Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewa...Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewable,and clinical demand is increasingly difficult to meet,leading to a proliferation of counterfeit products.During prolonged geological burial,static pressure from the surrounding strata severely compromises the microstructural integrity of osteons in Os Draconis,but Os Draconis still largely retains the structural features of mammalian bone.Methods:Using verified authentic Os Draconis samples over 10,000 years old as a baseline,this study summarizes the ultrastructural characteristics of genuine Os Draconis.Employing electron probe microanalysis and optical polarized light microscopy,we examined 28 batches of authentic Os Draconis and 31 batches of counterfeits to identify their ultrastructural differences.Key points for ultrastructural identification of Os Draconis were compiled,and a new identification approach was proposed based on these differences.Results:Authentic Os Draconis exhibited distinct ultrastructural markers:irregularly shaped osteons with traversing fissures,deformed/displaced Haversian canals,and secondary mineral infill(predominantly calcium carbonate).Counterfeits showed regular osteon arrangements,absent traversal fissures,and homogeneous hydroxyapatite composition.Lab-simulated samples lacked structural degradation features.EPMA confirmed calcium carbonate infill in fossilized Haversian canals,while elemental profiles differentiated lacunae types(void vs.mineral-packed).Conclusion:The study established ultrastructural criteria for authentic Os Draconis identification:osteon deformation,geological fissures penetrating bone units,and heterogenous mineral deposition.These features,unattainable in counterfeits or modern processed bones,provide a cost-effective,accurate identification method.This approach bridges gaps in TCM material standardization and supports quality control for clinical applications.展开更多
Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide ...Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts.For Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse.The researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of ALL.The systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this field.Different image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,Science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were presented.Convolutional Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the field.Finally,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.展开更多
In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering...In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.展开更多
Background:Gene expression profiling plays a key role in cancer research,but its high dimensionality and redundancy pose challenges for effective analysis.Feature selection and robust classification are critical for i...Background:Gene expression profiling plays a key role in cancer research,but its high dimensionality and redundancy pose challenges for effective analysis.Feature selection and robust classification are critical for improving predictive performance,while explainable machine learning techniques support transparency and biomarker discovery.Methods:To propose a hybrid explainable machine learning framework that combines stability-guided multi-source(SGMS)feature selection with classification models for gene expression-based cancer prediction and biomarker identification.SGMS integrates Mutual Information,F-statistic,and random forest(RF)importance to select informative genes.These features are used to train classifiers,including novel elasticnet logistic regression(NEN-LR),RF,and Support Vector Machine(SVM).Performance is evaluated using accuracy,precision,recall,F1-score,and Matthews correlation coefficient(MCC).SHapley Additive exPlanations(SHAP)values are used to interpret gene-level contributions,and co-expression networks help identify functional gene modules.Results:The proposed NEN-LR classifier achieved the highest performance with 99.8%accuracy,99.9%precision,and 0.997 MCC using the top 200 SGMS-selected genes.Biomarker discovery identified both class-specific and shared genes across five cancer types,with top genes like gene_230,gene_5380,and gene_18570 consistently appearing across multiple classes.Visualization tools,including heatmaps,Venn diagrams,and co-expression networks,were used to interpret expression dynamics and regulatory patterns,enhancing the biological relevance of findings.SHAP analysis revealed top biomarkers with strong predictive influence,while co-expression clustering uncovered biologically meaningful gene modules.Other models also showed marked improvement using SGMS-selected features.Conclusion:The proposed framework successfully integrates feature selection,interpretable classification,and biomarker discovery,providing a powerful tool for precision oncology and molecular diagnostics.展开更多
The risk factors for type 2 diabetes mellitus(T2DM)have been increasingly researched,but the lack of systematic identification and categorization makes it difficult for clinicians to quickly and accurately access and ...The risk factors for type 2 diabetes mellitus(T2DM)have been increasingly researched,but the lack of systematic identification and categorization makes it difficult for clinicians to quickly and accurately access and understand all the risk factors,which are categorized in this paper into five categories:Social determinants,lifestyle,checkable/testable risk factors,history of illness and medication,and other factors,which are discussed in a narrative review.Meanwhile,this paper points out the problems of the current research,helps to improve the systematic categorisation and practicality of T2DM risk factors,and provides a professional research basis for clinical practice and industry decision-making.展开更多
