Once more to the lake is a representative work of the American Writer, Elwyn Brooks White, and has been acclaimed by people in the world. The paper offers a three-layered contrasting analysis of the essay. By detailed...Once more to the lake is a representative work of the American Writer, Elwyn Brooks White, and has been acclaimed by people in the world. The paper offers a three-layered contrasting analysis of the essay. By detailed analysis of the contrasts running through the whole essay, the author finds that those contrasts are symbol of White's internal struggle and reflection for life.On the foundation of the three-layered contrasts, the paper presents the theme analysis and clarifies the content of the essay.展开更多
Using the monthly geopotential heights and winds for 700 and 200 hPa for India during July and August, and the weekly M-100 Soviet rocketsonde temperature and wind data for Thumba (8.5 ° N, 76.9 ° E) during ...Using the monthly geopotential heights and winds for 700 and 200 hPa for India during July and August, and the weekly M-100 Soviet rocketsonde temperature and wind data for Thumba (8.5 ° N, 76.9 ° E) during the last week of June and the first week of September for the two contrasting summer monsoon years 1975 (a very strong monsoon year) and 1979 (a very weak monsoon year), a study has been made to examine the mean circulation features of the troposphere over India, and the structures of the temperatures and the winds of the middle atmosphere over Thumba. The study suggested that the axis of the monsoon trough (AMT) at 700 hPa shifted southward in 1975 and northward towards the foothills of the Himalayas in 1979, from its normal position. Superimposed on the low-pressure area (AMT) at 700 hPa, a well-defined divergence was noticed at 200 hPa over the northern India in 1975.The mean temperatures at 25,50 and 60 km (middle atmosphere) over Thumba were cooler in 1975 than in 1979. While a cooling trend in 1975 and warming trend in 1979 were observed at 25 and 50 km, a reversed picture was noticed at 60 km. There was a weak easterly / westerly (weak westerly phase) zonal wind in 1975 and a strong easterly zonal wind in 1979. A phase reversal of the zonal wind was observed at 50 km. A tentative physical mechanism was offered, in terms of upward propagation of the two equatorially trapped planetary waves i.e. the Kelvin and the mixed Rossby-gravity waves, to explain the occurrence of the two spells of strong warmings in the mesosphere in 1975.展开更多
The iconic image of a giant radiating dyke swarm subsequently fragmented into three pieces via supercontinental breakup was produced by Paul May in1971(see next page).That figure presented a large part of
As the risk of malware is sharply increasing in Android platform,Android malware detection has become an important research topic.Existing works have demonstrated that required permissions of Android applications are ...As the risk of malware is sharply increasing in Android platform,Android malware detection has become an important research topic.Existing works have demonstrated that required permissions of Android applications are valuable for malware analysis,but how to exploit those permission patterns for malware detection remains an open issue.In this paper,we introduce the contrasting permission patterns to characterize the essential differences between malwares and clean applications from the permission aspect Then a framework based on contrasting permission patterns is presented for Android malware detection.According to the proposed framework,an ensemble classifier,Enclamald,is further developed to detect whether an application is potentially malicious.Every contrasting permission pattern is acting as a weak classifier in Enclamald,and the weighted predictions of involved weak classifiers are aggregated to the final result.Experiments on real-world applications validate that the proposed Enclamald classifier outperforms commonly used classifiers for Android Malware Detection.展开更多
The Awakening is about the heroine Edna Pontellier awakes and tries to reset her relation with the world in New Orleans,a place the family chose to spend the summer, where there also are influences working their way t...The Awakening is about the heroine Edna Pontellier awakes and tries to reset her relation with the world in New Orleans,a place the family chose to spend the summer, where there also are influences working their way to awake Edna. Among them, theeffects from Madame Ratignolle and Madame Reisz are of great importance in the process of Edna's awakening and to her final sui-cide. This dissertation will see how Edna is affected by and distinguished from them, and get the conclusion that Edna fails being alife mediator by contrasting with the two women.展开更多
