Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in...Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.展开更多
Flat optical elements have attracted enormous attentions and act as promising candidates for the next generation of optical components.As one of the most outstanding representatives,liquid crystal(LC)has been widely a...Flat optical elements have attracted enormous attentions and act as promising candidates for the next generation of optical components.As one of the most outstanding representatives,liquid crystal(LC)has been widely applied in flat panel display industries and inspires the wavefront modulation with the development of LC alignment techniques.However,most LC elements perform only one type of optical manipulation and are difficult to realize the multifunctionality and light integration.Here,flat multifunctional liquid crystal elements(FMLCEs),merely composed of anisotropic LC molecules with space-variant orientations,are presented for multichannel information manipulation by means of polarization,space and wavelength multiplexing.Specifically,benefiting from the unique light response with the change of the incident polarization,observation plane,and working wavelength,a series of FMLCEs are demonstrated to achieve distinct near-and far-field display functions.The proposed strategy takes full advantage of basic optical parameters as the decrypted keys to improve the information capacity and security,and we expect it to find potential applications in information encryption,optical anti-counterfeiting,virtual/augmented reality,etc.展开更多
The learning status of learners directly affects the quality of learning.Compared with offline teachers,it is difficult for online teachers to capture the learning status of students in the whole class,and it is even ...The learning status of learners directly affects the quality of learning.Compared with offline teachers,it is difficult for online teachers to capture the learning status of students in the whole class,and it is even more difficult to continue to pay attention to studentswhile teaching.Therefore,this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion.Specifically,a facial expression recognition model and an eye state recognition model are constructed to detect students’emotions and fatigue,respectively.By integrating the detected data with the homework test score data after online learning,an analysis model of students’online learning status is constructed.According to the PAD model,the learning state is expressed as three dimensions of students’understanding,engagement and interest,and then analyzed from multiple perspectives.Finally,the proposed model is applied to actual teaching,and procedural analysis of 5 different types of online classroom learners is carried out,and the validity of the model is verified by comparing with the results of the manual analysis.展开更多
Objective: To investigate the efficacy of multidimensional protective nursing interventions in reducing occupational exposure risks to hydrofluoric acid in dental departments. Methods: The study was conducted between ...Objective: To investigate the efficacy of multidimensional protective nursing interventions in reducing occupational exposure risks to hydrofluoric acid in dental departments. Methods: The study was conducted between January 2024 and January 2025, involving 60 healthcare workers who were randomly assigned to groups. The control group (n = 30) received conventional occupational protective nursing care, while the observation group underwent multidimensional protective nursing interventions. Results: Following intervention, the observation group demonstrated significantly higher scores than the control group in knowledge of hydrofluoric acid occupational exposure, occupational exposure risk perception, and protective skill operation (p < 0.05). Emergency response capability scores and protective behavior implementation scores were also higher in the observation group (p < 0.05). Compared to the control group, the observation group exhibited higher occupational health satisfaction rates and lower occupational exposure incidence rates (p < 0.05). Conclusion: Multidimensional protective nursing interventions effectively enhance dental healthcare personnel’s awareness and protective capabilities regarding hydrofluoric acid occupational exposure, improve compliance with protective measures, and significantly reduce occupational exposure risks. This approach warrants promotion and application in occupational protective nursing interventions within dental departments.展开更多
Succinonitrile(SN)-based polymer plastic crystal electrolytes(PPCEs)are regarded as promising candidates for lithium metal batteries but suffer from serious side reactions with Li metal.Herein,we propose a multi-dimen...Succinonitrile(SN)-based polymer plastic crystal electrolytes(PPCEs)are regarded as promising candidates for lithium metal batteries but suffer from serious side reactions with Li metal.Herein,we propose a multi-dimensional optimization strategy to alleviate the side reactions between SN and Li metal,and develop a highly stable poly-vinylethylene carbonate-based PPCE(PPCE-VEC).Moreover,we identify the intrinsic factors of multi-dimensional polymer structures on the electrolyte stability by three typical classes of polyesters.The PPCE-VEC constructed by in situ polymerization exhibits much better stability than poly-vinylene carbonate-based PPCE(PPCE-VCA)and poly-trifluoroethyl acrylate-based PPCE(PPCE-TFA),which is verified by its fewer SN-decomposition species in X-ray photoelectron spectroscopy(XPS)and outstanding full cell performance.The PPCE-VEC-enabled LiNi_(0.6)Co_(0.2)Mn_(0.2)O_(2)full cell achieve 73.7%capacity retention after 1400 cycles,which outperforms PPCE-VCA-and PPCE-TFA-enabled full cells(61.9%and 46.9%).Spectral analysis and theoretical calculation reveal that the high solvation ability of the carbonyl site,flexible polymer chain,and homogeneous electrolyte phase of PPCE-VEC are favorable to maximizing competition coordination with Li^(+)to weaken the Li^(+)–SN binding and shape an anion-rich solvation structure.This optimized polymer-involved Li^(+)solvation enhances SN stability and facilitates the formation of B/F enriched solid-electrolyte interphase(SEI),thus significantly improving PPCE stability.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
