Predictive maintenance(PdM)is vital for ensuring the reliability,safety,and cost efficiency of heavyduty vehicle fleets.However,real-world sensor data are often highly imbalanced,noisy,and temporally irregular,posing ...Predictive maintenance(PdM)is vital for ensuring the reliability,safety,and cost efficiency of heavyduty vehicle fleets.However,real-world sensor data are often highly imbalanced,noisy,and temporally irregular,posing significant challenges to model robustness and deployment.Using multivariate time-series data from Scania trucks,this study proposes a novel PdM framework that integrates efficient feature summarization with cost-sensitive hierarchical classification.First,the proposed last_k_summary method transforms recent operational records into compact statistical and trend-based descriptors while preserving missingness,allowing LightGBM to leverage its inherent split rules without ad-hoc imputation.Then,a two-stage LightGBM framework is developed for fault detection and severity classification:Stage A performs safety-prioritized fault screening(normal vs.fault)with a false-negativeweighted objective,and Stage B refines the detected faults into four severity levels through a cascaded hierarchy of binary classifiers.Under the official cost matrix of the IDA Industrial Challenge,the framework achieves total misclassification costs of 36,113(validation)and 36,314(test),outperforming XGBoost and Bi-LSTM by 3.8%-13.5%while maintaining high recall for the safety-critical class(0.83 validation,0.77 test).These results demonstrate that the proposed approach not only improves predictive accuracy but also provides a practical and deployable PdM solution that reduces maintenance cost,enhances fleet safety,and supports data-driven decision-making in industrial environments.展开更多
Integrated-energy systems(IESs)are key to advancing renewable-energy utilization and addressing environmental challenges.Key components of IESs include low-carbon,economic dispatch and demand response,for maximizing r...Integrated-energy systems(IESs)are key to advancing renewable-energy utilization and addressing environmental challenges.Key components of IESs include low-carbon,economic dispatch and demand response,for maximizing renewable-energy consumption and supporting sustainable-energy systems.User participation is central to demand response;however,many users are not inclined to engage actively;therefore,the full potential of demand response remains unrealized.User satisfaction must be prioritized in demand-response assessments.This study proposed a two-stage,capacity-optimization configuration method for user-level energy systems con-sidering thermal inertia and user satisfaction.This method addresses load coordination and complementary issues within the IES and seeks to minimize the annual,total cost for determining equipment capacity configurations while introducing models for system thermal inertia and user satisfaction.Indoor heating is adjusted,for optimizing device output and load profiles,with a focus on typical,daily,economic,and environmental objectives.The studyfindings indicate that the system thermal inertia optimizes energy-system scheduling considering user satisfaction.This optimization mitigates environmental concerns and enhances clean-energy integration.展开更多
As a novel class of purely organic fluores-cent materials,multiple resonance thermal-ly activated delayed fluorescence(MR-TADF)compounds hold significant promise for next-generation display technologies.The efficiency...As a novel class of purely organic fluores-cent materials,multiple resonance thermal-ly activated delayed fluorescence(MR-TADF)compounds hold significant promise for next-generation display technologies.The efficiency of exciton utilization and the overall performance of organic light-emit-ting devices are closely linked to the singlet-triplet energy gap(ΔE_(ST))of MR-TADF emitters.Identifying an economic and accu-rate theoretical approach to predictΔE_(ST)would be beneficial for high-throughput screening and facilitate the inverse design of MR-TADF molecules.In this study,we evaluated the S_(1)state energy(E(S_(1))),T_(1)state ener-gy(E(T_(1))),andΔE_(ST)using three different physical interpretations:adiabatic excitation ener-gy,vertical absorption energy,and vertical emission energy.We employed the time-depen-dent density functional theory(TDDFT)and delta self-consistent field(ΔSCF)methods to calculate E(S_(1)),E(T_(1)),andΔE_(ST)for 20 MR-TADF molecules reported in the literature.We compared these calculated values with experimental data obtained from fluorescence spec-troscopy at room-temperature(or 77 K)and phosphorescence spectroscopy conducted at 77 K.Our findings indicate that the vertical absorption energy at the S0 state minimum,deter-mined by theΔSCF method,accurately predicts the S_(1)state energy.Similarly,the vertical absorption energy at the S0 state minimum,calculated using the TDDFT method,effectively predicts the T_(1)state energy.TheΔE_(ST)derived from the difference between these two excita-tion energies exhibited the smallest mean absolute error of only 0.039 eV compared to the ex-perimental values.This combination represents the most accurate and cost-effective method reported to date for predicting theΔE_(ST)of MR-TADF molecules,and can be integrated into AI-driven inverse design workflows for new emitters.展开更多
Objectives This review aimed to systematically synthesize the available research on the disclosure of diagnosis and related issues in childhood cancer from the perspectives of healthcare professionals,with the goal of...Objectives This review aimed to systematically synthesize the available research on the disclosure of diagnosis and related issues in childhood cancer from the perspectives of healthcare professionals,with the goal of informing the optimization of disclosure processes and meeting the communication needs of affected families.Methods In accordance with the Joanna Briggs Institute(JBI)methodology for mixed methods systematic reviews,the convergent segregated approach was used in this review.Articles were retrieved from 11 databases,including PubMed,Web of Science,CINAHL,CENTRAL,Embase,Ovid/Medline,PsycINFO,PsycArticles,Scopus,ERIC,and China National Knowledge Infrastructure(CNKI).The quality of the selected articles was assessed using the Mixed Method Appraisal Tool(MMAT).The review protocol was registered on PROSPERO(CRD42024542746).Results A total of 21 studies from 10 countries were included.Their methodological quality was generally medium to high,with MMAT scores ranging from 60%to 100%.The synthesis yielded three core themes:1)the spectrum of professional and societal attitudes toward disclosure;2)the dynamic practices of navigating disclosure amid uncertainty,including timing and environment,stakeholders,and content of disclosure;and 3)factors influencing disclosure,including children’s,parental,healthcare professionals’,and socio-cultural factors.Conclusions This review synthesized the perspectives and experiences of healthcare professionals regarding disclosure in childhood cancer,highlighting the complexity and multidimensional nature of this process in clinical practice.Future research should further investigate the experiences and needs of children and their parents,explore cultural variations in disclosure practices,develop context-appropriate assessment tools,and construct multidimensional intervention strategies to enhance the humanistic care and professional effectiveness of the disclosure process.展开更多