Drill string vibration during drilling plays a vital and potentially decisive role in maintaining wellbore stability,as repeated impacts may lead to fatigue and borehole collapse.While drilling through geological laye...Drill string vibration during drilling plays a vital and potentially decisive role in maintaining wellbore stability,as repeated impacts may lead to fatigue and borehole collapse.While drilling through geological layers,a material contrast may act as a localization point for wellbore damage.The hypothesis tested in this paper is that wellbore instability is focused on the boundary between the layers and that mechanical contrasts accelerate the wellbore collapse.In this study,an elastic-plastic damage model was employed to investigate the effects of repeated mechanical impacts on wellbore stability.A 2-dimensional(2D)model of a wellbore surrounded by contrasting materials was developed,and the accumulated damage caused by repeated lateral impacts was monitored.It was found that damage develops not only around the wall of the wellbore but also along the material boundaries.A sensitivity analysis was carried out to identify the impact of contrasts in both elastic(Young's modulus and Poisson's ratio)and plastic(cohesion,friction angle,and dilation angle)parameters between layers.Four damage patterns were identifiedin the simulated models.The results also suggested that the number of impacts required to reach the critical damage was highly affected by the contrast in elastic parameters,while cohesion and friction angle contrasts had a lesser effect.Additionally,increasing the contrast in the dilation angle localized the damage,thus reducing the number of impacts required to trigger wellbore failure.展开更多
In high-intensity electromagnetic warfare,radar systems are persistently subjected to multi-jammer attacks,including potentially novel unknown jamming types that may emerge exclusively under wartime conditions.These j...In high-intensity electromagnetic warfare,radar systems are persistently subjected to multi-jammer attacks,including potentially novel unknown jamming types that may emerge exclusively under wartime conditions.These jamming signals severely degrade radar detection performance.Precise recognition of these unknown and compound jamming signals is critical to enhancing the anti-jamming capabilities and overall reliability of radar systems.To address this challenge,this article proposes a novel open-set compound jamming cognition(OSCJC)method.The proposed method employs a detection-classification dual-network architecture,which not only overcomes the false alarm and misdetection issues of traditional closed-set recognition methods when dealing with unknown jamming but also effectively addresses the performance bottleneck of existing open-set recognition techniques focusing on single jamming scenarios in compound jamming environments.To achieve unknown jamming detection,we first employ a consistency labeling strategy to train the detection network using diverse known jamming samples.This strategy enables the network to acquire highly generalizable jamming features,thereby accurately localizing candidate regions for individual jamming components within compound jamming.Subsequently,we introduce contrastive learning to optimize the classification network,significantly enhancing both intra-class clustering and inter-class separability in the jamming feature space.This method not only improves the recognition accuracy of the classification network for known jamming types but also enhances its sensitivity to unknown jamming types.Simulations and experimental data are used to verify the effectiveness of the proposed OSCJC method.Compared with the state-of-the-art open-set recognition methods,the proposed method demonstrates superior recognition accuracy and enhanced environmental adaptability.展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
This study aims to develop a novel,cost-effective method for fabricating silicone vascular phantoms(SVPs)using"chewy candy"as a dissolvable core material.The study explores the feasibility of using chewy can...This study aims to develop a novel,cost-effective method for fabricating silicone vascular phantoms(SVPs)using"chewy candy"as a dissolvable core material.The study explores the feasibility of using chewy candy to create detailed and intricate vascular models for clinical applications.The chewy candy,an amorphous material,was manually extruded to form vascular models of varying diameters.These models were embedded in a silicone mixture,which was then cured.The chewy candy was subsequently dissolved,leaving behind hollow silicone vascular channels.The SVPs were evaluated for their morphological accuracy and functionality through laser speckle contrast imaging.The SVPs successfully replicated vascular channels with consistent diameters,demonstrating minimal variation across different regions.Functional evaluation using laser speckle contrast imaging revealed distinct flow dynamics in Y-shaped and H-shaped SVPs,highlighting the potential for these phantoms to simulate realistic fluid dynamics in vascular systems.This study presents a simple,time-saving,and innovative approach to fabricating complex 3D SVPs using chewy candy.This method offers a viable alternative to traditional fabrication techniques,with potential applications in various biomedical fields.展开更多
