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
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
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 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
Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal sca...Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications.展开更多
The variation in microbiota during pit fermentation is the main reason for the distinct characteristics of the 7 types of base Baijiu in jiang-flavor Baijiu(JFB)brewing.However,the specific structure,succession,and fu...The variation in microbiota during pit fermentation is the main reason for the distinct characteristics of the 7 types of base Baijiu in jiang-flavor Baijiu(JFB)brewing.However,the specific structure,succession,and functional differentiation of microbial communities across different fermentation rounds remain unclear.Therefore,this study compared the differences in microbiota structure,environmental factors driving community assembly,and functional differentiations throughout 1–7 rounds(JC1–JC7)of pit fermentation in JFB production.Results showed that Lactobacillus dominated all rounds and complied with declining relative abundance from rounds JC1–JC7.The mould composition was similar in JC3–JC5 while the yeast structure in JC4 was found intermediate between JC3 and JC5.LEf Se analysis unveiled aroma-producing microorganisms as prominent biomarkers in JC1,strong enzyme-producing attributes in JC2,JC6,and JC7 biomarkers,and an enzyme and aroma-producing focus with robust tolerance in JC3–JC5 biomarkers.Acidity mainly regulated the microbial community in the first 4 rounds,with nutrient limitation drove microbial succession from the fifth round onward.Functional predictions underscored enriched amino acid metabolism enzymes in JC6 and JC1,while carbohydrate degradation exhibited predominant enzymatic profiles in JC2,JC6,and JC7.This study laid a foundation for comprehending community composition,succession,and flavor regulatory mechanisms throughout JFB brewing.展开更多
Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused ...Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused on the deep double-line Sejila Mountain tunnel to systematically analyze the spatial response of blasting-induced vibration and to develop a prediction model through field tests and numerical simulations.The results revealed that the presence of a cross passage significantly altered propagation paths and the spatial distribution of blasting-induced vibration velocity.The peak particle velocity(PPV)at the cross-passage corner was amplified by approximately 1.92 times due to wave reflection and geometric focusing.Blasting-induced vibration waves attenuated non-uniformly across the tunnel cross-section,where PPV on the blast-face side was 1.54–6.56 times higher than that on the opposite side.We propose an improved PPV attenuation model that accounts for the propagation path effect.This model significantly improved fitting accuracy and resolved anomalous parameter(k and a)estimates in traditional equations,thereby improving prediction reliability.Furthermore,based on the observed spatial distribution of blasting-induced vibration,optimal monitoring point placement and targeted vibration control measures for tunnel blasting were discussed.These findings provide a scientific basis for designing blasting schemes and vibration mitigation strategies in deep tunnels.展开更多
Background:Gastric cancer(GC)continues to pose a significant global health challenge due to its high rates of incidence and mortality,with the majority of cases identified at advanced stages.Immunotherapy,particularly...Background:Gastric cancer(GC)continues to pose a significant global health challenge due to its high rates of incidence and mortality,with the majority of cases identified at advanced stages.Immunotherapy,particularly immune checkpoint blockades(ICBs),has demonstrated considerable therapeutic potential;however,many patients do not exhibit a favorable response.As a result,constructing a predictive model to assess ICBs'responsiveness is essential for enhancing treatment outcomes.Methods:Using consensus clustering based on anoikis-related gene expression,GC patients were stratified into two subclusters.Differences in tumor immune microenvironment,ICB resistance,genomic alterations,methylation profiles,and transcriptional networks were analyzed.A machine learning-based strategy was employed to develop a consensus anoikis-related gene signature(ARGS).Potential therapeutic targets were identified through single-cell RNA sequencing(scRNA-seq),and validation was conducted using multiplex immunofluorescence and immunohistochemistry in an in-house cohort(n=28),including 14 ICB responders and 14 nonresponders.Results:The anoikis-resistant cluster(Cluster A)was associated with poorer survival,immunosuppressive infiltration,lower tumor mutation burden,and ICB resistance.ScRNA-seq revealed high fibroblast and endothelial infiltration,with GLI3+cancer-associated fibroblasts suggesting Hedgehog pathway involvement.The ARGS model effectively stratified patients,with elevated scores associ-ated with immunotherapy resistance,enhanced AR characteristics,and poorer clinical outcomes.展开更多
