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Warming Increases the Carbon Sequestration Capacity of Picea schrenkiana in the Tianshan Mountains, China

FORESTS 2021年第8期12卷 1066-1066页

作者： Zhou, Honghua Chen, Yaning Zhu, Chenggang Chen, Yapeng Yang, Yuhai Li, Weihong Chen, Shifeng Chinese Acad Sci Xinjiang Inst Ecol & Geog State Key Lab Desert & Oasis Ecol Urumqi 830011 Peoples R China

As an essential part of terrestrial ecosystems, convenient and accurate reconstruction of the past carbon sequestration capacity of forests is critical to assess future trends of aboveground carbon storage and ecosystem carbon cycles. In addition, the relationship between climate change and carbon sequestration of forests has been vigorously debated. In this study, dynamic change of carbon sequestration capacity in aboveground biomass of Picea schrenkiana (hereinafter abbreviated as P. schrenkiana) in the Tianshan Mountains, northwestern China, from 1850-2017, were reconstructed using dendrochronology. The main climate drivers that affected carbon sequestration capacity in aboveground biomass of P. schrenkiana were then investigated. The results showed that: (1) tree-ring width and diameter at breast height (DBH) of P. schrenkiana obtained from different altitudes and ages were an effective and convenient estimation index for reconstructing the carbon sequestration capacity of P. schrenkiana. The carbon storage of P. schrenkiana forest in 2016 in the Tianshan Mountains was 50.08 Tg C calculated using tree-ring width and DBH, which was very close to the value determined by direct field investigation data. (2) The annual carbon sequestration potential capacity of P. schrenkiana exhibited an increasing trend from 1850-2017. Temperature, especially minimum temperature, constituted the key climatic driver resulting in increased carbon sequestration capacity. The contribution rates of temperature and minimum temperature to the change of P. schrenkiana carbon sequestration capacity was 75% and 44%, respectively. (3) The significant increase of winter temperature and minimum temperature led to warming in the Tianshan Mountains, resulting in a significant increase in carbon sequestration capacity of P. schrenkiana. The results indicate that, with the continuous increase of winter temperature and minimum temperature, carbon sequestration of P. schrenkiana in the Tianshan Moun

关键词： tree rings carbon sequestration classification and regression tree climate change dendrochronology

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Subgroup causal effect identification and estimation via matching tree

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2021年 159卷 107188-107188页

作者： Zhang, Yuyang Schnell, Patrick Song, Chi Huang, Bin Lu, Bo Ohio State Univ Coll Publ Hlth Div Biostat Columbus OH 43210 USA Univ Cincinnati Div Biostat & Epidemiol Cincinnati Childrens Hosp Med Ctr Dept PediatColl Med Cincinnati OH USA

Inferring causal effect from observational studies is a central topic in many scientific fields, including social science, health and medicine. The statistical methodology for estimating population average causal effect has been well established. However, the methods for identifying and estimating subpopulation causal effects are relatively less developed. Part of the challenge is that the subgroup structure is usually unknown, therefore, methods working well for population level effect need to be modified to address this. A tree method based on a matched design is proposed to identify subgroups with differential treatment effects. To remove observed confounding, propensity-score-matched pairs are first created. Then the classification and regression tree is applied to the within-pair outcome differences to identify the subgroup structure. This non-parametric approach is robust to model misspecification, which is important because it becomes much harder to specify a parametric outcome model in the presence of subgroup effects. In addition to describing assumptions under which our matching estimator is unbiased, algorithms for identifying subgroup structures are provided. Simulation results indicate that the proposed approach compares favorably, in terms of the percentage of correctly identifying true tree structure, with other competing tree-based methods, including causal trees, causal inference trees and the virtual twins approach. Finally the proposed method is implemented to examine the potential subgroup effect of the timing of Tobramycin use on chronic infection among pediatric Cystic Fibrosis patients. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Potential outcomes Propensity score Matched design classification and regression tree Virtual twins

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Investigating Influence Factors on Injury Severity of Electric and Non-electric Bicycle Crashes in Beijing 5

Investigating Influence Factors on Injury Severity of Electr...