The Good Wife is an American TV series that focuses on women’s independence,politics,and law.The drama has been remade in China,Japan,and South Korea.This research aims to use Nida’s Functional Equivalence Theory to...The Good Wife is an American TV series that focuses on women’s independence,politics,and law.The drama has been remade in China,Japan,and South Korea.This research aims to use Nida’s Functional Equivalence Theory to analyze the methods of its English-to-Chinese subtitle translation by considering social,cultural,and historic backgrounds between China and America.After data collection and case analysis,the study found that:(1)Five major translation methods are adopted in the subtitle translation of The Good Wife.They are free translation,variation,literal translation,addition,and omission.Among them,free translation is the most frequently used,while omission is used least.(2)The subtitle translation of films and TV series is limited by time and space restrictions,social-cultural differences,and other factors.When translating,translators should try to use humorous words,euphemism,intonation,and other ways,and combine different methods such as literal translation,free translation,variation,addition,omission,and other methods to seek equivalence both in the meaning and function of subtitles under the guidance of Functional Equivalence Theory.展开更多
This mixed-methods study investigated how AI-enhanced English as a Foreign Language(EFL)learning environments influence students’psychological well-being through the mediating roles of motivation and language learnin...This mixed-methods study investigated how AI-enhanced English as a Foreign Language(EFL)learning environments influence students’psychological well-being through the mediating roles of motivation and language learning anxiety and the moderating role of trust.Participants were Chinese university students(N=310,62%female,mean age=18.9,SD=0.8),of whom 15 completed interviews to both add to and to clarify the evidence from the surveys.Structural equation modeling results revealed that AI use had significant indirect effects on well-being through increased motivation and reduced language learning anxiety.Trust in AI significantly moderated both paths,amplifying the motivational benefits and anxiety reduction associated with AI use.Thematic analysis supported these results,identifying three experiential themes:(1)motivational empowerment through personalization,(2)anxiety regulation through safe practice and feedback,and(3)trust as the emotional bridge between AI and well-being.The study extends AI psychology applications by empirically linking technology engagement with affective outcomes and underscores the need for human-centered and trust-enhancing design in AI-supported education.From these findings,we conclude that adaptive,transparent,and autonomy-supportive AI systems promote self-determined motivation,emotional safety,and overall psychological health among EFL learners.展开更多
The Thyristor-Controlled Series Compensator(TCSC)presents an effective solution for mitigating transmission congestion in power systems by regulating the distribution of line power flow.However,inherent faults within ...The Thyristor-Controlled Series Compensator(TCSC)presents an effective solution for mitigating transmission congestion in power systems by regulating the distribution of line power flow.However,inherent faults within the TCSC may lead to an unintended intensification of transmission congestion in other sections of the system post-installation,resulting in non-coherent phenomena of line blocking.In response to this challenge,this paper introduces a novel two-stage site selectionmethod for TCSC,emphasizing the enhancement of coherence in addressing line-blocking issues.Through rigorous non-coherent verification,this method mitigates the risk of line congestion deterioration due to TCSC faults.In the initial stage of the proposed method,TCSC faults are not considered during the extraction of system states.System state analysis is performed based on the TCSC site selection model,aiming to minimize system load reduction.The preliminary recommended installation position for TCSC is determined by sorting the frequency of TCSC installation occurrences on lines extracted from the analyzed system states.In the subsequent stage,accounting for the influence of TCSC faults on line faults,system operating states are extracted.Line and system congestion indices are calculated through the statistical analysis of the system state analysis results.The installation of TCSC at the preliminary position is scrutinized to identify non-coherent phenomena of line congestion on other lines.If such phenomena are observed,the installation position is excluded,and the TCSC site selection process is reinitiated based on the methodology from the first stage.To validate the effectiveness of the proposed method,a case study is conducted on a modified RBTS test system.The case study results indicate that,compared with TCSC siting schemes that do not consider transmission congestion non-coherency,the proposed non-coherency-based siting scheme reduces the system congestion expectation(SCE)and system congestion probability(SCP)by 17.7%and 11.4%,respectively,while lowering the LOLP and EENS by 2.56% and 4.55%,respectively.These results demonstrate that the proposed method can effectively alleviate transmission congestion and enhance the overall reliability of the system.展开更多
