BACKGROUND Donor-specific antibodies(DSAs)against human leukocyte antigen(HLA)-DQ are increasingly recognized as major contributors to antibody-mediated rejection(AMR)and graft failure in kidney transplantation.Howeve...BACKGROUND Donor-specific antibodies(DSAs)against human leukocyte antigen(HLA)-DQ are increasingly recognized as major contributors to antibody-mediated rejection(AMR)and graft failure in kidney transplantation.However,their clinical impact remains understudied in Morocco.AIM To evaluate the presence and implications of anti-HLA-DQ DSAs in Moroccan kidney transplant recipients.METHODS We retrospectively analyzed the immunological profiles and clinical outcomes of kidney transplant recipients screened for anti-HLA antibodies between 2015 and 2020,who developed anti-HLA-DQ DSAs either before or after transplantation.Anti-HLA antibodies were identified using Luminex®single antigen bead technology,and clinical follow-up included graft function assessment,biopsy interpretation,and evaluation of immunosuppression.RESULTS In the pre-transplant group(n=6 with confirmed donor typing),patients with low to moderate median fluorescence intensity(MFI)anti-HLA-DQ DSAs(MFI 561-1581)underwent successful transplantation and maintained stable graft function under optimized immunosuppression.In contrast,in the post-transplant group(n=6 with confirmed donor typing),the emergence of de novo anti-HLA-DQ DSAs was consistently associated with AMR,with MFI values reaching up to 19473,with biopsy-proven AMR in 5 of 6 cases and suspicion of AMR in 1 case.Two representative cases are detailed to illustrate the clinical impact of DQ DSAs:one patient developed high-level anti-DQB1*02 de novo DSA(MFI 12029)with persistent AMR after 5 years,while another developed anti-DQA1*05:01 de novo DSA after an early AMR episode but maintained stable graft function after 5 years(creatinine 1.48 mg/dL).CONCLUSION Our findings underscore the clinical significance of anti-HLA-DQ DSAs in Moroccan kidney transplant recipients.While preformed DSAs with low immunogenicity may permit successful transplantation,de novo DSAs strongly correlate with AMR.Proactive monitoring,including routine DSA screening and HLA-DQ typing,could improve graft outcomes by enabling early intervention and better donor selection.展开更多
BACKGROUND In an era leaning toward a personalized alignment of total knee arthroplasty,coronal plane alignment of the knee(CPAK)phenotypes for each population are studied;furthermore,other possible variables affectin...BACKGROUND In an era leaning toward a personalized alignment of total knee arthroplasty,coronal plane alignment of the knee(CPAK)phenotypes for each population are studied;furthermore,other possible variables affecting the alignment,such as ankle joint alignment,should be considered.AIM To determine CPAK distribution in the North African(Egyptian)population with knee osteoarthritis and to assess ankle joint line orientation(AJLO)adaptations across different CPAK types.METHODS A cross-sectional study was conducted on patients with primary knee osteoarthritis and normal ankle joints.Radiographic parameters included the mechanical lateral distal femoral angle,medial proximal tibial angle,and the derived calculations of joint line obliquity(JLO)and arithmetic hip-knee-ankle angle(aHKA).The tibial plafond horizontal angle(TPHA)was used for AJLO assessment,where 0°is neutral(type N),<0°is varus(type A),and>0°is valgus(type B).The nine CPAK types were further divided into 27 subtypes after incorporating the three AJLO types.RESULTS A total of 527 patients(1054 knees)were included for CPAK classification,and 435 patients(870 knees and ankles)for AJLO assessment.The mean age was 57.2±7.8 years,with 79.5%females.Most knees(76.4%)demonstrated varus alignment(mean aHKA was-5.51°±4.84°)and apex distal JLO(55.3%)(mean JLO was 176.43°±4.53°).CPAK types I(44.3%),IV(28.6%),and II(10%)were the most common.Regarding AJLO,70.2%of ankles exhibited varus orientation(mean TPHA was-5.21°±6.45°).The most frequent combined subtypes were CPAK type I-A(33.7%),IV-A(21.5%),and I-N(6.9%).A significant positive correlation was found between the TPHA and aHKA(r=0.40,P<0.001).CONCLUSION In this North African cohort,varus knee alignment with apex distal JLO and varus AJLO predominated.CPAK types I,IV,and II were the most common types,while subtypes I-A,IV-A,and I-N were commonly occurring after incorporating AJLO types;furthermore,the AJLO was significantly correlated to aHKA.展开更多
Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services...Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.展开更多
Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns(SWPs),however,the consistency of different classification methods is rarely examine...Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns(SWPs),however,the consistency of different classification methods is rarely examined.In this study,we apply two widely-used objective methods,the self-organizing map(SOM)and K-means clustering analysis,to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022.We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities.In the case of classifying six SWPs,the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods,and the difference in themean frequency of each SWP is less than 7%.The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature,lower cloud cover,relative humidity,and wind speed,and stronger subsidence compared to climatology mean.We find that during 2015-2022,the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 days/year,faster than the increases in the ozone exceedance days(3.0 days/year).The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6.In particular,the significant increase in ozone-favorable SWPs in 2022,especially the Subtropical High type which typically occurs in September,is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022.Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.展开更多
