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
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
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
A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation,assisting rail operators in efficiently allocating resources and timely relieving pres...A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation,assisting rail operators in efficiently allocating resources and timely relieving pressure on passenger safety and operation.First,the passenger flow sequence models in the study are broken down using VMD for noise reduction.The objective environment features are then added to the characteristic factors that affect the passenger flow.The target station serves as an additional spatial feature and is mined concurrently using the KNN algorithm.It is shown that the hybrid model VMD-CLSMT has a higher prediction accuracy,by setting BP,CNN,and LSTM reference experiments.All models’second order prediction effects are superior to their first order effects,showing that the residual network can significantly raise model prediction accuracy.Additionally,it confirms the efficacy of supplementary and objective environmental features.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
Short-term load forecasting (STLF) is part and parcel of theefficient working of power grid stations. Accurate forecasts help to detect thefault and enhance grid reliability for organizing sufficient energy transactio...Short-term load forecasting (STLF) is part and parcel of theefficient working of power grid stations. Accurate forecasts help to detect thefault and enhance grid reliability for organizing sufficient energy transactions.STLF ranges from an hour ahead prediction to a day ahead prediction. Variouselectric load forecasting methods have been used in literature for electricitygeneration planning to meet future load demand. A perfect balance regardinggeneration and utilization is still lacking to avoid extra generation and misusageof electric load. Therefore, this paper utilizes Levenberg–Marquardt(LM) based Artificial Neural Network (ANN) technique to forecast theshort-term electricity load for smart grids in a much better, more precise,and more accurate manner. For proper load forecasting, we take the mostcritical weather parameters along with historical load data in the form of timeseries grouped into seasons, i.e., winter and summer. Further, the presentedmodel deals with each season’s load data by splitting it into weekdays andweekends. The historical load data of three years have been used to forecastweek-ahead and day-ahead load demand after every thirty minutes makingload forecast for a very short period. The proposed model is optimized usingthe Levenberg-Marquardt backpropagation algorithm to achieve results withcomparable statistics. Mean Absolute Percent Error (MAPE), Root MeanSquared Error (RMSE), R2, and R are used to evaluate the model. Comparedwith other recent machine learning-based mechanisms, our model presentsthe best experimental results with MAPE and R2 scores of 1.3 and 0.99,respectively. The results prove that the proposed LM-based ANN modelperforms much better in accuracy and has the lowest error rates as comparedto existing work.展开更多
BACKGROUND Obesity is a state in which excess heat is converted into excess fat,which accumulates in the body and may cause damage to multiple organs of the circulatory,endocrine,and digestive systems.Studies have sho...BACKGROUND Obesity is a state in which excess heat is converted into excess fat,which accumulates in the body and may cause damage to multiple organs of the circulatory,endocrine,and digestive systems.Studies have shown that the accumulation of abdominal fat and mesenteric fat hypertrophy in patients with obesity makes laparoscopic surgery highly difficult,which is not conducive to operation and affects patient prognosis.However,there is still controversy regarding these conclusions.AIM To explore the relationship between body mass index(BMI)and short-term prognosis after surgery for colorectal cancer.METHODS PubMed,Embase,Ovid,Web of Science,CNKI,and China Biology Medicine Disc databases were searched to obtain relevant articles on this topic.After the articles were screened according to the inclusion and exclusion criteria and the risk of literature bias was assessed using the Newcastle-Ottawa Scale,the prognostic indicators were combined and analyzed.RESULTS A total of 16 articles were included for quantitative analysis,and 15588 patients undergoing colorectal cancer surgery were included in the study,including 3775 patients with obesity and 11813 patients without obesity.Among them,12 articles used BMI≥30 kg/m^(2)and 4 articles used BMI≥25 kg/m^(2)for the definition of obesity.Four patients underwent robotic colorectal surgery,whereas 12 underwent conventional laparoscopic colorectal resection.The quality of the literature was good.Meta-combined analysis showed that the overall complication rate of patients with obesity after surgery was higher than that of patients without obesity[OR=1.35,95%CI:1.23-1.48,Z=6.25,P<0.0001].The incidence of anastomotic leak after surgery in patients with obesity was not significantly different from that in patients without obesity[OR=0.99,95%CI:0.70-1.41),Z=-0.06,P=0.956].The incidence of surgical site infection(SSI)after surgery in patients with obesity was