The operation furnace profile for the high heat load zone was one of the important factors affecting the stable and high-quality production of the blast furnace,but it was difficult to monitor directly.To address this...The operation furnace profile for the high heat load zone was one of the important factors affecting the stable and high-quality production of the blast furnace,but it was difficult to monitor directly.To address this issue,an online calculation model for the operation furnace profile was proposed based on a dual-driven approach combining data and mechanisms,by integrating mechanism experiment,numerical simulation,and machine learning.The experimentally determined slag layer hanging temperature was 1130℃,and the thermal conductivity ranged from 1.32 to 1.96 m^(2)℃^(-1).Based on the 3D slag-hanging numerical simulation model,a database was constructed,containing 2294 sets of mechanism cases for the slag layer.The fusion of data modeling,heat transfer theory,and expert experience enabled the online calculation of key input variables for the operation furnace profile,particularly the quantification of the“black-box”variable of gas temperature.Simulated data were used as inputs,and light gradient boosting machine was applied to construct the online calculation model for the operation furnace profile.This model facilitated the online calculation of the slag layer thickness and other key indices.The coefficient of determination of the model exceeded 0.98,indicating high accuracy.A slag layer state judgment model was constructed,categorizing states as shedding,too thin,normal,and too thick.Real-time data were applied,and the average slag thickness in the high heat load area of the test data ranged from 40 to 80 mm,which was consistent with field experience.The absolute value of the Pearson correlation coefficient between slag layer thickness,thermocouple temperature,and heat load data was above 0.85,indicating that the calculated results closely aligned with the actual trends.A 3D visual online monitoring system for the operation furnace profile was created,and it has been successfully implemented at the blast furnace site.展开更多
With the rapid development of the internet,the dissemination of public opinion in online social networks has become increasingly complex.Existing dissemination models rarely consider group phenomena and the simultaneo...With the rapid development of the internet,the dissemination of public opinion in online social networks has become increasingly complex.Existing dissemination models rarely consider group phenomena and the simultaneous spread of competing public opinion information in online social networks.This paper introduces the UHNPR information dissemination model to study the dynamic spread and interaction of positive and negative public opinion information in hypernetworks.To improve the accuracy of modeling of information dissemination,we revise the traditional assumptions of constant propagation and decay rates by redefining these rates based on factors that influence the spread of public opinion information.Subsequently,we validate the effectiveness of the UHNPR model using numerical simulations and analyze the impact of factors such as authority effect,user intimacy,information content and information timeliness on the spread of public opinion,providing corresponding suggestions for public opinion control.Our research results demonstrate that this model outperforms the SIR,SEIR and SEIDR models in describing public opinion propagation in real social networks.Compared with complex networks,information spreads faster and more extensively in hypernetworks.展开更多
We propose a novel type of nonlinear solver acceleration for systems of nonlinear partial differential equations(PDEs)that is based on online/adaptive learning.It is applied in the context of multiphase flow in porous...We propose a novel type of nonlinear solver acceleration for systems of nonlinear partial differential equations(PDEs)that is based on online/adaptive learning.It is applied in the context of multiphase flow in porous media.The proposed method rely on four pillars:(i)dimensionless numbers as input parameters for the machine learning model,(ii)simplified numerical model(two-dimensional)for the offline training,(iii)dynamic control of a nonlinear solver tuning parameter(numerical relaxation),(iv)and online learning for time real-improvement of the machine learning model.This strategy decreases the number of nonlinear iterations by dynamically modifying a single global parameter,the relaxation factor,and by adaptively learning the attributes of each numerical model on-the-run.Furthermore,this work performs a sensitivity study in the dimensionless parameters(machine learning features),assess the efficacy of various machine learning models,demonstrate a decrease in nonlinear iterations using our method in more intricate,realistic three-dimensional models,and fully couple a machine learning model into an open-source multiphase flow simulator achieving up to 85%reduction in computational time.展开更多
In this paper, a fault-tolerant-based online critic learning algorithm is developed to solve the optimal tracking control issue for nonaffine nonlinear systems with actuator faults.First, a novel augmented plant is co...In this paper, a fault-tolerant-based online critic learning algorithm is developed to solve the optimal tracking control issue for nonaffine nonlinear systems with actuator faults.First, a novel augmented plant is constructed by fusing the system state and the reference trajectory, which aims to transform the optimal fault-tolerant tracking control design with actuator faults into the optimal regulation problem of the conventional nonlinear error system. Subsequently, in order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain an initial admissible tracking policy. Then, the constructed model neural network(NN) is pretrained to recognize the system dynamics and calculate trajectory control. The critic and action NNs are constructed to output the approximate cost function and approximate tracking control,respectively. The Hamilton-Jacobi-Bellman equation of the error system is solved online through the action-critic framework. In theoretical analysis, it is proved that all concerned signals are uniformly ultimately bounded according to the Lyapunov principle.The tracking control law can approach the optimal tracking control within a finite approximation error. Finally, two experimental examples are conducted to indicate the effectiveness and superiority of the developed fault-tolerant tracking control scheme.展开更多
