Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in speci...Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.展开更多
The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the...The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the equivalent conditions of complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space.The results complement the corresponding results in probability space to those for sequences of independent,identically distributed random variables under sublinear expectation space.展开更多
In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergen...In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergence of NSD random variables.We establish and improve a general result on the complete f-moment convergence for Sung’s type randomly weighted sums of NSD random variables under some general assumptions.As an application,we show the complete consistency for the randomly weighted estimator in a nonparametric regression model based on NSD errors.展开更多
Cold seeps are oases for biological communities on the sea floor around hydrocarbon emission pathways.Microbial utilization of methane and other hydrocarbons yield products that fuel rich chemosynthetic communities at...Cold seeps are oases for biological communities on the sea floor around hydrocarbon emission pathways.Microbial utilization of methane and other hydrocarbons yield products that fuel rich chemosynthetic communities at these sites.One such site in the cold seep ecosystem of Krishna-Godavari basin(K-G basin)along the east coast of India,discovered in Feb 2018 at a depth of 1800 m was assessed for its bacterial diversity.The seep bacterial communities were dominated by phylum Proteobacteria(57%),Firmicutes(16%)and unclassified species belonging to the family Helicobacteriaceae.The surface sediments of the seep had maximum OTUs(operational taxonomic units)(2.27×10^(3))with a Shannon alpha diversity index of 8.06.In general,environmental parameters like total organic carbon(p<0.01),sulfate(p<0.001),sulfide(p<0.05)and methane(p<0.01)were responsible for shaping the bacterial community of the cold seep ecosystem in the K-G Basin.Environmental parameters play a significant role in changing the bacterial diversity richness between different cold seep environments in the oceans.展开更多
Accurately mapping the spatial distribution of soil organic carbon(SOC)is crucial for guiding agricultural management and improving soil carbon sequestration,especially in fragmented agricultural landscapes.Although r...Accurately mapping the spatial distribution of soil organic carbon(SOC)is crucial for guiding agricultural management and improving soil carbon sequestration,especially in fragmented agricultural landscapes.Although remote sensing provides spatially continuous environmental information about heterogeneous agricultural landscapes,its relationship with SOC remains unclear.In this study,we hypothesized that multi-category remote sensing-derived variables can enhance our understanding of SOC variation within complex landscape conditions.Taking the Qilu Lake watershed in Yunnan,China,as a case study area and based on 216 topsoil samples collected from irrigation areas,we applied the extreme gradient boosting(XGBoost)model to investigate the contributions of vegetation indices(VI),brightness indices(BI),moisture indices(MI),and spectral transformations(ST,principal component analysis and tasseled cap transformation)to SOC mapping.The results showed that ST contributed the most to SOC prediction accuracy,followed by MI,VI,and BI,with improvements in R2 of 29.27,26.83,19.51,and 14.43%,respectively.The dominance of ST can be attributed to the fact that it contains richer remote sensing spectral information.The optimal SOC prediction model integrated soil properties,topographic factors,location factors,and landscape metrics,as well as remote sensing-derived variables,and achieved RMSE and MAE of 15.05 and 11.42 g kg-1,and R2 and CCC of 0.57 and 0.72,respectively.The Shapley additive explanations deciphered the nonlinear and threshold effects that exist between soil moisture,vegetation status,soil brightness and SOC.Compared with traditional linear regression models,interpretable machine learning has advantages in prediction accuracy and revealing the influences of variables that reflect landscape characteristics on SOC.Overall,this study not only reveals how remote sensing-derived variables contribute to our understanding of SOC distribution in fragmented agricultural landscapes but also clarifies their efficacy.Through interpretable machine learning,we can further elucidate the causes of SOC variation,which is important for sustainable soil management and agricultural practices.展开更多
In this paper,we establish characterizations of α-Bloch functions and little α-Bloch functions on the unit ball as well as the unit polydisk of C^(m),which generalize and improve results of Aulaskari-Lappan,Minda,Au...In this paper,we establish characterizations of α-Bloch functions and little α-Bloch functions on the unit ball as well as the unit polydisk of C^(m),which generalize and improve results of Aulaskari-Lappan,Minda,Aulaskari-Wulan,and Wu.Some examples are also given to complement our theory.展开更多
