Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives ...Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.展开更多
Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over p...Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over predictive control input sequences,deriving multiple optimal predictive control input sequences from its solution.展开更多
This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mi...This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.展开更多
The objective of the current study is to investigate an adaptive predictive observer-based autopilot for a skid-to-turn(STT)missile model with uncertainties and unknown dynamic equations.A predictive control for the S...The objective of the current study is to investigate an adaptive predictive observer-based autopilot for a skid-to-turn(STT)missile model with uncertainties and unknown dynamic equations.A predictive control for the STT missile is designed based on nonlinear model predictive control(NMPC)using Taylor series expansion,after which,via a neural network(NN),unknown functions are approximated.The present study also evaluates an adaptive optimal observer of a new strategy-based nonlinear system.Specifically,to estimate the missile states such as normal acceleration and its derivatives for the future,originally the Taylor series states expansion was gained to any specified order,based on their receding horizons.To address the problem of prediction error,an analytic solution was prepared that led to a closed form regarding the nonlinear optimal observer.Out of the gains resulting from the analytic solution,as developed for the problem of prediction error,the selection of the proposed observer gain was optimally conducted to meet the stability condition.Thus,combining the adaptive predictive autopilot and the adaptive optimal observer scheme was implemented to secure the performance,which needed only estimated normal acceleration and its derivatives.Meanwhile,no angular velocity measurement or wind angle estimation was required.Ultimately,the proposed technique was found effective,as confirmed by the qualitative simulation results.展开更多
Pinus radiata(D.Don)dominates New Zealand's forestry industry,constituting 91%of plantations,and is among the world's most important plantation species.Given the socio-economic and environmental importance of ...Pinus radiata(D.Don)dominates New Zealand's forestry industry,constituting 91%of plantations,and is among the world's most important plantation species.Given the socio-economic and environmental importance of this species,it is important to have accurate and precise projections over time to make efficient decisions for forest management and greenfield investments in afforestation projects,especially for permanent carbon forests.Future projections of any natural resource systems rely on modeling;however,the acceleration of climate change makes future projections of yield less certain.These challenges also impact national expectations of the contribution planted forests will provide to address climate change and meet international commitments under the Paris Agreement.Using a large national-scale set of contemporary ground-measured data(2013–2023),this study investigates the performance of two growth models developed over 30 years ago that are widely used by NZ plantation growers:1)the Pumice Plateau Model 1988(PPM88)and 2)the 300-index(including a model variant of regional drift).Model simulations were made using the FORECASTER modeling suite with geographic boundaries to adjust for drift in space and time.Basal area(BA,m^(2)⋅ha^(-1))and volume(m^(3)⋅ha^(-1))were simulated,and standard errors and goodness-of-fit metrics calculated up to a typical rotation age of 30 years.Model residuals were then separated and analysed for the main plantation growing regions.The models overpredicted observed growth by between 6.8%and 16.2%,but model predictions and errors varied significantly between regions.The results of this study provided clear evidence of divergence between the outputs of both models and the measured data.Finally,this study suggests future measures to address challenges posed by these discrepancies that will provide better information for forest management and investment decisions in a changing climate.展开更多
Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charg...Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charge(RIC)mode facilitates the ER driven by SEA to provide the required assistance and support for the subject.展开更多
Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying clima...Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying climatic conditions.This study presents a scale separation hybrid statistical model with recurrent neural network(SS-RNN)to predict the summer monthly NEC-PR.The SS-RNN model decomposes the multiple scales of the NEC-PR into several spatiotemporal intrinsic mode functions covering annual to decadal time scales.This strategy provides a way to derive appropriate predictors and establish predictive models for the primary spatial modes of the NEC-PR at various time scales.Our results demonstrate substantial improvements by the SS-RNN model in predicting the summer monthly NEC-PR as compared with dynamic models,particularly in predicting the spatial pattern of the NEC-PR.In this paper we take August,the month of the highest NEC-PR,to assess our model skill.Independent forecasts of the August NEC-PR over the period 2021–24 achieve significant spatial anomaly correlation coefficients,reaching a maximum value of 0.83.Additional verifications by station observations show that the model hits most station anomalies,achieving a mean predictive skill score of 90.展开更多
Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both g...Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.展开更多
