The relationship between meteorological factors and mumps based on Boosted regression tree model

2019 ◽  
Vol 695 ◽  
pp. 133758 ◽  
Author(s):  
Dandan Zhang ◽  
Yuming Guo ◽  
Shannon Rutherford ◽  
Chang Qi ◽  
Xu Wang ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Regression Tree ◽  
Tree Model ◽  
Boosted Regression Tree ◽  
The Relationship ◽  
Regression Tree Model

scholarly journals A global bibliometric perspective on soil erosion modelling

2020 ◽  
Author(s):  
Nejc Bezak ◽  
Keyword(s):  
Conference Proceedings ◽  
Soil Erosion ◽  
Regression Tree ◽  
Tree Model ◽  
Boosted Regression Tree ◽  
Field Activity ◽  
Erosion Modelling ◽  
Meta Information ◽  
Number Of Citations ◽  
Regression Tree Model

<p>Systematic bibliometric investigations are useful to evaluate and compare the scientific impact of journal papers, book chapters and conference proceedings. Such studies allow the detection of emerging research topics, the analyses of cooperation networks, and the collection of in-depth insights into a specific research topic. In the presented work, we carried out a bibliometric study in order to obtain an in-depth knowledge on soil erosion modelling applications worldwide.</p><p>As a starting point, we used the soil erosion modelling meta-analysis data collection generated by the authors of this abstract in a joint community effort. This database contains meta-information of more than 3,000 documents published between 1994 and 2018 that are indexed in the SCOPUS database. The documents were reviewed and database entries verified. The database contains various types of meta-information about the modelling studies (e.g., model used, study area, input data, calibration, etc.). The bibliometric information was also included in the database (e.g., number of citations, type of publication, Scopus category, etc.). We investigated differences among publication types and differences between papers published in journals that are part of various Scopus categories. Moreover, relationships between publication CiteScore, number of authors, and number of citations were analyzed. A boosted regression tree model was used to detect the relative impact of the selected meta-information such as erosion model used, spatial modelling scale, study period, field activity on the total number of citations. Detailed investigation of the most cited papers was also conducted. The VOSviewer software was used to analyze citations, co-citations, bibliographic coupling, and co-authorship networks of the database entries.  </p><p>Our bibliometric investigations demonstrated that journal publications, on average, receive more citations than book series or conference proceedings. There were differences among the erosion models used, and some specific models such as the WaTEM/SEDEM model, on average, receive more citations than other models (e.g., USLE). It should also be noted that self-citation rates in case of most frequently used models were similar. Global studies, on average, receive more citations than studies dealing with plot, regional, or national scales. According to the boosted regression tree model, model calibration, validation, or field activity do not have significant impact on the obtained publication citations. Co-citation investigation revealed some interesting patterns. Our results also indicate that papers about soil erosion modeling also attract citations from different fields and better international cooperation is needed to advance this field of research with regard to its visibility and impact on human societies.    </p>

scholarly journals Research on the Relationship Between Teachers’ Professional Skills and Students’ Expectations for Improving the Study Environment

2018 ◽  
Vol 4 (8) ◽  
pp. 109
Author(s):  
Aida Stikliene
Keyword(s):  
Regression Tree ◽  
Point Of View ◽  
Learning Problems ◽  
Data Collection Tool ◽  
Tree Model ◽  
Professional Skills ◽  
Working Students ◽  
Variate Analysis ◽  
The Relationship ◽  
Regression Tree Model

The teacher's attitude towards the teaching process and communication skills is of particular importance and plays a crucial role in today’s rapidly changing world. It has to go together, raising consciousness and awareness of individuals on study environment issues and ensuring that they contribute to solutions of learning problems. The research was conducted with 405 prospective professionals from the Faculty of Forest Sciences and Ecology, Aleksandras Stulginskis University. An interactive questionnaire ‘Study subject in student’s eyes’ (SSSE) developed at Aleksandras Stulginskis University (2014–2017) was used as the data collection tool. This article analyses the teachers’ pedagogical work from the students’ point of view. The multi-variate analysis and regression tree model were used in the interpretation of results. The results confirmed the hypothesis that hard working students better evaluate teachers’ professional skills. It seems that elder course students with age have higher expectations from the teaching environment. Keywords:

