Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rel...Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.展开更多
To address the issue of incorrect fusion results caused by conflicting evidence due to inaccurate evidence and incomplete recognition frameworks in radar airborne target tactical intention recognition,a spatiotemporal...To address the issue of incorrect fusion results caused by conflicting evidence due to inaccurate evidence and incomplete recognition frameworks in radar airborne target tactical intention recognition,a spatiotemporal evidence fusion algorithm is proposed.To resolve the conflict evidence fusion problem caused by inaccurate evidence,the algorithm performs discounting of evidence from both spatial and temporal dimensions.Spatial discounting is influenced by both inter-evidence inconsistency and intra-evidence inconsistency,while temporal discounting is determined by time intervals and information entropy.For the problem of conflicting evidence fusion due to an incomplete recognition framework,an open recognition architecture based on dynamic composite focal elements is proposed.This approach allocates some conflicting information to temporary composite focal elements,avoiding excessive basic probability assignment(BPA)of the empty set after fusion,which can lead to deviations from the actual fusion results.Simulation experiments comparing various methods indicate that the proposed method can effectively improve target intention recognition accuracy and demonstrates good stability.展开更多
Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy cl...Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy clustering techniques,such as Fuzzy C-Means(FCM),face significant challenges in handling uncertainty and the dependencies between different views.To overcome these limitations,we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data,aiming to enhance clustering accuracy and robustness,termed Multi-view Picture Fuzzy Clustering(MPFC).In particular,the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels:membership degrees,neutral degrees,and refusal degrees.This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models.Meanwhile,dual-anchor graphs exploit the similarity relationships between data points and integrate information across views.This combination improves stability,scalability,and robustness when handling noisy and heterogeneous data.Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency,outperforming traditional methods.Specifically,the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets,attaining a Purity(PUR)score of 0.6440 and an Accuracy(ACC)score of 0.6213 for the 3 Sources dataset,underscoring its robustness and efficiency.The proposed approach significantly contributes to fields such as pattern recognition,multi-view relational data analysis,and large-scale clustering problems.Future work will focus on extending the method for semi-supervised multi-view clustering,aiming to enhance adaptability,scalability,and performance in real-world applications.展开更多
The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches...The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches face challenges with data sparsity and information loss due to single-molecule representation limitations and isolated predictive tasks.This research proposes molecular properties prediction with parallel-view and collaborative learning(MolP-PC),a multi-view fusion and multi-task deep learning framework that integrates 1D molecular fingerprints(MFs),2D molecular graphs,and 3D geometric representations,incorporating an attention-gated fusion mechanism and multi-task adaptive learning strategy for precise ADMET property predictions.Experimental results demonstrate that MolP-PC achieves optimal performance in 27 of 54 tasks,with its multi-task learning(MTL)mechanism significantly enhancing predictive performance on small-scale datasets and surpassing single-task models in 41 of 54 tasks.Additional ablation studies and interpretability analyses confirm the significance of multi-view fusion in capturing multi-dimensional molecular information and enhancing model generalization.A case study examining the anticancer compound Oroxylin A demonstrates MolP-PC’s effective generalization in predicting key pharmacokinetic parameters such as half-life(T0.5)and clearance(CL),indicating its practical utility in drug modeling.However,the model exhibits a tendency to underestimate volume of distribution(VD),indicating potential for improvement in analyzing compounds with high tissue distribution.This study presents an efficient and interpretable approach for ADMET property prediction,establishing a novel framework for molecular optimization and risk assessment in drug development.展开更多
Phenotypic prediction is a promising strategy for accelerating plant breeding.Data from multiple sources(called multi-view data)can provide complementary information to characterize a biological object from various as...Phenotypic prediction is a promising strategy for accelerating plant breeding.Data from multiple sources(called multi-view data)can provide complementary information to characterize a biological object from various aspects.By integrating multi-view information into phenotypic prediction,a multi-view best linear unbiased prediction(MVBLUP)method is proposed in this paper.To measure the importance of multiple data views,the differential evolution algorithm with an early stopping mechanism is used,by which we obtain a multi-view kinship matrix and then incorporate it into the BLUP model for phenotypic prediction.To further illustrate the characteristics of MVBLUP,we perform the empirical experiments on four multi-view datasets in different crops.Compared to the single-view method,the prediction accuracy of the MVBLUP method has improved by 0.038–0.201 on average.The results demonstrate that the MVBLUP is an effective integrative prediction method for multi-view data.展开更多
Air target intent recognition holds significant importance in aiding commanders to assess battlefield situations and secure a competitive edge in decision-making.Progress in this domain has been hindered by challenges...Air target intent recognition holds significant importance in aiding commanders to assess battlefield situations and secure a competitive edge in decision-making.Progress in this domain has been hindered by challenges posed by imbalanced battlefield data and the limited robustness of traditional recognition models.Inspired by the success of diffusion models in addressing visual domain sample imbalances,this paper introduces a new approach that utilizes the Markov Transfer Field(MTF)method for time series data visualization.This visualization,when combined with the Denoising Diffusion Probabilistic Model(DDPM),effectively enhances sample data and mitigates noise within the original dataset.Additionally,a transformer-based model tailored for time series visualization and air target intent recognition is developed.Comprehensive experimental results,encompassing comparative,ablation,and denoising validations,reveal that the proposed method achieves a notable 98.86%accuracy in air target intent recognition while demonstrating exceptional robustness and generalization capabilities.This approach represents a promising avenue for advancing air target intent recognition.展开更多