BACKGROUND Laparoscopic distal pancreatectomy(LDP)has emerged as the preferred approach for both benign and malignant lesions located in the pancreatic body and tail.Nevertheless,a notable deficiency persists in the a...BACKGROUND Laparoscopic distal pancreatectomy(LDP)has emerged as the preferred approach for both benign and malignant lesions located in the pancreatic body and tail.Nevertheless,a notable deficiency persists in the absence of a standardized,procedure-specific metric for evaluating and comparing surgical quality.A composite measure termed“textbook outcome(TO)”,which encompasses key short-term endpoints,has been validated in laparoscopic pancreatoduodenectomy but has not yet been established in dedicated LDP cohorts.The definition and prediction of TO in this context could aid in facilitating cross-institutional benchmarking and fostering advancements in quality improvement.AIM To establish procedure-specific criteria for TO and identify independent predictors of TO failure in patients undergoing LDP.METHODS Consecutive patients who underwent LDP at a single high-volume pancreatic center between January 2015 and August 2022 were retrospectively analyzed.TO was defined as the absence of clinically relevant postoperative pancreatic fistula(grade B/C),post-pancreatectomy hemorrhage(grade B/C),severe complications(Clavien-Dindo≥III),readmission within 30 days,and in-hospital or 30-day mortality.Multivariable logistic regression was employed to identify independent predictors of TO failure,and a nomogram was constructed and internally validated.RESULTS Among 405 eligible patients,286(70.6%)attained TO.Multivariable analysis revealed that female sex[odds ratio(OR)=0.62,95%confidence interval(CI):0.39-0.99]conferred a protective effect,while preoperative endoscopic ultrasound-guided fine-needle aspiration(OR=2.66,95%CI:1.05-6.73),pancreatic portal hypertension(OR=2.81,95%CI:1.06-7.45),and cystic-solid(OR=2.51,95%CI:1.34-4.69)or solid lesions(OR=1.91,95%CI:1.06-3.44)were independently associated with TO failure(all P<0.05).The derived nomogram exhibited modest discrimination and calibration when assessed in both the training and validation datasets.CONCLUSION The proposed LDP-specific definition of TO is feasible and discriminative,and the developed nomogram provides an objective tool for individualized risk assessment.展开更多
Titanium-based semiconductors are known for their high chemical stability and suitable band gap widths.However,the conventional experimental screening methods are inefficient due to the wide variety of materials.To sp...Titanium-based semiconductors are known for their high chemical stability and suitable band gap widths.However,the conventional experimental screening methods are inefficient due to the wide variety of materials.To speed up the selection process,this work focuses on interpretable feature learning and band gap prediction for titanium-based semiconductors.First,titanium compounds were selected from the Materials Project database by machine learning,and elemental features were extracted using the Magpie descriptors.Then,principal component analysis(PCA)was applied to reduce the data dimensionality,creating a representative dataset.Meantime,heatmaps and SHAP(SHapley Additive exPlanations)methods were used to demonstrate the influence of key features such as electronegativity,covalent radius,period number,and unit cell volume on the bandgap,understanding the relationship between the material’s properties and performance.After comparing different machine learning models,including Random Forest(RF),Support Vector Machines(SVM),Linear Regression(LR),and Gradient Boosting Regression(GBR),the RF was found to be the most accurate for band gap prediction.Finally,the model performance was improved through parameter tuning,showing high accuracy.These findings provide strong data support and design guidance for the development of materials in fields like photocatalysis and solar cells.展开更多
The genetic basis of early-stage salt tolerance in alfalfa(Medicago sativa L.),a key factor limiting its productivity,remains poorly understood.To dissect this complex trait,we integrate genome-wide association studie...The genetic basis of early-stage salt tolerance in alfalfa(Medicago sativa L.),a key factor limiting its productivity,remains poorly understood.To dissect this complex trait,we integrate genome-wide association studies(GWAS)and transcriptomics from 176 accessions within a machine learning based genomic prediction framework.Analysis reveals weak genetic correlations among four salt-tolerance traits and a gradual decline in performance under increasing salt stress.GWAS identify 60 significant associated SNPs,with the highest number detected under 100 mM salt stress.Salt tolerance exhibits an additive effect from favorable haplotypes,which are most abundant in Chinese accessions.GWAS-associated genes are related to key regulators of hormone signaling and osmotic adjustment,while transcriptome analysis indicates a global repression of stress-responsive transcription factors.Integrating these multi-omics datasets allows us to identify 14 candidate genes,including MsHSD1(seed dormancy)and MsMTATP6(energy metabolism).Crucially,incorporating these markers into genomic prediction models improve cross-population predictive accuracy to an average of 54.4%.This study provides insights into the genetic architecture of salt tolerance in alfalfa and offers valuable markers to facilitate molecular breeding.展开更多
Deep learning has undeniably sharpened our ability to forecast risk in neuropsychiatry[1].Yet the very success of prediction has exposed a deeper limitation:we are still remarkably uncertain about which levers to pull...Deep learning has undeniably sharpened our ability to forecast risk in neuropsychiatry[1].Yet the very success of prediction has exposed a deeper limitation:we are still remarkably uncertain about which levers to pull to change patient trajectories[2].Accurate risk scores that cannot be translated into credible actions leave clinicians where they began,testing symptomatic fixes and hoping for the best.If we want to move beyond this impasse,the next step is not simply to train larger models,but to rethink what we ask of them.展开更多
