Integration of Forecasting, Scheduling, Machine Learning, and Efficiency Improvement Methods into the Sport Management Industry

Sport management is a complicated and economically impactful industry and involves many crucial decisions: such as which players to retain or release, how many concession vendors to add, how many fans to expect, what teams to schedule, and many others are made each offseason and changed frequently. The task of making such decisions effectively is difficult, but the process can be made easier using methods of industrial and systems engineering (ISE). Integrating methods such as forecasting, scheduling, machine learning, and efficiency improvement from ISE can be revolutionary in helping sports organizations and franchises be consistently successful. Research shows areas including player evaluation, analytics, fan attendance, stadium design, accurate scheduling, play prediction, player development, prevention of cheating, and others can be improved when ISE methods are used to target inefficient or wasteful areas

Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

Marital satisfaction, communication and coping strategy among Malaysian married couples: factors prediction and model testing

Empirical evidences on the understanding of marital satisfaction as a multidimensional construct in the context of a multicultural multireligious society are found to be limited. In this study, an attempt has been made to predict and hypothesize the factors in influencing marital satisfaction, communication and coping strategy in marriage among Malaysian married couples. Using 5-Likert scale agreement, the new Marital Satisfaction Scale has been constructed and administered among 150 respondents in a cross-sectional survey, Descriptive statistics and backward Multiple Regression Analysis (MRA) were adopted to ensure the models were estimated based on the contributions from each predictor to determine the model specification. The results have shown that the best predicted models of Marital Satisfaction and Communication are explained by 76% of variance while the prediction of Coping strategy is explained by 30% of variance. This study has recognized the usefulness of multiple regression analysis in model testing before further research on model prediction via Structural Equation Modeling is conducted. A newly hypothesized Marital Satisfaction Model was initiated by integrating the VSA Model of Marriage (Karney and Bradburry, 1995). This study is significant in contributing to pre/post-marital education and counseling fields as well as in crafting a better intervention strategy to promote a more satisfying marital institution

The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge

Measuring Well-Being: A Review of Instruments

Interest in the study of psychological health and well-being has increased significantly in recent decades. A variety of conceptualizations of psychological health have been proposed including hedonic and eudaimonic well-being, quality-of-life, and wellness approaches. Although instruments for measuring constructs associated with each of these approaches have been developed, there has been no comprehensive review of well-being measures. The present literature review was undertaken to identify self-report instruments measuring well-being or closely related constructs (i.e., quality of life and wellness) and critically evaluate them with regard to their conceptual basis and psychometric properties. Through a literature search, we identified 42 instruments that varied significantly in length, psychometric properties, and their conceptualization and operationalization of well-being. Results suggest that there is considerable disagreement regarding how to properly understand and measure well-being. Research and clinical implications are discussed