In this paper,we establish some strong laws of large numbers,which are for nonindependent random variables under the framework of sublinear expectations.One of our main results is for blockwise m-dependent random vari...In this paper,we establish some strong laws of large numbers,which are for nonindependent random variables under the framework of sublinear expectations.One of our main results is for blockwise m-dependent random variables,and another is for sub-orthogonal random variables.Both extend the strong law of large numbers for independent random variables under sublinear expectations to the non-independent case.展开更多
In this paper,we consider the fourth-order parabolic equation with p(x)Laplacian and variable exponent source ut+∆^(2)u−div(|■u|^(p(x)−2■u))=|u|^(q(x))−1u.By applying potential well method,we obtain global existence...In this paper,we consider the fourth-order parabolic equation with p(x)Laplacian and variable exponent source ut+∆^(2)u−div(|■u|^(p(x)−2■u))=|u|^(q(x))−1u.By applying potential well method,we obtain global existence,asymptotic behavior and blow-up of solutions with initial energy J(u_(0))≤d.Moreover,we estimate the upper bound of the blow-up time for J(u_(0))≤0.展开更多
In this article,we prove the boundedness for commutators of fractional Hardy and Hardy-Littlewood-Pólya operators on grand p-adic variable Herz spaces,where the symbols of the commutators belong to Lipschitz spaces.
This study investigates the reduction in polarization measurement accuracy caused by varying in-cident angles in a liquid crystal variable retarder(LCVR).The phase delay characteristics of the LCVR were examined,with ...This study investigates the reduction in polarization measurement accuracy caused by varying in-cident angles in a liquid crystal variable retarder(LCVR).The phase delay characteristics of the LCVR were examined,with particular emphasis on the influence of different two-dimensional incident angles on phase delay behavior.Building upon the calibration of phase delay under normal incidence,a phase delay calibra-tion model was developed to account for variations in incident angle and driving voltage.A mathematical re-lationship was established between phase delay and the azimuth angle(α)and pitch angle(β).Experimental validation was conducted under three conditions:α=20°,β=0°;α=0°,β=20°;and an arbitrary angle whereα=5°,β=15°.The results demonstrated that the maximum average deviation between theoretical pre-dictions and experimental measurements did not exceed 0.059 rad.The proposed calibration method proved to be both accurate and practical.This approach offers robust support for LCVR parameter calibration and performance optimization in optical systems,particularly in polarization imaging applications.展开更多
Background:Studies have shown that heart rate variability(HRV)is a predictor of the prognosis of cardiovascular diseases.Contact heartbeat monitoring equipment is widely used,especially in hospitals,and benefits from ...Background:Studies have shown that heart rate variability(HRV)is a predictor of the prognosis of cardiovascular diseases.Contact heartbeat monitoring equipment is widely used,especially in hospitals,and benefits from the rapidity and accuracy of the detection of physiological health indicators.However,long-term contact with equipment has many adverse effects.The purpose of this study was to improve the accuracy of HRV detection via noncontact equipment,thus enabling HRV to be assessed in various scenarios.Methods:A novel deep learning approach was proposed for measuring heartbeats through camera videos.First,we performed facial segmentation and divided the face into 16 grid cells with different light balance scores.After the trend is filtered by the Hamming window,a transformer-based neural network is used to further filter the signal.Finally,heart rate(HR)and HRV are estimated.Results:We used 1 million synthetic data points for pretraining and a public dataset in combination with a dataset that we constructed for task training.The final results were obtained on a test dataset that we constructed.The accuracy for HR with a low light balance score(0.867-0.983)was greater than that with a high score(0.667-0.750).Our method had higher accuracy in estimating HR than traditional filtering methods(0.167-0.417)and state-of-the-art neural network filtering methods(0.783-0.917)did.The root mean square error of the HRV from the time domain was the lowest,and the correlation index score was the highest for the HRV from the frequency domain estimated by our method compared with those estimated by two neural networks.Conclusions:Light balance,large sample training,and two-stage training can improve the accuracy of HRV estimation.展开更多
The Moroccan populations of Alnus glutinosa(L.)Gaertn.(Betulaceae)They are located at the southern limit of the species'distribution and are represented by tetraploid cytotypes.Assessing phenotypic variability in ...The Moroccan populations of Alnus glutinosa(L.)Gaertn.(Betulaceae)They are located at the southern limit of the species'distribution and are represented by tetraploid cytotypes.Assessing phenotypic variability in reproductive traits is crucial for understanding the persistence,evolution,and range dynamics of plant populations.However,no previous studies have analyzed the relative importance of variability in explaining inter-or intra-population differences in reproductive traits.To address this gap,we investigated phenotypic variation in reproductive organs by examining 10 traits in 3.600 male catkins,3.600 female catkins,and seeds from 12 populations across the Moroccan Rif Mountains.Our results highlighted the significance of inter-population variability.However,we found that the contribution of within-tree variation to total phenotypic variability was greater than that of both inter-and intra-population variation.Principal component analysis(PCA)revealed a phenotypic gradient among populations,primarily driven by female catkin size,though this gradient was not associated with geographic conditions.This finding was further supported by Mantel test results,which showed no correlation between phenotypic variability and population conditions.These findings have important implications for the genetic improvement,conservation,and resource management of Alnus glutinosa in the future.展开更多