Objective:The World Health Organization(WHO)grading based on histopathology cannot always accurately predict tumor behavior of meningiomas.To overcome the limitations of the WHO grading,the study aims to propose a nov...Objective:The World Health Organization(WHO)grading based on histopathology cannot always accurately predict tumor behavior of meningiomas.To overcome the limitations of the WHO grading,the study aims to propose a novel oxidative stress-based molecular classification for WHO grade 2/3 meningiomas.Methods:Differentially expressed oxidative stress-related genes were analyzed between 86 WHO grade 1(low grade)meningiomas and 99 grade 2/3(high grade)meningiomas.An oxidative stress-based molecular classification was developed in high-grade meningiomas through consensus clustering analysis.Immune microenvironment features,responses to immunotherapy and chemotherapy,and targeted drugs were evaluated.Three machine learning models:logistic regression,support vector machine,and random forest,were built for differentiating the classification.Key oxidative stress-related geneswere verified in humanmeningeal cells(HMC)and two meningioma cells(CH-157MN and IOMMLee)via reverse transcription quantitative polymerase chain reaction(RT-qPCR)and western blot.After knockdown of Forkhead Box M1(FOXM1)or Prion Protein(PRNP),cell growth,migration,and reactive oxygen species(ROS)levels were measured through cell counting kit-8(CCK-8),transwell,and immunofluorescence,respectively.Results:We classified high-grade meningiomas into two oxidative stress-based clusters,termed cluster 1 and cluster 2.Cluster 1 exhibited higher infiltrations of immune and stromal cells and higher expression of classic immune checkpoints:Cluster of Differentiation 86(CD86),Programmed Cell Death 1(PDCD1),and Leukocyte-Associated Immunoglobulin-Like Receptor 1(LAIR1),indicating that cluster 1 meningiomas might respond to immunotherapy.Drug sensitivity was heterogeneous between the two clusters.Three classifiers were established,which could accurately differentiate this molecular classification.FOXM1 and PRNP were experimentally evidenced to be highly expressed inmeningioma cells,and their knockdown hindered cell growth and migration and triggered ROS accumulation.Conclusion:In summary,our findings established a novel oxidative stress-based molecular classification and identified potential treatment vulnerabilities in high-grade meningiomas,which might assist personalized clinical management.展开更多
Resource-dependent cities are cities whose economic development depends on the exploitation and processing of natural resources.Their transformation and sustainable development are an important area of research on reg...Resource-dependent cities are cities whose economic development depends on the exploitation and processing of natural resources.Their transformation and sustainable development are an important area of research on regional industrial development,regional economy and urban development.Since the Chinese government launched a pilot project to transform resource-dependent cities,starting with Fuxin in Liaoning Province in 2001,accurately identifying and classifying China’s resource-dependent cities has become a focus of geographical research.Based on previous studies,this paper uses the theory and methods of urban function classification to analyze indicators and threshold values for identifying and classifying resource-dependent cities.It has thus identified 262 cities as being resource-dependent.Looking at the development levels,problems,characteristics and developmental direction of such cities,this paper attempts to establish a comprehensive analytical framework using the two evaluation indicators of resource security and sustainable development.It also creates a model to classify the 262 cities identified as resource-dependent cities into four types:growing cities,mature cities,declining cities and regenerating cities.The different connotations and characteristics of the city types were then analyzed.The results of this research support the delineation of scopes and categories of resource-dependent cities set out in the National Sustainable Development Plan for Resource-Dependent Cities published by the State Council,and they serve as a foundation for formulating policies on planning,classification and guidance.展开更多
The clinical course of infections with the hepatitis B virus (HBV) substantially varies between individuals, as a consequence of a complex interplay between viral, host, environmental and other factors. Due to the hig...The clinical course of infections with the hepatitis B virus (HBV) substantially varies between individuals, as a consequence of a complex interplay between viral, host, environmental and other factors. Due to the high genetic variability of HBV, the virus can be categorized into different HBV genotypes and subgenotypes, which considerably differ with respect to geographical distribution, transmission routes, disease progression, responses to antiviral therapy or vaccination, and clinical outcome measures such as cirrhosis or hepatocellular carcinoma. However, HBV (sub)genotyping has caused some controversies in the past due to misclassifications and incorrect interpretations of different genotyping methods. Thus, an accurate, holistic and dynamic classification system is essential. In this review article, we aimed at highlighting potential pitfalls in genetic and phylogenetic analyses of HBV and suggest novel terms for HBV classification. Analyzing full-length genome sequences when classifying genotypes and subgenotypes is the foremost prerequisite of this classification system. Careful attention must be paid to all aspects of phylogenetic analysis, such as bootstrapping values and meeting the necessary thresholds for (sub)genotyping. Quasi-subgenotype refers to subgenotypes that were incorrectly suggested to be novel. As many of these strains were misclassified due to genetic differences resulting from recombination, we propose the term “recombino-subgenotype”. Moreover, immigration is an important confounding facet of global HBV distribution and substantially changes the geographic pattern of HBV (sub)genotypes. We therefore suggest the term “immigro-subgenotype” to distinguish exotic (sub)genotypes from native ones. We are strongly convinced that applying these two proposed terms in HBV classification will help harmonize this rapidly progressing field and allow for improved prophylaxis, diagnosis and treatment.展开更多
Eclogite, one of the important lithologies in the main hole of the Chinese Continental Scientific Drilling （CCSD） Project, exists above the depth of 3 245 m and has distinctive responses of gamma-ray, compensating d...Eclogite, one of the important lithologies in the main hole of the Chinese Continental Scientific Drilling （CCSD） Project, exists above the depth of 3 245 m and has distinctive responses of gamma-ray, compensating density and neutron well-logging, and so on. In this study, according to the diversities of minerals and chemical components and well-logging responses, edogites are classified from three aspects of origin, content of oxygen, and sub-mineral. We studied the logging identification method for eclogite sub-classes based on multi-element statistics and reconstructed 11 kinds of eclogite. As a result, eclogites can be divided into 6 types using well logs. In the light of this recognition, the eclogite in the main hole is divided into 20 sections, and the distribution characters of all sub-classes of eclogite are analyzed, which will provide important data for geological research of CCSD.展开更多