Large-scale complex systems are integral to the functioning of various organizations within the national economy.Despite their significance,the lengthy construction cycles and the involvement of multiple entities ofte...Large-scale complex systems are integral to the functioning of various organizations within the national economy.Despite their significance,the lengthy construction cycles and the involvement of multiple entities often result in the deprioritization of standardized management practices,as they do not yield immediate benefits.The implementation of such systems typically encompasses the integrated phases of "development,construction,utiliz ation,and operation and maintenance".To enhance the overall delivery quality of these systems,it is imperative to dismantle the management barriers among these phases and adopt a holistic approach to standardized management.This paper takes a specific system project as a research object to identify common challenges,and proposes improvement strategies in the implementation of standar dized management.Empirical results indicate a substantial reduction in the system s full-lifecycle costs.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
Single Image Super-Resolution(SISR)seeks to reconstruct high-resolution(HR)images from lowresolution(LR)inputs,thereby enhancing visual fidelity and the perception of fine details.While Transformer-based models—such ...Single Image Super-Resolution(SISR)seeks to reconstruct high-resolution(HR)images from lowresolution(LR)inputs,thereby enhancing visual fidelity and the perception of fine details.While Transformer-based models—such as SwinIR,Restormer,and HAT—have recently achieved impressive results in super-resolution tasks by capturing global contextual information,these methods often suffer from substantial computational and memory overhead,which limits their deployment on resource-constrained edge devices.To address these challenges,we propose a novel lightweight super-resolution network,termed Binary Attention-Guided Information Distillation(BAID),which integrates frequency-aware modeling with a binary attention mechanism to significantly reduce computational complexity and parameter count whilemaintaining strong reconstruction performance.The network combines a high–low frequency decoupling strategy with a local–global attention sharing mechanism,enabling efficient compression of redundant computations through binary attention guidance.At the core of the architecture lies the Attention-Guided Distillation Block(AGDB),which retains the strengths of the information distillation framework while introducing a sparse binary attention module to enhance both inference efficiency and feature representation.Extensive×4 superresolution experiments on four standard benchmarks—Set5,Set14,BSD100,and Urban100—demonstrate that BAID achieves Peak Signal-to-Noise Ratio(PSNR)values of 32.13,28.51,27.47,and 26.15,respectively,with only 1.22 million parameters and 26.1 G Floating-Point Operations(FLOPs),outperforming other state-of-the-art lightweight methods such as Information Multi-Distillation Network(IMDN)and Residual Feature Distillation Network(RFDN).These results highlight the proposed model’s ability to deliver high-quality image reconstruction while offering strong deployment efficiency,making it well-suited for image restoration tasks in resource-limited environments.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
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
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
Rapid urbanization and digital transformation are reshaping how cities address challenges related to security,governance,and sustainable development.Geospatial information technology(GIT)has become a base infrastructu...Rapid urbanization and digital transformation are reshaping how cities address challenges related to security,governance,and sustainable development.Geospatial information technology(GIT)has become a base infrastructure for smart cities,where the gathering,synthesis,and examination of spatially explicit information are used to deliver data to make decisions in cities.Even after its increasing significance,the current body of research on geospatial innovation is still divided into the spheres of urban security,spatial governance,and smart city development.Such fragmentation restricts the integration of theoretical work,as well as limits the possibility of developing coherent policies and governance institutions.This article includes a systematic and integrative review of innovation in geospatial information technology to analyze the relationship between technological,data-driven,and institutional innovation and urban security practices,spatial governance processes,and smart city initiatives.Based on peer-reviewed sources on urban studies,geography,planning,and information science,the review generalizes the main trends in real-time spatial analytics,artificial intelligence,participatory geospatial platforms,and urban digital twins.The review shows that geospatial systems facilitate anticipatory governance,cross-sector coordination,and evidence-based urban management,and that it provides an integrative conceptual lens on the existing literature on smart cities and urban governance,as it positions geospatial information technology as a socio-technical infrastructure,as opposed to a technical tool.The paper recognizes the voids in critical research and the directions into the future on how to build ethical,inclusive,and context-sensitive geospatial systems that can allow the creation of secure,governable,and sustainable urban futures.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