Tree growth variability is a key determinant of forest stabilities.Previous studies have shown that recent climate change has increased variability in tree growth,while others have challenged this viewpoint,leading to...Tree growth variability is a key determinant of forest stabilities.Previous studies have shown that recent climate change has increased variability in tree growth,while others have challenged this viewpoint,leading to ongoing debate in this field.Moreover,gaps remain in understanding the climatic mechanisms driving increased tree growth variability,particularly for species simultaneously limited by multiple climate factors.In this study,we assessed the temporal trends in variability of Picea purpurea radial growth and its linkage with growth-climate sensitivity utilizing dendrochronological methods.Our results revealed a significant increase in P.purpurea radial growth variability from 1960 to 2020,as indicated by continuous rises in the standard deviation,coefficient of variation,and mean sensitivity of tree-ring width indices.The increased frequency of extreme growth declines further supported this finding.Furthermore,moisture condition in July was identified as a key limiting factor of P.purpurea growth.Notably,the strengthening relationship between tree-ring width indices and vapor pressure deficit(VPD)suggests that the moisture sensitivity for P.purpurea growth has increased over the period 1960-2020.This enhanced sensitivity to VPD,whose interannual variability has also increased synchronously,may have contributed to the rise in P.purpurea growth variability.Additionally,the maximum temperature in May was positively correlated with P.purpurea growth;however,there is little evidence that this factor contributed to the observed increase in growth variability.These findings provide new insights into P.purpurea growth trends and improve our understanding of the potential future impacts of climate change on forest ecosystems.展开更多
As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy...As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy decisions related to rice.With the increasing application of satellite remote sensing technology in crop monitoring,remote sensing for rice cultivation has emerged as a novel approach,offering new perspectives for monitoring rice planting.This paper briefly outlined the current research and development status of satellite remote sensing for monitoring rice cultivation both at home and abroad.Foreign scholars have made innovations in data sources and methodologies for satellite remote sensing monitoring,and utilized multi-source satellite information and machine learning algorithms to enhance the accuracy of rice planting monitoring.Scholars in China have achieved significant results in the study of satellite remote sensing for monitoring rice cultivation.Their research and application in monitoring rice planting areas provide valuable references for agricultural production management.However,satellite remote sensing monitoring of rice still faces challenges such as low spatiotemporal resolution and difficulties related to cloud cover and data fusion,which require further in-depth investigation.Additionally,there are shortcomings in the accuracy of remote sensing monitoring for fragmented farmland plots and smallholder farming.To address these issues,future efforts should focus on developing multi-source heterogeneous data fusion analysis technologies and researching monitoring systems.These advancements are expected to enable high-precision large-scale acquisition of rice planting information,laying a foundation for future smart agriculture.展开更多
Thermal-mechanical damage and deformation at the interface between shotcrete linings and the surrounding rock of tunnels under high-temperature and variable-temperature conditions are critical to the safe construction...Thermal-mechanical damage and deformation at the interface between shotcrete linings and the surrounding rock of tunnels under high-temperature and variable-temperature conditions are critical to the safe construction and operation of tunnel engineering.This study investigated the thermo-mechanical damage behavior of the composite interface between alkali-resistant glass fiber-reinforced concrete(ARGFRC)and granite,focusing on a plateau railway tunnel.Laboratory triaxial tests,laser scanning,XRD analysis,numerical simulations,and theoretical analyses were employed to investigate how different initial curing temperatures and joint roughness coefficient(JRC)influence interfacial damage behavior.The results indicate that an increase in interface roughness exacerbates the structural damage at the interface.At a JRC of 19.9 and a temperature of 70℃,crack initiation in granite was notably restrained when the confining pressure rose from 7 MPa to 10 MPa.Roughness-induced stress distribution at the interface was notably altered,although this effect became less pronounced under high confining pressure conditions.Additionally,during high-temperature curing,thermal stress concentration at the tips of micro-convex protrusions on the granite surface induced microcracks in the adjacent ARGFRC matrix,followed by deformation.These findings provide practical guidelines for designing concrete support systems to ensure tunnel structural safety in high-altitude regions with harsh thermal environments.展开更多
Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is q...Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.展开更多