Hyperspectral images in remote sensing include hundreds of spectral bands that provide valuable information for accurately identify objects.In this paper,a new method of classifying hyperspectral images using spectral...Hyperspectral images in remote sensing include hundreds of spectral bands that provide valuable information for accurately identify objects.In this paper,a new method of classifying hyperspectral images using spectral spatial information has been presented.Here,using the hyperspectral signal subspace identification(HYSIME)method which estimates the signal and noise correlation matrix and selects a subset of eigenvalues for the best representation of the signal subspace in order to minimize the mean square error,subsets from the main sample space have been extracted.After subspace extraction with the help of the HYSIME method,the edge-preserving filtering(EPF),and classification of the hyperspectral subspace using a support vector machine(SVM),results were then merged into the decision-making level using majority rule to create the spectral-spatial classifier.The simulation results showed that the spectral-spatial classifier presented leads to significant improvement in the accuracy and validity of the classification of Indiana,Pavia and Salinas hyperspectral images,such that it can classify these images with 98.79%,98.88% and 97.31% accuracy,respectively.展开更多
The efficacy of traditional Chinese medicine （TCM） treatments for Western medicine （WM） diseases relies heavily on the proper classification of patients into TCM syndrome types. The authors developed a data-driven...The efficacy of traditional Chinese medicine （TCM） treatments for Western medicine （WM） diseases relies heavily on the proper classification of patients into TCM syndrome types. The authors developed a data-driven method for solving the classification problem, where syndrome types were identified and quantified based on statistical patterns detected in unlabeled symptom survey data. The new method is a generalization of latent class analysis （LCA）, which has been widely applied in WM research to solve a similar problem, i.e., to identify subtypes of a patient population in the absence of a gold standard. A well-known weakness of LCA is that it makes an unrealistically strong independence assumption. The authors relaxed the assumption by first detecting symptom co-occurrence patterns from survey data and used those statistical patterns instead of the symptoms as features for LCA. This new method consists of six steps： data collection, symptom co-occurrence pattern discovery, statistical pattern interpretation, syndrome identification, syndrome type identification and syndrome type classification. A software package called Lantern has been developed to support the application of the method. The method was illustrated using a data set on vascular mild cognitive impairment.展开更多
We developed a forest type classification technology for the Daxing＇an Mountains of northeast China using multisource remote sensing data.A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR wer...We developed a forest type classification technology for the Daxing＇an Mountains of northeast China using multisource remote sensing data.A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR were used to identify forest types in the Pangu Forest Farm of the Daxing＇an Mountains.Forest types were identified using random forest（RF） classification with the following data combination types： SPOT-5 alone,SPOT-5 and SAR images in August or November,and SPOT-5 and two temporal SAR images.We identified many forest types using a combination of multitemporal SAR and SPOT-5 images,including Betula platyphylla,Larix gmelinii,Pinus sylvestris and Picea koraiensis forests.The accuracy of classification exceeded 88% and improved by 12% when compared to the classification results obtained using SPOT data alone.RF classification using a combination of multisource remote sensing data improved classification accuracy compared to that achieved using single-source remote sensing data.展开更多
Despite extensive studies on cultivated rice, the population structure and genetic diversity of the indica photoperiod-and thermo-sensitive genic male sterility(P/TGMS) lines in China remains unclear. Using 48 simple ...Despite extensive studies on cultivated rice, the population structure and genetic diversity of the indica photoperiod-and thermo-sensitive genic male sterility(P/TGMS) lines in China remains unclear. Using 48 simple sequence repeat(SSR) markers, we genotyped a panel of 208 indica P/TGMS lines and confirmed three subgroups, named indica-I, indica-II and indica-III, in indica P/TGMS lines. Further diversity analysis indicated indica-II had the highest genetic diversity. The genetic differentiation between indica-II and indica-III was demonstrated as the largest among the three subgroups. Moreover, indica/japonica component identification was detected that five P/TGMS lines possess indica components less than 0.900. These results improve our knowledge on the genetic background for P/TGMS lines in China and will be beneficial for hybrid rice breeding programs.展开更多