Due to the centralization of charging stations(CSs),CSs are organized as charging station alliances(CSAs)in the commercial competition.Under this situation,this paper studies the profit-oriented dynamic pricing strate...Due to the centralization of charging stations(CSs),CSs are organized as charging station alliances(CSAs)in the commercial competition.Under this situation,this paper studies the profit-oriented dynamic pricing strategy of CSAs.As the practicability basis,a privacy-protected bidirectional real-time information interaction framework is designed,under which the status of EVs is utilized as the reference for pricing,and the prices of CSs are the reference for charging decisions.Based on this framework,the decision-making models of EVs and CSs are established,in which the uncertainty caused by the information asymmetry between EVs and CSs and the bounded rationality of EV users are integrated.To solve the pricing decision model,the evolutionary game theory is adopted to describe the dynamic pricing game among CSAs,the equilibrium of which gives the optimal pricing strategy.Finally,the case study conducted in an urban area of Shanghai,China,validates the practicability of the framework and the effectiveness of the dynamic pricing strategy.展开更多
Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contempo...Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contemporary enterprises typically operate 200+interconnected systems,with research indicating that 52% of organizations manage three or more enterprise content management systems,creating information silos that reduce operational efficiency by up to 35%.While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision,their systematic application to business information systems remains largely unexplored.This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System(HABIS)framework that applies multi-level attention mechanisms to enterprise environments.We provide a comprehensive mathematical formulation of the framework,analyze its computational complexity,and present a proof-of-concept implementation with simulation-based validation that demonstrates a 42% reduction in crosssystem query latency compared to legacy ERP modules and 70% improvement in prediction accuracy over baseline methods.The theoretical framework introduces four hierarchical attention levels:system-level attention for dynamic weighting of business systems,process-level attention for business process prioritization,data-level attention for critical information selection,and temporal attention for time-sensitive pattern recognition.Our complexity analysis demonstrates that the framework achieves O(n log n)computational complexity for attention computation,making it scalable to large enterprise environments including retail supply chains with 200+system-scale deployments.The proof-of-concept implementation validates the theoretical framework’s feasibility withMSE loss of 0.439 and response times of 0.000120 s per query,demonstrating its potential for addressing key challenges in business information systems.This work establishes a foundation for future empirical research and practical implementation of attention-driven enterprise systems.展开更多
General Journal of Beijing Institute of Technology (JBIT)is a periodical publication on science and technology published by Beijing Institute of Technology under the sponsorship of the Ministry of Industry and Informa...General Journal of Beijing Institute of Technology (JBIT)is a periodical publication on science and technology published by Beijing Institute of Technology under the sponsorship of the Ministry of Industry and Information Technology of the People’s Republic of China.JBIT was inaugurated in 1992.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
Graph neural networks(GNNs)have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control-and data-flow graphs.Despite their...Graph neural networks(GNNs)have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control-and data-flow graphs.Despite their effectiveness,most GNN-based vulnerability detectors operate as black boxes,making their decisions difficult to interpret and thus less suitable for critical security auditing.The information bottleneck(IB)principle provides a theoretical framework for isolating task-relevant graph components.However,existing IB-based implementations often encounter unstable optimization and limited understanding of code semantics.To address these issues,we introduce ContractGIB,an interpretable graph information bottleneck framework for function-level vulnerability analysis.ContractGIB introduces three main advances.First,ContractGIB introduces an Hilbert–Schmidt Independence Criterion(HSIC)based estimator that provides stable dependence measurement.Second,it incorporates a CodeBERT semantic module to improve node representations.Third,it initializes all nodes with pretrained CodeBERT embeddings,removing the need for hand-crafted features.For each contract function,ContractGIB identifies themost informative nodes forming an instance-specific explanatory subgraph that supports the model’s prediction.Comprehensive experiments on public smart contract datasets,including ESC andVSC,demonstrate thatContractGIB achieves superior performance compared to competitive GNN baselines,while offering clearer,instance-level interpretability.展开更多