Fuel cell electric vehicles hold great promise for a diverse range of applications in reducing greenhouse gas emissions.In power fuel cell systems,hydrogen fuel serves as an energy vector.To ensure its suitability,it ...Fuel cell electric vehicles hold great promise for a diverse range of applications in reducing greenhouse gas emissions.In power fuel cell systems,hydrogen fuel serves as an energy vector.To ensure its suitability,it is necessary for the quality of hydrogen to adhere to the standards set by ISO 14687:2019,which sets maximum limits for 14 impurities in hydrogen,aiming to prevent any degradation of fuel cell performance.Ammonia(NH_(3))is a prominent pollutant in fuel cells,and accurate measurements of its concentration are crucial for hydrogen fuel cell quantity.In this study,a novel detection platform was developed for determining NH_(3)in real hydrogen samples.The online analysis platform integrates a self-developed online dilution module with a Fourier transform infrared spectrometer(ODM-FTIR).The ODM-FTIR can be operated fully automatically with remote operation.Under the optimum conditions,this method achieved a wide linear range between(50∼1000)nmol/mol.The limit of detection(LOD)was as low as 2 nmol/mol with a relative standard deviation(RSD,n=7)of 3.6%at a content of 50 nmol/mol.To ensure that the quality of the hydrogen products meets the requirement of proton exchange membrane fuel cell vehicles(PEMFCV),the developed ODM-FTIR system was applied to monitor the NH_(3)content in Chengdu Hydrogen Energy Co.,Ltd.for 21 days during Chengdu 2021 FISU World University Games.The proposed method retains several unique advantages,including a low detection limit,excellent repeatability,high accuracy,high speed,good stability,and calibration flexibility.It is an effective analytical method for accurately quantifying NH_(3)in hydrogen,especially suitable for online analysis.It also provides a new idea for the analysis of other impurity components in hydrogen.展开更多
To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy...To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.展开更多
Improvements in aero-engine performance have made the structures of the aero-engine components increasingly complex.To better adapt to the processing requirements of narrow twisted channels such as an integral shroude...Improvements in aero-engine performance have made the structures of the aero-engine components increasingly complex.To better adapt to the processing requirements of narrow twisted channels such as an integral shrouded blisk,this study proposes an innovative method of electrochemical cutting in which a flexible tube electrode is controlled by online deformation during processing.In this study,the processing principle of electrochemical cutting with a flexible electrode for controlled online deformation(FECC)was revealed for the first time.The online deformation process of flexible electrodes and the machining process of profiles were analysed in depth,and the corresponding theoretical models were established.Conventional electrochemical machining(ECM)is a multi-physical field-coupled process involving electric and flow fields.In FECC,classical mechanics are introduced into the tool cathode,which must be loaded at all times during the machining process.Therefore,in this study,before and after the deformation of the flexible electrode,a corresponding simulation study was conducted to understand the influence of the online deformation of the flexible electrode on the flow and electric fields.The feasibility of flexible electrodes for online deformation and the validity of the theoretical model were verified by deformation measurements and in situ observation experiments.Finally,the method was successfully applied to the machining of nickel-based high-temperature alloys,and different specifications of flexible electrodes were used to complete the machining of the corresponding complex profiles,thereby verifying the feasibility and versatility of the method.The method proposed in this study breaks the tradition of using a non-deformable cathode for ECM and adopts a flexible electrode that can be deformed during the machining process as the tool cathode,which improves machining flexibility and provides a valuable reference to promote the ECM of complex profiles.展开更多
In order to address the current inability of screen printing to monitor printing pressure online,an online printing pressure monitoring system applied to screen printing machines was designed in this study.In this stu...In order to address the current inability of screen printing to monitor printing pressure online,an online printing pressure monitoring system applied to screen printing machines was designed in this study.In this study,the consistency of printed electrodes was measured by using a confocal microscope and the pressure distribution detected by online pressure monitoring system was compared to investigate the relationship.The results demonstrated the relationship between printing pressure and the consistency of printed electrodes.As printing pressure increases,the ink layer at the corresponding position becomes thicker and that higher printing pressure enhances the consistency of the printed electrodes.The experiment confirms the feasibility of the online pressure monitoring system,which aids in predicting and controlling the consistency of printed electrodes,thereby improving their performance.展开更多
As world events have morphed teachers’roles within English medium of instruction(EMI)contexts to incorporate more online teaching practices,teachers’integration of digital tools has faced technological and curricula...As world events have morphed teachers’roles within English medium of instruction(EMI)contexts to incorporate more online teaching practices,teachers’integration of digital tools has faced technological and curricular challenges.While previous research has examined the integration of digital tools in face-to-face and hybrid EMI settings(e.g.,Finardi,2015;O’Dowd,2018),more research is needed to understand the familiarization process teachers engage in as they implement fully-online teaching to support their content and language integrated learning(CLIL)teaching.As part of a larger project,this case study sets out to fill this gap by examining the practices and perspectives of 30 Kazakhstani university teachers who adopted CLIL approaches while needing to adapt to fully-online teaching contexts.Using the concept of technological pedagogical content knowledge(Mishra&Koehler,2006)in tandem with Ball et al.’s(2016)seven CLIL principles as a framework,this study thematically analyzed workshop artifacts,survey responses,semi-structured interview transcripts,and videos from online class lessons to find that teachers were mediators and curators of content,language,pedagogy,and digital tools.The findings offer pedagogical insights for the implementation of professional development(PD)to prepare teachers to meaningfully curate and mediate technology into their CLIL pedagogy to teach content within EMI contexts.展开更多
