检索结果-内蒙古大学图书馆

Methane hydrate: Modeling and assessing the experimental data of incipient stability conditions

JOURNAL OF DISPERSION SCIENCE AND TECHNOLOGY 2018年第6期39卷 848-861页

作者： Ghiasi, Mohammad M. Noorollahi, Younes Aslani, Alireza Univ Tehran Fac New Sci & Technol Dept Renewable Energies North Kargar St Tehran *** Iran

Accurate knowledge of equilibrium conditions for methane hydrates dissociation is crucial to implement an appropriate hydrate-based technology/process. Hence, much importance is attached to utilize techniques for the modeling of hydrate stability zones and the existence of reliable data as well. This research employs the published experimental data, from 1940 to 2016, for modeling the incipient stability conditions of methane hydrate in pure water using classification and regression tree (CART) as a novel methodology in gas hydrates research. Least squares support vector machine (LSSVM) and artificial neural network (ANN) methods were selected as a basis for comparison. Furthermore, the Leverage mathematical approach was used for evaluating the quality of the data as well as the correctness of the CART model. Results confirmed that the developed tree-based model provides excellent outcomes and no model can rival it for accuracy. In addition, applying the Leverage algorithm specified that: (a) the CART model is statistically valid and correct;(b) the published experimental data in the literature are different in quality;(c) there is no doubtful data in the studied database;and (d) the published data in the temperature range from 148.8 to 238.8K are not sufficient compared with the whole of the collected database. [GRAPHICS] .

关键词： classification and regression tree clathrate methane hydrate modeling outlier detection

来源：评论

学校读者我要写书评

暂无评论

Thermal decomposition kinetics of poly(vinyl chloride) insulation for overloaded and non-overloaded wires

引用

JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2024年 1-16页

作者： Man, Peng Rui Lin, Qing Wen Xu, Jie Wang, Huai Bin Zhao, Yan Hong Su, Wen Wei Lyu, Hui Fei Li, Yang China Peoples Police Univ Forens Sci Inst Langfang 065000 Hebei Peoples R China Xian Univ Sci & Technol Sch Safety Sci & Engn Xian 710054 Shaanxi Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Univ Sci & Technol China State Key Lab Fire Sci Hefei 230026 Anhui Peoples R China

Polyvinyl chloride (PVC) is the most common insulation material applied to wires and cables. The hot internal wire is overloaded in household appliances with some faults, which can affect the fire characteristics and insulation property. This study performs non-isothermal thermogravimetric analysis to investigate the kinetic in the thermal decomposition of PVC insulation of overloaded electrical wires using peak deconvolution, four model-free methods, M & aacute;lek model method. The thermal decomposition process of overloaded PVC insulation can be divided into three stages using peak deconvolution. As the overload value increases, the reaction shifts to the high-temperature section. An analysis of the activation energy and the modeling method reveals that PVC overload failure leads to a transformation in the decomposition mechanism and a shift in the reaction order model to an exponent power model. And the critical overload value for thermal failure of insulation is 3Ie. In addition, the classification and regression tree was used to predict the thermal decomposition process at different overload values. The results show that these methods are effective ways for studying the mechanism of overload thermal decomposition and predicting the overload value in conductors.

关键词： Overloaded insulation Polyvinyl chloride Thermal decomposition Peak deconvolution classification and regression tree

来源：评论

学校读者我要写书评

暂无评论

The predicted effects of climate change on local species distributions around Beijing,China

引用

Journal of Forestry Research 2020年第5期31卷 1539-1550页

作者： Lichun Mo Jiakai Liu Hui Zhang Yi Xie School of Economies and Management Beijing Forestry UniversityBeijingPeople’s Republic of China School of Nature Conservation Beijing Forestry UniversityBeijingPeople’s Republic of China

To assist conservationists and policymakers in managing and protecting forests in Beijing from the effects of climate change,this study predicts changes for 2012–2112 in habitable areas of three tree species—Betula platyphylla,Quercus palustris,Platycladus orientalis,plus other mixed broadleaf species—in Beijing using a classification and regression tree niche model under the International Panel on Climate Change’s A2 and B2 emissions scenarios(SRES).The results show that climate change will increase annual average temperatures in the Beijing area by 2.0–4.7℃,and annual precipitation by 4.7–8.5 mm,depending on the emissions scenario *** changes result in shifts in the range of each of the *** suitable areas for distributions of *** and *** will decrease in the *** model points to significant shifts in the distributions of these species,withdrawing from their current ranges and pushing southward towards central *** of the ranges decline during the initial 2012–2040 period before shifting southward and ending up larger overall at the end of the 88-year *** mixed broadleaf forests expand their ranges *** *** forests,on the other hand,expand their range *** results indicate that climate change and its effects will accelerate significantly in Beijing over the next 88 *** stress is likely to be a major limiting factor on the distribution of forests and the most important factor affecting migration of species into and out of existing nature *** is a potential for the extinction of some ***,long-term vegetation monitoring and warning systems will be needed to protect local species from habitat loss and genetic swamping of native species by hybrids.