Further investigation is warranted into the collaborative function of carbon capture and electrolysis-to-gas conversion technologies within integrated electro-gas energy systems,as well as optimized scheduling that ad...Further investigation is warranted into the collaborative function of carbon capture and electrolysis-to-gas conversion technologies within integrated electro-gas energy systems,as well as optimized scheduling that addresses the variability of wind and solar energy,to promote multi-energy complementarity and energy decarbonization while enhancing the capacity to absorb new energy.This work presents an optimized scheduling model for electro-gas integrated energy systems that include hydrogen storage,utilizing information gap decision theory(IGDT).A model is constructed that integrates the synergistic functions of carbon capture and storage(CCS),power-to-gas(P2G),and gas turbine units through electrical coupling.A carbon ladder trading mechanism is implemented to mitigate carbon emissions inside the system.A day-ahead optimization scheduling model is subsequently built to maximize system operational profit and ensure hydrogen storage safety,while considering economic viability,low-carbon performance,and safety.Secondly,the trinitrotoluene(TNT)equivalent approach and the half-lethal range were employed to quantify the safety concerns associated with hydrogen storage tanks,offering the model optimization guidance and conservative management.Ultimately,the CCS-P2G integrated operation accounted for the unpredictability in wind and solar energy production through the application of information gap decision theory.The model was solved using the GUROBI solver.The findings indicate that the proposed approach diminishes system carbon emissions by 66%,attains complete integration of wind and solar energy,and eliminates hazardous working time for hydrogen storage tanks,reducing it from 10 h to zero.It ensures system safety while guaranteeing profits of at least 90%of the anticipated value,accounting for changes in wind and solar output within±14%.This confirms the model’s efficacy in improving renewable energy integration rates,facilitating low-carbon,cost-effective,and secure system operation,while mitigating the unpredictability of renewable energy production.展开更多
文摘Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.
基金the supports from the National Natural Science Foundation of China (61905073, 61835004, 62134001, 61905031, 62105263, 62275077)Fundamental Research Fund for the Central Universities (531118010189, 310202011qd002)+1 种基金the support from Xi’an Science and Technology Association Youth Talent Support Project (095920211306)the Postdoctoral Innovation Talent Support Program of China (BX20220388)
文摘Flat optical elements have attracted enormous attentions and act as promising candidates for the next generation of optical components.As one of the most outstanding representatives,liquid crystal(LC)has been widely applied in flat panel display industries and inspires the wavefront modulation with the development of LC alignment techniques.However,most LC elements perform only one type of optical manipulation and are difficult to realize the multifunctionality and light integration.Here,flat multifunctional liquid crystal elements(FMLCEs),merely composed of anisotropic LC molecules with space-variant orientations,are presented for multichannel information manipulation by means of polarization,space and wavelength multiplexing.Specifically,benefiting from the unique light response with the change of the incident polarization,observation plane,and working wavelength,a series of FMLCEs are demonstrated to achieve distinct near-and far-field display functions.The proposed strategy takes full advantage of basic optical parameters as the decrypted keys to improve the information capacity and security,and we expect it to find potential applications in information encryption,optical anti-counterfeiting,virtual/augmented reality,etc.
基金supported by the Chongqing Normal University Graduate Scientific Research Innovation Project (Grants YZH21014 and YZH21010).
文摘The learning status of learners directly affects the quality of learning.Compared with offline teachers,it is difficult for online teachers to capture the learning status of students in the whole class,and it is even more difficult to continue to pay attention to studentswhile teaching.Therefore,this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion.Specifically,a facial expression recognition model and an eye state recognition model are constructed to detect students’emotions and fatigue,respectively.By integrating the detected data with the homework test score data after online learning,an analysis model of students’online learning status is constructed.According to the PAD model,the learning state is expressed as three dimensions of students’understanding,engagement and interest,and then analyzed from multiple perspectives.Finally,the proposed model is applied to actual teaching,and procedural analysis of 5 different types of online classroom learners is carried out,and the validity of the model is verified by comparing with the results of the manual analysis.