In this paper,we study the nonlinear Riemann boundary value problem with square roots that is represented by a Cauchy-type integral with kernel density in variable exponent Lebesgue spaces.We discuss the odd-order zer...In this paper,we study the nonlinear Riemann boundary value problem with square roots that is represented by a Cauchy-type integral with kernel density in variable exponent Lebesgue spaces.We discuss the odd-order zero-points distribution of the solutions and separate the single valued analytic branch of the solutions with square roots,then convert the problem to a Riemann boundary value problem in variable exponent Lebesgue spaces and discuss the singularity of solutions at individual zeros belonging to curve.We consider two types of cases those where the coefficient is Hölder and those where it is piecewise Hölder.Then we solve the Hilbert boundary value problem with square roots in variable exponent Lebesgue spaces.By discussing the distribution of the odd-order zero-points for solutions and the method of symmetric extension,we convert the Hilbert problem to a Riemann boundary value problem.The equivalence of the transformation is discussed.Finally,we get the solvable conditions and the direct expressions of the solutions in variable exponent Lebesgue spaces.展开更多
Marine forecasting is critical for navigation safety and disaster prevention.However,traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial fe...Marine forecasting is critical for navigation safety and disaster prevention.However,traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial features.To address the limitations,the paper proposes a TimeXer-based numerical forecast correction model optimized by an exogenous-variable attention mechanism.The model treats target forecast values as internal variables,and incorporates historical temporal-spatial data and seven-day numerical forecast results from traditional models as external variables based on the embedding strategy of TimeXer.Using a self-attention structure,the model captures correlations between exogenous variables and target sequences,explores intrinsic multi-dimensional relationships,and subsequently corrects endogenous variables with the mined exogenous features.The model’s performance is evaluated using metrics including MSE(Mean Squared Error),MAE(Mean Absolute Error),RMSE(Root Mean Square Error),MAPE(Mean Absolute Percentage Error),MSPE(Mean Square Percentage Error),and computational time,with TimeXer and PatchTST models serving as benchmarks.Experiment results show that the proposed model achieves lower errors and higher correction accuracy for both one-day and seven-day forecasts.展开更多
Cerebral ischemia restricts cerebral blood flow(CBF),leading to unstable hemodynamics.Past studies of ischemia mainly focused on cortical CBF reduction.However,its impact on hemodynamic changes,especially temporal var...Cerebral ischemia restricts cerebral blood flow(CBF),leading to unstable hemodynamics.Past studies of ischemia mainly focused on cortical CBF reduction.However,its impact on hemodynamic changes,especially temporal varying characteristics,remains poorly understood.Here,we collected cortical resting-state CBF in rats with left carotid artery blockage during occlusion–reperfusion,and measured the temporal variability and changes in laterality using a novel state-space method.This method was also applied to stroke EEG datasets to validate its effectiveness.After arterial occlusion,the left marginal motor,sensory,auditory,and visual cortices exhibited severe temporal variability impairments.The laterality analysis indicated that affected left regions showed inferior unilateral mean,inter-hemispheric transition probability,time fraction,and laterality duration,while the right side had a higher laterality time fraction and duration.These impairments recovered partially following blood flow restoration.Besides,the ischemic state-space metrics were positively correlated with the pre-occlusion baseline appearance.Stroke patients exhibited impaired temporal variability in the affected ischemic hemisphere.The state-space analysis revealed damaged CBF temporal variability during cerebral ischemia and predicted baseline-ischemia connections.展开更多
Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although la...Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although land cover information has long been recognized as an essential component for monitoring SDGs,a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist.Therefore,we propose a novel expert-and data-driven framework for identifying,refining,and selecting a priority list of Essential Land cover-related Variables for SDGs(ELcV4SDGs).This framework integrates methods including expert knowledge-based analysis,clustering of variables with similar attributes,and quantified index calculation to establish the priority list.Applying the framework to 15 specific SDG indicators,we found that the ELcV4SDGs priority list comprises three main categories,type and structure,pattern and intensity,and process and evolution of land cover,which are further divided into 19 subcategories and ultimately encompass 50 general variables.The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment,thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local,national,and global levels.展开更多
Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers of...Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers offer advantages such as reduced material usage,lower refrigerant charge,and compact structure.However,they also face challenges,including increased refrigerant pressure drop and smaller heat transfer area inside the tubes.This paper combines the advantages and disadvantages of both small and large-diameter tubes and proposes a combined-diameter heat exchanger,consisting of large and small diameters,for use in the indoor units of split-type air conditioners.There are relatively few studies in this area.In this paper,A theoretical and numerical computation method is employed to establish a theoretical-numerical calculation model,and its reliability is verified through experiments.Using this model,the optimal combined diameters and flow path design for a combined-diameter heat exchanger using R32 as the working fluid are derived.The results show that the heat transfer performance of all combined diameter configurations improves by 2.79%to 8.26%compared to the baseline design,with the coefficient of performance(COP)increasing from 4.15 to 4.27~4.5.These designs can save copper material,but at the cost of an increase in pressure drop by 66.86%to 131.84%.The scheme IIIH,using R32,is the optimal combined-diameter and flow path configuration that balances both heat transfer performance and economic cost.展开更多
