With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or p...With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.展开更多
Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental con...Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.展开更多
The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,s...The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.展开更多
The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defendin...The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defending against jamming and interference through spectrum allocation becomes challenging,especially when each UAV pair makes decisions independently.In this paper,we propose a cooperative multi-agent reinforcement learning(MARL)-based anti-jamming framework for I-UAVs,enabling UAV pairs to learn their own policies cooperatively.Specifically,we first model the problem as a modelfree multi-agent Markov decision process(MAMDP)to maximize the long-term expected system throughput.Then,for improving the exploration of the optimal policy,we resort to optimizing a MARL objective function with a mutual-information(MI)regularizer between states and actions,which can dynamically assign the probability for actions frequently used by the optimal policy.Next,through sharing their current channel selections and local learning experience(their soft Q-values),the UAV pairs can learn their own policies cooperatively relying on only preceding observed information and predicting others’actions.Our simulation results show that for both sweep jamming and Markov jamming patterns,the proposed scheme outperforms the benchmarkers in terms of throughput,convergence and stability for different numbers of jammers,channels and UAV pairs.展开更多
Lutetium oxide(Lu_(2)O_(3))is recognized as a potential laser crystal material,and it is noted for its high ther⁃mal conductivity,low phonon energy,and strong crystal field.Nevertheless,its high melting point of 2450...Lutetium oxide(Lu_(2)O_(3))is recognized as a potential laser crystal material,and it is noted for its high ther⁃mal conductivity,low phonon energy,and strong crystal field.Nevertheless,its high melting point of 2450℃induces significant temperature gradients,resulting in a proliferation of defects.The scarcity of comprehensive research on this crystal’s defects hinders the enhancement of crystal quality.In this study,we employed the chemical etching method to examine the etching effects on Lu_(2)O_(3)crystals under various conditions and to identify the optimal conditions for investi⁃gating the dislocation defects of Lu_(2)O_(3)crystals(mass fraction 70%H3PO4,160℃,15-18 min).The morphologies of dislocation etch pits on the(111)-and(110)-oriented Lu_(2)O_(3)wafers were characterized using microscopy,scanning electron microscopy and atomic force microscopy.This research addresses the gap in understanding Lu_(2)O_(3)line defects and offers guidance for optimizing the crystal growth process and improving crystal quality.展开更多
Quantifying and mapping how ecosystem services impact agricultural competitiveness is crucial for attaining the Sustainable Development Goals of United Nations.However,few study quantified agricultural competitiveness...Quantifying and mapping how ecosystem services impact agricultural competitiveness is crucial for attaining the Sustainable Development Goals of United Nations.However,few study quantified agricultural competitiveness and mapped the effects of ecosystem services on agricultural competitiveness using multiple models.In this study,multi-source data from 2000 to 2020 were utilized to establish the indicator system of agricultural competitiveness;five ecosystem services were quantified using computation models;Geographic Information System(GIS)spatial analysis was used to explore the spatial patterns of agricultural competitiveness and ecosystem services;geographic detector models were applied to investigate the effects and driving mechanisms of ecosystem services on agricultural competitiveness.Shandong Province of China was selected as the case study area.The results demonstrated that:1)there was a significant increase in agricultural competitiveness during the study period,with high levels observed mainly in the east region of the study area.2)The spatial distribution patterns of ecosystem services and agricultural competitiveness primarily exhibited High-High and Low-Low Cluster types.3)Habitat quality emerged as the main driving factor of agricultural competitiveness in 2000 and 2020,while water yield played a substantial role in 2010.4)The coupling of two ecosystem services exerted a greater effect on agricultural competitiveness compared to individual ecosystem service.The innovations of this study are constructing an indicator system to quantify agricultural competitiveness,and exploring the effects of ecosystem services on agricultural competitiveness.This study proposed an indicator system to quantify agricultural competitiveness,which can be applied in other regions,and explored the effects of ecosystem services on agricultural competitiveness.The findings of this study can serve as valuable insights for policymakers to formulate tailored agricultural development policies that take into account the synergistic effects of ecosystem services on agricultural competitiveness.展开更多
Based on the features of marine environmental data and processing requirements, a cloud computing archi- tecture of marine environment information is proposed, which provides a new cloud technology framework for the i...Based on the features of marine environmental data and processing requirements, a cloud computing archi- tecture of marine environment information is proposed, which provides a new cloud technology framework for the integration and sharing of marine environmental information resources. A physical layer, software platform layer and an application layer are illustrated systematically, at the same time, a corresponding solu- tions for many difficult technical problems such as parallel query processing of multi-dimensional, spatio- temporal information, data slice storage, software service flow customization, analysis, reorganization and so on. A prototype system is developed and many different data-size experiments and a comparative analy- sis are done based on it. The experiment results show that the cloud platform based on this framework can achieve high performance and scalability when dealing with large-scale marine data.展开更多