higher than that in patients without obesity[OR=1.43,95%CI:1.16-1.78,Z=3.31,P<0.001].The incidence of reoperation in patients with obesity after surgery was higher than that in patients without obesity;however,the difference was not statistically significant[OR=1.15,95%CI:0.92-1.45,Z=1.23,P=0.23];Patients with obesity had lower mortality after surgery than patients without obesity;however,the difference was not statistically significant[OR=0.61,95%CI:0.35-1.06,Z=-1.75,P=0.08].Subgroup analysis revealed that the geographical location of the institute was one of the sources of heterogeneity.Robot-assisted surgery was not significantly different from traditional laparoscopic resection in terms of the incidence of complications.CONCLUSION Obesity increases the overall complication and SSI rates of patients undergoing colorectal cancer surgery but has no influence on the incidence of anastomotic leak,reoperation rate,and short-term mortality rate.展开更多
Night lighting has been shown to affect wild animals.To date,the effects of night lighting on the metabolic homeostasis of birds that spend short time in urban environments remain unclear.Using model bird species Zebr...Night lighting has been shown to affect wild animals.To date,the effects of night lighting on the metabolic homeostasis of birds that spend short time in urban environments remain unclear.Using model bird species Zebra Finch(Taeniopygia guttata),we investigated the effects of short-term night lighting on liver transcriptome,blood glucose,triglyceride,and thyroxine(T4 and T3)levels in birds exposed to two different night lighting duration periods(three days and six days).After three days of night lighting exposure,the expression of genes involved in fat synthesis in the liver was upregulated while the expression of genes involved in fatty acid oxidation and triglyceride decomposition was downregulated.There was also a reduction in blood triglyceride,glucose,and T3 concentrations.However,after six days of night lighting,the expression of genes associated with fatty acid decomposition and hyperglycemia in the liver was upregulated,while the expression of genes involved in fat synthesis was downregulated.Simultaneously,blood glucose levels and T3 concentration increased.These findings indicate that short-term exposure to night lighting can disrupt the lipid and glucose metabolism of small passerine birds,and longer stopovers in urban area with intense night lighting may cause birds to consume more lipid energy.展开更多
This article presents a novel approach to integrate a throughput prediction model for the ball mill into short-term stochastic production scheduling in mining complexes.The datasets for the throughput prediction model...This article presents a novel approach to integrate a throughput prediction model for the ball mill into short-term stochastic production scheduling in mining complexes.The datasets for the throughput prediction model include penetration rates from blast hole drilling(measurement while drilling),geological domains,material types,rock density,and throughput rates of the operating mill,offering an accessible and cost-effective method compared to other geometallurgical programs.First,the comminution behavior of the orebody was geostatistically simulated by building additive hardness proportions from penetration rates.A regression model was constructed to predict throughput rates as a function of blended rock properties,which are informed by a material tracking approach in the mining complex.Finally,the throughput prediction model was integrated into a stochastic optimization model for short-term production scheduling.This way,common shortfalls of existing geometallurgical throughput prediction models,that typically ignore the non-additive nature of hardness and are not designed to interact with mine production scheduling,are overcome.A case study at the Tropicana Mining Complex shows that throughput can be predicted with an error less than 30 t/h and a correlation coefficient of up to 0.8.By integrating the prediction model and new stochastic components into optimization,the production schedule achieves weekly planned production reliably because scheduled materials match with the predicted performance of the mill.Comparisons to optimization using conventional mill tonnage constraints reveal that expected production shortfalls of up to 7%per period can be mitigated this way.展开更多
A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively ...A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively used and still have considerable potential. In recent years, methods based on deep neural networks have made significant breakthroughs, and fault diagnosis methods for industrial processes based on deep learning have attracted considerable research attention. Therefore, we propose a fusion deeplearning algorithm based on a fully convolutional neural network(FCN) to extract features and build models to correctly diagnose all types of faults. We use long short-term memory(LSTM) units to expand our proposed FCN so that our proposed deep learning model can better extract the time-domain features of chemical process data. We also introduce the attention mechanism into the model, aimed at highlighting the importance of features, which is significant for the fault diagnosis of chemical processes with many features. When applied to the benchmark Tennessee Eastman process, our proposed model exhibits impressive performance, demonstrating the effectiveness of the attention-based LSTM FCN in chemical process fault diagnosis.展开更多