关键词： Climate change classification and regression tree Plant distribution Scenario A2 and B2 Simulation analysis

来源：评论

学校读者我要写书评

暂无评论

Southern pine beetle regional outbreaks modeled on landscape, climate and infestation history

引用

FOREST ECOLOGY AND MANAGEMENT 2011年第3期261卷 473-479页

作者： Duehl, Adrian J. Koch, Frank H. Hain, Fred P. USDA ARS CMAVE Gainesville FL 32608 USA N Carolina State Univ Dept Forestry & Environm Resources Eastern Forest Environm Threat Assessment Ctr USDA Forest Serv Res Triangle Pk NC 27709 USA N Carolina State Univ Dept Entomol Raleigh NC 27695 USA

The southern pine beetle (Dendroctonus frontalis, SPB) is the major insect pest of pine species in the southeastern United States. It attains outbreak population levels sufficient to mass attack host pines across the landscape at scales ranging from a single forest stand to interstate epidemics. This county level analysis selected and examined the best climatic and landscape variables for predicting infestations at regional scales. The analysis showed that, for a given county, the most important factor in predicting outbreaks was that the county was classified as in outbreak status in the previous year. Other important factors included minimum winter temperature and the greatest difference between the average of daily minimums and a subsequent low temperature point, precipitation history either seasonally in the previous year or difference from average over the previous 2 years, the synchronizing effect of seasonal temperatures on beetle populations and the relative percentage of total forest area composed of host species. The statistical models showed that climatic variables are stronger indicators of outbreak likelihood than landscape structure and cover variables. Average climatic conditions were more likely to lead to outbreaks than extreme conditions, supporting the notion of coupling between a native insect and its native host. Still, some extreme events (i.e., periods of very low temperature or very high precipitation) did precede beetle infestation. This analysis suggested that there are predisposing and inciting factors at the large scale but the driving factors leading to individual infestations operate at smaller scales. Published by Elsevier B.V

关键词： classification and regression tree Insect infestation Logistic regression Dendroctonus frontalis Pinus taeda

来源：评论

学校读者我要写书评

暂无评论

An analysis of assessment outcomes from eight years' operation of the Australian border weed risk assessment system

引用

JOURNAL OF ENVIRONMENTAL MANAGEMENT 2009年第2期90卷 798-807页

作者： Weber, Jason Panetta, F. Dane Virtue, John Pheloung, Paul Dept Primary Ind & Fisheries Cooperat Res Ctr Australian Weed Management Alan Fletcher Res Stn Sherwood Qld 4075 Australia Cooperat Res Ctr Australian Weed Management Dept Water Land & Biodivers Conservat Adelaide SA 5001 Australia Dept Agr Fisheries & Forestry Cooperat Res Ctr Australian Weed Management Canberra ACT 2601 Australia

The majority of Australian weeds are exotic plant species that were intentionally introduced for a variety of horticultural and agricultural purposes. A border weed risk assessment system (WRA) was implemented in 1997 in order to reduce the high economic costs and massive environmental damage associated with introducing serious weeds. We review the behaviour of this system with regard to eight years of data collected from the assessment of species proposed for importation or held within genetic resource centres in Australia. From a taxonomic perspective, species from the Chenopodiaceae and Poaceae were most likely to be rejected and those from the Arecaceae and Flacourtiaceae were most likely to be accepted. Dendrogram analysis and classification and regression tree (tree) models were also used to analyse the data. The latter revealed that a small subset of the 35 variables assessed was highly associated with the outcome of the original assessment. The tree model examining all of the data contained just five variables: unintentional human dispersal, congeneric weed, weed elsewhere, tolerates or benefits from mutilation, cultivation or fire, and reproduction by vegetative propagation. It gave the same outcome as the full WRA model for 71% of species. Weed elsewhere was not the first splitting variable in this model, indicating that the WRA has a capacity for capturing species that have no history of weediness. A reduced tree model (in which human-mediated variables had been removed) contained four variables: broad climate suitability, reproduction in less or than equal to I year, self-fertilisation, and tolerates and benefits from mutilation, cultivation or fire. It yielded the same outcome as the full WRA model for 65% of species. Data inconsistencies and the relative importance of questions arc discussed, with some recommendations made for improving the use of the system. Crown Copyright (C) 2008 Published by Elsevier Ltd. All rights reserved.