Geological prospecting and the identification of adverse geological features are essential in tunnel construction,providing critical information to ensure safety and guide engineering decisions.As tunnel projects exte...Geological prospecting and the identification of adverse geological features are essential in tunnel construction,providing critical information to ensure safety and guide engineering decisions.As tunnel projects extend into deeper and more mountainous terrains,engineers face increasingly complex geological conditions,including high water pressure,intense geo-stress,elevated geothermal gradients,and active fault zones.These conditions pose substantial risks such as high-pressure water inrush,largescale collapses,and tunnel boring machine(TBM)blockages.Addressing these challenges requires advanced detection technologies capable of long-distance,high-precision,and intelligent assessments of adverse geology.This paper presents a comprehensive review of recent advancements in tunnel geological ahead prospecting methods.It summarizes the fundamental principles,technical maturity,key challenges,development trends,and real-world applications of various detection techniques.Airborne and semi-airborne geophysical methods enable large-scale reconnaissance for initial surveys in complex terrain.Tunnel-and borehole-based approaches offer high-resolution detection during excavation,including seismic ahead prospecting(SAP),TBM rock-breaking source seismic methods,fulltime-domain tunnel induced polarization(TIP),borehole electrical resistivity,and ground penetrating radar(GPR).To address scenarios involving multiple,coexisting adverse geologies,intelligent inversion and geological identification methods have been developed based on multi-source data fusion and artificial intelligence(AI)techniques.Overall,these advances significantly improve detection range,resolution,and geological characterization capabilities.The methods demonstrate strong adaptability to complex environments and provide reliable subsurface information,supporting safer and more efficient tunnel construction.展开更多
In this paper we discuss two-stage Miistein methods for solving Ito stochastic differential equations （SDEs）. Six fully explicit methods （TSM 1 -- TSM 6） are given in this paper. Their order of strong convergence ...In this paper we discuss two-stage Miistein methods for solving Ito stochastic differential equations （SDEs）. Six fully explicit methods （TSM 1 -- TSM 6） are given in this paper. Their order of strong convergence is proved. The stability properties and numerical results show the effectiveness of these methods in the pathwise approximation of Ito SDEs.展开更多
We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training ph...We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.展开更多
Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters accordi...Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.展开更多
Since the connection of small-scale wind farms to distribution networks,power grid voltage stability has been reduced with increasing wind penetration in recent years,owing to the variable reactive power consumption o...Since the connection of small-scale wind farms to distribution networks,power grid voltage stability has been reduced with increasing wind penetration in recent years,owing to the variable reactive power consumption of wind generators.In this study,a two-stage reactive power optimization method based on the alternating direction method of multipliers(ADMM)algorithm is proposed for achieving optimal reactive power dispatch in wind farm-integrated distribution systems.Unlike existing optimal reactive power control methods,the proposed method enables distributed reactive power flow optimization with a two-stage optimization structure.Furthermore,under the partition concept,the consensus protocol is not needed to solve the optimization problems.In this method,the influence of the wake effect of each wind turbine is also considered in the control design.Simulation results for a mid-voltage distribution system based on MATLAB verified the effectiveness of the proposed method.展开更多
Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream.To improve the reliability and safety of the oil and gas pipeline network, inspections are implem...Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream.To improve the reliability and safety of the oil and gas pipeline network, inspections are implemented to minimize the risk of leakage, spill and theft, as well as documenting actual incidents. In recent years, unmanned aerial vehicles have been recognized as a promising option for inspection due to their high efficiency. However, the integrated optimization of unmanned aerial vehicle inspection for oil and gas pipeline networks, including physical feasibility, the performance of mission, cooperation, real-time implementation and three-dimensional(3-D) space, is a strategic problem due to its large-scale,complexity as well as the need for efficiency. In this work, a novel mixed-integer nonlinear programming model is proposed that takes into account the constraints of the mission scenario and the safety performance of unmanned aerial vehicles. To minimize the total length of the inspection path, the model is solved by a two-stage solution method. Finally, a virtual pipeline network and a practical pipeline network are set as two examples to demonstrate the performance of the optimization schemes. Moreover, compared with the traditional genetic algorithm and simulated annealing algorithm, the self-adaptive genetic simulated annealing algorithm proposed in this paper provides strong stability.展开更多
Two-stage underground coal gasification was studied to improve the caloric value of the syngas and to extend gas production times.A model test using the oxygen-enriched two-stage coal gasification method was carried o...Two-stage underground coal gasification was studied to improve the caloric value of the syngas and to extend gas production times.A model test using the oxygen-enriched two-stage coal gasification method was carried out.The composition of the gas produced,the time ratio of the two stages,and the role of the temperature field were analysed.The results show that oxygen-enriched two-stage gasification shortens the time of the first stage and prolongs the time of the second stage.Feed oxygen concentrations of 30%, 35%,40%,45%.60%,or 80%gave time ratios（first stage to second stage） of 1：0.12,1：0.21.1：0.51,1：0.64, 1：0.90.and 1：4.0 respectively.Cooling rates of the temperature field after steam injection decreased with time from about 19.1-27.4℃/min to 2.3-6.8℃/min.But this rate increased with increasing oxygen concentrations in the first stage.The caloric value of the syngas improves with increased oxygen concentration in the first stage.Injection of 80%oxygen-enriched air gave gas with the highest caloric value and also gave the longest production time.The caloric value of the gas obtained from the oxygenenriched two-stage gasification method lies in the range from 5.31 MJ/Nm^3 to 10.54 MJ/Nm^3.展开更多