Traditional oilfields face increasing extraction challenges, primarily due to reservoir quality degradation and production decline, which are further exacerbated by volatile international crude oil prices—illustrated...Traditional oilfields face increasing extraction challenges, primarily due to reservoir quality degradation and production decline, which are further exacerbated by volatile international crude oil prices—illustrated by Brent Crude’s trajectory from pandemic-induced negative pricing to geopolitically driven surges exceeding USD 100 per barrel. This study addresses these complexities through an integrated methodological framework applied to medium-permeability sandstone reservoirs in the Xinjiang oilfield by combining advanced numerical simulations with multivariate regression analysis. The methodology employs Latin Hypercube Sampling (LHS) to stratify geological parameter distributions and constructs heterogeneous reservoir models using Petrel software, rigorously validated through historical production data matching. Production forecasting integrates numerical simulation and Decline Curve Analysis (DCA), while investment estimation utilizes Ordinary Least Squares (OLS) regression to correlate engineering parameters with drilling and completion costs. Economic evaluation incorporates Discounted Cash Flow (DCF) modeling and breakeven analysis, establishing techno-economic boundaries via oil price sensitivity analysis ranging from USD 40 to 90 per barrel. Visualization tools, including 3D heatmaps, delineate nonlinear interactions among engineering, geological, and investment datasets under economic constraints. Key findings demonstrate that for the target reservoirs, as oil prices increase from USD 40 to USD 90 per barrel, the minimum economic thickness threshold decreases from approximately 5.7 m to about 2.5 m, with model prediction errors consistently below 25% across validation datasets. This framework provides scientifically grounded decision support for optimizing capital allocation and offers actionable insights to enhance undeveloped hydrocarbon development planning amid market uncertainty. Ultimately, it supports national energy security through technically robust and economically viable resource exploitation strategies.展开更多
Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that empl...Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.展开更多
Ungulates serve as key components in maintaining ecosystem stability,and their ecological functions are closely linked to the integrity of giant panda habitats within Wanglang National Nature Reserve.Assessment of ung...Ungulates serve as key components in maintaining ecosystem stability,and their ecological functions are closely linked to the integrity of giant panda habitats within Wanglang National Nature Reserve.Assessment of ungulate habitat suitability in this reserve can provide critical insights into the distribution patterns of ungulate communities across protected areas while informing conservation strategy optimization.Therefore,six ungulate species were monitored,including Tufted deer(Elaphodus cephalophus),Chinese serow(Capricornis milneedwardsii),Chinese goral(Naemorhedus griseus),Sichuan takin(Budorcas taxicolor),Reeve's muntjac(Muntiacus reevesi),and Wild boar(Sus scrofa)in Wanglang National Nature Reserve.The infrared camera monitoring data(a total of 83 sites)and 23 environmental variables were collected from January 2011 to May 2019,the relative abundance index(RAI),independent samples t-test,and optimized MaxEnt model were employed to evaluate the habitat suitability of six ungulate species.Our findings were as follows:(1)there were significant differences in the annual RAI among the six ungulate species(P<0.01).(2)the suitable habitat area varied among species:Tufted deer occupied 9578 hm^(2)(31.08%of the total),Chinese serow 10,093 hm^(2)(32.75%);Chinese goral 9936 hm^(2)(32.24%);Sichuan takin 10,992 hm^(2)(35.67%);Reeve's muntjac 9542 hm^(2)(30.96%);and Wild boar 9642 hm^(2)(31.28%).(3)the spatial niche overlaps between each pair of the six ungulates were all relatively high(D=0.77-0.89).(4)the annual average temperature,precipitation during the coldest season,and vegetation were the key factors influencing habitat selection.These findings offer valuable references for the conservation of ungulates in natural reserve and are conducive to formulating more scientific and effective.展开更多