Assume that{a_(i),−∞<i<∞}is an absolutely summable sequence of real numbers.We establish the complete q-order moment convergence for the partial sums of moving average processes{X_(n)=Σ_(i=−∞)^(∞)a_(i)Y_(i+...Assume that{a_(i),−∞<i<∞}is an absolutely summable sequence of real numbers.We establish the complete q-order moment convergence for the partial sums of moving average processes{X_(n)=Σ_(i=−∞)^(∞)a_(i)Y_(i+n),n≥1}under some proper conditions,where{Yi,-∞<i<∞}is a doubly infinite sequence of negatively dependent random variables under sub-linear expectations.These results extend and complement the relevant results in probability space.展开更多
In the current paper,we present a study of the spatial distribution of luminous blue variables(LBVs)and various LBV candidates(c LBVs)with respect to OB associations in the galaxy M33.The identification of blue star g...In the current paper,we present a study of the spatial distribution of luminous blue variables(LBVs)and various LBV candidates(c LBVs)with respect to OB associations in the galaxy M33.The identification of blue star groups was based on the LGGS data and was carried out by two clustering algorithms with initial parameters determined during simulations of random stellar fields.We have found that the distribution of distances to the nearest OB association obtained for the LBV/c LBV sample is close to that for massive stars with Minit>20 M⊙and WolfRayet stars.This result is in good agreement with the standard assumption that LBVs represent an intermediate stage in the evolution of the most massive stars.However,some objects from the LBV/cLBV sample,particularly Fe II-emission stars,demonstrated severe isolation compared to other massive stars,which,together with certain features of their spectra,implicitly indicates that the nature of these objects and other LBVs/cLBVs may differ radically.展开更多
The problem of multiple attribute decision making under fuzzy linguistic environments, in which decision makers can only provide their preferences （attribute values）in the form of trapezoid fuzzy linguistic variable...The problem of multiple attribute decision making under fuzzy linguistic environments, in which decision makers can only provide their preferences （attribute values）in the form of trapezoid fuzzy linguistic variables（TFLV）, is studied. The formula of the degree of possibility between two TFLVs is defined, and some of its characteristics are studied. Based on the degree of possibility of fuzzy linguistic variables, an approach to ranking the decision alternatives in multiple attribute decision making with TFLV is developed. The trapezoid fuzzy linguistic weighted averaging （TFLWA） operator method is utilized to aggregate the decision information, and then all the alternatives are ranked by comparing the degree of possibility of TFLV. The method can carry out linguistic computation processes easily without loss of linguistic information, and thus makes the decision results reasonable and effective. Finally, the implementation process of the proposed method is illustrated and analyzed by a practical example.展开更多
Species dynamics in terms of both plant biological traits, ecological strategies and species richness as well as soil chemical variables during a secondary succession in abandoned fields on the Loess Plateau along a t...Species dynamics in terms of both plant biological traits, ecological strategies and species richness as well as soil chemical variables during a secondary succession in abandoned fields on the Loess Plateau along a temporal sere from 3 a to 149 a were studied. The results indicated that (I) Soil total C and N increased while soil pH, total K and Na decreased with years since abandonment. No noticeable trend was found in the case of soil P along the successional sere. On the other hand, total CaO of the surface layer (0 - 10 cm) decreased, but that of the two deeper layer, (20 - 30 cm, 40 - 50 cm) increased with years since abandonment. Soil C, N, K and P decreased, while Na, CaO and soil pH increased with increasing soil depth. (2) Species richness peaked at both mid-stage of the successional sere and the intermediate portion of soil chemical variables gradient. (3) An ideal dominant species in the early successional stage were annuals with stable seed pool, CR-life strategy, S-regeneration strategy, and strong competitive ability on relatively poor soil, while perennials capable of intensive lateral spread and colonal ability, requiring high nutrient supply, and having Clife strategy would be the dominant species in the subsequent stages. Plant traits, such as perennial-life history, C-, CR-, SC-, SR-, S- and R-life strategies, W-, S-, Bs- VBs- and V-regeneration strategies, were over- represented throughout the whole sere among the other species. (4) Some traits, such as C-, SC-life strategies, ability of clonality, perennial-life history, well-developed lateral spread ability, V- and VBs-regeneration strategies, seed animal. dispersal mode, flowering time of autumn, fruit types of legumen and nut, were more or less correlated with increased soil total C, N and K, while S-, SR-, R-, CR-life strategies, annual-, biannual-life history, non-clonal ability, S-regeneration strategy, poor lateral spread ability, and fruit types of utricle, capsule were associated with increased soil total Na, CaO and pH. The results suggested that steppes should be the dominant native vegetation coinciding with the large-scaled eco-climatic conditions on the Loess Plateau.展开更多
