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
Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does no...Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.展开更多
The chaotic characteristics and maximum predictable time scale of the observation series of hourly water consumption in Hangzhou were investigated using the advanced algorithm presented here is based on the convention...The chaotic characteristics and maximum predictable time scale of the observation series of hourly water consumption in Hangzhou were investigated using the advanced algorithm presented here is based on the conventional Wolf's algorithm for the largest Lyapunov exponent. For comparison, the largest Lyapunov exponents of water consumption series with one-hour and 24-hour intervals were calculated respectively. The results indicated that chaotic characteristics obviously exist in the hourly water consumption system; and that observation series with 24-hour interval have longer maximum predictable scale than hourly series. These findings could have significant practical application for better prediction of urban hourly water consumption.展开更多
In this paper, the method which can combine different seismic data with the different precision and completeness, even the palaeo-earthquake data, has been applied to estimate the yearly seismic moment rate in the sei...In this paper, the method which can combine different seismic data with the different precision and completeness, even the palaeo-earthquake data, has been applied to estimate the yearly seismic moment rate in the seismic region. Based on this, the predictable model of regional time-magnitude has been used in North China and Southwest China. The normal correlation between the time interval of the events and the magnitude of the last strong earthquake shows that the model is suitable. The value of the parameter c is less than the average value of 0.33 that is obtained from the events occurred in the plate boundary in the world. It is explained that the correlativity between the recurrence interval of the earthquake and the magnitude of the last strong event is not obvious. It is shown that the continental earthquakes in China are different from that occurred in the plate boundary and the recurrence model for the continental events are different from the one for the plate boundary events. Finally the seismic risk analysis based on this model for North China and Southwest China is given in this paper.展开更多
In this paper,the optional and predictable projections of set-valued measurable processes are studied.The existence and uniqueness of optional and predictable projections of set-valued measurable processes are proved ...In this paper,the optional and predictable projections of set-valued measurable processes are studied.The existence and uniqueness of optional and predictable projections of set-valued measurable processes are proved under proper circumstances.展开更多
Making accurate forecast or prediction is a challenging task in the big data era, in particular for those datasets involving high-dimensional variables but short-term time series points,which are generally available f...Making accurate forecast or prediction is a challenging task in the big data era, in particular for those datasets involving high-dimensional variables but short-term time series points,which are generally available from real-world systems.To address this issue, Prof.展开更多
During the course of the disease,most patients with Crohn's disease(CD) may eventually develop a stricturing or a perforating complication,and a significant number of patients with both CD and ulcerative colitis w...During the course of the disease,most patients with Crohn's disease(CD) may eventually develop a stricturing or a perforating complication,and a significant number of patients with both CD and ulcerative colitis will undergo surgery.In recent years,research has focused on the determination of factors important in the prediction of disease course in inflammatory bowel diseases to improve stratification of patients,identify individual patient profiles,including clinical,laboratory and molecular markers,which hopefully will allow physicians to choose the most appropriate management in terms of therapy and intensity of follow-up.This review summarizes the available evidence on clinical,endoscopic variables and biomarkers in the prediction of short and long-term outcome in patients with inflammatory bowel diseases.展开更多
This paper proposes a performance prediction model for grid computing model ServiceBSP to support developing high quality applications in grid environment. In ServiceBSP model, the agents carrying computing tasks are ...This paper proposes a performance prediction model for grid computing model ServiceBSP to support developing high quality applications in grid environment. In ServiceBSP model, the agents carrying computing tasks are dispatched to the local domain of the selected computation services. By using the IP （integer program） approach, the Service Selection Agent selects the computation services with global optimized QoS （quality of service） consideration. The performance of a ServiceBSP application can be predicted according to the performance prediction model based on the QoS of the selected services. The performance prediction model can help users to analyze their applications and improve them by optimized the factors which affects the performance. The experiment shows that the Service Selection Agent can provide ServiceBSP users with satisfied QoS of applications.展开更多
Graphics Processing Units(GPUs)are used to accelerate computing-intensive tasks,such as neural networks,data analysis,high-performance computing,etc.In the past decade or so,researchers have done a lot of work on GPU ...Graphics Processing Units(GPUs)are used to accelerate computing-intensive tasks,such as neural networks,data analysis,high-performance computing,etc.In the past decade or so,researchers have done a lot of work on GPU architecture and proposed a variety of theories and methods to study the microarchitectural characteristics of various GPUs.In this study,the GPU serves as a co-processor and works together with the CPU in an embedded real-time system to handle computationally intensive tasks.It models the architecture of the GPU and further considers it based on some excellent work.The SIMT mechanism and Cache-miss situation provide a more detailed analysis of the GPU architecture.In order to verify the GPU architecture model proposed in this article,10 GPU kernel_task and an Nvidia GPU device were used to perform experiments.The experimental results showed that the minimum error between the kernel task execution time predicted by the GPU architecture model proposed in this article and the actual measured kernel task execution time was 3.80%,and the maximum error was 8.30%.展开更多
