Based on the analysis of status quo of metadata technology and the informationized freezing construction of an auxiliary shaft in Longgu mine, the necessity and feasibility of applying metadata standards for informati...Based on the analysis of status quo of metadata technology and the informationized freezing construction of an auxiliary shaft in Longgu mine, the necessity and feasibility of applying metadata standards for informationized construction is discussed. The prototype of metadata standard for the construction is designed and established by using a modeling method, and the framework with XML/RDF for such standard is given.展开更多
Objective:The study was to analyze the application effect of informationized teaching method based on evidence-based nursing in surgical nursing teaching.Methods:From December 2019 to December 2020,60 students were se...Objective:The study was to analyze the application effect of informationized teaching method based on evidence-based nursing in surgical nursing teaching.Methods:From December 2019 to December 2020,60 students were selected as the research objects and randomly divided into two groups,each with 30 students in the teaching group.The observation group applied informationized teaching based on evidence-based nursing method,and the control group used the traditional teaching model.The teaching effect was evaluated.Results:The test scores of subjective theoretical knowledge and objective theoretical knowledge of the observation group were significantly higher than those of the control group,and the comprehensive ability evaluation of the observation group was also higher(P<0.05).The majority of students accepted the informationized teaching method based on evidence-based nursing,and a few held a neutral or disapproval attitude.Conclusion:Informationized teaching method based on evidencebased nursing can improve students'theoretical and practical levels in surgical nursing teaching,and most students also accept this teaching method,which has application value.展开更多
The Chinese Journal of Chemical Engineering is the official journal of The Chemical Industry and Engineering Society of China and published by the Chemical Industry Press Co.,Ltd.The aim of the journal is to develop t...The Chinese Journal of Chemical Engineering is the official journal of The Chemical Industry and Engineering Society of China and published by the Chemical Industry Press Co.,Ltd.The aim of the journal is to develop the international exchange of scientific and technical information in the field of chemical engineering.Submission of Papers All papers will be submitted on line.展开更多
Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approa...Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.展开更多
Quantitative analysis of aluminum-silicon(Al-Si)alloy microstructure is crucial for evaluating and controlling alloy performance.Conventional analysis methods rely on manual segmentation,which is inefficient and subje...Quantitative analysis of aluminum-silicon(Al-Si)alloy microstructure is crucial for evaluating and controlling alloy performance.Conventional analysis methods rely on manual segmentation,which is inefficient and subjective,while fully supervised deep learning approaches require extensive and expensive pixel-level annotated data.Furthermore,existing semi-supervised methods still face challenges in handling the adhesion of adjacent primary silicon particles and effectively utilizing consistency in unlabeled data.To address these issues,this paper proposes a novel semi-supervised framework for Al-Si alloy microstructure image segmentation.First,we introduce a Rotational Uncertainty Correction Strategy(RUCS).This strategy employs multi-angle rotational perturbations andMonte Carlo sampling to assess prediction consistency,generating a pixel-wise confidence weight map.By integrating this map into the loss function,the model dynamically focuses on high-confidence regions,thereby improving generalization ability while reducing manual annotation pressure.Second,we design a Boundary EnhancementModule(BEM)to strengthen boundary feature extraction through erosion difference and multi-scale dilated convolutions.This module guides the model to focus on the boundary regions of adjacent particles,effectively resolving particle adhesion and improving segmentation accuracy.Systematic experiments were conducted on the Aluminum-Silicon Alloy Microstructure Dataset(ASAD).Results indicate that the proposed method performs exceptionally well with scarce labeled data.Specifically,using only 5%labeled data,our method improves the Jaccard index and Adjusted Rand Index(ARI)by 2.84 and 1.57 percentage points,respectively,and reduces the Variation of Information(VI)by 8.65 compared to stateof-the-art semi-supervised models,approaching the performance levels of 10%labeled data.These results demonstrate that the proposed method significantly enhances the accuracy and robustness of quantitative microstructure analysis while reducing annotation costs.展开更多
