Effects of Nitrogen Quantity on Grain-filling Characters of Two-line Hybrid Rice with Large Ear

[Objective] The aim was to research effects of N quantity on grain-filling characters of two-line hybrid rice cultivars with large ears. [Method] Peiza 67 and 88, two-line hybridized rice with large ears, were made use of to study on effects of N fertilizer in different quantities （LN： 90 kg/hm2;MN： 180 kg/hm2;HN： 270 kg/hm2） on plumpness and grain-filling characters. [Result] When N fertilizers were excessive, for inferior grains, grain-filling rate decreased and grain-filling time extended, resulting in plumpness decline after degradation of leaves＇ function. When N fertilizers were inadequate, maximal and average grain-filling rates decreased and the differences between superior and inferior grains in grain-filling rate increased, leading to decline of grain＇s weight and plumpness degree. On the other hand, quantity of N fertilizers had little effect on superior grains in plumpness. [Conclusion] The research provided references for reasonable use of N fertilizer and improvement of rice yield and N use.

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.

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.

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.

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.

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.

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.

Study on Quantitative Character for Anatomy in Tomato Seedling Stem

The results of quantitative characters for anatomy in stems of three varieties tomatoes seedlings showed that the cell population between vascular bundle and epidermis, the cellular layers among vascular bundles and the cell population in an unit area （mm^2） of no vascular bundle areas were similar and there had small difference among three varieties. On the foundation of these studies, the developmental mechanism of tomato seedling stem was discussed.

A Practical Method of Car License Plate Character Segmentation Based on Morphology and Labeling

In this paper, a kind of practical image segmentation algorithm for segment characters from car license plate is presented, based on morphology and labeling. First by morphological operation, noise in the binary image of license plate can be greatly decreased. Then, by labeling, each connected pixel component is given a unique label. Finally, by the known data of license plate, each character is extracted correctly. The advantage of this method is that it can deal with plates with different sizes and connected characters plates, and inclined plates. The experiment results show that it is an effective way to extract characters from the license plate, and can be put into practical use.

Swelling Index of Glutenin (SIG) Related to Protein Quality, DoughCharacters and Noodle Quality

The swelling index of glutenin (SIG) and the protein fraction of 25 Chinese wheat varieties were studied with new protein fractions extracting method. The protein fractions compose of monomeric protein, soluble glutenin and insoluble glutenin. The relations between other protein index, dough character, and fresh noodle quality were also discussed. The SIG results at different time is positively and highly significantly related to the insoluble glutenin content (r= 0.808 -0.867, P< 0.01). The SIG result can reflect the insoluble glutenin content. The protein content, gluten index, farinograph stability time, extensibility length and extensigram energy were positively and significantly correlated with SIG5 and SIG20 (r= 0.516 - 0.734, P<0.05, 0.01).SIG proved to be applicable in Chinese wheat dough evaluation. Fresh noodle making quality parameters were evaluated by fresh noodle length, thickness, maximum resistance to extension, extension area and extension distance, while cooked noodle texture was determined by cutting firmness, compression recovery, surface firmness and TPA by using a texture analyzer of TA.XT2i. The noodle cooking quality was significantly correlated with SIG value. The surface firmness and cutting firmness were more desirable for predicating the quality difference than TPA test and compression. Cooking loss and water absorption were negatively related to SIG value and insoluble content (r = -0.556 - - 0.787, P < 0.05, 0.01). The results showed that SIG test was also suitable in evaluating noodle making and cooking quality in very small sample size, which was very important in wheat breeding programs. Therefore, SIG test, as a small scale test, is suitable to evaluate dough rheological properties for Chinese wheat varieties, and will be helpful in cereal research and wheat breeding program, especially, in early generations.