The morphological description of wear particles in lubricating oil is crucial for wear state monitoring and fault diagnosis in aero-engines.Accurately and comprehensively acquiring three-dimensional(3D)morphological d...The morphological description of wear particles in lubricating oil is crucial for wear state monitoring and fault diagnosis in aero-engines.Accurately and comprehensively acquiring three-dimensional(3D)morphological data of these particles has became a key focus in wear debris analysis.Herein,we develop a novel multi-view polarization-sensitive optical coherence tomography(PS-OCT)method to achieve accurate 3D morphology detection and reconstruction of aero-engine lubricant wear particles,effectively resolving occlusion-induced information loss while enabling material-specific characterization.The particle morphology is captured by multi-view imaging,followed by filtering,sharpening,and contour recognition.The method integrates advanced registration algorithms with Poisson reconstruction to generate high-precision 3D models.This approach not only provides accurate 3D morphological reconstruction but also mitigates information loss caused by particle occlusion,ensuring model completeness.Furthermore,by collecting polarization characteristics of typical metals and their oxides in aero-engine lubricants,this work comprehensively characterizes and comparatively analyzes particle polarization properties using Stokes vectors,polarization uniformity,and cumulative phase retardation,and obtains a three-dimensional model containing polarization information.Ultimately,the proposed method enables multidimensional information acquisition for the reliable identification of abrasive particle types.展开更多
The evacuation of people under threat is an effective disaster prevention and mitigation measure in response to flash floods and geological hazards,and it is also an essential element of pre-disaster planning.However,...The evacuation of people under threat is an effective disaster prevention and mitigation measure in response to flash floods and geological hazards,and it is also an essential element of pre-disaster planning.However,the effect of the interactions between perception factors on residents'willingness to evacuate is an urgent problem to be solved.Therefore,this paper introduces risk,stakeholder,and protective action perceptions from the protective action decision model as the main explanatory variables.These three core perceptions are subdivided into affective risk perception,cognitive risk perception,government perception,other-stakeholder perception,resourcerelated attributes,and hazard-related attributes.A questionnaire survey was conducted from June to July 2023 among residents of mountainous communities in nine villages in three towns in Sichuan Province,China.359 cross-sectional data were analyzed using structural equation modeling to explore the effects of six perception factors on evacuation intentions.The results of the study showed that:(1)affective risk perception,government perception,other-stakeholder perception,and hazard-related attributes all directly and positively influence residents'intentions to evacuate;(2)cognitive risk perception is mediated by stakeholder and protective action perceptions,which indirectly and positively affect residents'intentions to evacuate.Based on the hypothesized paths,strategies to improve residents'willingness to evacuate are discussed from the perspective of three core perceptions:strengthening disaster risk education,improving residents'cohesion,and building government credibility.The results of this study can provide theoretical support and practical suggestions for emergency management departments to formulate emergency evacuation strategies,which can aid decision-makers in better understanding residents'intentions to evacuate,optimizing evacuation information dissemination pathways,and strengthening disaster risk management capabilities.展开更多
Traditional sheep identification is based on ear tags.However,the application of ear tags not only causes stress to the animals but also leads to loss of ear tags,which affects the correct recognition of sheep identit...Traditional sheep identification is based on ear tags.However,the application of ear tags not only causes stress to the animals but also leads to loss of ear tags,which affects the correct recognition of sheep identity.In contrast,the acquisition of sheep face images offers the advantages of being non-invasive and stress-free for the animals.Nevertheless,the extant convolutional neural network-based sheep face identification model is prone to the issue of inadequate refinement,which renders its implementation on farms challenging.To address this issue,this study presented a novel sheep face recognition model that employs advanced feature fusion techniques and precise image segmentation strategies.The images were preprocessed and accurately segmented using deep learning techniques,with a dataset constructed containing sheep face images from multiple viewpoints(left,front,and right faces).In particular,the model employs a segmentation algorithm to delineate the sheep face region accurately,utilizes the Improved Convolutional Block Attention Module(I-CBAM)to emphasize the salient features of the sheep face,and achieves multi-scale fusion of the features through a Feature Pyramid Network(FPN).This process guarantees that the features captured from disparate viewpoints can be efficiently integrated to enhance recognition accuracy.Furthermore,the model guarantees the precise delineation of sheep facial contours by streamlining the image segmentation procedure,thereby establishing a robust basis for the precise identification of sheep identity.The findings demonstrate that the recognition accuracy of the Sheep Face Mask Region-based Convolutional Neural Network(SFMask RCNN)model has been enhanced by 9.64%to 98.65%in comparison to the original model.The method offers a novel technological approach to the management of animal identity in the context of sheep husbandry.展开更多
To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition.This paper proposes a transfer learning-based intention predicti...To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition.This paper proposes a transfer learning-based intention prediction model for drone formation targets in air combat.This model recognizes the intentions of multiple aerial targets by extracting spatial features among the targets at each moment.Simulation results demonstrate that,compared to classical intention recognition models,the proposed model in this paper achieves higher accuracy in identifying the intentions of drone swarm targets in air combat scenarios.展开更多
Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin s...Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin samples,especially the high-order neighbor relationship between samples.To overcome the above challenges,this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model.We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module.By this design,the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix;then,we can obtain an optimal global affinity matrix where each connected node belongs to one cluster.In addition,the discriminative constraint between views is designed to further improve the clustering performance.A range of experiments on six public datasets demonstrates that the method performs better than other advanced multi-view clustering methods.The code is available at https://github.com/songzuolong/MNF-MDSC(accessed on 25 December 2024).展开更多
Sustainable development has become a critical global priority,and green transportation solutions,such as electric taxis,play a vital role in achieving this goal.This study examines the factors influencing students’in...Sustainable development has become a critical global priority,and green transportation solutions,such as electric taxis,play a vital role in achieving this goal.This study examines the factors influencing students’intentions to adopt electric taxi services in Hanoi,Vietnam,as a step toward sustainable urban mobility.We surveyed 573 students and ana-lyzed key determinants using reliability tests,exploratory factor analysis(EFA),and linear regression.The results indi-cate that four factors significantly influence adoption intentions:Perceived Usefulness and Sustainability,Price,Brand Awareness,and Service Quality.Among these,Perceived Usefulness and Sustainability had the strongest positive im-pact,while Service Quality exerted the weakest influence.Notably,Habit and Ease of Use&Transaction Convenience were found to be statistically insignificant in the final model.These findings provide practical implications for business-es and policymakers aiming to use electric taxi adoption.To enhance appeal,stakeholders should emphasize environ-mental benefits,competitive pricing,and brand recognition while improving service reliability.By addressing these fac-tors,electric taxi services can accelerate the transition to sustainable transportation,aligning with global climate goals and transforming urban mobility.This study offers actionable insights for encouraging greener travel behaviors among students,a key demographic for long-term sustainability impact.展开更多
The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show...The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data,as they rely on a single-dimensional membership value.To overcome these limitations,we propose an auto-weighted multi-view neutrosophic fuzzy clustering(AW-MVNFC)algorithm.Our method leverages the neutrosophic framework,an extension of fuzzy sets,to explicitly model imprecision and ambiguity through three membership degrees.The core novelty of AWMVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions of both individual data views and the importance of each feature within a view.Through a unified objective function,AW-MVNFC jointly optimizes the neutrosophic membership assignments,cluster centers,and the distributions of view and feature weights.Comprehensive experiments conducted on synthetic and real-world datasets demonstrate that our algorithm achieves more accurate and stable clustering than existing methods,demonstrating its effectiveness in handling the complexities of multi-view data.展开更多
Objective The effects of prolonged exposure to persistently elevated atmospheric pollutants,commonly termed air pollution waves,on fertility intentions remain inadequately understood.This study aims to investigate the...Objective The effects of prolonged exposure to persistently elevated atmospheric pollutants,commonly termed air pollution waves,on fertility intentions remain inadequately understood.This study aims to investigate the association between particulate matter(PM)exposure and fertility intentions.Methods In this nationwide cross-sectional study,we analyzed data from 10,747 participants(5496 females and 5251 males).PM waves were defined as periods lasting 3‒6 consecutive days during which the daily average concentrations exceeded China’s Ambient Air Quality Standards Grade II thresholds(PM2.5>75μg/m3 and PM10>150μg/m3).We employed multivariate logistic regression models to assess the association between exposure to PM waves and fertility intentions.Results Significant inverse associations were detected between exposure to PM2.5 wave events(characterized by concentrations exceeding 75μg/m3 for durations of 4‒6 days,P<0.05)and PM10 wave events(defined as concentrations exceeding 150μg/m3 for 6 consecutive days,P<0.05)and fertility intentions among females.In contrast,neither the PM2.5 wave nor the PM10 wave events demonstrated statistically significant correlations with fertility intentions in males(P>0.05 for all comparisons).The potentially susceptible subgroup was identified as females aged 20–30 years.Conclusions Our results provide the first evidence that PM2.5 and PM10 waves are associated with a reduction in female fertility intentions,offering critical insights for the development of public health policies and strategies aimed at individual protection.展开更多
In the contemporary digital landscape,the proliferation of information has led to an increasing diversity of channels through which consumers obtain information,resulting in a gradual transformation of shopping habits...In the contemporary digital landscape,the proliferation of information has led to an increasing diversity of channels through which consumers obtain information,resulting in a gradual transformation of shopping habits.Consumers now frequently rely on external sources to make well-informed purchasing decisions,leading to the emergence of live shopping as a prominent avenue for gathering product information and completing transactions.E-commerce live streaming has experienced rapid growth,leveraging its ability to generate traffic and capture consumer attention.The integration of content and live streaming not only meets users’psychological needs but also facilitates seamless communication between buyers and sellers.From the perspective of content marketing typologies,this paper examines content marketing across three key dimensions:informational content,entertainment content,and emotional content.It further explores the impact of content marketing on consumers’purchase intentions within the context of e-commerce live streaming.展开更多
This study focuses on the relationship between job stress and intention to leave among obstetric(OB)nurses in the context of China's birth policy adjustment,and provides a scientific basis for policymakers and hea...This study focuses on the relationship between job stress and intention to leave among obstetric(OB)nurses in the context of China's birth policy adjustment,and provides a scientific basis for policymakers and healthcare administrators.This study used a non-experimental descriptive correlation design with a purposive sampling of 230 OB nurses from three tertiary hospitals in Jinan,Shandong Province.Participants were surveyed using three questionnaires and descriptive analysis;ANOVA and correlation analyses were used to analyze the relationship between participants'stressor levels and turnover intention.Pearson's correlation coefficient analysis showed that there was a positive correlation between nurses‘work stressors and turnover intention,with a correlation coefficient of r=0.53,a moderate positive correlation(P<0.001).Based on the survey data from three tertiary hospitals in Shandong Province,the obstetric nurses group has a medium level of work stressors,but a high turnover intention,highlighting the professional identity crisis.展开更多