Net Losses and the Relationship between Auditor Independence and Client Importance: Evidence from a Cubist Regression-Tree Model

2017 ◽  
Vol 14 (1) ◽  
pp. 13-25 ◽  
Author(s):  
Yu-Cheng Lin ◽  
Yu-Hsin Lu ◽  
Fang-Chi Lin ◽  
Yi-Chen Lu
Keyword(s):  
Positive Relationship ◽  
Regression Tree ◽  
Financial Statements ◽  
Discretionary Accruals ◽  
Auditor Independence ◽  
Tree Model ◽  
Audit Risk ◽  
Using Data ◽  
The Relationship ◽  
Regression Tree Model

ABSTRACT This paper uses a cubist regression-tree model to explore when and why auditors compromise their independence. Using data from companies in Taiwan, we study the association between client importance and auditor independence. The results show a positive relationship between client importance and auditor dependence when clients report net losses in the current year. We also find that auditors allow more important clients to manage their discretionary accruals slightly upward, but the clients still report net losses on their financial statements. This suggests that auditors may impair their independence for clients with certain characteristics and acceptable levels of audit risk.

The research of the relationship between teachers’ professional skills and student expectations improving study environment

2017 ◽  
Vol 4 (6) ◽  
pp. 196-201
Author(s):  
Aida Stikliene ◽  
Anzelika Dautarte ◽  
Edmundas Bartkevicius ◽  
Remigijus Zalkauskas ◽  
Algirdas Gavenauskas
Keyword(s):  
Regression Tree ◽  
Point Of View ◽  
Learning Problems ◽  
Data Collection Tool ◽  
Student Expectations ◽  
Tree Model ◽  
Professional Skills ◽  
Working Students ◽  
The Relationship ◽  
Regression Tree Model

The teacher’s attitude towards the teaching process and communication skills is of particular importance and plays a crucial role in today’s rapidly changing world. It has to go together with raising the consciousness and awareness of individuals on study environment issues and ensuring them contribute to solutions of learning problems. Research was conducted with 405 prospective professionals who study at Aleksandras Stulginskis University Faculty of Forest Sciences and Ecology. An interactive questionnaire ‘Study subject in student’s eyes’ developed at Aleksandras Stulginskis University (2014–2017) was used as data collection tool. This article analyses the teacher’s pedagogical work from the student’s point of view. Multivariate analysis and regression tree model were used in the interpretation of the results. The results confirmed the hypothesis that hard-working students better evaluate teachers’ professional skills. It seems that elder course students with age have higher expectations from the teaching environment. Keywords: teacher’s attitude, Aleksandras Stulginskis University, pedagogical

Relationship between neighborhood land use structure and the spatiotemporal pattern of PM2.5 at the microscale: Evidence from the central area of Guangzhou, China

2021 ◽  
pp. 239980832110078
Author(s):  
Jie Song ◽  
Suhong Zhou ◽  
Yinong Peng ◽  
Jianbin Xu ◽  
Rongping Lin
Keyword(s):  
Land Use ◽  
Regression Tree ◽  
Spatiotemporal Patterns ◽  
Classification And Regression Tree ◽  
Tree Model ◽  
Land Use Structure ◽  
Land Use Types ◽  
Classification And Regression ◽  
The Relationship ◽  
Regression Tree Model

Fine particulate matter (PM2.5) is harmful to human health. Although the relationship between urban land use and PM2.5 has been studied in recent years, there has been little consideration of the relationship between land use structure and PM2.5 spatiotemporal patterns at the microscale. Based on mobile monitoring PM2.5 data and point of interest data, this paper explored their relationship with a classification and regression tree model. The results showed that PM2.5 exhibits spatiotemporal heterogeneity at the microscale. The neighborhoods’ land use structure can explain 60.4% of the PM2.5 spatiotemporal patterns. Transportation and ecology are the two most significant land use types that correlated with PM2.5 spatiotemporal patterns. Fourteen rules of neighborhood land use structures with different land use types are identified land use structure which leads to different spatiotemporal patterns of PM2.5. The higher the PM2.5 risk, the stronger the correlation with neighborhood land use structure is. The classification and regression tree model can be effectively used to judge the relationship between neighborhood land use structure and PM2.5 spatiotemporal patterns. The results provide a basis for developing appropriate measures, based on local conditions, to predict PM2.5 pollution levels at the microscale, and reduce the risk of neighborhood exposure to PM2.5.

