In recent years,the global installed capacity of wind power has grown rapidly,making the enhancement of wind power prediction accuracy crucial for facilitating the integration and consumption of renewable energy.Curre...In recent years,the global installed capacity of wind power has grown rapidly,making the enhancement of wind power prediction accuracy crucial for facilitating the integration and consumption of renewable energy.Current research on ultra-short-term wind power prediction often overlooks load characteristics,resulting in an inability to adequately address grid connection requirements and load dispatching demands across different time periods.To address this limitation,this study proposes a novel approach to ultra-short-term wind power prediction error correction that incorporates load peak-valley characteristics.The methodology involves three key steps:first,deriving interannual prediction error characteristics from ultra-short-term prediction results of wind farm clusters;second,establishing error correction intervals for load peak and valley periods,calculating corresponding correction coefficients,and analyzing the impact of varying correction radii on the final results;third,validating the proposed method through empirical analysis of wind farm clusters in three northeastern provinces.The results demonstrate that this approach not only improves wind power prediction accuracy but also significantly reduces the occurrence of harmful error days,thereby better meeting the operational requirements of power system dispatch.展开更多
Aerial surveys are dynamic and continuous processes,and there are different height distributions of the ground in the measurement area,which leads to problems such as overlapping measurement areas and inaccurate altit...Aerial surveys are dynamic and continuous processes,and there are different height distributions of the ground in the measurement area,which leads to problems such as overlapping measurement areas and inaccurate altitude correction during the survey process.Commonly used terrain correction methods are based on the concept of finite elementization of ground surface radioactive sources,using GPS coordinates,radar altitude,and ground elevation distribution information from aerial surveys,combined with the sourceless efficiency calibration method to construct a response matrix,which is then inverted for surface nuclide content.However,most of the sourceless efficiency calibration methods used are numerical calculations that consider the body detector as a point detector and do not consider the changes in intrinsic detection efficiency under different incident directions of gamma rays.Therefore,when the altitude of the measurement area varies significantly or the flight altitude of the aerial survey is relatively low,such sourceless efficiency calibration method calculations tend to have a large bias,which affects the accuracy of the terrain correction.To address the above problems,this study employs a novel sourceless efficiency calibration method based on the Boolean operation of the ray deposition process and simplifies the traditional body source measurement model to a surface source measurement model to achieve fast and accurate efficiency calibration.Then,through the discretization of the measurement process,the static measurement process is superposed as equivalent to the dynamic measurement process,and the dynamic measurement response matrix is built and optimized based on the calibration method.Finally,the PSO-MLEM algorithm was used to solve the dynamic measurement response matrix to achieve dynamic terrain correction of aerial survey data.Analysis of the Baiyun'ebo test area revealed that,after applying dynamic terrain correction,the inverted anomalies in uranium(eU),thorium(eTh),and potassium(K)concentrations were closer to ground measurements(within 5.72%-30.79%)and exhibited clearer anomaly boundaries compared to traditional height-based corrections.However,owing to the inherent statistical fluctuations and characteristics of matrix inversion,higher measurement values tend to absorb lower ones,potentially enlarging the anomalous regions.Nevertheless,the highanomaly regions after inversion largely coincided with the ground truth validation,demonstrating that the proposed method can effectively correct airborne gamma spectrometry data.展开更多
Correction to“Liu QQ,Li YD,Chen JX,Zhang LL,Guan RC,Zhao W,Meng LY.Prognostic value of preoperative fibrinogen,neutrophil-to-lymphocyte ratio,serum alpha-fetoprotein,and prealbumin for patients with primary liver can...Correction to“Liu QQ,Li YD,Chen JX,Zhang LL,Guan RC,Zhao W,Meng LY.Prognostic value of preoperative fibrinogen,neutrophil-to-lymphocyte ratio,serum alpha-fetoprotein,and prealbumin for patients with primary liver cancer undergoing transarterial chemoembolization.World J Gastrointest Oncol 2025;17(6):103198 PMID:40547171 DOI:10.4251/wjgo.v17.i6.103198”.The funding number listed in the"Supported by"section of this article needs to be corrected.展开更多
The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navi...The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.展开更多
Aerodynamic performances of axial compressors are significantly affected by variation of Reynolds number in aero-engines.In the design and analysis of compressors,previous correction methods for cascades and stages ha...Aerodynamic performances of axial compressors are significantly affected by variation of Reynolds number in aero-engines.In the design and analysis of compressors,previous correction methods for cascades and stages have difficulties in predicting comprehensively Reynolds number effects on airfoils,matching and characteristics curves.This study proposes Re-correction models for loss,deviation angle and endwall blockage based on classical theories and cascade tests,and loss and deviation models show good agreement in test data of NACA65 and C4 cascades.Throughflow method considering Reynolds number effects is developed by integrating the correction models into a verified Streamline Curvature(SLC)tool.A three-stage axial compressor is investigated through SLC and CFD methods from design Reynolds number(Red=2106)to low Re=4104,and the numerical methods are validated with test data of characteristic curves and spanwise distributions at Red.With Re reduction,SLC method with correction models well predicts variation in overall performances compared with CFD calculations and Wassell's model.Streamwise and spanwise matching such as total pressure and loss distributions in SLC predictions are basically consistent with those in CFD results at near-stall points under design and low Reynolds numbers.SLC and CFD methods share similar detections of stall risks in the third stage(Stg3),and their analyses of diffusion processes deviate to some extent due to different predictions in separated endwall flow.The correction models can be adopted to consider Reynolds number effects in through-flow design and analysis of axial compressors.展开更多
