Multi-label learning deals with objects associated with multiple class labels,and aims to induce a predictive model which can assign a set of relevant class labels for an unseen instance.Since each class might possess...Multi-label learning deals with objects associated with multiple class labels,and aims to induce a predictive model which can assign a set of relevant class labels for an unseen instance.Since each class might possess its own characteristics,the strategy of extracting label-specific features has been widely employed to improve the discrimination process in multi-label learning,where the predictive model is induced based on tailored features specific to each class label instead of the identical instance representations.As a representative approach,LIFT generates label-specific features by conducting clustering analysis.However,its performance may be degraded due to the inherent instability of the single clustering algorithm.To improve this,a novel multi-label learning approach named SENCE(stable label-Specific features gENeration for multi-label learning via mixture-based Clustering Ensemble)is proposed,which stabilizes the generation process of label-specific features via clustering ensemble techniques.Specifically,more stable clustering results are obtained by firstly augmenting the original instance repre-sentation with cluster assignments from base clusters and then fitting a mixture model via the expectation-maximization(EM)algorithm.Extensive experiments on eighteen benchmark data sets show that SENCE performs better than LIFT and other well-established multi-label learning algorithms.展开更多
Aiming at the problem of multi-label classification, a multi-label classification algorithm based on label-specific features is proposed in this paper. In this algorithm, we compute feature density on the positive and...Aiming at the problem of multi-label classification, a multi-label classification algorithm based on label-specific features is proposed in this paper. In this algorithm, we compute feature density on the positive and negative instances set of each class firstly and then select mk features of high density from the positive and negative instances set of each class, respectively; the intersec- tion is taken as the label-specific features of the corresponding class. Finally, multi-label data are classified on the basis of la- bel-specific features. The algorithm can show the label-specific features of each class. Experiments show that our proposed method, the MLSF algorithm, performs significantly better than the other state-of-the-art multi-label learning approaches.展开更多
In multi-label learning,the label-specific features learning framework can effectively solve the dimensional catastrophe problem brought by high-dimensional data.The classification performance and robustness of the mo...In multi-label learning,the label-specific features learning framework can effectively solve the dimensional catastrophe problem brought by high-dimensional data.The classification performance and robustness of the model are effectively improved.Most existing label-specific features learning utilizes the cosine similarity method to measure label correlation.It is well known that the correlation between labels is asymmetric.However,existing label-specific features learning only considers the private features of labels in classification and does not take into account the common features of labels.Based on this,this paper proposes a Causality-driven Common and Label-specific Features Learning,named CCSF algorithm.Firstly,the causal learning algorithm GSBN is used to calculate the asymmetric correlation between labels.Then,in the optimization,both l_(2,1)-norm and l_(1)-norm are used to select the corresponding features,respectively.Finally,it is compared with six state-of-the-art algorithms on nine datasets.The experimental results prove the effectiveness of the algorithm in this paper.展开更多
Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output codes.Existing works solve t...Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output codes.Existing works solve these binary classification problems in the original feature space,while it might be suboptimal as different binary classification problems correspond to different positive and negative examples.In this paper,we propose to learn label-specific features for each decomposed binary classification problem to consider the specific characteristics containing in its positive and negative examples.Specifically,to generate the label-specific features,clustering analysis is respectively conducted on the positive and negative examples in each decomposed binary data set to discover their inherent information and then label-specific features for one example are obtained by measuring the similarity between it and all cluster centers.Experiments clearly validate the effectiveness of learning label-specific features for decomposition-based multi-class classification.展开更多
AIM:To investigate the clinical features and prognosis of patients with orbital inflammatory myofibroblastic tumor(IMT).METHODS:This retrospective study collected clinical data from 22 patients diagnosed with orbital ...AIM:To investigate the clinical features and prognosis of patients with orbital inflammatory myofibroblastic tumor(IMT).METHODS:This retrospective study collected clinical data from 22 patients diagnosed with orbital IMT based on histopathological examination.The patients were followed up to assess their prognosis.Clinical data from patients,including age,gender,course of disease,past medical history,primary symptoms,ophthalmologic examination findings,general condition,as well as imaging,laboratory,histopathological,and immunohistochemical results from digital records were collected.Orbital magnetic resonance imaging(MRI)and(or)computed tomography(CT)scans were performed to assess bone destruction of the mass,invasion of surrounding tissues,and any inflammatory changes in periorbital areas.RESULTS:The mean age of patients with orbital IMT was 28.24±3.30y,with a male-to-female ratio of 1.2:1.Main clinical manifestations were proptosis,blurred vision,palpable mass,and pain.Bone destruction and surrounding