At present, Chinas civil aviation industry has entered a new stage of steady development, and various types of civil aircraft emerge in one after another. Aircraft as a special equipment, in order to ensure the safety...At present, Chinas civil aviation industry has entered a new stage of steady development, and various types of civil aircraft emerge in one after another. Aircraft as a special equipment, in order to ensure the safety of flight, airworthiness information management has extremely high requirements. In order to improve the level of airworthiness management, the introduction of information management means, make airworthiness management more scientific, consider from multiple goals and multiple dimensions, and improve the scientific nature and reliability of airworthiness management, so as to further improve the safety and stability of aircraft operation.展开更多
2025年12月22日,上海交通大学医学院医学技术学院李文杰在人工智能与信息融合领域国际顶级期刊Information Fusion发表了一项研究成果“CX-Mind:a pioneering multimodal large language model for interleaved reasoning in chest X-ra...2025年12月22日,上海交通大学医学院医学技术学院李文杰在人工智能与信息融合领域国际顶级期刊Information Fusion发表了一项研究成果“CX-Mind:a pioneering multimodal large language model for interleaved reasoning in chest X-ray via curriculum-guided reinforcement learning”。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
The relentless evolution of Information and Communication Technology(ICT) stands as a testament to the synergistic power of collaboration. It thrives at the dynamic intersection of industrial insight, academic rigor, ...The relentless evolution of Information and Communication Technology(ICT) stands as a testament to the synergistic power of collaboration. It thrives at the dynamic intersection of industrial insight, academic rigor, and dedicated research. This special issue, “Achievements of ZTE's Industry-University-Institute Cooperation Projects,” presents a curated collection of cutting-edge research that embodies the fruitful outcomes of deep collaboration, addressing some of the most pressing challenges across wireless communications, artificial intelligence(AI), software engineering, and industrial digitization.展开更多
Journal Customer services For ordering information,claims and any enquiry concerning your journal subscription please go to contact your nearest office.America:Tel:(401)515-4764 Europe,Middle East,Africa and Asia Paci...Journal Customer services For ordering information,claims and any enquiry concerning your journal subscription please go to contact your nearest office.America:Tel:(401)515-4764 Europe,Middle East,Africa and Asia Pacific:Email:asiaar@163.com;523404392@qq.com;434421224@qq.com Tel:+86-551-65148112 Annual subscription rate(12 issues):US$300.展开更多
Generative steganography uses generative stego images to transmit secret message.It also effectively defends against statistical steganalysis.However,most existing methods focus primarily on matching the feature distr...Generative steganography uses generative stego images to transmit secret message.It also effectively defends against statistical steganalysis.However,most existing methods focus primarily on matching the feature distribution of training data,often neglecting the sequential continuity between moves in the game.This oversight can result in unnatural patterns that deviate from real user behavior,thereby reducing the security of the hidden communication.To address this issue,we design a Gomoku agent based on the AlphaZero algorithm.The model engages in self-play to generate a sequence of plausible moves.These moves formthe basis of the stego images.We then apply an attractionmatrix at each step.It guides themove selection so that themoves appearmore natural.Thismethod helps maintain logical flow between moves.It also extends the game length,which increases the embedding capacity.Next,we filter and prioritize the generated moves.The selected moves are embedded into a move pool.Secret message is mapped to thesemoves.It is then embedded step by step as the game progresses.The finalmove sequence constitutes a complete steganographic game record.The receiver can extract the secret message using this record and a predefined mapping rule.Experiments show that our method reaches a maximum embedding capacity of 223 bits per carrier.Detection accuracy is 0.500 under XuNet and 0.498 under YeNet.These results are equal to random guessing,showing strong imperceptibility.The proposed method demonstrates superior concealment,higher embedding capacity,and greater robustness against common image distortions and steganalysis attacks.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
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
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
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
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 inform...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