Adjuvants enhance and prolong the immune response to therapeutic agents,such as drugs and vaccines.However,conventional adjuvants have limitations in terms of immune specificity and duration.Nanoadjuvants can leverage...Adjuvants enhance and prolong the immune response to therapeutic agents,such as drugs and vaccines.However,conventional adjuvants have limitations in terms of immune specificity and duration.Nanoadjuvants can leverage their nanoscale size to increase the capture efficacy of antigens by antigen-presenting cells and improve immunogen presentation for targeted delivery.Furthermore,noninvasive visualization of bifunctional nanoadjuvants with integrated efficacy and imaging postdelivery can provide insights into in vivo distribution and performance,aiding in the optimization and design of new dosage forms.This review systematically summarizes the structure,assembly,and function of nanoadjuvants alongside contrast agents.It delves into the impact of complex structures formed by nanoadjuvant-contrast agent interactions on antigen presentation,migration,imaging tracking,and visualization of immune cell recruitment.It also discusses how imaging can determine optimal immune intervals,vaccine safety,and toxicity while enabling diagnostic and therapeutic integration.Moreover,this paper discusses potential applications of novel adjuvants and promising imaging technologies that could have implications for future vaccine and drug development endeavors.展开更多
Global land monsoon precipitation(GLMP)is highly sensitive to changes in interhemispheric thermal contrast(ITC).Amplified interhemispheric asymmetries of GLMP due to enhanced ITC driven by high-level anthropogenic emi...Global land monsoon precipitation(GLMP)is highly sensitive to changes in interhemispheric thermal contrast(ITC).Amplified interhemispheric asymmetries of GLMP due to enhanced ITC driven by high-level anthropogenic emissions are expected to simultaneously increase the probability of regional floods and droughts,threatening ecosystems within global terrestrial monsoon regions and the freshwater supply for billions of residents in these areas.In this study,the responses of GLMP to the evolution of ITC toward the carbon neutrality goal are assessed using multimodel outputs from a new model intercomparison project(CovidMIP).The results show that the Northern Hemisphere-Southern Hemisphere(NH-SH)asymmetry of GLMP in boreal summer weakens during the 2040s,as a persistent reduction in well-mixed greenhouse gas(WMGHG)emissions leads to a downward trend in the ITC after 2040.At the same time,the reduction in WMGHG emissions dampens the Eastern Hemisphere-Western Hemisphere(EH-WH)asymmetry of GLMP by inducing La Niña-like cooling and enhancing moisture transport to Inner America.The resulting increases in land monsoon precipitation(LMP)may alleviate drought under the global warming scenario by about 19%-25%and 7%-9%in the WH and SH monsoon regions,respectively.However,a persistent reduction in aerosol emissions in Asia will dominate the increases in LMP in this region until the mid-21st century,and these increases may be approximately 23%-60%of the growth under the global warming scenario.Our results highlight the different rates of response of aerosol and WMGHG concentrations to the carbon neutrality goal,leading to various changes in LMP at global and regional scales.展开更多
Automated grading of dandruff severity is a clinically significant but challenging task due to the inherent ordinal nature of severity levels and the high prevalence of label noise from subjective expert annotations.S...Automated grading of dandruff severity is a clinically significant but challenging task due to the inherent ordinal nature of severity levels and the high prevalence of label noise from subjective expert annotations.Standard classification methods fail to address these dual challenges,limiting their real-world performance.In this paper,a novel,three-phase training framework is proposed that learns a robust ordinal classifier directly from noisy labels.The approach synergistically combines a rank-based ordinal regression backbone with a cooperative,semi-supervised learning strategy to dynamically partition the data into clean and noisy subsets.A hybrid training objective is then employed,applying a supervised ordinal loss to the clean set.The noisy set is simultaneously trained using a dualobjective that combines a semi-supervised ordinal loss with a parallel,label-agnostic contrastive loss.This design allows themodel to learn fromthe entire noisy subset while using contrastive learning to mitigate the risk of error propagation frompotentially corrupt supervision.Extensive experiments on a new,large-scale,multi-site clinical dataset validate our approach.Themethod achieves state-of-the-art performance with 80.71%accuracy and a 76.86%F1-score,significantly outperforming existing approaches,including a 2.26%improvement over the strongest baseline method.This work provides not only a robust solution for a practical medical imaging problem but also a generalizable framework for other tasks plagued by noisy ordinal labels.展开更多