The niche discipline of Indo-European Studies has proven itself to be prevailingly au courant by launching several projects in the field of online etymological dictionaries.My paper will offer an overview of these pro...The niche discipline of Indo-European Studies has proven itself to be prevailingly au courant by launching several projects in the field of online etymological dictionaries.My paper will offer an overview of these projects(including the Lexicon Etymologicum Digitale Indoeuropaeum(LEDI)directed by me)and analyse their approaches,features,and peculiarities(e.g.,commercial vs.open access).Special attention will paid to the projects’inclusions of phonetic rules and affixes,which makes derivation transparent and is helpful for didactic purposes.展开更多
When estimating the capacity of lithium-ion batteries offline or online,it is essential to extract a health feature(HF)that can effectively characterize capacity degradation under both conventional ideal and complex d...When estimating the capacity of lithium-ion batteries offline or online,it is essential to extract a health feature(HF)that can effectively characterize capacity degradation under both conventional ideal and complex dynamic operating conditions.However,the extraction of most HFs relies on complete charge-discharge cycle data,making them less adaptable to complex dynamic operating conditions.Existing mechanism HFs,while capable of characterizing capacity degradation from a mechanism perspective,suffer from limitations such as insufficient physical model expressiveness,high dimension,and redundancy of the mechanism HF.These issues increase the complexity of subsequent modeling of the relationship between HFs and capacity,thereby restricting their promotion in engineering practice.To meet this gap,this paper proposes a novel mechanism-based HF.Firstly,a multi-physical fields coupling model is developed to describe the interactions between electrochemical,thermal,and aging behaviors of the battery.Secondly,based on the aging mechanism,the accumulated charge of lithium lost during the formation of the solid electrolyte interphase(SEI)film is extracted as HF to provide a more intuitive representation of capacity degradation.Then,to reduce estimation errors caused by considering only a single aging mechanism,multiple representative regression models are employed to establish the mapping relationship between the mechanism HF and capacity,further enhancing the accuracy of final results.Finally,the proposed method is implemented and validated using real battery data under three different types of operating conditions.Experimental results demonstrate that,compared to other commonly used HFs,the proposed HF exhibits significant competitive advantages in handling incomplete cycle data,unknown operating conditions,and capacity estimation models.The minimum estimation error under ideal conditions is 0.0074,and the minimum estimation error under complex dynamic conditions is 0.0268.展开更多
Online learning has taken root and with the advancement in technology more and more educators are embracing online learning.During the COVID-19 pandemic,with total shut down of face-to-face learning,the Kenyan governm...Online learning has taken root and with the advancement in technology more and more educators are embracing online learning.During the COVID-19 pandemic,with total shut down of face-to-face learning,the Kenyan government had moved learning online and remotely.To find out on how learners in remote areas were experiencing learning during the COVID-19 pandemic,a study was carried out using rapid ethnography design.Five final year secondary students were sampled.Data was collected through interviews,observations,and document analysis.This paper reports on the findings of online and remote learning platforms,which were available,accessed,and preferred by the learners in remote areas of Kenya during the pandemic.It also highlights the importance of e-learning platforms in addressing learning experiences and success.展开更多
The rapid development of online education has posed brand-new challenges to the teaching quality monitoring system in colleges and universities.This paper systematically explores the three major characteristics of onl...The rapid development of online education has posed brand-new challenges to the teaching quality monitoring system in colleges and universities.This paper systematically explores the three major characteristics of online teaching quality monitoring in colleges and universities:the complexity of monitoring brought by the separation of time and space,the enhanced accuracy based on technology dependence,and the monitoring dimensions expanded by the diversification of interaction.The research reveals the key existing problems at present,including the analytical predicament caused by data fragmentation,the stability crisis triggered by technical failures,and the validity limitations due to the insufficient adaptability of teachers and students.In response to these challenges,this paper proposes systematic solutions such as building a unified data platform,strengthening the technical support system,and conducting targeted training.Through multi-dimensional analysis,this study provides a theoretical framework and practical path for constructing a quality monitoring system that ADAPTS to the characteristics of online education,and has important reference value for improving the quality of online teaching in colleges and universities.展开更多
To bridge the gap between curriculum content and clinical needs,as well as address the insufficient quality of online educational resources in current postgraduate anesthesiology education,this study proposes a“libra...To bridge the gap between curriculum content and clinical needs,as well as address the insufficient quality of online educational resources in current postgraduate anesthesiology education,this study proposes a“library-style”network resource platform for anesthesia graduate students,grounded in the competency-based medical education framework.The platform’s primary focus is“classical knowledge module”,which covers six core domains in anesthesiology,such as clinical anesthesia management,anesthesia technical operation,and anesthesia pharmacology.The platform also integrates‘Internet+’technology,creating a multifunctional network resource to support comprehensive learning.The platform is characterized by modularized knowledge,diversified resources,dynamic updates,and universal accessibility,enabling postgraduate students to engage in independent and lifelong learning.This flexibility fosters the innovation of hybrid teaching models,combining both online and offline components.The study aims to strengthen the competency-oriented anesthesia training system,providing robust support for both clinical practice and academic research.展开更多
The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children a...The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.展开更多