基金Construction of Standardized Protective Nursing Plan for Hydrofluoric Acid Operations in Dentistry and Research on Injury Prevention Effects(Project No.:FZ2025101)。
文摘Objective: To investigate the efficacy of multidimensional protective nursing interventions in reducing occupational exposure risks to hydrofluoric acid in dental departments. Methods: The study was conducted between January 2024 and January 2025, involving 60 healthcare workers who were randomly assigned to groups. The control group (n = 30) received conventional occupational protective nursing care, while the observation group underwent multidimensional protective nursing interventions. Results: Following intervention, the observation group demonstrated significantly higher scores than the control group in knowledge of hydrofluoric acid occupational exposure, occupational exposure risk perception, and protective skill operation (p < 0.05). Emergency response capability scores and protective behavior implementation scores were also higher in the observation group (p < 0.05). Compared to the control group, the observation group exhibited higher occupational health satisfaction rates and lower occupational exposure incidence rates (p < 0.05). Conclusion: Multidimensional protective nursing interventions effectively enhance dental healthcare personnel’s awareness and protective capabilities regarding hydrofluoric acid occupational exposure, improve compliance with protective measures, and significantly reduce occupational exposure risks. This approach warrants promotion and application in occupational protective nursing interventions within dental departments.
基金supported by the National Natural Science Foundation of China(22072048)the Guangdong Provincial Department of Science and Technology(2021A1515010128 and 2022A0505050013).
文摘Succinonitrile(SN)-based polymer plastic crystal electrolytes(PPCEs)are regarded as promising candidates for lithium metal batteries but suffer from serious side reactions with Li metal.Herein,we propose a multi-dimensional optimization strategy to alleviate the side reactions between SN and Li metal,and develop a highly stable poly-vinylethylene carbonate-based PPCE(PPCE-VEC).Moreover,we identify the intrinsic factors of multi-dimensional polymer structures on the electrolyte stability by three typical classes of polyesters.The PPCE-VEC constructed by in situ polymerization exhibits much better stability than poly-vinylene carbonate-based PPCE(PPCE-VCA)and poly-trifluoroethyl acrylate-based PPCE(PPCE-TFA),which is verified by its fewer SN-decomposition species in X-ray photoelectron spectroscopy(XPS)and outstanding full cell performance.The PPCE-VEC-enabled LiNi_(0.6)Co_(0.2)Mn_(0.2)O_(2)full cell achieve 73.7%capacity retention after 1400 cycles,which outperforms PPCE-VCA-and PPCE-TFA-enabled full cells(61.9%and 46.9%).Spectral analysis and theoretical calculation reveal that the high solvation ability of the carbonyl site,flexible polymer chain,and homogeneous electrolyte phase of PPCE-VEC are favorable to maximizing competition coordination with Li^(+)to weaken the Li^(+)–SN binding and shape an anion-rich solvation structure.This optimized polymer-involved Li^(+)solvation enhances SN stability and facilitates the formation of B/F enriched solid-electrolyte interphase(SEI),thus significantly improving PPCE stability.
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Large-scale complex systems are integral to the functioning of various organizations within the national economy.Despite their significance,the lengthy construction cycles and the involvement of multiple entities often result in the deprioritization of standardized management practices,as they do not yield immediate benefits.The implementation of such systems typically encompasses the integrated phases of "development,construction,utiliz ation,and operation and maintenance".To enhance the overall delivery quality of these systems,it is imperative to dismantle the management barriers among these phases and adopt a holistic approach to standardized management.This paper takes a specific system project as a research object to identify common challenges,and proposes improvement strategies in the implementation of standar dized management.Empirical results indicate a substantial reduction in the system s full-lifecycle costs.
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.
基金funded by Project of Sichuan Provincial Department of Science and Technology under 2025JDKP0150the Fundamental Research Funds for the Central Universities under 25CAFUC03093.