Intentional tooth replantation(ITR)is an advanced treatment modality and the procedure of last resort for preserving teeth with inaccessible endodontic or resorptive lesions.ITR is defined as the deliberate extraction...Intentional tooth replantation(ITR)is an advanced treatment modality and the procedure of last resort for preserving teeth with inaccessible endodontic or resorptive lesions.ITR is defined as the deliberate extraction of a tooth;evaluation of the root surface,endodontic manipulation,and repair;and placement of the tooth back into its original socket.Case reports,case series,cohort studies,and randomized controlled trials have demonstrated the efficacy of ITR in the retention of natural teeth that are untreatable or difficult to manage with root canal treatment or endodontic microsurgery.However,variations in clinical protocols for ITR exist due to the empirical nature of the original protocols and rapid advancements in the field of oral biology and dental materials.This heterogeneity in protocols may cause confusion among dental practitioners;therefore,guidelines and considerations for ITR should be explicated.This expert consensus discusses the biological foundation of ITR,the available clinical protocols and current status of ITR in treating teeth with refractory apical periodontitis or anatomical aberration,and the main complications of this treatment,aiming to refine the clinical management of ITR in accordance with the progress of basic research and clinical studies;the findings suggest that ITR may become a more consistent evidence-based option in dental treatment.展开更多
Background of the study:The Bangladeshi cosmetics market has witnessed significant growth in recent years,driven by changing consumer lifestyles,increased disposable incomes,and rising awareness of personal grooming.T...Background of the study:The Bangladeshi cosmetics market has witnessed significant growth in recent years,driven by changing consumer lifestyles,increased disposable incomes,and rising awareness of personal grooming.The study investigates the impact of content cues which influence on purchasing intention towards cosmetic brands in Bangladesh.Purpose:The basic purpose of this study was to evaluate the factors influencing consumers’purchasing intentions for cosmetic brands in Bangladesh.Specifically,the study explored the roles of various cosmetic-related attributes and their impact on purchasing intentions within the context of Bangladesh’s cosmetic industry.Research methods:A quantitative research approach was adopted,and data were collected through a structured survey targeting Bangladeshi consumers who frequently engage with cosmetic products.All the valid responses were analyzed using SmartPLS 4.0 to perform structural equation modeling.Research findings:The findings revealed that trust in cosmetic brands and competitive pricing significantly influence consumers’purchasing intentions,highlighting the importance of fostering trust and affordability.However,certain constructs,such as ethnocentric tendencies and concerns about ingredient safety,showed limited impact on consumers’decisions.Conclusion:This study contributes to the existing literature by offering empirical insights into the Bangladeshi context,particularly within the rapidly growing cosmetics market.The findings provide actionable recommendations for cosmetic brands aiming to strengthen their market position through trust-building initiatives,competitive pricing strategies,and educational campaigns to enhance consumer awareness.These insights are particularly relevant for marketing practitioners seeking to understand and respond to the unique dynamics of the Bangladeshi cosmetics industry.展开更多
This study investigated the role of intentional self-regulation and the moderating role of peer relationship in the relationship between teacher-student relationship and learning engagement.The study sample comprised ...This study investigated the role of intentional self-regulation and the moderating role of peer relationship in the relationship between teacher-student relationship and learning engagement.The study sample comprised 540 Chinese senior secondary school students between the ages of 15–18(51.67%boys;Mage=16.56 years;SDage=0.90).They completed surveys on the Teacher-Student Relationship Scale,the Selection,Optimization,and Compensation(SOC)Scale,the Peer Relationship Scale for Children and Adolescents,and the Learning Engagement Scale.The results following regression analysis showed that teacher-student relationship predicted higher learning engagement among senior secondary school students.Intentional self-regulation partially mediated the link between teacher-student relationship and learning engagement for higher learning engagement.Peer relationship moderated the relationships between teacher-student relationship and learning engagement and moderated the relationship between teacher-student relationship and intentional self-regulation for higher learning engagement.Thesefindings imply learning engagement can be enhanced by optimizing teacher-student relationship and strengthening intentional self-regulation interventions.展开更多
In this study,we analyzed the processes involved in the resolution and enforcement of multi-domain network intent policies for intent-based networking(IBN).Previous studies on IBN analyzed the basis of the network int...In this study,we analyzed the processes involved in the resolution and enforcement of multi-domain network intent policies for intent-based networking(IBN).Previous studies on IBN analyzed the basis of the network intent resolution processes.These processes produce the artifacts required by network intent policy enforcement.Thus,we continued such studies with the inclusion of network intent policy enforcement in the analysis,for which we constructed a model that predicts the accuracy of a multi-domain network intent policy enforcement system.We validated the model by designing a new multi-domain network intent policy enforcement system,and evaluated the accuracy and performance of the new system through experimentation over a large-scale multi-domain platform that involves sites separated by more than ten thousand kilometers.The results show that,on the one hand,the new system improves accuracy by 10%and,on the other hand,that policies obtained from the multi-domain network intents,including the most complex ones,can be enforced in less than 1.75 s in a platform comprising sites located in almost opposite sides of the world.The experiment confirmed that the long distance existing between the sites involved in our experimental multi-domain IBN platform had little impact on the performance of the new system,and that the predictions obtained with the new model are as much as 99%accurate with respect to the behavior observed in the experiment.展开更多
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.
