In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and cha...In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.展开更多
Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables...Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.展开更多
With the intelligent transformation of process manufacturing,accurate and comprehensive perception information is fundamental for application of artificial intelligence methods.In zinc smelting,the fluidized bed roast...With the intelligent transformation of process manufacturing,accurate and comprehensive perception information is fundamental for application of artificial intelligence methods.In zinc smelting,the fluidized bed roaster is a key piece of large-scale equipment and plays a critical role in the manufacturing industry;its internal temperature field directly determines the quality of zinc calcine and other related products.However,due to its vast spatial dimensions,the limited observation methods,and the complex multiphase,multifield coupled reaction atmosphere inside it,accurately and timely perceiving its temperature field remains a significant challenge.To address these challenges,a spatial-temporal reduced-order model(STROM)is proposed,which can realize fast and accurate temperature field perception based on sparse observation data.Specifically,to address the difficulty in matching the initial physical field with the sparse observation data,an initial field construction based on data assimilation(IFCDA)method is proposed to ensure that the initial conditions of the model can be matched with the actual operation state,which provides a basis for constructing a high-precision computational fluid dynamics(CFD)model.Then,to address the high simulation cost of high-precision CFD models under full working conditions,a high uniformity(HU)-orthogonal test design(OTD)method with the centered L2 deviation is innovatively proposed to ensure high information coverage of the temperature field dataset under typical working conditions in terms of multiple factors and levels of the component,feed,and blast parameters.Finally,to address the difficulty in real-time and accurate temperature field prediction,considering the spatial correlation between the observed temperature and the temperature field,as well as the dynamic correlation of the observed temperature in the time dimension,a spatial-temporal predictive model(STPM)is established,which realizes rapid prediction of the temperature field through sparse observa-tion data.To verify the accuracy and validity of the proposed method,CFD model validation and reduced-order model prediction experiments are designed,and the results show that the proposed method can realize high-precision and fast prediction of the roaster temperature field under different working conditions through sparse observation data.Compared with the CFD model,the prediction root-mean-square error(RMSE)of STROM is less than 0.038,and the computational efficiency is improved by 3.4184×10^(4)times.In particular,STROM also has a good prediction ability for unmodeled conditions,with a prediction RMSE of less than 0.1089.展开更多
Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surround...Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.展开更多
As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limite...As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.展开更多
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
The spatial distribution and seasonal variation of the tide-induced Lagrangian Residual Circulations （LRC hereafter）, wind-driven LRC, and the coupling dynamic characteristics were simulated using ECOM, given the He...The spatial distribution and seasonal variation of the tide-induced Lagrangian Residual Circulations （LRC hereafter）, wind-driven LRC, and the coupling dynamic characteristics were simulated using ECOM, given the Hellerman and Rosenstein global monthly-mean wind stresses. The results showed that the tide-induced LRC of the harmonic constituent M2 bears an identical pattern in four seasons in the Bohai Sea： the surface one is weak with random directions; however, there exist a southeast current from the Bohai Strait to the Laizhou bay, and a weakly anticlockwise gyre in the south of the Bohai Strait for the bottom layer LRC. The magnitude of bottom layer tide-induced LRC is larger than the surface one, and moreover, it contributes significantly to the whole LRC in the Bohai Sea. Unlike the identical structure of the tide-induced LRC, the wind driven LRC varies seasonally under the prevailing monsoon. It forms a distinct gyre under the summer and winter monsoons in July and January respectively, but it seems weak and non-directional in April and September.展开更多
With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration wi...With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.展开更多
The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most exi...The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.展开更多
As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