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IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)

作者： Xing, Yuxuan Sun, Zhiyuan Wang, Duo Beijing Univ Technol Beijing Key Lab Traff Engn Beijing Peoples R China

ISBN: (纸本)9781728194097

The objective of this study was to identify influence factors on injury severity of electric and non-electric bicycle crashes and discuss the differences between them in Beijing, China. Generalized linear model (GLM) and classification and regression tree (CART) were proposed to investigate significant influence factors and the importance order of influence factors, respectively. Based on GLM, seven factors were significant in electric bicycle crashes whereas five factors were significant in non-electric bicycle crashes. CART implied the most important factors was type of motor vehicle both in electric and non-electric bicycle crashes. However, other important factors showed different characteristic in the two type of crashes. This paper gives detailed information for electric and non-electric bicycle crashes, which provides reference for government to implement measures precisely.

关键词： injury severity electric bicycle crashes non-electric bicycle crashes generalized linear model classification and regression tree

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Hospitalization Outcomes for Rural Children with Mental Health Conditions

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JOURNAL OF PEDIATRICS 2021年 229卷 240-+页

作者： Bettenhausen, Jessica L. Hall, Matt Doupnik, Stephanie K. Markham, Jessica L. Feinstein, James A. Berry, Jay G. Gay, James C. Univ Missouri Dept Pediat Childrens Mercy Kansas City Sch Med Kansas City MO USA Childrens Hosp Assoc Lenexa KS USA Univ Penn Childrens Hosp Philadelphia Dept Pediat Philadelphia PA 19104 USA Univ Colorado Childrens Hosp Colorado Dept Pediat Sch Med Aurora CO USA Harvard Med Sch Boston Childrens Hosp Dept Pediat Boston MA 02115 USA Vanderbilt Univ Med Ctr Dept Pediat Nashville TN 37232 USA

Objective To identify where rural children with mental health conditions are hospitalized and to determine differences in outcomes based on location of hospitalization. Study design This is a retrospective cohort analysis of US rural children aged 0-18 years with a mental health hospitalization between January 1, 2014, and November 30, 2014, using the 2014 Agency for Healthcare Research and Quality's Nationwide Readmissions Database. Hospitalizations for rural children were categorized by children's hospitals, metropolitan non-children's hospitals, or rural hospitals. Associations between hospital location and outcomes were assessed with logistic (readmission) and negative binomial regression (length of stay [LOS]) models. classification and regression trees (CART) were used to describe the characteristics of most common hospitalizations at a rural hospital. Results Of 21 666 mental health hospitalizations of rural children, 20.6% were at rural hospitals. After adjustment for clinical and demographic characteristics, LOS was higher at metropolitan non-children's and children's hospitals compared with rural hospitals (LOS: adjusted rate ratio [aRR], 1.35 [95% CI 1.29-1.41] and 1.33 [95% CI, 1.25-1.41];P < .01 for all). The 30-day readmission was lower at metropolitan non-children's and children's hospitals compared with rural hospitals (aOR, 0.73 [95% CI, 0.63-0.84] and 0.59 [95% CI, 0.48-0.71];P < .001 for all). Adolescent males living in poverty with externalizing behavior disorder had the highest percentage of hospitalization at rural hospitals (69.4%). Conclusions Although hospitalizations at children's and metropolitan non-children's hospitals were longer, patient outcomes were more favorable.

关键词： aRR Adjusted rate ratio CART classification and regression tree CCC Complex chronic conditions HRISK Hospital Resource Intensity Scores for Kids LOS Length of stay NRD Nationwide Readmissions Database

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Evaluation of water status of wheat genotypes to aid prediction of yield on sodic soils using UAV-thermal imaging and machine learning

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AGRICULTURAL AND FOREST METEOROLOGY 2021年 307卷 108477-108477页