This study proposes a two-stage photovoltaic(PV)voltage control strategy for centralized control that ignores short-term load fluctuations.In the first stage,a deterministic power flow model optimizes the 15-minute ac...This study proposes a two-stage photovoltaic(PV)voltage control strategy for centralized control that ignores short-term load fluctuations.In the first stage,a deterministic power flow model optimizes the 15-minute active cycle of the inverter and reactive outputs to reduce network loss and light rejection.In the second stage,the local control stabilizes the fluctuations and tracks the system state of the first stage.The uncertain interval model establishes a chance constraint model for the inverter voltage-reactive power local control.Second-order cone optimization and sensitivity theories were employed to solve the models.The effectiveness of the model was confirmed using a modified IEEE 33 bus example.The intraday control outcome for distributed power generation considering the effects of fluctuation uncertainty,PV penetration rate,and inverter capacity is analyzed.展开更多
Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural ne...Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural network model based on the temporal pattern attention(TPA)mechanism.Firstly,based on the grey relational analysis,datasets similar to forecast day are obtained.Secondly,thebidirectional LSTM layermodels the data of thehistorical load,temperature,humidity,and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network,so that the influencing factors(with different characteristics)can select relevant information from different time steps to reduce the prediction error of the model.Simultaneously,the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism,so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input.Finally,the chaotic sparrow search algorithm(CSSA)is used to optimize the hyperparameter selection of the model.The short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238,respectively,which is lower than other forecastingmethods,demonstrating the accuracy and stability of our model.展开更多
An improved fuzzy time series algorithmbased on clustering is designed in this paper.The algorithm is successfully applied to short-term load forecasting in the distribution stations.Firstly,the K-means clustering met...An improved fuzzy time series algorithmbased on clustering is designed in this paper.The algorithm is successfully applied to short-term load forecasting in the distribution stations.Firstly,the K-means clustering method is used to cluster the data,and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division.On this basis,the data is fuzzed to form a fuzzy time series.Secondly,a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load,which is used to predict the short-term trend change of load in the distribution stations.Matlab/Simulink simulation results show that the load forecasting errors of the typical fuzzy time series on the time scale of one day and one week are[−50,20]and[−50,30],while the load forecasting errors of the improved fuzzy time series on the time scale of one day and one week are[−20,15]and[−20,25].It shows that the fuzzy time series algorithm improved by clustering improves the prediction accuracy and can effectively predict the short-term load trend of distribution stations.展开更多
There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement an...There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.展开更多
Background:This study was designed to investigate the feasibility of tumor-infiltrating immune cells with different phenotypic characteristics for predicting short-term clinical responses in patients with locally adva...Background:This study was designed to investigate the feasibility of tumor-infiltrating immune cells with different phenotypic characteristics for predicting short-term clinical responses in patients with locally advanced cervical cancer(LACC).Methods:Thirty-four patients who received concurrent chemoradiotherapy and twenty-one patients who merely underwent radiotherapy were enrolled in this study.We retrospectively analyzed the T cell markers(i.e.,CD3,CD4,CD8),memory markers(i.e.,CD45,CCR7),and differentiation markers(i.e.,CD27)in the peripheral blood and tumor tissues of patients with LACC before treatment based on flow cytometry.We also analyzed the relationship of T cell subsets between peripheral blood and tumor tissues,and their correlation with complete response or partial response.Results:The percentage of central memory CD8^(+)TCM(CD8^(+)CD45RA^(−)CD27^(+)CCR7^(+))cells in LACC patients was significantly lower than that of the control group.The percentage of CD8^(+)TN in the peripheral blood of LACC patients was significantly higher than that of tumor tissues.CD8^(+)TEM in the peripheral blood was significantly lower than that of tumor tissues.The percentage of CD8^(+)TN and CD8^(+)TCM in human papillomavirus(HPV)positive samples was significantly higher than that of HPV-negative samples.Similarly,the percentage of CD8^(+)TCM in tumor tissues was significantly higher in cancer tissue samples with lymph nodes compared with those without.Conclusion:A higher proportion of CD4^(+)TCM and a lower proportion of CD8^(+)TN in the tumor microenvironment of LACC may contribute to the therapy response prediction.展开更多
