bootstrapping method
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AMBIO ◽  
2021 ◽  
Author(s):  
Runsheng Song ◽  
Dingsheng Li ◽  
Alexander Chang ◽  
Mengya Tao ◽  
Yuwei Qin ◽  
...  

AbstractSpecies Sensitivity Distribution (SSD) is a key metric for understanding the potential ecotoxicological impacts of chemicals. However, SSDs have been developed to estimate for only handful of chemicals due to the scarcity of experimental toxicity data. Here we present a novel approach to expand the chemical coverage of SSDs using Artificial Neural Network (ANN). We collected over 2000 experimental toxicity data in Lethal Concentration 50 (LC50) for 8 aquatic species and trained an ANN model for each of the 8 aquatic species based on molecular structure. The R2 values of resulting ANN models range from 0.54 to 0.75 (median R2 = 0.69). We applied the predicted LC50 values to fit SSD curves using bootstrapping method, generating SSDs for 8424 chemicals in the ToX21 database. The dataset is expected to serve as a screening-level reference SSD database for understanding potential ecotoxicological impacts of chemicals.


Author(s):  
Paweł Piepiora ◽  
Zbigniew Piepiora

The aim of the study is to describe personality profiles and determinants of success in sports in relation to the Big Five Personality Model. In order to achieve this aim, personality profiles of players from various sports disciplines was set against the personality profile of champions—players who are considerably successful in sports competitions. Subsequently, an attempt was made to determine which personality traits significantly determine belonging to the group of champions—and therefore determine success in sport. The participants were men aged between 20 and 29 from the Polish population of sportsmen. A total of 1260 athletes were tested, out of whom 118 were qualified to the champions sample—those athletes had significant sports achievements. The research used the NEO-FFI Personality Questionnaire. Basic descriptive statistics, a series of Student’s t-tests for independent samples using the bootstrapping method, as well as a logistic regression model were performed. In relation to other athletes, champions were characterized by a lower level of neuroticism and a higher level of extraversion, openness to experience, agreeableness, and conscientiousness. An important personality determinant was neuroticism: the lower the level of neuroticism, the greater the probability of an athlete being classified as a champion. There are differences between champions and other athletes in all personality dimensions in terms of the Big Five. Based on the result of the research, it can be stated that personality differences should be seen as a consequence of athletes’ success, rather than as a reason for athletes’ success, based on their age between 20 and 29.


2021 ◽  
Vol 34 (3) ◽  
pp. 89-102
Author(s):  
Mahmoudreza Rahbarqazi ◽  
Raza Mahmoudoghli

The present study aims to examine the indirect effects of social media on political distrust among Lebanese citizens using data based on the Arab Barometer Wave V. The Arab Barometer Wave V was obtained in 2018-2019 via which 2,400 Lebanese citizens were surveyed. Using the Preacher and Hayes Bootstrapping method, the results of the test the hypotheses indicate that, firstly, social media has a positive effect on citizens’ political distrust and causes the increase in their level of distrust in political institutions with the mediator variables corruption perception and poor government performance; and secondly, the results show that although the lack of guaranteed freedoms has a positive effect on increasing political distrust in society, this variable cannot mediate the relationship between social media and political distrust among Lebanese citizens.


2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Can Chen ◽  
Yumin Cao ◽  
Keshuang Tang ◽  
Keping Li

Dynamic path flows, referring to the number of vehicles that choose each path in a network over time, are generally estimated with the partial observations as the input. The automatic vehicle identification (AVI) system and probe vehicle trajectories are now popular and can provide rich and complementary trip information, but the data fusion was rarely explored. Therefore, in this paper, the dynamic path flow estimation is based on these two data sources and transformed into a feature learning problem. To fuse the two data sources belonging to different detection ways at the data level, the virtual AVI points, analogous to the real AVI points (turning movements at nodes with AVI detectors), are defined and selected to statically observe the dynamic movement of the probe vehicles. The corresponding selection principles and a programming model considering the distribution of real AVI points are first established. The selected virtual AVI points are used to construct the input tensor, and the turning movement-based observations from both the data sources can be extracted and fused. Then, a three-dimensional (3D) convolutional neural network (CNN) model is designed to exploit the hidden patterns from the tensor and establish the high-dimensional correlations with path flows. As the path flow labels commonly with noises, the bootstrapping method is adopted for model training and the corresponding relabeling principle is defined to purify the noisy labels. The entire model is extensively tested based on a realistic road network, and the results show that the designed CNN model with the presented data fusion method can perform well in training time and estimation accuracy. The robustness of a model to noisy labels is also improved through the bootstrapping method. The dynamic path flows estimated by the trained model can be applied to travel information provision, proactive route guidance, and signal control with high real-time requirements.