Light-harvesting chlorophyll a/b-binding （LHC） proteins are a group of nuclear-encoded thylakoid proteins that play a key role in plant photosynthesis and are widely involved in light harvesting, energy transfer to ...Light-harvesting chlorophyll a/b-binding （LHC） proteins are a group of nuclear-encoded thylakoid proteins that play a key role in plant photosynthesis and are widely involved in light harvesting, energy transfer to the reaction center, maintenance of thylakoid membrane structure, photoprotection and response to en- vironmental conditions, etc. Although/dw supergene family is well characterized in model plants such as Arabidopsis, rice and poplar, little information is available in castor bean （Ricinus communis L. ）. In this study, a genome-wide search was carried out for the first time to identify castor bean L/w genes and analyze the gene structures, biochemical properties, evolutionary relationships and expression characteristics based on the published data of castor bean genome and ESTs. According to the results, a total of 28 Rclhcs genes representing 13 gene families （ l_hca , l_hcb , Elip , Ohpl , Ohp2 , SEP1, SEP2 , SEP3 , SEP4 , SEP5 , PsbS , Rieske and FCII） and 25 subgene families were identified in castor bean genome; to be specific, 25 and 5 genes were found to have corresponding ESTs in NCBI and have al- ternative splicing isoforlns, respectively. These RcLhcs contain 0 to 9 introns and distribute on 26 of the 25 878 released scaffolds. All RcLhcs genes were found to be expressed in all examined tissues, i.e. leaf, flower, II/III stage endosperm, V/VI stage endosperm and seed, with the highest expression level in leaf tissue.展开更多
Hyperspectral remote sensing has become one of the research frontiers in ground object identification and classification. On the basis of reviewing the application of hyperspectral remote sensing in identification and...Hyperspectral remote sensing has become one of the research frontiers in ground object identification and classification. On the basis of reviewing the application of hyperspectral remote sensing in identification and classification of ground objects at home and abroad. The research results of identification and classification of forest tree species, grassland and urban land features were summarized. Then the researches of classification methods were summarized. Finally the prospects of hyperspectral remote sensing in ground object identification and classification were prospected.展开更多
Purpose:Interdisciplinary research has become a critical approach to addressing complex societal,economic,technological,and environmental challenges,driving innovation and integrating scientific knowledge.While interd...Purpose:Interdisciplinary research has become a critical approach to addressing complex societal,economic,technological,and environmental challenges,driving innovation and integrating scientific knowledge.While interdisciplinarity indicators are widely used to evaluate research performance,the impact of classification granularity on these assessments remains underexplored.Design/methodology/approach:This study investigates how different levels of classification granularity-macro,meso,and micro-affect the evaluation of interdisciplinarity in research institutes.Using a dataset of 262 institutes from four major German non-university organizations(FHG,HGF,MPG,WGL)from 2018 to 2022,we examine inconsistencies in interdisciplinarity across levels,analyze ranking changes,and explore the influence of institutional fields and research focus(applied vs.basic).Findings:Our findings reveal significant inconsistencies in interdisciplinarity across classification levels,with rankings varying substantially.Notably,the Fraunhofer Society(FHG),which performs well at the macro level,experiences significant ranking declines at meso and micro levels.Normalizing interdisciplinarity by research field confirmed that these declines persist.The research focus of institutes,whether applied,basic,or mixed,does not significantly explain the observed ranking dynamics.Research limitations:This study has only considered the publication-based dimension of institutional interdisciplinarity and has not explored other aspects.Practical implications:The findings provide insights for policymakers,research managers,and scholars to better interpret interdisciplinarity metrics and support interdisciplinary research effectively.Originality/value:This study underscores the critical role of classification granularity in interdisciplinarity assessment and emphasizes the need for standardized approaches to ensure robust and fair evaluations.展开更多
Tegillarca granosa(T.granosa)is susceptible to heavy metals,which may pose a threat to consumer health.Thus,healthy and polluted T.granosa should be distinguished quickly.This study aimed to rapidly identify heavy met...Tegillarca granosa(T.granosa)is susceptible to heavy metals,which may pose a threat to consumer health.Thus,healthy and polluted T.granosa should be distinguished quickly.This study aimed to rapidly identify heavy metal pollution by using laser-induced breakdown spectroscopy(LIBS)coupled with linear regression classification(LRC).Five types of T.granosa were studied,namely,Cd-,Zn-,Pb-contaminated,mixed contaminated,and control samples.Threshold method was applied to extract the significant variables from LIBS spectra.Then,LRC was used to classify the different types of T.granosa.Other classification models and feature selection methods were used for comparison.LRC was the best model,achieving an accuracy of 90.67%.Results indicated that LIBS combined with LRC is effective and feasible for T.granosa heavy metal detection.展开更多
As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and...As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and it has been widely applied across various fields.In the field of astronomy,AI techniques have demonstrated unique advantages,particularly in the identification of pulsars and their candidates.AI is able to address the challenges of pulsar celestial body identification and classification because of its accuracy and efficiency.This paper systematically surveys commonly used AI models for pulsar candidate identification,analyzing and discussing the typical applications of machine learning,artificial neural networks,convolutional neural networks,and generative adversarial networks in candidate identification.Furthermore,it explores how th.e introduction of AI techniques not only enhances the efficiency and accuracy of pulsar identification but also provides new perspectives and tools for pulsar survey data processing,thus playing a significant role in advancing pulsar research and the field of astronomy.展开更多
Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome...Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.展开更多
The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textile...The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.展开更多
基金supported by the Scientific and Technological Innovation Project of the China Academy of Chinese Medical Sciences(CI2021A04013)the National Natural Science Foundation of China(82204610)+1 种基金the Qihang Talent Program(L2022046)the Fundamental Research Funds for the Central Public Welfare Research Institutes(ZZ15-YQ-041 and L2021029).