Tuberculosis(TB),one of the oldest infectious diseases caused by Mycobacterium tuberculosis,poses a considerable challenge to global public health.There are approximately 10 million new TB cases worldwide annually,and...Tuberculosis(TB),one of the oldest infectious diseases caused by Mycobacterium tuberculosis,poses a considerable challenge to global public health.There are approximately 10 million new TB cases worldwide annually,and TB claims the lives of nearly 3 million people each year,making it one of the leading causes of death from a single infectious disease[1].China ranks third globally in terms of TB burden,with approximately 733,000 TB cases reported in 2023[2].Based on the ecological model of health determinants developed by Whitehead and Dahlgren,health determinants can be classified into direct causes.展开更多
Plants play an essential role in matter and energy transformations and are key messengers in the carbon and energy cycle. Net primary productivity (NPP) reflects the capability of plants to transform solar energy into...Plants play an essential role in matter and energy transformations and are key messengers in the carbon and energy cycle. Net primary productivity (NPP) reflects the capability of plants to transform solar energy into photosynthesis. It is very sensible for factors affecting on vegetation variability such as climate, soils, plant characteristics and human activities. So, it can be used as an indicator of actual and potential trend of vegetation. In this study we used the actual NPP which was derived from MODIS to assess the response of NPP to climate variables in Gadarif State, from 2000 to 2010. The correlations between NPP and climate variables (temperature and precipitation) are calculated using Pearson’s Correlation Coefficient and ordinary least squares regression. The main results show the following 1) the correlation Coefficient between NPP and mean annual temperature is Somewhat negative for Feshaga, Rahd, Gadarif and Galabat areas and weakly negative in Faw area;2) the correlation Coefficient between NPP and annual total precipitation is weakly negative in Faw, Rahd and Galabat areas and somewhat negative in Galabat and Rahd areas. This study demonstrated that the correlation analysis between NPP and climate variables (precipitation and temperature) gives reliably result of NPP responses to climate variables that is clearly in a very large scale of study area.展开更多
An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of t...An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of the internal variables were considered in this ISV model,and the parameters were optimized by genetic algorithm.After validation,the ISV model was used to simulate the evolution of grain size(GS)and dynamic recrystallization(DRX)fraction during hot spinning via Abaqus and its subroutine Vumat.By comparing the simulated results with the experimental results,the application of the ISV model was proven to be reliable.Meanwhile,the strength of the thin-walled spun ZK61 tube increased from 303 to 334 MPa due to grain refinement by DRX and texture strengthening.Besides,some ultrafine grains(0.5μm)that played an important role in mechanical properties were formed due to the proliferation,movement,and entanglement of dislocations during the spinning process.展开更多
In order to solve the problem of the variable coefficient ordinary differen-tial equation on the bounded domain,the Lagrange interpolation method is used to approximate the exact solution of the equation,and the error...In order to solve the problem of the variable coefficient ordinary differen-tial equation on the bounded domain,the Lagrange interpolation method is used to approximate the exact solution of the equation,and the error between the numerical solution and the exact solution is obtained,and then compared with the error formed by the difference method,it is concluded that the Lagrange interpolation method is more effective in solving the variable coefficient ordinary differential equation.展开更多
As a crucial component of the Earth’s climate system,Antarctic sea ice has demonstrated significant variability over the satellite era.Here,we identify a remarkable decadal transition in the total Antarctic Sea Ice E...As a crucial component of the Earth’s climate system,Antarctic sea ice has demonstrated significant variability over the satellite era.Here,we identify a remarkable decadal transition in the total Antarctic Sea Ice Extent(SIE).The stage from 1979 to 2006 is characterized by high-frequency(i.e.,seasonal to interannual)temporal variability in SIE and zonal asymmetry in Sea Ice Concentration(SIC),which is primarily under the control of the Amundsen Sea Low(ASL).After 2007,however,sea ice changes exhibit a more spatially homogeneous pattern in SIC and a more temporally long-lasting mode in SIE.Further analysis reveals that sea ice-ocean interaction plays a major role in the low-frequency(i.e.,multiannual)variability of Antarctic sea ice from 2007−22.The related physical process is inferred to manifest as a strong coupling between the surface and the subsurface ocean layers,involving enhanced vertical convection and the downward delivery of the surface anomalies related to ice melting and freezing processes,thus maintaining the SIE anomalies for a longer time.Furthermore,this process mainly occurs in the Amundsen-Bellingshausen Sea(ABS)sector,and the weakened subsurface ocean stratification is the key factor triggering the coupling process in this region.We find that the Circumpolar Deep Water(CDW)over the ABS sector continued to shoal before 2007 and remained stable thereafter.It is speculated that the shoaling of the CDW may be a possible driver leading to the weakening of the subsurface stratification.展开更多
文摘In this paper,we establish some strong laws of large numbers,which are for nonindependent random variables under the framework of sublinear expectations.One of our main results is for blockwise m-dependent random variables,and another is for sub-orthogonal random variables.Both extend the strong law of large numbers for independent random variables under sublinear expectations to the non-independent case.