Accurate prediction of environmental temperature is pivotal for promoting sustainable crop growth.At present,the most effective temperature sensing and prediction system is the Agricultural Internet of Things(AIoT),wh...Accurate prediction of environmental temperature is pivotal for promoting sustainable crop growth.At present,the most effective temperature sensing and prediction system is the Agricultural Internet of Things(AIoT),which deploys a large number of sensors to collect meteorological data and transmits them to the cloud server for prediction.However,this procedure is computationally and communicationally expensive for resourceconstrained AIoT.Recently,Semantic Communication(SC)has shown potential in efficient data transmission,but existing methods overlook the repetitive semantic information whilst sensing data,bringing additional overheads.With the resource-constraint nature of AIoT in mind,we propose the Semantic Communication-enabled Cognitive Agriculture Framework(SC-CAF)for delivering accurate temperature predictions.The proposed SC-CAF incorporates an intelligent analysis layer that performs the temperature prediction and model training and distribution,while a semantic layer transmitting the semantic information extracted from raw data based on the download model,ultimately to reduce communication overheads in AIoT.Furthermore,we propose a novel model called the Light Temperature Semantic Communication(LTSC)by adopting skip-attention and semantic compressor to avoid unnecessary computation and repetitive information,thereby addressing the semantic redundancy issues in sensing data.We also develop a Semantic-based Model Compression(SCMC)algorithm to alleviate the computation and bandwidth burden,enabling AIoT to explore the extensive usage of SC.Experimental results demonstrate that the proposed SC-CAF achieves the lowest prediction error while reducing Floating Point Operations(FLOPs)by 95.88%,memory requirements by 78.30%,Graphics Processing Unit(GPU)power by 50.77%,and time latency by 84.44%,outperforming notable state-of-the-art methods.展开更多
Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the ...Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the authors present a machine learning-based risk prediction approach using routinely available clinical and laboratory parameters.Among the evaluated algorithms,a decision tree model demonstrated excellent discrimination,achieving an area under the curve of 0.951 in the validation set and notably identifying all true cases of delayed wound healing at the Youden index threshold.The inclusion of variables such as drainage duration,preoperative white blood cell and neutrophil counts,alongside age and sex,highlights the pragmatic appeal of the model for early postoperative monitoring.Nevertheless,several aspects warrant critical reflection,including the reliance on a postoperative variable(drainage duration),internal validation only,and certain reporting inconsistencies.This letter underscores both the promise and the limitations of adopting interpretable machine learning models in perioperative care.We advocate for transparent reporting,external validation,and careful consideration of clinically actionable timepoints before integration into practice.Ultimately,this work represents a valuable step toward precision risk stratification in gastric cancer surgery,and sets the stage for multicenter,prospective evaluations.展开更多
Seismic intensity is critical for post-earthquake hazard assessment and response,but is often delayed because field surveys are required.Here,we propose a simple scheme for quick prediction of earthquake ground shakin...Seismic intensity is critical for post-earthquake hazard assessment and response,but is often delayed because field surveys are required.Here,we propose a simple scheme for quick prediction of earthquake ground shaking intensity using high-rate Global Navigation Satellite System(GNSS)data.In the scheme,high-rate GNSS displacement waveforms and static GNSS coseismic offsets are first used to invert the fault rupture process based on a one-fault model.The kinematic slip model is then employed as input for kinematic forward simulation to predict strong ground motion,which is subsequently convert into seismic intensities according to the China seismic intensity scale(GB/T 17742–2020).We take the 2021 Mw 7.3 Maduo Earthquake as a case study to illustrate the feasibility of this scheme.Our results show that the seismic intensity produced by the one-fault model is consistent with that from field investigations,especially in meizoseismal zones,suggesting that the scheme may serve as a potential solution for quick prediction of seismic intensity,which helps to disaster relief efforts after strong earthquakes.展开更多
The coal dynamic characteristic stress identification under dynamic load is important for guiding underground mineral mining and predicting underground dynamic disasters.In this article,the dynamic compression test of...The coal dynamic characteristic stress identification under dynamic load is important for guiding underground mineral mining and predicting underground dynamic disasters.In this article,the dynamic compression test of anthracite under five strain rates is carried out,the evolution law of three kinds of crack characteristic stress is analyzed,and a prediction model of the crack characteristic stress threshold considering the strain rate effect is established.Then,the rationality of crack characteristic stress under dynamic loading is discussed from the damage evolution standpoint,and the crack extension response mechanism during dynamic compression of anthracite is discussed.The result shows that the crack characteristic stress threshold is significantly influenced by the strain rate.The three characteristic stress thresholds are positively correlated with the strain rate,but the ratios to the crest stress gradually decrease.The increase in the strain rate strongly contributes to the crack extension behavior of anthracite.In the crack unstable extension phase,because of the increase of the strain rate,anthracite shows more energy dissipation under the same deformation in association with the stress concentration effect and the dynamic strength enhancement effect.The crack propagation rate is increased,the crack propagation path of the section is more complex,and more severe damage occurs before the dynamic failure of anthracite,which leads to even more severe damage.展开更多