Thermal power plants are the main contributors to greenhouse gas emissions.The prediction of the emission supports the decision makers and environmental sustainability.The objective of this study is to enhance the acc...Thermal power plants are the main contributors to greenhouse gas emissions.The prediction of the emission supports the decision makers and environmental sustainability.The objective of this study is to enhance the accuracy of emission prediction models,supporting more effective real-time monitoring and enabling informed operational decisions that align with environmental compliance efforts.This paper presents a data-driven approach for the accurate prediction of gas emissions,specifically nitrogen oxides(NOx)and carbon monoxide(CO),in natural gas power plants using an optimized hybrid machine learning framework.The proposed model integrates a Feedforward Neural Network(FFNN)trained using Particle Swarm Optimization to capture the nonlinear emission dynamics under varying gas turbine operating conditions.To further enhance predictive performance,the K-Nearest Neighbor(K-NN)algorithm serves as a post-processing method to enhance IPSO-FFNN predictions through adjustment and refinement,improving overall prediction accuracy,while Neighbor Component Analysis is used to identify and rank the most influential operational variables.The study makes a significant contribution through the combination of NCA feature selection with PSO global optimization,FFNN nonlinear modelling,and K-NN error correction into one unified system,which delivers precise emission predictions.The model was developed and tested using a real-world dataset collected from gas-fired turbine operations,with validated results demonstrating robust accuracy,achieving Root Mean Square Error values of 0.355 for CO and 0.368 for NOx.When benchmarked against conventional models such as standard FFNN,Support Vector Regression,and Long Short-Term Memory networks,the hybrid model achieved substantial improvements,up to 97.8%in Mean Squared Error,95%in Mean Absolute Error(MAE),and 85.19%in RMSE for CO;and 97.16%in MSE,93.4%in MAE,and 83.15%in RMSE for NOx.These results underscore the model’s potential for improving emission prediction,thereby supporting enhanced operational efficiency and adherence to environmental standards.展开更多
Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(O...Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(OGTT),and fasting plasma glucose(FPG)screening techniques,which are invasive and limited in scale.Machine learning(ML)and deep neural network(DNN)models that use large datasets to learn the complex,nonlinear feature interactions,but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy.Conversely,DNN models are more robust,though the ability to reach a high accuracy rate consistently on heterogeneous datasets is still an open challenge.For predicting diabetes,this work proposed a hybrid DNN approach by integrating a bidirectional long short-term memory(BiLSTM)network with a bidirectional gated recurrent unit(BiGRU).A robust DL model,developed by combining various datasets with weighted coefficients,dense operations in the connection of deep layers,and the output aggregation using batch normalization and dropout functions to avoid overfitting.The goal of this hybrid model is better generalization and consistency among various datasets,which facilitates the effective management and early intervention.The proposed DNN model exhibits an excellent predictive performance as compared to the state-of-the-art and baseline ML and DNN models for diabetes prediction tasks.The robust performance indicates the possible usefulness of DL-based models in the development of disease prediction in healthcare and other areas that demand high-quality analytics.展开更多
With the widespread deployment of assembly robots in smart manufacturing,efficiently offloading tasks and allocating resources in highly dynamic industrial environments has become a critical challenge for Mobile Edge ...With the widespread deployment of assembly robots in smart manufacturing,efficiently offloading tasks and allocating resources in highly dynamic industrial environments has become a critical challenge for Mobile Edge Computing(MEC).To address this challenge,this paper constructs a cloud-edge-end collaborative MEC system that enables assembly robots to offload complex workflow tasks via multiple paths(horizontal,vertical,and hybrid collaboration).Tomitigate uncertainties arising frommobility,the location predictionmodule is employed.This enables proactive channel-quality estimation,providing forward-looking insights for offloading decisions.Furthermore,we propose a fairness-aware joint optimization framework.Utilizing an improved Multi-Agent Deep Reinforcement Learning(MADRL)algorithm whose reward function incorporates total system cost,positional reliability,and timeout penalties,the framework aims to balance resource distribution among assembly robots while maximizing system utility.Simulation results demonstrate that the proposed framework outperforms traditional offloading strategies.By integrating predictive mobility management with fairness-aware optimization,the framework offers a robust solution for dynamic industrial MEC environments.展开更多
