Online Webcast Demand vs. Offline Spectating Channel Demand (Stadium and TV) in the Professional Sports League

This study investigates the online spectating behavior of sports fans. Due to the great mobility and low opportunity/switching costs, webcast sports fans’ spectating behaviors are distinct from those associated with traditional spectating channels such as stadium attendance or TV viewership. We explore the unique characteristics of online webcast demand in professional sports leagues by rigorously modeling all three spectating choices of sports fans. To consider the substitute relationship of the three spectating choices simultaneously, we employ a BLP (Berry–Levinsohn–Pakes)-style random coefficient model. For the analysis, we collect a comprehensive game-level dataset from the Korean Professional Baseball Organization (KBO) League fan samples from three different channels: online webcast viewership, stadium attendance, and TV viewership. We find that the demand for online webcasts is distinctive compared to that of traditional spectating channels. Notably, we find that the impact of team performance is three times stronger than that of TV viewership demand and that the impact of game quality is four times stronger than that of attendance demand. In contrast, a nonperformance variable is relatively less effective in attracting sports fans to online broadcasting. Furthermore, we find evidence of a strong retention effect of online webcast viewers. Our findings indicate that the previous spectating experience of online webcasts increases the next-time choice of sports fans for the webcast because the genuine spectating experience with distinctive webcast services (such as real-time interactive communication or various supplementary programs) can induce consumers to revisit the channel.

Bayesian Multilevel Analysis of Utilization of Antenatal Care Services in Ethiopia

In sub-Saharan Africa, 72% of pregnant women received an antenatal care visit at least once in their pregnancy period. Ethiopia has one of the highest rates of maternal mortality in sub-Saharan African countries. So, this high maternal mortality levels remain a major public health problem. According to EDHS, 2016, the antenatal care (ANC), delivery care (DC), and postnatal care (PNC) were 62%, 73%, and 13%, respectively, indicating that ANC is in a low level. The main objective of this study was to examine the factors that affect the utilization of antenatal care services in Ethiopia using Bayesian multilevel logistic regression models. The data used for this study comes from the 2016 Ethiopian Demographic and Health Survey which was conducted by the Central Statistical Agency (CSA). The statistical method of data analysis used for this study is the Bayesian multilevel binary logistic regression model in general and the Bayesian multilevel logistic regression for the random coefficient model in particular. The convergences of parameters are estimated by using Markov chain Monte-Carlo (MCMC) using SPSS and MLwiN software. The descriptive result revealed that out of the 7171 women who are supposed to use ANC services, 2479 (34.6%) women were not receiving ANC services, while 4692 (65.4%) women were receiving ANC services. Moreover, women in the Somali and Afar regions are the least users of ANC. Using the Bayesian multilevel binary logistic regression of random coefficient model factors, place of residence, religion, educational attainment of women, husband educational level, employment status of husband, beat, household wealth index, and birth order were found to be the significant factors for usage of ANC. Regional variation in the usage of ANC was significant.

Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction

Both numerical simulations and data-driven methods have been applied in dam’s displacement modeling. For monitored displacement data-driven methods, the physical mechanism and structural correlations were rarely discussed. In order to take the spatial and temporal correlations among all monitoring points into account, we took the first step toward integrating the finite element method into a data-driven model. As the data-driven method, we selected the random coefficient model, which can make each explanatory variable coefficient of all monitoring points following one or several normal distributions. In this way, explanatory variables are constrained. Another contribution of the proposed model is that the actual elastic modulus at each monitoring point can be back-calculated. Moreover, with a Lagrange polynomial interpolation, we can obtain the distribution field of elastic modulus, rather than gaining one value for the whole dam in previous studies. The proposed model was validated by a case study of the concrete arch dam in Jinping-I hydropower station. It has a better prediction precision than the random coefficient model without the finite element method.

Socioeconomic, Demographic, and Environmental Associated with Stunting among Children under Five Years old in Ethiopia: Evidence from Ethiopian Demographic and Health Survey, 2016

Background : Stunting is one of the most serious but least addressed health problems in the world. Adequate nutrition is essential for children’s health and development. Globally it is estimated that, directly or indirectly, for at least 35% of deaths in children less than five years of age. Under nutrition is also a major cause of disability preventing children who survive from reaching their full development potential. Methods: Statistical models that can treat the categorical response variable like binary logistic regression model will be employed. Beside this study will include Socio –economic and demographic factors; Sex and age of child, age of mother, Educational status, occupation, health status, religion, sex of household head, number of children under five years, Household income, family size, land ownership and time of cultivation, income source of household, wealth index as independent variables. Empty model, random intercept and fixed slope with random coefficient are the method of analyzing the dataset. Result: The prevalence of stunting among children ages under five years old were about 49.3%. Months of breastfeeding, educational level, and wealth index, currently pregnant and child food nutrient are significantly associated with stunting presence. The odds of stunting status of child from women who are pregnant is more likely to be stunted 4.157 compared to non-pregnant women controlling for other variables in the model and random effects at level two. Women who feed nutrient food to their child are 1.239 more likely to be stunted (OR=1.239) than women who didn’t feed nutrient food controlling for other variables in the model and random effects at level two. Conclusions : Age of child, breast feeding, sex, pregnant status, and food nutrient were found to be significantly associated with stunting in multilevel modeling of random coefficient model. Finally random coefficient model best fit the EDHS 2016 dataset. Therefore, interventions that focus on breast feeding, period of next pregnancy, food nutrient taken by children are required for improving child stunting in Ethiopia.

Concrete Dam Displacement Prediction Based on an ISODATA-GMM Clustering and Random Coefficient Model

Displacement data modelling is of great importance for the safety control of concrete dams. The commonly used artificial intelligence method modelled the displacement data at each monitoring point individually, i.e., the data correlations between the monitoring points are overlooked, which leads to the over-fitting problem and the limitations in the generalization of model. A novel model combines Gaussian mixture model and Iterative self-organizing data analysing (ISODATA-GMM) clustering and the random coefficient method is proposed in this article, which takes the temporal-spatial correlation among the monitoring points into account. By taking the temporal-spatial correlation among the monitoring points into account and building models for all the points simultaneously, the random coefficient model improves the generalization ability of the model through reducing the number of free model variables. Since the random coefficient model supposed the data follows normal distributions, we use an ISODATA-GMM clustering algorithm to classify the measuring points into several groups according to its temporal and spatial characteristics, so that each group follows one distribution. Our model has the advantage of having a stronger generalization ability.