关键词： Weed risk assessment classification and regression tree Import Australia

来源：评论

学校读者我要写书评

暂无评论

Survival analysis with time-varying regression effects using a tree-based approach

引用

BIOMETRICS 2002年第2期58卷 305-315页

作者： Xu, RH Adak, S Harvard Univ Sch Publ Hlth Dept Biostat Boston MA 02115 USA Dana Farber Canc Inst Boston MA 02115 USA Indian Inst Technol Bioinformat Grp IBM India Res Lab New Delhi 110016 India

Nonproportional hazards often arise in survival analysis, as is evident in the data from the International Non-Hodgkin's Lymphoma Prognostic Factors Project. A tree-based method to handle such survival data is developed for the assessment and estimation of time-dependent regression effects under a Cox-type model. The tree method approximates the time-varying regression effects as piecewise constants and is designed to estimate change points in the regression parameters. A fast algorithm that relies on maximized score statistics is used in recursive segmentation of the time axis. Following the segmentation, a pruning algorithm with optimal properties similar to those of classification and regression trees (CART) is used to determine a sparse segmentation. Bootstrap resampling is used in correcting for overoptimism due to split point optimization. The piecewise constant model is often more suitable for clinical interpretation of the regression parameters than the more flexible spline models. The utility of the algorithm is shown oil the lymploma data, where. we further develop the, published International Risk Index into a time-varying risk index for non-Hodgkin's lymphoma.

关键词： change paint classification and regression tree maximized score test nonproportional hazards time-varying regression effect

来源：评论

学校读者我要写书评

暂无评论

Characterization of runoff generation in a mountainous hillslope according to multiple threshold behavior and hysteretic loop features

引用

JOURNAL OF HYDROLOGY 2020年 590卷 125534-125534页

作者： Lee, Eunhyung Kim, Sanghyun Pusan Natl Univ Dept Environm Engn Busandaehakro 63 Beongil 2 Pusan 46241 South Korea

This study explores the rainfall and soil moisture thresholds for runoff using a classification and regression tree (CART) algorithm for a large dataset (146 events) of hydrologic variables collected from a humid hillslope. The common CART structure for the runoff coefficient successfully separated rainfall events with zero runoff generation and further identified five distinct clusters with distinct runoff generation and hysteretic loop features. The total amount of rainfall was the primary driver of runoff, with the highest relative importance in the CART model. In contrast, the antecedent soil moisture and rainfall intensity helped explain the hysteretic loop features as well as exceptional events through hysteretic loop analysis. The multinomial logistic regression model was introduced to determine the probability of different hysteretic loop variations according to the rainfall characteristics. By identifying the distinctive hysteretic loop patterns between upslope and downslope regions, these findings demonstrate the sequential development of hydrological processes from zero runoff to extreme runoff events.

关键词： Hillslope hydrology Runoff generation threshold classification and regression tree Multinomial logistic regression model Hysteretic loop features

来源：评论

学校读者我要写书评

暂无评论

Exploring the feasibility of classification trees versus ordinal discrete choice models for analyzing crash severity

引用

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2015年 50卷 86-96页

作者： Khan, Ghazan Bill, Andrea R. Noyce, David A. Calif State Univ Sacramento Sacramento CA 95819 USA Univ Wisconsin Traff Operat & Safety TOPS Lab Madison WI 53706 USA

A cross-median crash (CMC) is one of the most severe types of crashes in which a vehicle crosses the median and sometimes collides with opposing traffic. A study of severity of CMCs in the state of Wisconsin was conducted by Lu et al. in 2010. Discrete choice models, namely ordinal logit and probit models were used to analyze factors related to the severity of CMCs. Separate models were developed for single and multi-vehicle CMCs. Although 25 different crash, roadway, and geometric variables were used, only 3 variables were found to be statistically significant which were alcohol usage, posted speed, and road conditions. The objective of this research was to explore the feasibility of GUIDE classification tree method to analyze the severity of CMCs to discover if any additional information could be revealed. A dataset of CMCs in the state of Wisconsin between 2001 and 2007, used in the study by Lu et al. was used to develop three different GUIDE classification trees. Additionally, the effects of variable types (continuous or discrete), misclassification costs, and tree pruning characteristics on models results were also explored. The results were directly compared with discrete choice models developed in the study by Lu et al. showing that the GUIDE classification trees revealed new variables (median width and traffic volume) that affect CMC severity and provided useful insight on the data. The results of this research suggest that the use of classification tree analysis should at least be considered in conjunction with regression-based crash models to better understand factors affecting crashes. classification tree models were able to reveal additional information about the dependent variable and offer advantages with respect to multicollinearity and variable redundancy issues. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： classification and regression tree GUIDE Cross-median crashes Crash severity models Multicollinearity