Existing Chinese named entity recognition(NER)research utilises 1D lexicon-based sequence labelling frameworks,which can only recognise flat entities.While lexicons serve as prior knowledge and enhance semantic inform...Existing Chinese named entity recognition(NER)research utilises 1D lexicon-based sequence labelling frameworks,which can only recognise flat entities.While lexicons serve as prior knowledge and enhance semantic information,they also pose completeness and resource requirements limitations.This paper proposes a template-based classification(TC)model to avoid lexicon issues and to identify nested entities.Template-based classification provides a template word for each entity type,which utilises contrastive learning to integrate the common characteristics among entities with the same category.Contrastive learning makes template words the centre points of their category in the vector space,thus improving generalisation ability.Additionally,TC presents a 2D tablefilling label scheme that classifies entities based on the attention distribution of template words.The proposed novel decoder algorithm enables TC recognition of both flat and nested entities simultaneously.Experimental results show that TC achieves the state-ofthe-art performance on five Chinese datasets.展开更多
Urbanization and industrialization have escalated water pollution,threatening ecosystems and human health.Water pollution not only degrades water quality but also poses long-term risks to human health through the food...Urbanization and industrialization have escalated water pollution,threatening ecosystems and human health.Water pollution not only degrades water quality but also poses long-term risks to human health through the food chain.The development of efficient wastewater detection and treatment methods is essential for mitigating this environmental hazard.Carbon dots(CDs),as emerging carbon-based nanomaterials,exhibit properties such as biocompatibility,photoluminescence(PL),water solubility,and strong adsorption,positioning them as promising candidates for environmental monitoring and management.Particularly in wastewater treatment,their optical and electron transfer properties make them ideal for pollutant detection and removal.Despite their potential,comprehensive reviews on CDs'role in wastewater treatment are scarce,often lacking detailed insights into their synthesis,PL mechanisms,and practical applications.This review systematically addresses the synthesis,PL mechanisms,and wastewater treatment applications of CDs,aiming to bridge existing research gaps.It begins with an overview of CDs structure and classification,essential for grasping their properties and uses.The paper then explores the pivotal PL mechanisms of CDs,crucial for their sensing capabilities.Next,comprehensive synthesis strategies are presented,encompassing both top-down and bottom-up strategies such as arc discharge,chemical oxidation,and hydrothermal/solvothermal synthesis.The diversity of these methods highlights the potential for tailored CDs production to suit specific environmental applications.Furthermore,the review systematically discusses the applications of CDs in wastewater treatment,including sensing,inorganic removal,and organic degradation.Finally,it delves into the research prospects and challenges of CDs,proposing future directions to enhance their role in wastewater treatment.展开更多
The leucine-rich repeat(LRR)protein family is involved in a variety of fundamental metabolic and signaling processes in plants,including growth and defense responses.LRR proteins can be divided into two categories:tho...The leucine-rich repeat(LRR)protein family is involved in a variety of fundamental metabolic and signaling processes in plants,including growth and defense responses.LRR proteins can be divided into two categories:those containing LRR domains along with other structural elements,which are further subdivided into five groups,LRR receptor-like kinases,LRR receptor-like proteins,nucleotide-binding site LRR proteins,LRR-extensin proteins,and polygalacturonase-inhibiting proteins,and those containing only LRR domains.Functionally,various LRR proteins are primarily involved in plant development and responses to environmental stress.Notably,the LRR protein family plays a central role in signal transduction pathways related to stress adaptation.In this review,we classify and analyze the functions of LRR proteins in plants.While extensive research has been conducted on the roles of LRR proteins in disease resistance signaling,these proteins also play important roles in abiotic stress responses.This review highlights recent advances in understanding how LRR proteins mediate responses to biotic and abiotic stresses.Building upon these insights,further exploration of the roles of LRR proteins in abiotic stress resistance may aid efforts to develop rice varieties with enhanced stress and disease tolerance.展开更多