Although extended-range forecasting has exceeded the limit of daily predictability of weather,there are still partially predictable characteristics of meteorological fields in such forecasts.A targeted forecast scheme...Although extended-range forecasting has exceeded the limit of daily predictability of weather,there are still partially predictable characteristics of meteorological fields in such forecasts.A targeted forecast scheme and strategy for extended-range predictable components is proposed.Based on chaotic characteristics of the atmosphere,predictable components and unpredictable random components are separated by using the standpoint of error growth in a numerical model.The predictable components are defined as those with slow error growth at a given range,which are not sensitive to small errors in initial conditions. A numerical model for predictable components(NMPC)is established,by filtering random components with poor predictability.The aim is to maintain predictable components and avoid the influence of rapidly growing forecast errors on small scales. Meanwhile,the analogue-dynamical approach(ADA)is used to correct forecast errors of predictable components,to decrease model error and statistically take into account the influence of random components.The scheme is applied to operational dynamical extended-range forecast(DERF)model of the National Climate Center of China Meteorological Administration (NCC/CMA).Prediction results show that the scheme can improve forecast skill of predictable components to some extent, especially in high predictability regions.Forecast skill at zonal wave zero is improved more than for ultra-long waves and synoptic-scale waves.Results show good agreement with predictability of spatial scale.As a result,the scheme can reduce forecast errors and improve forecast skill,which favors operational use.展开更多
Ever since gene targeting or specific modification of genome sequences in mice was achieved in the early 1980s,the reverse genetic approach of precise editing of any genomic locus has greatly accelerated biomedical re...Ever since gene targeting or specific modification of genome sequences in mice was achieved in the early 1980s,the reverse genetic approach of precise editing of any genomic locus has greatly accelerated biomedical research and biotechnology development.In particular,the recent development of the CRISPR/Cas9 system has greatly expedited genetic dissection of 3D genomes.CRISPR gene-editing outcomes result from targeted genome cleavage by ectopic bacterial Cas9 nuclease followed by presumed random ligations via the host double-strand break repair machineries.Recent studies revealed,however,that the CRISPR genomeediting system is precise and predictable because of cohesive Cas9 cleavage of targeting DNA.Here,we synthesize the current understanding of CRISPR DNA fragment-editing mechanisms and recent progress in predictable outcomes from precise genetic engineering of 3D genomes.Specifically,we first briefly describe historical genetic studies leading to CRISPR and 3D genome engineering.We then summarize different types of chromosomal rearrangements by DNA fragment editing.Finally,we review significant progress from precise ID gene editing toward predictable 3D genome engineering and synthetic biology.The exciting and rapid advances in this emerging field provide new opportunities and challenges to understand or digest 3D genomes.展开更多
We establish existence of Predictable Forward Performance Processes(PFPPs)in conditionally complete markets,which has been previously shown only in the binomial setting.Our market model can be a discrete-time or a con...We establish existence of Predictable Forward Performance Processes(PFPPs)in conditionally complete markets,which has been previously shown only in the binomial setting.Our market model can be a discrete-time or a continuous-time model,and the investment horizon can be finite or infinite.We show that the main step in construction of PFPPs is solving a one-period problem involving an integral equation,which is the counterpart of the functional equation found in the binomial case.Although this integral equation has been partially studied in the existing literature,we provide a new solution method using the Fourier transform for tempered distributions.We also provide closedform solutions for PFPPs with inverse marginal functions that are completely monotonic and establish uniqueness of PFPPs within this class.We apply our results to two special cases.The first one is the binomial market and is included to relate our work to the existing literature.The second example considers a generalized Black–Scholes model which,to the best of our knowledge,is a new result.展开更多
This paper refers to the CNOP-related algorithms and formulates the practical method and forecast techniques of extracting predictable components in a numerical model for predictable components on extended-range scale...This paper refers to the CNOP-related algorithms and formulates the practical method and forecast techniques of extracting predictable components in a numerical model for predictable components on extended-range scales.Model variables are divided into predictable components and unpredictable chaotic components from the angle of model prediction error growth.The predictable components are defined as those with a slow error growth at a given range.A targeted numerical model for predictable components is established based on the operational dynamical extended-range forecast(DERF)model of the National Climate Center.At the same time,useful information in historical data are combined to find the fields for predictable components in the numerical model that are similar to those for the predictable components in historical data,reducing the variable dimensions in a similar judgment process and further correcting prediction errors of predictable components.Historical data is used to obtain the expected value and variance of the chaotic components through the ensemble forecast method.The numerical experiment results show that this method can effectively improve the forecast skill of the atmospheric circulation field in the 10–30 days extended-range numerical model and has good prospects for operational applications.展开更多
