Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currentl...Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.展开更多
Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same g...Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.展开更多
The fluctuation of wind power affects the operating safety and power consumption of the electric power grid and restricts the grid connection of wind power on a large scale.Therefore,wind power forecasting plays a key...The fluctuation of wind power affects the operating safety and power consumption of the electric power grid and restricts the grid connection of wind power on a large scale.Therefore,wind power forecasting plays a key role in improving the safety and economic benefits of the power grid.This paper proposes a wind power predicting method based on a convolutional graph attention deep neural network with multi-wind farm data.Based on the graph attention network and attention mechanism,the method extracts spatial-temporal characteristics from the data of multiple wind farms.Then,combined with a deep neural network,a convolutional graph attention deep neural network model is constructed.Finally,the model is trained with the quantile regression loss function to achieve the wind power deterministic and probabilistic prediction based on multi-wind farm spatial-temporal data.A wind power dataset in the U.S.is taken as an example to demonstrate the efficacy of the proposed model.Compared with the selected baseline methods,the proposed model achieves the best prediction performance.The point prediction errors(i.e.,root mean square error(RMSE)and normalized mean absolute percentage error(NMAPE))are 0.304 MW and 1.177%,respectively.And the comprehensive performance of probabilistic prediction(i.e.,con-tinuously ranked probability score(CRPS))is 0.580.Thus,the significance of multi-wind farm data and spatial-temporal feature extraction module is self-evident.展开更多
Storm surges pose significant danger and havoc to the coastal residents’safety,property,and lives,particularly at offshore locations with shallow water levels.Predictions of storm surges with hours of warning time ar...Storm surges pose significant danger and havoc to the coastal residents’safety,property,and lives,particularly at offshore locations with shallow water levels.Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans.In addition to experienced predictions and numerical models,artificial intelligence(AI)techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations.Convolutional neural network(CNN)and long short-term memory(LSTM)are two of the most important models among AI techniques.However,they have been scarcely utilised for surge level(SL)forecasting,and combinations of the two models are even rarer.This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information.The architectures of the CNN,LSTM,and two sequential techniques of combining the models(LSTM–CNN and CNN–LSTM)were constructed via a trial-and-error approach and knowledge obtained from previous studies.As a case study,11 a of hourly observed SL and wind data of the Xiuying Station,Hainan Province,China,were organised as inputs for training to verify the feasibility and superiority of the proposed models.The results show that CNN and LSTM had evident advantages over support vector regression(SVR)and multilayer perceptron(MLP),and the combined models outperformed the individual models(CNN and LSTM),mostly by 4%–6%.However,on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges,the accuracy was found to improve by over 10%at all forecasting steps.展开更多
In this paper, the process of medium- and short-term prediction (submitted in special cards) of the Artux earthquake (MS=6.9) and the Usurian earthquake (MS=5.8) in Xinjiang area, is introduced. The imminent seismic r...In this paper, the process of medium- and short-term prediction (submitted in special cards) of the Artux earthquake (MS=6.9) and the Usurian earthquake (MS=5.8) in Xinjiang area, is introduced. The imminent seismic risk regions are judged based on long- and medium-term seismic risk regions and annual seismic risk regions determined by national seismologic analysis, combined with large seismic situation analysis. We trace and analyze the seismic situation in large areas, and judge principal risk regions or belts of seismic activity in a year, by integrating the large area’s seismicity with geodetic deformation evolutional characteristics. As much as possible using information, we study synthetically observational information for long-medium- and short-term (time domain) and large-medium -small dimensions (space domain), and approach the forecast region of forthcoming earthquakes from the large to small magnitude. A better effect has been obtained. Some questions about earthquake prediction are discussed.展开更多