This paper investigates mobility-aware online optimization for digital twin(DT)-assisted task execution in edge computing environments.In such systems,DTs,hosted on edge servers(ESs),require proactive migration to mai...This paper investigates mobility-aware online optimization for digital twin(DT)-assisted task execution in edge computing environments.In such systems,DTs,hosted on edge servers(ESs),require proactive migration to maintain proximity to their mobile physical twin(PT)counterparts.To minimize task response latency under a stringent energy consumption constraint,we jointly optimize three key components:the status data uploading frequency fromthe PT,theDT migration decisions,and the allocation of computational and communication resources.To address the asynchronous nature of these decisions,we propose a novel two-timescale mobility-aware online optimization(TMO)framework.The TMO scheme leverages an extended two-timescale Lyapunov optimization framework to decompose the long-term problem into sequential subproblems.At the larger timescale,a multi-armed bandit(MAB)algorithm is employed to dynamically learn the optimal status data uploading frequency.Within each shorter timescale,we first employ a gated recurrent unit(GRU)-based predictor to forecast the PT’s trajectory.Based on this prediction,an alternate minimization(AM)algorithm is then utilized to solve for the DT migration and resource allocation variables.Theoretical analysis confirms that the proposed TMO scheme is asymptotically optimal.Furthermore,simulation results demonstrate its significant performance gains over existing benchmark methods.展开更多
BACKGROUND Online adaptive radiotherapy(oART)has demonstrated improved target volume coverage and enhanced sparing of surrounding pelvic organs through daily reoptimization based on pretreatment imaging.Recently,itera...BACKGROUND Online adaptive radiotherapy(oART)has demonstrated improved target volume coverage and enhanced sparing of surrounding pelvic organs through daily reoptimization based on pretreatment imaging.Recently,iterative cone-beam computed tomography(iCBCT)has been integrated into oART workflows,facilitating precise daily adaptation.However,the dosimetric consequences of intra-fractional variations for clinical target volume(CTV)and organs at risk(OARs)remain insufficiently characterized.AIM To investigate intra-fractional CTV and OARs variation and their impact on iCBCT guided daily oART for postoperative cervical and endometrial cancer.METHODS Seventeen patients treated with daily postoperative iCBCT guided oART with rigorous bladder and rectal preparation protocols were enrolled.CTV and OARs were contoured on pre-and post-treatment iCBCT scans.The average surface distance(ASD),dice similarity coefficient(DSC),and 95%Hausdorff distance(HD)were utilized to evaluate the difference between pre-and post-treatment structures.Dosimetric outcomes for the pretreatment target volumes and OARs were recalculated using posttreatment contours to assess the impact of intrafractional variation.RESULTS A total of 434 treatment fractions were analyzed,with an average interval time of 22 minutes between two iCBCT scans.Minimal variations were observed in the bladder,rectum,and CTV both pre-and post-treatment,with DSC exceeding 0.8.The vaginal CTV exhibited centroid deviations of 0.46 mm anteriorly,0.11 mm laterally,and 0.58 mm superiorly,along with ASD of 1.69 mm and 95%HD of 6.42 mm.Weak correlations were observed between vaginal CTV posterior-anterior centroid deviations and rectal superior-inferior deviations(P=0.017).Minimal dosimetric differences were observed pre-and post-treatment,with V100%for the adapted plan of nodal CTV being 99.94%vs 99.08%and vaginal CTV being 99.97%vs 98.66%.CONCLUSION Daily iCBCT-guided oART with strict bladder and rectal preparation effectively compensates for intra-fractional variations,maintaining CTV coverage and OAR sparing across all treatment fractions.展开更多
In the current trend of educational digitization,online learning platforms have proliferated,making the visual design of digital learning resources increasingly critical.However,existing visual designs for online lear...In the current trend of educational digitization,online learning platforms have proliferated,making the visual design of digital learning resources increasingly critical.However,existing visual designs for online learning resources face numerous challenges.The emergence of AIGC(Artificial Intelligence-Generated Content)technology offers innovative solutions to these issues.This paper explores the application of AIGC technology in enhancing the“new quality productive forces”of visual design for online learning resources.It emphasizes the need to balance technological innovation with humanistic care and highlights the importance of human intervention in the design process.展开更多
Online interactive learning plays a crucial role in improving online education quality.This grounded theory study examines:(1)what key factors shape EFL learners’online interactive learning,(2)how these factors form ...Online interactive learning plays a crucial role in improving online education quality.This grounded theory study examines:(1)what key factors shape EFL learners’online interactive learning,(2)how these factors form an empirically validated model,and(3)how they interact within this model,through systematic analysis of 9,207 discussion forum posts from a Chinese University MOOC platform.Results demonstrate that learning drive,course structure,teaching competence,interaction behavior,expected outcomes,and online learning context significantly influence EFL online interactive learning.The analysis reveals two key mechanisms:expected outcomes mediate the effects of learning drive(β=0.45),course structure,teaching competence,and interaction behavior(β=0.35)on learning outcomes,while online learning context moderates these relationships(β=0.25).Specifically,learning drive provides intrinsic/extrinsic motivation,whereas course structure,teaching competence,interaction behavior,and expected outcomes collectively enhance interaction quality and sustainability.These findings,derived through rigorous grounded theory methodology involving open,axial,and selective coding of large-scale interaction data,yield three key contributions:(1)a comprehensive theoretical model of EFL online learning dynamics,(2)empirical validation of mediation/moderation mechanisms,and(3)practical strategies for designing scaffolded interaction protocols and adaptive feedback systems.The study establishes that its theoretically saturated model(achieved after analyzing 7,366 posts with 1,841 verification cases)offers educators evidence-based approaches to optimize collaborative interaction in digital EFL environments.展开更多
基金financially supported by the National Natural Science Foundation of China(Grant Nos.52404343 and 52274326)the Fundamental Research Funds for the Central Universities(Grant Nos.N2425031 and N25BJD007)+1 种基金the China Postdoctoral Science Foundation(Grant No.2024M760370)the Liaoning Province Science and Technology Plan Joint Program(Key Research and Development Program Project)(Grant No.2023JH2/101800058).