River bending is the major effect responsible for bed topography and bank changes.In this study,fluid velocity(measured by a three-dimensional Doppler advanced point current meter)and bed topographical data have been ...River bending is the major effect responsible for bed topography and bank changes.In this study,fluid velocity(measured by a three-dimensional Doppler advanced point current meter)and bed topographical data have been collected in 40 sections of an experimental model.The whole flume was composed of an organic glass bend,upstream and downstream water tanks,two transition straight sections,a circulation pump,and a connection pipeline.Each section has been found to be characterized by a primary circulation and a small reverse circulation,with some sections even presenting three more or more circulation structures.The minimum circulation intensity has been detected in proximity to the top of the curved channel,while a region with small longitudinal velocity has been observed near the concave bank of each bend,corresponding to the flat bed formed after a short period of scouring.The maximum sediment deposition and scour depth in the presence of a uniform distribution of living flexible vegetation within 10 cm of the flume wall have been found to be smaller than those observed in the tests conducted without vegetation.展开更多
Passive-warming, open-top chambers(OTCs) are widely applied for studying the effects of future climate warming on coastal wetlands. In this study, a set of six OTCs were established at a Phragmites wetland located in ...Passive-warming, open-top chambers(OTCs) are widely applied for studying the effects of future climate warming on coastal wetlands. In this study, a set of six OTCs were established at a Phragmites wetland located in the Yellow River Delta of Dongying City, China. With data collected through online transmission and in-situ sensors, the attributes and patterns of realized OTCs warming are demonstrated.The authors also quantified the preliminary influence of experimental chamber warming on plant traits.OTCs produced an elevated average air temperature of 0.8°C(relative to controls) during the growing season(June to October) of 2018, and soil temperatures actually decreased by 0.54°C at a depth of 5 cm and 0.46°C at a depth of 30 cm in the OTCs. Variations in diel patterns of warming depend greatly on the heat sources of incoming radiation in the daytime versus soil heat flux at night. Warming effects were often larger during instantaneous analyses and influenced OTCs air temperatures from-2.5°C to 8.3°C dependent on various meteorological conditions at any given time, ranging from cooling influences from vertical heat exchange and vegetation to radiation-associated warming. Night-time temperature depressions in the OTCs were due to the low turbulence inside OTCs and changes in surface soilatmosphere heat transfer. Plant shoot density, basal diameter, and biomass of Phragmites decreased by23.2%, 6.3%, and 34.0%, respectively, under experimental warming versus controls, and plant height increased by 4.3%, reflecting less carbon allocation to stem structures as plants in the OTCs experienced simultaneous wind buffering. While these passive-warming OTCs created the desired warming effects both to the atmosphere and soils, pest damages on the plant leaves and lodging within the OTCs were extensive and serious, creating the need to consider control options for these chambers and the replicated OTCs studies underway in other Chinese Phragmites marshes(Panjin and Yancheng).展开更多
Wind power prediction is very important for the economic dispatching of power systems containing wind power.In this work,a novel short-term wind power prediction method based on improved complete ensemble empirical mo...Wind power prediction is very important for the economic dispatching of power systems containing wind power.In this work,a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and(long short-term memory)LSTM neural network is proposed and studied.First,the original data is prepossessed including removing outliers and filling in the gaps.Then,the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast model.In addition,this study conducts seasonal classification of the annual data where ICEEMDAN is adopted to divide the original wind power sequence into numerous modal components according to different seasons.On this basis,sample entropy is used to calculate the complexity of each component and reconstruct them into trend components,oscillation components,and random components.Then,these three components are input into the LSTM neural network,respectively.Combined with the predicted values of the three components,the overall power prediction results are obtained.The simulation shows that ICEEMDAN-SE-LSTM achieves higher prediction accuracy ranging from 1.57%to 9.46%than other traditional models,which indicates the reliability and effectiveness of the proposed method for power prediction.展开更多
The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-...The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.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 Major Projects of the National Social Science Fund in China(21&ZD127).
