scholarly journals Differences in Cloud Radar Phase and Power in Co- and Cross-Channel—Indicator of Lightning

2021 ◽  
Vol 13 (3) ◽  
pp. 503
Author(s):  
Zbyněk Sokol ◽  
Jana Popová
Keyword(s):  
Power Spectra ◽  
Regression Tree ◽  
Radar Data ◽  
Tree Model ◽  
Cloud Radar ◽  
Critical Success ◽  
Lightning Discharges ◽  
First Time ◽  
Critical Success Index ◽  
Regression Tree Model

Thunderstorms and especially induced lightning discharges have still not been fully understood, although they are known to cause many casualties yearly worldwide. This study aims at filling the gap of knowledge by investigating the potential of phase and power of the co- and cross-channels of a vertical cloud radar to indicate lightning close to the radar site. We performed statistical and correlation analyses of vertical profiles of phase and power spectra in the co- and the cross-channel for 38 days of thunderstorms producing lightning up to 20 km from the radar in 2018–2019. Specifically, we divided the dataset into “near” and “far” data according to the observed distance of lightning to the radar and analyzed it separately. Although the results are quite initial given the limited number of “near” data, they clearly showed different structures of “near” and “far” data, thus confirming the potential of radar data to indicate lightning. Moreover, for the first time in this study the predictability of lightning using cloud radar quantities was evaluated. We applied a Regression Tree Model to diagnose lightning and verified it using Receiver Operating Characteristic (ROC) and Critical Success Index (CSI). ROC provided surprisingly good results, while CSI was not that good but considering the very rare nature of lightning its values are high as well.

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 A Dynamic Adjustment Model of Cruising Taxicab Fleet Size Combined the Operating and Flied Survey Data

2020 ◽  
Vol 12 (7) ◽  
pp. 2776 ◽  
Author(s):  
Xiaofei Ye ◽  
Min Li ◽  
Zhongzhen Yang ◽  
Xingchen Yan ◽  
Jun Chen
Keyword(s):  
Waiting Time ◽  
Layer Structure ◽  
Operating Time ◽  
Regression Tree ◽  
Threshold Value ◽  
Dynamic Adjustment ◽  
Tree Model ◽  
Fleet Size ◽  
Average Waiting Time ◽  
Regression Tree Model

Due to the lack of adjustment index systems for taxi fleet sizes in China, this paper used the taxi operating datasets from Ningbo City and established a regression tree model to consider the endogenous indicators that affect taxi fleet sizes. Then, a dynamic adjustment mechanism of taxi fleet sizes was proposed by combining the exogenous and endogenous indicators. The importance of the exogenous and endogenous indicators was sorted using the Delphi method. The threshold value of each indicator was also given. The results indicated that (1) in the three-layer structure of the regression tree model, the mileage utilization had the strongest effect on the fleet size of taxis, and the F statistic was 63.73; followed by the average daily revenue of a single taxi, the average waiting time to catch a single taxi, the average operating time of a single taxi, and the revenue per 100 km. The overall accuracy of the model was found to be valid. (2) When the mileage utilization was less than 0.6179 and the average daily revenue of a single taxi was less than 798.38 Yuan, the fleet size of cruising taxis was surplus and should be reduced by 362 vehicles. (3) When the mileage utilization was more than 0.6774 and the average waiting time to catch a single taxi was more than 259.09 s, the fleet size of cruising taxis was insufficient, and we suggest an increase of 463 taxis.

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:

scholarly journals Negatively phrased items of the Autism Spectrum Quotient function differently for groups with and without autism

Autism ◽  
2019 ◽  
Vol 23 (7) ◽  
pp. 1752-1764 ◽  
Author(s):  
Joost A Agelink van Rentergem ◽  
Anne Geeke Lever ◽  
Hilde M Geurts
Keyword(s):  
Differential Item Functioning ◽  
Latent Trait ◽  
Regression Tree ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Autism Spectrum Quotient ◽  
Tree Model ◽  
Item Functioning ◽  
Regression Tree Model ◽  
Group Comparisons

The Autism Spectrum Quotient is a widely used instrument for the detection of autistic traits. However, the validity of comparisons of Autism Spectrum Quotient scores between groups may be threatened by differential item functioning. Differential item functioning entails a bias in items, where participants with equal values of the latent trait give different answers because of their group membership. In this article, items of the Autism Spectrum Quotient were studied for differential item functioning between different groups within a single sample ( N = 408). Three analyses were conducted. First, using a Rasch mixture model, two latent groups were detected that show differential item functioning. Second, using a Rasch regression tree model, four groups were found that show differential item functioning: men without autism, women without autism, people 50 years and younger with autism, and people older than 50 years with autism. Third, using traditional methods, differential item functioning was detected between groups with and without autism. Therefore, group comparisons with the Autism Spectrum Quotient are at risk of being affected by bias. Eight items emerged that consistently show differences in response tendencies between groups across analyses, and these items were generally negatively phrased. Two often-used short forms of the Autism Spectrum Quotient, the AQ-28 and AQ-10, may be more suitable for group comparisons.

scholarly journals Comparing Machine Learning Approaches for Predicting Spatially Explicit Life Cycle Global Warming and Eutrophication Impacts from Corn Production

2020 ◽  
Vol 12 (4) ◽  
pp. 1481 ◽  
Author(s):  
Xiaobo Xue Romeiko ◽  
Zhijian Guo ◽  
Yulei Pang ◽  
Eun Kyung Lee ◽  
Xuesong Zhang
Keyword(s):  
Global Warming ◽  
Life Cycle ◽  
Predictive Accuracy ◽  
Regression Tree ◽  
Gradient Boosting ◽  
Spatially Explicit ◽  
Tree Model ◽  
Corn Production ◽  
County Scale ◽  
Regression Tree Model

Agriculture ranks as one of the top contributors to global warming and nutrient pollution. Quantifying life cycle environmental impacts from agricultural production serves as a scientific foundation for forming effective remediation strategies. However, methods capable of accurately and efficiently calculating spatially explicit life cycle global warming (GW) and eutrophication (EU) impacts at the county scale over a geographic region are lacking. The objective of this study was to determine the most efficient and accurate model for estimating spatially explicit life cycle GW and EU impacts at the county scale, with corn production in the U.S.’s Midwest region as a case study. This study compared the predictive accuracies and efficiencies of five distinct supervised machine learning (ML) algorithms, testing various sample sizes and feature selections. The results indicated that the gradient boosting regression tree model built with approximately 4000 records of monthly weather features yielded the highest predictive accuracy with cross-validation (CV) values of 0.8 for the life cycle GW impacts. The gradient boosting regression tree model built with nearly 6000 records of monthly weather features showed the highest predictive accuracy with CV values of 0.87 for the life cycle EU impacts based on all modeling scenarios. Moreover, predictive accuracy was improved at the cost of simulation time. The gradient boosting regression tree model required the longest training time. ML algorithms demonstrated to be one million times faster than the traditional process-based model with high predictive accuracy. This indicates that ML can serve as an alternative surrogate of process-based models to estimate life-cycle environmental impacts, capturing large geographic areas and timeframes.

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