The spatial interaction model is an effective way to explore the geographical disparities inherent in the Belt and Road Initiative(BRI) by simulating spatial flows. The traditional gravity model implies the hypothesis...The spatial interaction model is an effective way to explore the geographical disparities inherent in the Belt and Road Initiative(BRI) by simulating spatial flows. The traditional gravity model implies the hypothesis of equilibrium points without any reference to when or how to achieve it. In this paper, a dynamic gravity model was established based on the Maximum Entropy(MaxEnt) theory to estimate and monitor the interconnection intensity and dynamic characters of bilateral relations. In order to detect the determinants of interconnection intensity, a Geodetector method was applied to identify and evaluate the determinants of spatial networks in five dimensions. The empirical study clearly demonstrates a heterogeneous and non-circular spatial structure. The main driving forces of spatial-temporal evolution are foreign direct investment, tourism and railway infrastructure construction, while determinants in different sub-regions show obvious spatial differentiation. Southeast Asian countries are typically multi-island area where aviation infrastructure plays a more important role. North and Central Asian countries regard oil as a pillar industry where power and port facilities have a greater impact on the interconnection. While Western Asian countries are mostly influenced by the railway infrastructure, Eastern European countries already have relatively robust infrastructure where tariff policies provide a greater impetus.展开更多
Using a dynamic laser monitoring technique,the solubility of 3-nitro-1,2,4-triazole-5-one(NTO)was investigated in two different binary systems,namely hydroxylamine nitrate(HAN)-water and boric acid(HB)-water ranging f...Using a dynamic laser monitoring technique,the solubility of 3-nitro-1,2,4-triazole-5-one(NTO)was investigated in two different binary systems,namely hydroxylamine nitrate(HAN)-water and boric acid(HB)-water ranging from 278.15 K to 318.15 K.The solubility in each system was found to be positively correlated with temperature.Furthermore,solubility data were analyzed using four equations:the modified Apelblat equation,Van’t Hoff equation,λh equation and CNIBS/R-K equations,and they provided satisfactory results for both two systems.The average root-mean-square deviation(105RMSD)values for these models were less than 13.93.Calculations utilizing the Van’t Hoff equation and Gibbs equations facilitated the derivation of apparent thermodynamic properties of NTO dissolution in the two systems,including values for Gibbs free energy,enthalpy and entropy.The%ζ_(H)is larger than%ζ_(TS),and all the%ζ_(H)data are≥58.63%,indicating that the enthalpy make a greater contribution than entropy to theΔG_(soln)^(Θ).展开更多
BACKGROUND Emerging evidence implicates Candida albicans(C.albicans)in human oncogenesis.Notably,studies have supported its involvement in regulating outcomes in colorectal cancer(CRC).This study investigated the para...BACKGROUND Emerging evidence implicates Candida albicans(C.albicans)in human oncogenesis.Notably,studies have supported its involvement in regulating outcomes in colorectal cancer(CRC).This study investigated the paradoxical role of C.albicans in CRC,aiming to determine whether it promotes or suppresses tumor development,with a focus on the mechanistic basis linked to its metabolic profile.AIM To investigate the dual role of C.albicans in the development and progression of CRC through metabolite profiling and to establish a prognostic model that integrates the microbial and metabolic interactions in CRC,providing insights into potential therapeutic strategies and clinical outcomes.METHODSA prognostic model integrating C. albicans with CRC was developed, incorporating enrichment analysis, immuneinfiltration profiling, survival analysis, Mendelian randomization, single-cell sequencing, and spatial transcriptomics.The effects of the C. albicans metabolite mixture on CRC cells were subsequently validated in vitro. Theprimary metabolite composition was characterized using liquid chromatography-mass spectrometry.RESULTSA prognostic model based on five specific mRNA markers, EHD4, LIME1, GADD45B, TIMP1, and FDFT1, wasestablished. The C. albicans metabolite mixture significantly reduced CRC cell viability. Post-treatment analysisrevealed a significant decrease in gene expression in HT29 cells, while the expression levels of TIMP1, EHD4, andGADD45B were significantly elevated in HCT116 cells. Conversely, LIME1 expression and that of other CRC celllines showed reductions. In normal colonic epithelial cells (NCM460), GADD45B, TIMP1, and FDFT1 expressionlevels were significantly increased, while LIME1 and EHD4 levels were markedly reduced. Following metabolitetreatment, the invasive and migratory capabilities of NCM460, HT29, and HCT116 cells were reduced. Quantitativeanalysis of extracellular ATP post-treatment showed a significant elevation (P < 0.01). The C. albicans metabolitemixture had no effect on reactive oxygen species accumulation in CRC cells but led to a reduction in mitochondrialmembrane potential, increased intracellular lipid peroxidation, and induced apoptosis. Metabolomic profilingrevealed significant alterations, with 516 metabolites upregulated and 531 downregulated.CONCLUSIONThis study introduced a novel prognostic model for CRC risk assessment. The findings suggested that the C.albicans metabolite mixture exerted an inhibitory effect on CRC initiation.展开更多
Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these i...Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these internal magnetic fields accurately,data selection based on specific criteria is often employed to minimize the influence of rapidly changing current systems in the ionosphere and magnetosphere.However,the quantitative impact of various data selection criteria on internal geomagnetic field modeling is not well understood.This study aims to address this issue and provide a reference for constructing and applying geomagnetic field models.First,we collect the latest MSS-1 and Swarm satellite magnetic data and summarize widely used data selection criteria in geomagnetic field modeling.Second,we briefly describe the method to co-estimate the core,crustal,and large-scale magnetospheric fields using satellite magnetic data.Finally,we conduct a series of field modeling experiments with different data selection criteria to quantitatively estimate their influence.Our numerical experiments confirm that without selecting data from dark regions and geomagnetically quiet times,the resulting internal field differences at the Earth’s surface can range from tens to hundreds of nanotesla(nT).Additionally,we find that the uncertainties introduced into field models by different data selection criteria are significantly larger than the measurement accuracy of modern geomagnetic satellites.These uncertainties should be considered when utilizing constructed magnetic field models for scientific research and applications.展开更多
A 3D crustal model was constructed using a combination of cutting-edge techniques,which were integrated to provide a density model for Egypt and address the sporadic distribution of seismic data.These techniques inclu...A 3D crustal model was constructed using a combination of cutting-edge techniques,which were integrated to provide a density model for Egypt and address the sporadic distribution of seismic data.These techniques include obtaining gravity data from the Gravity Field and Steady-State Ocean Circulation Explorer(GOCE),creating seismic profiles,analyzing the receiver functions of seismic data,obtaining information from boreholes,and providing geological interpretations.GOCE satellite gravity data were processed to construct a preliminary model based on nonlinear inversions of the data.A regional crustal thickness model was developed using receiver functions,seismic refraction profiles,and geological insights.The inverted model was validated using borehole data and compared with seismic estimates.The model exhibited strong consistency and revealed a correlation between crustal thickness,geology,and tectonics of Egypt.It showed that the shallowest depths of the Moho are located in the north along the Mediterranean Sea and in the eastern part along the Red Sea,reflecting an oceanic plate with a thin,high-density crust.The deepest Moho depths are located in the southwestern part of Egypt,Red Sea coastal mountains,and Sinai Peninsula.The obtained 3D model of crustal thickness provided finely detailed Moho depth estimates that aligned closely with geology and tectonic characteristics of Egypt,contributing valuable insights into the subsurface structure and tectonic processes of region.展开更多
This review explores the use of agent-based modeling(ABM)within the framework of study human emotion and cognition in the context of its ability to simulate complex social interactions,adaptive changes,and evolutionar...This review explores the use of agent-based modeling(ABM)within the framework of study human emotion and cognition in the context of its ability to simulate complex social interactions,adaptive changes,and evolutionary processes.By representing agents and their defined environments with probabilistic interactions,ABM allows the assessment of the effects of individual behavior at the micro level on the greater social phenomena at the macro level.The review looks into the applications of ABM in portraying some of the key components of emotions and cognition-empathy,cooperation,decision making,and emotional transmission-and analyzes the problems including scalability,empirical validation,and description of sensitive emotional states.The most important conclusion is that merging ABM with information neurobiological data and artificial intelligence(AI)techniques would allow for deepening the interactions within the system and enhancing its responsiveness to stimuli.This review highlights approaches that aim to exploit the ABM methodology more fully and integrates methods from biology,neuroscience,and engineering.This integration could contribute to our understanding of the human behavior evolution and adaptation within systems relevant to policymaking,healthcare,and education.展开更多
It is well known that coarse-grained super-elastic NiTi shape memory alloys(SMAs)exhibit localized rather than homogeneous martensite transformation(MT),which,however,can be strongly influenced by either internal size...It is well known that coarse-grained super-elastic NiTi shape memory alloys(SMAs)exhibit localized rather than homogeneous martensite transformation(MT),which,however,can be strongly influenced by either internal size(grain size,GS)or the external size(geometric size).The coupled effect of GS and geometric size on the functional properties has not been clearly understood yet.In this work,the super-elasticity,one-way,and stress-assisted two-way shape memory effects of the polycrystalline NiTi SMAs with different aspect ratios(length/width for the gauge section)and different GSs are investigated based on the phase field method.The coupled effect of the aspect ratio and GS on the functional properties is adequately revealed.The simulated results indicate that when