Hydraulic asphalt concrete(HAC)has been increasingly employed as an appropriate impervious structure in hydraulic and hydropower engineering.However,asphalt mortar,usually seen as the matrix of HAC composite,is partic...Hydraulic asphalt concrete(HAC)has been increasingly employed as an appropriate impervious structure in hydraulic and hydropower engineering.However,asphalt mortar,usually seen as the matrix of HAC composite,is particularly prone to damage under combined stress and seepage interactions,and the mesoscale investigations on the damage-seepage coupling behavior of HAC under complex stress states remain limited.This research develops a numerical three-dimensional mesoscale model composed of asphalt mortar and polyhedral aggregate to investigate the stress-damage-seepage coupling behavior in HAC.In this model,asphalt mortar yields the viscoelastic continuum damage law and aggregate obeys the Mazars’elastic-brittle damage law;simultaneously,the effective permeability coefficient of asphalt mortar is assumed to follow an exponential function of damage.The predicted deviatoric stress-strain and hydraulic gradient-seepage curves both are in good agreement with the reported experimental results,which shows the proposed model is valid and reasonable.The simulated results indicate that the damaged asphalt mortar can induce localized areas of high permeability,which in turn affects the overall impervious performance of HAC.展开更多
Although traditional gamma-gamma density(GGD)logging technology is widely utilized,its potential environmental risks have prompted the development of more environmentally friendly neutron-gamma density(NGD)logging tec...Although traditional gamma-gamma density(GGD)logging technology is widely utilized,its potential environmental risks have prompted the development of more environmentally friendly neutron-gamma density(NGD)logging technology.However,NGD measurements are influenced by both neutron and gamma radiations.In the logging environment,variations in the formation composition indicate different elemental compositions,which affect the neutron-gamma reaction cross-sections and gamma generation.Compared to traditional gamma sources such as Cs-137,these changes significantly affect the generation and transport of neutron-induced inelastic gamma rays and hinder accurate measurements.To address this,a novel method is proposed that incorporates the mass attenuation coefficient function to account for the effects of various lithologies and pore contents on gamma-ray attenuation,thereby achieving more accurate density measurements by clarifying the transport processes of inelastic gamma rays with varying energies and spatial distributions in varied logging environments.The proposed method avoids the complex correction of neutron transport and is verified through Monte Carlo simulations for its applicability across various lithologies and pore contents,demonstrating absolute density errors that are less than 0.02 g/cm^(3)in clean formations and indicating good accuracy.This study clarifies the NGD mechanism and provides theoretical guidance for the application of NGD logging methods.Further studies will be conducted on extreme environmental conditions and tool calibration.展开更多
The infrared channels of the FY-4B advanced geosynchronous radiation imagers(AGRI) play a crucial role in temperature and humidity analyses for mesoscale numerical weather prediction, particularly in enhancing the ini...The infrared channels of the FY-4B advanced geosynchronous radiation imagers(AGRI) play a crucial role in temperature and humidity analyses for mesoscale numerical weather prediction, particularly in enhancing the initial field quality and the forecasting accuracy of the model. This study assimilated FY-4B AGRI data into the CMA-MESO model and analyzed the bias characteristics and correction methods. Analysis of the AGRI data revealed a clear diurnal variation in the bias, which was positively correlated with the solar elevation angle. However, the diurnal variation in the bias lagged behind the solar elevation angle, likely owing to temperature changes and delayed instrument responses resulting from solar radiation. To address this issue, we propose a correction method that utilizes the solar elevation angle after an optimal time shift. Using the time-shifted solar elevation angle as a predictor effectively reduces the diurnal variation in bias and significantly improves the correction effect. This approach provides theoretical support for the assimilation of FY-4B AGRI data into mesoscale numerical weather predictions, thereby enhancing the reliability of the assimilation results.展开更多
Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,i...Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.展开更多
Marine forecasting is critical for navigation safety and disaster prevention.However,traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial fe...Marine forecasting is critical for navigation safety and disaster prevention.However,traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial features.To address the limitations,the paper proposes a TimeXer-based numerical forecast correction model optimized by an exogenous-variable attention mechanism.The model treats target forecast values as internal variables,and incorporates historical temporal-spatial data and seven-day numerical forecast results from traditional models as external variables based on the embedding strategy of TimeXer.Using a self-attention structure,the model captures correlations between exogenous variables and target sequences,explores intrinsic multi-dimensional relationships,and subsequently corrects endogenous variables with the mined exogenous features.The model’s performance is evaluated using metrics including MSE(Mean Squared Error),MAE(Mean Absolute Error),RMSE(Root Mean Square Error),MAPE(Mean Absolute Percentage Error),MSPE(Mean Square Percentage Error),and computational time,with TimeXer and PatchTST models serving as benchmarks.Experiment results show that the proposed model achieves lower errors and higher correction accuracy for both one-day and seven-day forecasts.展开更多