Exploring the factors driving the decoupling of China’s sulfur dioxide(SO_(2))emissions from economic growth(DEI)is crucial for achieving sustainable development.By analyzing the decoupling indicators and driving fac...Exploring the factors driving the decoupling of China’s sulfur dioxide(SO_(2))emissions from economic growth(DEI)is crucial for achieving sustainable development.By analyzing the decoupling indicators and driving factors at both the generation and treatment stages of SO_(2),more effective targeted mitigation strategies can be developed.We employ the Tapio decoupling model and propose a two-stage method to examine the decoupling issues related to SO_(2).Our findings indicate that:①DEI shows a steady and significant improvement,with SO_(2)emission intensity identified as the primary driver.②for the decoupling of economic growth and SO_(2)generation,energy scale serves as the largest stimulator,while the effect of energy intensity changes from negative to positive,and pollution intensity is first positive and then negative.③For the decoupling of SO_(2)generation and SO_(2)removal,treatment efficiency leads as the largest promoter,followed by treatment intensity.Based on these results,this study recommends that China focuses more on enhancing clean energy utilization and the effectiveness of treatment processes.展开更多
In this paper, period-doubling bifurcation in a two-stage power factor correction converter is analyzed by using the method of incremental harmonic balance （IHB） and Floquet theory. A two-stage power factor correcti...In this paper, period-doubling bifurcation in a two-stage power factor correction converter is analyzed by using the method of incremental harmonic balance （IHB） and Floquet theory. A two-stage power factor correction converter typically employs a cascade configuration of a pre-regulator boost power factor correction converter with average current mode control to achieve a near unity power factor and a tightly regulated post-regulator DC-DC Buck converter with voltage feedback control to regulate the output voltage. Based on the assumption that the tightly regulated postregulator DC-DC Buck converter is represented as a constant power sink and some other assumptions, the simplified model of the two-stage power factor correction converter is derived and its approximate periodic solution is calculated by the method of IHB. And then, the stability of the system is investigated by using Floquet theory and the stable boundaries are presented on the selected parameter spaces. Finally, some experimental results are given to confirm the effectiveness of the theoretical analysis.展开更多
A new unified constitutive model was developed to predict the two-stage creep-aging(TSCA)behavior of Al-Zn-Mg-Cu alloys.The particular bimodal precipitation feature was analyzed and modeled by considering the primary ...A new unified constitutive model was developed to predict the two-stage creep-aging(TSCA)behavior of Al-Zn-Mg-Cu alloys.The particular bimodal precipitation feature was analyzed and modeled by considering the primary micro-variables evolution at different temperatures and their interaction.The dislocation density was incorporated into the model to capture the effect of creep deformation on precipitation.Quantitative transmission electron microscopy and experimental data obtained from a previous study were used to calibrate the model.Subsequently,the developed constitutive model was implemented in the finite element(FE)software ABAQUS via the user subroutines for TSCA process simulation and the springback prediction of an integral panel.A TSCA test was performed.The result shows that the maximum radius deviation between the formed plate and the simulation results is less than 0.4 mm,thus validating the effectiveness of the developed constitutive model and FE model.展开更多
A two-stage hybrid method is proposed to predict the phosphorus content of molten steel at the endpoint of steelmaking in BOF(Basic Oxygen Furnace). At the first clustering stage, the weighted K-means is performed to ...A two-stage hybrid method is proposed to predict the phosphorus content of molten steel at the endpoint of steelmaking in BOF(Basic Oxygen Furnace). At the first clustering stage, the weighted K-means is performed to produce clusters with homogeneous data. At the second predicting stage, each fuzzy neural network is carried out on each cluster and the results from all fuzzy neural networks are combined to be the final result of the hybrid method. The hybrid method and single fuzzy neural network are compared and the results show that the hybrid method outperforms single fuzzy neural network.展开更多
Based on the evaluation of dynamic performance for feed drives in machine tools, this paper presents a two-stage tuning method of servo parameters. In the first stage, the evaluation of dynamic performance, parameter ...Based on the evaluation of dynamic performance for feed drives in machine tools, this paper presents a two-stage tuning method of servo parameters. In the first stage, the evaluation of dynamic performance, parameter tuning and optimization on a mechatronic integrated system simulation platform of feed drives are performed. As a result, a servo parameter combination is acquired. In the second stage, the servo parameter combination from the first stage is set and tuned further in a real machine tool whose dynamic performance is measured and evaluated using the cross grid encoder developed by Heidenhain GmbH. A case study shows that this method simplifies the test process effectively and results in a good dynamic performance in a real machine tool.展开更多
As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and s...As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and smart maintenance.While promising,both methods have issues that need to be addressed.For example,model-based methods are limited by low computational accuracy and a high computational burden,and data-driven methods always suffer from poor interpretability and redundant features.To address these issues,the concept of data-model fusion(DMF)emerges as a promising solution.DMF involves integrating model-based methods with data-driven methods by incorporating big data into model-based methods or embedding relevant domain knowledge into data-driven methods.Despite growing efforts in the field of DMF,a unanimous definition of DMF remains elusive,and a general framework of DMF has been rarely discussed.This paper aims to address this gap by providing a thorough overview and categorization of both data-driven methods and model-based methods.Subsequently,this paper also presents the definition and categorization of DMF and discusses the general framework of DMF.Moreover,the primary seven applications of DMF are reviewed within the context of smart manufacturing and digital engineering.Finally,this paper directs the future directions of DMF.展开更多
基金supported by the GRRC program of Gyeonggi province[GRRC KGU 2023-B01,Research on Intelligent Industrial Data Analytics].