the aspect ratio is lower than about 4:1,the stress biaxiality and stress heterogeneity in the gauge section of the sample become more and more obvious with decreasing the aspect ratio,which can significantly influence the microstructure evolution in the process involving external stress.Therefore,the corresponding functional property is strongly dependent on the aspect ratio.With decreasing the GS and the aspect ratio(to be lower than 4:1),both the aspect ratio and GS can affect the MT or martensite reorientation in each grain and the interaction among grains.Thus,due to the strong internal constraint(i.e.,the constraint of grain boundary)and the external constraint(i.e.,the constraint of geometric boundary),the capabilities of the functional properties of NiTi SMAs are gradually weakened and highly dependent on these two factors.展开更多
In order to enhance the control performance of piezo-positioning system,the influence of hysteresis characteristics and its compensation method are studied.Hammerstein model is used to represent the dynamic hysteresis...In order to enhance the control performance of piezo-positioning system,the influence of hysteresis characteristics and its compensation method are studied.Hammerstein model is used to represent the dynamic hysteresis nonlinear characteristics of piezo-positioning actuator.The static nonlinear part and dynamic linear part of the Hammerstein model are represented by models obtained through the Prandtl-Ishlinskii(PI)model and Hankel matrix system identification method,respectively.This model demonstrates good generalization capability for typical input frequencies below 200 Hz.A sliding mode inverse compensation tracking control strategy based on P-I inverse model and integral augmentation is proposed.Experimental results show that compared with PID inverse compensation control and sliding mode control without inverse compensation,the sliding mode inverse compensation control has a more ideal step response and no overshoot,moreover,the settling time is only 6.2 ms.In the frequency domain,the system closed-loop tracking bandwidth reaches 119.9 Hz,and the disturbance rejection bandwidth reaches 86.2 Hz.The proposed control strategy can effectively compensate the hysteresis nonlinearity,and improve the tracking accuracy and antidisturbance capability of piezo-positioning system.展开更多
In this paper,the small-signal modeling of the Indium Phosphide High Electron Mobility Transistor(InP HEMT)based on the Transformer neural network model is investigated.The AC S-parameters of the HEMT device are train...In this paper,the small-signal modeling of the Indium Phosphide High Electron Mobility Transistor(InP HEMT)based on the Transformer neural network model is investigated.The AC S-parameters of the HEMT device are trained and validated using the Transformer model.In the proposed model,the eight-layer transformer encoders are connected in series and the encoder layer of each Transformer consists of the multi-head attention layer and the feed-forward neural network layer.The experimental results show that the measured and modeled S-parameters of the HEMT device match well in the frequency range of 0.5-40 GHz,with the errors versus frequency less than 1%.Compared with other models,good accuracy can be achieved to verify the effectiveness of the proposed model.展开更多
The Electro–Hydrostatic Actuator(EHA)is applied to drive the control surface in flightcontrol system of more electric aircraft.In EHA,the Oil-Immersed Motor Pump(OMP)serves asthe core as a power assembly.However,the ...The Electro–Hydrostatic Actuator(EHA)is applied to drive the control surface in flightcontrol system of more electric aircraft.In EHA,the Oil-Immersed Motor Pump(OMP)serves asthe core as a power assembly.However,the compact integration of the OMP presents challenges inefficiently dissipating internal heat,leading to a performance degradation of the EHA due to ele-vated temperatures.Therefore,accurately modeling and predicting the internal thermal dynamicsof the OMP hold considerable significance for monitoring the operational condition of the EHA.In view of this,a modeling method considering cumulative thermal coupling was hereby proposed.Based on the proposed method,the thermal models of the motor and the pump were established,taking into account heat accumulation and transfer.Taking the leakage oil as the heat couplingpoint between the motor and the pump,the dynamic thermal coupling model of the OMP wasdeveloped,with the thermal characteristics of the oil considered.Additionally,the comparativeexperiments were conducted to illustrate the efficiency of the proposed model.The experimentalresults demonstrate that the proposed dynamic thermal coupling model accurately captured thethermal behavior of OMP,outperforming the static thermal parameter model.Overall,thisadvancement is crucial for effectively monitoring the health of EHA and ensuring flight safety.展开更多
基金the World Climate Research Programme(WCRP),Climate Variability and Predictability(CLIVAR),and Global Energy and Water Exchanges(GEWEX)for facilitating the coordination of African monsoon researchsupport from the Center for Earth System Modeling,Analysis,and Data at the Pennsylvania State Universitythe support of the Office of Science of the U.S.Department of Energy Biological and Environmental Research as part of the Regional&Global Model Analysis(RGMA)program area。
文摘In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.