The growing demands on global infrastructure highlight the critical need for durable and efficient pavement systems,particularly under the stress of repetitive heavy traffic loads.The use of geosynthetics within the p...The growing demands on global infrastructure highlight the critical need for durable and efficient pavement systems,particularly under the stress of repetitive heavy traffic loads.The use of geosynthetics within the pavement structure increases the load-carrying capacity of unbound pavement layers by providing lateral restraint,improving vertical stress distribution,and enhancing bearing capacity.Such reinforcement typically aims to either improve the service life of pavements or achieve equivalent performance with a reduced granular cover.Previous and ongoing research quantifies geosynthetic performance in pavement reinforcement using various testing methods.Among these,laboratory model box tests subjected to cyclic loading are pivotal,as they closely replicate real-world traffic conditions.Hence,these studies are essential for understanding how geosynthetics distribute loads and enhance pavement durability.This facilitates the development of optimized geosynthetic design and installation practices,accelerating the loading process to simulate years of traffic wear in a shorter period.This review discusses the improved rutting resistance of unbound pavements reinforced with geosynthetic materials,specifically drawing on data from cyclic plate load tests conducted on laboratory model boxes,as highlighted in the literature.Key variables such as optimum geosynthetic placement,geosynthetic material properties,performance of different geosynthetic materials and the effects of aperture shape and size on rutting resistance are discussed.Furthermore,the review assesses various predictive rutting models,analysing their applicability and accuracy in forecasting the rutting performance of geosynthetic-reinforced unbound pavements.This comprehensive literature review aids pavement engineers and researchers,in guiding the selection and design of geosynthetics to optimize pavement durability and functionality under repetitive traffic loads.展开更多
Developing advanced acoustic treatments,such as the Multi-Degree-of-Freedom(MDOF)septum liner,to realize the broadband noise reduction is critical for silent aeroengines.This study investigates experimentally the MDOF...Developing advanced acoustic treatments,such as the Multi-Degree-of-Freedom(MDOF)septum liner,to realize the broadband noise reduction is critical for silent aeroengines.This study investigates experimentally the MDOF septum liner and its impedance model on the Beihang Grazing Flow Duct(BGFD)setup,over a wide frequency range under grazing flows up to 0.5 Mach number and Sound Pressure Level(SPL)up to 150 dB,typically encountered in aeroengine nacelles.Several specimens varying in the numbers,types,and depths of septa among units are designed,fabricated,and measured.Their impedances and Transmission Losses(TL)are obtained using the mirror-based multimodal straightforward method and the mode decomposition technique,respectively.Generally,the model predictions show good agreement with the educed impedances in all cases,and such liners with a large-porosity facesheet exhibit low acoustic nonlinearities in the presence of high SPL,especially under high-velocity grazing flows.Moreover,a MDOF liner we delicately designed,compared with a conventional broadband three-layer perforated liner as the reference,is close to the resonant state at more frequencies and thus has higher and wider measured TL spectra almost from 1 kHz up to 10 kHz at studied Mach numbers,under the premise of saving 22.7 mm in the thickness.These show that,the MDOF septum liner,if well designed,can achieve an ultra-broadband efficient sound attenuation using more limited spaces in complex aeroacoustic environments.展开更多
Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instabili...Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.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.
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.
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supported by the National Key Research and Development Program of China under Grant 2022YFB3608300in part by the National Nature Science Foundation of China(NSFC)under Grants 62404050,U2341218,62574056,62204052。
文摘Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications.