基金Supported by NSFC(No.12101482)the Natural Science Foundation of Shaanxi Province,China(No.2018JQ1052)。
文摘In this paper,we consider the fourth-order parabolic equation with p(x)Laplacian and variable exponent source ut+∆^(2)u−div(|■u|^(p(x)−2■u))=|u|^(q(x))−1u.By applying potential well method,we obtain global existence,asymptotic behavior and blow-up of solutions with initial energy J(u_(0))≤d.Moreover,we estimate the upper bound of the blow-up time for J(u_(0))≤0.
基金Supported by Chizhou University High Level Talent Research Start up Fund (No.CZ2025YJRC52)。
文摘In this article,we prove the boundedness for commutators of fractional Hardy and Hardy-Littlewood-Pólya operators on grand p-adic variable Herz spaces,where the symbols of the commutators belong to Lipschitz spaces.
文摘This study investigates the reduction in polarization measurement accuracy caused by varying in-cident angles in a liquid crystal variable retarder(LCVR).The phase delay characteristics of the LCVR were examined,with particular emphasis on the influence of different two-dimensional incident angles on phase delay behavior.Building upon the calibration of phase delay under normal incidence,a phase delay calibra-tion model was developed to account for variations in incident angle and driving voltage.A mathematical re-lationship was established between phase delay and the azimuth angle(α)and pitch angle(β).Experimental validation was conducted under three conditions:α=20°,β=0°;α=0°,β=20°;and an arbitrary angle whereα=5°,β=15°.The results demonstrated that the maximum average deviation between theoretical pre-dictions and experimental measurements did not exceed 0.059 rad.The proposed calibration method proved to be both accurate and practical.This approach offers robust support for LCVR parameter calibration and performance optimization in optical systems,particularly in polarization imaging applications.
基金National Natural Science Foundation of China,Grant/Award Number:72204169Department of Science and Technology of Sichuan Province,Grant/Award Number:2021YFS0393。
文摘Background:Studies have shown that heart rate variability(HRV)is a predictor of the prognosis of cardiovascular diseases.Contact heartbeat monitoring equipment is widely used,especially in hospitals,and benefits from the rapidity and accuracy of the detection of physiological health indicators.However,long-term contact with equipment has many adverse effects.The purpose of this study was to improve the accuracy of HRV detection via noncontact equipment,thus enabling HRV to be assessed in various scenarios.Methods:A novel deep learning approach was proposed for measuring heartbeats through camera videos.First,we performed facial segmentation and divided the face into 16 grid cells with different light balance scores.After the trend is filtered by the Hamming window,a transformer-based neural network is used to further filter the signal.Finally,heart rate(HR)and HRV are estimated.Results:We used 1 million synthetic data points for pretraining and a public dataset in combination with a dataset that we constructed for task training.The final results were obtained on a test dataset that we constructed.The accuracy for HR with a low light balance score(0.867-0.983)was greater than that with a high score(0.667-0.750).Our method had higher accuracy in estimating HR than traditional filtering methods(0.167-0.417)and state-of-the-art neural network filtering methods(0.783-0.917)did.The root mean square error of the HRV from the time domain was the lowest,and the correlation index score was the highest for the HRV from the frequency domain estimated by our method compared with those estimated by two neural networks.Conclusions:Light balance,large sample training,and two-stage training can improve the accuracy of HRV estimation.
文摘The Moroccan populations of Alnus glutinosa(L.)Gaertn.(Betulaceae)They are located at the southern limit of the species'distribution and are represented by tetraploid cytotypes.Assessing phenotypic variability in reproductive traits is crucial for understanding the persistence,evolution,and range dynamics of plant populations.However,no previous studies have analyzed the relative importance of variability in explaining inter-or intra-population differences in reproductive traits.To address this gap,we investigated phenotypic variation in reproductive organs by examining 10 traits in 3.600 male catkins,3.600 female catkins,and seeds from 12 populations across the Moroccan Rif Mountains.Our results highlighted the significance of inter-population variability.However,we found that the contribution of within-tree variation to total phenotypic variability was greater than that of both inter-and intra-population variation.Principal component analysis(PCA)revealed a phenotypic gradient among populations,primarily driven by female catkin size,though this gradient was not associated with geographic conditions.This finding was further supported by Mantel test results,which showed no correlation between phenotypic variability and population conditions.These findings have important implications for the genetic improvement,conservation,and resource management of Alnus glutinosa in the future.
基金funded by the Science and Technology Program of Gansu Province(25JRRA487)the National Natural Science Foundation of China(42101072)the Key Research and Development Program of Gansu Province(22YF7FA029).