Reconfigurable array architecture has become an important hardware platform for edge-side deployment of convolutional neural networks due to their high parallelism and flexible programmability.However,traditional mult...Reconfigurable array architecture has become an important hardware platform for edge-side deployment of convolutional neural networks due to their high parallelism and flexible programmability.However,traditional multi-branch convolutional networks suffer from computational redundancy,high memory access overhead,and inefficient branch fusion.Therefore,this paper proposes an adaptive multi-branch convolutional module(AMBC)that integrates software-hardware co-optimization.During training,the learnable fusion coefficients are introduced to enable adaptive fusion of multi-scale features,while in the inference phase,the multiple branches and their normalization parameters are merged with the fusion coefficients into a single 3×3 convolutional kernel through operator fusion.On the SIREA-288 reconfigurable platform,compared with unoptimized multi-branch networks,the proposed AMBC reduces external memory accesses by 47.91%and inference latency by 47.20%,achieving a 1.90×speedup.This approach maximizes the utilization of the reconfigurable logic while minimizing both reconfiguration and data-movement overheads in edge inference.展开更多
Sensor noise is a critical factor that degrades the performance of image processing systems.In traditional computing systems,noise correction is implemented in the digital domain,resulting in redundant latency and pow...Sensor noise is a critical factor that degrades the performance of image processing systems.In traditional computing systems,noise correction is implemented in the digital domain,resulting in redundant latency and power consumption overhead in the analog-to-digital conversion.In this work,we propose an analog-domain image correction architecture based on a proposed small-scale UNet,which implements a compact noise correction network within a one-transistor-one-memristor(1T1R)array.The statistical non-idealities of the fabricated 1T1R array(e.g.,device variability)are rigorously incorporated into the network's training and inference simulations.This correction network architecture leverages memristors for conducting multiply-accumulate operations aimed at rectifying non-uniform noise,defective pixels(stuck-at-bright/dark),and exposure mismatch.Compared to systems without correction,the proposed architecture achieves up to 50.13%improvement in recognition accuracy while demonstrating robust tolerance to memristor device-level errors.The proposed system achieves a 2.13-fold latency reduction and three orders of magnitude higher energy efficiency compared to conventional architecture.This work establishes a new paradigm for advancing the development of low-power,low-latency,and high-precision image processing systems.展开更多
In remote sensing sea surface temperature （SST）, the traditional fusion method is used to compute the dot product of a subjective weight vector with a satellite measurement vector, while the result requires validati...In remote sensing sea surface temperature （SST）, the traditional fusion method is used to compute the dot product of a subjective weight vector with a satellite measurement vector, while the result requires validation by field measurement. However, field measurement that relative to the satellite measurement is very sparse, many information may not be verified. A relative objective weight vector is constructed by using the limited field measurement, which is based on coefficient of variation method. And then it make an application of the data fusion by the weighted average method in the SST data. fuse SST data with the weighted average method. In this way, some posteriori information can be added to the fusion process. The model reduces the dependence on verification, and some of the satellite measurement can be handled without corresponding to the field measurement, and the fusion result matches transfer errors theory.展开更多
In this article, principle and mathematical method of determining the phase fractions of multiphase flows by using a dual-energy γ -ray system have been described. The dual-energy γ -ray device is composed of radioa...In this article, principle and mathematical method of determining the phase fractions of multiphase flows by using a dual-energy γ -ray system have been described. The dual-energy γ -ray device is composed of radioactive isotopes of 241Am and 137Cs with γ -ray energies of 59.5 and 662 keV, respectively. A rational method to calibrate the absorption coefficient was introduced in detail. The modified arithmetic is beneficial to removing the extra Compton scattering from the measured value. The result shows that the dual-energy γ -ray technique can be used in three-phase flow with average accuracy greater than 95%, which enables us to determine phase fractions almost independent of the flow regime. Improvement has been achieved on measurement accuracy of phase fractions.展开更多