作者： Das, Sumanta Christopher, Jack Apan, Armando Choudhury, Malini Roy Chapman, Scott Menzies, Neal W. Dang, Yash P. Univ Queensland Sch Agr & Food Sci St Lucia Qld 4072 Australia Univ Queensland Leslie Res Facil Queensland Alliance Agr & Food Innovat Toowoomba Qld 4350 Australia Univ Southern Queensland Sch Civil Engn & Surveying Toowoomba Qld 4350 Australia Univ Philippines Diliman Inst Environm Sci & Meteorol Quezon City 1101 Philippines

Water stress limits wheat growth and the yield on rain-fed sodic soils. Appropriate selection of traits and novel methods are required to forecast yield and to identify water stress tolerant wheat genotypes on sodic soils. In this study, we proposed a thermal remote sensing and machine learning-based approach to help predict the biomass and grain yields of wheat genotypes grown with variable water stress in sodic soil environments. We employed unmanned aerial vehicle-based thermal imaging to quantify water stress of 18 contrasting wheat genotypes grown on moderately sodic (MS) and highly sodic (HS) soils in north-eastern grains growing regions of Australia and related these to ground-measured plant biomass and grain yields. We evaluated crop water stress indices;standardized canopy temperature index, crop water stress index, stomatal conductance index, vapour pressure deficit, and crop stress index, which were computed from thermal imagery and on-site agro-meteorological parameters close to flowering. We then employed a classification and regression tree (CRT) as a supervised machine learning algorithm to classify crop water stress and predict biomass and grain yields as a function of crop water stress indices. The CRT accurately predicted biomass yield (coefficient of determination (R-2) = 0.86;root mean square error (RMSE) = 41.3 g/m(2) and R-2 = 0.75;RMSE = 47.7 g/m(2) for the MS and HS site) and grain yield (R-2 = 0.78;RMSE = 16.7 g/m(2) and R-2 = 0.69;RMSE = 23.2 g/m(2) for the MS and HS site, respectively). High sodic soil constraints increased crop water stress more than moderately sodic constraints soil that limits wheat yield similar to 40%. Wheat genotypes;Bremer, Gregory, Lancer, Mace, and Mitch were more productive than Gladius, Flanker, Scout, Emu Rock, and Janz in sodic soil environments. The study improves our ability to develop decision-making tools to assist farmers and breeders in securing agricultural productivity on sodic soils.

关键词： Thermal remote sensing Crop water stress classification and regression tree Biomass yield Grain yield Sodic soils

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Association between polypharmacy and the persistence of delirium: a retrospective cohort study

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BIOPSYCHOSOCIAL MEDICINE 2020年第1期14卷 25-25页

作者： Kurisu, Ken Miyabe, Daisuke Furukawa, Yoshiko Shibayama, Osamu Yoshiuchi, Kazuhiro Yokohama Rosai Hosp Dept Psychosomat Med Yokohama Kanagawa Japan Univ Tokyo Grad Sch Med Dept Stress Sci & Psychosomat Med Bunkyo Ku 7-3-1 Hongo Tokyo 1138655 Japan

Background Although the association between polypharmacy and the occurrence of delirium has been well studied, the influence of polypharmacy on the persistence of delirium remains unclear. We aimed to explore the effect of polypharmacy on the persistence of delirium. Methods This retrospective cohort study was conducted at a tertiary hospital. The medical records of patients diagnosed with delirium who were referred to the Department of Psychosomatic Medicine were reviewed. Presentation with delirium on day 3 was set as the outcome in this study. We counted the number of drugs prescribed on the date of referral, excluding general infusion fluids, nutritional or electrolytic products, and psychotropics. To define polypharmacy, we developed a classification and regression tree (CART) model and drew a receiver operating characteristic (ROC) curve. The odds ratio (OR) of polypharmacy for the persistence of delirium on day 3 was calculated using a logistic regression model with the propensity score as a covariate. Results We reviewed the data of 113 patients. The CART model and ROC curve indicated an optimal polypharmacy cutoff of six drugs. Polypharmacy was significantly associated with the persistence of delirium both before [OR, 3.02;95% confidence interval (CI), 1.39-6.81;P = 0.0062] and after (OR, 3.19;95% CI, 1.32-8.03;P = 0.011) propensity score adjustment. Conclusion We discovered an association between polypharmacy and worsening courses of delirium and hypothesize that polypharmacy might be a prognostic factor for delirium.