Laser wakefield accelerators(LWFAs)offer acceleration gradients up to 1000 times higher than those of conventional radio-frequency accelerators,offering a pathway to significantly more compact and cost-effective accel...Laser wakefield accelerators(LWFAs)offer acceleration gradients up to 1000 times higher than those of conventional radio-frequency accelerators,offering a pathway to significantly more compact and cost-effective accelerator systems.This breakthrough opens up new possibilities for laboratory-scale light sources.All-optical inverse Compton scattering(AOCS)sources driven by LWFAs produce high-brightness,quasimonochromatic X rays with micrometer-scale source sizes,delivering the spatial coherence and resolution required for X-ray phase-contrast imaging(XPCI).These features position AOCS X-ray sources as promising tools for applications in biology,medicine,physics,and materials science.However,previous AOCS-based imaging studies have primarily focused on X-ray absorption imaging.In this work,we report successful experimental demonstrations of edge-enhanced in-line XPCI using energy-tunable,quasi-monochromatic AOCS X rays.With a spatial resolution of~20μm,our results clearly show the potential of high-resolution,AOCS-based XPCI applications.展开更多
AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited...AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.展开更多
P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of Pgp inhibitors and substrates...P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of Pgp inhibitors and substrates is crucial for drug discovery and toxicological assessment.However,existing models rely on limited molecular information,leading to suboptimal model performance for predicting P-gp inhibitors and substrates.To overcome this challenge,we compiled an extensive dataset from public databases and literature,consisting of 5,943 P-gp inhibitors and 4,018 substrates,notable for their high quantity,quality,and structural uniqueness.In addition,we curated two external test sets to validate the model's generalization capability.Subsequently,we developed a multimodal graph contrastive learning(GCL)model for the prediction of P-gp inhibitors and substrates(MC-PGP).This framework integrates three types of features from Simplified Molecular Input Line Entry System(SMILES)sequences,molecular fingerprints,and molecular graphs using an attention-based fusion strategy to generate a unified molecular representation.Furthermore,we employed a GCL approach to enhance structural representations by aligning local and global structures.Extensive experimental results highlight the superior performance of MC-PGP,which achieves improvements in the area under the curve of receiver operating characteristic(AUC-ROC)of 9.82%and 10.62%on the external P-gp inhibitor and external P-gp substrate datasets,respectively,compared with 12 state-of-the-art methods.Furthermore,the interpretability analysis of all three molecular feature types offers comprehensive and complementary insights,demonstrating that MC-PGP effectively identifies key functional groups involved in P-gp interactions.These chemically intuitive insights provide valuable guidance for the design and optimization of drug candidates.展开更多
Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the pro...Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.展开更多
Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)t...Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.展开更多
文摘Once more to the lake is a representative work of the American Writer, Elwyn Brooks White, and has been acclaimed by people in the world. The paper offers a three-layered contrasting analysis of the essay. By detailed analysis of the contrasts running through the whole essay, the author finds that those contrasts are symbol of White's internal struggle and reflection for life.On the foundation of the three-layered contrasts, the paper presents the theme analysis and clarifies the content of the essay.
文摘Using the monthly geopotential heights and winds for 700 and 200 hPa for India during July and August, and the weekly M-100 Soviet rocketsonde temperature and wind data for Thumba (8.5 ° N, 76.9 ° E) during the last week of June and the first week of September for the two contrasting summer monsoon years 1975 (a very strong monsoon year) and 1979 (a very weak monsoon year), a study has been made to examine the mean circulation features of the troposphere over India, and the structures of the temperatures and the winds of the middle atmosphere over Thumba. The study suggested that the axis of the monsoon trough (AMT) at 700 hPa shifted southward in 1975 and northward towards the foothills of the Himalayas in 1979, from its normal position. Superimposed on the low-pressure area (AMT) at 700 hPa, a well-defined divergence was noticed at 200 hPa over the northern India in 1975.The mean temperatures at 25,50 and 60 km (middle atmosphere) over Thumba were cooler in 1975 than in 1979. While a cooling trend in 1975 and warming trend in 1979 were observed at 25 and 50 km, a reversed picture was noticed at 60 km. There was a weak easterly / westerly (weak westerly phase) zonal wind in 1975 and a strong easterly zonal wind in 1979. A phase reversal of the zonal wind was observed at 50 km. A tentative physical mechanism was offered, in terms of upward propagation of the two equatorially trapped planetary waves i.e. the Kelvin and the mixed Rossby-gravity waves, to explain the occurrence of the two spells of strong warmings in the mesosphere in 1975.