文摘A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation,assisting rail operators in efficiently allocating resources and timely relieving pressure on passenger safety and operation.First,the passenger flow sequence models in the study are broken down using VMD for noise reduction.The objective environment features are then added to the characteristic factors that affect the passenger flow.The target station serves as an additional spatial feature and is mined concurrently using the KNN algorithm.It is shown that the hybrid model VMD-CLSMT has a higher prediction accuracy,by setting BP,CNN,and LSTM reference experiments.All models’second order prediction effects are superior to their first order effects,showing that the residual network can significantly raise model prediction accuracy.Additionally,it confirms the efficacy of supplementary and objective environmental features.
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.
基金support provided in part by the National Key Research and Development Program of China (No.2020YFB1005804)in part by the National Natural Science Foundation of China under Grant 61632009+1 种基金in part by the High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01in part by the NCRA-017,NUST,Islamabad.
文摘Short-term load forecasting (STLF) is part and parcel of theefficient working of power grid stations. Accurate forecasts help to detect thefault and enhance grid reliability for organizing sufficient energy transactions.STLF ranges from an hour ahead prediction to a day ahead prediction. Variouselectric load forecasting methods have been used in literature for electricitygeneration planning to meet future load demand. A perfect balance regardinggeneration and utilization is still lacking to avoid extra generation and misusageof electric load. Therefore, this paper utilizes Levenberg–Marquardt(LM) based Artificial Neural Network (ANN) technique to forecast theshort-term electricity load for smart grids in a much better, more precise,and more accurate manner. For proper load forecasting, we take the mostcritical weather parameters along with historical load data in the form of timeseries grouped into seasons, i.e., winter and summer. Further, the presentedmodel deals with each season’s load data by splitting it into weekdays andweekends. The historical load data of three years have been used to forecastweek-ahead and day-ahead load demand after every thirty minutes makingload forecast for a very short period. The proposed model is optimized usingthe Levenberg-Marquardt backpropagation algorithm to achieve results withcomparable statistics. Mean Absolute Percent Error (MAPE), Root MeanSquared Error (RMSE), R2, and R are used to evaluate the model. Comparedwith other recent machine learning-based mechanisms, our model presentsthe best experimental results with MAPE and R2 scores of 1.3 and 0.99,respectively. The results prove that the proposed LM-based ANN modelperforms much better in accuracy and has the lowest error rates as comparedto existing work.
文摘BACKGROUND Obesity is a state in which excess heat is converted into excess fat,which accumulates in the body and may cause damage to multiple organs of the circulatory,endocrine,and digestive systems.Studies have shown that the accumulation of abdominal fat and mesenteric fat hypertrophy in patients with obesity makes laparoscopic surgery highly difficult,which is not conducive to operation and affects patient prognosis.However,there is still controversy regarding these conclusions.AIM To explore the relationship between body mass index(BMI)and short-term prognosis after surgery for colorectal cancer.METHODS PubMed,Embase,Ovid,Web of Science,CNKI,and China Biology Medicine Disc databases were searched to obtain relevant articles on this topic.After the articles were screened according to the inclusion and exclusion criteria and the risk of literature bias was assessed using the Newcastle-Ottawa Scale,the prognostic indicators were combined and analyzed.RESULTS A total of 16 articles were included for quantitative analysis,and 15588 patients undergoing colorectal cancer surgery were included in the study,including 3775 patients with obesity and 11813 patients without obesity.Among them,12 articles used BMI≥30 kg/m^(2)and 4 articles used BMI≥25 kg/m^(2)for the definition of obesity.Four patients underwent robotic colorectal surgery,whereas 12 underwent conventional laparoscopic colorectal resection.The quality of the literature was good.Meta-combined analysis showed that the overall complication rate of patients with obesity after surgery was higher than that of patients without obesity[OR=1.35,95%CI:1.23-1.48,Z=6.25,P<0.0001].The incidence of anastomotic leak after surgery in patients with obesity was not significantly different from that in patients without obesity[OR=0.99,95%CI:0.70-1.41),Z=-0.06,P=0.956].The incidence of surgical site infection(SSI)after surgery in patients with obesity was higher than that in patients without obesity[OR=1.43,95%CI:1.16-1.78,Z=3.31,P<0.001].The incidence of reoperation in patients with obesity after surgery was higher than that in patients without obesity;however,the difference was not statistically significant[OR=1.15,95%CI:0.92-1.45,Z=1.23,P=0.23];Patients with obesity had lower mortality after surgery than patients without obesity;however,the difference was not statistically significant[OR=0.61,95%CI:0.35-1.06,Z=-1.75,P=0.08].Subgroup analysis revealed that the geographical location of the institute was one of the sources of heterogeneity.Robot-assisted surgery was not significantly different from traditional laparoscopic resection in terms of the incidence of complications.CONCLUSION Obesity increases the overall complication and SSI rates of patients undergoing colorectal cancer surgery but has no influence on the incidence of anastomotic leak,reoperation rate,and short-term mortality rate.