文摘Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewable,and clinical demand is increasingly difficult to meet,leading to a proliferation of counterfeit products.During prolonged geological burial,static pressure from the surrounding strata severely compromises the microstructural integrity of osteons in Os Draconis,but Os Draconis still largely retains the structural features of mammalian bone.Methods:Using verified authentic Os Draconis samples over 10,000 years old as a baseline,this study summarizes the ultrastructural characteristics of genuine Os Draconis.Employing electron probe microanalysis and optical polarized light microscopy,we examined 28 batches of authentic Os Draconis and 31 batches of counterfeits to identify their ultrastructural differences.Key points for ultrastructural identification of Os Draconis were compiled,and a new identification approach was proposed based on these differences.Results:Authentic Os Draconis exhibited distinct ultrastructural markers:irregularly shaped osteons with traversing fissures,deformed/displaced Haversian canals,and secondary mineral infill(predominantly calcium carbonate).Counterfeits showed regular osteon arrangements,absent traversal fissures,and homogeneous hydroxyapatite composition.Lab-simulated samples lacked structural degradation features.EPMA confirmed calcium carbonate infill in fossilized Haversian canals,while elemental profiles differentiated lacunae types(void vs.mineral-packed).Conclusion:The study established ultrastructural criteria for authentic Os Draconis identification:osteon deformation,geological fissures penetrating bone units,and heterogenous mineral deposition.These features,unattainable in counterfeits or modern processed bones,provide a cost-effective,accurate identification method.This approach bridges gaps in TCM material standardization and supports quality control for clinical applications.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00460621,Developing BCI-Based Digital Health Technologies for Mental Illness and Pain Management).
文摘Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts.For Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse.The researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of ALL.The systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this field.Different image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,Science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were presented.Convolutional Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the field.Finally,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.
基金supported by the Science and Technology Development Plan Project of Jilin Provincial Department of Science and Technology (No.20220203112S)the Jilin Provincial Department of Education Science and Technology Research Project (No.JJKH20210039KJ)。
文摘In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.
文摘Background:Gene expression profiling plays a key role in cancer research,but its high dimensionality and redundancy pose challenges for effective analysis.Feature selection and robust classification are critical for improving predictive performance,while explainable machine learning techniques support transparency and biomarker discovery.Methods:To propose a hybrid explainable machine learning framework that combines stability-guided multi-source(SGMS)feature selection with classification models for gene expression-based cancer prediction and biomarker identification.SGMS integrates Mutual Information,F-statistic,and random forest(RF)importance to select informative genes.These features are used to train classifiers,including novel elasticnet logistic regression(NEN-LR),RF,and Support Vector Machine(SVM).Performance is evaluated using accuracy,precision,recall,F1-score,and Matthews correlation coefficient(MCC).SHapley Additive exPlanations(SHAP)values are used to interpret gene-level contributions,and co-expression networks help identify functional gene modules.Results:The proposed NEN-LR classifier achieved the highest performance with 99.8%accuracy,99.9%precision,and 0.997 MCC using the top 200 SGMS-selected genes.Biomarker discovery identified both class-specific and shared genes across five cancer types,with top genes like gene_230,gene_5380,and gene_18570 consistently appearing across multiple classes.Visualization tools,including heatmaps,Venn diagrams,and co-expression networks,were used to interpret expression dynamics and regulatory patterns,enhancing the biological relevance of findings.SHAP analysis revealed top biomarkers with strong predictive influence,while co-expression clustering uncovered biologically meaningful gene modules.Other models also showed marked improvement using SGMS-selected features.Conclusion:The proposed framework successfully integrates feature selection,interpretable classification,and biomarker discovery,providing a powerful tool for precision oncology and molecular diagnostics.
基金National Natural Science Foundation of China,No.T2341018Science and Technology Innovation Project of Chinese Academy of Traditional Chinese Medicine,No.CI2023C049YLL.
文摘The risk factors for type 2 diabetes mellitus(T2DM)have been increasingly researched,but the lack of systematic identification and categorization makes it difficult for clinicians to quickly and accurately access and understand all the risk factors,which are categorized in this paper into five categories:Social determinants,lifestyle,checkable/testable risk factors,history of illness and medication,and other factors,which are discussed in a narrative review.Meanwhile,this paper points out the problems of the current research,helps to improve the systematic categorisation and practicality of T2DM risk factors,and provides a professional research basis for clinical practice and industry decision-making.
基金supported by Hubei Provincial Natural Science Foundation of China(grants 2023AFB208)the Chinese Primary Health Care Foundation(Grant No.cphcf-2022-222)+2 种基金2025 Hubei Provincial Natural Science Foundation Innovation and Development Joint Fund Project:(JCZRLH202500457)Shanghai Foundation for Anti-Cancer&Cancer Prevention Development Phase II Exploration Oncology Research Fund Project:“Study on the Mechanism of ANO9-Mediated Cetuximab Resistance in Head and Neck Squamous Cell Carcinoma”(No Grant Number)Qingdao Sheci Public Welfare Relief Center Pan-Cancer Treatment Research Fund Project:(QD-HN30008).