文摘The operation furnace profile for the high heat load zone was one of the important factors affecting the stable and high-quality production of the blast furnace,but it was difficult to monitor directly.To address this issue,an online calculation model for the operation furnace profile was proposed based on a dual-driven approach combining data and mechanisms,by integrating mechanism experiment,numerical simulation,and machine learning.The experimentally determined slag layer hanging temperature was 1130℃,and the thermal conductivity ranged from 1.32 to 1.96 m^(2)℃^(-1).Based on the 3D slag-hanging numerical simulation model,a database was constructed,containing 2294 sets of mechanism cases for the slag layer.The fusion of data modeling,heat transfer theory,and expert experience enabled the online calculation of key input variables for the operation furnace profile,particularly the quantification of the“black-box”variable of gas temperature.Simulated data were used as inputs,and light gradient boosting machine was applied to construct the online calculation model for the operation furnace profile.This model facilitated the online calculation of the slag layer thickness and other key indices.The coefficient of determination of the model exceeded 0.98,indicating high accuracy.A slag layer state judgment model was constructed,categorizing states as shedding,too thin,normal,and too thick.Real-time data were applied,and the average slag thickness in the high heat load area of the test data ranged from 40 to 80 mm,which was consistent with field experience.The absolute value of the Pearson correlation coefficient between slag layer thickness,thermocouple temperature,and heat load data was above 0.85,indicating that the calculated results closely aligned with the actual trends.A 3D visual online monitoring system for the operation furnace profile was created,and it has been successfully implemented at the blast furnace site.
基金supported by Yunnan High-tech Industry Development Project(Grant No.201606)Yunnan Provincial Major Science and Technology Special Plan Projects(Grant Nos.202103AA080015 and 202002AD080001-5)+1 种基金Yunnan Basic Research Project(Grant No.202001AS070014)Talents and Platform Program of Science and Technology of Yunnan(Grant No.202105AC160018)。
文摘With the rapid development of the internet,the dissemination of public opinion in online social networks has become increasingly complex.Existing dissemination models rarely consider group phenomena and the simultaneous spread of competing public opinion information in online social networks.This paper introduces the UHNPR information dissemination model to study the dynamic spread and interaction of positive and negative public opinion information in hypernetworks.To improve the accuracy of modeling of information dissemination,we revise the traditional assumptions of constant propagation and decay rates by redefining these rates based on factors that influence the spread of public opinion information.Subsequently,we validate the effectiveness of the UHNPR model using numerical simulations and analyze the impact of factors such as authority effect,user intimacy,information content and information timeliness on the spread of public opinion,providing corresponding suggestions for public opinion control.Our research results demonstrate that this model outperforms the SIR,SEIR and SEIDR models in describing public opinion propagation in real social networks.Compared with complex networks,information spreads faster and more extensively in hypernetworks.
基金MUFFINS,MUltiphase Flow-induced Fluid-flexible structure InteractioN in Subsea applications(EP/P033180/1)the PREMIERE programme grant(EP/T000414/1)SMARTRES,Smart assessment,management and optimization of urban geothermal resources(NE/X005607/1).
文摘We propose a novel type of nonlinear solver acceleration for systems of nonlinear partial differential equations(PDEs)that is based on online/adaptive learning.It is applied in the context of multiphase flow in porous media.The proposed method rely on four pillars:(i)dimensionless numbers as input parameters for the machine learning model,(ii)simplified numerical model(two-dimensional)for the offline training,(iii)dynamic control of a nonlinear solver tuning parameter(numerical relaxation),(iv)and online learning for time real-improvement of the machine learning model.This strategy decreases the number of nonlinear iterations by dynamically modifying a single global parameter,the relaxation factor,and by adaptively learning the attributes of each numerical model on-the-run.Furthermore,this work performs a sensitivity study in the dimensionless parameters(machine learning features),assess the efficacy of various machine learning models,demonstrate a decrease in nonlinear iterations using our method in more intricate,realistic three-dimensional models,and fully couple a machine learning model into an open-source multiphase flow simulator achieving up to 85%reduction in computational time.
基金supported in part by the National Natural Science Foundation of China(62222301,62373012,62473012,62021003)the National Science and Technology Major Project(2021ZD0112302,2021ZD0112301)the Beijing Natural Science Foundation(JQ19013)
文摘In this paper, a fault-tolerant-based online critic learning algorithm is developed to solve the optimal tracking control issue for nonaffine nonlinear systems with actuator faults.First, a novel augmented plant is constructed by fusing the system state and the reference trajectory, which aims to transform the optimal fault-tolerant tracking control design with actuator faults into the optimal regulation problem of the conventional nonlinear error system. Subsequently, in order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain an initial admissible tracking policy. Then, the constructed model neural network(NN) is pretrained to recognize the system dynamics and calculate trajectory control. The critic and action NNs are constructed to output the approximate cost function and approximate tracking control,respectively. The Hamilton-Jacobi-Bellman equation of the error system is solved online through the action-critic framework. In theoretical analysis, it is proved that all concerned signals are uniformly ultimately bounded according to the Lyapunov principle.The tracking control law can approach the optimal tracking control within a finite approximation error. Finally, two experimental examples are conducted to indicate the effectiveness and superiority of the developed fault-tolerant tracking control scheme.