Satellite clock bias(SCB)prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning(RT-PPP)in Global Navigation Satellite Systems(GNSS).To address the nonlinearity,non-st...Satellite clock bias(SCB)prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning(RT-PPP)in Global Navigation Satellite Systems(GNSS).To address the nonlinearity,non-stationarity,and short-term interruptions of SCB data under complex environments,this paper proposes an enhanced SCB prediction model combining Temporal Convolutional Networks(TCN)and Transformers.Experimental results indicate that,in a 24-h prediction task,the proposed model reduces root mean square error(RMSE)and range error(RE)by 95.6%,86.0%,and 61.3%,and93.7%,86.3%,and 58.8%,respectively,compared with LSTM,Transformer,and CNN-BiGRU-Attention models,while improving computational efficiency by 48.6%over the Transformer.Moreover,although the clock bias products generated by the proposed method result in slightly higher static PPP positioning errors than the International GNSS Service(IGS)rapid clock products,the error differences are generally at the millimeter level,demonstrating the feasibility of using predicted clock bias products to replace rapid clock products in the short term.This method addresses the PPP positioning issue during short-term network service interruptions from the perspective of time series prediction and provides potential solutions for engineering applications such as landslide,earthquake,and subsidence monitoring.展开更多
This study reveals the critical role of multiscale interaction within the westerly wind bursts(WWBs)west of the MJO convection in modulating the prediction skill for the November MJO event during the DYNAMO(Dynamics o...This study reveals the critical role of multiscale interaction within the westerly wind bursts(WWBs)west of the MJO convection in modulating the prediction skill for the November MJO event during the DYNAMO(Dynamics of the Madden–Julian Oscillation)field campaign.The characteristics of the MJO convection envelope are obtained by the largescale precipitation tracking method,and a novel metric is introduced to quantify the prediction skill for the MJO convection in the ECMWF reforecast.The ECMWF forecast exhibits approximately 17 days in skillful prediction for the MJO convection—significantly lower than that derived from the global measure.The reforecast ensembles are further classified into high and low skill catalogs based on the mean prediction skill during the observed WWBs period.High-skill ensembles exhibit significantly enhanced low-level westerlies,amplified MJO convection,and reduced spatial separation between the low-level westerlies and MJO convection during the WWBs period,indicating stronger coupling between the large-scale circulation and the convection.Mechanistic analysis reveals that enhanced westerlies in high-skill ensembles can transfer more high-frequency energy to the MJO convection through the flux convergence of interaction energy for MJO convection development,resulting in better prediction skill.展开更多
Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between...Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).展开更多
Anthropogenic ammonia emissions primarily originate from agriculture,especially field fertilization.These emissions represent nitrogen loss for farmers and contribute to air pollution,posing risks to human health and ...Anthropogenic ammonia emissions primarily originate from agriculture,especially field fertilization.These emissions represent nitrogen loss for farmers and contribute to air pollution,posing risks to human health and the environment.Estimating ammonia emissions is crucial for national inventories and policy-making.Various models exist for predicting emissions,including mechanistic,empirical,and semi-empirical approaches.While machine learning(ML)is widely used in environmental science,its application to ammonia emissions remains limited.In this study,we used 5939 ammonia emission data from 538 trials,extracted from the ALFAM2 database,to train three machine learning methods-random forest,gradient boosting,and lasso-for predicting cumulative ammonia emissions 72 h after manure application.These methods were compared to the semi-empirical ALFAM2 model using an independent test dataset.Random forest(RMSE=4.51,r=0.94,MAE=3.28,Bias=0.92)and gradient boosting(RMSE=6.19,r=0.89,MAE=4.10,Bias=0.51)showed the best performance,while the lasso log-linear model(RMSE=7.30,r=0.84,MAE=5.57,Bias=-1.38)performed worst.Both random forest and gradient boosting outperformed the semi-empirical ALFAM2 model,which showed performance comparable to the lasso model.We then used these models and the ALFAM2 model to compare five slurry management techniques,varying in application method(trailing hoses,trailing shoes,and open slot)and post-application incorporation,across 128 scenarios with different manure types and weather conditions.Compared to broadcast application,alternative techniques reduced emissions by a median of-13.6%to-61.7%.This study highlights the promise of ML models in assessing ammonia emission reduction methods,while emphasizing the importance of evaluating model sensitivity to algorithm choice.展开更多
Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying ...Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.U2342208)support from NSF/Climate Dynamics Award#2025057。
文摘Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.
基金supported by the National Natural Science Foundation of China(62433014,62373287,62573324,62333005,62273255)in part by the International Exchange Program for Graduate Students of Tongji University(4360143306)+3 种基金in part by the Fundamental Research Funds for Central Universities(22120230311)supported by DeutscheForschungsgemeinschaft(DFG,German Research Foundation)under Germany’s Excellence Strategy(EXC 2075390740016,468094890)support by the Stuttgart Center for Simulation Science(SimTech)the International Max Planck Research School for Intelligent Systems(IMPRS-IS)for supporting Y.Xie。
文摘Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over predictive control input sequences,deriving multiple optimal predictive control input sequences from its solution.