来源：评论

学校读者我要写书评

暂无评论

Comparison of Spatial Interpolation Methods for Gridded Bias Removal in Surface Temperature Forecasts

引用

Journal of Meteorological Research 2017年第4期31卷 791-799页

作者： Seyedeh Atefeh MOHAMMADI Majid AZADI Morteza RAHMANI Technology Development Institute Tehran 13445-686 Iran Atmospheric Science and Meteorological Research Center Tehran 16385-14977 Iran Faculty of Basic and Advanced Technologies in Biology University of Science and Culture Tehran 14619-68151 Iran

All numerical weather prediction（NWP） models inherently have substantial biases, especially in the forecast of near-surface weather variables. Statistical methods can be used to remove the systematic error based on historical bias data at observation stations. However, many end users of weather forecasts need bias corrected forecasts at locations that scarcely have any historical bias data. To circumvent this limitation, the bias of surface temperature forecasts on a regular grid covering Iran is removed, by using the information available at observation stations in the vicinity of any given grid point. To this end, the running mean error method is first used to correct the forecasts at observation stations, then four interpolation methods including inverse distance squared weighting with constant lapse rate（IDSW-CLR）, Kriging with constant lapse rate（Kriging-CLR）, gradient inverse distance squared with linear lapse rate（GIDS-LR）, and gradient inverse distance squared with lapse rate determined by classification and regression tree（GIDS-CART）, are employed to interpolate the bias corrected forecasts at neighboring observation stations to any given location. The results show that all four interpolation methods used do reduce the model error significantly,but Kriging-CLR has better performance than the other methods. For Kriging-CLR, root mean square error（RMSE）and mean absolute error（MAE） were decreased by 26% and 29%, respectively, as compared to the raw forecasts. It is found also, that after applying any of the proposed methods, unlike the raw forecasts, the bias corrected forecasts do not show spatial or temporal dependency.

关键词： spatial interpolation bias correction lapse rate Kriging classification and regression tree

来源：评论

学校读者我要写书评

暂无评论

Predicting depressed patients with suicidal ideation from ECG recordings

引用

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2017年第5期55卷 793-805页

作者： Khandoker, A. H. Luthra, V. Abouallaban, Y. Saha, S. Ahmed, K. I. Mostafa, R. Chowdhury, N. Jelinek, H. F. Khalifa Univ Dept Biomed Engn POB 127788 Abu Dhabi U Arab Emirates Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia Amer Ctr Psychiat & Neurol Abu Dhabi U Arab Emirates United Int Univ Dhaka Bangladesh Charles Sturt Univ Sch Community Hlth Albury NSW Australia Charles Sturt Univ Ctr Res Complex Syst Albury NSW Australia Macquarie Univ Australian Sch Adv Med Sydney NSW Australia

Globally suicidal behavior is the third most common cause of death among patients with major depressive disorder (MDD). This study presents multi-lag tone-entropy (T-E) analysis of heart rate variability (HRV) as a screening tool for identifying MDD patients with suicidal ideation. Sixty-one ECG recordings (10 min) were acquired and analyzed from control subjects (29 CONT), 16 MDD subjects with (MDDSI+) and 16 without suicidal ideation (MDDSI-). After ECG preprocessing, tone and entropy values were calculated for multiple lags (m: 1-10). The MDDSI+ group was found to have a higher mean tone value compared to that of the MDDSI- group for lags 1-8, whereas the mean entropy value was lower in MDDSI+ than that in CONT group at all lags (1-10). Leave-one-out cross-validation tests, using a classification and regression tree (CART), obtained 94.83 % accuracy in predicting MDDSI+ subjects by using a combination of tone and entropy values at all lags and including demographic factors (age, BMI and waist circumference) compared to results with time and frequency domain HRV analysis. The results of this pilot study demonstrate the usefulness of multi-lag T-E analysis in identifying MDD patients with suicidal ideation and highlight the change in autonomic nervous system modulation of the heart rate associated with depression and suicidal ideation.

关键词： Major depressive disorder Heart rate variability Tone-entropy analysis classification and regression tree Suicidal ideation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：