In converged heterogeneous wireless networks, vertical handoff is an important issue in radio resource management and occurs when an end user switches from one network to another （e.g., from wireless local area netwo...In converged heterogeneous wireless networks, vertical handoff is an important issue in radio resource management and occurs when an end user switches from one network to another （e.g., from wireless local area network to wideband code division multiple access）. Efficient vertical handoff should allocate network resources efficiently and maintain good quality of service （QoS） for the end users. The objective of this work is to determine conditions under which vertical handoff can be performed. The channel usage situation of each access network is formulated as a birth-death process with the objective of predicting the avaliable bandwidth and the blocking probability. A reward function is used to capture the network bandwidth and the blocking probability is expressed as a cost function. An end user will access the certain network which maximizes the total function defined as the combination of the reward fimction and the cost function. Simulation results show that the proposed algorithm can significantly improve the network performance, including higher bandwidth for end users and lower new call blocking and handoff call blocking probability for networks.展开更多
For the issue of evaluation of capability of enterprise agent coalition,an evaluation model based on information fusion and entropy weighting method is presented. The attribute reduction method is utilized to reduce i...For the issue of evaluation of capability of enterprise agent coalition,an evaluation model based on information fusion and entropy weighting method is presented. The attribute reduction method is utilized to reduce indicators of the capability according to the theory of rough set. The new indicator system can be determined. Attribute reduction can also reduce the workload and remove the redundant information,when there are too many indicators or the indicators have strong correlation. The research complexity can be reduced and the efficiency can be improved. Entropy weighting method is used to determine the weights of the remaining indicators,and the importance of indicators is analyzed. The information fusion model based on nearest neighbor method is developed and utilized to evaluate the capability of multiple agent coalitions,compared to cloud evaluation model and D-S evidence method. Simulation results are reasonable and with obvious distinction. Thus they verify the effectiveness and feasibility of the model. The information fusion model can provide more scientific,rational decision support for choosing the best agent coalition,and provide innovative steps for the evaluation process of capability of agent coalitions.展开更多
To study the fluid dynamic response mechanism under the working condition of water injection well borehole,based on the microelement analysis of fluid mechanics and the classical theory of hydrodynamics,a fluid microe...To study the fluid dynamic response mechanism under the working condition of water injection well borehole,based on the microelement analysis of fluid mechanics and the classical theory of hydrodynamics,a fluid microelement pressure-flow rate relationship model is built to derive and solve the dynamic distribution of fluid pressure and flow rate in the space of well borehole.Combined with the production data of a typical deviated well in China,numerical simulations and analyses are carried out to analyze the dynamic distribution of wellbore pressure at different injection pressures and injection volumes,the delayed and attenuated characteristics of fluid transmission in tube,and the dynamic distribution of wellbore pressure amplitude under the fluctuation of wellhead pressure.The pressure loss along the wellbore has nothing to do with the absolute pressure,and the design of the coding and decoding scheme for wave code communication doesn’t need to consider the absolute pressure during injecting.When the injection pressure is constant,the higher the injection flow rate at the wellhead,the larger the pressure loss along the wellbore.The fluid wave signal delay amplitude mainly depends on the length of the wellbore.The smaller the tubing diameter,the larger the fluid wave signal attenuation amplitude.The higher the target wave code amplitude(differential pressure identification root mean square)generated at the same well depth,the greater the wellhead pressure wave amplitude required to overcome the wellbore pressure loss.展开更多
The method of singular coefficient of water inrush to achieve safety mining has limitation and one sidedness. Aiming at the problem above, large amounts of data about water inrush were collected. Then the data, includ...The method of singular coefficient of water inrush to achieve safety mining has limitation and one sidedness. Aiming at the problem above, large amounts of data about water inrush were collected. Then the data, including the maximum water inrush, water inrush coefficient and water abundance in aquifers of working face, were processed by the statistical analysis. The analysis results indicate that both water inrush coefficient and water abundance in aquifers should be taken into consideration when evaluating the danger of water inrush from coal seam floor. The prediction model of safe-mining evaluation grade was built by using the support vector machine, and the result shows that this model has high classification accuracy. A feasible classification system of water-inrush safety evaluation can be got by using the data visualization method which makes the implicit support vector machine models explicit.展开更多