基金supported by the National Natural Science Foundation of China(No.12372045)the National Key Research and the Development Program of China(Nos.2023YFC2205900,2023YFC2205901)。
文摘This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.
文摘The objective of the current study is to investigate an adaptive predictive observer-based autopilot for a skid-to-turn(STT)missile model with uncertainties and unknown dynamic equations.A predictive control for the STT missile is designed based on nonlinear model predictive control(NMPC)using Taylor series expansion,after which,via a neural network(NN),unknown functions are approximated.The present study also evaluates an adaptive optimal observer of a new strategy-based nonlinear system.Specifically,to estimate the missile states such as normal acceleration and its derivatives for the future,originally the Taylor series states expansion was gained to any specified order,based on their receding horizons.To address the problem of prediction error,an analytic solution was prepared that led to a closed form regarding the nonlinear optimal observer.Out of the gains resulting from the analytic solution,as developed for the problem of prediction error,the selection of the proposed observer gain was optimally conducted to meet the stability condition.Thus,combining the adaptive predictive autopilot and the adaptive optimal observer scheme was implemented to secure the performance,which needed only estimated normal acceleration and its derivatives.Meanwhile,no angular velocity measurement or wind angle estimation was required.Ultimately,the proposed technique was found effective,as confirmed by the qualitative simulation results.
基金funded by Scion's Strategic Science Investment Fund(SSIF)the Forest Growers Levy Trust(FGLT)through the Resilient Forests Programme(Task No.A89220)。
文摘Pinus radiata(D.Don)dominates New Zealand's forestry industry,constituting 91%of plantations,and is among the world's most important plantation species.Given the socio-economic and environmental importance of this species,it is important to have accurate and precise projections over time to make efficient decisions for forest management and greenfield investments in afforestation projects,especially for permanent carbon forests.Future projections of any natural resource systems rely on modeling;however,the acceleration of climate change makes future projections of yield less certain.These challenges also impact national expectations of the contribution planted forests will provide to address climate change and meet international commitments under the Paris Agreement.Using a large national-scale set of contemporary ground-measured data(2013–2023),this study investigates the performance of two growth models developed over 30 years ago that are widely used by NZ plantation growers:1)the Pumice Plateau Model 1988(PPM88)and 2)the 300-index(including a model variant of regional drift).Model simulations were made using the FORECASTER modeling suite with geographic boundaries to adjust for drift in space and time.Basal area(BA,m^(2)⋅ha^(-1))and volume(m^(3)⋅ha^(-1))were simulated,and standard errors and goodness-of-fit metrics calculated up to a typical rotation age of 30 years.Model residuals were then separated and analysed for the main plantation growing regions.The models overpredicted observed growth by between 6.8%and 16.2%,but model predictions and errors varied significantly between regions.The results of this study provided clear evidence of divergence between the outputs of both models and the measured data.Finally,this study suggests future measures to address challenges posed by these discrepancies that will provide better information for forest management and investment decisions in a changing climate.
基金supported in part by the National Natural Science Foundation of China(62173048,62373065,61873304,62106023)the Key Science and Technology Projects of Jilin Province,China(20230204081YY)the Research and Innovation Team of Anhui Province(2024AH010023)。
文摘Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charge(RIC)mode facilitates the ER driven by SEA to provide the required assistance and support for the subject.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3002803)the National Key Research and Development Program of China(Grant No.2024YFF0808402)the National Natural Science Foundation of China(Grant No.42375169)。
文摘Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying climatic conditions.This study presents a scale separation hybrid statistical model with recurrent neural network(SS-RNN)to predict the summer monthly NEC-PR.The SS-RNN model decomposes the multiple scales of the NEC-PR into several spatiotemporal intrinsic mode functions covering annual to decadal time scales.This strategy provides a way to derive appropriate predictors and establish predictive models for the primary spatial modes of the NEC-PR at various time scales.Our results demonstrate substantial improvements by the SS-RNN model in predicting the summer monthly NEC-PR as compared with dynamic models,particularly in predicting the spatial pattern of the NEC-PR.In this paper we take August,the month of the highest NEC-PR,to assess our model skill.Independent forecasts of the August NEC-PR over the period 2021–24 achieve significant spatial anomaly correlation coefficients,reaching a maximum value of 0.83.Additional verifications by station observations show that the model hits most station anomalies,achieving a mean predictive skill score of 90.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2342210 and 42275043)the National Institute of Natural Hazards,Ministry of Emergency Management of China(Grant Nos.J2223806,ZDJ2024-25 and ZDJ2025-34)。
文摘Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.