Extreme ozone pollution events(EOPEs)are associated with synoptic weather patterns(SWPs)and pose severe health and ecological risks.However,a systematic investigation of themeteorological causes,transport pathways,and...Extreme ozone pollution events(EOPEs)are associated with synoptic weather patterns(SWPs)and pose severe health and ecological risks.However,a systematic investigation of themeteorological causes,transport pathways,and source contributions to historical EOPEs is still lacking.In this paper,the K-means clustering method is applied to identify six dominant SWPs during the warm season in the Yangtze River Delta(YRD)region from 2016 to 2022.It provides an integrated analysis of the meteorological factors affecting ozone pollution in Hefei under different SWPs.Using the WRF-FLEXPART model,the transport pathways(TPPs)and geographical sources of the near-surface air masses in Hefei during EOPEs are investigated.The results reveal that Hefei experienced the highest ozone concentration(134.77±42.82μg/m^(3)),exceedance frequency(46 days(23.23%)),and proportion of EOPEs(21 instances,47.7%)under the control of peripheral subsidence of typhoon(Type 5).Regional southeast winds correlated with the ozone pollution in Hefei.During EOPEs,a high boundary layer height,solar radiation,and temperature;lowhumidity and cloud cover;and pronounced subsidence airflow occurred over Hefei and the broader YRD region.The East-South(E_S)patterns exhibited the highest frequency(28 instances,65.11%).Regarding the TPPs and geographical sources of the near-surface air masses during historical EOPEs.The YRD was the main source for land-originating air masses under E_S patterns(50.28%),with Hefei,southern Anhui,southern Jiangsu,and northern Zhejiang being key contributors.These findings can help improve ozone pollution early warning and control mechanisms at urban and regional scales.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
Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advanc...Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advancements in machine learning,achieving both high accuracy and low computational cost for arrhythmia classification remains a critical issue.Computer-aided diagnosis systems can play a key role in early detection,reducing mortality rates associated with cardiac disorders.This study proposes a fully automated approach for ECG arrhythmia classification using deep learning and machine learning techniques to improve diagnostic accuracy while minimizing processing time.The methodology consists of three stages:1)preprocessing,where ECG signals undergo noise reduction and feature extraction;2)feature Identification,where deep convolutional neural network(CNN)blocks,combined with data augmentation and transfer learning,extract key parameters;3)classification,where a hybrid CNN-SVM model is employed for arrhythmia recognition.CNN-extracted features were fed into a binary support vector machine(SVM)classifier,and model performance was assessed using five-fold cross-validation.Experimental findings demonstrated that the CNN2 model achieved 85.52%accuracy,while the hybrid CNN2-SVM approach significantly improved accuracy to 97.33%,outperforming conventional methods.This model enhances classification efficiency while reducing computational complexity.The proposed approach bridges the gap between accuracy and processing speed in ECG arrhythmia classification,offering a promising solution for real-time clinical applications.Its superior performance compared to nonlinear classifiers highlights its potential for improving automated cardiac diagnosis.展开更多
The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in spec...In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in specific genes,type ofβ-cell impairment,degree of insulin resistance,and clinical characteristics of metabolic profiles.Improved classification methods enable healthcare providers to formulate blood glucose management strategies more precisely.Applying these updated classification systems,will assist clinicians in further optimising treatment plans,including targeted drug therapies,personalized dietary advice,and specific exercise plans.Ultimately,this will facilitate stricter blood glucose control,minimize the risks of hypoglycaemia and hyperglycaemia,and reduce long-term complications associated with diabetes.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
Lunar impact glasses have been identified as crucial indicators of geochemical information regarding their source regions. Impact glasses can be categorized as either local or exotic. Those preserving geochemical sign...Lunar impact glasses have been identified as crucial indicators of geochemical information regarding their source regions. Impact glasses can be categorized as either local or exotic. Those preserving geochemical signatures matching local lithologies (e.g., mare basalts or their single minerals) or regolith bulk soil compositions are classified as “local”. Otherwise, they could be defined as “exotic”. The analysis of exotic glasses provides the opportunity to explore previously unsampled lunar areas. This study focuses on the identification of exotic glasses within the Chang’e-5 (CE-5) soil sample by analyzing the trace elements of 28 impact glasses with distinct major element compositions in comparison with the CE-5 bulk soil. However, the results indicate that 18 of the analyzed glasses exhibit trace element compositions comparable to those of the local CE-5 materials. In particular, some of them could match the local single mineral component in major and trace elements, suggesting a local