基金supported by the National Natural Science Foundation of China(No.42061065)the Third Xinjiang Comprehensive Scientific Expedition,China(No.2022xjkk03010102).
文摘Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.
基金supported in part by the National Key Research and Development Program of China(2022YFB3304900)in part by the National Natural Science Foundation of China(62394340 and 62073340)in part by the Science and Technology Innovation Program of Hunan Province(2022JJ10083).
文摘With the intelligent transformation of process manufacturing,accurate and comprehensive perception information is fundamental for application of artificial intelligence methods.In zinc smelting,the fluidized bed roaster is a key piece of large-scale equipment and plays a critical role in the manufacturing industry;its internal temperature field directly determines the quality of zinc calcine and other related products.However,due to its vast spatial dimensions,the limited observation methods,and the complex multiphase,multifield coupled reaction atmosphere inside it,accurately and timely perceiving its temperature field remains a significant challenge.To address these challenges,a spatial-temporal reduced-order model(STROM)is proposed,which can realize fast and accurate temperature field perception based on sparse observation data.Specifically,to address the difficulty in matching the initial physical field with the sparse observation data,an initial field construction based on data assimilation(IFCDA)method is proposed to ensure that the initial conditions of the model can be matched with the actual operation state,which provides a basis for constructing a high-precision computational fluid dynamics(CFD)model.Then,to address the high simulation cost of high-precision CFD models under full working conditions,a high uniformity(HU)-orthogonal test design(OTD)method with the centered L2 deviation is innovatively proposed to ensure high information coverage of the temperature field dataset under typical working conditions in terms of multiple factors and levels of the component,feed,and blast parameters.Finally,to address the difficulty in real-time and accurate temperature field prediction,considering the spatial correlation between the observed temperature and the temperature field,as well as the dynamic correlation of the observed temperature in the time dimension,a spatial-temporal predictive model(STPM)is established,which realizes rapid prediction of the temperature field through sparse observa-tion data.To verify the accuracy and validity of the proposed method,CFD model validation and reduced-order model prediction experiments are designed,and the results show that the proposed method can realize high-precision and fast prediction of the roaster temperature field under different working conditions through sparse observation data.Compared with the CFD model,the prediction root-mean-square error(RMSE)of STROM is less than 0.038,and the computational efficiency is improved by 3.4184×10^(4)times.In particular,STROM also has a good prediction ability for unmodeled conditions,with a prediction RMSE of less than 0.1089.
基金supported by the National Key Research and Development Program of China(2018AAA0101005,2018AAA0102404)the Program of the Huawei Technologies Co.Ltd.(FA2018111061SOW12)+1 种基金the National Natural Science Foundation of China(61773054)the Youth Research Fund of the State Key Laboratory of Complex Systems Management and Control(20190213)。
文摘Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,41975037,and 42105133)the National Key Research and Development Program of China(No.2022YFC3703502)+1 种基金the Plan for Anhui Major Provincial Science&Technology Project(No.202203a07020003)Hefei Ecological Environment Bureau Project(No.2020BFFFD01804).
文摘As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
基金N ational N atural Science Foundation of China, N o.40271020 K now ledge Innovation Project of the Institute ofG eographic Sciences and N aturalResources Research,CA S,N o.CX IO G -A 04-09
文摘The spatial distribution and seasonal variation of the tide-induced Lagrangian Residual Circulations （LRC hereafter）, wind-driven LRC, and the coupling dynamic characteristics were simulated using ECOM, given the Hellerman and Rosenstein global monthly-mean wind stresses. The results showed that the tide-induced LRC of the harmonic constituent M2 bears an identical pattern in four seasons in the Bohai Sea： the surface one is weak with random directions; however, there exist a southeast current from the Bohai Strait to the Laizhou bay, and a weakly anticlockwise gyre in the south of the Bohai Strait for the bottom layer LRC. The magnitude of bottom layer tide-induced LRC is larger than the surface one, and moreover, it contributes significantly to the whole LRC in the Bohai Sea. Unlike the identical structure of the tide-induced LRC, the wind driven LRC varies seasonally under the prevailing monsoon. It forms a distinct gyre under the summer and winter monsoons in July and January respectively, but it seems weak and non-directional in April and September.
基金Under the auspices of National Natural Science Foundation of China(No.42330510)。
文摘With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.