Real-time monitoring and wireless transmission of farmland soil moisture have been paid with more and more attention in the research of agricultural drought monitoring, early warning and prevention and control technol...Real-time monitoring and wireless transmission of farmland soil moisture have been paid with more and more attention in the research of agricultural drought monitoring, early warning and prevention and control technology. The hardware design and software design of soil moisture monitoring in farmland were carried out, and a monitoring system based on the principles of ZigBee and GPRS technologies was developed and applied to the actual monitoring of soil moisture in farmland. This study provides a good idea to promote real-time monitoring, wireless transmission and intelligent management of soil moisture in farmland.展开更多
The purpose of this research is a quantitative analysis of movement patterns of dance, which cannot be analyzed with a motion capture system alone, using simultaneous measurement of body motion and biophysical informa...The purpose of this research is a quantitative analysis of movement patterns of dance, which cannot be analyzed with a motion capture system alone, using simultaneous measurement of body motion and biophysical information. In this research, two kinds of same leg movement are captured by simultaneous measurement; one is a leg movement with given strength, the other is a leg movement without strength on condition of basic experiment using optical motion capture and electromyography （EMG） equipment in order to quantitatively analyze characteristics of leg movement. Also, we measured the motion of the traditional Japanese dance using the constructed system. We can visualize leg movement of Japanese dance by displaying a 3D CG character animation with motion data and EMG data. In addition, we expect that our research will help dancers and researchers on dance through giving new information on dance movement which cannot be analyzed with only motion capture.展开更多
Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as ...Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.展开更多
Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical devices.Most of the machine learning models are ...Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical devices.Most of the machine learning models are built on the assumption of a static learning environment,but in practical situations,the data generated by the process is dynamic.This evolution of the data is termed concept drift.This research paper presents an approach for predictingmechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment.The method proposed here is applicable to allmechanical devices that are susceptible to failure or operational degradation.The proposed method in this paper is equipped with the capacity to detect the drift in data generation and adaptation.The proposed approach evaluates the machine learning and deep learning models for their efficacy in handling the errors related to industrial machines due to their dynamic nature.It is observed that,in the settings without concept drift in the data,methods like SVM and Random Forest performed better compared to deep neural networks.However,this resulted in poor sensitivity for the smallest drift in the machine data reported as a drift.In this perspective,DNN generated the stable drift detection method;it reported an accuracy of 84%and an AUC of 0.87 while detecting only a single drift point,indicating the stability to performbetter in detecting and adapting to new data in the drifting environments under industrial measurement settings.展开更多
In this paper,a 4×4 wideband linearly po-larization(LP)antenna array is proposed by using pla-nar dual-arm spiral structures.Wideband balun struc-tures,composed of microstrip line-fed coupling slots,are adopted t...In this paper,a 4×4 wideband linearly po-larization(LP)antenna array is proposed by using pla-nar dual-arm spiral structures.Wideband balun struc-tures,composed of microstrip line-fed coupling slots,are adopted to feed two dual-arms spiral structures with opposite phases.Then,by combining the left-and right-hand circular polarizations,a linearly polar-ization is achieved.The proposed antenna has a wide operating bandwidth due to the wideband nature of the spiral structure.Simulated results show that the an-tenna element can achieve a 68.73%impedance band-width and a maximum gain of 6.64 dBi within 19.44–38.83 GHz.A 4×4 array prototype is designed to verify the concept.Measured results show that an impedance bandwidth of 63.73%is obtained.The pro-posed array has the merits of a wide bandwidth,a low profile,a low cost,and a small size,which is promis-ing for the application in millimeter wave wireless sys-tems.展开更多
Semiconductor colloidal quantum wells(CQWs)with atomic-precision layer thickness are rapidly gaining attention for next-generation optoelectronic applications due to their tunable optical and electronic properties.In ...Semiconductor colloidal quantum wells(CQWs)with atomic-precision layer thickness are rapidly gaining attention for next-generation optoelectronic applications due to their tunable optical and electronic properties.In this study,we investigate the dielectric and optical characteristics of CdSe CQWs with monolayer numbers ranging from 2 to 7,synthesized via thermal injection and atomic layer(c-ALD)deposition techniques.Through a combination of spectroscopic ellipsometry(SE)and first-principles calculations,we demonstrate the significant tunability of the bandgap,refractive index,and extinction coefficient,driven by quantum confinement effects.Our results show a decrease in bandgap from 3.1 to 2.0 eV as the layer thickness increases.Furthermore,by employing a detailed analysis of the absorption spectra,accounting for exciton localization and asymmetric broadening,we precisely capture the relationship between monolayer number and exciton binding energy.These findings offer crucial insights for optimizing CdSe CQWs in optoelectronic device design by leveraging their layer-dependent properties.展开更多