基金financial support by Sichuan Science and Technology,China(No.2023YFG0070).
文摘Fuel cell electric vehicles hold great promise for a diverse range of applications in reducing greenhouse gas emissions.In power fuel cell systems,hydrogen fuel serves as an energy vector.To ensure its suitability,it is necessary for the quality of hydrogen to adhere to the standards set by ISO 14687:2019,which sets maximum limits for 14 impurities in hydrogen,aiming to prevent any degradation of fuel cell performance.Ammonia(NH_(3))is a prominent pollutant in fuel cells,and accurate measurements of its concentration are crucial for hydrogen fuel cell quantity.In this study,a novel detection platform was developed for determining NH_(3)in real hydrogen samples.The online analysis platform integrates a self-developed online dilution module with a Fourier transform infrared spectrometer(ODM-FTIR).The ODM-FTIR can be operated fully automatically with remote operation.Under the optimum conditions,this method achieved a wide linear range between(50∼1000)nmol/mol.The limit of detection(LOD)was as low as 2 nmol/mol with a relative standard deviation(RSD,n=7)of 3.6%at a content of 50 nmol/mol.To ensure that the quality of the hydrogen products meets the requirement of proton exchange membrane fuel cell vehicles(PEMFCV),the developed ODM-FTIR system was applied to monitor the NH_(3)content in Chengdu Hydrogen Energy Co.,Ltd.for 21 days during Chengdu 2021 FISU World University Games.The proposed method retains several unique advantages,including a low detection limit,excellent repeatability,high accuracy,high speed,good stability,and calibration flexibility.It is an effective analytical method for accurately quantifying NH_(3)in hydrogen,especially suitable for online analysis.It also provides a new idea for the analysis of other impurity components in hydrogen.
基金Supported by State Grid Zhejiang Electric Power Co.,Ltd.Science and Technology Project Funding(No.B311DS230005).
文摘To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.
基金supported by the National Natural Science Foundation of China(52375443)the Innovative Research Group Project of the National Natural Science Foundation of China(51921003).
文摘Improvements in aero-engine performance have made the structures of the aero-engine components increasingly complex.To better adapt to the processing requirements of narrow twisted channels such as an integral shrouded blisk,this study proposes an innovative method of electrochemical cutting in which a flexible tube electrode is controlled by online deformation during processing.In this study,the processing principle of electrochemical cutting with a flexible electrode for controlled online deformation(FECC)was revealed for the first time.The online deformation process of flexible electrodes and the machining process of profiles were analysed in depth,and the corresponding theoretical models were established.Conventional electrochemical machining(ECM)is a multi-physical field-coupled process involving electric and flow fields.In FECC,classical mechanics are introduced into the tool cathode,which must be loaded at all times during the machining process.Therefore,in this study,before and after the deformation of the flexible electrode,a corresponding simulation study was conducted to understand the influence of the online deformation of the flexible electrode on the flow and electric fields.The feasibility of flexible electrodes for online deformation and the validity of the theoretical model were verified by deformation measurements and in situ observation experiments.Finally,the method was successfully applied to the machining of nickel-based high-temperature alloys,and different specifications of flexible electrodes were used to complete the machining of the corresponding complex profiles,thereby verifying the feasibility and versatility of the method.The method proposed in this study breaks the tradition of using a non-deformable cathode for ECM and adopts a flexible electrode that can be deformed during the machining process as the tool cathode,which improves machining flexibility and provides a valuable reference to promote the ECM of complex profiles.
文摘In order to address the current inability of screen printing to monitor printing pressure online,an online printing pressure monitoring system applied to screen printing machines was designed in this study.In this study,the consistency of printed electrodes was measured by using a confocal microscope and the pressure distribution detected by online pressure monitoring system was compared to investigate the relationship.The results demonstrated the relationship between printing pressure and the consistency of printed electrodes.As printing pressure increases,the ink layer at the corresponding position becomes thicker and that higher printing pressure enhances the consistency of the printed electrodes.The experiment confirms the feasibility of the online pressure monitoring system,which aids in predicting and controlling the consistency of printed electrodes,thereby improving their performance.
基金funding from the U.S.-Kazakhstan University Partnerships program funded by the U.S.Mission to Kazakhstan and administered by American Councils[Award number SKZ100-19-CA-0149].
