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
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
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 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
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
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
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
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
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
AIM: To evaluate the clinicopathological features of mixed-type gastric cancer and their influence on prognosis of mixed-type stageⅠgastric cancer.METHODS: We analyzed 446 patients who underwent curative gastrectomy ...AIM: To evaluate the clinicopathological features of mixed-type gastric cancer and their influence on prognosis of mixed-type stageⅠgastric cancer.METHODS: We analyzed 446 patients who underwent curative gastrectomy for stageⅠgastric cancer between 1999 and 2009. The patients were divided into two groups: those with differentiated or undifferentiated cancer(non-mixed-type, n = 333) and those with a mixture of differentiated and undifferentiated cancers(mixed-type, n = 113).RESULTS: The overall prevalence of mixed-type gastric cancer was 25.3%(113/446). Compared with patients with non-mixed-type gastric cancer, those with mixedtype gastric cancer tended to be older at onset(P = 0.1252) and have a higher incidence of lymph node metastasis(P = 0.1476). They also had significantly larger tumors(P < 0.0001), more aggressive lymphatic invasion(P = 0.0011), and deeper tumor invasion(P < 0.0001). In addition, they exhibited significantly worse overall survival rates than did patients with non-mixedtype gastric cancer(P = 0.0026). Furthermore, mixedtype gastric cancer was independently associated with a worse outcome in multivariate analysis [P = 0.0300, hazard ratio = 11.4(1.265-102.7)].CONCLUSION: Histological mixed-type of gastric cancer contributes to malignant outcomes and highlight its usefulness as a prognostic indicator in stageⅠgastric cancer.展开更多
AIM To investigate the relationship between histological mixed-type of early gastric cancer(EGC) in the mucosa and submucosa and lymph node metastasis(LNM).METHODS This study included 298 patients who underwent gastre...AIM To investigate the relationship between histological mixed-type of early gastric cancer(EGC) in the mucosa and submucosa and lymph node metastasis(LNM).METHODS This study included 298 patients who underwent gastrectomy for EGC between 2005 and 2012. Enrolled lesions were divided into groups of pure differentiated(pure D), pure undifferentiated(pure U), and mixed-type according to the proportion of the differentiated and undifferentiated components observed under a microscope. We reviewed the clinicopathological features, including age, sex, location, size, gross type, lymphovascular invasion, ulceration, and LNM, among the three groups. furthermore, we evaluated the predictors of LNM in the mucosa-confined EGC.RESULTS Of the 298 patients, 165(55.4%) had mucosa-confined EGC and 133(44.6%) had submucosa-invasive EGC. Only 13(7.9%) cases of mucosa-confined EGC and 30(22.6%) cases of submucosa-invasive EGC were observed to have LNM. The submucosal invasion(OR = 4.58, 95%CI: 1.23-16.97, P = 0.023), pure U type(OR = 4.97, 95%CI: 1.21-20.39, P = 0.026), and mixedtype(OR = 5.84, 95%CI: 1.05-32.61, P = 0.044) were independent risk factors for LNM in EGC. The rate of LNM in mucosa-confined EGC was higher in the mixedtype group(P = 0.012) and pure U group(P = 0.010) than in the pure D group, but no significant difference was found between the mixed-type group and pure U group(P = 0.739). Similarly, the rate of LNM in the submucosa-invasive EGC was higher in the mixedtype(P = 0.012) and pure U group(P = 0.009) than in the pure D group but was not significantly different between the mixed-type and pure U group(P = 0.375). Multivariate logistic analysis showed that only female sex(OR = 5.83, 95%CI: 1.64-20.70, P = 0.028) and presence of lymphovascular invasion(OR = 13.18, 95%CI: 1.39-125.30, P = 0.020) were independent risk factors for LNM in mucosa-confined EGC, while histological type was not an independent risk factor for LNM in mucosa-confined EGC(P = 0.106).CONCLUSION for mucosal EGC, histological mixed-type is not an independent risk factor for LNM and could be managed in the same way as the undifferentiated type.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 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.
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.
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.
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.
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.
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.
文摘AIM: To evaluate the clinicopathological features of mixed-type gastric cancer and their influence on prognosis of mixed-type stageⅠgastric cancer.METHODS: We analyzed 446 patients who underwent curative gastrectomy for stageⅠgastric cancer between 1999 and 2009. The patients were divided into two groups: those with differentiated or undifferentiated cancer(non-mixed-type, n = 333) and those with a mixture of differentiated and undifferentiated cancers(mixed-type, n = 113).RESULTS: The overall prevalence of mixed-type gastric cancer was 25.3%(113/446). Compared with patients with non-mixed-type gastric cancer, those with mixedtype gastric cancer tended to be older at onset(P = 0.1252) and have a higher incidence of lymph node metastasis(P = 0.1476). They also had significantly larger tumors(P < 0.0001), more aggressive lymphatic invasion(P = 0.0011), and deeper tumor invasion(P < 0.0001). In addition, they exhibited significantly worse overall survival rates than did patients with non-mixedtype gastric cancer(P = 0.0026). Furthermore, mixedtype gastric cancer was independently associated with a worse outcome in multivariate analysis [P = 0.0300, hazard ratio = 11.4(1.265-102.7)].CONCLUSION: Histological mixed-type of gastric cancer contributes to malignant outcomes and highlight its usefulness as a prognostic indicator in stageⅠgastric cancer.
基金Supported by Medical Science and Technology Development Foundation of Nanjing Department of Health,No.201402032
文摘AIM To investigate the relationship between histological mixed-type of early gastric cancer(EGC) in the mucosa and submucosa and lymph node metastasis(LNM).METHODS This study included 298 patients who underwent gastrectomy for EGC between 2005 and 2012. Enrolled lesions were divided into groups of pure differentiated(pure D), pure undifferentiated(pure U), and mixed-type according to the proportion of the differentiated and undifferentiated components observed under a microscope. We reviewed the clinicopathological features, including age, sex, location, size, gross type, lymphovascular invasion, ulceration, and LNM, among the three groups. furthermore, we evaluated the predictors of LNM in the mucosa-confined EGC.RESULTS Of the 298 patients, 165(55.4%) had mucosa-confined EGC and 133(44.6%) had submucosa-invasive EGC. Only 13(7.9%) cases of mucosa-confined EGC and 30(22.6%) cases of submucosa-invasive EGC were observed to have LNM. The submucosal invasion(OR = 4.58, 95%CI: 1.23-16.97, P = 0.023), pure U type(OR = 4.97, 95%CI: 1.21-20.39, P = 0.026), and mixedtype(OR = 5.84, 95%CI: 1.05-32.61, P = 0.044) were independent risk factors for LNM in EGC. The rate of LNM in mucosa-confined EGC was higher in the mixedtype group(P = 0.012) and pure U group(P = 0.010) than in the pure D group, but no significant difference was found between the mixed-type group and pure U group(P = 0.739). Similarly, the rate of LNM in the submucosa-invasive EGC was higher in the mixedtype(P = 0.012) and pure U group(P = 0.009) than in the pure D group but was not significantly different between the mixed-type and pure U group(P = 0.375). Multivariate logistic analysis showed that only female sex(OR = 5.83, 95%CI: 1.64-20.70, P = 0.028) and presence of lymphovascular invasion(OR = 13.18, 95%CI: 1.39-125.30, P = 0.020) were independent risk factors for LNM in mucosa-confined EGC, while histological type was not an independent risk factor for LNM in mucosa-confined EGC(P = 0.106).CONCLUSION for mucosal EGC, histological mixed-type is not an independent risk factor for LNM and could be managed in the same way as the undifferentiated type.