文摘The variation in microbiota during pit fermentation is the main reason for the distinct characteristics of the 7 types of base Baijiu in jiang-flavor Baijiu(JFB)brewing.However,the specific structure,succession,and functional differentiation of microbial communities across different fermentation rounds remain unclear.Therefore,this study compared the differences in microbiota structure,environmental factors driving community assembly,and functional differentiations throughout 1–7 rounds(JC1–JC7)of pit fermentation in JFB production.Results showed that Lactobacillus dominated all rounds and complied with declining relative abundance from rounds JC1–JC7.The mould composition was similar in JC3–JC5 while the yeast structure in JC4 was found intermediate between JC3 and JC5.LEf Se analysis unveiled aroma-producing microorganisms as prominent biomarkers in JC1,strong enzyme-producing attributes in JC2,JC6,and JC7 biomarkers,and an enzyme and aroma-producing focus with robust tolerance in JC3–JC5 biomarkers.Acidity mainly regulated the microbial community in the first 4 rounds,with nutrient limitation drove microbial succession from the fifth round onward.Functional predictions underscored enriched amino acid metabolism enzymes in JC6 and JC1,while carbohydrate degradation exhibited predominant enzymatic profiles in JC2,JC6,and JC7.This study laid a foundation for comprehending community composition,succession,and flavor regulatory mechanisms throughout JFB brewing.
基金financially supported by the National Natural Science Foundation of China(Nos.42577209 and U22A20239)the Key R&D Program of Hunan Province(No.2024WK2004)the Key Technologies for Accurate Diagnosis and Intelligent Prevention and Control of Slope Hazards in Open pit Mines,181 Major R&D projects of Metallurgical Corporation of China Ltd。
文摘Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused on the deep double-line Sejila Mountain tunnel to systematically analyze the spatial response of blasting-induced vibration and to develop a prediction model through field tests and numerical simulations.The results revealed that the presence of a cross passage significantly altered propagation paths and the spatial distribution of blasting-induced vibration velocity.The peak particle velocity(PPV)at the cross-passage corner was amplified by approximately 1.92 times due to wave reflection and geometric focusing.Blasting-induced vibration waves attenuated non-uniformly across the tunnel cross-section,where PPV on the blast-face side was 1.54–6.56 times higher than that on the opposite side.We propose an improved PPV attenuation model that accounts for the propagation path effect.This model significantly improved fitting accuracy and resolved anomalous parameter(k and a)estimates in traditional equations,thereby improving prediction reliability.Furthermore,based on the observed spatial distribution of blasting-induced vibration,optimal monitoring point placement and targeted vibration control measures for tunnel blasting were discussed.These findings provide a scientific basis for designing blasting schemes and vibration mitigation strategies in deep tunnels.
基金National Science and Technology Major Project,Grant/Award Number:2024ZD0533300Excellent Doctor Program of Zhongnan Hospital of Wuhan University,Grant/Award Number:ZNYB2021009。
文摘Background:Gastric cancer(GC)continues to pose a significant global health challenge due to its high rates of incidence and mortality,with the majority of cases identified at advanced stages.Immunotherapy,particularly immune checkpoint blockades(ICBs),has demonstrated considerable therapeutic potential;however,many patients do not exhibit a favorable response.As a result,constructing a predictive model to assess ICBs'responsiveness is essential for enhancing treatment outcomes.Methods:Using consensus clustering based on anoikis-related gene expression,GC patients were stratified into two subclusters.Differences in tumor immune microenvironment,ICB resistance,genomic alterations,methylation profiles,and transcriptional networks were analyzed.A machine learning-based strategy was employed to develop a consensus anoikis-related gene signature(ARGS).Potential therapeutic targets were identified through single-cell RNA sequencing(scRNA-seq),and validation was conducted using multiplex immunofluorescence and immunohistochemistry in an in-house cohort(n=28),including 14 ICB responders and 14 nonresponders.Results:The anoikis-resistant cluster(Cluster A)was associated with poorer survival,immunosuppressive infiltration,lower tumor mutation burden,and ICB resistance.ScRNA-seq revealed high fibroblast and endothelial infiltration,with GLI3+cancer-associated fibroblasts suggesting Hedgehog pathway involvement.The ARGS model effectively stratified patients,with elevated scores associ-ated with immunotherapy resistance,enhanced AR characteristics,and poorer clinical outcomes.