文摘The iconic image of a giant radiating dyke swarm subsequently fragmented into three pieces via supercontinental breakup was produced by Paul May in1971(see next page).That figure presented a large part of
基金This work was supported by Deakin Cyber Security Research Cluster National Natural Science Foundation of China under Grant Nos. 61304067 and 61202211 +1 种基金 Guangxi Key Laboratory of Trusted Software No. kx201325 the Fundamental Research Funds for the Central Universities under Grant No 31541311314.
文摘As the risk of malware is sharply increasing in Android platform,Android malware detection has become an important research topic.Existing works have demonstrated that required permissions of Android applications are valuable for malware analysis,but how to exploit those permission patterns for malware detection remains an open issue.In this paper,we introduce the contrasting permission patterns to characterize the essential differences between malwares and clean applications from the permission aspect Then a framework based on contrasting permission patterns is presented for Android malware detection.According to the proposed framework,an ensemble classifier,Enclamald,is further developed to detect whether an application is potentially malicious.Every contrasting permission pattern is acting as a weak classifier in Enclamald,and the weighted predictions of involved weak classifiers are aggregated to the final result.Experiments on real-world applications validate that the proposed Enclamald classifier outperforms commonly used classifiers for Android Malware Detection.
文摘The Awakening is about the heroine Edna Pontellier awakes and tries to reset her relation with the world in New Orleans,a place the family chose to spend the summer, where there also are influences working their way to awake Edna. Among them, theeffects from Madame Ratignolle and Madame Reisz are of great importance in the process of Edna's awakening and to her final sui-cide. This dissertation will see how Edna is affected by and distinguished from them, and get the conclusion that Edna fails being alife mediator by contrasting with the two women.
基金support from the Research Council of Norway,Equinor,and Sekal with NFR project(Grant No.308826).
文摘Drill string vibration during drilling plays a vital and potentially decisive role in maintaining wellbore stability,as repeated impacts may lead to fatigue and borehole collapse.While drilling through geological layers,a material contrast may act as a localization point for wellbore damage.The hypothesis tested in this paper is that wellbore instability is focused on the boundary between the layers and that mechanical contrasts accelerate the wellbore collapse.In this study,an elastic-plastic damage model was employed to investigate the effects of repeated mechanical impacts on wellbore stability.A 2-dimensional(2D)model of a wellbore surrounded by contrasting materials was developed,and the accumulated damage caused by repeated lateral impacts was monitored.It was found that damage develops not only around the wall of the wellbore but also along the material boundaries.A sensitivity analysis was carried out to identify the impact of contrasts in both elastic(Young's modulus and Poisson's ratio)and plastic(cohesion,friction angle,and dilation angle)parameters between layers.Four damage patterns were identifiedin the simulated models.The results also suggested that the number of impacts required to reach the critical damage was highly affected by the contrast in elastic parameters,while cohesion and friction angle contrasts had a lesser effect.Additionally,increasing the contrast in the dilation angle localized the damage,thus reducing the number of impacts required to trigger wellbore failure.
文摘In high-intensity electromagnetic warfare,radar systems are persistently subjected to multi-jammer attacks,including potentially novel unknown jamming types that may emerge exclusively under wartime conditions.These jamming signals severely degrade radar detection performance.Precise recognition of these unknown and compound jamming signals is critical to enhancing the anti-jamming capabilities and overall reliability of radar systems.To address this challenge,this article proposes a novel open-set compound jamming cognition(OSCJC)method.The proposed method employs a detection-classification dual-network architecture,which not only overcomes the false alarm and misdetection issues of traditional closed-set recognition methods when dealing with unknown jamming but also effectively addresses the performance bottleneck of existing open-set recognition techniques focusing on single jamming scenarios in compound jamming environments.To achieve unknown jamming detection,we first employ a consistency labeling strategy to train the detection network using diverse known jamming samples.This strategy enables the network to acquire highly generalizable jamming features,thereby accurately localizing candidate regions for individual jamming components within compound jamming.Subsequently,we introduce contrastive learning to optimize the classification network,significantly enhancing both intra-class clustering and inter-class separability in the jamming feature space.This method not only improves the recognition accuracy of the classification network for known jamming types but also enhances its sensitivity to unknown jamming types.Simulations and experimental data are used to verify the effectiveness of the proposed OSCJC method.Compared with the state-of-the-art open-set recognition methods,the proposed method demonstrates superior recognition accuracy and enhanced environmental adaptability.
