Almond pruning biomass is an important agricultural residue that has been scarcely studied for the co-production of sugars and solid biofuels.In this work,the production of monosaccharides from almond prunings was opt...Almond pruning biomass is an important agricultural residue that has been scarcely studied for the co-production of sugars and solid biofuels.In this work,the production of monosaccharides from almond prunings was optimised by a two-step process scheme:pretreatment with dilute sulphuric acid(0.025 M,at 185.9-214.1℃for 0.8-9.2 min)followed by enzyme saccharification of the pretreated cellulose.The application of a response surface methodology enabled the mathematical modelling of the process,establishing pretreatment conditions to maximise both the amount of sugar in the acid prehydrolysate(23.4 kg/100 kg raw material,at 195.7℃for 3.5 min)and the enzymatic digestibility of the pretreated cellulose(45.4%,at 210.0℃for 8.0 min).The highest overall sugar yield(36.8 kg/100 kg raw material,equivalent to 64.3%of all sugars in the feedstock)was obtained with a pretreatment carried out at 197.0℃for 4.0 min.Under these conditions,moreover,the final solids showed better properties for thermochemical utilisation(22.0 MJ/kg heating value,0.87%ash content,and 72.1 mg/g moisture adsorption capacity)compared to those of the original prunings.展开更多
Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a sign...Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision.This study introduces REPTF-TMDI,a novel method that combines a Reduced Error Pruning Tree Forest(REPTree Forest)with a newly proposed Time-based Missing Data Imputation(TMDI)approach.The REP Tree Forest,an ensemble learning approach,is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urbanmobility solutions.Meanwhile,the TMDI approach exploits temporal patterns to estimate missing values reliably whenever empty fields are encountered.The proposed method was evaluated using hourly traffic flow data from a major U.S.roadway spanning 2012-2018,incorporating temporal features(e.g.,hour,day,month,year,weekday),holiday indicator,and weather conditions(temperature,rain,snow,and cloud coverage).Experimental results demonstrated that the REPTF-TMDI method outperformed conventional imputation techniques across various missing data ratios by achieving an average 11.76%improvement in terms of correlation coefficient(R).Furthermore,REPTree Forest achieved improvements of 68.62%in RMSE and 70.52%in MAE compared to existing state-of-the-art models.These findings highlight the method’s ability to significantly boost traffic flow prediction accuracy,even in the presence of missing data,thereby contributing to the broader objectives of sustainable urban transportation systems.展开更多
Tree pruning is an effective algorithm to reduce the complexity of sphere detection （SD） for multiple-input multiple-output （MIMO） communication systems. How to determine the tree pruning rule, as well as by what ...Tree pruning is an effective algorithm to reduce the complexity of sphere detection （SD） for multiple-input multiple-output （MIMO） communication systems. How to determine the tree pruning rule, as well as by what the tradeoff between the performance and the complexity can be achieved, is still an open problem. In this paper, a tree pruning algorithm is proposed based on minimum mean square error （MMSE） detection. The proposed algorithm first preforms MMSE detection since the complexity of MMSE detection is very low. Then the pruning constraints will be set according to the scaled path metrics of the MMSE solution. The choice of the scale factors and their influences on the complexity and performance are also discussed. Through analysis and simulations, it is shown that the complexity is reduced significantly with negligible performance degradation and additional computations.展开更多
Slope stability prediction plays a significant role in landslide disaster prevention and mitigation.This paper’s reduced error pruning(REP)tree and random tree(RT)models are developed for slope stability evaluation a...Slope stability prediction plays a significant role in landslide disaster prevention and mitigation.This paper’s reduced error pruning(REP)tree and random tree(RT)models are developed for slope stability evaluation and meeting the high precision and rapidity requirements in slope engineering.The data set of this study includes five parameters,namely slope height,slope angle,cohesion,internal friction angle,and peak ground acceleration.The available data is split into two categories:training(75%)and test(25%)sets.The output of the RT and REP tree models is evaluated using performance measures including accuracy(Acc),Matthews correlation coefficient(Mcc),precision(Prec),recall(Rec),and F-score.The applications of the aforementionedmethods for predicting slope stability are compared to one another and recently established soft computing models in the literature.The analysis of the Acc together with Mcc,and F-score for the slope stability in the test set demonstrates that the RT achieved a better prediction performance with(Acc=97.1429%,Mcc=0.935,F-score for stable class=0.979 and for unstable case F-score=0.935)succeeded by the REP tree model with(Acc=95.4286%,Mcc=0.896,F-score stable class=0.967 and for unstable class F-score=0.923)for the slope stability dataset The analysis of performance measures for the slope stability dataset reveals that the RT model attains comparatively better and reliable results and thus should be encouraged in further research.展开更多