In order to improve the prediction precision of the safety performance function （SPF） of freeway basic segments, design and crash data of 640 segments are collected from different institutions. Three negative binomi...In order to improve the prediction precision of the safety performance function （SPF） of freeway basic segments, design and crash data of 640 segments are collected from different institutions. Three negative binomial （NB） regression models and three generalized negative binomial （GNB） regression models are built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The effective use of the GNB model in analyzing the interactive influence of explanatory variables and predicting freeway basic segments is demonstrated. Among six models, the two models （one is the NB model and the other is the GNB model. ） which consider the interactive influence of the annual average daily traffic （AADT） and length are more reasonable for predicting results. Furthermore, a comprehensive study is carried out to prove that when considering the interactive influence, the NB and GNB models have almost the same fitting performance in estimating the crashes, among which the GNB model is slightly better for prediction performance.展开更多
The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risk...The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.展开更多
The predictability of a coupled system composed of a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order three-variable tropical recharge-discharge model is explored with emphasis on its l...The predictability of a coupled system composed of a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order three-variable tropical recharge-discharge model is explored with emphasis on its long-term forecasting capabilities.Highly idealized ensemble forecasts are produced taking into account the uncertainties in the initial states of the system,with specific attention to the structure of the initial errors in the tropical model.Three main types of experiments are explored:with random perturbations along the three Lyapunov vectors of the tropical model;along the two dominant Lyapunov vectors;and along the first Lyapunov vector only.When perturbations are introduced along all vectors,forecasting biases develop even if in a perfect model framework and with known initial uncertainty properties.Theses biases are considerably reduced only when the perturbations are introduced along the dominant Lyapunov vector.Furthermore,this perturbation strategy allows a reduced mean square error to be obtained at long lead times of a few years,as well as reliable ensemble forecasts across the whole time range.These very counterintuitive findings further underline the importance of appropriately controlling the initial error structure in the tropics through data assimilation.展开更多
Admittedly cognitive variables such as intelligence and aptitude exert great impact on English learning. Affective variables, however, are of intense importance in determining English learning as well, because affect ...Admittedly cognitive variables such as intelligence and aptitude exert great impact on English learning. Affective variables, however, are of intense importance in determining English learning as well, because affect is a starting machine that sets the learning mechanism in motion and learning will run into difficulty if affect does not work properly. Besides, there is mounting interest in exploring the affective domain. Therefore, this paper focuses upon the analyses of four affective variables(attitude, motivation, self-esteem and anxiety) that have bearings on English learning and sets forth Implications for English teaching.展开更多
This paper first, illustrates the advantages of applying real time study to linguistic researches. Second, this paper also compares linguistic variables with linguistic variant; nasality, stronger constraint and weake...This paper first, illustrates the advantages of applying real time study to linguistic researches. Second, this paper also compares linguistic variables with linguistic variant; nasality, stronger constraint and weaker constraint have been clearly defined as well.展开更多
Objective To examine if the variations at sea level would be able to predict subsequent susceptibility to acute altitude sickness in subjects upon a rapid ascent to high altitude.Methods One hundred and six Han nation...Objective To examine if the variations at sea level would be able to predict subsequent susceptibility to acute altitude sickness in subjects upon a rapid ascent to high altitude.Methods One hundred and six Han nationality male individuals were recruited to this research.Dynamic electrocardiogram,treadmill exercise test,echocardiography,routine blood examination and biochemical analysis were performed when subjects at sea level and entering the plateau respectively.Then multiple regression analysis was performed to construct a multiple linear regression equation using the Lake Louise Score as dependent variable to predict the risk factors at sea level related to acute mountain sickness(AMS).Results Approximately 49.05%of the individuals developed AMS.The tricuspid annular plane systolic excursion(22.0+2.66 vs.23.2+3.19 mm,t=l.998,P=0.048)was significantly lower in the AMS group at sea level,while count of eosinophil[(0.264+0.393)×109/L vs.