tissue invasion occurred in 72.73%and 54.55%of cases,respectively.Inflammatory changes in the periorbital site were observed in 77.27%of the patients.Hematoxylin and eosin staining showed proliferation of fibroblasts and myofibroblasts,accompanied by infiltration of lymphocytes and plasma cells.Immunohistochemical staining revealed that smooth muscle actin(SMA)and vimentin were positive in 100%of cases,while anaplastic lymphoma kinase(ALK)showed positivity in 47.37%.The recurrence rate of orbital IMT was 27.27%,and sarcomatous degeneration could occur.There were no significant correlations between recurrence and factors such as age,gender,laterality,duration of the disease,periorbital tissue invasion,bone destruction,periorbital inflammation,tumor size,fever,leukocytosis,or treatment(P>0.05).However,lymphadenopathy and a Ki-67 index of 10%or higher may be risk factors for recurrence(P=0.046;P=0.023).CONCLUSION:Orbital IMT is a locally invasive disease that may recur or lead to sarcomatoid degeneration,primarily affecting young and middle-aged patients.The presence of lymphadenopathy and a Ki-67 index of 10%or higher may signify a poor prognosis.展开更多
The detection of steel surface anomalies has become an industrial challenge due to variations in production equipment,processes,and characteristics.To alleviate the problem,this paper proposes a detection and localiza...The detection of steel surface anomalies has become an industrial challenge due to variations in production equipment,processes,and characteristics.To alleviate the problem,this paper proposes a detection and localization method combining 3D depth and 2D RGB features.The framework comprises three stages:defect classification,defect location,an d warpage judgment.The first stage uses a dataefficient image Transformer model,the second stage utilizes reverse knowledge distillation,and the third stage performs feature fusion using3D depth and 2D RGB features.Experimental results show that the proposed algorithm achieves relatively high accuracy and feasibility,and can be effectively used in industrial scenarios.展开更多
Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish ...Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish a TCM-informed tool for early depression screening,thereby bridging traditional diagnostic principles with modern computational approaches.Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1,2022 to October 1,2023,as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group.Videos of 3–10 s were captured using a Xiaomi Pad 5,and the TCM spirit and expressions were determined by TCM experts(at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions).Basic information,facial images,and interview information were collected through a portable TCM intelligent analysis and diagnosis device,and facial diagnosis features were extracted using the Open CV computer vision library technology.Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data,TCM spirit and expression features,and facial diagnosis feature parameters of the two groups,to compare the differences in TCM spirit and expression and facial features.Five machine learning algorithms,including extreme gradient boosting(XGBoost),decision tree(DT),Bernoulli naive Bayes(BernoulliNB),support vector machine(SVM),and k-nearest neighbor(KNN)classification,were used to construct a depression recognition model based on the fusion of TCM spirit and expression features.The performance of the model was evaluated using metrics such as accuracy,precision,and the area under the receiver operating characteristic(ROC)curve(AUC).The model results were explained using the Shapley Additive exPlanations(SHAP).Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study.There was no statistically significant difference in the baseline characteristics between the two groups(P>0.05).The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows.(i)Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls(P<0.05),with characteristic features such as sad expressions,facial erythema,and changes in the lip color ranging from erythematous to cyanotic.(ii)Depressed patients exhibited significantly lower values in facial complexion L,lip L,and a values,and gloss index,but higher values in facial complexion a and b,lip b,low gloss index,and matte index(all P<0.05).(iii)The results of multiple models show that the XGBoost-based depression recognition model,integrating the TCM“spirit-expression”diagnostic framework,achieved an accuracy of 98.61%and significantly outperformed four benchmark algorithms—DT,BernoulliNB,SVM,and KNN(P<0.01).(iv)The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm,the complexion b value,categories of facial spirit,high gloss index,low gloss index,categories of facial expression and texture features have significant contribution to the model.Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model,offering a novel paradigm for objective depression diagnosis.展开更多
BACKGROUND SMARCB1/INI1-deficient pancreatic undifferentiated rhabdoid carcinoma is a highly aggressive tumor,and spontaneous splenic rupture(SSR)as its presenting manifestation is rarely reported among pancreatic mal...BACKGROUND SMARCB1/INI1-deficient pancreatic undifferentiated rhabdoid carcinoma is a highly aggressive tumor,and spontaneous splenic rupture(SSR)as its presenting manifestation is rarely reported among pancreatic malignancies.CASE SUMMARY We herein report a rare case of a 59-year-old female who presented with acute left upper quadrant abdominal pain without any history of trauma.Abdominal imaging demonstrated a heterogeneous splenic lesion with hemoperitoneum,raising clinical suspicion of SSR.Emergency laparotomy revealed a pancreatic tumor invading the spleen and left kidney,with associated splenic rupture and dense adhesions,necessitating en bloc resection of the distal pancreas,spleen,and left kidney.Histopathology revealed a biphasic malignancy composed of moderately differentiated pancreatic ductal adenocarcinoma and an undifferentiated carcinoma with rhabdoid morphology and loss of SMARCB1 expression.Immunohistochemical analysis confirmed complete loss of SMARCB1/INI1 in the undifferentiated component,along with a high Ki-67 index(approximately 80%)and CD10 positivity.The ductal adenocarcinoma component retained SMARCB1/INI1 expression and was positive for CK7 and CK-pan.Transitional zones between the two tumor components suggested progressive dedifferentiation and underlying genomic instability.The patient received adjuvant chemotherapy with gemcitabine and nab-paclitaxel and maintained a satisfactory quality of life at the 6-month follow-up.CONCLUSION This study reports a rare case of SMARCB1/INI1-deficient undifferentiated rhabdoid carcinoma of the pancreas combined with ductal adenocarcinoma,presenting as SSR-an exceptionally uncommon initial manifestation of pancreatic malignancy.展开更多