Although Named Entity Recognition(NER)in cybersecurity has historically concentrated on threat intelligence,vital security data can be found in a variety of sources,such as open-source intelligence and unprocessed too...Although Named Entity Recognition(NER)in cybersecurity has historically concentrated on threat intelligence,vital security data can be found in a variety of sources,such as open-source intelligence and unprocessed tool outputs.When dealing with technical language,the coexistence of structured and unstructured data poses serious issues for traditional BERT-based techniques.We introduce a three-phase approach for improved NER inmulti-source cybersecurity data that makes use of large language models(LLMs).To ensure thorough entity coverage,our method starts with an identification module that uses dynamic prompting techniques.To lessen hallucinations,the extraction module uses confidence-based self-assessment and cross-checking using regex validation.The tagging module links to knowledge bases for contextual validation and uses SecureBERT in conjunction with conditional random fields to detect entity boundaries precisely.Our framework creates efficient natural language segments by utilizing decoderbased LLMs with 10B parameters.When compared to baseline SecureBERT implementations,evaluation across four cybersecurity data sources shows notable gains,with a 9.4%–25.21%greater recall and a 6.38%–17.3%better F1-score.Our refined model matches larger models and achieves 2.6%–4.9%better F1-score for technical phrase recognition than the state-of-the-art alternatives Claude 3.5 Sonnet,Llama3-8B,and Mixtral-7B.The three-stage architecture identification-extraction-tagging pipeline tackles important cybersecurity NER issues.Through effective architectures,these developments preserve deployability while setting a new standard for entity extraction in challenging security scenarios.The findings show how specific enhancements in hybrid recognition,validation procedures,and prompt engineering raise NER performance above monolithic LLM approaches in cybersecurity applications,especially for technical entity extraction fromheterogeneous sourceswhere conventional techniques fall short.Because of itsmodular nature,the framework can be upgraded at the component level as new methods are developed.展开更多
Submission.Papers appearing in the Journal comprise Editorials,Rapid Communications,Perspectives,Tutorials,Feature Articles,Reviews,Research Articles,which should contain original information,theoretical or experiment...Submission.Papers appearing in the Journal comprise Editorials,Rapid Communications,Perspectives,Tutorials,Feature Articles,Reviews,Research Articles,which should contain original information,theoretical or experimental,on any topics in the field of polymer science and polymer material science.Papers already published or scheduled to be published elsewhere should not be submitted and certainly will not be accepted.展开更多
While biomaterials are endowed with sophisticated functions by the temporal dynamics and autonomy derived from non-equilibrium assemblies in biological systems,fabricating advanced materials counterparts with these fe...While biomaterials are endowed with sophisticated functions by the temporal dynamics and autonomy derived from non-equilibrium assemblies in biological systems,fabricating advanced materials counterparts with these features through kinetic control remains rare.Herein,we report a non-equilibrium hydrogel that exhibits autonomous time-dependent ultrabright fluorescence(quantum yield 0.90),achieved through the kinetically controlled incorporation of thermodynamic equilibrium host-vip complexes into a poly(2-hydroxyethyl methacrylate)(PHEMA)network.Transient complexes are programmed by coupling rapid assembly kinetics with the slow competitive binding of the polymer matrix.This kinetic mismatch converts a thermodynamic equilibrium supramolecular system into a non-equilibrium state,generating temporally dynamic fluorescence that cyclically shifts from yellow to green and self-reverts.The programmed temporal dynamics endow the hydrogel with high potential for information encryption applications.展开更多
People read many things online these days.It's an easy way to get a lot of information fast.They look at news,see posts and watch videos.But how much of the information is true?Some things online are fake.So it...People read many things online these days.It's an easy way to get a lot of information fast.They look at news,see posts and watch videos.But how much of the information is true?Some things online are fake.So it's important to check the facts before you believe or share anything.You can ask people or look at other sources first.Check newspapers or official websites.Always think carefully before you believe something online.展开更多
In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic(PV)modules,this study proposes a predictiv...In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic(PV)modules,this study proposes a predictive maintenance(PdM)strategy based on Remaining Useful Life(RUL)estimation.First,a RUL prediction model is established using the Transformer architecture,which enables the effective processing of sequential degradation data.By employing the historical degradation data of PV modules,the proposed model provides accurate forecasts of the remaining useful life,thereby supplying essential inputs for maintenance decision-making.Subsequently,the RUL information obtained from the prediction process is integrated into the optimization of maintenance policies.An opposition-based learning Harris Hawks Optimization(OHHO)algorithm is introduced to jointly optimize two critical parameters:the maintenance threshold L,which specifies the degradation level at which maintenance should be performed,and the recovery factor r,which reflects the extent to which the system performance is restored after maintenance.The objective of this joint optimization is to minimize the overall operation and maintenance cost while maintaining system availability.Finally,simulation experiments are conducted to evaluate the performance of the proposed PdM strategy.The results indicate that,compared with conventional corrective maintenance(CM)and periodic maintenance(PM)strategies,the RUL-driven PdM approach achieves a reduction in the average cost rate by approximately 20.7%and 17.9%,respectively,thereby demonstrating its potential effectiveness for practical PV maintenance applications.展开更多