For the two classes of stochastic processes, namely, martingale difference sequences withconstant conditional variances and processes with independent increments, each square-inte-grable functional of the process has ...For the two classes of stochastic processes, namely, martingale difference sequences withconstant conditional variances and processes with independent increments, each square-inte-grable functional of the process has been shown to have chaos decomposition if and only ifthe process has the property of predictable representation. The definition of chaos is thesame as P. A. Meyer’s, that is polynomial functional in discrete parameter case and ortho-gonal stochastic multiple integral in continuous parameter case. The proofs mainly rely onthe necessary and sufficient conditions for the property of predictable representation forthese two classes of processes, obtained previously by the authors.展开更多
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
In this study,we delve into the optimal stopping problem by examining the p(ϕ(τ),τ∈T_(0)^(p))case in which the reward is given by a family of nonnegative random variables indexed by predictable stopping times.We ai...In this study,we delve into the optimal stopping problem by examining the p(ϕ(τ),τ∈T_(0)^(p))case in which the reward is given by a family of nonnegative random variables indexed by predictable stopping times.We aim to elucidate various properties of the value function family within this context.We prove the existence of an optimal predictable stopping time,subject to specific assumptions regarding the reward functionϕ.展开更多
The predictability of a coupled system composed of a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order three-variable tropical recharge-discharge model is explored with emphasis on its l...The predictability of a coupled system composed of a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order three-variable tropical recharge-discharge model is explored with emphasis on its long-term forecasting capabilities.Highly idealized ensemble forecasts are produced taking into account the uncertainties in the initial states of the system,with specific attention to the structure of the initial errors in the tropical model.Three main types of experiments are explored:with random perturbations along the three Lyapunov vectors of the tropical model;along the two dominant Lyapunov vectors;and along the first Lyapunov vector only.When perturbations are introduced along all vectors,forecasting biases develop even if in a perfect model framework and with known initial uncertainty properties.Theses biases are considerably reduced only when the perturbations are introduced along the dominant Lyapunov vector.Furthermore,this perturbation strategy allows a reduced mean square error to be obtained at long lead times of a few years,as well as reliable ensemble forecasts across the whole time range.These very counterintuitive findings further underline the importance of appropriately controlling the initial error structure in the tropics through data assimilation.展开更多
The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n...The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.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
Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In...Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.展开更多
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Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.
基金Project (No. 50078048) supported by the National Natural Science Foundation of China
文摘The chaotic characteristics and maximum predictable time scale of the observation series of hourly water consumption in Hangzhou were investigated using the advanced algorithm presented here is based on the conventional Wolf's algorithm for the largest Lyapunov exponent. For comparison, the largest Lyapunov exponents of water consumption series with one-hour and 24-hour intervals were calculated respectively. The results indicated that chaotic characteristics obviously exist in the hourly water consumption system; and that observation series with 24-hour interval have longer maximum predictable scale than hourly series. These findings could have significant practical application for better prediction of urban hourly water consumption.
文摘In this paper, the method which can combine different seismic data with the different precision and completeness, even the palaeo-earthquake data, has been applied to estimate the yearly seismic moment rate in the seismic region. Based on this, the predictable model of regional time-magnitude has been used in North China and Southwest China. The normal correlation between the time interval of the events and the magnitude of the last strong earthquake shows that the model is suitable. The value of the parameter c is less than the average value of 0.33 that is obtained from the events occurred in the plate boundary in the world. It is explained that the correlativity between the recurrence interval of the earthquake and the magnitude of the last strong event is not obvious. It is shown that the continental earthquakes in China are different from that occurred in the plate boundary and the recurrence model for the continental events are different from the one for the plate boundary events. Finally the seismic risk analysis based on this model for North China and Southwest China is given in this paper.
基金National Natural Science Foundation of China(1 9971 0 72 )
文摘In this paper,the optional and predictable projections of set-valued measurable processes are studied.The existence and uniqueness of optional and predictable projections of set-valued measurable processes are proved under proper circumstances.
基金supported by the grants from CASthe National Key R&D Program of Chinathe National Natural Science Foundation of China
文摘Making accurate forecast or prediction is a challenging task in the big data era, in particular for those datasets involving high-dimensional variables but short-term time series points,which are generally available from real-world systems.To address this issue, Prof.