At present,the surrounding rock of the deep mine roadway is prone to post-peak stress under the action of high stress,and secondary rock burst disaster is prone to occur under complex stress disturbance.According to i...At present,the surrounding rock of the deep mine roadway is prone to post-peak stress under the action of high stress,and secondary rock burst disaster is prone to occur under complex stress disturbance.According to incomplete statistics,as of 2023,80%of coal mine rock bursts accidents in China occur in mining roadway.In view of this phenomenon,the cyclic impact test of post-peak sandstone is designed,focusing on the post-peak stress state of sandstone,and exploring the post-peak dynamic response of sandstone.The post-peak sandstone specimens were prepared by a uniaxial compressor,and then cyclic impact tests were carried out on the post-peak sandstone under different coaxial pressure conditions by an improved separated Hopkinson equipment.The results show that:1)The number of impact times required for sandstone failure after peak decreased with the increase of axial pressure,indicating that the impact tendency of sandstone after peak decreased under lower axial pressure.On the contrary,the post-peak sandstone had strong impact tendency under higher axial pressure;2)The higher the axial pressure,the lower the dynamic strength of the post-peak sandstone,indicating that the axial pressure promoted the failure process of the post peak sandstone;3)It was a nonlinear evolution of a quadratic polynomial function between the dissipation-energy release rate and axial pressure;4)Shear failure occurred mainly in post-peak impact sandstone with the increased axial pressure,and the composite failure of intergranular failure and transgranular failure changed to single intergranular failure at the microscopic level.The research shows that when the roadway surrounding rock was in the post-peak stress state,reducing the static stress was the key to prevent the secondary ground pressure disaster.The research results provide a theoretical basis for the prevention and control of roadway rock burst disaster under high ground stress environment,and promote the research and exploration of post-peak mechanical properties of coal and rock.展开更多
In the electroslag remelting(ESR)process,it mainly relies on thermal experiments or analysis via mechanistic models to realize the physical fields simulation of the electromagnetic field and temperature field coupled ...In the electroslag remelting(ESR)process,it mainly relies on thermal experiments or analysis via mechanistic models to realize the physical fields simulation of the electromagnetic field and temperature field coupled transfer,which has the limitations of high cost,a large amount of calculating data and high computing power requirements.A novel network based on physics-informed neural network(PINN)was designed to realize the fast and high-fidelity prediction of the distribution of electromagnetic field and temperature field in ESR process.The physical laws were combined with the deep learning network through PINN,and physical constraints were embedded to achieve effective solution of partial differential equations(PDEs).PINN was used to minimize the loss function consisting of data error,physical information error and boundary condition error.The physical laws and boundary condition constraints in the ESR process were considered to maintain high PDE solution accuracy under different spatial and temporal resolutions.Automatic differentiation(Autodiff)technique and gradient descent algorithm were used to optimize the network parameters.The experimental results show that compared with the mechanistic models,PINN can effectively replace thermal experiments to realize the physical field simulation of ESR process with only a few experimental data,which can avoid the disadvantages of pure data-driven network simulation that requires a large amount of training data.Moreover,the solution of PINN has good physical interpretability and reliability of simulation results.For simulating electromagnetic field and temperature field distribution,the training time of the network is only 140 and 203 s,and the regression indicators of root mean square error can reach 12.65 and 13.76,respectively.展开更多
The application of visual-language large models in the field of medical health has gradually become a research focus.The models combine the capability for image understanding and natural language processing,and can si...The application of visual-language large models in the field of medical health has gradually become a research focus.The models combine the capability for image understanding and natural language processing,and can simultaneously process multi-modality data such as medical images and medical reports.These models can not only recognize images,but also understand the semantic relationship between images and texts,effectively realize the integration of medical information,and provide strong support for clinical decision-making and disease diagnosis.The visual-language large model has good performance for specific medical tasks,and also shows strong potential and high intelligence in the general task models.This paper provides a comprehensive review of the visual-language large model in the field of medical health.Specifically,this paper first introduces the basic theoretical basis and technical principles.Then,this paper introduces the specific application scenarios in the field of medical health,including modality fusion,semi-supervised learning,weakly supervised learning,unsupervised learning,cross-domain model and general models.Finally,the challenges including insufficient data,interpretability,and practical deployment are discussed.According to the existing challenges,four potential future development directions are given.展开更多
基金funded by the Hunan Provincial Natural Science Foundation of China(Grant No.2025JJ70105)the Hunan Provincial College Students’Innovation and Entrepreneurship Training Program(Project No.S202411342056)The article processing charge(APC)was funded by the Project No.2025JJ70105.
文摘With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.
文摘Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.
基金supported in part by the National Natural Science Foundation of China under Grant 62371181in part by the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029+1 种基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2021R1A2B5B02087169)supported under the framework of international cooperation program managed by the National Research Foundation of Korea(2022K2A9A1A01098051)。
文摘The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.