This study was conducted to establish a simple convenient method for calculating crop coefficient, and provide a certain basis for the research of the empirical formula for calculating crop coefficient with plant heig...This study was conducted to establish a simple convenient method for calculating crop coefficient, and provide a certain basis for the research of the empirical formula for calculating crop coefficient with plant height which could be measured conveniently with regional differences, especially for the establishment of accurate irrigation schedule of potato in Yunnan. By the field experiment on potato under the condition of drip irrigation, it was found that the models of plant height with corrected FAO-56-recommended K and measured K were a quartic polynomial and a cubic polynomial, respectively, and the polynomial of potato plant height with measured crop coefficient was simpler with higher degree of fitting; and the differences between the period with the highest change rate of potato plant height and the periods with the greatest FAO-56-recommended K and measured K exhibited a differences of 3 d. In conclusion： In the future study of simple or empirical formula calculation of crop coefficient, plant height should be considered as a main dependent variable in that the calculation result would be closer to the measured crop coefficient with the problem of regional difference existing in the FAO method solved and the formula might be simpler; and the irrigation time of potato should be 3 d earlier than the irrigation time determined according to the corrected FAO-56-recommended crop coefficient, especially in the key water requirement stages of potato.展开更多
The tropospheric delay has a significant impact on high-accuracy positioning of the Global Navigation Satellite System(GNSS).Traditional solutions have their weaknesses.First,the estimation of tropospheric delay as a ...The tropospheric delay has a significant impact on high-accuracy positioning of the Global Navigation Satellite System(GNSS).Traditional solutions have their weaknesses.First,the estimation of tropospheric delay as a state parameter slows the positioning filter's convergence,especially critical for Precise Point Positioning(PPP).Second,correction-based approaches,including empirical model,meteorological model and GNSS network observations,have their corresponding limitations.The empirical model comprises yearly data-based statistics,which ignores high temporal-variation components,leading to decreased correction accuracy.The meteorological model requires real-time local weather observations.One can enable the network method of the expensive regional infrastructure of GNSS stations,of which performance depends on the rover-network geometry.In this study,we enable a real-time tropospheric regional correction service by polynomial coefficients from the Kalman filtering of multisource data,including the Global Pressure and Temperature 2 wet(GPT2w)model,weather observations from the National Oceanic and Atmospheric Administration(NOAA),and GNSS network observations.After discussing the weighting strategy examined by the regional dataset from Zhejiang Province,we evaluate the performance of the proposed fusion approach with post-processed PPP results as references.We obtained the optimal weightings for the corresponding dataset,and the average accuracy for Zenith Tropospheric Delay(ZTD)is 0.43,and 1.20 cm under static,active,and overall weather conditions,respectively.Compared with the real-time GNSS network ZTD solution,our proposed fusion solution is improved by 48.21%,55.20%,and 41.70%,respectively.In conclusion,the proposed approach makes the best of three traditional correction-based methods to provide optimized real-time tropospheric service.展开更多
By means of a logarithm law for the velocity profile, a corrected formula of bed resistance coefficient, which involves many factors such as gradient of still water depth, variation of surface elevation, flow directio...By means of a logarithm law for the velocity profile, a corrected formula of bed resistance coefficient, which involves many factors such as gradient of still water depth, variation of surface elevation, flow direction, and so on, is derived from the 3D governing equations of tidal current by averaging over the whole water depth. Theoretical analysis and application have shown that the 2D plane tidal current numerical model would be more reasonable and could be applied to steep bottom topography when the corrected bed resistance coefficient is used, therefore the results of reproduction simulation and engineering calculation would be more scientific and reasonable.展开更多
The morphological distribution of absorbent in composites is equally important with absorbents for the overall electromagnetic properties,but it is often ignored.Herein,a comprehensive consideration including electrom...The morphological distribution of absorbent in composites is equally important with absorbents for the overall electromagnetic properties,but it is often ignored.Herein,a comprehensive consideration including electromagnetic component regulation,layered arrangement structure,and gradient concentration distribution was used to optimize impedance matching and enhance electromagnetic loss.On the microscale,the incorporation of magnetic Ni nanoparticles into MXene nanosheets(Ni@MXene)endows suitable intrinsic permittivity and permeability.On the macroscale,the layered arrangement of Ni@MXene increases the effective interaction area with electromagnetic waves,inducing multiple reflection/scattering effects.On this basis,according to the analysis of absorption,reflection,and transmission(A-R-T)power coefficients of layered composites,the gradient concentration distribution was constructed to realize the impedance matching at low-concentration surface layer,electromagnetic loss at middle concentration interlayer and microwave reflection at high-concentration bottom layer.Consequently,the layered gradient composite(LG5-10-15)achieves complete absorption coverage of X-band at thickness of 2.00-2.20 mm with RL_(min) of-68.67 dB at 9.85 GHz in 2.05 mm,which is 199.0%,12.6%,and 50.6%higher than non-layered,layered and layered descending gradient composites,respectively.Therefore,this work confirms the importance of layered gradient structure in improving absorption performance and broadens the design of high-performance microwave absorption materials.展开更多
Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the int...Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.展开更多
Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination...Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.展开更多
基金supported by the National Key R&D Program of China(Technology and application of wind power/photovoltaic power prediction for promoting renewable energy consumption(2018YFB0904200).