(0.126+0.084)×109/L,t=-2.040,P—0.045],percentage of diflerences exceeding 50 ms between adjacent normal number of intervals(PNN50,9.66%±5.40%vs.6.98%±5.66%,t=-2.229,P=0.028)and heart rate variability triangle index(57.1+16.1 vs.50.6+12.7,t=-2.271,P=0.025)were significantly higher.After acute exposure to high altitude,C-reactive protein(0.098+0.103 vs.0.062+0.045 g/L,t=-2.132,P=0.037),aspartate aminotransferase(19.7+6.7275.17,3±3.95 U/L,t=-2.231,P=0.028)and creatinine(85.1±12.9 vs.77.7±11.2 mmol/L,t=3.162,P=0.002)were significantly higher in the AMS group,while alkaline phosphatase(71.7+18.2 vs.80.6+20.2 U/L,t=2.389,P=0.019),standard deviation of normal-to-normal RR intervals(126.5+35.9 vs.143.3+36.4 ms,t—2.320,P—0.022),ejection time(276.9+50.8 vs.313.8+48.9 ms,t—3.641,P—0.001)and heart rate variability triangle index(37.1+12.9 vs.41.9+11.1,t=2.O2O,P=0.047)were significantly lower.Using the Lake Louise Score as the dependent variable,prediction equation were established to estimate AMS:Lake Louise Score=3.783+0.281Xeosinophil-0.219Xalkaline phosphatase+O.O32XPNN50.Conclusions We elucidated the differences of pl^siological variables as well as noninvasive cardiovascular indicators for subjects after high altitude exposure compared with those at sea level.We also created an acute high altitude reaction early warning equation based on the physiological variables and noninvasive cardiovascular indicators at sea level.展开更多
The spatial interpolation for soil texture does not necessarily satisfy the constant sum and nonnegativity constraints. Meanwhile, although numeric and categorical variables have been used as auxiliary variables to im...The spatial interpolation for soil texture does not necessarily satisfy the constant sum and nonnegativity constraints. Meanwhile, although numeric and categorical variables have been used as auxiliary variables to improve prediction accuracy of soil attributes such as soil organic matter, they （especially the categorical variables） are rarely used in spatial prediction of soil texture. The objective of our study was to comparing the performance of the methods for spatial prediction of soil texture with consideration of the characteristics of compositional data and auxiliary variables. These methods include the ordinary kriging with the symmetry logratio transform, regression kriging with the symmetry logratio transform, and compositional kriging （CK） approaches. The root mean squared error （RMSE）, the relative improvement value of RMSE and Aitchison＇s distance （DA） were all utilized to assess the accuracy of prediction and the mean squared deviation ratio was used to evaluate the goodness of fit of the theoretical estimate of error. The results showed that the prediction methods utilized in this paper could enable interpolation results of soil texture to satisfy the constant sum and nonnegativity constraints. Prediction accuracy and model fitting effect of the CK approach were better, suggesting that the CK method was more appropriate for predicting soil texture. The CK method is directly interpolated on soil texture, which ensures that it is optimal unbiased estimator. If the environment variables are appropriately selected as auxiliary variables, spatial variability of soil texture can be predicted reasonably and accordingly the predicted results will be satisfied.展开更多
We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts ...We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature,precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area,a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01),and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China,NPP showed 16-d,48-d,and 96-d lagged correlation with air temperature,precipitation,and sunshine percentage,respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d,while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region,the spatial patterns of vegetation-climate relationship became complicated and diversiform,especially for precipitation influences on NPP. In the northern part of the study area,all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP.展开更多
Ecological methodology plus negative binomial regression were used to identify dengue fever （DF） epidemiological status and its relationship with meteorological variables. From 2007 to 2012, annual incidence rate of...Ecological methodology plus negative binomial regression were used to identify dengue fever （DF） epidemiological status and its relationship with meteorological variables. From 2007 to 2012, annual incidence rate of DF in Guangzhou was 0.33, 0.11, 0.15, 0.64, 0.45, and 1.34 （per 100 000） respectively, showing an increasing trend. Each 1℃ rise of temperature corresponded to an increase of 10.23% （95% CI 7.68% to 12.83%） in the monthly number of DF cases, whereas l hPa rise of atmospheric pressure corresponded to a decrease in the number of cases by 5.14% （95% CI： 7.10%-3.14%）. Likewise, each one meter per second rise in wind velocity led to an increase by 43.80% or 107.53%, and one percent rise of relative humidity led to an increase by 2.04% or 2.19%.展开更多
M-negatively associated random variables, which generalizes the classical one of negatively associated random variables and includes m-dependent sequences as its particular case, are introduced and studied. Large devi...M-negatively associated random variables, which generalizes the classical one of negatively associated random variables and includes m-dependent sequences as its particular case, are introduced and studied. Large deviation principles and moderate deviation upper bounds for stationary m-negatively associated random variables are proved. Kolmogorov-type and Marcinkiewicz-type strong laws of large numbers as well as the three series theorem for m-negatively associated random variables are also given.展开更多
基金supported by the National Key R&D Program of China(No.2021YFB0301200)National Natural Science Foundation of China(No.62025208).
文摘Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.