基金supported by grants from Key laboratory of Ecology and Environment in Minority Area,National Ethnic Affairs Commission(KLEEMA202207)the Graduate Research and Practice Projects of Minzu University of China(BZKY2022042).
文摘Night lighting has been shown to affect wild animals.To date,the effects of night lighting on the metabolic homeostasis of birds that spend short time in urban environments remain unclear.Using model bird species Zebra Finch(Taeniopygia guttata),we investigated the effects of short-term night lighting on liver transcriptome,blood glucose,triglyceride,and thyroxine(T4 and T3)levels in birds exposed to two different night lighting duration periods(three days and six days).After three days of night lighting exposure,the expression of genes involved in fat synthesis in the liver was upregulated while the expression of genes involved in fatty acid oxidation and triglyceride decomposition was downregulated.There was also a reduction in blood triglyceride,glucose,and T3 concentrations.However,after six days of night lighting,the expression of genes associated with fatty acid decomposition and hyperglycemia in the liver was upregulated,while the expression of genes involved in fat synthesis was downregulated.Simultaneously,blood glucose levels and T3 concentration increased.These findings indicate that short-term exposure to night lighting can disrupt the lipid and glucose metabolism of small passerine birds,and longer stopovers in urban area with intense night lighting may cause birds to consume more lipid energy.
基金the National Sciences and Engineering Research Council of Canada(NSERC)under CDR Grant CRDPJ 500414-16NSERC Discovery Grant 239019the COSMO mining industry consortium(AngloGold Ashanti,BHP,De Beers,AngloAmerican,IAMGOLD,Kinross Gold,Newmont Mining,and Vale).
文摘This article presents a novel approach to integrate a throughput prediction model for the ball mill into short-term stochastic production scheduling in mining complexes.The datasets for the throughput prediction model include penetration rates from blast hole drilling(measurement while drilling),geological domains,material types,rock density,and throughput rates of the operating mill,offering an accessible and cost-effective method compared to other geometallurgical programs.First,the comminution behavior of the orebody was geostatistically simulated by building additive hardness proportions from penetration rates.A regression model was constructed to predict throughput rates as a function of blended rock properties,which are informed by a material tracking approach in the mining complex.Finally,the throughput prediction model was integrated into a stochastic optimization model for short-term production scheduling.This way,common shortfalls of existing geometallurgical throughput prediction models,that typically ignore the non-additive nature of hardness and are not designed to interact with mine production scheduling,are overcome.A case study at the Tropicana Mining Complex shows that throughput can be predicted with an error less than 30 t/h and a correlation coefficient of up to 0.8.By integrating the prediction model and new stochastic components into optimization,the production schedule achieves weekly planned production reliably because scheduled materials match with the predicted performance of the mill.Comparisons to optimization using conventional mill tonnage constraints reveal that expected production shortfalls of up to 7%per period can be mitigated this way.
文摘A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively used and still have considerable potential. In recent years, methods based on deep neural networks have made significant breakthroughs, and fault diagnosis methods for industrial processes based on deep learning have attracted considerable research attention. Therefore, we propose a fusion deeplearning algorithm based on a fully convolutional neural network(FCN) to extract features and build models to correctly diagnose all types of faults. We use long short-term memory(LSTM) units to expand our proposed FCN so that our proposed deep learning model can better extract the time-domain features of chemical process data. We also introduce the attention mechanism into the model, aimed at highlighting the importance of features, which is significant for the fault diagnosis of chemical processes with many features. When applied to the benchmark Tennessee Eastman process, our proposed model exhibits impressive performance, demonstrating the effectiveness of the attention-based LSTM FCN in chemical process fault diagnosis.
