Sports fans are able to watch games from many locations using TV services while interacting with other fans online. In this paper, we identify the factors that affect sports viewers’ online interactions. Using a large-scale dataset of more than 25 million chat messages from a popular social TV site for baseball, we extract various game-related factors, and investigate the relationships between these factors and fans’ interactions using a series of multiple regression analyses. As a result, we identify several factors that are significantly related to viewer interactions. In addition, we determine that the influence of these factors varies according to the user group; i.e., active vs. less active users, and loyal vs. non-loyal users.
Please scroll down for article-it is on subsequent pagesWith 12,500 members from nearly 90 countries, INFORMS is the largest international association of operations research (O.R.) and analytics professionals and students. INFORMS provides unique networking and learning opportunities for individual professionals, and organizations of all types and sizes, to better understand and use O.R. and analytics tools and methods to transform strategic visions and achieve better outcomes. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org Abstract. Kolon Sport (K/S), a leading outdoor fashion brand in South Korea, must deal with a large variety of items during each selling season. In doing so, the company has encountered a challenging operational problem-the assort-packing and distribution problem. The problem involves making decisions on the optimal method to use in packing a set of different items in a box and allocating the boxes to stores to meet the stores' demands. In this paper, we introduce an analytics project initiated to improve the assortpacking and distribution process, and we describe the formulation and solution approach we developed to solve this industrial problem in a timely manner. We validated the proposed approach by computational and onsite pilot testing, which demonstrated that the decisions made using this approach are superior to those made with the manual method K/S used previously. Inventory is distributed to all stores in a more balanced way by considering the demands of the stores. K/S, which implemented the proposed approach into its internal system in July 2015, estimates that the new system improves sales by approximately eight percent.
The purpose of this study was to investigate the muscle activity ratio of the lower limb according to changes in straight leg raise (SLR) test angles on hamstring muscle shortening during squat exercises. Design: Randomized controlled trial. Methods: The subjects were 14 healthy adults who were informed of and agreed to the method and purpose of the study. The participants were classified into SLR groups according to two angles (over 80° or under 80°) assessed using the SLR tests. After training and practicing the wall squat posture to be applied to the experiment, electromyography (EMG) was used to measure changes in muscle activity during the performance of a wall squat. After stretching, a sequence of pre-stretch tests were performed again, and the active and passive SLR tests were also reconducted; thereafter, a wall squat was performed again by attaching EMG electrodes. The EMG results before and after stretching were compared. Results: The muscle activity of the vastus lateralis oblique muscle increased in both groups. The muscle activity of the vastus medialis oblique muscle decreased in over both group. Rectus femorus activity increased in the under 80-degree groups but decreased in the over 80-degree group. The muscle activity of the biceps femoris muscle decreased after stretching in the over 80-degree group and increased in the under 80-degree group, and the semitendinosus muscle activity after stretching was decreased. The quadriceps-to-hamstring muscle (Q:H) ratio before and after stretching between groups showed that the hamstring muscle ratio decreased after stretching in both groups. Conclusions: The results of this study showed that the Q:H ratio before and after stretching between groups was not significantly different.
While smartphone addiction is becoming a recent concern with the exponential increase in the number of smartphone users, it is difficult to predict problematic smartphone users based on the usage characteristics of individual smartphone users. This study aimed to explore the possibility of predicting smartphone addiction level with mobile phone log data. By Korea Internet and Security Agency (KISA), 29,712 respondents completed the Smartphone Addiction Scale developed in 2017. Integrating basic personal characteristics and smartphone usage information, the data were analyzed using machine learning techniques (decision tree, random forest, and Xgboost) in addition to hypothesis tests. In total, 27 variables were employed to predict smartphone addiction and the accuracy rate was the highest for the random forest (82.59%) model and the lowest for the decision tree model (74.56%). The results showed that users’ general information, such as age group, job classification, and sex did not contribute much to predicting their smartphone addiction level. The study can provide directions for future work on the detection of smartphone addiction with log-data, which suggests that more detailed smartphone’s log-data will enable more accurate results.
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