Improved delay detached eddy simulation is performed to explore the flow features and aero-optical effects of turrets with different bottom cylinder height at a freestream Mach number Ma=0.7.Analysis of both the time-...Improved delay detached eddy simulation is performed to explore the flow features and aero-optical effects of turrets with different bottom cylinder height at a freestream Mach number Ma=0.7.Analysis of both the time-averaged and instantaneous flow features demonstrate that the shock motion causes the oscillation of separated shear layer.In flow analysis,two unsteady shock-wake-correlated modes are discerned:the asymmetric shifting mode and the symmetric breathing mode.With the increase of cylinder height,the relative energy of shock gradually increases,which goes from 26%to 59%.The proper orthogonal decomposition analysis yields the single frequency peak for the two dominant modes.The frequency peaks of shifting mode are generally at StD<0.23,while the frequency peaks of breathing mode are generally at StD>0.26.The dynamic mode decomposition analysis gives range of frequency peak.The frequency peaks of shifting mode are in the range of StD=0.11-0.23,and the frequency peaks of breathing mode are in range of StD=0.26-0.41.Optical distortion analysis indicates that the distortion calculated in five cases is linked to the breathing mode.When the beam passes through the turbulent wake,it exhibits the high-frequency and high-amplitude characteristics.展开更多
This study proposes a multimodal deep learning framework for joint prediction of the state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries.Twelve representative impedance features-covering charge-...This study proposes a multimodal deep learning framework for joint prediction of the state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries.Twelve representative impedance features-covering charge-transfer resistance,solid electrolyte interface(SEI)layer impedance,and ion diffusion-are extracted from electrochemical impedance spectroscopy(EIS)and combined with short voltage/current segments to form a compact,interpretable feature set.A residual multi-layer perceptron(ResMLP)is employed for SOH regression,and a temporal convolutional network with attention(TCNAttention)is used for RUL estimation.Lifetime experiments on two battery types with different chemistries and form factors,evaluated through three rounds of paired cross-validation,validate the approach.Results show that the proposed features significantly reduce dimensionality and computational cost while substantially lowering SOH error,achieving an average normalized root mean square error of 2.3%.The RUL prediction reaches an average error of 14.8%.Overall,the framework balances interpretability,robustness,and feasibility,providing a practical solution for battery management systems(BMS)monitoring and life prediction.展开更多
Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies ha...Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.展开更多
In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,...In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.展开更多
Phishing email detection represents a critical research challenge in cybersecurity.To address this,this paper proposes a novel Double-S(statistical-semantic)feature model based on three core entities involved in email...Phishing email detection represents a critical research challenge in cybersecurity.To address this,this paper proposes a novel Double-S(statistical-semantic)feature model based on three core entities involved in email communication:the sender,recipient,and email content.We employ strategic game theory to analyze the offensive strategies of phishing attackers and defensive strategies of protectors,extracting statistical features from these entities.We also leverage the Qwen large language model to excavate implicit semantic features(e.g.,emotional manipulation and social engineering tactics)from email content.By integrating statistical and semantic features,our model achieves a robust representation of phishing emails.We introduce a hybrid detection model that integrates a convolutional neural network(CNN)module with the XGBoost(Extreme Gradient Boosting)classifier,effectively capturing local correlations in high-dimensional features.Experimental results on real-world phishing email datasets demonstrate the superiority of our approach,achieving an F1-score of 0.9587,precision of 0.9591,and recall of 0.9583,representing improvements of 1.3%–10.6%compared to state-of-the-art methods.展开更多
By integrating self-localization,environment mapping,and dynamic object tracking into a unified framework,visual simultaneous localization and mapping with multiple object tracking(SLAMMOT)enhances decision-making and...By integrating self-localization,environment mapping,and dynamic object tracking into a unified framework,visual simultaneous localization and mapping with multiple object tracking(SLAMMOT)enhances decision-making and interaction capabilities in applications such as autonomous driving,robotic navigation,and augmented reality.While numerous outstanding visual SLAMMOT methods have been proposed,the majority rely only on point features,overlooking the abundant and stable planar features in artificial objects that can provide valuable constraints.To address this limitation,we propose OP(object planar)-SLAM,an RGB-D SLAMMOT system that leverages planar features to improve object pose estimation and reconstruction accuracy.Specifically,we introduce an accurate object planar feature extraction and association method using normal images,alongside a novel object bundle adjustment framework that incorporates planar constraints for enhanced optimization.The proposed system is evaluated on both synthetic and public real-world datasets,including Oxford multimotion dataset(OMD)and KITTI tracking dataset.Especially on the OMD,where planar features are prominent,our method improves object pose estimation accuracy by approximately 60%.Extensive experiments demonstrate its effectiveness in enhancing object pose estimation and reconstruction,achieving notable performance compared with existing methods.Furthermore,OP-SLAM runs in real time,making it suitable for practical robots and augmented reality applications.展开更多