Ecological Coupling Analysis and Genetic Effect Evaluation of Quantitative Characters in Lonicera edulis

Lonicera edulis is a perennial berry shrub that prefers the cold and wet climate, with high nutritional and medicinal value. In this study, ecological cou- pling capability of L. edulis with light, heat, water and soil resources in cold regions was investigated to analyze comprehensively ecological effects of quantitative characters and genetic effects of parents, aiming at providing a theoretical basis for the breeding, introduction and domestication of fine varieties of L. edulis in cold regions of China. The results showed that fruit characters in L. edulis exhibited certain variations among different habitat types. To be specific, fruit vertical diame- ter varied slightly, fruit horizontal diameter varied greatly and seed number varied maximally, with the average variance coefficients of 9.38%, 11.92% and 20. 64%, respectively; in addition, fruit characters in L. edtdis exhibited moderate herilability, moderate genetic gain and low level of genetic differentiation; the heritability of fruit vertical diameter, fruit horizontal diameter and seed number was 0.825, 0. 559 and 0. 627, respectively; the genetic gain of these three fruit characters was 7.53%, 5.72% and 11. 94%, respectively, resulting in significant economical benefits.

Character Development in a Multilingual International School and Its Use of Self-Assessment Tools

This article describes a multiyear initiative of a multilingual multicultural international school that has come to adopt and internalize character development as part of its identity.That is,character education has been treated as a central tenet and core value that permeates the school and binds the community.It has not been regarded as a supplemental or enhancement project,but rather integral to the general educational program.Built from a principled framework with sound theoretical backing,the infusion of character education at this international school has resulted in the crafting of new standards and the introduction of teacher and student self-assessment tools.In that vein,in this article,we share how the school has come to embrace character development and has forged personalized ways for stakeholders,including teachers and multilingual learners,to engage in improving teaching and learning.

Incorporating Linguistic Rules in Statistical Chinese Language Model for Pinyin-to-character Conversion

An N-gram Chinese language model incorporating linguistic rules is presented. By constructing elements lattice, rules information is incorporated in statistical frame. To facilitate the hybrid modeling, novel methods such as MI-based rule evaluating, weighted rule quantification and element-based n-gram probability approximation are presented. Dynamic Viterbi algorithm is adopted to search the best path in lattice. To strengthen the model, transformation-based error-driven rules learning is adopted. Applying proposed model to Chinese Pinyin-to-character conversion, high performance has been achieved in accuracy, flexibility and robustness simultaneously. Tests show correct rate achieves 94.81% instead of 90.53% using bi-gram Markov model alone. Many long-distance dependency and recursion in language can be processed effectively.

Genetic and Correlation Analysis of Grain Characters in Three-line Hybrid Rice

Abst[Objective] This study was to understand the genetic dynamics of three-line hybrid rice, and explore the respective effect of sterile line and restoring line on grain characters of hybrid rica. [Method] Four three-line stedle lines and 27 restoring lines（cultivars） commonly culti- vated in Central China region were regarded as expadmental materials to conduct 4 x27NCII cross design, and the grain characters of three-line hybrid rico were analyzed at genetic and correlation levels. [ Result] Four characters of grain length, grain width, 1 000-grain weight and length- width ratio play the leading role in additive gene effect; these four characters were simultaneously influenced by male parent and female parent, but the effect from male parent was relatively larger. The grain length, grain width, 1 000-grain weight and length-width ratio all have high brood hedtabUities（ respectively 99.65%, 98.31%, 95.27% and 98.81% ）. Correlation analysis showed that grain length was positively correlated with 1 000-grain weight and length-width ratio at extremely significant level; 1 000-grain weight was positively correlated with grain length and length- width ratio at extremely significant level, and was insignificantly correlated with grain width; grain width was negatively correlated with grain length and length-width ratio at extremely significant level. Path analysis showed that the direct path coefficients of grain length, grain width and 1 0（30- grain weight to length-width ratio were 0.624 6, -0.555 9 and -0.015 8, respectively. [ Conclusion] This study systematically analyzed the effects of stedle line and restoring line on grain characters of hybrid rice, which provided theoretical basis for breeding high quality and yield hy- brid dce.