文摘During the course of the disease,most patients with Crohn's disease(CD) may eventually develop a stricturing or a perforating complication,and a significant number of patients with both CD and ulcerative colitis will undergo surgery.In recent years,research has focused on the determination of factors important in the prediction of disease course in inflammatory bowel diseases to improve stratification of patients,identify individual patient profiles,including clinical,laboratory and molecular markers,which hopefully will allow physicians to choose the most appropriate management in terms of therapy and intensity of follow-up.This review summarizes the available evidence on clinical,endoscopic variables and biomarkers in the prediction of short and long-term outcome in patients with inflammatory bowel diseases.
基金Supported by the National Natural Science Foundation of China (60573109)Shanghai Municipal Committee of Science and Tech-nology (05dz15005)Shanghai High Institution Grid Project
文摘This paper proposes a performance prediction model for grid computing model ServiceBSP to support developing high quality applications in grid environment. In ServiceBSP model, the agents carrying computing tasks are dispatched to the local domain of the selected computation services. By using the IP （integer program） approach, the Service Selection Agent selects the computation services with global optimized QoS （quality of service） consideration. The performance of a ServiceBSP application can be predicted according to the performance prediction model based on the QoS of the selected services. The performance prediction model can help users to analyze their applications and improve them by optimized the factors which affects the performance. The experiment shows that the Service Selection Agent can provide ServiceBSP users with satisfied QoS of applications.
文摘Graphics Processing Units(GPUs)are used to accelerate computing-intensive tasks,such as neural networks,data analysis,high-performance computing,etc.In the past decade or so,researchers have done a lot of work on GPU architecture and proposed a variety of theories and methods to study the microarchitectural characteristics of various GPUs.In this study,the GPU serves as a co-processor and works together with the CPU in an embedded real-time system to handle computationally intensive tasks.It models the architecture of the GPU and further considers it based on some excellent work.The SIMT mechanism and Cache-miss situation provide a more detailed analysis of the GPU architecture.In order to verify the GPU architecture model proposed in this article,10 GPU kernel_task and an Nvidia GPU device were used to perform experiments.The experimental results showed that the minimum error between the kernel task execution time predicted by the GPU architecture model proposed in this article and the actual measured kernel task execution time was 3.80%,and the maximum error was 8.30%.
基金supported by National Natural Science Foundation of China (Grant Nos.41105070,40930952 and 41005041)State Key Program of Science and Technology of China(Grant No.2009BAC51B04)Meteorological Special Project of China(Grant No.GYHY 201106016)
文摘Although extended-range forecasting has exceeded the limit of daily predictability of weather,there are still partially predictable characteristics of meteorological fields in such forecasts.A targeted forecast scheme and strategy for extended-range predictable components is proposed.Based on chaotic characteristics of the atmosphere,predictable components and unpredictable random components are separated by using the standpoint of error growth in a numerical model.The predictable components are defined as those with slow error growth at a given range,which are not sensitive to small errors in initial conditions. A numerical model for predictable components(NMPC)is established,by filtering random components with poor predictability.The aim is to maintain predictable components and avoid the influence of rapidly growing forecast errors on small scales. Meanwhile,the analogue-dynamical approach(ADA)is used to correct forecast errors of predictable components,to decrease model error and statistically take into account the influence of random components.The scheme is applied to operational dynamical extended-range forecast(DERF)model of the National Climate Center of China Meteorological Administration (NCC/CMA).Prediction results show that the scheme can improve forecast skill of predictable components to some extent, especially in high predictability regions.Forecast skill at zonal wave zero is improved more than for ultra-long waves and synoptic-scale waves.Results show good agreement with predictability of spatial scale.As a result,the scheme can reduce forecast errors and improve forecast skill,which favors operational use.
基金This work was supported by grants from the National Natural Science Foundation of China(31630039 and 32000425)the Ministry of Science and Technology of China(2017YFA0504203 and 2018YFC1004504)the Science and Technology Commission of Shanghai Municipality(19JC1412500).