基金supported by the Key Research and Development Program of Shaanxi Province(2023-GHZD-33)the Open Project of the State Key Laboratory of Intelligent Game(ZBKF-23-05)the National Nature Science Foundation of China(62003267)。
文摘To address the issue of incorrect fusion results caused by conflicting evidence due to inaccurate evidence and incomplete recognition frameworks in radar airborne target tactical intention recognition,a spatiotemporal evidence fusion algorithm is proposed.To resolve the conflict evidence fusion problem caused by inaccurate evidence,the algorithm performs discounting of evidence from both spatial and temporal dimensions.Spatial discounting is influenced by both inter-evidence inconsistency and intra-evidence inconsistency,while temporal discounting is determined by time intervals and information entropy.For the problem of conflicting evidence fusion due to an incomplete recognition framework,an open recognition architecture based on dynamic composite focal elements is proposed.This approach allocates some conflicting information to temporary composite focal elements,avoiding excessive basic probability assignment(BPA)of the empty set after fusion,which can lead to deviations from the actual fusion results.Simulation experiments comparing various methods indicate that the proposed method can effectively improve target intention recognition accuracy and demonstrates good stability.
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy clustering techniques,such as Fuzzy C-Means(FCM),face significant challenges in handling uncertainty and the dependencies between different views.To overcome these limitations,we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data,aiming to enhance clustering accuracy and robustness,termed Multi-view Picture Fuzzy Clustering(MPFC).In particular,the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels:membership degrees,neutral degrees,and refusal degrees.This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models.Meanwhile,dual-anchor graphs exploit the similarity relationships between data points and integrate information across views.This combination improves stability,scalability,and robustness when handling noisy and heterogeneous data.Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency,outperforming traditional methods.Specifically,the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets,attaining a Purity(PUR)score of 0.6440 and an Accuracy(ACC)score of 0.6213 for the 3 Sources dataset,underscoring its robustness and efficiency.The proposed approach significantly contributes to fields such as pattern recognition,multi-view relational data analysis,and large-scale clustering problems.Future work will focus on extending the method for semi-supervised multi-view clustering,aiming to enhance adaptability,scalability,and performance in real-world applications.
基金supported by the research on key technologies for monitoring and identifying drug abuse of anesthetic drugs and psychotropic drugs,and intervention for addiction(No.2023YFC3304200)the program of a study on the diagnosis of addiction to synthetic cannabinoids and methods of assessing the risk of abuse(No.2022YFC3300905)+1 种基金the program of Ab initio design and generation of AI models for small molecule ligands based on target structures(No.2022PE0AC03)ZHIJIANG LAB.
文摘The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches face challenges with data sparsity and information loss due to single-molecule representation limitations and isolated predictive tasks.This research proposes molecular properties prediction with parallel-view and collaborative learning(MolP-PC),a multi-view fusion and multi-task deep learning framework that integrates 1D molecular fingerprints(MFs),2D molecular graphs,and 3D geometric representations,incorporating an attention-gated fusion mechanism and multi-task adaptive learning strategy for precise ADMET property predictions.Experimental results demonstrate that MolP-PC achieves optimal performance in 27 of 54 tasks,with its multi-task learning(MTL)mechanism significantly enhancing predictive performance on small-scale datasets and surpassing single-task models in 41 of 54 tasks.Additional ablation studies and interpretability analyses confirm the significance of multi-view fusion in capturing multi-dimensional molecular information and enhancing model generalization.A case study examining the anticancer compound Oroxylin A demonstrates MolP-PC’s effective generalization in predicting key pharmacokinetic parameters such as half-life(T0.5)and clearance(CL),indicating its practical utility in drug modeling.However,the model exhibits a tendency to underestimate volume of distribution(VD),indicating potential for improvement in analyzing compounds with high tissue distribution.This study presents an efficient and interpretable approach for ADMET property prediction,establishing a novel framework for molecular optimization and risk assessment in drug development.
基金supported by National Natural Science Foundation of China(32122066,32201855)STI2030—Major Projects(2023ZD04076).
文摘Phenotypic prediction is a promising strategy for accelerating plant breeding.Data from multiple sources(called multi-view data)can provide complementary information to characterize a biological object from various aspects.By integrating multi-view information into phenotypic prediction,a multi-view best linear unbiased prediction(MVBLUP)method is proposed in this paper.To measure the importance of multiple data views,the differential evolution algorithm with an early stopping mechanism is used,by which we obtain a multi-view kinship matrix and then incorporate it into the BLUP model for phenotypic prediction.To further illustrate the characteristics of MVBLUP,we perform the empirical experiments on four multi-view datasets in different crops.Compared to the single-view method,the prediction accuracy of the MVBLUP method has improved by 0.038–0.201 on average.The results demonstrate that the MVBLUP is an effective integrative prediction method for multi-view data.