文摘Objective:The World Health Organization(WHO)grading based on histopathology cannot always accurately predict tumor behavior of meningiomas.To overcome the limitations of the WHO grading,the study aims to propose a novel oxidative stress-based molecular classification for WHO grade 2/3 meningiomas.Methods:Differentially expressed oxidative stress-related genes were analyzed between 86 WHO grade 1(low grade)meningiomas and 99 grade 2/3(high grade)meningiomas.An oxidative stress-based molecular classification was developed in high-grade meningiomas through consensus clustering analysis.Immune microenvironment features,responses to immunotherapy and chemotherapy,and targeted drugs were evaluated.Three machine learning models:logistic regression,support vector machine,and random forest,were built for differentiating the classification.Key oxidative stress-related geneswere verified in humanmeningeal cells(HMC)and two meningioma cells(CH-157MN and IOMMLee)via reverse transcription quantitative polymerase chain reaction(RT-qPCR)and western blot.After knockdown of Forkhead Box M1(FOXM1)or Prion Protein(PRNP),cell growth,migration,and reactive oxygen species(ROS)levels were measured through cell counting kit-8(CCK-8),transwell,and immunofluorescence,respectively.Results:We classified high-grade meningiomas into two oxidative stress-based clusters,termed cluster 1 and cluster 2.Cluster 1 exhibited higher infiltrations of immune and stromal cells and higher expression of classic immune checkpoints:Cluster of Differentiation 86(CD86),Programmed Cell Death 1(PDCD1),and Leukocyte-Associated Immunoglobulin-Like Receptor 1(LAIR1),indicating that cluster 1 meningiomas might respond to immunotherapy.Drug sensitivity was heterogeneous between the two clusters.Three classifiers were established,which could accurately differentiate this molecular classification.FOXM1 and PRNP were experimentally evidenced to be highly expressed inmeningioma cells,and their knockdown hindered cell growth and migration and triggered ROS accumulation.Conclusion:In summary,our findings established a novel oxidative stress-based molecular classification and identified potential treatment vulnerabilities in high-grade meningiomas,which might assist personalized clinical management.
基金National Natural Science Foundation of China,No.41671166,No.41701128
文摘Resource-dependent cities are cities whose economic development depends on the exploitation and processing of natural resources.Their transformation and sustainable development are an important area of research on regional industrial development,regional economy and urban development.Since the Chinese government launched a pilot project to transform resource-dependent cities,starting with Fuxin in Liaoning Province in 2001,accurately identifying and classifying China’s resource-dependent cities has become a focus of geographical research.Based on previous studies,this paper uses the theory and methods of urban function classification to analyze indicators and threshold values for identifying and classifying resource-dependent cities.It has thus identified 262 cities as being resource-dependent.Looking at the development levels,problems,characteristics and developmental direction of such cities,this paper attempts to establish a comprehensive analytical framework using the two evaluation indicators of resource security and sustainable development.It also creates a model to classify the 262 cities identified as resource-dependent cities into four types:growing cities,mature cities,declining cities and regenerating cities.The different connotations and characteristics of the city types were then analyzed.The results of this research support the delineation of scopes and categories of resource-dependent cities set out in the National Sustainable Development Plan for Resource-Dependent Cities published by the State Council,and they serve as a foundation for formulating policies on planning,classification and guidance.
基金Supported by Mahmoud Reza Pourkarim is supported by a postdoctoral grant from the''Fonds voor Wetenschappelijk Onderzoek Vlaanderen''
文摘The clinical course of infections with the hepatitis B virus (HBV) substantially varies between individuals, as a consequence of a complex interplay between viral, host, environmental and other factors. Due to the high genetic variability of HBV, the virus can be categorized into different HBV genotypes and subgenotypes, which considerably differ with respect to geographical distribution, transmission routes, disease progression, responses to antiviral therapy or vaccination, and clinical outcome measures such as cirrhosis or hepatocellular carcinoma. However, HBV (sub)genotyping has caused some controversies in the past due to misclassifications and incorrect interpretations of different genotyping methods. Thus, an accurate, holistic and dynamic classification system is essential. In this review article, we aimed at highlighting potential pitfalls in genetic and phylogenetic analyses of HBV and suggest novel terms for HBV classification. Analyzing full-length genome sequences when classifying genotypes and subgenotypes is the foremost prerequisite of this classification system. Careful attention must be paid to all aspects of phylogenetic analysis, such as bootstrapping values and meeting the necessary thresholds for (sub)genotyping. Quasi-subgenotype refers to subgenotypes that were incorrectly suggested to be novel. As many of these strains were misclassified due to genetic differences resulting from recombination, we propose the term “recombino-subgenotype”. Moreover, immigration is an important confounding facet of global HBV distribution and substantially changes the geographic pattern of HBV (sub)genotypes. We therefore suggest the term “immigro-subgenotype” to distinguish exotic (sub)genotypes from native ones. We are strongly convinced that applying these two proposed terms in HBV classification will help harmonize this rapidly progressing field and allow for improved prophylaxis, diagnosis and treatment.
基金This paper is supported by the Engineering Center of Chinese Continental Scientific Drilling (No. CCSD2004-04-01)the Focused Subject Program of Beijing (No. XK104910598).
文摘Eclogite, one of the important lithologies in the main hole of the Chinese Continental Scientific Drilling （CCSD） Project, exists above the depth of 3 245 m and has distinctive responses of gamma-ray, compensating density and neutron well-logging, and so on. In this study, according to the diversities of minerals and chemical components and well-logging responses, edogites are classified from three aspects of origin, content of oxygen, and sub-mineral. We studied the logging identification method for eclogite sub-classes based on multi-element statistics and reconstructed 11 kinds of eclogite. As a result, eclogites can be divided into 6 types using well logs. In the light of this recognition, the eclogite in the main hole is divided into 20 sections, and the distribution characters of all sub-classes of eclogite are analyzed, which will provide important data for geological research of CCSD.