Multi-label learning deals with the problem where each instance is associated with a set of class labels.In multilabel learning,different labels may have their own inherent characteristics for distinguishing each othe...Multi-label learning deals with the problem where each instance is associated with a set of class labels.In multilabel learning,different labels may have their own inherent characteristics for distinguishing each other,and the correlation information has shown promising strength in improving multi-label learning.In this study,we propose a novel multilabel learning method by simultaneously taking into account both the learning of label-specific features and the correlation information during the learning process.Firstly,we learn a sparse weight parameter vector for each label based on the linear regression model,and the label-specific features can be extracted according to the corresponding weight parameters.Secondly,we constrain label correlations directly on the output of labels,not on the corresponding parameter vectors which conflicts with the label-specific feature learning.Specifically,for any two related labels,their corresponding models should have similar outputs rather than similar parameter vectors.Thirdly,we also exploit the sample correlations through sparse reconstruction.The experimental results on 12 benchmark datasets show that the proposed method performs better than the existing methods.The proposed method ranks in the 1st place at 66.7%case and achieves optimal average rank in terms of all evaluation measures.展开更多
In the article“A Lightweight Approach for Skin Lesion Detection through Optimal Features Fusion”by Khadija Manzoor,Fiaz Majeed,Ansar Siddique,Talha Meraj,Hafiz Tayyab Rauf,Mohammed A.El-Meligy,Mohamed Sharaf,Abd Ela...In the article“A Lightweight Approach for Skin Lesion Detection through Optimal Features Fusion”by Khadija Manzoor,Fiaz Majeed,Ansar Siddique,Talha Meraj,Hafiz Tayyab Rauf,Mohammed A.El-Meligy,Mohamed Sharaf,Abd Elatty E.Abd Elgawad Computers,Materials&Continua,2022,Vol.70,No.1,pp.1617–1630.DOI:10.32604/cmc.2022.018621,URL:https://www.techscience.com/cmc/v70n1/44361,there was an error regarding the affiliation for the author Hafiz Tayyab Rauf.Instead of“Centre for Smart Systems,AI and Cybersecurity,Staffordshire University,Stoke-on-Trent,UK”,the affiliation should be“Independent Researcher,Bradford,BD80HS,UK”.展开更多
The wheat above-ground biomass(AGB)is an important index that shows the life activity of vegetation,which is of great significance for wheat growth monitoring and yield prediction.Traditional biomass estimation method...The wheat above-ground biomass(AGB)is an important index that shows the life activity of vegetation,which is of great significance for wheat growth monitoring and yield prediction.Traditional biomass estimation methods specifically include sample surveys and harvesting statistics.Although these methods have high estimation accuracy,they are time-consuming,destructive,and difficult to implement to monitor the biomass at a large scale.The main objective of this study is to optimize the traditional remote sensing methods to estimate the wheat AGBbased on improved convolutional features(CFs).Low-cost unmanned aerial vehicles(UAV)were used as the main data acquisition equipment.This study acquired image data acquired by RGB camera(RGB)and multi-spectral(MS)image data of the wheat population canopy for two wheat varieties and five key growth stages.Then,field measurements were conducted to obtain the actual wheat biomass data for validation.Based on the remote sensing indices(RSIs),structural features(SFs),and CFs,this study proposed a new feature named AUR-50(multi-source combination based on convolutional feature optimization)to estimate the wheat AGB.The results show that AUR-50 could estimate the wheat AGB more accurately than RSIs and SFs,and the average R^(2) exceeded 0.77.In the overwintering period,AUR-50_(MS)(multi-source combination with convolutional feature optimization using multispectral imagery)had the highest estimation accuracy(R^(2) of 0.88).In addition,AUR-50 reduced the effect of the vegetation index saturation on the biomass estimation accuracy by adding CFs,where the highest R^(2) was 0.69 at the flowering stage.The results of this study provide an effective method to evaluate the AGB in wheat with high throughput and a research reference for the phenotypic parameters of other crops.展开更多
BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features ...BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.展开更多
During Donald Trump’s first term,the“Trump Shock”brought world politics into an era of uncertainties and pulled the transatlantic alliance down to its lowest point in history.The Trump 2.0 tsunami brewed by the 202...During Donald Trump’s first term,the“Trump Shock”brought world politics into an era of uncertainties and pulled the transatlantic alliance down to its lowest point in history.The Trump 2.0 tsunami brewed by the 2024 presidential election of the United States has plunged the U.S.-Europe relations into more gloomy waters,ushering in a more complex and turbulent period of adjustment.展开更多
The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method f...The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.展开更多
基金This work was supported by the National Science Foundation of China(62176055)the China University S&T Innovation Plan Guided by the Ministry of Education.