基金supported in part by the National Natural Science Foundation of China under Grants 62001225,62071236,62071234 and U22A2002in part by the Major Science and Technology plan of Hainan Province under Grant ZDKJ2021022+1 种基金in part by the Scientific Research Fund Project of Hainan University under Grant KYQD(ZR)-21008in part by the Key Technologies R&D Program of Jiangsu(Prospective and Key Technologies for Industry)under Grants BE2023022 and BE2023022-2.
文摘The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defending against jamming and interference through spectrum allocation becomes challenging,especially when each UAV pair makes decisions independently.In this paper,we propose a cooperative multi-agent reinforcement learning(MARL)-based anti-jamming framework for I-UAVs,enabling UAV pairs to learn their own policies cooperatively.Specifically,we first model the problem as a modelfree multi-agent Markov decision process(MAMDP)to maximize the long-term expected system throughput.Then,for improving the exploration of the optimal policy,we resort to optimizing a MARL objective function with a mutual-information(MI)regularizer between states and actions,which can dynamically assign the probability for actions frequently used by the optimal policy.Next,through sharing their current channel selections and local learning experience(their soft Q-values),the UAV pairs can learn their own policies cooperatively relying on only preceding observed information and predicting others’actions.Our simulation results show that for both sweep jamming and Markov jamming patterns,the proposed scheme outperforms the benchmarkers in terms of throughput,convergence and stability for different numbers of jammers,channels and UAV pairs.
基金Supported by National Key Research and Development Program of China(2021YFB3601403)National Natural Science Foundation of China(62105181)Taishan Scholar of Shandong Province(tsqn202306014)。
文摘Lutetium oxide(Lu_(2)O_(3))is recognized as a potential laser crystal material,and it is noted for its high ther⁃mal conductivity,low phonon energy,and strong crystal field.Nevertheless,its high melting point of 2450℃induces significant temperature gradients,resulting in a proliferation of defects.The scarcity of comprehensive research on this crystal’s defects hinders the enhancement of crystal quality.In this study,we employed the chemical etching method to examine the etching effects on Lu_(2)O_(3)crystals under various conditions and to identify the optimal conditions for investi⁃gating the dislocation defects of Lu_(2)O_(3)crystals(mass fraction 70%H3PO4,160℃,15-18 min).The morphologies of dislocation etch pits on the(111)-and(110)-oriented Lu_(2)O_(3)wafers were characterized using microscopy,scanning electron microscopy and atomic force microscopy.This research addresses the gap in understanding Lu_(2)O_(3)line defects and offers guidance for optimizing the crystal growth process and improving crystal quality.
基金Under the auspices of the National Key Research and Development Program of China(No.2022YFC3204404)。
文摘Quantifying and mapping how ecosystem services impact agricultural competitiveness is crucial for attaining the Sustainable Development Goals of United Nations.However,few study quantified agricultural competitiveness and mapped the effects of ecosystem services on agricultural competitiveness using multiple models.In this study,multi-source data from 2000 to 2020 were utilized to establish the indicator system of agricultural competitiveness;five ecosystem services were quantified using computation models;Geographic Information System(GIS)spatial analysis was used to explore the spatial patterns of agricultural competitiveness and ecosystem services;geographic detector models were applied to investigate the effects and driving mechanisms of ecosystem services on agricultural competitiveness.Shandong Province of China was selected as the case study area.The results demonstrated that:1)there was a significant increase in agricultural competitiveness during the study period,with high levels observed mainly in the east region of the study area.2)The spatial distribution patterns of ecosystem services and agricultural competitiveness primarily exhibited High-High and Low-Low Cluster types.3)Habitat quality emerged as the main driving factor of agricultural competitiveness in 2000 and 2020,while water yield played a substantial role in 2010.4)The coupling of two ecosystem services exerted a greater effect on agricultural competitiveness compared to individual ecosystem service.The innovations of this study are constructing an indicator system to quantify agricultural competitiveness,and exploring the effects of ecosystem services on agricultural competitiveness.This study proposed an indicator system to quantify agricultural competitiveness,which can be applied in other regions,and explored the effects of ecosystem services on agricultural competitiveness.The findings of this study can serve as valuable insights for policymakers to formulate tailored agricultural development policies that take into account the synergistic effects of ecosystem services on agricultural competitiveness.