文摘Hyperspectral images in remote sensing include hundreds of spectral bands that provide valuable information for accurately identify objects.In this paper,a new method of classifying hyperspectral images using spectral spatial information has been presented.Here,using the hyperspectral signal subspace identification(HYSIME)method which estimates the signal and noise correlation matrix and selects a subset of eigenvalues for the best representation of the signal subspace in order to minimize the mean square error,subsets from the main sample space have been extracted.After subspace extraction with the help of the HYSIME method,the edge-preserving filtering(EPF),and classification of the hyperspectral subspace using a support vector machine(SVM),results were then merged into the decision-making level using majority rule to create the spectral-spatial classifier.The simulation results showed that the spectral-spatial classifier presented leads to significant improvement in the accuracy and validity of the classification of Indiana,Pavia and Salinas hyperspectral images,such that it can classify these images with 98.79%,98.88% and 97.31% accuracy,respectively.
基金supported by Hong Kong Research Grants Council under grants No.16202515 and16212516Guangzhou HKUST Fok Ying Tung Research Institute,China Ministry of Science and Technology TCM Special Research Projects Program under grants No.200807011,No.201007002 and No.201407001-8+2 种基金Beijing Science and Technology Program under grant No.Z111107056811040Beijing New Medical Discipline Development Program under grant No.XK100270569Beijing University of Chinese Medicine under grant No.2011-CXTD-23
文摘The efficacy of traditional Chinese medicine （TCM） treatments for Western medicine （WM） diseases relies heavily on the proper classification of patients into TCM syndrome types. The authors developed a data-driven method for solving the classification problem, where syndrome types were identified and quantified based on statistical patterns detected in unlabeled symptom survey data. The new method is a generalization of latent class analysis （LCA）, which has been widely applied in WM research to solve a similar problem, i.e., to identify subtypes of a patient population in the absence of a gold standard. A well-known weakness of LCA is that it makes an unrealistically strong independence assumption. The authors relaxed the assumption by first detecting symptom co-occurrence patterns from survey data and used those statistical patterns instead of the symptoms as features for LCA. This new method consists of six steps： data collection, symptom co-occurrence pattern discovery, statistical pattern interpretation, syndrome identification, syndrome type identification and syndrome type classification. A software package called Lantern has been developed to support the application of the method. The method was illustrated using a data set on vascular mild cognitive impairment.
基金supported by the National Natural Science Foundation of China(Nos.31500518,31500519,and 31470640)
文摘We developed a forest type classification technology for the Daxing＇an Mountains of northeast China using multisource remote sensing data.A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR were used to identify forest types in the Pangu Forest Farm of the Daxing＇an Mountains.Forest types were identified using random forest（RF） classification with the following data combination types： SPOT-5 alone,SPOT-5 and SAR images in August or November,and SPOT-5 and two temporal SAR images.We identified many forest types using a combination of multitemporal SAR and SPOT-5 images,including Betula platyphylla,Larix gmelinii,Pinus sylvestris and Picea koraiensis forests.The accuracy of classification exceeded 88% and improved by 12% when compared to the classification results obtained using SPOT data alone.RF classification using a combination of multisource remote sensing data improved classification accuracy compared to that achieved using single-source remote sensing data.
基金supported by the Chinese Academy of Agricultural Sciences (Grant No. CAAS-ASTIP-201X-CNRRI)the Major Scientific and Technological Project for New Varieties Breeding of Zhejiang Province, China (Grant No. 2016C02050-6-1)
文摘Despite extensive studies on cultivated rice, the population structure and genetic diversity of the indica photoperiod-and thermo-sensitive genic male sterility(P/TGMS) lines in China remains unclear. Using 48 simple sequence repeat(SSR) markers, we genotyped a panel of 208 indica P/TGMS lines and confirmed three subgroups, named indica-I, indica-II and indica-III, in indica P/TGMS lines. Further diversity analysis indicated indica-II had the highest genetic diversity. The genetic differentiation between indica-II and indica-III was demonstrated as the largest among the three subgroups. Moreover, indica/japonica component identification was detected that five P/TGMS lines possess indica components less than 0.900. These results improve our knowledge on the genetic background for P/TGMS lines in China and will be beneficial for hybrid rice breeding programs.