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
基金supported by the Regional Innovation System&Education(RISE)program through the Gangwon RISE Center,funded by the Ministry of Education(MOE)and the Gangwon State(G.S.),Republic of Korea(2025-RISE-10-006).
文摘This study aims to develop a novel,cost-effective method for fabricating silicone vascular phantoms(SVPs)using"chewy candy"as a dissolvable core material.The study explores the feasibility of using chewy candy to create detailed and intricate vascular models for clinical applications.The chewy candy,an amorphous material,was manually extruded to form vascular models of varying diameters.These models were embedded in a silicone mixture,which was then cured.The chewy candy was subsequently dissolved,leaving behind hollow silicone vascular channels.The SVPs were evaluated for their morphological accuracy and functionality through laser speckle contrast imaging.The SVPs successfully replicated vascular channels with consistent diameters,demonstrating minimal variation across different regions.Functional evaluation using laser speckle contrast imaging revealed distinct flow dynamics in Y-shaped and H-shaped SVPs,highlighting the potential for these phantoms to simulate realistic fluid dynamics in vascular systems.This study presents a simple,time-saving,and innovative approach to fabricating complex 3D SVPs using chewy candy.This method offers a viable alternative to traditional fabrication techniques,with potential applications in various biomedical fields.
基金supported by the National Key R&D Project of China(Nos.2022YFC2304205,2022YFC2304202)Key scientific research project of universities in Guangdong Province(No.2023KCXTD026)the Major Project of Guangzhou National Laboratory(No.GZNL2023A03002).
文摘Adjuvants enhance and prolong the immune response to therapeutic agents,such as drugs and vaccines.However,conventional adjuvants have limitations in terms of immune specificity and duration.Nanoadjuvants can leverage their nanoscale size to increase the capture efficacy of antigens by antigen-presenting cells and improve immunogen presentation for targeted delivery.Furthermore,noninvasive visualization of bifunctional nanoadjuvants with integrated efficacy and imaging postdelivery can provide insights into in vivo distribution and performance,aiding in the optimization and design of new dosage forms.This review systematically summarizes the structure,assembly,and function of nanoadjuvants alongside contrast agents.It delves into the impact of complex structures formed by nanoadjuvant-contrast agent interactions on antigen presentation,migration,imaging tracking,and visualization of immune cell recruitment.It also discusses how imaging can determine optimal immune intervals,vaccine safety,and toxicity while enabling diagnostic and therapeutic integration.Moreover,this paper discusses potential applications of novel adjuvants and promising imaging technologies that could have implications for future vaccine and drug development endeavors.
基金funded by the National Natural Science Foundation of China(Grant No.42275039)the Meteorological Joint Fund by NSF and CMA(Grant No.U2342224)+1 种基金the National Key R&D Program of China(Grant No.2022YFC3701202)the S&T Development Fund of CAMS(Grant No.2024KJ019)。
文摘Global land monsoon precipitation(GLMP)is highly sensitive to changes in interhemispheric thermal contrast(ITC).Amplified interhemispheric asymmetries of GLMP due to enhanced ITC driven by high-level anthropogenic emissions are expected to simultaneously increase the probability of regional floods and droughts,threatening ecosystems within global terrestrial monsoon regions and the freshwater supply for billions of residents in these areas.In this study,the responses of GLMP to the evolution of ITC toward the carbon neutrality goal are assessed using multimodel outputs from a new model intercomparison project(CovidMIP).The results show that the Northern Hemisphere-Southern Hemisphere(NH-SH)asymmetry of GLMP in boreal summer weakens during the 2040s,as a persistent reduction in well-mixed greenhouse gas(WMGHG)emissions leads to a downward trend in the ITC after 2040.At the same time,the reduction in WMGHG emissions dampens the Eastern Hemisphere-Western Hemisphere(EH-WH)asymmetry of GLMP by inducing La Niña-like cooling and enhancing moisture transport to Inner America.The resulting increases in land monsoon precipitation(LMP)may alleviate drought under the global warming scenario by about 19%-25%and 7%-9%in the WH and SH monsoon regions,respectively.However,a persistent reduction in aerosol emissions in Asia will dominate the increases in LMP in this region until the mid-21st century,and these increases may be approximately 23%-60%of the growth under the global warming scenario.Our results highlight the different rates of response of aerosol and WMGHG concentrations to the carbon neutrality goal,leading to various changes in LMP at global and regional scales.