基金partially supported by the National Key Research and Development Program of China(2020YFB2104001)。
文摘The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金the auspices of A Category of Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA20010101)。
文摘The spatial interaction model is an effective way to explore the geographical disparities inherent in the Belt and Road Initiative(BRI) by simulating spatial flows. The traditional gravity model implies the hypothesis of equilibrium points without any reference to when or how to achieve it. In this paper, a dynamic gravity model was established based on the Maximum Entropy(MaxEnt) theory to estimate and monitor the interconnection intensity and dynamic characters of bilateral relations. In order to detect the determinants of interconnection intensity, a Geodetector method was applied to identify and evaluate the determinants of spatial networks in five dimensions. The empirical study clearly demonstrates a heterogeneous and non-circular spatial structure. The main driving forces of spatial-temporal evolution are foreign direct investment, tourism and railway infrastructure construction, while determinants in different sub-regions show obvious spatial differentiation. Southeast Asian countries are typically multi-island area where aviation infrastructure plays a more important role. North and Central Asian countries regard oil as a pillar industry where power and port facilities have a greater impact on the interconnection. While Western Asian countries are mostly influenced by the railway infrastructure, Eastern European countries already have relatively robust infrastructure where tariff policies provide a greater impetus.
文摘Using a dynamic laser monitoring technique,the solubility of 3-nitro-1,2,4-triazole-5-one(NTO)was investigated in two different binary systems,namely hydroxylamine nitrate(HAN)-water and boric acid(HB)-water ranging from 278.15 K to 318.15 K.The solubility in each system was found to be positively correlated with temperature.Furthermore,solubility data were analyzed using four equations:the modified Apelblat equation,Van’t Hoff equation,λh equation and CNIBS/R-K equations,and they provided satisfactory results for both two systems.The average root-mean-square deviation(105RMSD)values for these models were less than 13.93.Calculations utilizing the Van’t Hoff equation and Gibbs equations facilitated the derivation of apparent thermodynamic properties of NTO dissolution in the two systems,including values for Gibbs free energy,enthalpy and entropy.The%ζ_(H)is larger than%ζ_(TS),and all the%ζ_(H)data are≥58.63%,indicating that the enthalpy make a greater contribution than entropy to theΔG_(soln)^(Θ).
基金Supported by Gansu Province Joint Fund General Program,No.24JRRA878Gansu Provincial Science and Technology Program Project,No.24JRRA1020+2 种基金Gansu Province Key Talent Program,No.2025RCXM006Teaching Research and Reform Program for Postgraduate Education at Gansu University of Traditional Chinese Medicine(GUSTCM),No.YBXM-202406Special Fund for Mentors of“Qihuang Talents”in the First-Level Discipline of Chinese Medicine,No.ZYXKBD-202415。
文摘BACKGROUND Emerging evidence implicates Candida albicans(C.albicans)in human oncogenesis.Notably,studies have supported its involvement in regulating outcomes in colorectal cancer(CRC).This study investigated the paradoxical role of C.albicans in CRC,aiming to determine whether it promotes or suppresses tumor development,with a focus on the mechanistic basis linked to its metabolic profile.AIM To investigate the dual role of C.albicans in the development and progression of CRC through metabolite profiling and to establish a prognostic model that integrates the microbial and metabolic interactions in CRC,providing insights into potential therapeutic strategies and clinical outcomes.METHODSA prognostic model integrating C. albicans with CRC was developed, incorporating enrichment analysis, immuneinfiltration profiling, survival analysis, Mendelian randomization, single-cell sequencing, and spatial transcriptomics.The effects of the C. albicans metabolite mixture on CRC cells were subsequently validated in vitro. Theprimary metabolite composition was characterized using liquid chromatography-mass spectrometry.RESULTSA prognostic model based on five specific mRNA markers, EHD4, LIME1, GADD45B, TIMP1, and FDFT1, wasestablished. The C. albicans metabolite mixture significantly reduced CRC cell viability. Post-treatment analysisrevealed a significant decrease in gene expression in HT29 cells, while the expression levels of TIMP1, EHD4, andGADD45B were significantly elevated in HCT116 cells. Conversely, LIME1 expression and that of other CRC celllines showed reductions. In normal colonic epithelial cells (NCM460), GADD45B, TIMP1, and FDFT1 expressionlevels were significantly increased, while LIME1 and EHD4 levels were markedly reduced. Following metabolitetreatment, the invasive and migratory capabilities of NCM460, HT29, and HCT116 cells were reduced. Quantitativeanalysis of extracellular ATP post-treatment showed a significant elevation (P < 0.01). The C. albicans metabolitemixture had no effect on reactive oxygen species accumulation in CRC cells but led to a reduction in mitochondrialmembrane potential, increased intracellular lipid peroxidation, and induced apoptosis. Metabolomic profilingrevealed significant alterations, with 516 metabolites upregulated and 531 downregulated.CONCLUSIONThis study introduced a novel prognostic model for CRC risk assessment. The findings suggested that the C.albicans metabolite mixture exerted an inhibitory effect on CRC initiation.