基金the Ocean Public Welfare Scientific Research Project of State Oceanic Administration of China under contract No.201105033
文摘Based on the features of marine environmental data and processing requirements, a cloud computing archi- tecture of marine environment information is proposed, which provides a new cloud technology framework for the integration and sharing of marine environmental information resources. A physical layer, software platform layer and an application layer are illustrated systematically, at the same time, a corresponding solu- tions for many difficult technical problems such as parallel query processing of multi-dimensional, spatio- temporal information, data slice storage, software service flow customization, analysis, reorganization and so on. A prototype system is developed and many different data-size experiments and a comparative analy- sis are done based on it. The experiment results show that the cloud platform based on this framework can achieve high performance and scalability when dealing with large-scale marine data.
基金Project(20040533035) supported by the National Research Foundation for the Doctoral Program of Higher Education of ChinaProject (50275150) supported by the National Natural Science Foundation of China
文摘In converged heterogeneous wireless networks, vertical handoff is an important issue in radio resource management and occurs when an end user switches from one network to another （e.g., from wireless local area network to wideband code division multiple access）. Efficient vertical handoff should allocate network resources efficiently and maintain good quality of service （QoS） for the end users. The objective of this work is to determine conditions under which vertical handoff can be performed. The channel usage situation of each access network is formulated as a birth-death process with the objective of predicting the avaliable bandwidth and the blocking probability. A reward function is used to capture the network bandwidth and the blocking probability is expressed as a cost function. An end user will access the certain network which maximizes the total function defined as the combination of the reward fimction and the cost function. Simulation results show that the proposed algorithm can significantly improve the network performance, including higher bandwidth for end users and lower new call blocking and handoff call blocking probability for networks.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61173052)the China Postdoctoral Scinece Foundation(Grant No.2014M561363)
文摘For the issue of evaluation of capability of enterprise agent coalition,an evaluation model based on information fusion and entropy weighting method is presented. The attribute reduction method is utilized to reduce indicators of the capability according to the theory of rough set. The new indicator system can be determined. Attribute reduction can also reduce the workload and remove the redundant information,when there are too many indicators or the indicators have strong correlation. The research complexity can be reduced and the efficiency can be improved. Entropy weighting method is used to determine the weights of the remaining indicators,and the importance of indicators is analyzed. The information fusion model based on nearest neighbor method is developed and utilized to evaluate the capability of multiple agent coalitions,compared to cloud evaluation model and D-S evidence method. Simulation results are reasonable and with obvious distinction. Thus they verify the effectiveness and feasibility of the model. The information fusion model can provide more scientific,rational decision support for choosing the best agent coalition,and provide innovative steps for the evaluation process of capability of agent coalitions.
基金Supported by the National Natural Science Foundation of China(52074345)CNPC Research and Technology Development Project(2021ZG12).
文摘To study the fluid dynamic response mechanism under the working condition of water injection well borehole,based on the microelement analysis of fluid mechanics and the classical theory of hydrodynamics,a fluid microelement pressure-flow rate relationship model is built to derive and solve the dynamic distribution of fluid pressure and flow rate in the space of well borehole.Combined with the production data of a typical deviated well in China,numerical simulations and analyses are carried out to analyze the dynamic distribution of wellbore pressure at different injection pressures and injection volumes,the delayed and attenuated characteristics of fluid transmission in tube,and the dynamic distribution of wellbore pressure amplitude under the fluctuation of wellhead pressure.The pressure loss along the wellbore has nothing to do with the absolute pressure,and the design of the coding and decoding scheme for wave code communication doesn’t need to consider the absolute pressure during injecting.When the injection pressure is constant,the higher the injection flow rate at the wellhead,the larger the pressure loss along the wellbore.The fluid wave signal delay amplitude mainly depends on the length of the wellbore.The smaller the tubing diameter,the larger the fluid wave signal attenuation amplitude.The higher the target wave code amplitude(differential pressure identification root mean square)generated at the same well depth,the greater the wellhead pressure wave amplitude required to overcome the wellbore pressure loss.
基金Financial supports for this work, provided by National Natural Key Science Foundation of China (No. 50539080)Ministry of Education Research Fund for the doctoral program of China (No. 20133718110004)+2 种基金the Natural Science Key Foundation of Shandong Province of China (No. ZR2011EEZ002)the Technology Project Development Plan of Qingdao Economic and Technological Development Zone of China (No. 2013-1-62)SDUST Research Fund of China (No. 2012KYTD101)
文摘The method of singular coefficient of water inrush to achieve safety mining has limitation and one sidedness. Aiming at the problem above, large amounts of data about water inrush were collected. Then the data, including the maximum water inrush, water inrush coefficient and water abundance in aquifers of working face, were processed by the statistical analysis. The analysis results indicate that both water inrush coefficient and water abundance in aquifers should be taken into consideration when evaluating the danger of water inrush from coal seam floor. The prediction model of safe-mining evaluation grade was built by using the support vector machine, and the result shows that this model has high classification accuracy. A feasible classification system of water-inrush safety evaluation can be got by using the data visualization method which makes the implicit support vector machine models explicit.