文摘In recent years,the global installed capacity of wind power has grown rapidly,making the enhancement of wind power prediction accuracy crucial for facilitating the integration and consumption of renewable energy.Current research on ultra-short-term wind power prediction often overlooks load characteristics,resulting in an inability to adequately address grid connection requirements and load dispatching demands across different time periods.To address this limitation,this study proposes a novel approach to ultra-short-term wind power prediction error correction that incorporates load peak-valley characteristics.The methodology involves three key steps:first,deriving interannual prediction error characteristics from ultra-short-term prediction results of wind farm clusters;second,establishing error correction intervals for load peak and valley periods,calculating corresponding correction coefficients,and analyzing the impact of varying correction radii on the final results;third,validating the proposed method through empirical analysis of wind farm clusters in three northeastern provinces.The results demonstrate that this approach not only improves wind power prediction accuracy but also significantly reduces the occurrence of harmful error days,thereby better meeting the operational requirements of power system dispatch.
基金supported by the National Key Research and Development Program(No.2022YFC2807400)the National Natural Science Foundation of China(Nos.12265003 and 12205044)。
文摘Aerial surveys are dynamic and continuous processes,and there are different height distributions of the ground in the measurement area,which leads to problems such as overlapping measurement areas and inaccurate altitude correction during the survey process.Commonly used terrain correction methods are based on the concept of finite elementization of ground surface radioactive sources,using GPS coordinates,radar altitude,and ground elevation distribution information from aerial surveys,combined with the sourceless efficiency calibration method to construct a response matrix,which is then inverted for surface nuclide content.However,most of the sourceless efficiency calibration methods used are numerical calculations that consider the body detector as a point detector and do not consider the changes in intrinsic detection efficiency under different incident directions of gamma rays.Therefore,when the altitude of the measurement area varies significantly or the flight altitude of the aerial survey is relatively low,such sourceless efficiency calibration method calculations tend to have a large bias,which affects the accuracy of the terrain correction.To address the above problems,this study employs a novel sourceless efficiency calibration method based on the Boolean operation of the ray deposition process and simplifies the traditional body source measurement model to a surface source measurement model to achieve fast and accurate efficiency calibration.Then,through the discretization of the measurement process,the static measurement process is superposed as equivalent to the dynamic measurement process,and the dynamic measurement response matrix is built and optimized based on the calibration method.Finally,the PSO-MLEM algorithm was used to solve the dynamic measurement response matrix to achieve dynamic terrain correction of aerial survey data.Analysis of the Baiyun'ebo test area revealed that,after applying dynamic terrain correction,the inverted anomalies in uranium(eU),thorium(eTh),and potassium(K)concentrations were closer to ground measurements(within 5.72%-30.79%)and exhibited clearer anomaly boundaries compared to traditional height-based corrections.However,owing to the inherent statistical fluctuations and characteristics of matrix inversion,higher measurement values tend to absorb lower ones,potentially enlarging the anomalous regions.Nevertheless,the highanomaly regions after inversion largely coincided with the ground truth validation,demonstrating that the proposed method can effectively correct airborne gamma spectrometry data.
基金Supported by Health Commission of Heilongjiang Province,No.20230404080031.
文摘Correction to“Liu QQ,Li YD,Chen JX,Zhang LL,Guan RC,Zhao W,Meng LY.Prognostic value of preoperative fibrinogen,neutrophil-to-lymphocyte ratio,serum alpha-fetoprotein,and prealbumin for patients with primary liver cancer undergoing transarterial chemoembolization.World J Gastrointest Oncol 2025;17(6):103198 PMID:40547171 DOI:10.4251/wjgo.v17.i6.103198”.The funding number listed in the"Supported by"section of this article needs to be corrected.
基金supported by the National Natural Science Foundation of China(No.41971339)the SDUST Research Fund(No.2019TDJH103)。
文摘The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.
基金supported by the National Science and Tech-nology Major Project of China(Nos.2017-II-0007-0021 and J2019-II-0017-0038)。
文摘Aerodynamic performances of axial compressors are significantly affected by variation of Reynolds number in aero-engines.In the design and analysis of compressors,previous correction methods for cascades and stages have difficulties in predicting comprehensively Reynolds number effects on airfoils,matching and characteristics curves.This study proposes Re-correction models for loss,deviation angle and endwall blockage based on classical theories and cascade tests,and loss and deviation models show good agreement in test data of NACA65 and C4 cascades.Throughflow method considering Reynolds number effects is developed by integrating the correction models into a verified Streamline Curvature(SLC)tool.A three-stage axial compressor is investigated through SLC and CFD methods from design Reynolds number(Red=2106)to low Re=4104,and the numerical methods are validated with test data of characteristic curves and spanwise distributions at Red.With Re reduction,SLC method with correction models well predicts variation in overall performances compared with CFD calculations and Wassell's model.Streamwise and spanwise matching such as total pressure and loss distributions in SLC predictions are basically consistent with those in CFD results at near-stall points under design and low Reynolds numbers.SLC and CFD methods share similar detections of stall risks in the third stage(Stg3),and their analyses of diffusion processes deviate to some extent due to different predictions in separated endwall flow.The correction models can be adopted to consider Reynolds number effects in through-flow design and analysis of axial compressors.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3005603-01)the Natural Science Foundation Science of Anhui Province(Grant No.2308085US02).