文摘Traditional oilfields face increasing extraction challenges, primarily due to reservoir quality degradation and production decline, which are further exacerbated by volatile international crude oil prices—illustrated by Brent Crude’s trajectory from pandemic-induced negative pricing to geopolitically driven surges exceeding USD 100 per barrel. This study addresses these complexities through an integrated methodological framework applied to medium-permeability sandstone reservoirs in the Xinjiang oilfield by combining advanced numerical simulations with multivariate regression analysis. The methodology employs Latin Hypercube Sampling (LHS) to stratify geological parameter distributions and constructs heterogeneous reservoir models using Petrel software, rigorously validated through historical production data matching. Production forecasting integrates numerical simulation and Decline Curve Analysis (DCA), while investment estimation utilizes Ordinary Least Squares (OLS) regression to correlate engineering parameters with drilling and completion costs. Economic evaluation incorporates Discounted Cash Flow (DCF) modeling and breakeven analysis, establishing techno-economic boundaries via oil price sensitivity analysis ranging from USD 40 to 90 per barrel. Visualization tools, including 3D heatmaps, delineate nonlinear interactions among engineering, geological, and investment datasets under economic constraints. Key findings demonstrate that for the target reservoirs, as oil prices increase from USD 40 to USD 90 per barrel, the minimum economic thickness threshold decreases from approximately 5.7 m to about 2.5 m, with model prediction errors consistently below 25% across validation datasets. This framework provides scientifically grounded decision support for optimizing capital allocation and offers actionable insights to enhance undeveloped hydrocarbon development planning amid market uncertainty. Ultimately, it supports national energy security through technically robust and economically viable resource exploitation strategies.
基金Project(42077244)supported by the National Natural Science Foundation of ChinaProject(2020-05)supported by the Open Research Fund of Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization,China。
文摘Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.
基金project titled“Research on the Distribution Trend of Suitable Habitats for Terrestrial Wild Animals”(Number:SXBYKY2021029).
文摘Ungulates serve as key components in maintaining ecosystem stability,and their ecological functions are closely linked to the integrity of giant panda habitats within Wanglang National Nature Reserve.Assessment of ungulate habitat suitability in this reserve can provide critical insights into the distribution patterns of ungulate communities across protected areas while informing conservation strategy optimization.Therefore,six ungulate species were monitored,including Tufted deer(Elaphodus cephalophus),Chinese serow(Capricornis milneedwardsii),Chinese goral(Naemorhedus griseus),Sichuan takin(Budorcas taxicolor),Reeve's muntjac(Muntiacus reevesi),and Wild boar(Sus scrofa)in Wanglang National Nature Reserve.The infrared camera monitoring data(a total of 83 sites)and 23 environmental variables were collected from January 2011 to May 2019,the relative abundance index(RAI),independent samples t-test,and optimized MaxEnt model were employed to evaluate the habitat suitability of six ungulate species.Our findings were as follows:(1)there were significant differences in the annual RAI among the six ungulate species(P<0.01).(2)the suitable habitat area varied among species:Tufted deer occupied 9578 hm^(2)(31.08%of the total),Chinese serow 10,093 hm^(2)(32.75%);Chinese goral 9936 hm^(2)(32.24%);Sichuan takin 10,992 hm^(2)(35.67%);Reeve's muntjac 9542 hm^(2)(30.96%);and Wild boar 9642 hm^(2)(31.28%).(3)the spatial niche overlaps between each pair of the six ungulates were all relatively high(D=0.77-0.89).(4)the annual average temperature,precipitation during the coldest season,and vegetation were the key factors influencing habitat selection.These findings offer valuable references for the conservation of ungulates in natural reserve and are conducive to formulating more scientific and effective.