In order to achieve high short-term prediction accuracy of ionospheric TEC,first,we transform a seasonal time series for ionospheric Total Electron Content (TEC) into a stationary time series by seasonal differences a...In order to achieve high short-term prediction accuracy of ionospheric TEC,first,we transform a seasonal time series for ionospheric Total Electron Content (TEC) into a stationary time series by seasonal differences and regular differences with a full consideration of the Multiplicative Seasonal model.Next,we use the Autoregressive Integrated Moving Average (ARIMA) model taken from time series analysis theory for modeling the stationary TEC values to predict the TEC series.Using TEC data from 2008 to 2012 provided by the Center for Orbit Determination in Europe (CODE) as sample data,we analyzed the precision of this method for prediction of ionospheric TEC values which vary from high to low latitudes during both quiet and active ionospheric periods.The effect of the TEC sample’s length on the predicted accuracy is analyzed,too.Results from numerical experiments show that during the ionospheric quiet period the average relative prediction accuracy for a six day time span reaches up to 83.3% with average prediction residual errors of about 0.18±1.9 TECu.During ionospheric active periods it changes to 86.6% with an average prediction residual error of about 0.69±2.6 TECu.For the quiet periods,above 90% of predicted residual is less than ±3 TECu while during active periods,it is only about 81%.The two periods show that that the higher the latitude,the higher the absolute precision,and the lower the predicted relative accuracy.In addition,the results show that prediction accuracy will improve with an increase of the TEC sample sequences length,but it will gradually reduce if the length exceeds the optimal length,about 30 days.On the other hand,with the same TEC sample,as the predicted days increase,the predictive accuracy decreases.Athough the predictive accuracy is not apparent at the beginning,it will be significantly reduced after 30 days.展开更多
A statistical downscaling approach was developed to improve seasonal-to-interannual prediction of summer rainfall over North China by considering the effect of decadal variability based on observational datasets and d...A statistical downscaling approach was developed to improve seasonal-to-interannual prediction of summer rainfall over North China by considering the effect of decadal variability based on observational datasets and dynamical model outputs. Both predictands and predictors were first decomposed into interannual and decadal components. Two predictive equations were then built separately for the two distinct timescales by using multivariate linear regressions based on independent sample validation. For the interannual timescale, 850-hPa meridional wind and 500-hPa geopotential heights from multiple dynamical models' hindcasts and SSTs from observational datasets were used to construct predictors. For the decadal timescale, two well-known basin-scale SST decadal oscillation(the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation) indices were used as predictors. Then, the downscaled predictands were combined to represent the predicted/hindcasted total rainfall. The prediction was compared with the models' raw hindcasts and those from a similar approach but without timescale decomposition. In comparison to hindcasts from individual models or their multi-model ensemble mean, the skill of the present scheme was found to be significantly higher, with anomaly correlation coefficients increasing from nearly neutral to over 0.4 and with RMSE decreasing by up to 0.6 mm d-1. The improvements were also seen in the station-based temporal correlation of the predictions with observed rainfall, with the coefficients ranging from-0.1 to 0.87, obviously higher than the models' raw hindcasted rainfall results. Thus, the present approach exhibits a great advantage and may be appropriate for use in operational predictions.展开更多
We present a verification of the short-term predictions of solar Xray bursts for the maximum phase (2000-2001) of Solar Cycle 23, issued by two prediction centers. The results are that the rate of correct predictions ...We present a verification of the short-term predictions of solar Xray bursts for the maximum phase (2000-2001) of Solar Cycle 23, issued by two prediction centers. The results are that the rate of correct predictions is about equal for RWC-China and WWA; the rate of too high predictions is greater for RWC-China than for WWA, while the rate of too low predictions is smaller for RWC-China than for WWA.展开更多
Input data of the system are two-dimensional images and one-dimensional distributions of total and polarized solar emission at 5.2 cm wavelength obtained with SSRT. Together with photoheliograms, magnetograms, Hα-fil...Input data of the system are two-dimensional images and one-dimensional distributions of total and polarized solar emission at 5.2 cm wavelength obtained with SSRT. Together with photoheliograms, magnetograms, Hα-filtergrams and characteristics of active regions received from other sources, they form the initial database. The first stage includes superimposing the images, identifying microwave sources with active regions, assigning NOAA numbers to the sources, and determining for each active region the heliolatitude, extent, and inclination angle of the group's axis to the equator. These data are used to calculate the boundaries of longitude zones for each active region. A next stage involves determining the brightness temperatures of microwave sources less than the polarization distribution, the degree of polarization, and microwave emission flux, as well as calculating the parameters of microwave sources. Each parameter is assigned its own value of the weight factor, and the sum of values is used to draw the conclusion about the flare occurrence probability in each active region and on the Sun in general.展开更多