文摘Single Image Super-Resolution(SISR)seeks to reconstruct high-resolution(HR)images from lowresolution(LR)inputs,thereby enhancing visual fidelity and the perception of fine details.While Transformer-based models—such as SwinIR,Restormer,and HAT—have recently achieved impressive results in super-resolution tasks by capturing global contextual information,these methods often suffer from substantial computational and memory overhead,which limits their deployment on resource-constrained edge devices.To address these challenges,we propose a novel lightweight super-resolution network,termed Binary Attention-Guided Information Distillation(BAID),which integrates frequency-aware modeling with a binary attention mechanism to significantly reduce computational complexity and parameter count whilemaintaining strong reconstruction performance.The network combines a high–low frequency decoupling strategy with a local–global attention sharing mechanism,enabling efficient compression of redundant computations through binary attention guidance.At the core of the architecture lies the Attention-Guided Distillation Block(AGDB),which retains the strengths of the information distillation framework while introducing a sparse binary attention module to enhance both inference efficiency and feature representation.Extensive×4 superresolution experiments on four standard benchmarks—Set5,Set14,BSD100,and Urban100—demonstrate that BAID achieves Peak Signal-to-Noise Ratio(PSNR)values of 32.13,28.51,27.47,and 26.15,respectively,with only 1.22 million parameters and 26.1 G Floating-Point Operations(FLOPs),outperforming other state-of-the-art lightweight methods such as Information Multi-Distillation Network(IMDN)and Residual Feature Distillation Network(RFDN).These results highlight the proposed model’s ability to deliver high-quality image reconstruction while offering strong deployment efficiency,making it well-suited for image restoration tasks in resource-limited environments.
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project supported by the Scientific Research Fund of the Zhejiang Provincial Education Department(grant number Y202456064).
文摘Rapid urbanization and digital transformation are reshaping how cities address challenges related to security,governance,and sustainable development.Geospatial information technology(GIT)has become a base infrastructure for smart cities,where the gathering,synthesis,and examination of spatially explicit information are used to deliver data to make decisions in cities.Even after its increasing significance,the current body of research on geospatial innovation is still divided into the spheres of urban security,spatial governance,and smart city development.Such fragmentation restricts the integration of theoretical work,as well as limits the possibility of developing coherent policies and governance institutions.This article includes a systematic and integrative review of innovation in geospatial information technology to analyze the relationship between technological,data-driven,and institutional innovation and urban security practices,spatial governance processes,and smart city initiatives.Based on peer-reviewed sources on urban studies,geography,planning,and information science,the review generalizes the main trends in real-time spatial analytics,artificial intelligence,participatory geospatial platforms,and urban digital twins.The review shows that geospatial systems facilitate anticipatory governance,cross-sector coordination,and evidence-based urban management,and that it provides an integrative conceptual lens on the existing literature on smart cities and urban governance,as it positions geospatial information technology as a socio-technical infrastructure,as opposed to a technical tool.The paper recognizes the voids in critical research and the directions into the future on how to build ethical,inclusive,and context-sensitive geospatial systems that can allow the creation of secure,governable,and sustainable urban futures.
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sponsored in part by the National Natural Science Foundation of China(52167014)in part by the Science and Technology Commission of Shanghai Municipality(23XD1422000,23QB1400500).
文摘Due to the centralization of charging stations(CSs),CSs are organized as charging station alliances(CSAs)in the commercial competition.Under this situation,this paper studies the profit-oriented dynamic pricing strategy of CSAs.As the practicability basis,a privacy-protected bidirectional real-time information interaction framework is designed,under which the status of EVs is utilized as the reference for pricing,and the prices of CSs are the reference for charging decisions.Based on this framework,the decision-making models of EVs and CSs are established,in which the uncertainty caused by the information asymmetry between EVs and CSs and the bounded rationality of EV users are integrated.To solve the pricing decision model,the evolutionary game theory is adopted to describe the dynamic pricing game among CSAs,the equilibrium of which gives the optimal pricing strategy.Finally,the case study conducted in an urban area of Shanghai,China,validates the practicability of the framework and the effectiveness of the dynamic pricing strategy.