Information collaboration is crucial for optimizing resource allocation and improving diagnostic efficiency across hospital tiers through enhanced information technology capacity.To characterize the dynamic decision-m...Information collaboration is crucial for optimizing resource allocation and improving diagnostic efficiency across hospital tiers through enhanced information technology capacity.To characterize the dynamic decision-making mechanism between general hospitals(GHs)and primary healthcare centers(PHCs),a two-player differential game model was constructed to analyze the relationship between optimal investment levels and corresponding payoffs and explore how GHs can incentivize collaboration by adjusting their investment intensity and sharing PHCs’costs.The results indicate that information collaboration is a win-win strategy.Its dynamic equilibrium shows that GHs make intensive efforts in the early stage of digital construction.However,such investment decreases over time as patient information accessibility becomes limited.Under the collaboration mode,although GHs’digital investment is lower than that in the independent operation,the total system payoff significantly increases.This improvement arises because PHCs,with their locational and informational advantages,undertake major digitalization tasks,allowing GHs to focus resources on disease treatment.The introduction of collaboration incentives strengthens this performance improvement.展开更多
The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issu...The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issue is the formalisation of extracting meaning from information.Meaning emerges through a three-stage interpretative process,where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context.However,this approach currently lacks practical grounding.In this research,we developed a model based on contexts,which applies interpretation principles to the visual information to address this gap.The field of computer vision and object recognition has progressed essentially with artificial neural networks,but these models struggle with geometrically transformed images,such as those that are rotated or shifted,limiting their robustness in real-world applications.Various approaches have been proposed to address this problem.Some of them(Hu moments,spatial transformers,capsule networks,attention and memory mechanisms)share a conceptual connection with the contextual model(CM)discussed in this study.This paper investigates whether CM principles are applicable for interpreting rotated images from the MNIST and Fashion MNIST datasets.The model was implemented in the Rust programming language.It consists of a contextual module and a convolutional neural network(CNN).The CMwas trained on the rotated Mono Icons dataset,which is significantly different from the testing datasets.The CNN module was trained on the original MNIST and Fashion MNIST datasets for interpretation recognition.As a result,the CM was able to recognise the original datasets but encountered rotated images only during testing.The findings show that the model effectively interpreted transformed images by considering them in all available contexts and restoring their original form.This provides a practical foundation for further development of the contextual hypothesis and its relation to theAGI domain.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 inform...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
Should the article be accepted and published by Meteorological and Environmental Research,the author hereby grants exclusively to the editorial department of Meteorological and Environmental Research the digital repro...Should the article be accepted and published by Meteorological and Environmental Research,the author hereby grants exclusively to the editorial department of Meteorological and Environmental Research the digital reproduction,distribution,compilation and information network transmission rights.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
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
文摘At present, Chinas civil aviation industry has entered a new stage of steady development, and various types of civil aircraft emerge in one after another. Aircraft as a special equipment, in order to ensure the safety of flight, airworthiness information management has extremely high requirements. In order to improve the level of airworthiness management, the introduction of information management means, make airworthiness management more scientific, consider from multiple goals and multiple dimensions, and improve the scientific nature and reliability of airworthiness management, so as to further improve the safety and stability of aircraft operation.
文摘2025年12月22日,上海交通大学医学院医学技术学院李文杰在人工智能与信息融合领域国际顶级期刊Information Fusion发表了一项研究成果“CX-Mind:a pioneering multimodal large language model for interleaved reasoning in chest X-ray via curriculum-guided reinforcement learning”。
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.
文摘The relentless evolution of Information and Communication Technology(ICT) stands as a testament to the synergistic power of collaboration. It thrives at the dynamic intersection of industrial insight, academic rigor, and dedicated research. This special issue, “Achievements of ZTE's Industry-University-Institute Cooperation Projects,” presents a curated collection of cutting-edge research that embodies the fruitful outcomes of deep collaboration, addressing some of the most pressing challenges across wireless communications, artificial intelligence(AI), software engineering, and industrial digitization.
文摘Journal Customer services For ordering information,claims and any enquiry concerning your journal subscription please go to contact your nearest office.America:Tel:(401)515-4764 Europe,Middle East,Africa and Asia Pacific:Email:asiaar@163.com;523404392@qq.com;434421224@qq.com Tel:+86-551-65148112 Annual subscription rate(12 issues):US$300.