origin. Therefore, it is recommended that the investigation be expanded from using major elements to including nonvolatile trace elements, with a view to enhancing our understanding on the provenance of lunar impact glasses. To achieve a more accurate identification of exotic glasses within the CE-5 soil sample, a novel classification plot of Mg# versus La is proposed. The remaining 10 glasses, which exhibit diverse trace element variations, were identified as exotic. A comparative analysis of their chemical characteristics with remote sensing data indicates that they may have originated from the Aristarchus, Mairan, Sharp, or Pythagoras craters. This study elucidates the classification and possible provenance of exotic materials within the CE-5 soil sample, thereby providing constraints for the enhanced identification of local and exotic components at the CE-5 landing site.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
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
Accurate spatio-temporal land cover information in agricultural irrigation districts is crucial for effective agricultural management and crop production.Therefore,a spectralphenological-based land cover classificatio...Accurate spatio-temporal land cover information in agricultural irrigation districts is crucial for effective agricultural management and crop production.Therefore,a spectralphenological-based land cover classification(SPLC)method combined with a fusion model(flexible spatiotemporal data fusion,FSDAF)(abbreviated as SPLC-F)was proposed to map multi-year land cover and crop type(LC-CT)distribution in agricultural irrigated areas with complex landscapes and cropping system,using time series optical images(Landsat and MODIS).The SPLC-F method was well validated and applied in a super-large irrigated area(Hetao)of the upper Yellow River Basin(YRB).Results showed that the SPLC-F method had a satisfactory performance in producing long-term LC-CT maps in Hetao,without the requirement of field sampling.Then,the spatio-temporal variation and the driving factors of the cropping systems were further analyzed with the aid of detailed household surveys and statistics.We clarified that irrigation and salinity conditions were the main factors that had impacts on crop spatial distribution in the upper YRB.Investment costs,market demand,and crop price are the main driving factors in determining the temporal variations in cropping distribution.Overall,this study provided essential multi-year LC-CT maps for sustainable management of agriculture,eco-environments,and food security in the upper YRB.展开更多
Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injec...Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity.To overcome these obstacles,researchers have created several network IDS models,such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques.This study provides an updated learning strategy for artificial neural network(ANN)to address data categorization problems caused by unbalanced data.Compared to traditional approaches,the augmented ANN’s 92%accuracy is a significant improvement owing to the network’s increased resilience to disturbances and computational complexity,brought about by the addition of a random weight and standard scaler.Considering the ever-evolving nature of cybersecurity threats,this study introduces a revolutionary intrusion detection method.展开更多
Given the scarcity of Satellite Frequency and Orbit(SFO)resources,it holds paramount importance to establish a comprehensive knowledge graph of SFO field(SFO-KG)and employ knowledge reasoning technology to automatical...Given the scarcity of Satellite Frequency and Orbit(SFO)resources,it holds paramount importance to establish a comprehensive knowledge graph of SFO field(SFO-KG)and employ knowledge reasoning technology to automatically mine available SFO resources.An essential aspect of constructing SFO-KG is the extraction of Chinese entity relations.Unfortunately,there is currently no publicly available Chinese SFO entity Relation Extraction(RE)dataset.Moreover,publicly available SFO text data contain numerous NA(representing for“No Answer”)relation category sentences that resemble other relation sentences and pose challenges in accurate classification,resulting in low recall and precision for the NA relation category in entity RE.Consequently,this issue adversely affects both the accuracy of constructing the knowledge graph and the efficiency of RE processes.To address these challenges,this paper proposes a method for extracting Chinese SFO text entity relations based on dynamic integrated learning.This method includes the construction of a manually annotated Chinese SFO entity RE dataset and a classifier combining features of SFO resource data.The proposed approach combines integrated learning and pre-training models,specifically utilizing Bidirectional Encoder Representation from Transformers(BERT).In addition,it incorporates one-class classification,attention mechanisms,and dynamic feedback mechanisms to improve the performance of the RE model.Experimental results show that the proposed method outperforms the traditional methods in terms of F1 value when extracting entity relations from both balanced and long-tailed datasets.展开更多
Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronar...Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.展开更多
基金Supported by the National Science and Technology Research Center(Morocco)“PhD-Associate Scholarship-PASS”Program,No.88UH2C2023.
