Morphological and molecular evidence for natural hybridization between Sorbus pohuashanensis and S. discolor (Rosaceae)

In overlapping distribution areas of Sorbus pohuashanensis and S.discolor in North China(Mount Tuoliang,Mount Xiling and Mount Baihua),Sorbus indi-viduals were found with pink fruit,which have never been recorded for the flora of China.Fourteen morphological characters combined with four chloroplast DNA markers and internal transcribed spacer(ITS)were used to analyze the origin of the Sorbus individuals with pink fruits and their relationship to S.pohuashanensis and S.discolor.PCA,SDA and one-way(taxon)ANOVA of morphological characters provided convincing evidence of the hybrid ori-gin of Sorbus individuals with pink fruits based on a novel morphological character and many intermediate characters.Haplotype analysis based on four cpDNA markers showed that either S.pohuashanensis or S.discolor were maternal parents of Sorbus individuals with pink fruits.Incongru-ence of the position of Sorbus individuals with pink fruits between cpDNA and ITS in cluster trees supported by DNA sequence comparative analysis,implying former hybridiza-tion events between S.pohuashanensis and S.discolor.Mul-tiple hybridization events between S.pohuashanensis and S.discolor might have contributed to the generation of Sorbus individuals with pink fruits.This study has provided insights into hybridization between species of the same genus in sympatric areas,which is of great significance for the study of interspecific hybridization.

A new infrageneric classification of Gastrochilus(Orchidaceae:Epidendroideae)based on molecular and morphological data

Gastrochilus is an orchid genus containing 73 species of mainly epiphytic on trees or rocks in mountain forests of tropical and subtropical Asia.Previous phylogenetic analyses and morphological assessments have failed to produce a well-resolved phylogeny at the infrageneric level.In the present study,a new infrageneric classification of Gastrochilus is proposed based on thoroughly morphological and phylogenetic analyses based on 52 species.Our phylogenetic analysis divided the genus into six sections including three new sections,G.sect.Pseudodistichi,G.sect.Brachycaules and G.sect.Acinacifolii.We also reinstate G.suavis to the specific rank.Furthermore,two new species,G.armeniacus Jun Y.Zhang,B.Xu&Yue H.Cheng and G.minjiangensis Jun Y.Zhang,B.Xu&Yue H.Cheng,are described and illustrated.A key to six sections of the genus is presented.

Flower morphology of Allium(Amaryllidaceae)and its systematic significance

Allium is a complicated genus that includes approximately 1000 species.Although its morphology is well studied,the taxonomic importance of many morphological traits,including floral traits,are poorly understood.Here,we examined and measured the floral characteristics of 87 accessions of 74 Allium taxa(belonging to 30 sections and nine subgenera)from Central to Eastern Asian countries.We then examined the taxonomic relationships between select flower characteristics and a phylogenetic tree based on ITS sequences.Our results confirm that floral morphology provides key taxonomic information to assess species delimitation in Allium.We found that perianth color is an important characteristic within the subg.Melanocrommyum,Polyprason,and Reticulatobulbosa.In subg.Allium,Cepa,and Rhizirideum,significant characteristics include ovary shape,perianth shape,and inner tepal apex.For species in subg.Angunium,the key taxonomic character is ovule number(only one ovule in per locule).In the subg.Allium,Cepa,Polyprason,and Reticulatobulbosa,which belong to the third evolutionary line of Allium,hood-like appendages occur in the ovary,although these do not occur in subg.Rhizirideum.Our results also indicated that the flower morphology of several species in some sections are not clearly distinguished,e.g.,sect.Sacculiferum(subg.Cepa)and sect.Tenuissima(subg.Rhizirideum).This study provides detailed photographs and descriptions of floral characteristics and information on general distributions,habitats,and phenology of the studied taxa.

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.

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.

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.

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.

Combined Application of Biostimulants and EDTA Improved Wheat Productivity under Cadmium Stress

Wheat(Triticum aestivum L.)exhibits a greater capacity for cadmium(Cd)absorption compared to other cereal crops,leading to elevated daily Cd intake,and posing a significant threat to public health.For the mitigation of Cd stress in sustainable and environmentally friendly way,a pot study was designed by using exogenous application of various biostimulants,i.e.,Nigella sativa and Ocimum sanctum extracts:0%,10%,and 20%in combination with the chelating agent ethylenediaminetetraacetic acid(EDTA)using 0 and 5 mg kg^(-1) under various levels of Cd stress(i.e.,0,5,10,and 15 mg kg^(-1) soil).Results revealed that Cd stress significantly reduced the seed emergence,growth,root,and allometric characters and yield-related parameters of wheat crops.The most observable reduc-tion was documented in wheat plants exposed to a higher Cd concentration(15 mg kg^(-1)),followed by the lower Cd level(control).The combined application of bio-stimulants and EDTA minimized the negative impacts of Cd stress.The highest increase in seedling emergence(5.44%),leaf area(50.60%),number of tillers(31.02%),grain yield per plant(24.28%),biological yield(13.97%),and decrease in Cd levels in grains(40%)was noticed where 20%foliar application of N.sativa and 10%of O.sanctum biostimulants were done using 5 mg kg^(-1) of soil-applied EDTA.This intervention demonstrated a notable reduction in Cd-induced negative effects,highlighting the potential of these substances in promoting sustainable wheat cultivation in contaminated environments.Moreover,it is an eco-friendly and approachable method at thefield level able to ensure food safety.