文摘Hydraulic asphalt concrete(HAC)has been increasingly employed as an appropriate impervious structure in hydraulic and hydropower engineering.However,asphalt mortar,usually seen as the matrix of HAC composite,is particularly prone to damage under combined stress and seepage interactions,and the mesoscale investigations on the damage-seepage coupling behavior of HAC under complex stress states remain limited.This research develops a numerical three-dimensional mesoscale model composed of asphalt mortar and polyhedral aggregate to investigate the stress-damage-seepage coupling behavior in HAC.In this model,asphalt mortar yields the viscoelastic continuum damage law and aggregate obeys the Mazars’elastic-brittle damage law;simultaneously,the effective permeability coefficient of asphalt mortar is assumed to follow an exponential function of damage.The predicted deviatoric stress-strain and hydraulic gradient-seepage curves both are in good agreement with the reported experimental results,which shows the proposed model is valid and reasonable.The simulated results indicate that the damaged asphalt mortar can induce localized areas of high permeability,which in turn affects the overall impervious performance of HAC.
基金supported by the National Natural Science Foundation of China(U23B20151 and 52171253).
文摘Although traditional gamma-gamma density(GGD)logging technology is widely utilized,its potential environmental risks have prompted the development of more environmentally friendly neutron-gamma density(NGD)logging technology.However,NGD measurements are influenced by both neutron and gamma radiations.In the logging environment,variations in the formation composition indicate different elemental compositions,which affect the neutron-gamma reaction cross-sections and gamma generation.Compared to traditional gamma sources such as Cs-137,these changes significantly affect the generation and transport of neutron-induced inelastic gamma rays and hinder accurate measurements.To address this,a novel method is proposed that incorporates the mass attenuation coefficient function to account for the effects of various lithologies and pore contents on gamma-ray attenuation,thereby achieving more accurate density measurements by clarifying the transport processes of inelastic gamma rays with varying energies and spatial distributions in varied logging environments.The proposed method avoids the complex correction of neutron transport and is verified through Monte Carlo simulations for its applicability across various lithologies and pore contents,demonstrating absolute density errors that are less than 0.02 g/cm^(3)in clean formations and indicating good accuracy.This study clarifies the NGD mechanism and provides theoretical guidance for the application of NGD logging methods.Further studies will be conducted on extreme environmental conditions and tool calibration.
基金National Key Research and Development Program of China (2022YFC3004004)National Natural Science Foundation of China (42075155,12241104)National Natural Science Foundation of China Joint Fund (U2342213)。
文摘The infrared channels of the FY-4B advanced geosynchronous radiation imagers(AGRI) play a crucial role in temperature and humidity analyses for mesoscale numerical weather prediction, particularly in enhancing the initial field quality and the forecasting accuracy of the model. This study assimilated FY-4B AGRI data into the CMA-MESO model and analyzed the bias characteristics and correction methods. Analysis of the AGRI data revealed a clear diurnal variation in the bias, which was positively correlated with the solar elevation angle. However, the diurnal variation in the bias lagged behind the solar elevation angle, likely owing to temperature changes and delayed instrument responses resulting from solar radiation. To address this issue, we propose a correction method that utilizes the solar elevation angle after an optimal time shift. Using the time-shifted solar elevation angle as a predictor effectively reduces the diurnal variation in bias and significantly improves the correction effect. This approach provides theoretical support for the assimilation of FY-4B AGRI data into mesoscale numerical weather predictions, thereby enhancing the reliability of the assimilation results.
基金supported by the Meteorological Joint Funds of the National Natural Science Foundation of China(Grant No.U2142211)the National Natural Science Foundation of China(Grant Nos.42075141,42341202 and 62088101)+1 种基金the National Key Research and Development Program of China(Grant No.2020YFA0608000)the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100).
文摘Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.
基金supported by the National Key Research and Development Program Project(2023YFC3107804)Planning Fund Project of Humanities and Social Sciences Research of the Ministry of Education(24YJA880097)the Graduate Education Reform Project in North China University of Technology(217051360025XN095-17)。
文摘Marine forecasting is critical for navigation safety and disaster prevention.However,traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial features.To address the limitations,the paper proposes a TimeXer-based numerical forecast correction model optimized by an exogenous-variable attention mechanism.The model treats target forecast values as internal variables,and incorporates historical temporal-spatial data and seven-day numerical forecast results from traditional models as external variables based on the embedding strategy of TimeXer.Using a self-attention structure,the model captures correlations between exogenous variables and target sequences,explores intrinsic multi-dimensional relationships,and subsequently corrects endogenous variables with the mined exogenous features.The model’s performance is evaluated using metrics including MSE(Mean Squared Error),MAE(Mean Absolute Error),RMSE(Root Mean Square Error),MAPE(Mean Absolute Percentage Error),MSPE(Mean Square Percentage Error),and computational time,with TimeXer and PatchTST models serving as benchmarks.Experiment results show that the proposed model achieves lower errors and higher correction accuracy for both one-day and seven-day forecasts.