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ...High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.展开更多
1 General information Journal of Geographical Sciences is an international academic journal that publishes papers of the highest quality in physical geography, natural resources, environmental sciences, geographic inf...1 General information Journal of Geographical Sciences is an international academic journal that publishes papers of the highest quality in physical geography, natural resources, environmental sciences, geographic information sciences, remote sensing and cartography. Manuscripts come from different parts of the world.展开更多
Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automaticall...Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.展开更多
Human activities have significantly impacted the land surface temperature(LST),endangering human health;however,the relationship between these two factors has not been adequately quantified.This study comprehensively ...Human activities have significantly impacted the land surface temperature(LST),endangering human health;however,the relationship between these two factors has not been adequately quantified.This study comprehensively constructs a Human Activity Intensity(HAI)index and employs the Maximal Information Coefficient,four-quadrant model,and XGBoostSHAP model to investigate the spatiotemporal relationship and influencing factors of HAI-LST in the Yellow River Basin(YRB)from 2000 to 2020.The results indicated that from 2000 to 2020,as HAI and LST increased,the static HAI-LST relationship in the YRB showed a positive correlation that continued to strengthen.This dynamic relationship exhibited conflicting development,with the proportion of coordinated to conflicting regions shifting from 1:4 to 1:2,indicating a reduction in conflict intensity.Notably,only the degree of conflict in the source area decreased significantly,whereas it intensified in the upper and lower reaches.The key factors influencing the HAI-LST relationship include fractional vegetation cover,slope,precipitation,and evapotranspiration,along with region-specific factors such as PM_(2.5),biodiversity,and elevation.Based on these findings,region-specific ecological management strategies have been proposed to mitigate conflict-prone areas and alleviate thermal stress,thereby providing important guidance for promoting harmonious development between humans and nature.展开更多
Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instabili...Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.展开更多
Rock mass stability is significantly influenced by the heterogeneity of rock joint roughness and shear strength.While modern technology facilitates assessing roughness heterogeneity,evaluating shear strength heterogen...Rock mass stability is significantly influenced by the heterogeneity of rock joint roughness and shear strength.While modern technology facilitates assessing roughness heterogeneity,evaluating shear strength heterogeneity remains challenging.To address this,this study first captures the morphology of large-scale(1000 mm × 1000 mm) slate and granite joints via 3D laser scanning.Analysis of these surfaces and corresponding push/pull tests on carved specimens revealed a potential correlation between the heterogeneity of roughness and shear strength.A comparative evaluation of five statistical metrics identified information entropy(Hs) as the most robust indicator for quantifying rock joint heterogeneity.Further analysis using Hsreveals that the heterogeneity is anisotropic and,critically,that shear strength heterogeneity is governed not only by roughness heterogeneity but is also significantly influenced by the mean roughness value,normal stress,and intact rock tensile strength.Consequently,a simple comparison of roughness Hsvalues is insufficient for reliably comparing shear strength heterogeneity.To overcome this limitation,a theoretical framework is developed to explicitly map fundamental roughness statistics(mean and heterogeneity) to shear strength heterogeneity.This framework culminates in a practical workflow that allows for the rapid,field-based assessment of shear strength heterogeneity using readily obtainable rock joint roughness data.展开更多
With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State I...With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.展开更多