基金co-supported by the National Natural Science Foundation of China(Nos.61806219,61876189 and 61703426)the Young Talent Fund of University Association for Science and Technology in Shaanxi,China(Nos.20190108 and 20220106)the Innvation Talent Supporting Project of Shaanxi,China(No.2020KJXX-065)。
文摘Air target intent recognition holds significant importance in aiding commanders to assess battlefield situations and secure a competitive edge in decision-making.Progress in this domain has been hindered by challenges posed by imbalanced battlefield data and the limited robustness of traditional recognition models.Inspired by the success of diffusion models in addressing visual domain sample imbalances,this paper introduces a new approach that utilizes the Markov Transfer Field(MTF)method for time series data visualization.This visualization,when combined with the Denoising Diffusion Probabilistic Model(DDPM),effectively enhances sample data and mitigates noise within the original dataset.Additionally,a transformer-based model tailored for time series visualization and air target intent recognition is developed.Comprehensive experimental results,encompassing comparative,ablation,and denoising validations,reveal that the proposed method achieves a notable 98.86%accuracy in air target intent recognition while demonstrating exceptional robustness and generalization capabilities.This approach represents a promising avenue for advancing air target intent recognition.
文摘The morphological description of wear particles in lubricating oil is crucial for wear state monitoring and fault diagnosis in aero-engines.Accurately and comprehensively acquiring three-dimensional(3D)morphological data of these particles has became a key focus in wear debris analysis.Herein,we develop a novel multi-view polarization-sensitive optical coherence tomography(PS-OCT)method to achieve accurate 3D morphology detection and reconstruction of aero-engine lubricant wear particles,effectively resolving occlusion-induced information loss while enabling material-specific characterization.The particle morphology is captured by multi-view imaging,followed by filtering,sharpening,and contour recognition.The method integrates advanced registration algorithms with Poisson reconstruction to generate high-precision 3D models.This approach not only provides accurate 3D morphological reconstruction but also mitigates information loss caused by particle occlusion,ensuring model completeness.Furthermore,by collecting polarization characteristics of typical metals and their oxides in aero-engine lubricants,this work comprehensively characterizes and comparatively analyzes particle polarization properties using Stokes vectors,polarization uniformity,and cumulative phase retardation,and obtains a three-dimensional model containing polarization information.Ultimately,the proposed method enables multidimensional information acquisition for the reliable identification of abrasive particle types.
基金supported by the National Natural Science Foundation of China(U20A20111)the National key R&D Program(2022YFC3080100)。
文摘The evacuation of people under threat is an effective disaster prevention and mitigation measure in response to flash floods and geological hazards,and it is also an essential element of pre-disaster planning.However,the effect of the interactions between perception factors on residents'willingness to evacuate is an urgent problem to be solved.Therefore,this paper introduces risk,stakeholder,and protective action perceptions from the protective action decision model as the main explanatory variables.These three core perceptions are subdivided into affective risk perception,cognitive risk perception,government perception,other-stakeholder perception,resourcerelated attributes,and hazard-related attributes.A questionnaire survey was conducted from June to July 2023 among residents of mountainous communities in nine villages in three towns in Sichuan Province,China.359 cross-sectional data were analyzed using structural equation modeling to explore the effects of six perception factors on evacuation intentions.The results of the study showed that:(1)affective risk perception,government perception,other-stakeholder perception,and hazard-related attributes all directly and positively influence residents'intentions to evacuate;(2)cognitive risk perception is mediated by stakeholder and protective action perceptions,which indirectly and positively affect residents'intentions to evacuate.Based on the hypothesized paths,strategies to improve residents'willingness to evacuate are discussed from the perspective of three core perceptions:strengthening disaster risk education,improving residents'cohesion,and building government credibility.The results of this study can provide theoretical support and practical suggestions for emergency management departments to formulate emergency evacuation strategies,which can aid decision-makers in better understanding residents'intentions to evacuate,optimizing evacuation information dissemination pathways,and strengthening disaster risk management capabilities.
基金Fundamental Research Funds for Inner Mongolia Directly Affiliated Universities(Grant No.BR221032)the First Class Disciplines Research Special Project(Grant No.YLXKZX-NND-009)。
文摘Traditional sheep identification is based on ear tags.However,the application of ear tags not only causes stress to the animals but also leads to loss of ear tags,which affects the correct recognition of sheep identity.In contrast,the acquisition of sheep face images offers the advantages of being non-invasive and stress-free for the animals.Nevertheless,the extant convolutional neural network-based sheep face identification model is prone to the issue of inadequate refinement,which renders its implementation on farms challenging.To address this issue,this study presented a novel sheep face recognition model that employs advanced feature fusion techniques and precise image segmentation strategies.The images were preprocessed and accurately segmented using deep learning techniques,with a dataset constructed containing sheep face images from multiple viewpoints(left,front,and right faces).In particular,the model employs a segmentation algorithm to delineate the sheep face region accurately,utilizes the Improved Convolutional Block Attention Module(I-CBAM)to emphasize the salient features of the sheep face,and achieves multi-scale fusion of the features through a Feature Pyramid Network(FPN).This process guarantees that the features captured from disparate viewpoints can be efficiently integrated to enhance recognition accuracy.Furthermore,the model guarantees the precise delineation of sheep facial contours by streamlining the image segmentation procedure,thereby establishing a robust basis for the precise identification of sheep identity.The findings demonstrate that the recognition accuracy of the Sheep Face Mask Region-based Convolutional Neural Network(SFMask RCNN)model has been enhanced by 9.64%to 98.65%in comparison to the original model.The method offers a novel technological approach to the management of animal identity in the context of sheep husbandry.
文摘To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition.This paper proposes a transfer learning-based intention prediction model for drone formation targets in air combat.This model recognizes the intentions of multiple aerial targets by extracting spatial features among the targets at each moment.Simulation results demonstrate that,compared to classical intention recognition models,the proposed model in this paper achieves higher accuracy in identifying the intentions of drone swarm targets in air combat scenarios.