基金supported by the Key Research and Development Project of Hubei Province(No.2024BAB070),China。
文摘Accurate prediction of environmental temperature is pivotal for promoting sustainable crop growth.At present,the most effective temperature sensing and prediction system is the Agricultural Internet of Things(AIoT),which deploys a large number of sensors to collect meteorological data and transmits them to the cloud server for prediction.However,this procedure is computationally and communicationally expensive for resourceconstrained AIoT.Recently,Semantic Communication(SC)has shown potential in efficient data transmission,but existing methods overlook the repetitive semantic information whilst sensing data,bringing additional overheads.With the resource-constraint nature of AIoT in mind,we propose the Semantic Communication-enabled Cognitive Agriculture Framework(SC-CAF)for delivering accurate temperature predictions.The proposed SC-CAF incorporates an intelligent analysis layer that performs the temperature prediction and model training and distribution,while a semantic layer transmitting the semantic information extracted from raw data based on the download model,ultimately to reduce communication overheads in AIoT.Furthermore,we propose a novel model called the Light Temperature Semantic Communication(LTSC)by adopting skip-attention and semantic compressor to avoid unnecessary computation and repetitive information,thereby addressing the semantic redundancy issues in sensing data.We also develop a Semantic-based Model Compression(SCMC)algorithm to alleviate the computation and bandwidth burden,enabling AIoT to explore the extensive usage of SC.Experimental results demonstrate that the proposed SC-CAF achieves the lowest prediction error while reducing Floating Point Operations(FLOPs)by 95.88%,memory requirements by 78.30%,Graphics Processing Unit(GPU)power by 50.77%,and time latency by 84.44%,outperforming notable state-of-the-art methods.
文摘Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the authors present a machine learning-based risk prediction approach using routinely available clinical and laboratory parameters.Among the evaluated algorithms,a decision tree model demonstrated excellent discrimination,achieving an area under the curve of 0.951 in the validation set and notably identifying all true cases of delayed wound healing at the Youden index threshold.The inclusion of variables such as drainage duration,preoperative white blood cell and neutrophil counts,alongside age and sex,highlights the pragmatic appeal of the model for early postoperative monitoring.Nevertheless,several aspects warrant critical reflection,including the reliance on a postoperative variable(drainage duration),internal validation only,and certain reporting inconsistencies.This letter underscores both the promise and the limitations of adopting interpretable machine learning models in perioperative care.We advocate for transparent reporting,external validation,and careful consideration of clinically actionable timepoints before integration into practice.Ultimately,this work represents a valuable step toward precision risk stratification in gastric cancer surgery,and sets the stage for multicenter,prospective evaluations.
基金supported by the Basic Scientific Funding of the Institute of Geology,China Earthquake Administration(No.IGCEA2120)the National Natural Science Foundation of China(Nos.U2139202 and 42104007)the Innovation Fund Project for College Teachers of Gansu Provincial Education Department(No.2025A-041)。
文摘Seismic intensity is critical for post-earthquake hazard assessment and response,but is often delayed because field surveys are required.Here,we propose a simple scheme for quick prediction of earthquake ground shaking intensity using high-rate Global Navigation Satellite System(GNSS)data.In the scheme,high-rate GNSS displacement waveforms and static GNSS coseismic offsets are first used to invert the fault rupture process based on a one-fault model.The kinematic slip model is then employed as input for kinematic forward simulation to predict strong ground motion,which is subsequently convert into seismic intensities according to the China seismic intensity scale(GB/T 17742–2020).We take the 2021 Mw 7.3 Maduo Earthquake as a case study to illustrate the feasibility of this scheme.Our results show that the seismic intensity produced by the one-fault model is consistent with that from field investigations,especially in meizoseismal zones,suggesting that the scheme may serve as a potential solution for quick prediction of seismic intensity,which helps to disaster relief efforts after strong earthquakes.