文摘Ever since gene targeting or specific modification of genome sequences in mice was achieved in the early 1980s,the reverse genetic approach of precise editing of any genomic locus has greatly accelerated biomedical research and biotechnology development.In particular,the recent development of the CRISPR/Cas9 system has greatly expedited genetic dissection of 3D genomes.CRISPR gene-editing outcomes result from targeted genome cleavage by ectopic bacterial Cas9 nuclease followed by presumed random ligations via the host double-strand break repair machineries.Recent studies revealed,however,that the CRISPR genomeediting system is precise and predictable because of cohesive Cas9 cleavage of targeting DNA.Here,we synthesize the current understanding of CRISPR DNA fragment-editing mechanisms and recent progress in predictable outcomes from precise genetic engineering of 3D genomes.Specifically,we first briefly describe historical genetic studies leading to CRISPR and 3D genome engineering.We then summarize different types of chromosomal rearrangements by DNA fragment editing.Finally,we review significant progress from precise ID gene editing toward predictable 3D genome engineering and synthetic biology.The exciting and rapid advances in this emerging field provide new opportunities and challenges to understand or digest 3D genomes.
基金supported by the National Science Foundation(Grant No.DMS-1929348).
文摘We establish existence of Predictable Forward Performance Processes(PFPPs)in conditionally complete markets,which has been previously shown only in the binomial setting.Our market model can be a discrete-time or a continuous-time model,and the investment horizon can be finite or infinite.We show that the main step in construction of PFPPs is solving a one-period problem involving an integral equation,which is the counterpart of the functional equation found in the binomial case.Although this integral equation has been partially studied in the existing literature,we provide a new solution method using the Fourier transform for tempered distributions.We also provide closedform solutions for PFPPs with inverse marginal functions that are completely monotonic and establish uniqueness of PFPPs within this class.We apply our results to two special cases.The first one is the binomial market and is included to relate our work to the existing literature.The second example considers a generalized Black–Scholes model which,to the best of our knowledge,is a new result.
基金supported by the National Natural Science Foundation of China (Grant Nos. 40930952, 41105055)Global Change Study of Major National Scientific Research Plan of China (Grant No. 2012CB955902)Meteorological Special Project of China (Grant Nos. GYHY201106016, GYHY201106015)
文摘This paper refers to the CNOP-related algorithms and formulates the practical method and forecast techniques of extracting predictable components in a numerical model for predictable components on extended-range scales.Model variables are divided into predictable components and unpredictable chaotic components from the angle of model prediction error growth.The predictable components are defined as those with a slow error growth at a given range.A targeted numerical model for predictable components is established based on the operational dynamical extended-range forecast(DERF)model of the National Climate Center.At the same time,useful information in historical data are combined to find the fields for predictable components in the numerical model that are similar to those for the predictable components in historical data,reducing the variable dimensions in a similar judgment process and further correcting prediction errors of predictable components.Historical data is used to obtain the expected value and variance of the chaotic components through the ensemble forecast method.The numerical experiment results show that this method can effectively improve the forecast skill of the atmospheric circulation field in the 10–30 days extended-range numerical model and has good prospects for operational applications.
基金Supported by the National Natural Science Foundation of China.
文摘For the two classes of stochastic processes, namely, martingale difference sequences withconstant conditional variances and processes with independent increments, each square-inte-grable functional of the process has been shown to have chaos decomposition if and only ifthe process has the property of predictable representation. The definition of chaos is thesame as P. A. Meyer’s, that is polynomial functional in discrete parameter case and ortho-gonal stochastic multiple integral in continuous parameter case. The proofs mainly rely onthe necessary and sufficient conditions for the property of predictable representation forthese two classes of processes, obtained previously by the authors.
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.
文摘In this study,we delve into the optimal stopping problem by examining the p(ϕ(τ),τ∈T_(0)^(p))case in which the reward is given by a family of nonnegative random variables indexed by predictable stopping times.We aim to elucidate various properties of the value function family within this context.We prove the existence of an optimal predictable stopping time,subject to specific assumptions regarding the reward functionϕ.
基金supported by the National Key R&D Program of China(Grant No.2023YFF0805100)。
文摘The predictability of a coupled system composed of a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order three-variable tropical recharge-discharge model is explored with emphasis on its long-term forecasting capabilities.Highly idealized ensemble forecasts are produced taking into account the uncertainties in the initial states of the system,with specific attention to the structure of the initial errors in the tropical model.Three main types of experiments are explored:with random perturbations along the three Lyapunov vectors of the tropical model;along the two dominant Lyapunov vectors;and along the first Lyapunov vector only.When perturbations are introduced along all vectors,forecasting biases develop even if in a perfect model framework and with known initial uncertainty properties.Theses biases are considerably reduced only when the perturbations are introduced along the dominant Lyapunov vector.Furthermore,this perturbation strategy allows a reduced mean square error to be obtained at long lead times of a few years,as well as reliable ensemble forecasts across the whole time range.These very counterintuitive findings further underline the importance of appropriately controlling the initial error structure in the tropics through data assimilation.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950).
文摘The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.
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.
文摘Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.