基金supported by the National Key R&D Program of China(2023YFC3304600).
文摘Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin samples,especially the high-order neighbor relationship between samples.To overcome the above challenges,this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model.We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module.By this design,the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix;then,we can obtain an optimal global affinity matrix where each connected node belongs to one cluster.In addition,the discriminative constraint between views is designed to further improve the clustering performance.A range of experiments on six public datasets demonstrates that the method performs better than other advanced multi-view clustering methods.The code is available at https://github.com/songzuolong/MNF-MDSC(accessed on 25 December 2024).
文摘Sustainable development has become a critical global priority,and green transportation solutions,such as electric taxis,play a vital role in achieving this goal.This study examines the factors influencing students’intentions to adopt electric taxi services in Hanoi,Vietnam,as a step toward sustainable urban mobility.We surveyed 573 students and ana-lyzed key determinants using reliability tests,exploratory factor analysis(EFA),and linear regression.The results indi-cate that four factors significantly influence adoption intentions:Perceived Usefulness and Sustainability,Price,Brand Awareness,and Service Quality.Among these,Perceived Usefulness and Sustainability had the strongest positive im-pact,while Service Quality exerted the weakest influence.Notably,Habit and Ease of Use&Transaction Convenience were found to be statistically insignificant in the final model.These findings provide practical implications for business-es and policymakers aiming to use electric taxi adoption.To enhance appeal,stakeholders should emphasize environ-mental benefits,competitive pricing,and brand recognition while improving service reliability.By addressing these fac-tors,electric taxi services can accelerate the transition to sustainable transportation,aligning with global climate goals and transforming urban mobility.This study offers actionable insights for encouraging greener travel behaviors among students,a key demographic for long-term sustainability impact.
文摘The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data,as they rely on a single-dimensional membership value.To overcome these limitations,we propose an auto-weighted multi-view neutrosophic fuzzy clustering(AW-MVNFC)algorithm.Our method leverages the neutrosophic framework,an extension of fuzzy sets,to explicitly model imprecision and ambiguity through three membership degrees.The core novelty of AWMVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions of both individual data views and the importance of each feature within a view.Through a unified objective function,AW-MVNFC jointly optimizes the neutrosophic membership assignments,cluster centers,and the distributions of view and feature weights.Comprehensive experiments conducted on synthetic and real-world datasets demonstrate that our algorithm achieves more accurate and stable clustering than existing methods,demonstrating its effectiveness in handling the complexities of multi-view data.
基金supported by grants from the National Key Research and Development Program of China(No.2023YFC2705700)Guangdong Basic and Applied Basic Research Foundation(No.2024A1515012355)+1 种基金the Shenzhen Science and Technology Program(No.JCYJ20220530140609020)the Scientific Research Project of Wuhan Municipal Health Commission(No.WX21Q36).
文摘Objective The effects of prolonged exposure to persistently elevated atmospheric pollutants,commonly termed air pollution waves,on fertility intentions remain inadequately understood.This study aims to investigate the association between particulate matter(PM)exposure and fertility intentions.Methods In this nationwide cross-sectional study,we analyzed data from 10,747 participants(5496 females and 5251 males).PM waves were defined as periods lasting 3‒6 consecutive days during which the daily average concentrations exceeded China’s Ambient Air Quality Standards Grade II thresholds(PM2.5>75μg/m3 and PM10>150μg/m3).We employed multivariate logistic regression models to assess the association between exposure to PM waves and fertility intentions.Results Significant inverse associations were detected between exposure to PM2.5 wave events(characterized by concentrations exceeding 75μg/m3 for durations of 4‒6 days,P<0.05)and PM10 wave events(defined as concentrations exceeding 150μg/m3 for 6 consecutive days,P<0.05)and fertility intentions among females.In contrast,neither the PM2.5 wave nor the PM10 wave events demonstrated statistically significant correlations with fertility intentions in males(P>0.05 for all comparisons).The potentially susceptible subgroup was identified as females aged 20–30 years.Conclusions Our results provide the first evidence that PM2.5 and PM10 waves are associated with a reduction in female fertility intentions,offering critical insights for the development of public health policies and strategies aimed at individual protection.
文摘In the contemporary digital landscape,the proliferation of information has led to an increasing diversity of channels through which consumers obtain information,resulting in a gradual transformation of shopping habits.Consumers now frequently rely on external sources to make well-informed purchasing decisions,leading to the emergence of live shopping as a prominent avenue for gathering product information and completing transactions.E-commerce live streaming has experienced rapid growth,leveraging its ability to generate traffic and capture consumer attention.The integration of content and live streaming not only meets users’psychological needs but also facilitates seamless communication between buyers and sellers.From the perspective of content marketing typologies,this paper examines content marketing across three key dimensions:informational content,entertainment content,and emotional content.It further explores the impact of content marketing on consumers’purchase intentions within the context of e-commerce live streaming.
文摘This study focuses on the relationship between job stress and intention to leave among obstetric(OB)nurses in the context of China's birth policy adjustment,and provides a scientific basis for policymakers and healthcare administrators.This study used a non-experimental descriptive correlation design with a purposive sampling of 230 OB nurses from three tertiary hospitals in Jinan,Shandong Province.Participants were surveyed using three questionnaires and descriptive analysis;ANOVA and correlation analyses were used to analyze the relationship between participants'stressor levels and turnover intention.Pearson's correlation coefficient analysis showed that there was a positive correlation between nurses‘work stressors and turnover intention,with a correlation coefficient of r=0.53,a moderate positive correlation(P<0.001).Based on the survey data from three tertiary hospitals in Shandong Province,the obstetric nurses group has a medium level of work stressors,but a high turnover intention,highlighting the professional identity crisis.