There is a contradiction between the evolution rate of materials and the time resolution of SR-CT characterization in the in situ synchrotron radiation computed tomography(SR-CT)characterization of ultrafast evolution...There is a contradiction between the evolution rate of materials and the time resolution of SR-CT characterization in the in situ synchrotron radiation computed tomography(SR-CT)characterization of ultrafast evolution process.The sampling strategy of the ultra-sparse angle is an effective method for improving time resolution.Accurate reconstruction under sparse sampling conditions has always been a bottleneck problem.In recent years,convolutional neural networks have shown outstanding advantages in sparse-angle CT reconstruction given the development of deep learning.However,existing ideas did not consider the expression of high-frequency details in neural networks,limiting their application in accurate SR-CT characterization.A novel high-frequency information-constrained deep learning network(HFIC-Net)is proposed in response to this problem.Additional high-frequency information constraints are added to improve the accuracy of the reconstruction results.Further,a series of numerical reconstruction experiments are conducted to verify this new method,and the results indicate that the reconstruction results of HFIC-Net method effectively improve reconstruction quality.This new method uses only eight-angle projections to achieve the reconstruction effect of the filtered backprojection method(FBP)method in 360 projections.The results of the HFIC-Net method demonstrate clear boundaries and accurate detailed structures,correcting the misinformation caused by using other methods.For quantitative evaluation,the SSIM used to evaluate image structure similarity is increased from 0.1951,0.9212,and 0.9308 for FBP,FBP-Conv,and DDC-Net,respectively,to 0.9620 for HFIC-Net.Finally,the results of actual SR-CT experimental data indicate that the new method can suppress artifacts and achieve accurate reconstruction,and it is suitable for the in situ SR-CT accurate characterization of ultxafast evolution process.展开更多
Quantum control allows a wide range of quantum operations employed in molecular physics,nuclear magnetic resonance and quantum information processing.Thanks to the existing microelectronics industry,semiconducting qub...Quantum control allows a wide range of quantum operations employed in molecular physics,nuclear magnetic resonance and quantum information processing.Thanks to the existing microelectronics industry,semiconducting qubits,where quantum information is encoded in spin or charge degree freedom of electrons or nuclei in semiconductor quantum dots,constitute a highly competitive candidate for scalable solid-state quantum technologies.In quantum information processing,advanced control techniques are needed to realize quantum manipulations with both high precision and noise resilience.In this review,we first introduce the basics of various widely-used control methods,including resonant excitation,adabatic passage,shortcuts to adiabaticity,composite pulses,and quantum optimal control.Then we review the practical aspects in applying these methods to realize accurate and robust quantum gates for single semiconductor qubits,such as Loss–DiVincenzo spin qubit,spinglet-triplet qubit,exchange-only qubit and charge qubit.展开更多
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
文摘Based on the analysis of status quo of metadata technology and the informationized freezing construction of an auxiliary shaft in Longgu mine, the necessity and feasibility of applying metadata standards for informationized construction is discussed. The prototype of metadata standard for the construction is designed and established by using a modeling method, and the framework with XML/RDF for such standard is given.
文摘Objective:The study was to analyze the application effect of informationized teaching method based on evidence-based nursing in surgical nursing teaching.Methods:From December 2019 to December 2020,60 students were selected as the research objects and randomly divided into two groups,each with 30 students in the teaching group.The observation group applied informationized teaching based on evidence-based nursing method,and the control group used the traditional teaching model.The teaching effect was evaluated.Results:The test scores of subjective theoretical knowledge and objective theoretical knowledge of the observation group were significantly higher than those of the control group,and the comprehensive ability evaluation of the observation group was also higher(P<0.05).The majority of students accepted the informationized teaching method based on evidence-based nursing,and a few held a neutral or disapproval attitude.Conclusion:Informationized teaching method based on evidencebased nursing can improve students'theoretical and practical levels in surgical nursing teaching,and most students also accept this teaching method,which has application value.
文摘The Chinese Journal of Chemical Engineering is the official journal of The Chemical Industry and Engineering Society of China and published by the Chemical Industry Press Co.,Ltd.The aim of the journal is to develop the international exchange of scientific and technical information in the field of chemical engineering.Submission of Papers All papers will be submitted on line.
基金supported by the National Natural Science Foundation of China(Grant Nos.62572057,62272049,U24A20331)Beijing Natural Science Foundation(Grant Nos.4232026,4242020)Academic Research Projects of Beijing Union University(Grant No.ZK10202404).
文摘Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.
基金funded by the National Natural Science Foundation of China (52061020).