文摘BACKGROUND Donor-specific antibodies(DSAs)against human leukocyte antigen(HLA)-DQ are increasingly recognized as major contributors to antibody-mediated rejection(AMR)and graft failure in kidney transplantation.However,their clinical impact remains understudied in Morocco.AIM To evaluate the presence and implications of anti-HLA-DQ DSAs in Moroccan kidney transplant recipients.METHODS We retrospectively analyzed the immunological profiles and clinical outcomes of kidney transplant recipients screened for anti-HLA antibodies between 2015 and 2020,who developed anti-HLA-DQ DSAs either before or after transplantation.Anti-HLA antibodies were identified using Luminex®single antigen bead technology,and clinical follow-up included graft function assessment,biopsy interpretation,and evaluation of immunosuppression.RESULTS In the pre-transplant group(n=6 with confirmed donor typing),patients with low to moderate median fluorescence intensity(MFI)anti-HLA-DQ DSAs(MFI 561-1581)underwent successful transplantation and maintained stable graft function under optimized immunosuppression.In contrast,in the post-transplant group(n=6 with confirmed donor typing),the emergence of de novo anti-HLA-DQ DSAs was consistently associated with AMR,with MFI values reaching up to 19473,with biopsy-proven AMR in 5 of 6 cases and suspicion of AMR in 1 case.Two representative cases are detailed to illustrate the clinical impact of DQ DSAs:one patient developed high-level anti-DQB1*02 de novo DSA(MFI 12029)with persistent AMR after 5 years,while another developed anti-DQA1*05:01 de novo DSA after an early AMR episode but maintained stable graft function after 5 years(creatinine 1.48 mg/dL).CONCLUSION Our findings underscore the clinical significance of anti-HLA-DQ DSAs in Moroccan kidney transplant recipients.While preformed DSAs with low immunogenicity may permit successful transplantation,de novo DSAs strongly correlate with AMR.Proactive monitoring,including routine DSA screening and HLA-DQ typing,could improve graft outcomes by enabling early intervention and better donor selection.
基金approved by Institutional Review Board of Faculty of Medicine in Assiut University,No.04-2024-300470.
文摘BACKGROUND In an era leaning toward a personalized alignment of total knee arthroplasty,coronal plane alignment of the knee(CPAK)phenotypes for each population are studied;furthermore,other possible variables affecting the alignment,such as ankle joint alignment,should be considered.AIM To determine CPAK distribution in the North African(Egyptian)population with knee osteoarthritis and to assess ankle joint line orientation(AJLO)adaptations across different CPAK types.METHODS A cross-sectional study was conducted on patients with primary knee osteoarthritis and normal ankle joints.Radiographic parameters included the mechanical lateral distal femoral angle,medial proximal tibial angle,and the derived calculations of joint line obliquity(JLO)and arithmetic hip-knee-ankle angle(aHKA).The tibial plafond horizontal angle(TPHA)was used for AJLO assessment,where 0°is neutral(type N),<0°is varus(type A),and>0°is valgus(type B).The nine CPAK types were further divided into 27 subtypes after incorporating the three AJLO types.RESULTS A total of 527 patients(1054 knees)were included for CPAK classification,and 435 patients(870 knees and ankles)for AJLO assessment.The mean age was 57.2±7.8 years,with 79.5%females.Most knees(76.4%)demonstrated varus alignment(mean aHKA was-5.51°±4.84°)and apex distal JLO(55.3%)(mean JLO was 176.43°±4.53°).CPAK types I(44.3%),IV(28.6%),and II(10%)were the most common.Regarding AJLO,70.2%of ankles exhibited varus orientation(mean TPHA was-5.21°±6.45°).The most frequent combined subtypes were CPAK type I-A(33.7%),IV-A(21.5%),and I-N(6.9%).A significant positive correlation was found between the TPHA and aHKA(r=0.40,P<0.001).CONCLUSION In this North African cohort,varus knee alignment with apex distal JLO and varus AJLO predominated.CPAK types I,IV,and II were the most common types,while subtypes I-A,IV-A,and I-N were commonly occurring after incorporating AJLO types;furthermore,the AJLO was significantly correlated to aHKA.
文摘Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.
基金supported by the Guangdong Basic and Applied Basic Research project (No.2020B0301030004)the Key-Area Research and Development Program of Guangdong Province (No.2020B1111360003)+1 种基金the National Natural Science Foundation of China (No.42105103)the Guangdong Basic and Applied Basic Research Foundation (No.2022A1515011554).
