deep learning-based AHCR(FWODL-AHCR)technique.Themajor intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic language.It initially pre-processes the handwritten images to improve their quality of them.Then,the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature vectors.Next,the deep echo state network(DESN)model is utilized to classify handwritten characters.Finally,the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition performance.Various simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR technique.The comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches,with 99.91%and 98.94%on Hijja and AHCD datasets,respectively.

How Does Social Media Usage Intensity Influence Adolescents’Social Anxiety:The Chain Mediating Role of Imaginary Audience and Appearance Self-Esteem

Background:To reduce adolescents’social anxiety,the study integrates external factors(social media usage)with internal factors(imaginary audience and appearance-based self-esteem)to internal mechanisms of adolescents’social anxiety in the Internet age based on objective self-awareness theory and self-esteem importance weighting model.Methods:Utilizing the Social Media Usage Intensity Scale,Social Anxiety Scale,imaginary Audience Scale,and Physical Self Questionnaire,we surveyed 400 junior high school students from three schools in Hubei province,China.Results:A significantly positive correlation is revealed between the intensity of social media usage and both social anxiety and imaginary audience(p<0.001).Conversely,social media usage intensity and appearance self-esteem are significantly negatively correlated(p<0.001).Additionally,the perception of an imaginary audience was negatively correlated with appearance self-esteem(p<0.001).Furthermore,we found that imaginary audience(indirect effect of 0.14,95%CI=[0.02,0.07])and appearance self-esteem(indirect effect of 0.14,95%CI=[0.02,0.07])can respectively act as independent mediators between social networking site use intensity and social anxiety,Additionally,the relationship between imaginary audience and appearance self-esteem can also be chain-mediated(indirect effect of 0.03,95%CI=[0.00,0.02])separately affect the relationship between the two.Conclusion:The imaginary audience serves as an independent mediator that links social media usage intensity to social anxiety among adolescents.Additionally,the observed chain mediation effect involving both the imaginary audience and appearance self-esteem provides novel insights for developing strategies aimed at addressing adolescent social anxiety.

Audio2AB:Audio-driven collaborative generation of virtual character animation

Background Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of success.However,few methods exist for generating full-body animations,and the portability of virtual character gestures and facial animations has not received sufficient attention.Methods Therefore,we propose a deep-learning-based audio-to-animation-and-blendshape(Audio2AB)network that generates gesture animations and ARK it's 52 facial expression parameter blendshape weights based on audio,audio-corresponding text,emotion labels,and semantic relevance labels to generate parametric data for full-body animations.This parameterization method can be used to drive full-body animations of virtual characters and improve their portability.In the experiment,we first downsampled the gesture and facial data to achieve the same temporal resolution for the input,output,and facial data.The Audio2AB network then encoded the audio,audio-corresponding text,emotion labels,and semantic relevance labels,and then fused the text,emotion labels,and semantic relevance labels into the audio to obtain better audio features.Finally,we established links between the body,gestures,and facial decoders and generated the corresponding animation sequences through our proposed GAN-GF loss function.Results By using audio,audio-corresponding text,and emotional and semantic relevance labels as input,the trained Audio2AB network could generate gesture animation data containing blendshape weights.Therefore,different 3D virtual character animations could be created through parameterization.Conclusions The experimental results showed that the proposed method could generate significant gestures and facial animations.

A Novel 6G Scalable Blockchain Clustering-Based Computer Vision Character Detection for Mobile Images

6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and transparency.With the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly important.Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare monitoring.Users can control their visual data and grant or revoke access as needed.Recognizing Chinese characters from images can provide convenience in various aspects of people’s lives.However,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character identification.In contrast,computer vision technologies have significantly improved image recognition accuracy.This paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G technology.The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain model.The data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character estimation.With the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G network.The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication.Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional methods.For complex Chinese characters,the average recognition accuracy improves to 84.4%with our system,compared to 72.8%with traditional methods.Additionally,deploying the WHCC model improves data security with the increased data encryption rate complexity of∼12&higher than the traditional techniques.

From Society to Solitude:An In-depth Analysis of Thoreau’s Characters in Walden

With the deterioration of the environment and the unbalanced relationship between man and nature,people are plagued by varieties of social problems and rising spiritual crises.This paper aims to analyze Thoreau’s characters in Walden and attempts to provide the implications for our contemporary life.Combined with Thoreau’s life experience and the background of Walden,the paper starts with the analysis of Thoreau’s characters from three aspects concerning his pursuit of inner serenity,search for true essence of life and abiding passion for life,which constitute his unique and independent characters and provide the enlightenment for contemporary life.We concluded that unique thoughts,attitudes,and spirit embedded in Thoreau’s characters are inextricably connected with our contemporary life.His personal experience and practice of solitary life at Walden Pond is a testimony to the necessity of closing to nature actively and confronting life passionately.

Frontier Progress of Landscape Character Assessment in China

The role of Landscape Character Assessment(LCA)at the level of territorial landscape governance spans both natural and social sciences.By analyzing the development history,research distribution,methods and applications of cutting-edge cases of LCA in China,the following conclusions are drawn:①the LCA research in China originated earlier than that in Europe,but has not yet been systematically applied to the implementation of urban and rural planning at all levels;②the fundamental theory of LCA in China has been well constructed,with three main research directions:technologyled,assessment-led,and assessment combined with other theories;③the development of LCA in rural areas is more mature than in urban areas,but the progress of research is uneven across regions;④the current research presents significant“bottom-up”academic characteristics,and there is an urgent need for government decision-making authorities and academia to jointly promote a“top-down”standardized governance mechanism to comprehensively promote the modernization of territorial landscape governance.