基金supported by Doctoral Scientific Research Starting Foundation of Jingdezhen Ceramic University(Grant No.102/01003002031)Re-accompanying Funding Project of Academic Achievements of Jingdezhen Ceramic University(Grant Nos.215/20506277,215/20506341)。
文摘The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the equivalent conditions of complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space.The results complement the corresponding results in probability space to those for sequences of independent,identically distributed random variables under sublinear expectation space.
基金supported by the National Social Science Fundation(Grant No.21BTJ040)the Project of Outstanding Young People in University of Anhui Province(Grant Nos.2023AH020037,SLXY2024A001).
文摘In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergence of NSD random variables.We establish and improve a general result on the complete f-moment convergence for Sung’s type randomly weighted sums of NSD random variables under some general assumptions.As an application,we show the complete consistency for the randomly weighted estimator in a nonparametric regression model based on NSD errors.
文摘Cold seeps are oases for biological communities on the sea floor around hydrocarbon emission pathways.Microbial utilization of methane and other hydrocarbons yield products that fuel rich chemosynthetic communities at these sites.One such site in the cold seep ecosystem of Krishna-Godavari basin(K-G basin)along the east coast of India,discovered in Feb 2018 at a depth of 1800 m was assessed for its bacterial diversity.The seep bacterial communities were dominated by phylum Proteobacteria(57%),Firmicutes(16%)and unclassified species belonging to the family Helicobacteriaceae.The surface sediments of the seep had maximum OTUs(operational taxonomic units)(2.27×10^(3))with a Shannon alpha diversity index of 8.06.In general,environmental parameters like total organic carbon(p<0.01),sulfate(p<0.001),sulfide(p<0.05)and methane(p<0.01)were responsible for shaping the bacterial community of the cold seep ecosystem in the K-G Basin.Environmental parameters play a significant role in changing the bacterial diversity richness between different cold seep environments in the oceans.
基金supported by the National Key Research and Development Program of China(2022YFB3903302).
文摘Accurately mapping the spatial distribution of soil organic carbon(SOC)is crucial for guiding agricultural management and improving soil carbon sequestration,especially in fragmented agricultural landscapes.Although remote sensing provides spatially continuous environmental information about heterogeneous agricultural landscapes,its relationship with SOC remains unclear.In this study,we hypothesized that multi-category remote sensing-derived variables can enhance our understanding of SOC variation within complex landscape conditions.Taking the Qilu Lake watershed in Yunnan,China,as a case study area and based on 216 topsoil samples collected from irrigation areas,we applied the extreme gradient boosting(XGBoost)model to investigate the contributions of vegetation indices(VI),brightness indices(BI),moisture indices(MI),and spectral transformations(ST,principal component analysis and tasseled cap transformation)to SOC mapping.The results showed that ST contributed the most to SOC prediction accuracy,followed by MI,VI,and BI,with improvements in R2 of 29.27,26.83,19.51,and 14.43%,respectively.The dominance of ST can be attributed to the fact that it contains richer remote sensing spectral information.The optimal SOC prediction model integrated soil properties,topographic factors,location factors,and landscape metrics,as well as remote sensing-derived variables,and achieved RMSE and MAE of 15.05 and 11.42 g kg-1,and R2 and CCC of 0.57 and 0.72,respectively.The Shapley additive explanations deciphered the nonlinear and threshold effects that exist between soil moisture,vegetation status,soil brightness and SOC.Compared with traditional linear regression models,interpretable machine learning has advantages in prediction accuracy and revealing the influences of variables that reflect landscape characteristics on SOC.Overall,this study not only reveals how remote sensing-derived variables contribute to our understanding of SOC distribution in fragmented agricultural landscapes but also clarifies their efficacy.Through interpretable machine learning,we can further elucidate the causes of SOC variation,which is important for sustainable soil management and agricultural practices.
基金Supported by Natural Science Research Project for Colleges and Universities of Anhui Province(Grant No.2022AH050329)Yunnan Provincial Department of Education Fund(Grant No.2025J0376).
文摘In this paper,we establish characterizations of α-Bloch functions and little α-Bloch functions on the unit ball as well as the unit polydisk of C^(m),which generalize and improve results of Aulaskari-Lappan,Minda,Aulaskari-Wulan,and Wu.Some examples are also given to complement our theory.