Short-term prediction of on-street parking occupancy is essential to the ITS system,which can guide drivers in finding vacant parking spaces.And the spatial dependencies and exogenous dependencies need to be considere...Short-term prediction of on-street parking occupancy is essential to the ITS system,which can guide drivers in finding vacant parking spaces.And the spatial dependencies and exogenous dependencies need to be considered simultaneously,which makes short-term prediction of on-street parking occupancy challenging.Therefore,this paper proposes a deep learning model for predicting block-level parking occupancy.First,the importance of multiple points of interest(POI)in different buffers is sorted by Boruta,used for feature selection.The results show that different types of POI data should consider different buffer radii.Then based on the real on-street parking data,long short-term memory(LSTM)that can address the time dependencies is applied to predict the parking occupancy.The results demonstrate that LSTM considering POI data after Boruta selection(LSTM(+BORUTA))outperforms other baseline methods,including LSTM,with an average testing MAPE of 11.78%.The selection process of POI data helps LSTM reduce training time and slightly improve the prediction performance,which indicates that complex correlations among the same type of POI data in different buffer zones will also affect the prediction accuracy of LSTM.When there are more restaurants on both sides of the street,the prediction performance of LSTM(+BORUTA)is significantly better than that of LSTM.展开更多
Based on the data recorded by the regional digital seismic network of Yunnan and using new methods, the short-term variations of the ambient stress field of Yunnan and its adjacent areas are monitored in real time. Wi...Based on the data recorded by the regional digital seismic network of Yunnan and using new methods, the short-term variations of the ambient stress field of Yunnan and its adjacent areas are monitored in real time. With the in-depth analyses of the spatial-temporal evolution of the ambient stress field prior to the 2004, Shuangbai M_S5.0 earthquake, concrete procedures for predicting the three elements of the earthquake are presented.展开更多
With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning ...With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.展开更多
Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy.However,owing to the stochastic and intermittent of wind speed,predicting ...Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy.However,owing to the stochastic and intermittent of wind speed,predicting wind speed accurately is difficult.A new hybrid deep learning model based on empirical wavelet transform,recurrent neural network and error correction for short-term wind speed prediction is proposed in this paper.The empirical wavelet transformation is applied to decompose the original wind speed series.The long short term memory network and the Elman neural network are adopted to predict low-frequency and high-frequency wind speed sub-layers respectively to balance the calculation efficiency and prediction accuracy.The error correction strategy based on deep long short term memory network is developed to modify the prediction errors.Four actual wind speed series are utilized to verify the effectiveness of the proposed model.The empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction.展开更多
Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep lear...Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.展开更多
The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning mode...The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.展开更多
A recently published modeling approach for the penetration into adobe and previous approaches implicitly criticized are reviewed and discussed.This article contains a note on the paper titled“Ballistic model for the ...A recently published modeling approach for the penetration into adobe and previous approaches implicitly criticized are reviewed and discussed.This article contains a note on the paper titled“Ballistic model for the prediction of penetration depth and residual velocity in adobe:A new interpretation of the ballistic resistance of earthen masonry”(DOI:https://doi.org/10.1016/j.dt.2018.07.017).Reply to the Note from Li Piani et al is linked to this article.展开更多