Every mobile operator of today's world switches their technology over from 2G(second generation)to 3G(third generation)network.Operators are keen analyzing their CDR(call detail record)obtained over the past usage...Every mobile operator of today's world switches their technology over from 2G(second generation)to 3G(third generation)network.Operators are keen analyzing their CDR(call detail record)obtained over the past usage for predicting the behavior of their customers and their usage.The operators are willing to mine knowledge from real-world dataset which implies the pattern of user mentality on this changing world.To identify the usage of 2G and 3G services the classification models were trained using the data collected from PAKDD 2006 dataset.In order to obtain the prediction accuracy,the classifiers were evaluated using 10 folds cross validation.On comparing the results of the experiment,J48 performed more accurately and random tree consumed less time.展开更多
Tree-based models have been widely applied in both academic and industrial settings due to the natural interpretability, good predictive accuracy, and high scalability. In this paper, we focus on improving the single-...Tree-based models have been widely applied in both academic and industrial settings due to the natural interpretability, good predictive accuracy, and high scalability. In this paper, we focus on improving the single-tree method and propose the segmented linear regression trees(SLRT) model that replaces the traditional constant leaf model with linear ones. From the parametric view, SLRT can be employed as a recursive change point detect procedure for segmented linear regression(SLR) models,which is much more efficient and flexible than the traditional grid search method. Along this way,we propose to use the conditional Kendall's τ correlation coefficient to select the underlying change points. From the non-parametric view, we propose an efficient greedy splitting method that selects the splits by analyzing the association between residuals and each candidate split variable. Further, with the SLRT as a single-tree predictor, we propose a linear random forest approach that aggregates the SLRTs by a weighted average. Both simulation and empirical studies showed significant improvements than the CART trees and even the random forest.展开更多
Phylogenetic trees have been widely used in the study of evolutionary biology for representing the tree-like evolution of a collection of species. However, different data sets and different methods often lead to the c...Phylogenetic trees have been widely used in the study of evolutionary biology for representing the tree-like evolution of a collection of species. However, different data sets and different methods often lead to the construction of different phylogenetic trees for the same set of species. Therefore, comparing these trees to determine similarities or, equivalently, dissimilarities, becomes the fundamental issue. Typically, Tree Bisection and Reconnection（TBR）and Subtree Prune and Regraft（SPR） distances have been proposed to facilitate the comparison between different phylogenetic trees. In this paper, we give a survey on the aspects of computational complexity, fixed-parameter algorithms, and approximation algorithms for computing the TBR and SPR distances of phylogenetic trees.展开更多
基金supported by the Operative Program FEDER Andalucía 2014-2020(Junta de Andalucía-MINECO-FEDER)by the grant funded 2021/00591/001using the support to the research Action 1 of University of Jaén.
文摘Almond pruning biomass is an important agricultural residue that has been scarcely studied for the co-production of sugars and solid biofuels.In this work,the production of monosaccharides from almond prunings was optimised by a two-step process scheme:pretreatment with dilute sulphuric acid(0.025 M,at 185.9-214.1℃for 0.8-9.2 min)followed by enzyme saccharification of the pretreated cellulose.The application of a response surface methodology enabled the mathematical modelling of the process,establishing pretreatment conditions to maximise both the amount of sugar in the acid prehydrolysate(23.4 kg/100 kg raw material,at 195.7℃for 3.5 min)and the enzymatic digestibility of the pretreated cellulose(45.4%,at 210.0℃for 8.0 min).The highest overall sugar yield(36.8 kg/100 kg raw material,equivalent to 64.3%of all sugars in the feedstock)was obtained with a pretreatment carried out at 197.0℃for 4.0 min.Under these conditions,moreover,the final solids showed better properties for thermochemical utilisation(22.0 MJ/kg heating value,0.87%ash content,and 72.1 mg/g moisture adsorption capacity)compared to those of the original prunings.
文摘Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision.This study introduces REPTF-TMDI,a novel method that combines a Reduced Error Pruning Tree Forest(REPTree Forest)with a newly proposed Time-based Missing Data Imputation(TMDI)approach.The REP Tree Forest,an ensemble learning approach,is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urbanmobility solutions.Meanwhile,the TMDI approach exploits temporal patterns to estimate missing values reliably whenever empty fields are encountered.The proposed method was evaluated using hourly traffic flow data from a major U.S.roadway spanning 2012-2018,incorporating temporal features(e.g.,hour,day,month,year,weekday),holiday indicator,and weather conditions(temperature,rain,snow,and cloud coverage).Experimental results demonstrated that the REPTF-TMDI method outperformed conventional imputation techniques across various missing data ratios by achieving an average 11.76%improvement in terms of correlation coefficient(R).Furthermore,REPTree Forest achieved improvements of 68.62%in RMSE and 70.52%in MAE compared to existing state-of-the-art models.These findings highlight the method’s ability to significantly boost traffic flow prediction accuracy,even in the presence of missing data,thereby contributing to the broader objectives of sustainable urban transportation systems.