基金Supported by Special Scientific Research Fund of Meteorology in the Public Welfare Profession of China(GYHY201306046-05)
文摘Real-time monitoring and wireless transmission of farmland soil moisture have been paid with more and more attention in the research of agricultural drought monitoring, early warning and prevention and control technology. The hardware design and software design of soil moisture monitoring in farmland were carried out, and a monitoring system based on the principles of ZigBee and GPRS technologies was developed and applied to the actual monitoring of soil moisture in farmland. This study provides a good idea to promote real-time monitoring, wireless transmission and intelligent management of soil moisture in farmland.
基金This work was partly supported by the"21st Century COE program",the"Open Research Center program"the"Grantin-in-Aid for Scientific Research"of the Ministry of Education,Science,Sports and Culture(No.(B)16300035).
文摘The purpose of this research is a quantitative analysis of movement patterns of dance, which cannot be analyzed with a motion capture system alone, using simultaneous measurement of body motion and biophysical information. In this research, two kinds of same leg movement are captured by simultaneous measurement; one is a leg movement with given strength, the other is a leg movement without strength on condition of basic experiment using optical motion capture and electromyography （EMG） equipment in order to quantitatively analyze characteristics of leg movement. Also, we measured the motion of the traditional Japanese dance using the constructed system. We can visualize leg movement of Japanese dance by displaying a 3D CG character animation with motion data and EMG data. In addition, we expect that our research will help dancers and researchers on dance through giving new information on dance movement which cannot be analyzed with only motion capture.
基金funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region:No.22D01B148Bidding Topics for the Center for Integration of Education and Production and Development of New Business in 2024:No.2024-KYJD05+1 种基金Basic Scientific Research Business Fee Project of Colleges and Universities in Autonomous Region:No.XJEDU2025P126Xinjiang College of Science&Technology School-level Scientific Research Fund Project:No.2024-KYTD01.
文摘Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.
文摘Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical devices.Most of the machine learning models are built on the assumption of a static learning environment,but in practical situations,the data generated by the process is dynamic.This evolution of the data is termed concept drift.This research paper presents an approach for predictingmechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment.The method proposed here is applicable to allmechanical devices that are susceptible to failure or operational degradation.The proposed method in this paper is equipped with the capacity to detect the drift in data generation and adaptation.The proposed approach evaluates the machine learning and deep learning models for their efficacy in handling the errors related to industrial machines due to their dynamic nature.It is observed that,in the settings without concept drift in the data,methods like SVM and Random Forest performed better compared to deep neural networks.However,this resulted in poor sensitivity for the smallest drift in the machine data reported as a drift.In this perspective,DNN generated the stable drift detection method;it reported an accuracy of 84%and an AUC of 0.87 while detecting only a single drift point,indicating the stability to performbetter in detecting and adapting to new data in the drifting environments under industrial measurement settings.
基金supported in part by the National Natural Science Foundation of China under Grant 62131008the Fundamental Research Funds for the Central Universities 2242022k60003.
文摘In this paper,a 4×4 wideband linearly po-larization(LP)antenna array is proposed by using pla-nar dual-arm spiral structures.Wideband balun struc-tures,composed of microstrip line-fed coupling slots,are adopted to feed two dual-arms spiral structures with opposite phases.Then,by combining the left-and right-hand circular polarizations,a linearly polar-ization is achieved.The proposed antenna has a wide operating bandwidth due to the wideband nature of the spiral structure.Simulated results show that the an-tenna element can achieve a 68.73%impedance band-width and a maximum gain of 6.64 dBi within 19.44–38.83 GHz.A 4×4 array prototype is designed to verify the concept.Measured results show that an impedance bandwidth of 63.73%is obtained.The pro-posed array has the merits of a wide bandwidth,a low profile,a low cost,and a small size,which is promis-ing for the application in millimeter wave wireless sys-tems.
基金supported by the National Natural Science Foundation of China(62205180)the Natural Science Foundation of Shandong Province(ZR2022QF029)the Taishan Scholar Program of Shandong Province(Young Scientist).
文摘Semiconductor colloidal quantum wells(CQWs)with atomic-precision layer thickness are rapidly gaining attention for next-generation optoelectronic applications due to their tunable optical and electronic properties.In this study,we investigate the dielectric and optical characteristics of CdSe CQWs with monolayer numbers ranging from 2 to 7,synthesized via thermal injection and atomic layer(c-ALD)deposition techniques.Through a combination of spectroscopic ellipsometry(SE)and first-principles calculations,we demonstrate the significant tunability of the bandgap,refractive index,and extinction coefficient,driven by quantum confinement effects.Our results show a decrease in bandgap from 3.1 to 2.0 eV as the layer thickness increases.Furthermore,by employing a detailed analysis of the absorption spectra,accounting for exciton localization and asymmetric broadening,we precisely capture the relationship between monolayer number and exciton binding energy.These findings offer crucial insights for optimizing CdSe CQWs in optoelectronic device design by leveraging their layer-dependent properties.