关键词： classification and regression tree Delirium Polypharmacy Propensity score Receiver operating characteristic curve

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Decision tree analysis for pathogen identification based on circumstantial factors in outbreaks of bovine respiratory disease in calves

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PREVENTIVE VETERINARY MEDICINE 2021年 196卷 105469-105469页

作者： Lowie, T. Callens, J. Maris, J. Ribbens, S. Pardon, B. Univ Ghent Fac Vet Med Dept Large Anim Internal Med Salisburylaan 133 B-9820 Merelbeke Belgium Anim Hlth Serv Flanders DGZ Vlaanderen Ind Laan 29 B-8820 Torhout Belgium Boehringer Ingelheim Belgium Arianelaan 16 B-1200 Sint Lambrechts Wolume Belgium

Respiratory tract infections continue to be a leading cause of economic loss, hampered animal welfare and intensive antimicrobial use in cattle operations, worldwide. To better target antimicrobial therapy, control and prevention towards the involved pathogens, there is a growing interest in microbiological tests on respiratory samples. However, these tests are time consuming, cost money and sampling might compromise animal welfare. Therefore, the objective of the present study was to develop immediately applicable decision trees for pathogen identification in outbreaks of bovine respiratory disease based on circumstantial factors. Data from a cross sectional study, involving 201 outbreaks of bovine respiratory disease in dairy and beef farms between 2016 and 2019 was used. Pathogens were identified by a semi-quantitative PCR (polymerase chain reaction) on a pooled non-endoscopic broncho-alveolar lavage sample from clinically affected animals. Potential risk factors of involved animals, environment, management and housing were obtained by enquiry. classification and regression tree analysis was used for decision tree development with cross-validation. Different trees were constructed, involving a general 3-group classification tree (viruses, Mycoplasma bovis or Pasteurellaceae family) and a tree for each single pathogen. The general 3-group classification tree was 52.7 % accurate and had a sensitivity of 81.5 % and a specificity 52.2 % for viruses, respectively 51.7 % and 84.4 % for M. bovis and 28.9 % and 93.6 % for Pasteurellaceae. The single-pathogen trees were more specific than sensitive: Histophilus somni (Se = 25.8 %;Sp = 94.5 %), Mannheimia haemolytica (Se = 69.2 %;Sp = 70.6 %), bovine coronavirus (Se = 42.2 %;Sp = 89.6 %) and bovine respiratory syncytial virus (Se = 34.0 %;Sp = 96.6 %). For Pasteurella multocida, M. bovis and parainfluenzavirus type 3 no meaningful tree was obtained. The concept and trees are promising, but currently lack sensitivity and sp

关键词： Bovine respiratory syncytial virus Pneumonia classification and regression tree Predictive modeling Mycoplasma bovis

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A performance analysis of prediction techniques for impacting vehicles in hit-and-run road accidents

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ACCIDENT ANALYSIS AND PREVENTION 2021年 157卷 106164-106164页

作者： Jha, Alok Nikhil Chatterjee, Niladri Tiwari, Geetam Indian Inst Technol TRIPP Delhi India Indian Inst Technol Dept Math Delhi India