文摘Multi-label learning deals with objects associated with multiple class labels,and aims to induce a predictive model which can assign a set of relevant class labels for an unseen instance.Since each class might possess its own characteristics,the strategy of extracting label-specific features has been widely employed to improve the discrimination process in multi-label learning,where the predictive model is induced based on tailored features specific to each class label instead of the identical instance representations.As a representative approach,LIFT generates label-specific features by conducting clustering analysis.However,its performance may be degraded due to the inherent instability of the single clustering algorithm.To improve this,a novel multi-label learning approach named SENCE(stable label-Specific features gENeration for multi-label learning via mixture-based Clustering Ensemble)is proposed,which stabilizes the generation process of label-specific features via clustering ensemble techniques.Specifically,more stable clustering results are obtained by firstly augmenting the original instance repre-sentation with cluster assignments from base clusters and then fitting a mixture model via the expectation-maximization(EM)algorithm.Extensive experiments on eighteen benchmark data sets show that SENCE performs better than LIFT and other well-established multi-label learning algorithms.
基金Supported by the Opening Fund of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education (93K-17-2010-K02)the Opening Fund of Key Discipline of Computer Soft-Ware and Theory of Zhejiang Province at Zhejiang Normal University (ZSDZZZZXK05)
文摘Aiming at the problem of multi-label classification, a multi-label classification algorithm based on label-specific features is proposed in this paper. In this algorithm, we compute feature density on the positive and negative instances set of each class firstly and then select mk features of high density from the positive and negative instances set of each class, respectively; the intersec- tion is taken as the label-specific features of the corresponding class. Finally, multi-label data are classified on the basis of la- bel-specific features. The algorithm can show the label-specific features of each class. Experiments show that our proposed method, the MLSF algorithm, performs significantly better than the other state-of-the-art multi-label learning approaches.
基金2022 University Research Priorities,No.2022AH051989.
文摘In multi-label learning,the label-specific features learning framework can effectively solve the dimensional catastrophe problem brought by high-dimensional data.The classification performance and robustness of the model are effectively improved.Most existing label-specific features learning utilizes the cosine similarity method to measure label correlation.It is well known that the correlation between labels is asymmetric.However,existing label-specific features learning only considers the private features of labels in classification and does not take into account the common features of labels.Based on this,this paper proposes a Causality-driven Common and Label-specific Features Learning,named CCSF algorithm.Firstly,the causal learning algorithm GSBN is used to calculate the asymmetric correlation between labels.Then,in the optimization,both l_(2,1)-norm and l_(1)-norm are used to select the corresponding features,respectively.Finally,it is compared with six state-of-the-art algorithms on nine datasets.The experimental results prove the effectiveness of the algorithm in this paper.
基金supported by the National Natural Science Foundation of China(Grant No.62225602).
文摘Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output codes.Existing works solve these binary classification problems in the original feature space,while it might be suboptimal as different binary classification problems correspond to different positive and negative examples.In this paper,we propose to learn label-specific features for each decomposed binary classification problem to consider the specific characteristics containing in its positive and negative examples.Specifically,to generate the label-specific features,clustering analysis is respectively conducted on the positive and negative examples in each decomposed binary data set to discover their inherent information and then label-specific features for one example are obtained by measuring the similarity between it and all cluster centers.Experiments clearly validate the effectiveness of learning label-specific features for decomposition-based multi-class classification.
基金Supported by the National Key R&D Program of China(No.2023YFC2410203)Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support(No.ZLRK202503).
文摘AIM:To investigate the clinical features and prognosis of patients with orbital inflammatory myofibroblastic tumor(IMT).METHODS:This retrospective study collected clinical data from 22 patients diagnosed with orbital IMT based on histopathological examination.The patients were followed up to assess their prognosis.Clinical data from patients,including age,gender,course of disease,past medical history,primary symptoms,ophthalmologic examination findings,general condition,as well as imaging,laboratory,histopathological,and immunohistochemical results from digital records were collected.Orbital magnetic resonance imaging(MRI)and(or)computed tomography(CT)scans were performed to assess bone destruction of the mass,invasion of surrounding tissues,and any inflammatory changes in periorbital areas.RESULTS:The mean age of patients with orbital IMT was 28.24±3.30y,with a male-to-female ratio of 1.2:1.Main clinical manifestations were proptosis,blurred vision,palpable mass,and pain.Bone destruction and surrounding tissue invasion occurred in 72.73%and 54.55%of cases,respectively.Inflammatory changes in the periorbital site were observed in 77.27%of the patients.Hematoxylin and eosin staining showed proliferation of fibroblasts and myofibroblasts,accompanied by infiltration of lymphocytes and plasma cells.Immunohistochemical staining revealed that smooth muscle actin(SMA)and vimentin were positive in 100%of cases,while anaplastic lymphoma kinase(ALK)showed positivity in 47.37%.The recurrence rate of orbital IMT was 27.27%,and sarcomatous degeneration could occur.There were no significant correlations between recurrence and factors such as age,gender,laterality,duration of the disease,periorbital tissue invasion,bone destruction,periorbital inflammation,tumor size,fever,leukocytosis,or treatment(P>0.05).However,lymphadenopathy and a Ki-67 index of 10%or higher may be risk factors for recurrence(P=0.046;P=0.023).CONCLUSION:Orbital IMT is a locally invasive disease that may recur or lead to sarcomatoid degeneration,primarily affecting young and middle-aged patients.The presence of lymphadenopathy and a Ki-67 index of 10%or higher may signify a poor prognosis.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No. HC-CN-20221107001。
文摘The detection of steel surface anomalies has become an industrial challenge due to variations in production equipment,processes,and characteristics.To alleviate the problem,this paper proposes a detection and localization method combining 3D depth and 2D RGB features.The framework comprises three stages:defect classification,defect location,an d warpage judgment.The first stage uses a dataefficient image Transformer model,the second stage utilizes reverse knowledge distillation,and the third stage performs feature fusion using3D depth and 2D RGB features.Experimental results show that the proposed algorithm achieves relatively high accuracy and feasibility,and can be effectively used in industrial scenarios.