文摘Predictive maintenance(PdM)is vital for ensuring the reliability,safety,and cost efficiency of heavyduty vehicle fleets.However,real-world sensor data are often highly imbalanced,noisy,and temporally irregular,posing significant challenges to model robustness and deployment.Using multivariate time-series data from Scania trucks,this study proposes a novel PdM framework that integrates efficient feature summarization with cost-sensitive hierarchical classification.First,the proposed last_k_summary method transforms recent operational records into compact statistical and trend-based descriptors while preserving missingness,allowing LightGBM to leverage its inherent split rules without ad-hoc imputation.Then,a two-stage LightGBM framework is developed for fault detection and severity classification:Stage A performs safety-prioritized fault screening(normal vs.fault)with a false-negativeweighted objective,and Stage B refines the detected faults into four severity levels through a cascaded hierarchy of binary classifiers.Under the official cost matrix of the IDA Industrial Challenge,the framework achieves total misclassification costs of 36,113(validation)and 36,314(test),outperforming XGBoost and Bi-LSTM by 3.8%-13.5%while maintaining high recall for the safety-critical class(0.83 validation,0.77 test).These results demonstrate that the proposed approach not only improves predictive accuracy but also provides a practical and deployable PdM solution that reduces maintenance cost,enhances fleet safety,and supports data-driven decision-making in industrial environments.
基金supported by the science and technology foundation of Guizhou province[2022]general 013the science and technology foundation of Guizhou province[2022]general 014+1 种基金the science and technology foundation of Guizhou province GCC[2022]016-1the educational technology foundation of Guizhou province[2022]043.
文摘Integrated-energy systems(IESs)are key to advancing renewable-energy utilization and addressing environmental challenges.Key components of IESs include low-carbon,economic dispatch and demand response,for maximizing renewable-energy consumption and supporting sustainable-energy systems.User participation is central to demand response;however,many users are not inclined to engage actively;therefore,the full potential of demand response remains unrealized.User satisfaction must be prioritized in demand-response assessments.This study proposed a two-stage,capacity-optimization configuration method for user-level energy systems con-sidering thermal inertia and user satisfaction.This method addresses load coordination and complementary issues within the IES and seeks to minimize the annual,total cost for determining equipment capacity configurations while introducing models for system thermal inertia and user satisfaction.Indoor heating is adjusted,for optimizing device output and load profiles,with a focus on typical,daily,economic,and environmental objectives.The studyfindings indicate that the system thermal inertia optimizes energy-system scheduling considering user satisfaction.This optimization mitigates environmental concerns and enhances clean-energy integration.
基金support provided by the National Natural Science Foundation of China(No.22273043).
文摘As a novel class of purely organic fluores-cent materials,multiple resonance thermal-ly activated delayed fluorescence(MR-TADF)compounds hold significant promise for next-generation display technologies.The efficiency of exciton utilization and the overall performance of organic light-emit-ting devices are closely linked to the singlet-triplet energy gap(ΔE_(ST))of MR-TADF emitters.Identifying an economic and accu-rate theoretical approach to predictΔE_(ST)would be beneficial for high-throughput screening and facilitate the inverse design of MR-TADF molecules.In this study,we evaluated the S_(1)state energy(E(S_(1))),T_(1)state ener-gy(E(T_(1))),andΔE_(ST)using three different physical interpretations:adiabatic excitation ener-gy,vertical absorption energy,and vertical emission energy.We employed the time-depen-dent density functional theory(TDDFT)and delta self-consistent field(ΔSCF)methods to calculate E(S_(1)),E(T_(1)),andΔE_(ST)for 20 MR-TADF molecules reported in the literature.We compared these calculated values with experimental data obtained from fluorescence spec-troscopy at room-temperature(or 77 K)and phosphorescence spectroscopy conducted at 77 K.Our findings indicate that the vertical absorption energy at the S0 state minimum,deter-mined by theΔSCF method,accurately predicts the S_(1)state energy.Similarly,the vertical absorption energy at the S0 state minimum,calculated using the TDDFT method,effectively predicts the T_(1)state energy.TheΔE_(ST)derived from the difference between these two excita-tion energies exhibited the smallest mean absolute error of only 0.039 eV compared to the ex-perimental values.This combination represents the most accurate and cost-effective method reported to date for predicting theΔE_(ST)of MR-TADF molecules,and can be integrated into AI-driven inverse design workflows for new emitters.
基金supported by the Fuxing Nursing Research Foundation of Fudan University[FNF202352].