文摘Intentional tooth replantation(ITR)is an advanced treatment modality and the procedure of last resort for preserving teeth with inaccessible endodontic or resorptive lesions.ITR is defined as the deliberate extraction of a tooth;evaluation of the root surface,endodontic manipulation,and repair;and placement of the tooth back into its original socket.Case reports,case series,cohort studies,and randomized controlled trials have demonstrated the efficacy of ITR in the retention of natural teeth that are untreatable or difficult to manage with root canal treatment or endodontic microsurgery.However,variations in clinical protocols for ITR exist due to the empirical nature of the original protocols and rapid advancements in the field of oral biology and dental materials.This heterogeneity in protocols may cause confusion among dental practitioners;therefore,guidelines and considerations for ITR should be explicated.This expert consensus discusses the biological foundation of ITR,the available clinical protocols and current status of ITR in treating teeth with refractory apical periodontitis or anatomical aberration,and the main complications of this treatment,aiming to refine the clinical management of ITR in accordance with the progress of basic research and clinical studies;the findings suggest that ITR may become a more consistent evidence-based option in dental treatment.
基金Correspondence concerning this article should be addressed to Meher Neger,Comilla University,Comilla,Bangladesh.
文摘Background of the study:The Bangladeshi cosmetics market has witnessed significant growth in recent years,driven by changing consumer lifestyles,increased disposable incomes,and rising awareness of personal grooming.The study investigates the impact of content cues which influence on purchasing intention towards cosmetic brands in Bangladesh.Purpose:The basic purpose of this study was to evaluate the factors influencing consumers’purchasing intentions for cosmetic brands in Bangladesh.Specifically,the study explored the roles of various cosmetic-related attributes and their impact on purchasing intentions within the context of Bangladesh’s cosmetic industry.Research methods:A quantitative research approach was adopted,and data were collected through a structured survey targeting Bangladeshi consumers who frequently engage with cosmetic products.All the valid responses were analyzed using SmartPLS 4.0 to perform structural equation modeling.Research findings:The findings revealed that trust in cosmetic brands and competitive pricing significantly influence consumers’purchasing intentions,highlighting the importance of fostering trust and affordability.However,certain constructs,such as ethnocentric tendencies and concerns about ingredient safety,showed limited impact on consumers’decisions.Conclusion:This study contributes to the existing literature by offering empirical insights into the Bangladeshi context,particularly within the rapidly growing cosmetics market.The findings provide actionable recommendations for cosmetic brands aiming to strengthen their market position through trust-building initiatives,competitive pricing strategies,and educational campaigns to enhance consumer awareness.These insights are particularly relevant for marketing practitioners seeking to understand and respond to the unique dynamics of the Bangladeshi cosmetics industry.
文摘This study investigated the role of intentional self-regulation and the moderating role of peer relationship in the relationship between teacher-student relationship and learning engagement.The study sample comprised 540 Chinese senior secondary school students between the ages of 15–18(51.67%boys;Mage=16.56 years;SDage=0.90).They completed surveys on the Teacher-Student Relationship Scale,the Selection,Optimization,and Compensation(SOC)Scale,the Peer Relationship Scale for Children and Adolescents,and the Learning Engagement Scale.The results following regression analysis showed that teacher-student relationship predicted higher learning engagement among senior secondary school students.Intentional self-regulation partially mediated the link between teacher-student relationship and learning engagement for higher learning engagement.Peer relationship moderated the relationships between teacher-student relationship and learning engagement and moderated the relationship between teacher-student relationship and intentional self-regulation for higher learning engagement.Thesefindings imply learning engagement can be enhanced by optimizing teacher-student relationship and strengthening intentional self-regulation interventions.
基金supported by the Spanish Ministry of Science and Innovation under the project ONOFRE-4,Grant PID2023-148104OB-C43,funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU.by Horizon project RIGOUROUS funded by the European Commission,GA:101095933by the Spanish Ministry of Science and Innovation under the DIN2019-010827 Industrial PhD Grant,co-funded by Odin Solutions S.L.
文摘In this study,we analyzed the processes involved in the resolution and enforcement of multi-domain network intent policies for intent-based networking(IBN).Previous studies on IBN analyzed the basis of the network intent resolution processes.These processes produce the artifacts required by network intent policy enforcement.Thus,we continued such studies with the inclusion of network intent policy enforcement in the analysis,for which we constructed a model that predicts the accuracy of a multi-domain network intent policy enforcement system.We validated the model by designing a new multi-domain network intent policy enforcement system,and evaluated the accuracy and performance of the new system through experimentation over a large-scale multi-domain platform that involves sites separated by more than ten thousand kilometers.The results show that,on the one hand,the new system improves accuracy by 10%and,on the other hand,that policies obtained from the multi-domain network intents,including the most complex ones,can be enforced in less than 1.75 s in a platform comprising sites located in almost opposite sides of the world.The experiment confirmed that the long distance existing between the sites involved in our experimental multi-domain IBN platform had little impact on the performance of the new system,and that the predictions obtained with the new model are as much as 99%accurate with respect to the behavior observed in the experiment.