scholarly journals Spatial distribution of soil organic carbon stocks in France

2010 ◽  
Vol 7 (6) ◽  
pp. 8409-8443 ◽  
Author(s):  
M. P. Martin ◽  
M. Wattenbach ◽  
P. Smith ◽  
J. Meersmans ◽  
C. Jolivet ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Regression Tree ◽  
Climatic Variables ◽  
Tree Model ◽  
Boosted Regression Tree ◽  
Regression Tree Model

Abstract. Soil organic carbon plays a major role in the global carbon budget, and can act as a source or a sink of atmospheric carbon, whereby it can influence the course of climate change. Changes in soil organic soil stocks (SOCS) are now taken into account in international negotiations regarding climate change. Consequently, developing sampling schemes and models for estimating the spatial distribution of SOCS is a priority. The French soil monitoring network has been established on a 16 km × 16 km grid and the first sampling campaign has recently been completed, providing circa 2200 measurements of stocks of soil organic carbon, obtained through an in situ composite sampling, uniformly distributed over the French territory. We calibrated a boosted regression tree model on the observed stocks, modelling SOCS as a function of other variables such as climatic parameters, vegetation net primary productivity, soil properties and land use. The calibrated model was evaluated through cross-validation and eventually used for estimating SOCS for the whole of metropolitan France. Two other models were calibrated on forest and agricultural soils separately, in order to assess more precisely the influence of pedo-climatic variables on soil organic carbon for such soils. The boosted regression tree model showed good predictive ability, and enabled quantification of relationships between SOCS and pedo-climatic variables (plus their interactions) over the French territory. These relationship strongly depended on the land use, and more specifically differed between forest soils and cultivated soil. The total estimate of SOCS in France was 3.260 ± 0.872 PgC for the first 30 cm. It was compared to another estimate, based on the previously published European soil organic carbon and bulk density maps, of 5.303 PgC. We demonstrate that the present estimate might better represent the actual SOCS distributions of France, and consequently that the previously published approach at the European level greatly overestimates SOCS.

scholarly journals Spatial distribution of soil organic carbon stocks in France

2011 ◽  
Vol 8 (5) ◽  
pp. 1053-1065 ◽  
Author(s):  
M. P. Martin ◽  
M. Wattenbach ◽  
P. Smith ◽  
J. Meersmans ◽  
C. Jolivet ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Regression Tree ◽  
Tree Model ◽  
Boosted Regression Tree ◽  
Regression Tree Model ◽  
Soc Stocks

Abstract. Soil organic carbon plays a major role in the global carbon budget, and can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Changes in soil organic carbon (SOC) stocks are now taken into account in international negotiations regarding climate change. Consequently, developing sampling schemes and models for estimating the spatial distribution of SOC stocks is a priority. The French soil monitoring network has been established on a 16 km × 16 km grid and the first sampling campaign has recently been completed, providing around 2200 measurements of stocks of soil organic carbon, obtained through an in situ composite sampling, uniformly distributed over the French territory. We calibrated a boosted regression tree model on the observed stocks, modelling SOC stocks as a function of other variables such as climatic parameters, vegetation net primary productivity, soil properties and land use. The calibrated model was evaluated through cross-validation and eventually used for estimating SOC stocks for mainland France. Two other models were calibrated on forest and agricultural soils separately, in order to assess more precisely the influence of pedo-climatic variables on SOC for such soils. The boosted regression tree model showed good predictive ability, and enabled quantification of relationships between SOC stocks and pedo-climatic variables (plus their interactions) over the French territory. These relationships strongly depended on the land use, and more specifically, differed between forest soils and cultivated soil. The total estimate of SOC stocks in France was 3.260 ± 0.872 PgC for the first 30 cm. It was compared to another estimate, based on the previously published European soil organic carbon and bulk density maps, of 5.303 PgC. We demonstrate that the present estimate might better represent the actual SOC stock distributions of France, and consequently that the previously published approach at the European level greatly overestimates SOC stocks.

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