文摘As world events have morphed teachers’roles within English medium of instruction(EMI)contexts to incorporate more online teaching practices,teachers’integration of digital tools has faced technological and curricular challenges.While previous research has examined the integration of digital tools in face-to-face and hybrid EMI settings(e.g.,Finardi,2015;O’Dowd,2018),more research is needed to understand the familiarization process teachers engage in as they implement fully-online teaching to support their content and language integrated learning(CLIL)teaching.As part of a larger project,this case study sets out to fill this gap by examining the practices and perspectives of 30 Kazakhstani university teachers who adopted CLIL approaches while needing to adapt to fully-online teaching contexts.Using the concept of technological pedagogical content knowledge(Mishra&Koehler,2006)in tandem with Ball et al.’s(2016)seven CLIL principles as a framework,this study thematically analyzed workshop artifacts,survey responses,semi-structured interview transcripts,and videos from online class lessons to find that teachers were mediators and curators of content,language,pedagogy,and digital tools.The findings offer pedagogical insights for the implementation of professional development(PD)to prepare teachers to meaningfully curate and mediate technology into their CLIL pedagogy to teach content within EMI contexts.
文摘The niche discipline of Indo-European Studies has proven itself to be prevailingly au courant by launching several projects in the field of online etymological dictionaries.My paper will offer an overview of these projects(including the Lexicon Etymologicum Digitale Indoeuropaeum(LEDI)directed by me)and analyse their approaches,features,and peculiarities(e.g.,commercial vs.open access).Special attention will paid to the projects’inclusions of phonetic rules and affixes,which makes derivation transparent and is helpful for didactic purposes.
基金supported by the National Natural Science Foundation of China(NSFC,No.62303031)the Fundamental Research Funds for the Central Universities。
文摘When estimating the capacity of lithium-ion batteries offline or online,it is essential to extract a health feature(HF)that can effectively characterize capacity degradation under both conventional ideal and complex dynamic operating conditions.However,the extraction of most HFs relies on complete charge-discharge cycle data,making them less adaptable to complex dynamic operating conditions.Existing mechanism HFs,while capable of characterizing capacity degradation from a mechanism perspective,suffer from limitations such as insufficient physical model expressiveness,high dimension,and redundancy of the mechanism HF.These issues increase the complexity of subsequent modeling of the relationship between HFs and capacity,thereby restricting their promotion in engineering practice.To meet this gap,this paper proposes a novel mechanism-based HF.Firstly,a multi-physical fields coupling model is developed to describe the interactions between electrochemical,thermal,and aging behaviors of the battery.Secondly,based on the aging mechanism,the accumulated charge of lithium lost during the formation of the solid electrolyte interphase(SEI)film is extracted as HF to provide a more intuitive representation of capacity degradation.Then,to reduce estimation errors caused by considering only a single aging mechanism,multiple representative regression models are employed to establish the mapping relationship between the mechanism HF and capacity,further enhancing the accuracy of final results.Finally,the proposed method is implemented and validated using real battery data under three different types of operating conditions.Experimental results demonstrate that,compared to other commonly used HFs,the proposed HF exhibits significant competitive advantages in handling incomplete cycle data,unknown operating conditions,and capacity estimation models.The minimum estimation error under ideal conditions is 0.0074,and the minimum estimation error under complex dynamic conditions is 0.0268.
文摘Online learning has taken root and with the advancement in technology more and more educators are embracing online learning.During the COVID-19 pandemic,with total shut down of face-to-face learning,the Kenyan government had moved learning online and remotely.To find out on how learners in remote areas were experiencing learning during the COVID-19 pandemic,a study was carried out using rapid ethnography design.Five final year secondary students were sampled.Data was collected through interviews,observations,and document analysis.This paper reports on the findings of online and remote learning platforms,which were available,accessed,and preferred by the learners in remote areas of Kenya during the pandemic.It also highlights the importance of e-learning platforms in addressing learning experiences and success.
文摘The rapid development of online education has posed brand-new challenges to the teaching quality monitoring system in colleges and universities.This paper systematically explores the three major characteristics of online teaching quality monitoring in colleges and universities:the complexity of monitoring brought by the separation of time and space,the enhanced accuracy based on technology dependence,and the monitoring dimensions expanded by the diversification of interaction.The research reveals the key existing problems at present,including the analytical predicament caused by data fragmentation,the stability crisis triggered by technical failures,and the validity limitations due to the insufficient adaptability of teachers and students.In response to these challenges,this paper proposes systematic solutions such as building a unified data platform,strengthening the technical support system,and conducting targeted training.Through multi-dimensional analysis,this study provides a theoretical framework and practical path for constructing a quality monitoring system that ADAPTS to the characteristics of online education,and has important reference value for improving the quality of online teaching in colleges and universities.
基金supported by Planning Project of Shanghai Higher Education Association(2QYB24158)Collaborative Education Project of the Ministry of Education of China(250101414020206).
文摘To bridge the gap between curriculum content and clinical needs,as well as address the insufficient quality of online educational resources in current postgraduate anesthesiology education,this study proposes a“library-style”network resource platform for anesthesia graduate students,grounded in the competency-based medical education framework.The platform’s primary focus is“classical knowledge module”,which covers six core domains in anesthesiology,such as clinical anesthesia management,anesthesia technical operation,and anesthesia pharmacology.The platform also integrates‘Internet+’technology,creating a multifunctional network resource to support comprehensive learning.The platform is characterized by modularized knowledge,diversified resources,dynamic updates,and universal accessibility,enabling postgraduate students to engage in independent and lifelong learning.This flexibility fosters the innovation of hybrid teaching models,combining both online and offline components.The study aims to strengthen the competency-oriented anesthesia training system,providing robust support for both clinical practice and academic research.
基金supported by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korean government(Ministry of Science and ICT)(IITP-2025-RS-2024-00438056).
文摘The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.