文摘Objectives This review aimed to systematically synthesize the available research on the disclosure of diagnosis and related issues in childhood cancer from the perspectives of healthcare professionals,with the goal of informing the optimization of disclosure processes and meeting the communication needs of affected families.Methods In accordance with the Joanna Briggs Institute(JBI)methodology for mixed methods systematic reviews,the convergent segregated approach was used in this review.Articles were retrieved from 11 databases,including PubMed,Web of Science,CINAHL,CENTRAL,Embase,Ovid/Medline,PsycINFO,PsycArticles,Scopus,ERIC,and China National Knowledge Infrastructure(CNKI).The quality of the selected articles was assessed using the Mixed Method Appraisal Tool(MMAT).The review protocol was registered on PROSPERO(CRD42024542746).Results A total of 21 studies from 10 countries were included.Their methodological quality was generally medium to high,with MMAT scores ranging from 60%to 100%.The synthesis yielded three core themes:1)the spectrum of professional and societal attitudes toward disclosure;2)the dynamic practices of navigating disclosure amid uncertainty,including timing and environment,stakeholders,and content of disclosure;and 3)factors influencing disclosure,including children’s,parental,healthcare professionals’,and socio-cultural factors.Conclusions This review synthesized the perspectives and experiences of healthcare professionals regarding disclosure in childhood cancer,highlighting the complexity and multidimensional nature of this process in clinical practice.Future research should further investigate the experiences and needs of children and their parents,explore cultural variations in disclosure practices,develop context-appropriate assessment tools,and construct multidimensional intervention strategies to enhance the humanistic care and professional effectiveness of the disclosure process.
文摘The Good Wife is an American TV series that focuses on women’s independence,politics,and law.The drama has been remade in China,Japan,and South Korea.This research aims to use Nida’s Functional Equivalence Theory to analyze the methods of its English-to-Chinese subtitle translation by considering social,cultural,and historic backgrounds between China and America.After data collection and case analysis,the study found that:(1)Five major translation methods are adopted in the subtitle translation of The Good Wife.They are free translation,variation,literal translation,addition,and omission.Among them,free translation is the most frequently used,while omission is used least.(2)The subtitle translation of films and TV series is limited by time and space restrictions,social-cultural differences,and other factors.When translating,translators should try to use humorous words,euphemism,intonation,and other ways,and combine different methods such as literal translation,free translation,variation,addition,omission,and other methods to seek equivalence both in the meaning and function of subtitles under the guidance of Functional Equivalence Theory.
文摘This mixed-methods study investigated how AI-enhanced English as a Foreign Language(EFL)learning environments influence students’psychological well-being through the mediating roles of motivation and language learning anxiety and the moderating role of trust.Participants were Chinese university students(N=310,62%female,mean age=18.9,SD=0.8),of whom 15 completed interviews to both add to and to clarify the evidence from the surveys.Structural equation modeling results revealed that AI use had significant indirect effects on well-being through increased motivation and reduced language learning anxiety.Trust in AI significantly moderated both paths,amplifying the motivational benefits and anxiety reduction associated with AI use.Thematic analysis supported these results,identifying three experiential themes:(1)motivational empowerment through personalization,(2)anxiety regulation through safe practice and feedback,and(3)trust as the emotional bridge between AI and well-being.The study extends AI psychology applications by empirically linking technology engagement with affective outcomes and underscores the need for human-centered and trust-enhancing design in AI-supported education.From these findings,we conclude that adaptive,transparent,and autonomy-supportive AI systems promote self-determined motivation,emotional safety,and overall psychological health among EFL learners.
基金supported by Graduate Research and Innovation Program Project of Nanjing Institute of Technology(No.TB202517078).
文摘The Thyristor-Controlled Series Compensator(TCSC)presents an effective solution for mitigating transmission congestion in power systems by regulating the distribution of line power flow.However,inherent faults within the TCSC may lead to an unintended intensification of transmission congestion in other sections of the system post-installation,resulting in non-coherent phenomena of line blocking.In response to this challenge,this paper introduces a novel two-stage site selectionmethod for TCSC,emphasizing the enhancement of coherence in addressing line-blocking issues.Through rigorous non-coherent verification,this method mitigates the risk of line congestion deterioration due to TCSC faults.In the initial stage of the proposed method,TCSC faults are not considered during the extraction of system states.System state analysis is performed based on the TCSC site selection model,aiming to minimize system load reduction.The preliminary recommended installation position for TCSC is determined by sorting the frequency of TCSC installation occurrences on lines extracted from the analyzed system states.In the subsequent stage,accounting for the influence of TCSC faults on line faults,system operating states are extracted.Line and system congestion indices are calculated through the statistical analysis of the system state analysis results.The installation of TCSC at the preliminary position is scrutinized to identify non-coherent phenomena of line congestion on other lines.If such phenomena are observed,the installation position is excluded,and the TCSC site selection process is reinitiated based on the methodology from the first stage.To validate the effectiveness of the proposed method,a case study is conducted on a modified RBTS test system.The case study results indicate that,compared with TCSC siting schemes that do not consider transmission congestion non-coherency,the proposed non-coherency-based siting scheme reduces the system congestion expectation(SCE)and system congestion probability(SCP)by 17.7%and 11.4%,respectively,while lowering the LOLP and EENS by 2.56% and 4.55%,respectively.These results demonstrate that the proposed method can effectively alleviate transmission congestion and enhance the overall reliability of the system.