基金supported by the China National Natural Science Foundation(52177082)China National Key R&D Program(2020YFC0827001)Science and Technology Project of Jilin Electric Power Co.,Ltd(2020JBGS-03).
文摘This study proposes a two-stage photovoltaic(PV)voltage control strategy for centralized control that ignores short-term load fluctuations.In the first stage,a deterministic power flow model optimizes the 15-minute active cycle of the inverter and reactive outputs to reduce network loss and light rejection.In the second stage,the local control stabilizes the fluctuations and tracks the system state of the first stage.The uncertain interval model establishes a chance constraint model for the inverter voltage-reactive power local control.Second-order cone optimization and sensitivity theories were employed to solve the models.The effectiveness of the model was confirmed using a modified IEEE 33 bus example.The intraday control outcome for distributed power generation considering the effects of fluctuation uncertainty,PV penetration rate,and inverter capacity is analyzed.
基金supported by the Major Project of Basic and Applied Research in Guangdong Universities (2017WZDXM012)。
文摘Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural network model based on the temporal pattern attention(TPA)mechanism.Firstly,based on the grey relational analysis,datasets similar to forecast day are obtained.Secondly,thebidirectional LSTM layermodels the data of thehistorical load,temperature,humidity,and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network,so that the influencing factors(with different characteristics)can select relevant information from different time steps to reduce the prediction error of the model.Simultaneously,the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism,so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input.Finally,the chaotic sparrow search algorithm(CSSA)is used to optimize the hyperparameter selection of the model.The short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238,respectively,which is lower than other forecastingmethods,demonstrating the accuracy and stability of our model.
基金supported by the National Natural Science Foundation of China under Grant 51777193.
文摘An improved fuzzy time series algorithmbased on clustering is designed in this paper.The algorithm is successfully applied to short-term load forecasting in the distribution stations.Firstly,the K-means clustering method is used to cluster the data,and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division.On this basis,the data is fuzzed to form a fuzzy time series.Secondly,a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load,which is used to predict the short-term trend change of load in the distribution stations.Matlab/Simulink simulation results show that the load forecasting errors of the typical fuzzy time series on the time scale of one day and one week are[−50,20]and[−50,30],while the load forecasting errors of the improved fuzzy time series on the time scale of one day and one week are[−20,15]and[−20,25].It shows that the fuzzy time series algorithm improved by clustering improves the prediction accuracy and can effectively predict the short-term load trend of distribution stations.
文摘There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.
基金the Project of the Central Government Guiding Local Science and Technology under Grant Number ZYYD2022B18the Institutional Ethics Committee of Affiliated Cancer Hospital of Xinjiang Medical University(No.K-2019001).
文摘Background:This study was designed to investigate the feasibility of tumor-infiltrating immune cells with different phenotypic characteristics for predicting short-term clinical responses in patients with locally advanced cervical cancer(LACC).Methods:Thirty-four patients who received concurrent chemoradiotherapy and twenty-one patients who merely underwent radiotherapy were enrolled in this study.We retrospectively analyzed the T cell markers(i.e.,CD3,CD4,CD8),memory markers(i.e.,CD45,CCR7),and differentiation markers(i.e.,CD27)in the peripheral blood and tumor tissues of patients with LACC before treatment based on flow cytometry.We also analyzed the relationship of T cell subsets between peripheral blood and tumor tissues,and their correlation with complete response or partial response.Results:The percentage of central memory CD8^(+)TCM(CD8^(+)CD45RA^(−)CD27^(+)CCR7^(+))cells in LACC patients was significantly lower than that of the control group.The percentage of CD8^(+)TN in the peripheral blood of LACC patients was significantly higher than that of tumor tissues.CD8^(+)TEM in the peripheral blood was significantly lower than that of tumor tissues.The percentage of CD8^(+)TN and CD8^(+)TCM in human papillomavirus(HPV)positive samples was significantly higher than that of HPV-negative samples.Similarly,the percentage of CD8^(+)TCM in tumor tissues was significantly higher in cancer tissue samples with lymph nodes compared with those without.Conclusion:A higher proportion of CD4^(+)TCM and a lower proportion of CD8^(+)TN in the tumor microenvironment of LACC may contribute to the therapy response prediction.