文摘Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns(SWPs),however,the consistency of different classification methods is rarely examined.In this study,we apply two widely-used objective methods,the self-organizing map(SOM)and K-means clustering analysis,to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022.We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities.In the case of classifying six SWPs,the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods,and the difference in themean frequency of each SWP is less than 7%.The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature,lower cloud cover,relative humidity,and wind speed,and stronger subsidence compared to climatology mean.We find that during 2015-2022,the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 days/year,faster than the increases in the ozone exceedance days(3.0 days/year).The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6.In particular,the significant increase in ozone-favorable SWPs in 2022,especially the Subtropical High type which typically occurs in September,is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022.Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.
基金Sichuan Provincial Science and Technology Support Program,Grant/Award Number:2023YFG0151National Natural Science Foundation of China,Grant/Award Numbers:U22B2061,U2336204。
文摘Existing Chinese named entity recognition(NER)research utilises 1D lexicon-based sequence labelling frameworks,which can only recognise flat entities.While lexicons serve as prior knowledge and enhance semantic information,they also pose completeness and resource requirements limitations.This paper proposes a template-based classification(TC)model to avoid lexicon issues and to identify nested entities.Template-based classification provides a template word for each entity type,which utilises contrastive learning to integrate the common characteristics among entities with the same category.Contrastive learning makes template words the centre points of their category in the vector space,thus improving generalisation ability.Additionally,TC presents a 2D tablefilling label scheme that classifies entities based on the attention distribution of template words.The proposed novel decoder algorithm enables TC recognition of both flat and nested entities simultaneously.Experimental results show that TC achieves the state-ofthe-art performance on five Chinese datasets.
基金supported by the Natural Science Foundation of Hebei Province(No.E2022208046)National Science Foundation of China(No.52004080)+2 种基金Key project of National Natural Science Foundation of China(No.U20A20130)Key research and development project of Hebei Province(No.22373704D)2023 Central Government Guide Local Science and Technology Development Fund Project(No.236Z1812 G)。
文摘Urbanization and industrialization have escalated water pollution,threatening ecosystems and human health.Water pollution not only degrades water quality but also poses long-term risks to human health through the food chain.The development of efficient wastewater detection and treatment methods is essential for mitigating this environmental hazard.Carbon dots(CDs),as emerging carbon-based nanomaterials,exhibit properties such as biocompatibility,photoluminescence(PL),water solubility,and strong adsorption,positioning them as promising candidates for environmental monitoring and management.Particularly in wastewater treatment,their optical and electron transfer properties make them ideal for pollutant detection and removal.Despite their potential,comprehensive reviews on CDs'role in wastewater treatment are scarce,often lacking detailed insights into their synthesis,PL mechanisms,and practical applications.This review systematically addresses the synthesis,PL mechanisms,and wastewater treatment applications of CDs,aiming to bridge existing research gaps.It begins with an overview of CDs structure and classification,essential for grasping their properties and uses.The paper then explores the pivotal PL mechanisms of CDs,crucial for their sensing capabilities.Next,comprehensive synthesis strategies are presented,encompassing both top-down and bottom-up strategies such as arc discharge,chemical oxidation,and hydrothermal/solvothermal synthesis.The diversity of these methods highlights the potential for tailored CDs production to suit specific environmental applications.Furthermore,the review systematically discusses the applications of CDs in wastewater treatment,including sensing,inorganic removal,and organic degradation.Finally,it delves into the research prospects and challenges of CDs,proposing future directions to enhance their role in wastewater treatment.
基金supported by the National Natural Science Foundation of China(Grant Nos.32072048 and U2004204)National Key Research and Development Program of China(Grant No.2023YFF1001200)+2 种基金China Rice Research Institute Basal Research Fund(Grant No.CPSIBRF-CNRRI-202404)Academician Workstation of National Nanfan Research Institute(Sanya),Chinese Agricultural Academic Science(CAAS),(Grant Nos.YBXM2422 and YBXM2423)Agricultural Science and Technology Innovation Program of CAAS,China.
文摘The leucine-rich repeat(LRR)protein family is involved in a variety of fundamental metabolic and signaling processes in plants,including growth and defense responses.LRR proteins can be divided into two categories:those containing LRR domains along with other structural elements,which are further subdivided into five groups,LRR receptor-like kinases,LRR receptor-like proteins,nucleotide-binding site LRR proteins,LRR-extensin proteins,and polygalacturonase-inhibiting proteins,and those containing only LRR domains.Functionally,various LRR proteins are primarily involved in plant development and responses to environmental stress.Notably,the LRR protein family plays a central role in signal transduction pathways related to stress adaptation.In this review,we classify and analyze the functions of LRR proteins in plants.While extensive research has been conducted on the roles of LRR proteins in disease resistance signaling,these proteins also play important roles in abiotic stress responses.This review highlights recent advances in understanding how LRR proteins mediate responses to biotic and abiotic stresses.Building upon these insights,further exploration of the roles of LRR proteins in abiotic stress resistance may aid efforts to develop rice varieties with enhanced stress and disease tolerance.