基金supported by the National Natural Science Foundation of China(42250101)the Macao Foundation。
文摘Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these internal magnetic fields accurately,data selection based on specific criteria is often employed to minimize the influence of rapidly changing current systems in the ionosphere and magnetosphere.However,the quantitative impact of various data selection criteria on internal geomagnetic field modeling is not well understood.This study aims to address this issue and provide a reference for constructing and applying geomagnetic field models.First,we collect the latest MSS-1 and Swarm satellite magnetic data and summarize widely used data selection criteria in geomagnetic field modeling.Second,we briefly describe the method to co-estimate the core,crustal,and large-scale magnetospheric fields using satellite magnetic data.Finally,we conduct a series of field modeling experiments with different data selection criteria to quantitatively estimate their influence.Our numerical experiments confirm that without selecting data from dark regions and geomagnetically quiet times,the resulting internal field differences at the Earth’s surface can range from tens to hundreds of nanotesla(nT).Additionally,we find that the uncertainties introduced into field models by different data selection criteria are significantly larger than the measurement accuracy of modern geomagnetic satellites.These uncertainties should be considered when utilizing constructed magnetic field models for scientific research and applications.
文摘A 3D crustal model was constructed using a combination of cutting-edge techniques,which were integrated to provide a density model for Egypt and address the sporadic distribution of seismic data.These techniques include obtaining gravity data from the Gravity Field and Steady-State Ocean Circulation Explorer(GOCE),creating seismic profiles,analyzing the receiver functions of seismic data,obtaining information from boreholes,and providing geological interpretations.GOCE satellite gravity data were processed to construct a preliminary model based on nonlinear inversions of the data.A regional crustal thickness model was developed using receiver functions,seismic refraction profiles,and geological insights.The inverted model was validated using borehole data and compared with seismic estimates.The model exhibited strong consistency and revealed a correlation between crustal thickness,geology,and tectonics of Egypt.It showed that the shallowest depths of the Moho are located in the north along the Mediterranean Sea and in the eastern part along the Red Sea,reflecting an oceanic plate with a thin,high-density crust.The deepest Moho depths are located in the southwestern part of Egypt,Red Sea coastal mountains,and Sinai Peninsula.The obtained 3D model of crustal thickness provided finely detailed Moho depth estimates that aligned closely with geology and tectonic characteristics of Egypt,contributing valuable insights into the subsurface structure and tectonic processes of region.
文摘This review explores the use of agent-based modeling(ABM)within the framework of study human emotion and cognition in the context of its ability to simulate complex social interactions,adaptive changes,and evolutionary processes.By representing agents and their defined environments with probabilistic interactions,ABM allows the assessment of the effects of individual behavior at the micro level on the greater social phenomena at the macro level.The review looks into the applications of ABM in portraying some of the key components of emotions and cognition-empathy,cooperation,decision making,and emotional transmission-and analyzes the problems including scalability,empirical validation,and description of sensitive emotional states.The most important conclusion is that merging ABM with information neurobiological data and artificial intelligence(AI)techniques would allow for deepening the interactions within the system and enhancing its responsiveness to stimuli.This review highlights approaches that aim to exploit the ABM methodology more fully and integrates methods from biology,neuroscience,and engineering.This integration could contribute to our understanding of the human behavior evolution and adaptation within systems relevant to policymaking,healthcare,and education.
基金supported by the National Natural Science Foundation of China (Grant Nos.12202294 and 12022208)the Project funded by China Postdoctoral Science Foundation (Grant No.2022M712243)the Fundamental Research Funds for the Central Universities (Grant No.2023SCU12098).