文摘Quantitative analysis of aluminum-silicon(Al-Si)alloy microstructure is crucial for evaluating and controlling alloy performance.Conventional analysis methods rely on manual segmentation,which is inefficient and subjective,while fully supervised deep learning approaches require extensive and expensive pixel-level annotated data.Furthermore,existing semi-supervised methods still face challenges in handling the adhesion of adjacent primary silicon particles and effectively utilizing consistency in unlabeled data.To address these issues,this paper proposes a novel semi-supervised framework for Al-Si alloy microstructure image segmentation.First,we introduce a Rotational Uncertainty Correction Strategy(RUCS).This strategy employs multi-angle rotational perturbations andMonte Carlo sampling to assess prediction consistency,generating a pixel-wise confidence weight map.By integrating this map into the loss function,the model dynamically focuses on high-confidence regions,thereby improving generalization ability while reducing manual annotation pressure.Second,we design a Boundary EnhancementModule(BEM)to strengthen boundary feature extraction through erosion difference and multi-scale dilated convolutions.This module guides the model to focus on the boundary regions of adjacent particles,effectively resolving particle adhesion and improving segmentation accuracy.Systematic experiments were conducted on the Aluminum-Silicon Alloy Microstructure Dataset(ASAD).Results indicate that the proposed method performs exceptionally well with scarce labeled data.Specifically,using only 5%labeled data,our method improves the Jaccard index and Adjusted Rand Index(ARI)by 2.84 and 1.57 percentage points,respectively,and reduces the Variation of Information(VI)by 8.65 compared to stateof-the-art semi-supervised models,approaching the performance levels of 10%labeled data.These results demonstrate that the proposed method significantly enhances the accuracy and robustness of quantitative microstructure analysis while reducing annotation costs.
文摘Although Named Entity Recognition(NER)in cybersecurity has historically concentrated on threat intelligence,vital security data can be found in a variety of sources,such as open-source intelligence and unprocessed tool outputs.When dealing with technical language,the coexistence of structured and unstructured data poses serious issues for traditional BERT-based techniques.We introduce a three-phase approach for improved NER inmulti-source cybersecurity data that makes use of large language models(LLMs).To ensure thorough entity coverage,our method starts with an identification module that uses dynamic prompting techniques.To lessen hallucinations,the extraction module uses confidence-based self-assessment and cross-checking using regex validation.The tagging module links to knowledge bases for contextual validation and uses SecureBERT in conjunction with conditional random fields to detect entity boundaries precisely.Our framework creates efficient natural language segments by utilizing decoderbased LLMs with 10B parameters.When compared to baseline SecureBERT implementations,evaluation across four cybersecurity data sources shows notable gains,with a 9.4%–25.21%greater recall and a 6.38%–17.3%better F1-score.Our refined model matches larger models and achieves 2.6%–4.9%better F1-score for technical phrase recognition than the state-of-the-art alternatives Claude 3.5 Sonnet,Llama3-8B,and Mixtral-7B.The three-stage architecture identification-extraction-tagging pipeline tackles important cybersecurity NER issues.Through effective architectures,these developments preserve deployability while setting a new standard for entity extraction in challenging security scenarios.The findings show how specific enhancements in hybrid recognition,validation procedures,and prompt engineering raise NER performance above monolithic LLM approaches in cybersecurity applications,especially for technical entity extraction fromheterogeneous sourceswhere conventional techniques fall short.Because of itsmodular nature,the framework can be upgraded at the component level as new methods are developed.
文摘Submission.Papers appearing in the Journal comprise Editorials,Rapid Communications,Perspectives,Tutorials,Feature Articles,Reviews,Research Articles,which should contain original information,theoretical or experimental,on any topics in the field of polymer science and polymer material science.Papers already published or scheduled to be published elsewhere should not be submitted and certainly will not be accepted.
基金supported by the National Natural Science Foundation of China(22025503,22220102004,22401093)the Science and Technology Commission of Shanghai Municipality(24DX1400200)+2 种基金the Innovation Program of Shanghai Municipal Education Commission(2023ZKZD40)the Programme of Introducing Talents of Discipline to Universities(B16017)the China Postdoctoral Science Foundation(2023M741167,GZB20230211,2024T170273)。
文摘While biomaterials are endowed with sophisticated functions by the temporal dynamics and autonomy derived from non-equilibrium assemblies in biological systems,fabricating advanced materials counterparts with these features through kinetic control remains rare.Herein,we report a non-equilibrium hydrogel that exhibits autonomous time-dependent ultrabright fluorescence(quantum yield 0.90),achieved through the kinetically controlled incorporation of thermodynamic equilibrium host-vip complexes into a poly(2-hydroxyethyl methacrylate)(PHEMA)network.Transient complexes are programmed by coupling rapid assembly kinetics with the slow competitive binding of the polymer matrix.This kinetic mismatch converts a thermodynamic equilibrium supramolecular system into a non-equilibrium state,generating temporally dynamic fluorescence that cyclically shifts from yellow to green and self-reverts.The programmed temporal dynamics endow the hydrogel with high potential for information encryption applications.