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,and 41975037)the National Key Research and Development Programof China(No.2022YFC3700303).
文摘Extreme ozone pollution events(EOPEs)are associated with synoptic weather patterns(SWPs)and pose severe health and ecological risks.However,a systematic investigation of themeteorological causes,transport pathways,and source contributions to historical EOPEs is still lacking.In this paper,the K-means clustering method is applied to identify six dominant SWPs during the warm season in the Yangtze River Delta(YRD)region from 2016 to 2022.It provides an integrated analysis of the meteorological factors affecting ozone pollution in Hefei under different SWPs.Using the WRF-FLEXPART model,the transport pathways(TPPs)and geographical sources of the near-surface air masses in Hefei during EOPEs are investigated.The results reveal that Hefei experienced the highest ozone concentration(134.77±42.82μg/m^(3)),exceedance frequency(46 days(23.23%)),and proportion of EOPEs(21 instances,47.7%)under the control of peripheral subsidence of typhoon(Type 5).Regional southeast winds correlated with the ozone pollution in Hefei.During EOPEs,a high boundary layer height,solar radiation,and temperature;lowhumidity and cloud cover;and pronounced subsidence airflow occurred over Hefei and the broader YRD region.The East-South(E_S)patterns exhibited the highest frequency(28 instances,65.11%).Regarding the TPPs and geographical sources of the near-surface air masses during historical EOPEs.The YRD was the main source for land-originating air masses under E_S patterns(50.28%),with Hefei,southern Anhui,southern Jiangsu,and northern Zhejiang being key contributors.These findings can help improve ozone pollution early warning and control mechanisms at urban and regional scales.
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.
文摘Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advancements in machine learning,achieving both high accuracy and low computational cost for arrhythmia classification remains a critical issue.Computer-aided diagnosis systems can play a key role in early detection,reducing mortality rates associated with cardiac disorders.This study proposes a fully automated approach for ECG arrhythmia classification using deep learning and machine learning techniques to improve diagnostic accuracy while minimizing processing time.The methodology consists of three stages:1)preprocessing,where ECG signals undergo noise reduction and feature extraction;2)feature Identification,where deep convolutional neural network(CNN)blocks,combined with data augmentation and transfer learning,extract key parameters;3)classification,where a hybrid CNN-SVM model is employed for arrhythmia recognition.CNN-extracted features were fed into a binary support vector machine(SVM)classifier,and model performance was assessed using five-fold cross-validation.Experimental findings demonstrated that the CNN2 model achieved 85.52%accuracy,while the hybrid CNN2-SVM approach significantly improved accuracy to 97.33%,outperforming conventional methods.This model enhances classification efficiency while reducing computational complexity.The proposed approach bridges the gap between accuracy and processing speed in ECG arrhythmia classification,offering a promising solution for real-time clinical applications.Its superior performance compared to nonlinear classifiers highlights its potential for improving automated cardiac diagnosis.
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
文摘In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in specific genes,type ofβ-cell impairment,degree of insulin resistance,and clinical characteristics of metabolic profiles.Improved classification methods enable healthcare providers to formulate blood glucose management strategies more precisely.Applying these updated classification systems,will assist clinicians in further optimising treatment plans,including targeted drug therapies,personalized dietary advice,and specific exercise plans.Ultimately,this will facilitate stricter blood glucose control,minimize the risks of hypoglycaemia and hyperglycaemia,and reduce long-term complications associated with diabetes.