OBJECTIVES To establish a scoring system combining the ACEF score and the quantitative blood flow ratio(QFR) to improve the long-term risk prediction of patients undergoing percutaneous coronary intervention(PCI).METH...OBJECTIVES To establish a scoring system combining the ACEF score and the quantitative blood flow ratio(QFR) to improve the long-term risk prediction of patients undergoing percutaneous coronary intervention(PCI).METHODS In this population-based cohort study, a total of 46 features, including patient clinical and coronary lesion characteristics, were assessed for analysis through machine learning models. The ACEF-QFR scoring system was developed using 1263consecutive cases of CAD patients after PCI in PANDA Ⅲ trial database. The newly developed score was then validated on the other remaining 542 patients in the cohort.RESULTS In both the Random Forest Model and the Deep Surv Model, age, renal function(creatinine), cardiac function(LVEF)and post-PCI coronary physiological index(QFR) were identified and confirmed to be significant predictive factors for 2-year adverse cardiac events. The ACEF-QFR score was constructed based on the developmental dataset and computed as age(years)/EF(%) + 1(if creatinine ≥ 2.0 mg/d L) + 1(if post-PCI QFR ≤ 0.92). The performance of the ACEF-QFR scoring system was preliminarily evaluated in the developmental dataset, and then further explored in the validation dataset. The ACEF-QFR score showed superior discrimination(C-statistic = 0.651;95% CI: 0.611-0.691, P < 0.05 versus post-PCI physiological index and other commonly used risk scores) and excellent calibration(Hosmer–Lemeshow χ^(2)= 7.070;P = 0.529) for predicting 2-year patient-oriented composite endpoint(POCE). The good prognostic value of the ACEF-QFR score was further validated by multivariable Cox regression and Kaplan–Meier analysis(adjusted HR = 1.89;95% CI: 1.18–3.04;log-rank P < 0.01) after stratified the patients into high-risk group and low-risk group.CONCLUSIONS An improved scoring system combining clinical and coronary lesion-based functional variables(ACEF-QFR)was developed, and its ability for prognostic prediction in patients with PCI was further validated to be significantly better than the post-PCI physiological index and other commonly used risk scores.展开更多
BACKGROUND Previous studies have reported that low hematocrit levels indicate poor survival in patients with ovarian cancer and cervical cancer,the prognostic value of hematocrit for colorectal cancer(CRC)patients has...BACKGROUND Previous studies have reported that low hematocrit levels indicate poor survival in patients with ovarian cancer and cervical cancer,the prognostic value of hematocrit for colorectal cancer(CRC)patients has not been determined.The prognostic value of red blood cell distribution width(RDW)for CRC patients was controversial.AIM To investigate the impact of RDW and hematocrit on the short-term outcomes and long-term prognosis of CRC patients who underwent radical surgery.METHODS Patients who were diagnosed with CRC and underwent radical CRC resection between January 2011 and January 2020 at a single clinical center were included.The short-term outcomes,overall survival(OS)and disease-free survival(DFS)were compared among the different groups.Cox analysis was also conducted to identify independent risk factors for OS and DFS.RESULTS There were 4258 CRC patients who underwent radical surgery included in our study.A total of 1573 patients were in the lower RDW group and 2685 patients were in the higher RDW group.There were 2166 and 2092 patients in the higher hematocrit group and lower hematocrit group,respectively.Patients in the higher RDW group had more intraoperative blood loss(P<0.01)and more overall complications(P<0.01)than did those in the lower RDW group.Similarly,patients in the lower hematocrit group had more intraoperative blood loss(P=0.012),longer hospital stay(P=0.016)and overall complications(P<0.01)than did those in the higher hematocrit group.The higher RDW group had a worse OS and DFS than did the lower RDW group for tumor node metastasis(TNM)stage I(OS,P<0.05;DFS,P=0.001)and stage II(OS,P=0.004;DFS,P=0.01)than the lower RDW group;the lower hematocrit group had worse OS and DFS for TNM stage II(OS,P<0.05;DFS,P=0.001)and stage III(OS,P=0.001;DFS,P=0.001)than did the higher hematocrit group.Preoperative hematocrit was an independent risk factor for OS[P=0.017,hazard ratio(HR)=1.256,95%confidence interval(CI):1.041-1.515]and DFS(P=0.035,HR=1.194,95%CI:1.013-1.408).CONCLUSION A higher preoperative RDW and lower hematocrit were associated with more postoperative complications.However,only hematocrit was an independent risk factor for OS and DFS in CRC patients who underwent radical surgery,while RDW was not.展开更多
The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication e...The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.展开更多
Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The ris...Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The rising federated learning provides us with a promising solution to this problem,which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device,decoupling the ability to do machine learning from the need to store the data in the cloud.However,existing federated learningbased methods either do not provide privacy guarantees or have vulnerability in terms of privacy leakage.In this paper,we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm,where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction performance.Extensive experimental results show that our proposed method significantly outperforms the existing stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.展开更多
基金supported by National Natural Science Foundation of China,China(No.42004016)HuBei Natural Science Fund,China(No.2020CFB329)+1 种基金HuNan Natural Science Fund,China(No.2023JJ60559,2023JJ60560)the State Key Laboratory of Geodesy and Earth’s Dynamics self-deployment project,China(No.S21L6101)。
文摘Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.