文摘People read many things online these days.It's an easy way to get a lot of information fast.They look at news,see posts and watch videos.But how much of the information is true?Some things online are fake.So it's important to check the facts before you believe or share anything.You can ask people or look at other sources first.Check newspapers or official websites.Always think carefully before you believe something online.
基金supported by the National Natural Science Foundation of China(No.51767017)the Key Research and Development Program of Gansu Province(No.25YFGA032)the Industry Support and Guidance Project for Higher Education Institutions of Gansu Province(No.2022CYZC-22).
文摘In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic(PV)modules,this study proposes a predictive maintenance(PdM)strategy based on Remaining Useful Life(RUL)estimation.First,a RUL prediction model is established using the Transformer architecture,which enables the effective processing of sequential degradation data.By employing the historical degradation data of PV modules,the proposed model provides accurate forecasts of the remaining useful life,thereby supplying essential inputs for maintenance decision-making.Subsequently,the RUL information obtained from the prediction process is integrated into the optimization of maintenance policies.An opposition-based learning Harris Hawks Optimization(OHHO)algorithm is introduced to jointly optimize two critical parameters:the maintenance threshold L,which specifies the degradation level at which maintenance should be performed,and the recovery factor r,which reflects the extent to which the system performance is restored after maintenance.The objective of this joint optimization is to minimize the overall operation and maintenance cost while maintaining system availability.Finally,simulation experiments are conducted to evaluate the performance of the proposed PdM strategy.The results indicate that,compared with conventional corrective maintenance(CM)and periodic maintenance(PM)strategies,the RUL-driven PdM approach achieves a reduction in the average cost rate by approximately 20.7%and 17.9%,respectively,thereby demonstrating its potential effectiveness for practical PV maintenance applications.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2020-NR049579).
文摘High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.
文摘1 General information Journal of Geographical Sciences is an international academic journal that publishes papers of the highest quality in physical geography, natural resources, environmental sciences, geographic information sciences, remote sensing and cartography. Manuscripts come from different parts of the world.
文摘Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.
基金Shanxi Province Graduate Research Practice Innovation Project,No.2023KY465Project on the Reform of Graduate Education and Teaching in Shanxi Province,No.2021YJJG146+1 种基金Research Project of Shanxi Provincial Cultural Relics Bureau,No.22-8-14-1400-119National Key R&D Program of China,No.2021YFB3901300。
文摘Human activities have significantly impacted the land surface temperature(LST),endangering human health;however,the relationship between these two factors has not been adequately quantified.This study comprehensively constructs a Human Activity Intensity(HAI)index and employs the Maximal Information Coefficient,four-quadrant model,and XGBoostSHAP model to investigate the spatiotemporal relationship and influencing factors of HAI-LST in the Yellow River Basin(YRB)from 2000 to 2020.The results indicated that from 2000 to 2020,as HAI and LST increased,the static HAI-LST relationship in the YRB showed a positive correlation that continued to strengthen.This dynamic relationship exhibited conflicting development,with the proportion of coordinated to conflicting regions shifting from 1:4 to 1:2,indicating a reduction in conflict intensity.Notably,only the degree of conflict in the source area decreased significantly,whereas it intensified in the upper and lower reaches.The key factors influencing the HAI-LST relationship include fractional vegetation cover,slope,precipitation,and evapotranspiration,along with region-specific factors such as PM_(2.5),biodiversity,and elevation.Based on these findings,region-specific ecological management strategies have been proposed to mitigate conflict-prone areas and alleviate thermal stress,thereby providing important guidance for promoting harmonious development between humans and nature.
基金Supported by the National Defense Basic Scientific Research Program of China.
文摘Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.