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funded by the National Natural Science Foundation of China (Grant Nos. 42241103 and 62227901)the Key Research Program of the Institute of Geology and Geophysics, Chinese Academy of Sciences (Grant Nos. IGGCAS-202101 and IGGCAS-202401)
文摘Lunar impact glasses have been identified as crucial indicators of geochemical information regarding their source regions. Impact glasses can be categorized as either local or exotic. Those preserving geochemical signatures matching local lithologies (e.g., mare basalts or their single minerals) or regolith bulk soil compositions are classified as “local”. Otherwise, they could be defined as “exotic”. The analysis of exotic glasses provides the opportunity to explore previously unsampled lunar areas. This study focuses on the identification of exotic glasses within the Chang’e-5 (CE-5) soil sample by analyzing the trace elements of 28 impact glasses with distinct major element compositions in comparison with the CE-5 bulk soil. However, the results indicate that 18 of the analyzed glasses exhibit trace element compositions comparable to those of the local CE-5 materials. In particular, some of them could match the local single mineral component in major and trace elements, suggesting a local origin. Therefore, it is recommended that the investigation be expanded from using major elements to including nonvolatile trace elements, with a view to enhancing our understanding on the provenance of lunar impact glasses. To achieve a more accurate identification of exotic glasses within the CE-5 soil sample, a novel classification plot of Mg# versus La is proposed. The remaining 10 glasses, which exhibit diverse trace element variations, were identified as exotic. A comparative analysis of their chemical characteristics with remote sensing data indicates that they may have originated from the Aristarchus, Mairan, Sharp, or Pythagoras craters. This study elucidates the classification and possible provenance of exotic materials within the CE-5 soil sample, thereby providing constraints for the enhanced identification of local and exotic components at the CE-5 landing site.
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.
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National Natural Science Foundation of China,No.52379053,No.52022108The Key Research Project of Science and Technology in Inner Mongolia Autonomous Region of China,No.NMKJXM202208,No.NMKJXM202301The Project Funded by the Water Resources Department of Inner Mongolia Autonomous Region of China,No.NSK202103。
文摘Accurate spatio-temporal land cover information in agricultural irrigation districts is crucial for effective agricultural management and crop production.Therefore,a spectralphenological-based land cover classification(SPLC)method combined with a fusion model(flexible spatiotemporal data fusion,FSDAF)(abbreviated as SPLC-F)was proposed to map multi-year land cover and crop type(LC-CT)distribution in agricultural irrigated areas with complex landscapes and cropping system,using time series optical images(Landsat and MODIS).The SPLC-F method was well validated and applied in a super-large irrigated area(Hetao)of the upper Yellow River Basin(YRB).Results showed that the SPLC-F method had a satisfactory performance in producing long-term LC-CT maps in Hetao,without the requirement of field sampling.Then,the spatio-temporal variation and the driving factors of the cropping systems were further analyzed with the aid of detailed household surveys and statistics.We clarified that irrigation and salinity conditions were the main factors that had impacts on crop spatial distribution in the upper YRB.Investment costs,market demand,and crop price are the main driving factors in determining the temporal variations in cropping distribution.Overall,this study provided essential multi-year LC-CT maps for sustainable management of agriculture,eco-environments,and food security in the upper YRB.
文摘Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity.To overcome these obstacles,researchers have created several network IDS models,such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques.This study provides an updated learning strategy for artificial neural network(ANN)to address data categorization problems caused by unbalanced data.Compared to traditional approaches,the augmented ANN’s 92%accuracy is a significant improvement owing to the network’s increased resilience to disturbances and computational complexity,brought about by the addition of a random weight and standard scaler.Considering the ever-evolving nature of cybersecurity threats,this study introduces a revolutionary intrusion detection method.
文摘Given the scarcity of Satellite Frequency and Orbit(SFO)resources,it holds paramount importance to establish a comprehensive knowledge graph of SFO field(SFO-KG)and employ knowledge reasoning technology to automatically mine available SFO resources.An essential aspect of constructing SFO-KG is the extraction of Chinese entity relations.Unfortunately,there is currently no publicly available Chinese SFO entity Relation Extraction(RE)dataset.Moreover,publicly available SFO text data contain numerous NA(representing for“No Answer”)relation category sentences that resemble other relation sentences and pose challenges in accurate classification,resulting in low recall and precision for the NA relation category in entity RE.Consequently,this issue adversely affects both the accuracy of constructing the knowledge graph and the efficiency of RE processes.To address these challenges,this paper proposes a method for extracting Chinese SFO text entity relations based on dynamic integrated learning.This method includes the construction of a manually annotated Chinese SFO entity RE dataset and a classifier combining features of SFO resource data.The proposed approach combines integrated learning and pre-training models,specifically utilizing Bidirectional Encoder Representation from Transformers(BERT).In addition,it incorporates one-class classification,attention mechanisms,and dynamic feedback mechanisms to improve the performance of the RE model.Experimental results show that the proposed method outperforms the traditional methods in terms of F1 value when extracting entity relations from both balanced and long-tailed datasets.
基金the Research Grant of Kwangwoon University in 2024.
文摘Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.