文摘Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contemporary enterprises typically operate 200+interconnected systems,with research indicating that 52% of organizations manage three or more enterprise content management systems,creating information silos that reduce operational efficiency by up to 35%.While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision,their systematic application to business information systems remains largely unexplored.This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System(HABIS)framework that applies multi-level attention mechanisms to enterprise environments.We provide a comprehensive mathematical formulation of the framework,analyze its computational complexity,and present a proof-of-concept implementation with simulation-based validation that demonstrates a 42% reduction in crosssystem query latency compared to legacy ERP modules and 70% improvement in prediction accuracy over baseline methods.The theoretical framework introduces four hierarchical attention levels:system-level attention for dynamic weighting of business systems,process-level attention for business process prioritization,data-level attention for critical information selection,and temporal attention for time-sensitive pattern recognition.Our complexity analysis demonstrates that the framework achieves O(n log n)computational complexity for attention computation,making it scalable to large enterprise environments including retail supply chains with 200+system-scale deployments.The proof-of-concept implementation validates the theoretical framework’s feasibility withMSE loss of 0.439 and response times of 0.000120 s per query,demonstrating its potential for addressing key challenges in business information systems.This work establishes a foundation for future empirical research and practical implementation of attention-driven enterprise systems.
文摘General Journal of Beijing Institute of Technology (JBIT)is a periodical publication on science and technology published by Beijing Institute of Technology under the sponsorship of the Ministry of Industry and Information Technology of the People’s Republic of China.JBIT was inaugurated in 1992.
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 Natural Science Foundation of China(Grant Nos.52208424,52208416,52078091,and 52108399)the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0102).
文摘Graph neural networks(GNNs)have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control-and data-flow graphs.Despite their effectiveness,most GNN-based vulnerability detectors operate as black boxes,making their decisions difficult to interpret and thus less suitable for critical security auditing.The information bottleneck(IB)principle provides a theoretical framework for isolating task-relevant graph components.However,existing IB-based implementations often encounter unstable optimization and limited understanding of code semantics.To address these issues,we introduce ContractGIB,an interpretable graph information bottleneck framework for function-level vulnerability analysis.ContractGIB introduces three main advances.First,ContractGIB introduces an Hilbert–Schmidt Independence Criterion(HSIC)based estimator that provides stable dependence measurement.Second,it incorporates a CodeBERT semantic module to improve node representations.Third,it initializes all nodes with pretrained CodeBERT embeddings,removing the need for hand-crafted features.For each contract function,ContractGIB identifies themost informative nodes forming an instance-specific explanatory subgraph that supports the model’s prediction.Comprehensive experiments on public smart contract datasets,including ESC andVSC,demonstrate thatContractGIB achieves superior performance compared to competitive GNN baselines,while offering clearer,instance-level interpretability.
文摘Further investigation is warranted into the collaborative function of carbon capture and electrolysis-to-gas conversion technologies within integrated electro-gas energy systems,as well as optimized scheduling that addresses the variability of wind and solar energy,to promote multi-energy complementarity and energy decarbonization while enhancing the capacity to absorb new energy.This work presents an optimized scheduling model for electro-gas integrated energy systems that include hydrogen storage,utilizing information gap decision theory(IGDT).A model is constructed that integrates the synergistic functions of carbon capture and storage(CCS),power-to-gas(P2G),and gas turbine units through electrical coupling.A carbon ladder trading mechanism is implemented to mitigate carbon emissions inside the system.A day-ahead optimization scheduling model is subsequently built to maximize system operational profit and ensure hydrogen storage safety,while considering economic viability,low-carbon performance,and safety.Secondly,the trinitrotoluene(TNT)equivalent approach and the half-lethal range were employed to quantify the safety concerns associated with hydrogen storage tanks,offering the model optimization guidance and conservative management.Ultimately,the CCS-P2G integrated operation accounted for the unpredictability in wind and solar energy production through the application of information gap decision theory.The model was solved using the GUROBI solver.The findings indicate that the proposed approach diminishes system carbon emissions by 66%,attains complete integration of wind and solar energy,and eliminates hazardous working time for hydrogen storage tanks,reducing it from 10 h to zero.It ensures system safety while guaranteeing profits of at least 90%of the anticipated value,accounting for changes in wind and solar output within±14%.This confirms the model’s efficacy in improving renewable energy integration rates,facilitating low-carbon,cost-effective,and secure system operation,while mitigating the unpredictability of renewable energy production.