基金supported by the National Natural Science Foundation of China (Nos.42422705,42207175,42177117 and 42577170)the Ningbo Youth Leading Talent Project (No.2024QL051)+1 种基金the Chinese Academy of Engineering Science and Technology Strategy Consulting Project (No.2025-XZ-57)the Central Government Funding Program for Guiding Local Science and Technology Development (No.2025ZY01028)。
文摘Rock mass stability is significantly influenced by the heterogeneity of rock joint roughness and shear strength.While modern technology facilitates assessing roughness heterogeneity,evaluating shear strength heterogeneity remains challenging.To address this,this study first captures the morphology of large-scale(1000 mm × 1000 mm) slate and granite joints via 3D laser scanning.Analysis of these surfaces and corresponding push/pull tests on carved specimens revealed a potential correlation between the heterogeneity of roughness and shear strength.A comparative evaluation of five statistical metrics identified information entropy(Hs) as the most robust indicator for quantifying rock joint heterogeneity.Further analysis using Hsreveals that the heterogeneity is anisotropic and,critically,that shear strength heterogeneity is governed not only by roughness heterogeneity but is also significantly influenced by the mean roughness value,normal stress,and intact rock tensile strength.Consequently,a simple comparison of roughness Hsvalues is insufficient for reliably comparing shear strength heterogeneity.To overcome this limitation,a theoretical framework is developed to explicitly map fundamental roughness statistics(mean and heterogeneity) to shear strength heterogeneity.This framework culminates in a practical workflow that allows for the rapid,field-based assessment of shear strength heterogeneity using readily obtainable rock joint roughness data.
基金supported by National Natural Science Foundation of China(NSFC)under grant U23A20310.
文摘With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.
基金supported by the National Nature Science Foundation of China(Nos.12027901 and 12041202)Synchrotron Radiation Joint Fund of University of Science and Technology of China(Nos.KY2090000059 and KY2090000054)。
文摘There is a contradiction between the evolution rate of materials and the time resolution of SR-CT characterization in the in situ synchrotron radiation computed tomography(SR-CT)characterization of ultrafast evolution process.The sampling strategy of the ultra-sparse angle is an effective method for improving time resolution.Accurate reconstruction under sparse sampling conditions has always been a bottleneck problem.In recent years,convolutional neural networks have shown outstanding advantages in sparse-angle CT reconstruction given the development of deep learning.However,existing ideas did not consider the expression of high-frequency details in neural networks,limiting their application in accurate SR-CT characterization.A novel high-frequency information-constrained deep learning network(HFIC-Net)is proposed in response to this problem.Additional high-frequency information constraints are added to improve the accuracy of the reconstruction results.Further,a series of numerical reconstruction experiments are conducted to verify this new method,and the results indicate that the reconstruction results of HFIC-Net method effectively improve reconstruction quality.This new method uses only eight-angle projections to achieve the reconstruction effect of the filtered backprojection method(FBP)method in 360 projections.The results of the HFIC-Net method demonstrate clear boundaries and accurate detailed structures,correcting the misinformation caused by using other methods.For quantitative evaluation,the SSIM used to evaluate image structure similarity is increased from 0.1951,0.9212,and 0.9308 for FBP,FBP-Conv,and DDC-Net,respectively,to 0.9620 for HFIC-Net.Finally,the results of actual SR-CT experimental data indicate that the new method can suppress artifacts and achieve accurate reconstruction,and it is suitable for the in situ SR-CT accurate characterization of ultxafast evolution process.
基金support by the National Natural Science Foundation of China(Nos.12174379,E31Q02BG)the Chinese Academy of Sciences(Nos.E0SEBB11,E27RBB11)+1 种基金the Innovation Program for Quantum Science and Technology(No.2021ZD0302300)Chinese Academy of Sciences Project for Young Scientists in Basic Research(No.YSBR-090)。
文摘Quantum control allows a wide range of quantum operations employed in molecular physics,nuclear magnetic resonance and quantum information processing.Thanks to the existing microelectronics industry,semiconducting qubits,where quantum information is encoded in spin or charge degree freedom of electrons or nuclei in semiconductor quantum dots,constitute a highly competitive candidate for scalable solid-state quantum technologies.In quantum information processing,advanced control techniques are needed to realize quantum manipulations with both high precision and noise resilience.In this review,we first introduce the basics of various widely-used control methods,including resonant excitation,adabatic passage,shortcuts to adiabaticity,composite pulses,and quantum optimal control.Then we review the practical aspects in applying these methods to realize accurate and robust quantum gates for single semiconductor qubits,such as Loss–DiVincenzo spin qubit,spinglet-triplet qubit,exchange-only qubit and charge qubit.
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