基金supported by the National Natural Science Foundation of China(Grant Nos.52021005,52325904,and 51991391)。
文摘Geological prospecting and the identification of adverse geological features are essential in tunnel construction,providing critical information to ensure safety and guide engineering decisions.As tunnel projects extend into deeper and more mountainous terrains,engineers face increasingly complex geological conditions,including high water pressure,intense geo-stress,elevated geothermal gradients,and active fault zones.These conditions pose substantial risks such as high-pressure water inrush,largescale collapses,and tunnel boring machine(TBM)blockages.Addressing these challenges requires advanced detection technologies capable of long-distance,high-precision,and intelligent assessments of adverse geology.This paper presents a comprehensive review of recent advancements in tunnel geological ahead prospecting methods.It summarizes the fundamental principles,technical maturity,key challenges,development trends,and real-world applications of various detection techniques.Airborne and semi-airborne geophysical methods enable large-scale reconnaissance for initial surveys in complex terrain.Tunnel-and borehole-based approaches offer high-resolution detection during excavation,including seismic ahead prospecting(SAP),TBM rock-breaking source seismic methods,fulltime-domain tunnel induced polarization(TIP),borehole electrical resistivity,and ground penetrating radar(GPR).To address scenarios involving multiple,coexisting adverse geologies,intelligent inversion and geological identification methods have been developed based on multi-source data fusion and artificial intelligence(AI)techniques.Overall,these advances significantly improve detection range,resolution,and geological characterization capabilities.The methods demonstrate strong adaptability to complex environments and provide reliable subsurface information,supporting safer and more efficient tunnel construction.
文摘In this paper we discuss two-stage Miistein methods for solving Ito stochastic differential equations （SDEs）. Six fully explicit methods （TSM 1 -- TSM 6） are given in this paper. Their order of strong convergence is proved. The stability properties and numerical results show the effectiveness of these methods in the pathwise approximation of Ito SDEs.
基金supported by the Natural Science Research Project of Colleges and Universities in Anhui Province (No.KJ2021A0479)the Science Research Program of Anhui University of Finance and Economics (No.ACKYC22082)。
文摘We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.
基金supported by the Innovation Foundation of Provincial Education Department of Gansu(2024B-005)the Gansu Province National Science Foundation(22YF7GA182)the Fundamental Research Funds for the Central Universities(No.lzujbky2022-kb01)。
文摘Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.
基金support of The National Key Research and Development Program of China(Basic Research Class)(No.2017YFB0903000)the National Natural Science Foundation of China(No.U1909201)。
文摘Since the connection of small-scale wind farms to distribution networks,power grid voltage stability has been reduced with increasing wind penetration in recent years,owing to the variable reactive power consumption of wind generators.In this study,a two-stage reactive power optimization method based on the alternating direction method of multipliers(ADMM)algorithm is proposed for achieving optimal reactive power dispatch in wind farm-integrated distribution systems.Unlike existing optimal reactive power control methods,the proposed method enables distributed reactive power flow optimization with a two-stage optimization structure.Furthermore,under the partition concept,the consensus protocol is not needed to solve the optimization problems.In this method,the influence of the wake effect of each wind turbine is also considered in the control design.Simulation results for a mid-voltage distribution system based on MATLAB verified the effectiveness of the proposed method.
基金part of the Program of "Study on Optimization and Supply-side Reliability of Oil Product Supply Chain Logistics System" funded under the National Natural Science Foundation of China, Grant Number 51874325
文摘Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream.To improve the reliability and safety of the oil and gas pipeline network, inspections are implemented to minimize the risk of leakage, spill and theft, as well as documenting actual incidents. In recent years, unmanned aerial vehicles have been recognized as a promising option for inspection due to their high efficiency. However, the integrated optimization of unmanned aerial vehicle inspection for oil and gas pipeline networks, including physical feasibility, the performance of mission, cooperation, real-time implementation and three-dimensional(3-D) space, is a strategic problem due to its large-scale,complexity as well as the need for efficiency. In this work, a novel mixed-integer nonlinear programming model is proposed that takes into account the constraints of the mission scenario and the safety performance of unmanned aerial vehicles. To minimize the total length of the inspection path, the model is solved by a two-stage solution method. Finally, a virtual pipeline network and a practical pipeline network are set as two examples to demonstrate the performance of the optimization schemes. Moreover, compared with the traditional genetic algorithm and simulated annealing algorithm, the self-adaptive genetic simulated annealing algorithm proposed in this paper provides strong stability.
基金financial support of the National Natural Science Foundation of China(No.50876112)the Fundamental Research Funds for the Central Universities (No.2009QH13)the Program of International S&T Cooperation (No.2009DFR60180,No.2010DFR60610)
文摘Two-stage underground coal gasification was studied to improve the caloric value of the syngas and to extend gas production times.A model test using the oxygen-enriched two-stage coal gasification method was carried out.The composition of the gas produced,the time ratio of the two stages,and the role of the temperature field were analysed.The results show that oxygen-enriched two-stage gasification shortens the time of the first stage and prolongs the time of the second stage.Feed oxygen concentrations of 30%, 35%,40%,45%.60%,or 80%gave time ratios（first stage to second stage） of 1：0.12,1：0.21.1：0.51,1：0.64, 1：0.90.and 1：4.0 respectively.Cooling rates of the temperature field after steam injection decreased with time from about 19.1-27.4℃/min to 2.3-6.8℃/min.But this rate increased with increasing oxygen concentrations in the first stage.The caloric value of the syngas improves with increased oxygen concentration in the first stage.Injection of 80%oxygen-enriched air gave gas with the highest caloric value and also gave the longest production time.The caloric value of the gas obtained from the oxygenenriched two-stage gasification method lies in the range from 5.31 MJ/Nm^3 to 10.54 MJ/Nm^3.
基金the National Natural Science Foundation of China[Grant No.52270183].