基金Projects(U23B2093,52034009)supported by the National Natural Science Foundation of ChinaProject(2022YFC3004602)supported by the National Key Research and Development Program of ChinaProject(BBJ2024009)supported by the Fundamental Research Funds for the Central Universities,China。
文摘At present,the surrounding rock of the deep mine roadway is prone to post-peak stress under the action of high stress,and secondary rock burst disaster is prone to occur under complex stress disturbance.According to incomplete statistics,as of 2023,80%of coal mine rock bursts accidents in China occur in mining roadway.In view of this phenomenon,the cyclic impact test of post-peak sandstone is designed,focusing on the post-peak stress state of sandstone,and exploring the post-peak dynamic response of sandstone.The post-peak sandstone specimens were prepared by a uniaxial compressor,and then cyclic impact tests were carried out on the post-peak sandstone under different coaxial pressure conditions by an improved separated Hopkinson equipment.The results show that:1)The number of impact times required for sandstone failure after peak decreased with the increase of axial pressure,indicating that the impact tendency of sandstone after peak decreased under lower axial pressure.On the contrary,the post-peak sandstone had strong impact tendency under higher axial pressure;2)The higher the axial pressure,the lower the dynamic strength of the post-peak sandstone,indicating that the axial pressure promoted the failure process of the post peak sandstone;3)It was a nonlinear evolution of a quadratic polynomial function between the dissipation-energy release rate and axial pressure;4)Shear failure occurred mainly in post-peak impact sandstone with the increased axial pressure,and the composite failure of intergranular failure and transgranular failure changed to single intergranular failure at the microscopic level.The research shows that when the roadway surrounding rock was in the post-peak stress state,reducing the static stress was the key to prevent the secondary ground pressure disaster.The research results provide a theoretical basis for the prevention and control of roadway rock burst disaster under high ground stress environment,and promote the research and exploration of post-peak mechanical properties of coal and rock.
基金supported by National Natural Science Foundation of China(52274323 and 524743495)the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20240231.
文摘In the electroslag remelting(ESR)process,it mainly relies on thermal experiments or analysis via mechanistic models to realize the physical fields simulation of the electromagnetic field and temperature field coupled transfer,which has the limitations of high cost,a large amount of calculating data and high computing power requirements.A novel network based on physics-informed neural network(PINN)was designed to realize the fast and high-fidelity prediction of the distribution of electromagnetic field and temperature field in ESR process.The physical laws were combined with the deep learning network through PINN,and physical constraints were embedded to achieve effective solution of partial differential equations(PDEs).PINN was used to minimize the loss function consisting of data error,physical information error and boundary condition error.The physical laws and boundary condition constraints in the ESR process were considered to maintain high PDE solution accuracy under different spatial and temporal resolutions.Automatic differentiation(Autodiff)technique and gradient descent algorithm were used to optimize the network parameters.The experimental results show that compared with the mechanistic models,PINN can effectively replace thermal experiments to realize the physical field simulation of ESR process with only a few experimental data,which can avoid the disadvantages of pure data-driven network simulation that requires a large amount of training data.Moreover,the solution of PINN has good physical interpretability and reliability of simulation results.For simulating electromagnetic field and temperature field distribution,the training time of the network is only 140 and 203 s,and the regression indicators of root mean square error can reach 12.65 and 13.76,respectively.
基金The Natural Science Foundation of Hebei Province(F2024501044).
文摘The application of visual-language large models in the field of medical health has gradually become a research focus.The models combine the capability for image understanding and natural language processing,and can simultaneously process multi-modality data such as medical images and medical reports.These models can not only recognize images,but also understand the semantic relationship between images and texts,effectively realize the integration of medical information,and provide strong support for clinical decision-making and disease diagnosis.The visual-language large model has good performance for specific medical tasks,and also shows strong potential and high intelligence in the general task models.This paper provides a comprehensive review of the visual-language large model in the field of medical health.Specifically,this paper first introduces the basic theoretical basis and technical principles.Then,this paper introduces the specific application scenarios in the field of medical health,including modality fusion,semi-supervised learning,weakly supervised learning,unsupervised learning,cross-domain model and general models.Finally,the challenges including insufficient data,interpretability,and practical deployment are discussed.According to the existing challenges,four potential future development directions are given.