基金Supported by the National Science and Technology Major Project of China(2022ZD0119005)the Natural Science Project of Shaanxi Province(2025JC-YBMS-754,2024JC-YBMS-539)。
文摘Reconfigurable array architecture has become an important hardware platform for edge-side deployment of convolutional neural networks due to their high parallelism and flexible programmability.However,traditional multi-branch convolutional networks suffer from computational redundancy,high memory access overhead,and inefficient branch fusion.Therefore,this paper proposes an adaptive multi-branch convolutional module(AMBC)that integrates software-hardware co-optimization.During training,the learnable fusion coefficients are introduced to enable adaptive fusion of multi-scale features,while in the inference phase,the multiple branches and their normalization parameters are merged with the fusion coefficients into a single 3×3 convolutional kernel through operator fusion.On the SIREA-288 reconfigurable platform,compared with unoptimized multi-branch networks,the proposed AMBC reduces external memory accesses by 47.91%and inference latency by 47.20%,achieving a 1.90×speedup.This approach maximizes the utilization of the reconfigurable logic while minimizing both reconfiguration and data-movement overheads in edge inference.
基金Project supported by the National Key Research and Development Program of China(Grant No.2024YFA1208800)the National Natural Science Foundation of China(Grant Nos.62404253,62304254,U23A20322)。
文摘Sensor noise is a critical factor that degrades the performance of image processing systems.In traditional computing systems,noise correction is implemented in the digital domain,resulting in redundant latency and power consumption overhead in the analog-to-digital conversion.In this work,we propose an analog-domain image correction architecture based on a proposed small-scale UNet,which implements a compact noise correction network within a one-transistor-one-memristor(1T1R)array.The statistical non-idealities of the fabricated 1T1R array(e.g.,device variability)are rigorously incorporated into the network's training and inference simulations.This correction network architecture leverages memristors for conducting multiply-accumulate operations aimed at rectifying non-uniform noise,defective pixels(stuck-at-bright/dark),and exposure mismatch.Compared to systems without correction,the proposed architecture achieves up to 50.13%improvement in recognition accuracy while demonstrating robust tolerance to memristor device-level errors.The proposed system achieves a 2.13-fold latency reduction and three orders of magnitude higher energy efficiency compared to conventional architecture.This work establishes a new paradigm for advancing the development of low-power,low-latency,and high-precision image processing systems.
基金Project supported by the National Natural Science Foundation of China(Grant No.40976108)the Shanghai Leading Academic Discipline Project(Grant No.J50103)
文摘In remote sensing sea surface temperature （SST）, the traditional fusion method is used to compute the dot product of a subjective weight vector with a satellite measurement vector, while the result requires validation by field measurement. However, field measurement that relative to the satellite measurement is very sparse, many information may not be verified. A relative objective weight vector is constructed by using the limited field measurement, which is based on coefficient of variation method. And then it make an application of the data fusion by the weighted average method in the SST data. fuse SST data with the weighted average method. In this way, some posteriori information can be added to the fusion process. The model reduces the dependence on verification, and some of the satellite measurement can be handled without corresponding to the field measurement, and the fusion result matches transfer errors theory.
基金Supported by National Natural Science Foundation of China (No.10572143) and Joint Project between the Royal Society and the Chinese Academy of Sciences (No.15933).
文摘In this article, principle and mathematical method of determining the phase fractions of multiphase flows by using a dual-energy γ -ray system have been described. The dual-energy γ -ray device is composed of radioactive isotopes of 241Am and 137Cs with γ -ray energies of 59.5 and 662 keV, respectively. A rational method to calibrate the absorption coefficient was introduced in detail. The modified arithmetic is beneficial to removing the extra Compton scattering from the measured value. The result shows that the dual-energy γ -ray technique can be used in three-phase flow with average accuracy greater than 95%, which enables us to determine phase fractions almost independent of the flow regime. Improvement has been achieved on measurement accuracy of phase fractions.