文摘BACKGROUND Laparoscopic distal pancreatectomy(LDP)has emerged as the preferred approach for both benign and malignant lesions located in the pancreatic body and tail.Nevertheless,a notable deficiency persists in the absence of a standardized,procedure-specific metric for evaluating and comparing surgical quality.A composite measure termed“textbook outcome(TO)”,which encompasses key short-term endpoints,has been validated in laparoscopic pancreatoduodenectomy but has not yet been established in dedicated LDP cohorts.The definition and prediction of TO in this context could aid in facilitating cross-institutional benchmarking and fostering advancements in quality improvement.AIM To establish procedure-specific criteria for TO and identify independent predictors of TO failure in patients undergoing LDP.METHODS Consecutive patients who underwent LDP at a single high-volume pancreatic center between January 2015 and August 2022 were retrospectively analyzed.TO was defined as the absence of clinically relevant postoperative pancreatic fistula(grade B/C),post-pancreatectomy hemorrhage(grade B/C),severe complications(Clavien-Dindo≥III),readmission within 30 days,and in-hospital or 30-day mortality.Multivariable logistic regression was employed to identify independent predictors of TO failure,and a nomogram was constructed and internally validated.RESULTS Among 405 eligible patients,286(70.6%)attained TO.Multivariable analysis revealed that female sex[odds ratio(OR)=0.62,95%confidence interval(CI):0.39-0.99]conferred a protective effect,while preoperative endoscopic ultrasound-guided fine-needle aspiration(OR=2.66,95%CI:1.05-6.73),pancreatic portal hypertension(OR=2.81,95%CI:1.06-7.45),and cystic-solid(OR=2.51,95%CI:1.34-4.69)or solid lesions(OR=1.91,95%CI:1.06-3.44)were independently associated with TO failure(all P<0.05).The derived nomogram exhibited modest discrimination and calibration when assessed in both the training and validation datasets.CONCLUSION The proposed LDP-specific definition of TO is feasible and discriminative,and the developed nomogram provides an objective tool for individualized risk assessment.
文摘Titanium-based semiconductors are known for their high chemical stability and suitable band gap widths.However,the conventional experimental screening methods are inefficient due to the wide variety of materials.To speed up the selection process,this work focuses on interpretable feature learning and band gap prediction for titanium-based semiconductors.First,titanium compounds were selected from the Materials Project database by machine learning,and elemental features were extracted using the Magpie descriptors.Then,principal component analysis(PCA)was applied to reduce the data dimensionality,creating a representative dataset.Meantime,heatmaps and SHAP(SHapley Additive exPlanations)methods were used to demonstrate the influence of key features such as electronegativity,covalent radius,period number,and unit cell volume on the bandgap,understanding the relationship between the material’s properties and performance.After comparing different machine learning models,including Random Forest(RF),Support Vector Machines(SVM),Linear Regression(LR),and Gradient Boosting Regression(GBR),the RF was found to be the most accurate for band gap prediction.Finally,the model performance was improved through parameter tuning,showing high accuracy.These findings provide strong data support and design guidance for the development of materials in fields like photocatalysis and solar cells.
基金supported by the National Key Research and Development Program of China(2022YFF1003203)Biological Breeding-National Science and Technology Major Project(2022ZDo4011)+2 种基金the Central Public-interest Scientific Institution Basal Research Fund(Y2025YC44)the Central Public-interest Scientific Institution Basal Research Fund(2025-YWF-ZYSQ-04)the China Postdoctoral Science Foundation(2023M733832).
文摘The genetic basis of early-stage salt tolerance in alfalfa(Medicago sativa L.),a key factor limiting its productivity,remains poorly understood.To dissect this complex trait,we integrate genome-wide association studies(GWAS)and transcriptomics from 176 accessions within a machine learning based genomic prediction framework.Analysis reveals weak genetic correlations among four salt-tolerance traits and a gradual decline in performance under increasing salt stress.GWAS identify 60 significant associated SNPs,with the highest number detected under 100 mM salt stress.Salt tolerance exhibits an additive effect from favorable haplotypes,which are most abundant in Chinese accessions.GWAS-associated genes are related to key regulators of hormone signaling and osmotic adjustment,while transcriptome analysis indicates a global repression of stress-responsive transcription factors.Integrating these multi-omics datasets allows us to identify 14 candidate genes,including MsHSD1(seed dormancy)and MsMTATP6(energy metabolism).Crucially,incorporating these markers into genomic prediction models improve cross-population predictive accuracy to an average of 54.4%.This study provides insights into the genetic architecture of salt tolerance in alfalfa and offers valuable markers to facilitate molecular breeding.
文摘Deep learning has undeniably sharpened our ability to forecast risk in neuropsychiatry[1].Yet the very success of prediction has exposed a deeper limitation:we are still remarkably uncertain about which levers to pull to change patient trajectories[2].Accurate risk scores that cannot be translated into credible actions leave clinicians where they began,testing symptomatic fixes and hoping for the best.If we want to move beyond this impasse,the next step is not simply to train larger models,but to rethink what we ask of them.