基金supported in part by the Special Fund for Basic Scientific Research Business Expenses of Central Public Welfare Scientific Research Institutes under Grant TKS20210103the Open Fund of Key Laboratory of Ocean Observation Technology,Ministry of Natural Resources of China(2021klootA06).
文摘River bending is the major effect responsible for bed topography and bank changes.In this study,fluid velocity(measured by a three-dimensional Doppler advanced point current meter)and bed topographical data have been collected in 40 sections of an experimental model.The whole flume was composed of an organic glass bend,upstream and downstream water tanks,two transition straight sections,a circulation pump,and a connection pipeline.Each section has been found to be characterized by a primary circulation and a small reverse circulation,with some sections even presenting three more or more circulation structures.The minimum circulation intensity has been detected in proximity to the top of the curved channel,while a region with small longitudinal velocity has been observed near the concave bank of each bend,corresponding to the flat bed formed after a short period of scouring.The maximum sediment deposition and scour depth in the presence of a uniform distribution of living flexible vegetation within 10 cm of the flume wall have been found to be smaller than those observed in the tests conducted without vegetation.
基金jointly funded by the Marine S&T Fund of Shandong Province for the Pilot National Laboratory for Marine Science and Technology (Qingdao)(2022QNLM 040003-3)the National Key R&D Program of China (2016YFE0109600)+3 种基金National Natural Science Foundation of China (U22A20558, 41240022, 41876057, 40872167, 41602143)China Geological Survey (1212010611402, GZH201200503, and DD20160144)by in-kind support from the Land Carbon ProgramLand Change Science R&D Program of the United States Geological Survey。
文摘Passive-warming, open-top chambers(OTCs) are widely applied for studying the effects of future climate warming on coastal wetlands. In this study, a set of six OTCs were established at a Phragmites wetland located in the Yellow River Delta of Dongying City, China. With data collected through online transmission and in-situ sensors, the attributes and patterns of realized OTCs warming are demonstrated.The authors also quantified the preliminary influence of experimental chamber warming on plant traits.OTCs produced an elevated average air temperature of 0.8°C(relative to controls) during the growing season(June to October) of 2018, and soil temperatures actually decreased by 0.54°C at a depth of 5 cm and 0.46°C at a depth of 30 cm in the OTCs. Variations in diel patterns of warming depend greatly on the heat sources of incoming radiation in the daytime versus soil heat flux at night. Warming effects were often larger during instantaneous analyses and influenced OTCs air temperatures from-2.5°C to 8.3°C dependent on various meteorological conditions at any given time, ranging from cooling influences from vertical heat exchange and vegetation to radiation-associated warming. Night-time temperature depressions in the OTCs were due to the low turbulence inside OTCs and changes in surface soilatmosphere heat transfer. Plant shoot density, basal diameter, and biomass of Phragmites decreased by23.2%, 6.3%, and 34.0%, respectively, under experimental warming versus controls, and plant height increased by 4.3%, reflecting less carbon allocation to stem structures as plants in the OTCs experienced simultaneous wind buffering. While these passive-warming OTCs created the desired warming effects both to the atmosphere and soils, pest damages on the plant leaves and lodging within the OTCs were extensive and serious, creating the need to consider control options for these chambers and the replicated OTCs studies underway in other Chinese Phragmites marshes(Panjin and Yancheng).
基金supported by Science and Technology Project of State Grid Shandong Electric Power Company(52062622000R,Research on Aggregation and Regulation Technology of Regional Integrated Energy System).
文摘Wind power prediction is very important for the economic dispatching of power systems containing wind power.In this work,a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and(long short-term memory)LSTM neural network is proposed and studied.First,the original data is prepossessed including removing outliers and filling in the gaps.Then,the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast model.In addition,this study conducts seasonal classification of the annual data where ICEEMDAN is adopted to divide the original wind power sequence into numerous modal components according to different seasons.On this basis,sample entropy is used to calculate the complexity of each component and reconstruct them into trend components,oscillation components,and random components.Then,these three components are input into the LSTM neural network,respectively.Combined with the predicted values of the three components,the overall power prediction results are obtained.The simulation shows that ICEEMDAN-SE-LSTM achieves higher prediction accuracy ranging from 1.57%to 9.46%than other traditional models,which indicates the reliability and effectiveness of the proposed method for power prediction.
基金National Key R&D Program of China(No.2020YFB1707700)。
文摘The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.