基金Supported by the Academic Achievement Re-cultivation Projects of Jingdezhen Ceramic University(Grant Nos.215/20506341215/20506277)the Doctoral Scientific Research Starting Foundation of Jingdezhen Ceramic University(Grant No.102/01003002031)。
文摘Assume that{a_(i),−∞<i<∞}is an absolutely summable sequence of real numbers.We establish the complete q-order moment convergence for the partial sums of moving average processes{X_(n)=Σ_(i=−∞)^(∞)a_(i)Y_(i+n),n≥1}under some proper conditions,where{Yi,-∞<i<∞}is a doubly infinite sequence of negatively dependent random variables under sub-linear expectations.These results extend and complement the relevant results in probability space.
文摘In the current paper,we present a study of the spatial distribution of luminous blue variables(LBVs)and various LBV candidates(c LBVs)with respect to OB associations in the galaxy M33.The identification of blue star groups was based on the LGGS data and was carried out by two clustering algorithms with initial parameters determined during simulations of random stellar fields.We have found that the distribution of distances to the nearest OB association obtained for the LBV/c LBV sample is close to that for massive stars with Minit>20 M⊙and WolfRayet stars.This result is in good agreement with the standard assumption that LBVs represent an intermediate stage in the evolution of the most massive stars.However,some objects from the LBV/cLBV sample,particularly Fe II-emission stars,demonstrated severe isolation compared to other massive stars,which,together with certain features of their spectra,implicitly indicates that the nature of these objects and other LBVs/cLBVs may differ radically.
基金2008 Soft Science Program of Jiangsu Science and Technology Department (No.BR2008098)
文摘The problem of multiple attribute decision making under fuzzy linguistic environments, in which decision makers can only provide their preferences （attribute values）in the form of trapezoid fuzzy linguistic variables（TFLV）, is studied. The formula of the degree of possibility between two TFLVs is defined, and some of its characteristics are studied. Based on the degree of possibility of fuzzy linguistic variables, an approach to ranking the decision alternatives in multiple attribute decision making with TFLV is developed. The trapezoid fuzzy linguistic weighted averaging （TFLWA） operator method is utilized to aggregate the decision information, and then all the alternatives are ranked by comparing the degree of possibility of TFLV. The method can carry out linguistic computation processes easily without loss of linguistic information, and thus makes the decision results reasonable and effective. Finally, the implementation process of the proposed method is illustrated and analyzed by a practical example.
文摘Species dynamics in terms of both plant biological traits, ecological strategies and species richness as well as soil chemical variables during a secondary succession in abandoned fields on the Loess Plateau along a temporal sere from 3 a to 149 a were studied. The results indicated that (I) Soil total C and N increased while soil pH, total K and Na decreased with years since abandonment. No noticeable trend was found in the case of soil P along the successional sere. On the other hand, total CaO of the surface layer (0 - 10 cm) decreased, but that of the two deeper layer, (20 - 30 cm, 40 - 50 cm) increased with years since abandonment. Soil C, N, K and P decreased, while Na, CaO and soil pH increased with increasing soil depth. (2) Species richness peaked at both mid-stage of the successional sere and the intermediate portion of soil chemical variables gradient. (3) An ideal dominant species in the early successional stage were annuals with stable seed pool, CR-life strategy, S-regeneration strategy, and strong competitive ability on relatively poor soil, while perennials capable of intensive lateral spread and colonal ability, requiring high nutrient supply, and having Clife strategy would be the dominant species in the subsequent stages. Plant traits, such as perennial-life history, C-, CR-, SC-, SR-, S- and R-life strategies, W-, S-, Bs- VBs- and V-regeneration strategies, were over- represented throughout the whole sere among the other species. (4) Some traits, such as C-, SC-life strategies, ability of clonality, perennial-life history, well-developed lateral spread ability, V- and VBs-regeneration strategies, seed animal. dispersal mode, flowering time of autumn, fruit types of legumen and nut, were more or less correlated with increased soil total C, N and K, while S-, SR-, R-, CR-life strategies, annual-, biannual-life history, non-clonal ability, S-regeneration strategy, poor lateral spread ability, and fruit types of utricle, capsule were associated with increased soil total Na, CaO and pH. The results suggested that steppes should be the dominant native vegetation coinciding with the large-scaled eco-climatic conditions on the Loess Plateau.