基金Supported by National Natural Science Foundation of China(31100460)Natural Science Foundation of Hainan Province(312026)Fundamental Research Fund for the Rubber Research Institute in Chinese Academy of Tropical Agricultural Sciences(1630022011014)
文摘Light-harvesting chlorophyll a/b-binding （LHC） proteins are a group of nuclear-encoded thylakoid proteins that play a key role in plant photosynthesis and are widely involved in light harvesting, energy transfer to the reaction center, maintenance of thylakoid membrane structure, photoprotection and response to en- vironmental conditions, etc. Although/dw supergene family is well characterized in model plants such as Arabidopsis, rice and poplar, little information is available in castor bean （Ricinus communis L. ）. In this study, a genome-wide search was carried out for the first time to identify castor bean L/w genes and analyze the gene structures, biochemical properties, evolutionary relationships and expression characteristics based on the published data of castor bean genome and ESTs. According to the results, a total of 28 Rclhcs genes representing 13 gene families （ l_hca , l_hcb , Elip , Ohpl , Ohp2 , SEP1, SEP2 , SEP3 , SEP4 , SEP5 , PsbS , Rieske and FCII） and 25 subgene families were identified in castor bean genome; to be specific, 25 and 5 genes were found to have corresponding ESTs in NCBI and have al- ternative splicing isoforlns, respectively. These RcLhcs contain 0 to 9 introns and distribute on 26 of the 25 878 released scaffolds. All RcLhcs genes were found to be expressed in all examined tissues, i.e. leaf, flower, II/III stage endosperm, V/VI stage endosperm and seed, with the highest expression level in leaf tissue.
文摘Hyperspectral remote sensing has become one of the research frontiers in ground object identification and classification. On the basis of reviewing the application of hyperspectral remote sensing in identification and classification of ground objects at home and abroad. The research results of identification and classification of forest tree species, grassland and urban land features were summarized. Then the researches of classification methods were summarized. Finally the prospects of hyperspectral remote sensing in ground object identification and classification were prospected.
文摘Purpose:Interdisciplinary research has become a critical approach to addressing complex societal,economic,technological,and environmental challenges,driving innovation and integrating scientific knowledge.While interdisciplinarity indicators are widely used to evaluate research performance,the impact of classification granularity on these assessments remains underexplored.Design/methodology/approach:This study investigates how different levels of classification granularity-macro,meso,and micro-affect the evaluation of interdisciplinarity in research institutes.Using a dataset of 262 institutes from four major German non-university organizations(FHG,HGF,MPG,WGL)from 2018 to 2022,we examine inconsistencies in interdisciplinarity across levels,analyze ranking changes,and explore the influence of institutional fields and research focus(applied vs.basic).Findings:Our findings reveal significant inconsistencies in interdisciplinarity across classification levels,with rankings varying substantially.Notably,the Fraunhofer Society(FHG),which performs well at the macro level,experiences significant ranking declines at meso and micro levels.Normalizing interdisciplinarity by research field confirmed that these declines persist.The research focus of institutes,whether applied,basic,or mixed,does not significantly explain the observed ranking dynamics.Research limitations:This study has only considered the publication-based dimension of institutional interdisciplinarity and has not explored other aspects.Practical implications:The findings provide insights for policymakers,research managers,and scholars to better interpret interdisciplinarity metrics and support interdisciplinary research effectively.Originality/value:This study underscores the critical role of classification granularity in interdisciplinarity assessment and emphasizes the need for standardized approaches to ensure robust and fair evaluations.
基金This research was funded by National Natural Science Foundation of China(Nos.31571920,61671378)。
文摘Tegillarca granosa(T.granosa)is susceptible to heavy metals,which may pose a threat to consumer health.Thus,healthy and polluted T.granosa should be distinguished quickly.This study aimed to rapidly identify heavy metal pollution by using laser-induced breakdown spectroscopy(LIBS)coupled with linear regression classification(LRC).Five types of T.granosa were studied,namely,Cd-,Zn-,Pb-contaminated,mixed contaminated,and control samples.Threshold method was applied to extract the significant variables from LIBS spectra.Then,LRC was used to classify the different types of T.granosa.Other classification models and feature selection methods were used for comparison.LRC was the best model,achieving an accuracy of 90.67%.Results indicated that LIBS combined with LRC is effective and feasible for T.granosa heavy metal detection.
基金supported by the National Key R&D Program of China(2021YFC2203502 and 2022YFF0711502)the National Natural Science Foundation of China(NSFC)(12173077)+4 种基金the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095 and 2023TSYCCX0112)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(PTYQ2022YZZD01)China National Astronomical Data Center(NADC)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of Sciences(CAS)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360)。
文摘As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and it has been widely applied across various fields.In the field of astronomy,AI techniques have demonstrated unique advantages,particularly in the identification of pulsars and their candidates.AI is able to address the challenges of pulsar celestial body identification and classification because of its accuracy and efficiency.This paper systematically surveys commonly used AI models for pulsar candidate identification,analyzing and discussing the typical applications of machine learning,artificial neural networks,convolutional neural networks,and generative adversarial networks in candidate identification.Furthermore,it explores how th.e introduction of AI techniques not only enhances the efficiency and accuracy of pulsar identification but also provides new perspectives and tools for pulsar survey data processing,thus playing a significant role in advancing pulsar research and the field of astronomy.
基金supported by King Saud University,Riyadh,Saudi Arabia,through the Researchers Supporting Project under Grant RSPD2025R697.
文摘Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.
基金supported in part by the Six Talent Peaks Project in Jiangsu Province under Grant 013040315in part by the China Textile Industry Federation Science and Technology Guidance Project under Grant 2017107+1 种基金in part by the National Natural Science Foundation of China under Grant 31570714in part by the China Scholarship Council under Grant 202108320290。
文摘The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.