Grain boundary engineering for enhancing intergranular damage resistance of ferritic/martensitic steel P92

Ferritic/martensitic(F/M)steel is widely used as a structural material in thermal and nuclear power plants.However,it is susceptible to intergranular damage,which is a critical issue,under service conditions.In this study,to improve the resistance to intergranular damage of F/M steel,a thermomechanical process(TMP)was employed to achieve a grain boundary engineering(GBE)microstructure in F/M steel P92.The TMP,including cold-rolling thickness reduction of 6%,9%,and 12%,followed by austenitization at 1323 K for 40 min and tempering at 1053 K for 45 min,was applied to the as-received(AR)P92 steel.The prior austenite grain(PAG)size,prior austenite grain boundary character distribution(GBCD),and connectivity of prior austenite grain boundaries(PAGBs)were investigated.Compared to the AR specimen,the PAG size did not change significantly.The fraction of coincident site lattice boundaries(CSLBs,3≤Σ≤29)and Σ3^(n) boundaries along PAGBs decreased with increasing reduction ratio because the recrystallization fraction increased with increasing reduction ratio.The PAGB connectivity of the 6%deformed specimen slightly deteriorated compared with that of the AR specimen.Moreover,potentiodynamic polarization studies revealed that the intergranular damage resistance of the studied steel could be improved by increasing the fraction of CSLBs along the PAGBs,indicating that the TMP,which involves low deformation,could enhance the intergranular damage resistance.

Music-stylized hierarchical dance synthesis with user control

Background Synthesizing dance motions to match musical inputs is a significant challenge in animation research.Compared to functional human motions,such as locomotion,dance motions are creative and artistic,often influenced by music,and can be independent body language expressions.Dance choreography requires motion content to follow a general dance genre,whereas dance performances under musical influence are infused with diverse impromptu motion styles.Considering the high expressiveness and variations in space and time,providing accessible and effective user control for tuning dance motion styles remains an open problem.Methods In this study,we present a hierarchical framework that decouples the dance synthesis task into independent modules.We use a high-level choreography module built as a Transformer-based sequence model to predict the long-term structure of a dance genre and a low-level realization module that implements dance stylization and synchronization to match the musical input or user preferences.This novel framework allows the individual modules to be trained separately.Because of the decoupling,dance composition can fully utilize existing high-quality dance datasets that do not have musical accompaniments,and the dance implementation can conveniently incorporate user controls and edit motions through a decoder network.Each module is replaceable at runtime,which adds flexibility to the synthesis of dance sequences.Results Synthesized results demonstrate that our framework generates high-quality diverse dance motions that are well adapted to varying musical conditions and user controls.

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.

A Harmonic Approach to Handwriting Style Synthesis Using Deep Learning

The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting.The majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting styles.This is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly used.To resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles.This network excels in generating conditional text by extracting style vectors from a series of style images.The model performs admirably on a range of handwriting synthesis tasks,including the production of text that is out-of-vocabulary.It works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting styles.Applicable fields include digital forensics,creative writing,and document security.

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.

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.

The Inheritance and Application of Chinese Reverse Contrast Typeface Style

This paper discusses the inheritance and application of Chinese character reverse contrast typeface style.It begins by analyzing the visual features of Western reverse contrast typeface styles,with a focus on Caslon Italian and French Clarendon,providing a Western perspective reference for subsequent Chinese character reverse contrast typeface style designs.The paper then traces the origins of the Chinese reverse contrast style,from the calligraphy style"Lacquer Script"to the earliest printing type"フワンテール形",exploring the historical background and cultural significance of the Chinese reverse contrast style.In the methodology section of Chinese character reverse contrast typeface style design,the discussion is conducted from two dimensions:inheritance and application.In terms of inheritance,through an in-depth analysis of"Lacquer Script"and"フワンテール形"typeface style,the paper summarizes three basic theories for modern Chinese character reverse contrast typeface style design.In the application section,it examines in detail the two most influential recent typeface styles,"Ribaasu"and"Basic Artistic",outlining three directions of application:extreme horizontal stroke variations,exaggerated contrast,and diverse decorative strokes,showcasing new directions and possibilities for Chinese character reverse contrast typeface style design.This paper not only reviews the developmental history of the Chinese character reverse contrast typeface style but also analyzes the design methodology of Chinese character reverse contrast typeface style through specific case studies.

Influence of the Powder on the Properties of the Liquid Crystal Emulsions

The purpose of study was to evaluate the effect of four powder including titanium dioxide,bismuth oxychloride,silica,and kaolin on the properties of the liquid crystal emulsions.The results show that the addition of titanium dioxide and bismuth oxychloride had no obvious effect on the liquid crystal structure.In addition,the addition of Kaolin and silica have an effect on the stability of the liquid crystal structure.Sensory evaluation and Texture analyzer results shown that the addition of titanium dioxide and bismuth oxychloride had no obvious effect on the spreadability of liquid crystal system.The addition of silica and Kaolin was increased the hardness and adhesive of the liquid crystal system.Rheological experiments shown that the kaolin system had lower structural stability.the system with titanium dioxide,bismuth oxychloride,and silica has good stability.This paper provides data support for the application of powders in the formulation of liquid crystal system,which aims to provide a data basis for the preparation and applications of liquid crystal emulsion.

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.

Neural Network-Powered License Plate Recognition System Design

The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.

A Development of a Knowledge Management System for Water Situation Reports in Thailand

The development of a knowledge management system for the National Hydro Data Center of Thailand was described in this paper. The system was created after the major flood event in 2011 to improve water resource management. It addresses the need for easy access to water situation reports, which are crucial for informed decision-making on water usage, allocation, and reservoir management. The system utilizes Optical Character Recognition technique to convert scanned water situation reports into searchable text. It applied FastText and ElasticSearch for advanced search functionalities. FastText identified the documents related to the search query, even with typos or misspelled words. ElasticSearch allows for efficient searching of text data based on relevance. The system also integrates Google Search for additional information access. Therefore, this knowledge management system provides an efficient way to access and analyze water situation data in Thailand.