Road accidents are globally accepted challenges. They are one of the significant causes of deaths and injuries besides other direct and indirect losses. Countries and international organizations have designed technologies, systems, and policies to prevent accidents. However, hit-and-run accidents remain one of the most dangerous types of road accidents as the information about the vehicle responsible for the accident remain unknown. Therefore, any mechanism which can provide information about the impacting vehicle in hit-and-run accidents will be useful in planning and executing preventive measures to address this road menace. Since there exist several models to predict the impacting unknown vehicle, it becomes important to find which is the most accurate amongst those available. This research applies a process-based approach that identifies the most accurate model out of six supervised learning classification models viz. Logistic Reasoning, Linear Discriminant Analysis, Naive Bayes, classification and regression trees, k-Nearest Neighbor and Support Vector Machine. These models are implemented using five-fold and ten-fold cross validation, on road accident data collected from five mid-sized Indian cities: Agra, Amritsar, Bhopal, Ludhiana, and Vizag (Vishakhapatnam). This study investigates the possible input factors that may have effect on the performance of applied models. Based on the results of the experiment conducted in this study, Support Vector Machine has been found to have the maximum potentiality to predict unknown impacting vehicle type in hit-and-run accidents for all the cities except Amritsar. The result indicates that, classification and regression trees have maximum accuracy, for Amritsar. Naive Bayes performed very poorly for the five cities. These recommendations will help in predicting unknown impacting vehicles in hit-and-run accidents. The outcome is useful for transportation authorities and policymakers to implement effective road safety measu

关键词： Hit-and-run Road accident Vehicle type prediction Support vector machine Linear discriminant analysis Naive Bayes classification and regression tree k-Nearest neighbor Cross validation Road user safety

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An integration of geospatial and machine learning techniques for mapping groundwater potential: a case study of the Shipra river basin, India

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ARABIAN JOURNAL OF GEOSCIENCES 2021年第16期14卷 1645-1645页

作者： Patidar, Ruchir Pingale, Santosh Murlidhar Khare, Deepak Indian Inst Technol Dept Water Resources Dev & Management Roorkee Uttar Pradesh India Natl Inst Hydrol Hydrol Invest Div Roorkee Uttar Pradesh India

Groundwater is an important component of the hydrologic cycle and its significance is quite high due to the lack of surface water and is an important source of fresh water. The amount of surface water alone is not enough to meet the demands of increasing population and increased needs for different purposes due to technological advances. Hence, the need of the hour is to increase groundwater sources and manage them effectively for their sustainable growth. Therefore, the main objective of this study is to map groundwater potential (GWP) zones of the Shipra river basin in India using advanced machine learning and geospatial techniques. Nine factors were used as effective factors such as slope degree, altitude, plan curvature, topographic wetness index (TWI), profile curvature, topographic factor, drainage density, slope aspect, and land use/land cover. The models adopted in this study were classification and regression tree (CART), boosted regression tree (BRT), and random forest (RF). The integrated results of GIS and machine learning techniques were proved to be effective and successful in predicting GWP zones. The area under the curve (AUC) of three models namely BRT, CART, and RF came out to be 0.841, 0.880, and 0.899, respectively. This indicates that all the models are giving a good performance for the GWP zone mapping (>0.80). This study also found that the best technique for prediction is random forest followed by CART and BRT in the case of Shipra river basin. Therefore, this study outcome can prove to be beneficial for effective management, protection, and exploration of groundwater prospects for the different stakeholders.

关键词： Groundwater potential zones classification and regression tree Machine learning techniques Boosted regression tree Random forest GIS

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Severe Convection Weather Identification Model Based on Rough Set Theory and Artificial Fish Swarm Algorithm

Severe Convection Weather Identification Model Based on Roug...

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第40届中国控制会议

作者： Jianlin Guo Zhiying Lu Chenyi Wang Haopeng Wu Xudong Ding School of Electrical and Information Engineering Tianjin University

In order to solve the problem of identifying severe convection weather, this paper proposes a model of identifying severe convection weather based on rough set theory and artificial fish swarm algorithm. First, based on the radar reflectivity images, the echo attributes of the severe convection area are analyzed and extracted. Second, this paper proposes a discretization method based on rough set theory. This method discretizes the meteorological data and establishes a decision table. Third, the improved artificial fish swarm algorithm is used to reduce attributes and obtain the minimum attribute set. Finally, the classification and regression tree（CART） algorithm and the cost-complexity pruning（CCP） principle are used to establish an objective model for identifying severe convection weather. Experimental results show that the model in this paper can recognize severe convection weather under the condition of a few attributes and the identification model has high accuracy.

关键词： Severe convection rough set attribute reduction artificial fish swarm algorithm classification and regression tree

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