文摘Tree growth variability is a key determinant of forest stabilities.Previous studies have shown that recent climate change has increased variability in tree growth,while others have challenged this viewpoint,leading to ongoing debate in this field.Moreover,gaps remain in understanding the climatic mechanisms driving increased tree growth variability,particularly for species simultaneously limited by multiple climate factors.In this study,we assessed the temporal trends in variability of Picea purpurea radial growth and its linkage with growth-climate sensitivity utilizing dendrochronological methods.Our results revealed a significant increase in P.purpurea radial growth variability from 1960 to 2020,as indicated by continuous rises in the standard deviation,coefficient of variation,and mean sensitivity of tree-ring width indices.The increased frequency of extreme growth declines further supported this finding.Furthermore,moisture condition in July was identified as a key limiting factor of P.purpurea growth.Notably,the strengthening relationship between tree-ring width indices and vapor pressure deficit(VPD)suggests that the moisture sensitivity for P.purpurea growth has increased over the period 1960-2020.This enhanced sensitivity to VPD,whose interannual variability has also increased synchronously,may have contributed to the rise in P.purpurea growth variability.Additionally,the maximum temperature in May was positively correlated with P.purpurea growth;however,there is little evidence that this factor contributed to the observed increase in growth variability.These findings provide new insights into P.purpurea growth trends and improve our understanding of the potential future impacts of climate change on forest ecosystems.
基金Supported by Natural Science Foundation General Project of Heilongjiang Province(C2018050).
文摘As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy decisions related to rice.With the increasing application of satellite remote sensing technology in crop monitoring,remote sensing for rice cultivation has emerged as a novel approach,offering new perspectives for monitoring rice planting.This paper briefly outlined the current research and development status of satellite remote sensing for monitoring rice cultivation both at home and abroad.Foreign scholars have made innovations in data sources and methodologies for satellite remote sensing monitoring,and utilized multi-source satellite information and machine learning algorithms to enhance the accuracy of rice planting monitoring.Scholars in China have achieved significant results in the study of satellite remote sensing for monitoring rice cultivation.Their research and application in monitoring rice planting areas provide valuable references for agricultural production management.However,satellite remote sensing monitoring of rice still faces challenges such as low spatiotemporal resolution and difficulties related to cloud cover and data fusion,which require further in-depth investigation.Additionally,there are shortcomings in the accuracy of remote sensing monitoring for fragmented farmland plots and smallholder farming.To address these issues,future efforts should focus on developing multi-source heterogeneous data fusion analysis technologies and researching monitoring systems.These advancements are expected to enable high-precision large-scale acquisition of rice planting information,laying a foundation for future smart agriculture.
基金funded by the National Natural Science Foundation of China(Nos.52209130 and 52379100)Shandong Provincial Natural Science Foundation(No.ZR2024ME112).
文摘Thermal-mechanical damage and deformation at the interface between shotcrete linings and the surrounding rock of tunnels under high-temperature and variable-temperature conditions are critical to the safe construction and operation of tunnel engineering.This study investigated the thermo-mechanical damage behavior of the composite interface between alkali-resistant glass fiber-reinforced concrete(ARGFRC)and granite,focusing on a plateau railway tunnel.Laboratory triaxial tests,laser scanning,XRD analysis,numerical simulations,and theoretical analyses were employed to investigate how different initial curing temperatures and joint roughness coefficient(JRC)influence interfacial damage behavior.The results indicate that an increase in interface roughness exacerbates the structural damage at the interface.At a JRC of 19.9 and a temperature of 70℃,crack initiation in granite was notably restrained when the confining pressure rose from 7 MPa to 10 MPa.Roughness-induced stress distribution at the interface was notably altered,although this effect became less pronounced under high confining pressure conditions.Additionally,during high-temperature curing,thermal stress concentration at the tips of micro-convex protrusions on the granite surface induced microcracks in the adjacent ARGFRC matrix,followed by deformation.These findings provide practical guidelines for designing concrete support systems to ensure tunnel structural safety in high-altitude regions with harsh thermal environments.
基金supported by the National Key R&D Program of China under Grant No.2022YFA1003701the Open Research Fund of Yunnan Key Laboratory of Statistical Modeling and Data Analysis,Yunnan University under Grant No.SMDAYB2023004。
文摘Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.
基金supported by the National Natural Science Foundation of China(11601525)the Natural Science Foundation of Hunan Province(2024JJ5412),the Changsha Municipal Natural Science Foundation(kq2402193).
文摘In this paper,we study the nonlinear Riemann boundary value problem with square roots that is represented by a Cauchy-type integral with kernel density in variable exponent Lebesgue spaces.We discuss the odd-order zero-points distribution of the solutions and separate the single valued analytic branch of the solutions with square roots,then convert the problem to a Riemann boundary value problem in variable exponent Lebesgue spaces and discuss the singularity of solutions at individual zeros belonging to curve.We consider two types of cases those where the coefficient is Hölder and those where it is piecewise Hölder.Then we solve the Hilbert boundary value problem with square roots in variable exponent Lebesgue spaces.By discussing the distribution of the odd-order zero-points for solutions and the method of symmetric extension,we convert the Hilbert problem to a Riemann boundary value problem.The equivalence of the transformation is discussed.Finally,we get the solvable conditions and the direct expressions of the solutions in variable exponent Lebesgue spaces.