基金The National Natural Science Foundation of China(No.51408229,51278202)the Program of the Key Laboratory of Road and Traffic Engineering of the Ministry of Education,Tongji University(No.K201204)the Science and Technology Program of Guangdong Communication Department(No.2013-02-068)
文摘In order to improve the prediction precision of the safety performance function （SPF） of freeway basic segments, design and crash data of 640 segments are collected from different institutions. Three negative binomial （NB） regression models and three generalized negative binomial （GNB） regression models are built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The effective use of the GNB model in analyzing the interactive influence of explanatory variables and predicting freeway basic segments is demonstrated. Among six models, the two models （one is the NB model and the other is the GNB model. ） which consider the interactive influence of the annual average daily traffic （AADT） and length are more reasonable for predicting results. Furthermore, a comprehensive study is carried out to prove that when considering the interactive influence, the NB and GNB models have almost the same fitting performance in estimating the crashes, among which the GNB model is slightly better for prediction performance.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608904)the International Partnership Program of the Chinese Academy of Sciences(Grant Nos.060GJHZ2023079GC and 134111KYSB20160031)+1 种基金supported by the Office of Science,U.S.Department of Energy(DOE)Biological and Environmental Research as part of the Regional and Global Model Analysis program area through the Water Cycle and Climate Extremes Modeling(WACCEM)scientific focus areaoperated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830。
文摘The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.
基金supported by the National Key R&D Program of China(Grant No.2023YFF0805100)。
文摘The predictability of a coupled system composed of a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order three-variable tropical recharge-discharge model is explored with emphasis on its long-term forecasting capabilities.Highly idealized ensemble forecasts are produced taking into account the uncertainties in the initial states of the system,with specific attention to the structure of the initial errors in the tropical model.Three main types of experiments are explored:with random perturbations along the three Lyapunov vectors of the tropical model;along the two dominant Lyapunov vectors;and along the first Lyapunov vector only.When perturbations are introduced along all vectors,forecasting biases develop even if in a perfect model framework and with known initial uncertainty properties.Theses biases are considerably reduced only when the perturbations are introduced along the dominant Lyapunov vector.Furthermore,this perturbation strategy allows a reduced mean square error to be obtained at long lead times of a few years,as well as reliable ensemble forecasts across the whole time range.These very counterintuitive findings further underline the importance of appropriately controlling the initial error structure in the tropics through data assimilation.
文摘Admittedly cognitive variables such as intelligence and aptitude exert great impact on English learning. Affective variables, however, are of intense importance in determining English learning as well, because affect is a starting machine that sets the learning mechanism in motion and learning will run into difficulty if affect does not work properly. Besides, there is mounting interest in exploring the affective domain. Therefore, this paper focuses upon the analyses of four affective variables(attitude, motivation, self-esteem and anxiety) that have bearings on English learning and sets forth Implications for English teaching.
文摘This paper first, illustrates the advantages of applying real time study to linguistic researches. Second, this paper also compares linguistic variables with linguistic variant; nasality, stronger constraint and weaker constraint have been clearly defined as well.
基金National Science and Technology Major Projects for Major New Drugs Innovation and Development(2014ZX09J14102-02A)Special Topic on Military Health Care(17bjz41)National Natural Science Foundation of China(81170249 and 30700305).
文摘Objective To examine if the variations at sea level would be able to predict subsequent susceptibility to acute altitude sickness in subjects upon a rapid ascent to high altitude.Methods One hundred and six Han nationality male individuals were recruited to this research.Dynamic electrocardiogram,treadmill exercise test,echocardiography,routine blood examination and biochemical analysis were performed when subjects at sea level and entering the plateau respectively.Then multiple regression analysis was performed to construct a multiple linear regression equation using the Lake Louise Score as dependent variable to predict the risk factors at sea level related to acute mountain sickness(AMS).Results Approximately 49.05%of the individuals developed AMS.The tricuspid annular plane systolic excursion(22.0+2.66 vs.23.2+3.19 mm,t=l.998,P=0.048)was significantly lower in the AMS group at sea level,while count of eosinophil[(0.264+0.393)×109/L vs.(0.126+0.084)×109/L,t=-2.040,P—0.045],percentage of diflerences exceeding 50 ms between adjacent normal number of intervals(PNN50,9.66%±5.40%vs.6.98%±5.66%,t=-2.229,P=0.028)and heart rate variability triangle index(57.1+16.1 vs.50.6+12.7,t=-2.271,P=0.025)were significantly higher.After acute exposure to high altitude,C-reactive protein(0.098+0.103 vs.0.062+0.045 g/L,t=-2.132,P=0.037),aspartate aminotransferase(19.7+6.7275.17,3±3.95 U/L,t=-2.231,P=0.028)and creatinine(85.1±12.9 vs.77.7±11.2 mmol/L,t=3.162,P=0.002)were significantly higher in the AMS group,while alkaline phosphatase(71.7+18.2 vs.80.6+20.2 U/L,t=2.389,P=0.019),standard deviation of normal-to-normal RR intervals(126.5+35.9 vs.143.3+36.4 ms,t—2.320,P—0.022),ejection time(276.9+50.8 vs.313.8+48.9 ms,t—3.641,P—0.001)and heart rate variability triangle index(37.1+12.9 vs.41.9+11.1,t=2.O2O,P=0.047)were significantly lower.Using the Lake Louise Score as the dependent variable,prediction equation were established to estimate AMS:Lake Louise Score=3.783+0.281Xeosinophil-0.219Xalkaline phosphatase+O.O32XPNN50.Conclusions We elucidated the differences of pl^siological variables as well as noninvasive cardiovascular indicators for subjects after high altitude exposure compared with those at sea level.We also created an acute high altitude reaction early warning equation based on the physiological variables and noninvasive cardiovascular indicators at sea level.