基金General Program of National Natural Science Foundation of China(82474390)Construction Project of Pudong New Area Famous TCM Studios(National Pilot Zone for TCM Development,Shanghai)(PDZY-2025-0716)Shanghai Municipal Science and Technology Program Project Shanghai Key Laboratory of Health Identification and Assessment(21DZ2271000).
文摘Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish a TCM-informed tool for early depression screening,thereby bridging traditional diagnostic principles with modern computational approaches.Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1,2022 to October 1,2023,as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group.Videos of 3–10 s were captured using a Xiaomi Pad 5,and the TCM spirit and expressions were determined by TCM experts(at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions).Basic information,facial images,and interview information were collected through a portable TCM intelligent analysis and diagnosis device,and facial diagnosis features were extracted using the Open CV computer vision library technology.Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data,TCM spirit and expression features,and facial diagnosis feature parameters of the two groups,to compare the differences in TCM spirit and expression and facial features.Five machine learning algorithms,including extreme gradient boosting(XGBoost),decision tree(DT),Bernoulli naive Bayes(BernoulliNB),support vector machine(SVM),and k-nearest neighbor(KNN)classification,were used to construct a depression recognition model based on the fusion of TCM spirit and expression features.The performance of the model was evaluated using metrics such as accuracy,precision,and the area under the receiver operating characteristic(ROC)curve(AUC).The model results were explained using the Shapley Additive exPlanations(SHAP).Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study.There was no statistically significant difference in the baseline characteristics between the two groups(P>0.05).The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows.(i)Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls(P<0.05),with characteristic features such as sad expressions,facial erythema,and changes in the lip color ranging from erythematous to cyanotic.(ii)Depressed patients exhibited significantly lower values in facial complexion L,lip L,and a values,and gloss index,but higher values in facial complexion a and b,lip b,low gloss index,and matte index(all P<0.05).(iii)The results of multiple models show that the XGBoost-based depression recognition model,integrating the TCM“spirit-expression”diagnostic framework,achieved an accuracy of 98.61%and significantly outperformed four benchmark algorithms—DT,BernoulliNB,SVM,and KNN(P<0.01).(iv)The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm,the complexion b value,categories of facial spirit,high gloss index,low gloss index,categories of facial expression and texture features have significant contribution to the model.Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model,offering a novel paradigm for objective depression diagnosis.
文摘BACKGROUND SMARCB1/INI1-deficient pancreatic undifferentiated rhabdoid carcinoma is a highly aggressive tumor,and spontaneous splenic rupture(SSR)as its presenting manifestation is rarely reported among pancreatic malignancies.CASE SUMMARY We herein report a rare case of a 59-year-old female who presented with acute left upper quadrant abdominal pain without any history of trauma.Abdominal imaging demonstrated a heterogeneous splenic lesion with hemoperitoneum,raising clinical suspicion of SSR.Emergency laparotomy revealed a pancreatic tumor invading the spleen and left kidney,with associated splenic rupture and dense adhesions,necessitating en bloc resection of the distal pancreas,spleen,and left kidney.Histopathology revealed a biphasic malignancy composed of moderately differentiated pancreatic ductal adenocarcinoma and an undifferentiated carcinoma with rhabdoid morphology and loss of SMARCB1 expression.Immunohistochemical analysis confirmed complete loss of SMARCB1/INI1 in the undifferentiated component,along with a high Ki-67 index(approximately 80%)and CD10 positivity.The ductal adenocarcinoma component retained SMARCB1/INI1 expression and was positive for CK7 and CK-pan.Transitional zones between the two tumor components suggested progressive dedifferentiation and underlying genomic instability.The patient received adjuvant chemotherapy with gemcitabine and nab-paclitaxel and maintained a satisfactory quality of life at the 6-month follow-up.CONCLUSION This study reports a rare case of SMARCB1/INI1-deficient undifferentiated rhabdoid carcinoma of the pancreas combined with ductal adenocarcinoma,presenting as SSR-an exceptionally uncommon initial manifestation of pancreatic malignancy.