文摘It is well known that coarse-grained super-elastic NiTi shape memory alloys(SMAs)exhibit localized rather than homogeneous martensite transformation(MT),which,however,can be strongly influenced by either internal size(grain size,GS)or the external size(geometric size).The coupled effect of GS and geometric size on the functional properties has not been clearly understood yet.In this work,the super-elasticity,one-way,and stress-assisted two-way shape memory effects of the polycrystalline NiTi SMAs with different aspect ratios(length/width for the gauge section)and different GSs are investigated based on the phase field method.The coupled effect of the aspect ratio and GS on the functional properties is adequately revealed.The simulated results indicate that when the aspect ratio is lower than about 4:1,the stress biaxiality and stress heterogeneity in the gauge section of the sample become more and more obvious with decreasing the aspect ratio,which can significantly influence the microstructure evolution in the process involving external stress.Therefore,the corresponding functional property is strongly dependent on the aspect ratio.With decreasing the GS and the aspect ratio(to be lower than 4:1),both the aspect ratio and GS can affect the MT or martensite reorientation in each grain and the interaction among grains.Thus,due to the strong internal constraint(i.e.,the constraint of grain boundary)and the external constraint(i.e.,the constraint of geometric boundary),the capabilities of the functional properties of NiTi SMAs are gradually weakened and highly dependent on these two factors.
文摘In order to enhance the control performance of piezo-positioning system,the influence of hysteresis characteristics and its compensation method are studied.Hammerstein model is used to represent the dynamic hysteresis nonlinear characteristics of piezo-positioning actuator.The static nonlinear part and dynamic linear part of the Hammerstein model are represented by models obtained through the Prandtl-Ishlinskii(PI)model and Hankel matrix system identification method,respectively.This model demonstrates good generalization capability for typical input frequencies below 200 Hz.A sliding mode inverse compensation tracking control strategy based on P-I inverse model and integral augmentation is proposed.Experimental results show that compared with PID inverse compensation control and sliding mode control without inverse compensation,the sliding mode inverse compensation control has a more ideal step response and no overshoot,moreover,the settling time is only 6.2 ms.In the frequency domain,the system closed-loop tracking bandwidth reaches 119.9 Hz,and the disturbance rejection bandwidth reaches 86.2 Hz.The proposed control strategy can effectively compensate the hysteresis nonlinearity,and improve the tracking accuracy and antidisturbance capability of piezo-positioning system.
基金Supported by the National Natural Science Foundation of China(62201293,62034003)the Open-Foundation of State Key Laboratory of Millimeter-Waves(K202313)the Jiangsu Province Youth Science and Technology Talent Support Project(JSTJ-2024-040)。
文摘In this paper,the small-signal modeling of the Indium Phosphide High Electron Mobility Transistor(InP HEMT)based on the Transformer neural network model is investigated.The AC S-parameters of the HEMT device are trained and validated using the Transformer model.In the proposed model,the eight-layer transformer encoders are connected in series and the encoder layer of each Transformer consists of the multi-head attention layer and the feed-forward neural network layer.The experimental results show that the measured and modeled S-parameters of the HEMT device match well in the frequency range of 0.5-40 GHz,with the errors versus frequency less than 1%.Compared with other models,good accuracy can be achieved to verify the effectiveness of the proposed model.
基金supported by the National Key R&D Program of China(No.2021YFB2011300)the National Natural Science Foundation of China(Nos.52275044,U2233212)。
文摘The Electro–Hydrostatic Actuator(EHA)is applied to drive the control surface in flightcontrol system of more electric aircraft.In EHA,the Oil-Immersed Motor Pump(OMP)serves asthe core as a power assembly.However,the compact integration of the OMP presents challenges inefficiently dissipating internal heat,leading to a performance degradation of the EHA due to ele-vated temperatures.Therefore,accurately modeling and predicting the internal thermal dynamicsof the OMP hold considerable significance for monitoring the operational condition of the EHA.In view of this,a modeling method considering cumulative thermal coupling was hereby proposed.Based on the proposed method,the thermal models of the motor and the pump were established,taking into account heat accumulation and transfer.Taking the leakage oil as the heat couplingpoint between the motor and the pump,the dynamic thermal coupling model of the OMP wasdeveloped,with the thermal characteristics of the oil considered.Additionally,the comparativeexperiments were conducted to illustrate the efficiency of the proposed model.The experimentalresults demonstrate that the proposed dynamic thermal coupling model accurately captured thethermal behavior of OMP,outperforming the static thermal parameter model.Overall,thisadvancement is crucial for effectively monitoring the health of EHA and ensuring flight safety.