基金supported by the National Key Research and Development Program Project(2023YFC3107804)Planning Fund Project of Humanities and Social Sciences Research of the Ministry of Education(24YJA880097)the Graduate Education Reform Project in North China University of Technology(217051360025XN095-17)。
文摘Marine forecasting is critical for navigation safety and disaster prevention.However,traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial features.To address the limitations,the paper proposes a TimeXer-based numerical forecast correction model optimized by an exogenous-variable attention mechanism.The model treats target forecast values as internal variables,and incorporates historical temporal-spatial data and seven-day numerical forecast results from traditional models as external variables based on the embedding strategy of TimeXer.Using a self-attention structure,the model captures correlations between exogenous variables and target sequences,explores intrinsic multi-dimensional relationships,and subsequently corrects endogenous variables with the mined exogenous features.The model’s performance is evaluated using metrics including MSE(Mean Squared Error),MAE(Mean Absolute Error),RMSE(Root Mean Square Error),MAPE(Mean Absolute Percentage Error),MSPE(Mean Square Percentage Error),and computational time,with TimeXer and PatchTST models serving as benchmarks.Experiment results show that the proposed model achieves lower errors and higher correction accuracy for both one-day and seven-day forecasts.
基金supported by the National Natural Science Foundation of China(82250410380 and 62171101)the Natural Science Foundation of Sichuan Province(24NSFSC6257)the China MOST2030 Brain Project(2022ZD0208500).
文摘Cerebral ischemia restricts cerebral blood flow(CBF),leading to unstable hemodynamics.Past studies of ischemia mainly focused on cortical CBF reduction.However,its impact on hemodynamic changes,especially temporal varying characteristics,remains poorly understood.Here,we collected cortical resting-state CBF in rats with left carotid artery blockage during occlusion–reperfusion,and measured the temporal variability and changes in laterality using a novel state-space method.This method was also applied to stroke EEG datasets to validate its effectiveness.After arterial occlusion,the left marginal motor,sensory,auditory,and visual cortices exhibited severe temporal variability impairments.The laterality analysis indicated that affected left regions showed inferior unilateral mean,inter-hemispheric transition probability,time fraction,and laterality duration,while the right side had a higher laterality time fraction and duration.These impairments recovered partially following blood flow restoration.Besides,the ischemic state-space metrics were positively correlated with the pre-occlusion baseline appearance.Stroke patients exhibited impaired temporal variability in the affected ischemic hemisphere.The state-space analysis revealed damaged CBF temporal variability during cerebral ischemia and predicted baseline-ischemia connections.
基金supported by the Key Program of National Natural Science Foundation of China(Grant No.41930650)Young Scientists Fund of the National Natural Science Foundation of China(Grant No.42301310).
文摘Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although land cover information has long been recognized as an essential component for monitoring SDGs,a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist.Therefore,we propose a novel expert-and data-driven framework for identifying,refining,and selecting a priority list of Essential Land cover-related Variables for SDGs(ELcV4SDGs).This framework integrates methods including expert knowledge-based analysis,clustering of variables with similar attributes,and quantified index calculation to establish the priority list.Applying the framework to 15 specific SDG indicators,we found that the ELcV4SDGs priority list comprises three main categories,type and structure,pattern and intensity,and process and evolution of land cover,which are further divided into 19 subcategories and ultimately encompass 50 general variables.The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment,thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local,national,and global levels.
基金supported by Supported by the Scientific Research Foundation for High-Level Talents of Zhoukou Normal University(ZKNUC2024018).
文摘Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers offer advantages such as reduced material usage,lower refrigerant charge,and compact structure.However,they also face challenges,including increased refrigerant pressure drop and smaller heat transfer area inside the tubes.This paper combines the advantages and disadvantages of both small and large-diameter tubes and proposes a combined-diameter heat exchanger,consisting of large and small diameters,for use in the indoor units of split-type air conditioners.There are relatively few studies in this area.In this paper,A theoretical and numerical computation method is employed to establish a theoretical-numerical calculation model,and its reliability is verified through experiments.Using this model,the optimal combined diameters and flow path design for a combined-diameter heat exchanger using R32 as the working fluid are derived.The results show that the heat transfer performance of all combined diameter configurations improves by 2.79%to 8.26%compared to the baseline design,with the coefficient of performance(COP)increasing from 4.15 to 4.27~4.5.These designs can save copper material,but at the cost of an increase in pressure drop by 66.86%to 131.84%.The scheme IIIH,using R32,is the optimal combined-diameter and flow path configuration that balances both heat transfer performance and economic cost.