基金Supported by the Scientific Research Project of Yunnan Agricultural University(A3007680)the Fund for Scientific Research of Department of Education+1 种基金Yunnan(2014y1902014y193)~~
文摘This study was conducted to establish a simple convenient method for calculating crop coefficient, and provide a certain basis for the research of the empirical formula for calculating crop coefficient with plant height which could be measured conveniently with regional differences, especially for the establishment of accurate irrigation schedule of potato in Yunnan. By the field experiment on potato under the condition of drip irrigation, it was found that the models of plant height with corrected FAO-56-recommended K and measured K were a quartic polynomial and a cubic polynomial, respectively, and the polynomial of potato plant height with measured crop coefficient was simpler with higher degree of fitting; and the differences between the period with the highest change rate of potato plant height and the periods with the greatest FAO-56-recommended K and measured K exhibited a differences of 3 d. In conclusion： In the future study of simple or empirical formula calculation of crop coefficient, plant height should be considered as a main dependent variable in that the calculation result would be closer to the measured crop coefficient with the problem of regional difference existing in the FAO method solved and the formula might be simpler; and the irrigation time of potato should be 3 d earlier than the irrigation time determined according to the corrected FAO-56-recommended crop coefficient, especially in the key water requirement stages of potato.
基金supported by the National Natural Science Foundation of China under[Grants 42004019 and 41874033].
文摘The tropospheric delay has a significant impact on high-accuracy positioning of the Global Navigation Satellite System(GNSS).Traditional solutions have their weaknesses.First,the estimation of tropospheric delay as a state parameter slows the positioning filter's convergence,especially critical for Precise Point Positioning(PPP).Second,correction-based approaches,including empirical model,meteorological model and GNSS network observations,have their corresponding limitations.The empirical model comprises yearly data-based statistics,which ignores high temporal-variation components,leading to decreased correction accuracy.The meteorological model requires real-time local weather observations.One can enable the network method of the expensive regional infrastructure of GNSS stations,of which performance depends on the rover-network geometry.In this study,we enable a real-time tropospheric regional correction service by polynomial coefficients from the Kalman filtering of multisource data,including the Global Pressure and Temperature 2 wet(GPT2w)model,weather observations from the National Oceanic and Atmospheric Administration(NOAA),and GNSS network observations.After discussing the weighting strategy examined by the regional dataset from Zhejiang Province,we evaluate the performance of the proposed fusion approach with post-processed PPP results as references.We obtained the optimal weightings for the corresponding dataset,and the average accuracy for Zenith Tropospheric Delay(ZTD)is 0.43,and 1.20 cm under static,active,and overall weather conditions,respectively.Compared with the real-time GNSS network ZTD solution,our proposed fusion solution is improved by 48.21%,55.20%,and 41.70%,respectively.In conclusion,the proposed approach makes the best of three traditional correction-based methods to provide optimized real-time tropospheric service.
基金National Natural Science Foundation of China(Grant No.49971064)
文摘By means of a logarithm law for the velocity profile, a corrected formula of bed resistance coefficient, which involves many factors such as gradient of still water depth, variation of surface elevation, flow direction, and so on, is derived from the 3D governing equations of tidal current by averaging over the whole water depth. Theoretical analysis and application have shown that the 2D plane tidal current numerical model would be more reasonable and could be applied to steep bottom topography when the corrected bed resistance coefficient is used, therefore the results of reproduction simulation and engineering calculation would be more scientific and reasonable.
基金support for this work by Key Research and Development Project of Henan Province(Grant.No.241111232300)the National Natural Science Foundation of China(Grant.No.52273085 and 52303113)the Open Fund of Yaoshan Laboratory(Grant.No.2024003).
文摘The morphological distribution of absorbent in composites is equally important with absorbents for the overall electromagnetic properties,but it is often ignored.Herein,a comprehensive consideration including electromagnetic component regulation,layered arrangement structure,and gradient concentration distribution was used to optimize impedance matching and enhance electromagnetic loss.On the microscale,the incorporation of magnetic Ni nanoparticles into MXene nanosheets(Ni@MXene)endows suitable intrinsic permittivity and permeability.On the macroscale,the layered arrangement of Ni@MXene increases the effective interaction area with electromagnetic waves,inducing multiple reflection/scattering effects.On this basis,according to the analysis of absorption,reflection,and transmission(A-R-T)power coefficients of layered composites,the gradient concentration distribution was constructed to realize the impedance matching at low-concentration surface layer,electromagnetic loss at middle concentration interlayer and microwave reflection at high-concentration bottom layer.Consequently,the layered gradient composite(LG5-10-15)achieves complete absorption coverage of X-band at thickness of 2.00-2.20 mm with RL_(min) of-68.67 dB at 9.85 GHz in 2.05 mm,which is 199.0%,12.6%,and 50.6%higher than non-layered,layered and layered descending gradient composites,respectively.Therefore,this work confirms the importance of layered gradient structure in improving absorption performance and broadens the design of high-performance microwave absorption materials.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608000)the National Natural Science Foundation of China(Grant No.42030605)+1 种基金CAAI-MindSpore Academic Fund Research Projects(CAAIXSJLJJ2023MindSpore11)the program of China Scholarships Council(No.CXXM2101180001)。
文摘Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.
基金supports by the National Natural Science Foundation of China(Nos.82201135)"2015"Cultivation Program for Reserve Talents for Academic Leaders of Nanjing Stomatological School,Medical School of Nanjing University(No.0223A204).
文摘Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.