文摘Thermal power plants are the main contributors to greenhouse gas emissions.The prediction of the emission supports the decision makers and environmental sustainability.The objective of this study is to enhance the accuracy of emission prediction models,supporting more effective real-time monitoring and enabling informed operational decisions that align with environmental compliance efforts.This paper presents a data-driven approach for the accurate prediction of gas emissions,specifically nitrogen oxides(NOx)and carbon monoxide(CO),in natural gas power plants using an optimized hybrid machine learning framework.The proposed model integrates a Feedforward Neural Network(FFNN)trained using Particle Swarm Optimization to capture the nonlinear emission dynamics under varying gas turbine operating conditions.To further enhance predictive performance,the K-Nearest Neighbor(K-NN)algorithm serves as a post-processing method to enhance IPSO-FFNN predictions through adjustment and refinement,improving overall prediction accuracy,while Neighbor Component Analysis is used to identify and rank the most influential operational variables.The study makes a significant contribution through the combination of NCA feature selection with PSO global optimization,FFNN nonlinear modelling,and K-NN error correction into one unified system,which delivers precise emission predictions.The model was developed and tested using a real-world dataset collected from gas-fired turbine operations,with validated results demonstrating robust accuracy,achieving Root Mean Square Error values of 0.355 for CO and 0.368 for NOx.When benchmarked against conventional models such as standard FFNN,Support Vector Regression,and Long Short-Term Memory networks,the hybrid model achieved substantial improvements,up to 97.8%in Mean Squared Error,95%in Mean Absolute Error(MAE),and 85.19%in RMSE for CO;and 97.16%in MSE,93.4%in MAE,and 83.15%in RMSE for NOx.These results underscore the model’s potential for improving emission prediction,thereby supporting enhanced operational efficiency and adherence to environmental standards.
基金supported by the School of Digital Science,Universiti Brunei Darussalam,Brunei.
文摘Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(OGTT),and fasting plasma glucose(FPG)screening techniques,which are invasive and limited in scale.Machine learning(ML)and deep neural network(DNN)models that use large datasets to learn the complex,nonlinear feature interactions,but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy.Conversely,DNN models are more robust,though the ability to reach a high accuracy rate consistently on heterogeneous datasets is still an open challenge.For predicting diabetes,this work proposed a hybrid DNN approach by integrating a bidirectional long short-term memory(BiLSTM)network with a bidirectional gated recurrent unit(BiGRU).A robust DL model,developed by combining various datasets with weighted coefficients,dense operations in the connection of deep layers,and the output aggregation using batch normalization and dropout functions to avoid overfitting.The goal of this hybrid model is better generalization and consistency among various datasets,which facilitates the effective management and early intervention.The proposed DNN model exhibits an excellent predictive performance as compared to the state-of-the-art and baseline ML and DNN models for diabetes prediction tasks.The robust performance indicates the possible usefulness of DL-based models in the development of disease prediction in healthcare and other areas that demand high-quality analytics.
基金supported by the National Key R&D Program of China under Grant Nos.2024YFD2400200 and 2024YFD2400204supported in part by the Science and Technology Development Program for the Two Zones under Grant No.2023LQ02004.
文摘With the widespread deployment of assembly robots in smart manufacturing,efficiently offloading tasks and allocating resources in highly dynamic industrial environments has become a critical challenge for Mobile Edge Computing(MEC).To address this challenge,this paper constructs a cloud-edge-end collaborative MEC system that enables assembly robots to offload complex workflow tasks via multiple paths(horizontal,vertical,and hybrid collaboration).Tomitigate uncertainties arising frommobility,the location predictionmodule is employed.This enables proactive channel-quality estimation,providing forward-looking insights for offloading decisions.Furthermore,we propose a fairness-aware joint optimization framework.Utilizing an improved Multi-Agent Deep Reinforcement Learning(MADRL)algorithm whose reward function incorporates total system cost,positional reliability,and timeout penalties,the framework aims to balance resource distribution among assembly robots while maximizing system utility.Simulation results demonstrate that the proposed framework outperforms traditional offloading strategies.By integrating predictive mobility management with fairness-aware optimization,the framework offers a robust solution for dynamic industrial MEC environments.