基金supported by the National Natural Science Foundation of China(82574173,82003516)Jiangsu Provincial Natural Science Foundation(BK20251958)+2 种基金Jiangsu Provincial Medical Key Discipline(ZDXK202250)Top Talent Awards Project Fund(RDF-TP-0023,RDF-TP-0030)Postgraduate Research Fund(PGRS2112022)at Xi'an Jiaotong-Liverpool University.
文摘Tuberculosis(TB),one of the oldest infectious diseases caused by Mycobacterium tuberculosis,poses a considerable challenge to global public health.There are approximately 10 million new TB cases worldwide annually,and TB claims the lives of nearly 3 million people each year,making it one of the leading causes of death from a single infectious disease[1].China ranks third globally in terms of TB burden,with approximately 733,000 TB cases reported in 2023[2].Based on the ecological model of health determinants developed by Whitehead and Dahlgren,health determinants can be classified into direct causes.
文摘Plants play an essential role in matter and energy transformations and are key messengers in the carbon and energy cycle. Net primary productivity (NPP) reflects the capability of plants to transform solar energy into photosynthesis. It is very sensible for factors affecting on vegetation variability such as climate, soils, plant characteristics and human activities. So, it can be used as an indicator of actual and potential trend of vegetation. In this study we used the actual NPP which was derived from MODIS to assess the response of NPP to climate variables in Gadarif State, from 2000 to 2010. The correlations between NPP and climate variables (temperature and precipitation) are calculated using Pearson’s Correlation Coefficient and ordinary least squares regression. The main results show the following 1) the correlation Coefficient between NPP and mean annual temperature is Somewhat negative for Feshaga, Rahd, Gadarif and Galabat areas and weakly negative in Faw area;2) the correlation Coefficient between NPP and annual total precipitation is weakly negative in Faw, Rahd and Galabat areas and somewhat negative in Galabat and Rahd areas. This study demonstrated that the correlation analysis between NPP and climate variables (precipitation and temperature) gives reliably result of NPP responses to climate variables that is clearly in a very large scale of study area.
基金supported by the National Natural Science Foundation of China(No.51905123)Major Scientific and Technological Innovation Program of Shandong Province,China(Nos.2020CXGC010303,2022ZLGX04)Key R&D Programme of Shandong Province,China(No.2022JMRH0308).
文摘An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of the internal variables were considered in this ISV model,and the parameters were optimized by genetic algorithm.After validation,the ISV model was used to simulate the evolution of grain size(GS)and dynamic recrystallization(DRX)fraction during hot spinning via Abaqus and its subroutine Vumat.By comparing the simulated results with the experimental results,the application of the ISV model was proven to be reliable.Meanwhile,the strength of the thin-walled spun ZK61 tube increased from 303 to 334 MPa due to grain refinement by DRX and texture strengthening.Besides,some ultrafine grains(0.5μm)that played an important role in mechanical properties were formed due to the proliferation,movement,and entanglement of dislocations during the spinning process.
文摘In order to solve the problem of the variable coefficient ordinary differen-tial equation on the bounded domain,the Lagrange interpolation method is used to approximate the exact solution of the equation,and the error between the numerical solution and the exact solution is obtained,and then compared with the error formed by the difference method,it is concluded that the Lagrange interpolation method is more effective in solving the variable coefficient ordinary differential equation.
基金supported by the National Natural Science Foundation China(Grant No.42176222).
文摘As a crucial component of the Earth’s climate system,Antarctic sea ice has demonstrated significant variability over the satellite era.Here,we identify a remarkable decadal transition in the total Antarctic Sea Ice Extent(SIE).The stage from 1979 to 2006 is characterized by high-frequency(i.e.,seasonal to interannual)temporal variability in SIE and zonal asymmetry in Sea Ice Concentration(SIC),which is primarily under the control of the Amundsen Sea Low(ASL).After 2007,however,sea ice changes exhibit a more spatially homogeneous pattern in SIC and a more temporally long-lasting mode in SIE.Further analysis reveals that sea ice-ocean interaction plays a major role in the low-frequency(i.e.,multiannual)variability of Antarctic sea ice from 2007−22.The related physical process is inferred to manifest as a strong coupling between the surface and the subsurface ocean layers,involving enhanced vertical convection and the downward delivery of the surface anomalies related to ice melting and freezing processes,thus maintaining the SIE anomalies for a longer time.Furthermore,this process mainly occurs in the Amundsen-Bellingshausen Sea(ABS)sector,and the weakened subsurface ocean stratification is the key factor triggering the coupling process in this region.We find that the Circumpolar Deep Water(CDW)over the ABS sector continued to shoal before 2007 and remained stable thereafter.It is speculated that the shoaling of the CDW may be a possible driver leading to the weakening of the subsurface stratification.