基金funded by theWuxi“Taihu Light”Science and Technology Key Project(Basic Research)(K20241046)the National Natural Science Foundation of China(Grant Nos.62102189,62122032,42305158)+1 种基金the Open Project of the National Engineering Research Center for Sensor Networks(2024YJZXKFKT02)Wuxi University Research Start-up Fund for High-Level Talents(No.2022r043).
文摘Generative steganography uses generative stego images to transmit secret message.It also effectively defends against statistical steganalysis.However,most existing methods focus primarily on matching the feature distribution of training data,often neglecting the sequential continuity between moves in the game.This oversight can result in unnatural patterns that deviate from real user behavior,thereby reducing the security of the hidden communication.To address this issue,we design a Gomoku agent based on the AlphaZero algorithm.The model engages in self-play to generate a sequence of plausible moves.These moves formthe basis of the stego images.We then apply an attractionmatrix at each step.It guides themove selection so that themoves appearmore natural.Thismethod helps maintain logical flow between moves.It also extends the game length,which increases the embedding capacity.Next,we filter and prioritize the generated moves.The selected moves are embedded into a move pool.Secret message is mapped to thesemoves.It is then embedded step by step as the game progresses.The finalmove sequence constitutes a complete steganographic game record.The receiver can extract the secret message using this record and a predefined mapping rule.Experiments show that our method reaches a maximum embedding capacity of 223 bits per carrier.Detection accuracy is 0.500 under XuNet and 0.498 under YeNet.These results are equal to random guessing,showing strong imperceptibility.The proposed method demonstrates superior concealment,higher embedding capacity,and greater robustness against common image distortions and steganalysis attacks.
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.
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.
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.
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.
基金The National Natural Science Foundation of China(No.72071042).
文摘Information collaboration is crucial for optimizing resource allocation and improving diagnostic efficiency across hospital tiers through enhanced information technology capacity.To characterize the dynamic decision-making mechanism between general hospitals(GHs)and primary healthcare centers(PHCs),a two-player differential game model was constructed to analyze the relationship between optimal investment levels and corresponding payoffs and explore how GHs can incentivize collaboration by adjusting their investment intensity and sharing PHCs’costs.The results indicate that information collaboration is a win-win strategy.Its dynamic equilibrium shows that GHs make intensive efforts in the early stage of digital construction.However,such investment decreases over time as patient information accessibility becomes limited.Under the collaboration mode,although GHs’digital investment is lower than that in the independent operation,the total system payoff significantly increases.This improvement arises because PHCs,with their locational and informational advantages,undertake major digitalization tasks,allowing GHs to focus resources on disease treatment.The introduction of collaboration incentives strengthens this performance improvement.
文摘The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issue is the formalisation of extracting meaning from information.Meaning emerges through a three-stage interpretative process,where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context.However,this approach currently lacks practical grounding.In this research,we developed a model based on contexts,which applies interpretation principles to the visual information to address this gap.The field of computer vision and object recognition has progressed essentially with artificial neural networks,but these models struggle with geometrically transformed images,such as those that are rotated or shifted,limiting their robustness in real-world applications.Various approaches have been proposed to address this problem.Some of them(Hu moments,spatial transformers,capsule networks,attention and memory mechanisms)share a conceptual connection with the contextual model(CM)discussed in this study.This paper investigates whether CM principles are applicable for interpreting rotated images from the MNIST and Fashion MNIST datasets.The model was implemented in the Rust programming language.It consists of a contextual module and a convolutional neural network(CNN).The CMwas trained on the rotated Mono Icons dataset,which is significantly different from the testing datasets.The CNN module was trained on the original MNIST and Fashion MNIST datasets for interpretation recognition.As a result,the CM was able to recognise the original datasets but encountered rotated images only during testing.The findings show that the model effectively interpreted transformed images by considering them in all available contexts and restoring their original form.This provides a practical foundation for further development of the contextual hypothesis and its relation to theAGI domain.
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Should the article be accepted and published by Meteorological and Environmental Research,the author hereby grants exclusively to the editorial department of Meteorological and Environmental Research the digital reproduction,distribution,compilation and information network transmission rights.
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 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.