Grammar and usage板块教学设计

课堂是教学的主阵地。优秀的教学设计能为教师提供经验与启示,帮助教师提高教学质量。为此,2024年,本刊开设专栏《典型课例》。在该栏目中,我们以“教学设计+点评”的形式,呈现一线教师学习、理解新课标,深化素养导向的课堂教学改革和育人方式转变的实践与思考。本期典型课例是2023年江苏省高中英语优质课比赛一等奖课例——译林版普通高中英语选择性必修第二册Unit 4 Grammar and usage板块的教学,希望通过课例展示,引领教师关注三维动态语法观,提升英语教学质量。

Progress in the evaluation tool of Character Strengths and its application in nursing staff

Character Strengths is a group of positive personality traits reflected through cognition,behavior,and emotion,which play a positive role in improving happiness,alleviating negative emotions,and maintaining physical and mental health.This article reviews the concept,content,methods,evaluation tools,and application progress of Character Strengths in nurses,so as to provide a reference for clinical managers and improve the quality of life,mental health,and professional satisfaction of n urses.

Chip Surface Character Recognition Based on OpenCV

Chip surface character recognition is an important part of quality inspection in the field of microelectronics manufacturing.By recognizing the character information on the chip,automated production,quality control,and data collection and analysis can be achieved.This article studies a chip surface character recognition method based on the OpenCV vision library.Firstly,the obtained chip images are preprocessed.Secondly,the template matching method is used to locate the chip position.In addition,the surface characters on the chip are individually segmented,and each character image is extracted separately.Finally,a Support Vector Machine(SVM)is used to classify and recognize characters.The results show that this method can accurately recognize the surface characters of chips and meet the requirements of chip quality inspection.

An Empirical Study on the Improvement of College Students’Positive Mental Characters through the Sandplay Course

In recent years,psychological crisis events have occurred frequently in colleges.Therefore,cultivating students’rational and calm,positive and upward attitude is the current focus of mental health education in colleges.This study aims to explore the intervention effect of sandplay courses on students’positive mental characters.Through the voluntary registration and matching of the public elective course platform,35 students from a university were selected as subjects,including 17 students in the experimental group and 18 students in the control group.From March to July 2024,16 weeks of sandplay course intervention was carried out in the experimental group,while no intervention was done in the control group.The scores of the Positive Mental Characters Scale for Chinese College Students(PMCSCCS)and the Positive Mental Characters Self-Assessment before and after the intervention were statistically analyzed.Both the scale test(t=-3.62,P<0.01)and self-assessment(t=-3.86,P<0.01)of the experimental group had significant improvement in positive mental characters after the intervention,while the control group had no significant difference in positive mental characters scores before and after the intervention.The sandplay course can effectively improve the level of positive mental characters of college students.Schools and families should pay attention to and explore various channels to actively cultivate positive mental characters of college students,so as to lay a solid foundation for their future development and growth.

A Review of Research on Handwritten Chinese Character Recognition with Multi-Feature Fusion

This paper analyzes the progress of handwritten Chinese character recognition technology,from two perspectives:traditional recognition methods and deep learning-based recognition methods.Firstly,the complexity of Chinese character recognition is pointed out,including its numerous categories,complex structure,and the problem of similar characters,especially the variability of handwritten Chinese characters.Subsequently,recognition methods based on feature optimization,model optimization,and fusion techniques are highlighted.The fusion studies between feature optimization and model improvement are further explored,and these studies further enhance the recognition effect through complementary advantages.Finally,the article summarizes the current challenges of Chinese character recognition technology,including accuracy improvement,model complexity,and real-time problems,and looks forward to future research directions.

Analysis of Codon Usage Between Different Poplar Species

Codon usage is the selective and nonrandom use of synonymous codons to encode amino acids in genes for proteins. The analysis of codon usage may improve the understanding of cocion preferences between different species and allow to rebuild the codons of exogenous genes to increase the expression efficiency of exogenous genes, Here, codon DNA sequence （CDS） of four poplar species, including Populus tremuloides Michx., P. tomentosa Carr., P. deltoides Marsh., and P. trichocarpa Torr. ＆ Gray., is used to analyze the relative frequency of synonymous codon （RFSC）. High-frequency codons are selected by high-frequency （HF） codon analysis. The results indicate that the codon usage is common for all four poplar species and the codon preference is quite similar among the four poplar species. However, CCT encoding for Pro, and ACT coding for Thr are the preferred codons in P. tremuloides and P. tomentosa, whereas CCA coding for Pro, and ACA coding for Thr are preferred in P. deltoides and P. trichocarpa The codons such as TGC coding for Cys, TTC coding for Phe, and AAG coding for Lys, are preferred in the poplar species except P trichocarpa. GAG coding for Glu is preferred only in P deltoides, while the other three poplar species prefer to use GAA. The commonness of preferred codon allows exogenous gene designed by the preferred cocion of one of the different poplar species to be used in other poplar species.