基金funded by the Fujian Province Science and Technology Plan,China(Grant Number 2019H0017).
文摘Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.
基金supported by the Science and Technology Project of State Grid Corporation of China(4000-202122070A-0-0-00).
文摘The fluctuation of wind power affects the operating safety and power consumption of the electric power grid and restricts the grid connection of wind power on a large scale.Therefore,wind power forecasting plays a key role in improving the safety and economic benefits of the power grid.This paper proposes a wind power predicting method based on a convolutional graph attention deep neural network with multi-wind farm data.Based on the graph attention network and attention mechanism,the method extracts spatial-temporal characteristics from the data of multiple wind farms.Then,combined with a deep neural network,a convolutional graph attention deep neural network model is constructed.Finally,the model is trained with the quantile regression loss function to achieve the wind power deterministic and probabilistic prediction based on multi-wind farm spatial-temporal data.A wind power dataset in the U.S.is taken as an example to demonstrate the efficacy of the proposed model.Compared with the selected baseline methods,the proposed model achieves the best prediction performance.The point prediction errors(i.e.,root mean square error(RMSE)and normalized mean absolute percentage error(NMAPE))are 0.304 MW and 1.177%,respectively.And the comprehensive performance of probabilistic prediction(i.e.,con-tinuously ranked probability score(CRPS))is 0.580.Thus,the significance of multi-wind farm data and spatial-temporal feature extraction module is self-evident.
基金The National Key Research and Development Program of China under contract No.2016YFC1402609the Open Fund of the Key Laboratory of Marine Hazards Forecasting+1 种基金Ministry of Natural Resources under contract No.LOMF 1804the National Natural Science Foundation of China under contract No.42077438。
文摘Storm surges pose significant danger and havoc to the coastal residents’safety,property,and lives,particularly at offshore locations with shallow water levels.Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans.In addition to experienced predictions and numerical models,artificial intelligence(AI)techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations.Convolutional neural network(CNN)and long short-term memory(LSTM)are two of the most important models among AI techniques.However,they have been scarcely utilised for surge level(SL)forecasting,and combinations of the two models are even rarer.This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information.The architectures of the CNN,LSTM,and two sequential techniques of combining the models(LSTM–CNN and CNN–LSTM)were constructed via a trial-and-error approach and knowledge obtained from previous studies.As a case study,11 a of hourly observed SL and wind data of the Xiuying Station,Hainan Province,China,were organised as inputs for training to verify the feasibility and superiority of the proposed models.The results show that CNN and LSTM had evident advantages over support vector regression(SVR)and multilayer perceptron(MLP),and the combined models outperformed the individual models(CNN and LSTM),mostly by 4%–6%.However,on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges,the accuracy was found to improve by over 10%at all forecasting steps.
文摘In this paper, the process of medium- and short-term prediction (submitted in special cards) of the Artux earthquake (MS=6.9) and the Usurian earthquake (MS=5.8) in Xinjiang area, is introduced. The imminent seismic risk regions are judged based on long- and medium-term seismic risk regions and annual seismic risk regions determined by national seismologic analysis, combined with large seismic situation analysis. We trace and analyze the seismic situation in large areas, and judge principal risk regions or belts of seismic activity in a year, by integrating the large area’s seismicity with geodetic deformation evolutional characteristics. As much as possible using information, we study synthetically observational information for long-medium- and short-term (time domain) and large-medium -small dimensions (space domain), and approach the forecast region of forthcoming earthquakes from the large to small magnitude. A better effect has been obtained. Some questions about earthquake prediction are discussed.
基金The National Nature Science Foundation of China(41474025,41374035)The Fundamental Research Funds for the Central Universities(2014214020201).