基金National Natural Science Foundation of China,Grant/Award Numbers:12072363,12372373,51934007,52104234,52174091。
文摘The coal dynamic characteristic stress identification under dynamic load is important for guiding underground mineral mining and predicting underground dynamic disasters.In this article,the dynamic compression test of anthracite under five strain rates is carried out,the evolution law of three kinds of crack characteristic stress is analyzed,and a prediction model of the crack characteristic stress threshold considering the strain rate effect is established.Then,the rationality of crack characteristic stress under dynamic loading is discussed from the damage evolution standpoint,and the crack extension response mechanism during dynamic compression of anthracite is discussed.The result shows that the crack characteristic stress threshold is significantly influenced by the strain rate.The three characteristic stress thresholds are positively correlated with the strain rate,but the ratios to the crest stress gradually decrease.The increase in the strain rate strongly contributes to the crack extension behavior of anthracite.In the crack unstable extension phase,because of the increase of the strain rate,anthracite shows more energy dissipation under the same deformation in association with the stress concentration effect and the dynamic strength enhancement effect.The crack propagation rate is increased,the crack propagation path of the section is more complex,and more severe damage occurs before the dynamic failure of anthracite,which leads to even more severe damage.
基金financial and intellectual support provided by Queensland University of Technology(QUT)through its Higher Degree Research Program.
文摘The growing demands on global infrastructure highlight the critical need for durable and efficient pavement systems,particularly under the stress of repetitive heavy traffic loads.The use of geosynthetics within the pavement structure increases the load-carrying capacity of unbound pavement layers by providing lateral restraint,improving vertical stress distribution,and enhancing bearing capacity.Such reinforcement typically aims to either improve the service life of pavements or achieve equivalent performance with a reduced granular cover.Previous and ongoing research quantifies geosynthetic performance in pavement reinforcement using various testing methods.Among these,laboratory model box tests subjected to cyclic loading are pivotal,as they closely replicate real-world traffic conditions.Hence,these studies are essential for understanding how geosynthetics distribute loads and enhance pavement durability.This facilitates the development of optimized geosynthetic design and installation practices,accelerating the loading process to simulate years of traffic wear in a shorter period.This review discusses the improved rutting resistance of unbound pavements reinforced with geosynthetic materials,specifically drawing on data from cyclic plate load tests conducted on laboratory model boxes,as highlighted in the literature.Key variables such as optimum geosynthetic placement,geosynthetic material properties,performance of different geosynthetic materials and the effects of aperture shape and size on rutting resistance are discussed.Furthermore,the review assesses various predictive rutting models,analysing their applicability and accuracy in forecasting the rutting performance of geosynthetic-reinforced unbound pavements.This comprehensive literature review aids pavement engineers and researchers,in guiding the selection and design of geosynthetics to optimize pavement durability and functionality under repetitive traffic loads.
基金co-supported by the National Natural Science Foundation of China(No.52206039)the Taihang Laboratory of China(No.A2053)+1 种基金the Aeronautical Science Foundation of China(No.20240011051001)the National Key R&D Program of China(No.2021YFB3703900)。
文摘Developing advanced acoustic treatments,such as the Multi-Degree-of-Freedom(MDOF)septum liner,to realize the broadband noise reduction is critical for silent aeroengines.This study investigates experimentally the MDOF septum liner and its impedance model on the Beihang Grazing Flow Duct(BGFD)setup,over a wide frequency range under grazing flows up to 0.5 Mach number and Sound Pressure Level(SPL)up to 150 dB,typically encountered in aeroengine nacelles.Several specimens varying in the numbers,types,and depths of septa among units are designed,fabricated,and measured.Their impedances and Transmission Losses(TL)are obtained using the mirror-based multimodal straightforward method and the mode decomposition technique,respectively.Generally,the model predictions show good agreement with the educed impedances in all cases,and such liners with a large-porosity facesheet exhibit low acoustic nonlinearities in the presence of high SPL,especially under high-velocity grazing flows.Moreover,a MDOF liner we delicately designed,compared with a conventional broadband three-layer perforated liner as the reference,is close to the resonant state at more frequencies and thus has higher and wider measured TL spectra almost from 1 kHz up to 10 kHz at studied Mach numbers,under the premise of saving 22.7 mm in the thickness.These show that,the MDOF septum liner,if well designed,can achieve an ultra-broadband efficient sound attenuation using more limited spaces in complex aeroacoustic environments.
基金Supported by the National Defense Basic Scientific Research Program of China.
文摘Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.