基金funded by the National Key Lab Foundation,China(No.2020KLF030101)the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China(No.CX2025031)Shaanxi Innovative Research Team of Artificial Intelligence for Fluid Mechanics,China(No.2024RS-CXTD-16)。
文摘Improved delay detached eddy simulation is performed to explore the flow features and aero-optical effects of turrets with different bottom cylinder height at a freestream Mach number Ma=0.7.Analysis of both the time-averaged and instantaneous flow features demonstrate that the shock motion causes the oscillation of separated shear layer.In flow analysis,two unsteady shock-wake-correlated modes are discerned:the asymmetric shifting mode and the symmetric breathing mode.With the increase of cylinder height,the relative energy of shock gradually increases,which goes from 26%to 59%.The proper orthogonal decomposition analysis yields the single frequency peak for the two dominant modes.The frequency peaks of shifting mode are generally at StD<0.23,while the frequency peaks of breathing mode are generally at StD>0.26.The dynamic mode decomposition analysis gives range of frequency peak.The frequency peaks of shifting mode are in the range of StD=0.11-0.23,and the frequency peaks of breathing mode are in range of StD=0.26-0.41.Optical distortion analysis indicates that the distortion calculated in five cases is linked to the breathing mode.When the beam passes through the turbulent wake,it exhibits the high-frequency and high-amplitude characteristics.
基金financially supported by the National Natural Science Foundation of China(No.U22A20439)the Shenzhen Fundamental Research Program(No.JCYJ20220818100418040)+2 种基金the Guangdong-Hong Kong-Macao Joint Innovation Fund(No.2024A0505040001)the Guangdong Basic and Applied Basic Research Foundation(2023A1515011122)the Shenzhen ShowMac Network Technology Co.,Ltd.
文摘This study proposes a multimodal deep learning framework for joint prediction of the state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries.Twelve representative impedance features-covering charge-transfer resistance,solid electrolyte interface(SEI)layer impedance,and ion diffusion-are extracted from electrochemical impedance spectroscopy(EIS)and combined with short voltage/current segments to form a compact,interpretable feature set.A residual multi-layer perceptron(ResMLP)is employed for SOH regression,and a temporal convolutional network with attention(TCNAttention)is used for RUL estimation.Lifetime experiments on two battery types with different chemistries and form factors,evaluated through three rounds of paired cross-validation,validate the approach.Results show that the proposed features significantly reduce dimensionality and computational cost while substantially lowering SOH error,achieving an average normalized root mean square error of 2.3%.The RUL prediction reaches an average error of 14.8%.Overall,the framework balances interpretability,robustness,and feasibility,providing a practical solution for battery management systems(BMS)monitoring and life prediction.
基金supported by the Tianjin Manufacturing High Quality Development Special Foundation(No.20232185)the Roycom Foundation(No.70306901).
文摘Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.
文摘In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.
基金supported by the National Key Research and Development Program of China(No.2023YFB3105700).
文摘Phishing email detection represents a critical research challenge in cybersecurity.To address this,this paper proposes a novel Double-S(statistical-semantic)feature model based on three core entities involved in email communication:the sender,recipient,and email content.We employ strategic game theory to analyze the offensive strategies of phishing attackers and defensive strategies of protectors,extracting statistical features from these entities.We also leverage the Qwen large language model to excavate implicit semantic features(e.g.,emotional manipulation and social engineering tactics)from email content.By integrating statistical and semantic features,our model achieves a robust representation of phishing emails.We introduce a hybrid detection model that integrates a convolutional neural network(CNN)module with the XGBoost(Extreme Gradient Boosting)classifier,effectively capturing local correlations in high-dimensional features.Experimental results on real-world phishing email datasets demonstrate the superiority of our approach,achieving an F1-score of 0.9587,precision of 0.9591,and recall of 0.9583,representing improvements of 1.3%–10.6%compared to state-of-the-art methods.
基金Supported by Major Science and Technology Project of Hubei Province(2022AAA009)。
文摘By integrating self-localization,environment mapping,and dynamic object tracking into a unified framework,visual simultaneous localization and mapping with multiple object tracking(SLAMMOT)enhances decision-making and interaction capabilities in applications such as autonomous driving,robotic navigation,and augmented reality.While numerous outstanding visual SLAMMOT methods have been proposed,the majority rely only on point features,overlooking the abundant and stable planar features in artificial objects that can provide valuable constraints.To address this limitation,we propose OP(object planar)-SLAM,an RGB-D SLAMMOT system that leverages planar features to improve object pose estimation and reconstruction accuracy.Specifically,we introduce an accurate object planar feature extraction and association method using normal images,alongside a novel object bundle adjustment framework that incorporates planar constraints for enhanced optimization.The proposed system is evaluated on both synthetic and public real-world datasets,including Oxford multimotion dataset(OMD)and KITTI tracking dataset.Especially on the OMD,where planar features are prominent,our method improves object pose estimation accuracy by approximately 60%.Extensive experiments demonstrate its effectiveness in enhancing object pose estimation and reconstruction,achieving notable performance compared with existing methods.Furthermore,OP-SLAM runs in real time,making it suitable for practical robots and augmented reality applications.