文摘Automated grading of dandruff severity is a clinically significant but challenging task due to the inherent ordinal nature of severity levels and the high prevalence of label noise from subjective expert annotations.Standard classification methods fail to address these dual challenges,limiting their real-world performance.In this paper,a novel,three-phase training framework is proposed that learns a robust ordinal classifier directly from noisy labels.The approach synergistically combines a rank-based ordinal regression backbone with a cooperative,semi-supervised learning strategy to dynamically partition the data into clean and noisy subsets.A hybrid training objective is then employed,applying a supervised ordinal loss to the clean set.The noisy set is simultaneously trained using a dualobjective that combines a semi-supervised ordinal loss with a parallel,label-agnostic contrastive loss.This design allows themodel to learn fromthe entire noisy subset while using contrastive learning to mitigate the risk of error propagation frompotentially corrupt supervision.Extensive experiments on a new,large-scale,multi-site clinical dataset validate our approach.Themethod achieves state-of-the-art performance with 80.71%accuracy and a 76.86%F1-score,significantly outperforming existing approaches,including a 2.26%improvement over the strongest baseline method.This work provides not only a robust solution for a practical medical imaging problem but also a generalizable framework for other tasks plagued by noisy ordinal labels.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0530000)the Discipline Construction Foundation of“Double World-class Project”.
文摘Laser wakefield accelerators(LWFAs)offer acceleration gradients up to 1000 times higher than those of conventional radio-frequency accelerators,offering a pathway to significantly more compact and cost-effective accelerator systems.This breakthrough opens up new possibilities for laboratory-scale light sources.All-optical inverse Compton scattering(AOCS)sources driven by LWFAs produce high-brightness,quasimonochromatic X rays with micrometer-scale source sizes,delivering the spatial coherence and resolution required for X-ray phase-contrast imaging(XPCI).These features position AOCS X-ray sources as promising tools for applications in biology,medicine,physics,and materials science.However,previous AOCS-based imaging studies have primarily focused on X-ray absorption imaging.In this work,we report successful experimental demonstrations of edge-enhanced in-line XPCI using energy-tunable,quasi-monochromatic AOCS X rays.With a spatial resolution of~20μm,our results clearly show the potential of high-resolution,AOCS-based XPCI applications.
文摘AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.
基金supported by the National Key Research and Development Program of China(Program No.:2022YFF1203003)the National Natural Science Foundation of China(Grant No.:82373791).
文摘P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of Pgp inhibitors and substrates is crucial for drug discovery and toxicological assessment.However,existing models rely on limited molecular information,leading to suboptimal model performance for predicting P-gp inhibitors and substrates.To overcome this challenge,we compiled an extensive dataset from public databases and literature,consisting of 5,943 P-gp inhibitors and 4,018 substrates,notable for their high quantity,quality,and structural uniqueness.In addition,we curated two external test sets to validate the model's generalization capability.Subsequently,we developed a multimodal graph contrastive learning(GCL)model for the prediction of P-gp inhibitors and substrates(MC-PGP).This framework integrates three types of features from Simplified Molecular Input Line Entry System(SMILES)sequences,molecular fingerprints,and molecular graphs using an attention-based fusion strategy to generate a unified molecular representation.Furthermore,we employed a GCL approach to enhance structural representations by aligning local and global structures.Extensive experimental results highlight the superior performance of MC-PGP,which achieves improvements in the area under the curve of receiver operating characteristic(AUC-ROC)of 9.82%and 10.62%on the external P-gp inhibitor and external P-gp substrate datasets,respectively,compared with 12 state-of-the-art methods.Furthermore,the interpretability analysis of all three molecular feature types offers comprehensive and complementary insights,demonstrating that MC-PGP effectively identifies key functional groups involved in P-gp interactions.These chemically intuitive insights provide valuable guidance for the design and optimization of drug candidates.
基金supported by the Research Grant Fund from Kwangwoon University in 2023,the National Natural Science Foundation of China under Grant(62311540155)the Taishan Scholars Project Special Funds(tsqn202312035)the open research foundation of State Key Laboratory of Integrated Chips and Systems.
文摘Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.
基金supported by the Natural Science Foundation of China(No.41804112,author:Chengyun Song).
文摘Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.