基金funded by the State Key Laboratory of Massive Personalized Customization System and Technology,grant No.H&C-MPC-2023-04-01.
文摘This paper investigates mobility-aware online optimization for digital twin(DT)-assisted task execution in edge computing environments.In such systems,DTs,hosted on edge servers(ESs),require proactive migration to maintain proximity to their mobile physical twin(PT)counterparts.To minimize task response latency under a stringent energy consumption constraint,we jointly optimize three key components:the status data uploading frequency fromthe PT,theDT migration decisions,and the allocation of computational and communication resources.To address the asynchronous nature of these decisions,we propose a novel two-timescale mobility-aware online optimization(TMO)framework.The TMO scheme leverages an extended two-timescale Lyapunov optimization framework to decompose the long-term problem into sequential subproblems.At the larger timescale,a multi-armed bandit(MAB)algorithm is employed to dynamically learn the optimal status data uploading frequency.Within each shorter timescale,we first employ a gated recurrent unit(GRU)-based predictor to forecast the PT’s trajectory.Based on this prediction,an alternate minimization(AM)algorithm is then utilized to solve for the DT migration and resource allocation variables.Theoretical analysis confirms that the proposed TMO scheme is asymptotically optimal.Furthermore,simulation results demonstrate its significant performance gains over existing benchmark methods.
基金Supported by the National Key R&D Program of China,No.2022YFC2407100 and No.2022YFC2407101the National High Level Hospital Clinical Research Funding,No.2022-PUMCH-B-127.
文摘BACKGROUND Online adaptive radiotherapy(oART)has demonstrated improved target volume coverage and enhanced sparing of surrounding pelvic organs through daily reoptimization based on pretreatment imaging.Recently,iterative cone-beam computed tomography(iCBCT)has been integrated into oART workflows,facilitating precise daily adaptation.However,the dosimetric consequences of intra-fractional variations for clinical target volume(CTV)and organs at risk(OARs)remain insufficiently characterized.AIM To investigate intra-fractional CTV and OARs variation and their impact on iCBCT guided daily oART for postoperative cervical and endometrial cancer.METHODS Seventeen patients treated with daily postoperative iCBCT guided oART with rigorous bladder and rectal preparation protocols were enrolled.CTV and OARs were contoured on pre-and post-treatment iCBCT scans.The average surface distance(ASD),dice similarity coefficient(DSC),and 95%Hausdorff distance(HD)were utilized to evaluate the difference between pre-and post-treatment structures.Dosimetric outcomes for the pretreatment target volumes and OARs were recalculated using posttreatment contours to assess the impact of intrafractional variation.RESULTS A total of 434 treatment fractions were analyzed,with an average interval time of 22 minutes between two iCBCT scans.Minimal variations were observed in the bladder,rectum,and CTV both pre-and post-treatment,with DSC exceeding 0.8.The vaginal CTV exhibited centroid deviations of 0.46 mm anteriorly,0.11 mm laterally,and 0.58 mm superiorly,along with ASD of 1.69 mm and 95%HD of 6.42 mm.Weak correlations were observed between vaginal CTV posterior-anterior centroid deviations and rectal superior-inferior deviations(P=0.017).Minimal dosimetric differences were observed pre-and post-treatment,with V100%for the adapted plan of nodal CTV being 99.94%vs 99.08%and vaginal CTV being 99.97%vs 98.66%.CONCLUSION Daily iCBCT-guided oART with strict bladder and rectal preparation effectively compensates for intra-fractional variations,maintaining CTV coverage and OAR sparing across all treatment fractions.
文摘In the current trend of educational digitization,online learning platforms have proliferated,making the visual design of digital learning resources increasingly critical.However,existing visual designs for online learning resources face numerous challenges.The emergence of AIGC(Artificial Intelligence-Generated Content)technology offers innovative solutions to these issues.This paper explores the application of AIGC technology in enhancing the“new quality productive forces”of visual design for online learning resources.It emphasizes the need to balance technological innovation with humanistic care and highlights the importance of human intervention in the design process.
文摘Online interactive learning plays a crucial role in improving online education quality.This grounded theory study examines:(1)what key factors shape EFL learners’online interactive learning,(2)how these factors form an empirically validated model,and(3)how they interact within this model,through systematic analysis of 9,207 discussion forum posts from a Chinese University MOOC platform.Results demonstrate that learning drive,course structure,teaching competence,interaction behavior,expected outcomes,and online learning context significantly influence EFL online interactive learning.The analysis reveals two key mechanisms:expected outcomes mediate the effects of learning drive(β=0.45),course structure,teaching competence,and interaction behavior(β=0.35)on learning outcomes,while online learning context moderates these relationships(β=0.25).Specifically,learning drive provides intrinsic/extrinsic motivation,whereas course structure,teaching competence,interaction behavior,and expected outcomes collectively enhance interaction quality and sustainability.These findings,derived through rigorous grounded theory methodology involving open,axial,and selective coding of large-scale interaction data,yield three key contributions:(1)a comprehensive theoretical model of EFL online learning dynamics,(2)empirical validation of mediation/moderation mechanisms,and(3)practical strategies for designing scaffolded interaction protocols and adaptive feedback systems.The study establishes that its theoretically saturated model(achieved after analyzing 7,366 posts with 1,841 verification cases)offers educators evidence-based approaches to optimize collaborative interaction in digital EFL environments.