基金Supported by:This work was partially supported by the National Natural Science Foundation of China under Grant Nos.61773208 and 61906090the Natural Science Foundation of Jiangsu Province of China under Grant Nos.BK20191287 and BK20170809.
文摘Multi-label learning deals with the problem where each instance is associated with a set of class labels.In multilabel learning,different labels may have their own inherent characteristics for distinguishing each other,and the correlation information has shown promising strength in improving multi-label learning.In this study,we propose a novel multilabel learning method by simultaneously taking into account both the learning of label-specific features and the correlation information during the learning process.Firstly,we learn a sparse weight parameter vector for each label based on the linear regression model,and the label-specific features can be extracted according to the corresponding weight parameters.Secondly,we constrain label correlations directly on the output of labels,not on the corresponding parameter vectors which conflicts with the label-specific feature learning.Specifically,for any two related labels,their corresponding models should have similar outputs rather than similar parameter vectors.Thirdly,we also exploit the sample correlations through sparse reconstruction.The experimental results on 12 benchmark datasets show that the proposed method performs better than the existing methods.The proposed method ranks in the 1st place at 66.7%case and achieves optimal average rank in terms of all evaluation measures.
文摘In the article“A Lightweight Approach for Skin Lesion Detection through Optimal Features Fusion”by Khadija Manzoor,Fiaz Majeed,Ansar Siddique,Talha Meraj,Hafiz Tayyab Rauf,Mohammed A.El-Meligy,Mohamed Sharaf,Abd Elatty E.Abd Elgawad Computers,Materials&Continua,2022,Vol.70,No.1,pp.1617–1630.DOI:10.32604/cmc.2022.018621,URL:https://www.techscience.com/cmc/v70n1/44361,there was an error regarding the affiliation for the author Hafiz Tayyab Rauf.Instead of“Centre for Smart Systems,AI and Cybersecurity,Staffordshire University,Stoke-on-Trent,UK”,the affiliation should be“Independent Researcher,Bradford,BD80HS,UK”.
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(SJCX23_1973)the National Natural Science Foundation of China(32172110,32071945)+7 种基金the Key Research and Development Program(Modern Agriculture)of Jiangsu Province,China(BE2022342-2,BE2020319)the Anhui Province Crop Intelligent Planting and Processing Technology Engineering Research Center Open Project,China(ZHKF04)the National Key Research and Development Program of China(2023YFD2300201,2023YFD1202200)the Special Funds for Scientific and Technological Innovation of Jiangsu Province,China(BE2022425)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China(PAPD)the Central Publicinterest Scientific Institution Basal Research Fund,China(JBYW-AII-2023-08)the Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences(CAAS-CS-202201)the Special Fund for Independent Innovation of Agriculture Science and Technology in Jiangsu Province,China(CX(22)3112)。
文摘The wheat above-ground biomass(AGB)is an important index that shows the life activity of vegetation,which is of great significance for wheat growth monitoring and yield prediction.Traditional biomass estimation methods specifically include sample surveys and harvesting statistics.Although these methods have high estimation accuracy,they are time-consuming,destructive,and difficult to implement to monitor the biomass at a large scale.The main objective of this study is to optimize the traditional remote sensing methods to estimate the wheat AGBbased on improved convolutional features(CFs).Low-cost unmanned aerial vehicles(UAV)were used as the main data acquisition equipment.This study acquired image data acquired by RGB camera(RGB)and multi-spectral(MS)image data of the wheat population canopy for two wheat varieties and five key growth stages.Then,field measurements were conducted to obtain the actual wheat biomass data for validation.Based on the remote sensing indices(RSIs),structural features(SFs),and CFs,this study proposed a new feature named AUR-50(multi-source combination based on convolutional feature optimization)to estimate the wheat AGB.The results show that AUR-50 could estimate the wheat AGB more accurately than RSIs and SFs,and the average R^(2) exceeded 0.77.In the overwintering period,AUR-50_(MS)(multi-source combination with convolutional feature optimization using multispectral imagery)had the highest estimation accuracy(R^(2) of 0.88).In addition,AUR-50 reduced the effect of the vegetation index saturation on the biomass estimation accuracy by adding CFs,where the highest R^(2) was 0.69 at the flowering stage.The results of this study provide an effective method to evaluate the AGB in wheat with high throughput and a research reference for the phenotypic parameters of other crops.
文摘BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.
文摘During Donald Trump’s first term,the“Trump Shock”brought world politics into an era of uncertainties and pulled the transatlantic alliance down to its lowest point in history.The Trump 2.0 tsunami brewed by the 2024 presidential election of the United States has plunged the U.S.-Europe relations into more gloomy waters,ushering in a more complex and turbulent period of adjustment.
基金Supported by the Henan Province Key Research and Development Project(231111211300)the Central Government of Henan Province Guides Local Science and Technology Development Funds(Z20231811005)+2 种基金Henan Province Key Research and Development Project(231111110100)Henan Provincial Outstanding Foreign Scientist Studio(GZS2024006)Henan Provincial Joint Fund for Scientific and Technological Research and Development Plan(Application and Overcoming Technical Barriers)(242103810028)。
文摘The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.