2020 ◽  
Vol 20 (1) ◽  
pp. 75-84
Author(s):  
Ellys Ellys ◽  
Mei Ie

Job satisfaction is a feeling of pleasure or disappointed employees at work or the company and organizational culture are values ??or norms that are instilled by the company. These two important variables explain organizational commitment in the form of employee desires to maintain its membership in the company. The main purpose of this study is to find out the effect of job satisfaction and organizational culture on employee organizational commitment. The sample of this research was 50 respondents, who are employees in non-managerial positions. Sampling was done by non-probability sampling with purposive sampling. The data analysis method used by researchers was PLS-SEM with bootstrapping method. The results of this study indicated that job satisfaction has a positive effect on organizational commitment and organizational culture has a positive effect on organizational commitment.


2020 ◽  
Author(s):  
Leila Yousefi ◽  
Mashael Al-Luhaybi ◽  
Lucia Sacchi ◽  
Luca Chiovato ◽  
Allan Tucker

Abstract Background: Type 2 Diabetes is a chronic disease with an onset that is commonly associated with multiple life-threatening co morbidities (complications). Early prediction of diabetic complications while discovering the behaviour of associated aggressive risk factors can reduce the patients’ suffering time. Therefore, models of the time series diabetic data (which are often imbalanced, incomplete and involve complex interactions) are needed to better manage diabetic complications. Aims: The aim of this work is to both deals with imbalanced clinical data using a bootstrapping approach, whilst determining the precise position of latent variables within probabilistic networks generated from the observations. The main motivation behind this paper is to stratify patient groups by means of latent variables to discover how complications in diabetes interact. Methods: We propose a time series bootstrapping method for building Dynamic Bayesian Networks that includes hidden / latent variables, applied to a case for predicting T2DM complications. A combination of the IC* algorithm on time series bootstrapped data is utilized to identify the latent variables within Bayesian model. Then, an exploration of inference methods assessed the influences of these latent variables. Results: Our promising findings show how this targeted use of latent variables improves prediction accuracy, specificity, and sensitivity over standard approaches as well as aiding the understanding of relationships between these latent variable sand disease complications / risk factors. The contribution of this paper compared to the previous papers in which time series bootstrapping is used for re-balancing the data and providing confidence in the prediction results. Conclusion: Our results showed that our re-balancing approach by the use of Time Series bootstrapping method for an unequal number of time series visits demonstrated an improvement in the prediction performance. Additionally, the most highlighted contribution of this paper gained insight by interpreting the latent states (looking at the associated distributions of complications), which led to a better understanding of risk factors and patient-specific interventions: here the fact that the latent variable demonstrated that a patient falls into a sub-group that is hypertensive but not suffering from retinopathy.


2020 ◽  
Vol 20 (7) ◽  
pp. 1223-1241
Author(s):  
Ashok Vairavan ◽  
G. Peter Zhang

Purpose This paper aims to reexamine the link between board racial diversity and firm performance. It focuses on the mechanism through which board racial diversity could affect performance. The paper proposes and empirically tests the role of employee productivity and R&D productivity in the relationship between board racial diversity and firm financial performance. Design/methodology/approach The paper adopts a mediation analysis framework with the bootstrapping method to test both the direct and indirect effect of board diversity on firm performance. The data used in the study come from S&P 1500 with variables composed from COMPUSTAT, Institutional Shareholder Services and Wharton Research Data Services. Findings Contrary to prior findings, the results indicate that there is neither direct effect of board racial diversity on firm performance nor is there an indirect effect through either employee productivity or R&D productivity. Research limitations/implications Because the data used in the paper are based on large public firms, the results may not generalize to small or private firms. Practical implications The findings of the paper suggest that not all diversity measures matter in the same way and firms should carefully make board appointments to reduce the perception that they select directors for any reason other than qualifications. Originality/value The paper advances the literature on board diversity by examining two previously unexplored mediating variables of employee productivity and R&D productivity. It also uses more rigorous mediation analysis with bootstrapping method and a validation sample to improve robustness of the results.


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