基金supported by the National Natural Science Foundation of China (41071152)the Special Fund for Land and Resources Scientific Research in the Public Interest,China (201011006-3)the Special Fund for Agro-Scientific Research in the Public Interest,China (201103005-01-01)
文摘The spatial interpolation for soil texture does not necessarily satisfy the constant sum and nonnegativity constraints. Meanwhile, although numeric and categorical variables have been used as auxiliary variables to improve prediction accuracy of soil attributes such as soil organic matter, they （especially the categorical variables） are rarely used in spatial prediction of soil texture. The objective of our study was to comparing the performance of the methods for spatial prediction of soil texture with consideration of the characteristics of compositional data and auxiliary variables. These methods include the ordinary kriging with the symmetry logratio transform, regression kriging with the symmetry logratio transform, and compositional kriging （CK） approaches. The root mean squared error （RMSE）, the relative improvement value of RMSE and Aitchison＇s distance （DA） were all utilized to assess the accuracy of prediction and the mean squared deviation ratio was used to evaluate the goodness of fit of the theoretical estimate of error. The results showed that the prediction methods utilized in this paper could enable interpolation results of soil texture to satisfy the constant sum and nonnegativity constraints. Prediction accuracy and model fitting effect of the CK approach were better, suggesting that the CK method was more appropriate for predicting soil texture. The CK method is directly interpolated on soil texture, which ensures that it is optimal unbiased estimator. If the environment variables are appropriately selected as auxiliary variables, spatial variability of soil texture can be predicted reasonably and accordingly the predicted results will be satisfied.
基金Project supported by the National High-Tech Research and Development Program (863) of China (No. 2006AA120101)the National Natural Science Foundation of China (Nos. 40871158 and 40875070)the Key Technologies Research and Development Program of China (No. 2006BAD10A01)
文摘We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature,precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area,a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01),and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China,NPP showed 16-d,48-d,and 96-d lagged correlation with air temperature,precipitation,and sunshine percentage,respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d,while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region,the spatial patterns of vegetation-climate relationship became complicated and diversiform,especially for precipitation influences on NPP. In the northern part of the study area,all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP.
基金supported by the Research Fund from Health Bureau of Guangzhou(201102A212006)Science and Technology Bureau of Guangzhou(2012Y2-00020)Medical Sciences Program of Guangdong(A2011507)
文摘Ecological methodology plus negative binomial regression were used to identify dengue fever （DF） epidemiological status and its relationship with meteorological variables. From 2007 to 2012, annual incidence rate of DF in Guangzhou was 0.33, 0.11, 0.15, 0.64, 0.45, and 1.34 （per 100 000） respectively, showing an increasing trend. Each 1℃ rise of temperature corresponded to an increase of 10.23% （95% CI 7.68% to 12.83%） in the monthly number of DF cases, whereas l hPa rise of atmospheric pressure corresponded to a decrease in the number of cases by 5.14% （95% CI： 7.10%-3.14%）. Likewise, each one meter per second rise in wind velocity led to an increase by 43.80% or 107.53%, and one percent rise of relative humidity led to an increase by 2.04% or 2.19%.
基金Partly supported by the National Natural Science Foundation of China and the Ministry of Education of ChinaPartly supported by the Science and Technology Research Item of Hubei Provincial Department of Education,Jiaghan University
文摘M-negatively associated random variables, which generalizes the classical one of negatively associated random variables and includes m-dependent sequences as its particular case, are introduced and studied. Large deviation principles and moderate deviation upper bounds for stationary m-negatively associated random variables are proved. Kolmogorov-type and Marcinkiewicz-type strong laws of large numbers as well as the three series theorem for m-negatively associated random variables are also given.