基金supported by the Hi-Tech Research and Development Program of China (2011AA01A204)the Beijing University of Posts and Telecommunications Research and Innovation Fund for Youths
文摘Tree pruning is an effective algorithm to reduce the complexity of sphere detection （SD） for multiple-input multiple-output （MIMO） communication systems. How to determine the tree pruning rule, as well as by what the tradeoff between the performance and the complexity can be achieved, is still an open problem. In this paper, a tree pruning algorithm is proposed based on minimum mean square error （MMSE） detection. The proposed algorithm first preforms MMSE detection since the complexity of MMSE detection is very low. Then the pruning constraints will be set according to the scaled path metrics of the MMSE solution. The choice of the scale factors and their influences on the complexity and performance are also discussed. Through analysis and simulations, it is shown that the complexity is reduced significantly with negligible performance degradation and additional computations.
基金supported by the National Key Research and Development Plan of China under Grant No.2021YFB2600703.
文摘Slope stability prediction plays a significant role in landslide disaster prevention and mitigation.This paper’s reduced error pruning(REP)tree and random tree(RT)models are developed for slope stability evaluation and meeting the high precision and rapidity requirements in slope engineering.The data set of this study includes five parameters,namely slope height,slope angle,cohesion,internal friction angle,and peak ground acceleration.The available data is split into two categories:training(75%)and test(25%)sets.The output of the RT and REP tree models is evaluated using performance measures including accuracy(Acc),Matthews correlation coefficient(Mcc),precision(Prec),recall(Rec),and F-score.The applications of the aforementionedmethods for predicting slope stability are compared to one another and recently established soft computing models in the literature.The analysis of the Acc together with Mcc,and F-score for the slope stability in the test set demonstrates that the RT achieved a better prediction performance with(Acc=97.1429%,Mcc=0.935,F-score for stable class=0.979 and for unstable case F-score=0.935)succeeded by the REP tree model with(Acc=95.4286%,Mcc=0.896,F-score stable class=0.967 and for unstable class F-score=0.923)for the slope stability dataset The analysis of performance measures for the slope stability dataset reveals that the RT model attains comparatively better and reliable results and thus should be encouraged in further research.
文摘Every mobile operator of today's world switches their technology over from 2G(second generation)to 3G(third generation)network.Operators are keen analyzing their CDR(call detail record)obtained over the past usage for predicting the behavior of their customers and their usage.The operators are willing to mine knowledge from real-world dataset which implies the pattern of user mentality on this changing world.To identify the usage of 2G and 3G services the classification models were trained using the data collected from PAKDD 2006 dataset.In order to obtain the prediction accuracy,the classifiers were evaluated using 10 folds cross validation.On comparing the results of the experiment,J48 performed more accurately and random tree consumed less time.
文摘Tree-based models have been widely applied in both academic and industrial settings due to the natural interpretability, good predictive accuracy, and high scalability. In this paper, we focus on improving the single-tree method and propose the segmented linear regression trees(SLRT) model that replaces the traditional constant leaf model with linear ones. From the parametric view, SLRT can be employed as a recursive change point detect procedure for segmented linear regression(SLR) models,which is much more efficient and flexible than the traditional grid search method. Along this way,we propose to use the conditional Kendall's τ correlation coefficient to select the underlying change points. From the non-parametric view, we propose an efficient greedy splitting method that selects the splits by analyzing the association between residuals and each candidate split variable. Further, with the SLRT as a single-tree predictor, we propose a linear random forest approach that aggregates the SLRTs by a weighted average. Both simulation and empirical studies showed significant improvements than the CART trees and even the random forest.
基金supported by the National Natural Science Foundation of China (Nos.61103033,61173051, 61232001,and 70921001)
文摘Phylogenetic trees have been widely used in the study of evolutionary biology for representing the tree-like evolution of a collection of species. However, different data sets and different methods often lead to the construction of different phylogenetic trees for the same set of species. Therefore, comparing these trees to determine similarities or, equivalently, dissimilarities, becomes the fundamental issue. Typically, Tree Bisection and Reconnection（TBR）and Subtree Prune and Regraft（SPR） distances have been proposed to facilitate the comparison between different phylogenetic trees. In this paper, we give a survey on the aspects of computational complexity, fixed-parameter algorithms, and approximation algorithms for computing the TBR and SPR distances of phylogenetic trees.