文摘In order to achieve high short-term prediction accuracy of ionospheric TEC,first,we transform a seasonal time series for ionospheric Total Electron Content (TEC) into a stationary time series by seasonal differences and regular differences with a full consideration of the Multiplicative Seasonal model.Next,we use the Autoregressive Integrated Moving Average (ARIMA) model taken from time series analysis theory for modeling the stationary TEC values to predict the TEC series.Using TEC data from 2008 to 2012 provided by the Center for Orbit Determination in Europe (CODE) as sample data,we analyzed the precision of this method for prediction of ionospheric TEC values which vary from high to low latitudes during both quiet and active ionospheric periods.The effect of the TEC sample’s length on the predicted accuracy is analyzed,too.Results from numerical experiments show that during the ionospheric quiet period the average relative prediction accuracy for a six day time span reaches up to 83.3% with average prediction residual errors of about 0.18±1.9 TECu.During ionospheric active periods it changes to 86.6% with an average prediction residual error of about 0.69±2.6 TECu.For the quiet periods,above 90% of predicted residual is less than ±3 TECu while during active periods,it is only about 81%.The two periods show that that the higher the latitude,the higher the absolute precision,and the lower the predicted relative accuracy.In addition,the results show that prediction accuracy will improve with an increase of the TEC sample sequences length,but it will gradually reduce if the length exceeds the optimal length,about 30 days.On the other hand,with the same TEC sample,as the predicted days increase,the predictive accuracy decreases.Athough the predictive accuracy is not apparent at the beginning,it will be significantly reduced after 30 days.
基金supported by the Special Program in the Public Interest of the China Meteorological Administration (Grant No. GYHY201006022)the Strategic Special Projects of the Chinese Academy of Sciences (Grant No. XDA05090000)
文摘A statistical downscaling approach was developed to improve seasonal-to-interannual prediction of summer rainfall over North China by considering the effect of decadal variability based on observational datasets and dynamical model outputs. Both predictands and predictors were first decomposed into interannual and decadal components. Two predictive equations were then built separately for the two distinct timescales by using multivariate linear regressions based on independent sample validation. For the interannual timescale, 850-hPa meridional wind and 500-hPa geopotential heights from multiple dynamical models' hindcasts and SSTs from observational datasets were used to construct predictors. For the decadal timescale, two well-known basin-scale SST decadal oscillation(the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation) indices were used as predictors. Then, the downscaled predictands were combined to represent the predicted/hindcasted total rainfall. The prediction was compared with the models' raw hindcasts and those from a similar approach but without timescale decomposition. In comparison to hindcasts from individual models or their multi-model ensemble mean, the skill of the present scheme was found to be significantly higher, with anomaly correlation coefficients increasing from nearly neutral to over 0.4 and with RMSE decreasing by up to 0.6 mm d-1. The improvements were also seen in the station-based temporal correlation of the predictions with observed rainfall, with the coefficients ranging from-0.1 to 0.87, obviously higher than the models' raw hindcasted rainfall results. Thus, the present approach exhibits a great advantage and may be appropriate for use in operational predictions.
基金Supported by the National Natural Science Foundation of China
文摘We present a verification of the short-term predictions of solar Xray bursts for the maximum phase (2000-2001) of Solar Cycle 23, issued by two prediction centers. The results are that the rate of correct predictions is about equal for RWC-China and WWA; the rate of too high predictions is greater for RWC-China than for WWA, while the rate of too low predictions is smaller for RWC-China than for WWA.
基金Supported by the Education and Science Ministry of Russian Federation (477.2003.2)Russian Federal Program "Astronomy"the China-Russia Joint Research Center on Space Weather, Chinese Academy of Sciences
文摘Input data of the system are two-dimensional images and one-dimensional distributions of total and polarized solar emission at 5.2 cm wavelength obtained with SSRT. Together with photoheliograms, magnetograms, Hα-filtergrams and characteristics of active regions received from other sources, they form the initial database. The first stage includes superimposing the images, identifying microwave sources with active regions, assigning NOAA numbers to the sources, and determining for each active region the heliolatitude, extent, and inclination angle of the group's axis to the equator. These data are used to calculate the boundaries of longitude zones for each active region. A next stage involves determining the brightness temperatures of microwave sources less than the polarization distribution, the degree of polarization, and microwave emission flux, as well as calculating the parameters of microwave sources. Each parameter is assigned its own value of the weight factor, and the sum of values is used to draw the conclusion about the flare occurrence probability in each active region and on the Sun in general.
基金supported in part by the National Key Research and Development Program of China(Project No.2018YFB1600900)the Jiangsu Province Transportation Key Project of Science(Project No.2019Z01)Zhejiang Provincial Natural Science Foundation of China(No.LTGG23E080005).