基金supported by the National Natural Science Foundation of China(42304050)Major Science and Technology Projects in Anhui Province,grant number(202103a05020026)+1 种基金Open Foundation of the Key Laboratory of Universities in Anhui Province for Prevention of Mine Geological Disasters(2022-MGDP-08)University Natural Science Research Project of Anhui Province(2023AH051190)。
文摘Satellite clock bias(SCB)prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning(RT-PPP)in Global Navigation Satellite Systems(GNSS).To address the nonlinearity,non-stationarity,and short-term interruptions of SCB data under complex environments,this paper proposes an enhanced SCB prediction model combining Temporal Convolutional Networks(TCN)and Transformers.Experimental results indicate that,in a 24-h prediction task,the proposed model reduces root mean square error(RMSE)and range error(RE)by 95.6%,86.0%,and 61.3%,and93.7%,86.3%,and 58.8%,respectively,compared with LSTM,Transformer,and CNN-BiGRU-Attention models,while improving computational efficiency by 48.6%over the Transformer.Moreover,although the clock bias products generated by the proposed method result in slightly higher static PPP positioning errors than the International GNSS Service(IGS)rapid clock products,the error differences are generally at the millimeter level,demonstrating the feasibility of using predicted clock bias products to replace rapid clock products in the short term.This method addresses the PPP positioning issue during short-term network service interruptions from the perspective of time series prediction and provides potential solutions for engineering applications such as landslide,earthquake,and subsidence monitoring.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.U2442206,42205067,and 41922035)the National Key R&D Program of China(Grant No.2024YFC3013100)the Key Research Program of Frontier Sciences of CAS(Grant No.QYZDB-SSW-DQC017).
文摘This study reveals the critical role of multiscale interaction within the westerly wind bursts(WWBs)west of the MJO convection in modulating the prediction skill for the November MJO event during the DYNAMO(Dynamics of the Madden–Julian Oscillation)field campaign.The characteristics of the MJO convection envelope are obtained by the largescale precipitation tracking method,and a novel metric is introduced to quantify the prediction skill for the MJO convection in the ECMWF reforecast.The ECMWF forecast exhibits approximately 17 days in skillful prediction for the MJO convection—significantly lower than that derived from the global measure.The reforecast ensembles are further classified into high and low skill catalogs based on the mean prediction skill during the observed WWBs period.High-skill ensembles exhibit significantly enhanced low-level westerlies,amplified MJO convection,and reduced spatial separation between the low-level westerlies and MJO convection during the WWBs period,indicating stronger coupling between the large-scale circulation and the convection.Mechanistic analysis reveals that enhanced westerlies in high-skill ensembles can transfer more high-frequency energy to the MJO convection through the flux convergence of interaction energy for MJO convection development,resulting in better prediction skill.
基金J.YANG was supported by funding from the National Natural Science Foundation of China(Grant Nos.42475022,42261144671)the National Key R&D Program of China(Project No.2024YFC3013100)+2 种基金the Fundamental Research Funds for the Central UniversitiesM.LU was supported by the Otto Poon Centre of Climate Resilience and Sustainability at HKUST and the Hong Kong Research Grant Committee(Project No.16300424)Data processing and storage were supported by the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab).
文摘Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).
基金the French state aid managed by the ANR under the“Investissements d’avenir”programme with the reference ANR-16-CONV-0003from the AgroEcoSystem department of INRAE.We are grateful to the INRAE MIGALE bioinformatics facility(MIGALE,INRAE,2020.Migale bioinformatics Facility,doi:10.15454/1.5572390655343293E12)for providing help and/or computing and/or storage resources.We are also grateful to Sasha Hafner for his help in reproducing some of the results of Hafner et al.(2019).
文摘Anthropogenic ammonia emissions primarily originate from agriculture,especially field fertilization.These emissions represent nitrogen loss for farmers and contribute to air pollution,posing risks to human health and the environment.Estimating ammonia emissions is crucial for national inventories and policy-making.Various models exist for predicting emissions,including mechanistic,empirical,and semi-empirical approaches.While machine learning(ML)is widely used in environmental science,its application to ammonia emissions remains limited.In this study,we used 5939 ammonia emission data from 538 trials,extracted from the ALFAM2 database,to train three machine learning methods-random forest,gradient boosting,and lasso-for predicting cumulative ammonia emissions 72 h after manure application.These methods were compared to the semi-empirical ALFAM2 model using an independent test dataset.Random forest(RMSE=4.51,r=0.94,MAE=3.28,Bias=0.92)and gradient boosting(RMSE=6.19,r=0.89,MAE=4.10,Bias=0.51)showed the best performance,while the lasso log-linear model(RMSE=7.30,r=0.84,MAE=5.57,Bias=-1.38)performed worst.Both random forest and gradient boosting outperformed the semi-empirical ALFAM2 model,which showed performance comparable to the lasso model.We then used these models and the ALFAM2 model to compare five slurry management techniques,varying in application method(trailing hoses,trailing shoes,and open slot)and post-application incorporation,across 128 scenarios with different manure types and weather conditions.Compared to broadcast application,alternative techniques reduced emissions by a median of-13.6%to-61.7%.This study highlights the promise of ML models in assessing ammonia emission reduction methods,while emphasizing the importance of evaluating model sensitivity to algorithm choice.
文摘Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.