文摘Exploring the factors driving the decoupling of China’s sulfur dioxide(SO_(2))emissions from economic growth(DEI)is crucial for achieving sustainable development.By analyzing the decoupling indicators and driving factors at both the generation and treatment stages of SO_(2),more effective targeted mitigation strategies can be developed.We employ the Tapio decoupling model and propose a two-stage method to examine the decoupling issues related to SO_(2).Our findings indicate that:①DEI shows a steady and significant improvement,with SO_(2)emission intensity identified as the primary driver.②for the decoupling of economic growth and SO_(2)generation,energy scale serves as the largest stimulator,while the effect of energy intensity changes from negative to positive,and pollution intensity is first positive and then negative.③For the decoupling of SO_(2)generation and SO_(2)removal,treatment efficiency leads as the largest promoter,followed by treatment intensity.Based on these results,this study recommends that China focuses more on enhancing clean energy utilization and the effectiveness of treatment processes.
基金supported by the National Natural Science Foundation of China (Grant No.51007068)the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No.20100201120028)+1 种基金the Fundamental Research Funds for the Central Universities of Chinathe State Key Laboratory of Electrical Insulation and Power Equipment of China (Grant No.EIPE10303)
文摘In this paper, period-doubling bifurcation in a two-stage power factor correction converter is analyzed by using the method of incremental harmonic balance （IHB） and Floquet theory. A two-stage power factor correction converter typically employs a cascade configuration of a pre-regulator boost power factor correction converter with average current mode control to achieve a near unity power factor and a tightly regulated post-regulator DC-DC Buck converter with voltage feedback control to regulate the output voltage. Based on the assumption that the tightly regulated postregulator DC-DC Buck converter is represented as a constant power sink and some other assumptions, the simplified model of the two-stage power factor correction converter is derived and its approximate periodic solution is calculated by the method of IHB. And then, the stability of the system is investigated by using Floquet theory and the stable boundaries are presented on the selected parameter spaces. Finally, some experimental results are given to confirm the effectiveness of the theoretical analysis.
基金supported by the National Key R&D Program of China(No.2021YFB3400900)the National Natural Science Foundation of China(Nos.52175373,52205435)+1 种基金Natural Science Foundation of Hunan Province,China(No.2022JJ40621)the Innovation Fund of National Commercial Aircraft Manufacturing Engineering Technology Center,China(No.COMACSFGS-2022-1875)。
文摘A new unified constitutive model was developed to predict the two-stage creep-aging(TSCA)behavior of Al-Zn-Mg-Cu alloys.The particular bimodal precipitation feature was analyzed and modeled by considering the primary micro-variables evolution at different temperatures and their interaction.The dislocation density was incorporated into the model to capture the effect of creep deformation on precipitation.Quantitative transmission electron microscopy and experimental data obtained from a previous study were used to calibrate the model.Subsequently,the developed constitutive model was implemented in the finite element(FE)software ABAQUS via the user subroutines for TSCA process simulation and the springback prediction of an integral panel.A TSCA test was performed.The result shows that the maximum radius deviation between the formed plate and the simulation results is less than 0.4 mm,thus validating the effectiveness of the developed constitutive model and FE model.
基金Item Sponsored by Beijing Higher Education Young Elite Teacher Project(YETP0382)2012 Ladder Plan Project of Beijing Key Laboratory of Knowledge Engineering for Materials Science of China(Z121101002812005)
文摘A two-stage hybrid method is proposed to predict the phosphorus content of molten steel at the endpoint of steelmaking in BOF(Basic Oxygen Furnace). At the first clustering stage, the weighted K-means is performed to produce clusters with homogeneous data. At the second predicting stage, each fuzzy neural network is carried out on each cluster and the results from all fuzzy neural networks are combined to be the final result of the hybrid method. The hybrid method and single fuzzy neural network are compared and the results show that the hybrid method outperforms single fuzzy neural network.
基金This paper is supported by the Major State Basic Research Development Program of China under Grant No2005CB724101the Key Items Program of International Science and Technology Cooperation of China under Grant No2003DF000021
文摘Based on the evaluation of dynamic performance for feed drives in machine tools, this paper presents a two-stage tuning method of servo parameters. In the first stage, the evaluation of dynamic performance, parameter tuning and optimization on a mechatronic integrated system simulation platform of feed drives are performed. As a result, a servo parameter combination is acquired. In the second stage, the servo parameter combination from the first stage is set and tuned further in a real machine tool whose dynamic performance is measured and evaluated using the cross grid encoder developed by Heidenhain GmbH. A case study shows that this method simplifies the test process effectively and results in a good dynamic performance in a real machine tool.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grants(52275471 and 52120105008)the Beijing Outstanding Young Scientist Program,and the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and smart maintenance.While promising,both methods have issues that need to be addressed.For example,model-based methods are limited by low computational accuracy and a high computational burden,and data-driven methods always suffer from poor interpretability and redundant features.To address these issues,the concept of data-model fusion(DMF)emerges as a promising solution.DMF involves integrating model-based methods with data-driven methods by incorporating big data into model-based methods or embedding relevant domain knowledge into data-driven methods.Despite growing efforts in the field of DMF,a unanimous definition of DMF remains elusive,and a general framework of DMF has been rarely discussed.This paper aims to address this gap by providing a thorough overview and categorization of both data-driven methods and model-based methods.Subsequently,this paper also presents the definition and categorization of DMF and discusses the general framework of DMF.Moreover,the primary seven applications of DMF are reviewed within the context of smart manufacturing and digital engineering.Finally,this paper directs the future directions of DMF.