文摘Short-term prediction of on-street parking occupancy is essential to the ITS system,which can guide drivers in finding vacant parking spaces.And the spatial dependencies and exogenous dependencies need to be considered simultaneously,which makes short-term prediction of on-street parking occupancy challenging.Therefore,this paper proposes a deep learning model for predicting block-level parking occupancy.First,the importance of multiple points of interest(POI)in different buffers is sorted by Boruta,used for feature selection.The results show that different types of POI data should consider different buffer radii.Then based on the real on-street parking data,long short-term memory(LSTM)that can address the time dependencies is applied to predict the parking occupancy.The results demonstrate that LSTM considering POI data after Boruta selection(LSTM(+BORUTA))outperforms other baseline methods,including LSTM,with an average testing MAPE of 11.78%.The selection process of POI data helps LSTM reduce training time and slightly improve the prediction performance,which indicates that complex correlations among the same type of POI data in different buffer zones will also affect the prediction accuracy of LSTM.When there are more restaurants on both sides of the street,the prediction performance of LSTM(+BORUTA)is significantly better than that of LSTM.
基金the Key Science andTechnology R&D Project of the 10th "Five-Year Plan" of Yunnan Province , entitled "Study of Med- and Short-term Prediction Techniques for Strong Earthquakein Yunnan"(2001NG46) andthe construction of Earthquake Monitoring andPrevention Center of West Yunnan (YN150105T037-045)
文摘Based on the data recorded by the regional digital seismic network of Yunnan and using new methods, the short-term variations of the ambient stress field of Yunnan and its adjacent areas are monitored in real time. With the in-depth analyses of the spatial-temporal evolution of the ambient stress field prior to the 2004, Shuangbai M_S5.0 earthquake, concrete procedures for predicting the three elements of the earthquake are presented.
文摘With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.
基金the Gansu Province Soft Scientific Research Projects(No.2015GS06516)the Funds for Distinguished Young Scientists of Lanzhou University of Technology,China(No.J201304)。
文摘Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy.However,owing to the stochastic and intermittent of wind speed,predicting wind speed accurately is difficult.A new hybrid deep learning model based on empirical wavelet transform,recurrent neural network and error correction for short-term wind speed prediction is proposed in this paper.The empirical wavelet transformation is applied to decompose the original wind speed series.The long short term memory network and the Elman neural network are adopted to predict low-frequency and high-frequency wind speed sub-layers respectively to balance the calculation efficiency and prediction accuracy.The error correction strategy based on deep long short term memory network is developed to modify the prediction errors.Four actual wind speed series are utilized to verify the effectiveness of the proposed model.The empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction.
基金funded by the Natural Science Foundation of Fujian Province,China (Grant No.2022J05291)Xiamen Scientific Research Funding for Overseas Chinese Scholars.
文摘Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.
基金funded by the National Natural Science Foundation of China (41807285)。
文摘The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.
文摘A recently published modeling approach for the penetration into adobe and previous approaches implicitly criticized are reviewed and discussed.This article contains a note on the paper titled“Ballistic model for the prediction of penetration depth and residual velocity in adobe:A new interpretation of the ballistic resistance of earthen masonry”(DOI:https://doi.org/10.1016/j.dt.2018.07.017).Reply to the Note from Li Piani et al is linked to this article.
基金sponsored by Sino Medical,Tianjin,Chinasupported by the Beijing Municipal Science and Technology Project[Z191100006619107 to B.X.]Capital Health Development Research Project[20201–4032 to K.D.].
文摘OBJECTIVES To establish a scoring system combining the ACEF score and the quantitative blood flow ratio(QFR) to improve the long-term risk prediction of patients undergoing percutaneous coronary intervention(PCI).METHODS In this population-based cohort study, a total of 46 features, including patient clinical and coronary lesion characteristics, were assessed for analysis through machine learning models. The ACEF-QFR scoring system was developed using 1263consecutive cases of CAD patients after PCI in PANDA Ⅲ trial database. The newly developed score was then validated on the other remaining 542 patients in the cohort.RESULTS In both the Random Forest Model and the Deep Surv Model, age, renal function(creatinine), cardiac function(LVEF)and post-PCI coronary physiological index(QFR) were identified and confirmed to be significant predictive factors for 2-year adverse cardiac events. The ACEF-QFR score was constructed based on the developmental dataset and computed as age(years)/EF(%) + 1(if creatinine ≥ 2.0 mg/d L) + 1(if post-PCI QFR ≤ 0.92). The performance of the ACEF-QFR scoring system was preliminarily evaluated in the developmental dataset, and then further explored in the validation dataset. The ACEF-QFR score showed superior discrimination(C-statistic = 0.651;95% CI: 0.611-0.691, P < 0.05 versus post-PCI physiological index and other commonly used risk scores) and excellent calibration(Hosmer–Lemeshow χ^(2)= 7.070;P = 0.529) for predicting 2-year patient-oriented composite endpoint(POCE). The good prognostic value of the ACEF-QFR score was further validated by multivariable Cox regression and Kaplan–Meier analysis(adjusted HR = 1.89;95% CI: 1.18–3.04;log-rank P < 0.01) after stratified the patients into high-risk group and low-risk group.CONCLUSIONS An improved scoring system combining clinical and coronary lesion-based functional variables(ACEF-QFR)was developed, and its ability for prognostic prediction in patients with PCI was further validated to be significantly better than the post-PCI physiological index and other commonly used risk scores.
基金The study was approved by the ethics committee of the First Affiliated Hospital of Chongqing Medical University(2022-K205),this study was conducted in accordance with the World Medical Association Declaration of Helsinki as well。
文摘BACKGROUND Previous studies have reported that low hematocrit levels indicate poor survival in patients with ovarian cancer and cervical cancer,the prognostic value of hematocrit for colorectal cancer(CRC)patients has not been determined.The prognostic value of red blood cell distribution width(RDW)for CRC patients was controversial.AIM To investigate the impact of RDW and hematocrit on the short-term outcomes and long-term prognosis of CRC patients who underwent radical surgery.METHODS Patients who were diagnosed with CRC and underwent radical CRC resection between January 2011 and January 2020 at a single clinical center were included.The short-term outcomes,overall survival(OS)and disease-free survival(DFS)were compared among the different groups.Cox analysis was also conducted to identify independent risk factors for OS and DFS.RESULTS There were 4258 CRC patients who underwent radical surgery included in our study.A total of 1573 patients were in the lower RDW group and 2685 patients were in the higher RDW group.There were 2166 and 2092 patients in the higher hematocrit group and lower hematocrit group,respectively.Patients in the higher RDW group had more intraoperative blood loss(P<0.01)and more overall complications(P<0.01)than did those in the lower RDW group.Similarly,patients in the lower hematocrit group had more intraoperative blood loss(P=0.012),longer hospital stay(P=0.016)and overall complications(P<0.01)than did those in the higher hematocrit group.The higher RDW group had a worse OS and DFS than did the lower RDW group for tumor node metastasis(TNM)stage I(OS,P<0.05;DFS,P=0.001)and stage II(OS,P=0.004;DFS,P=0.01)than the lower RDW group;the lower hematocrit group had worse OS and DFS for TNM stage II(OS,P<0.05;DFS,P=0.001)and stage III(OS,P=0.001;DFS,P=0.001)than did the higher hematocrit group.Preoperative hematocrit was an independent risk factor for OS[P=0.017,hazard ratio(HR)=1.256,95%confidence interval(CI):1.041-1.515]and DFS(P=0.035,HR=1.194,95%CI:1.013-1.408).CONCLUSION A higher preoperative RDW and lower hematocrit were associated with more postoperative complications.However,only hematocrit was an independent risk factor for OS and DFS in CRC patients who underwent radical surgery,while RDW was not.
文摘The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.
基金supported in part by the National Key Research and Development Program of China under 2020AAA0106000the National Natural Science Foundation of China under U20B2060 and U21B2036supported by a grant from the Guoqiang Institute, Tsinghua University under 2021GQG1005
文摘Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The rising federated learning provides us with a promising solution to this problem,which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device,decoupling the ability to do machine learning from the need to store the data in the cloud.However,existing federated learningbased methods either do not provide privacy guarantees or have vulnerability in terms of privacy leakage.In this paper,we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm,where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction performance.Extensive experimental results show that our proposed method significantly outperforms the existing stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.
