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Abstract Background Gestational diabetes mellitus (GDM) is a common pregnancy complication with long-term health consequences for mothers and their children. The escalating trends of GDM coupled with the growing prevalence of maternal obesity, a significant GDM risk factor projected to approach nearly 60% by 2030 in Kansas, has emerged as a pressing public health issue. Methods The aim of this study was to compare GDM and maternal obesity trends in rural and urban areas and investigate maternal demographic characteristics influencing the risk of GDM development over a 15-year period. Trend analyses and a binary logistic regression were employed utilizing 2005 to 2019 de-identified birth record vital statistics from the Kansas Department of Health and Environment (N = 589,605). Results Over the cumulative 15-year period, a higher prevalence of GDM was observed across age, race/ethnicity, education, and insurance source. Throughout this period, there was an increasing trend in both GDM and obese pre-pregnancy BMI age-adjusted prevalence, with noticeable rural-urban disparities. From 2005 to 2019, women, including Asians (OR: 2.73, 95% CI 2.58%-2.88%), American Indian or Alaskan Natives (OR: 1.58, 95%, CI 1.44-1.73%), Hispanics (OR: 1.42, 95% CI 1.37%-1.48%), women residing in rural areas (OR: 1.09, 95%, CI 1.06-1.12%), with advanced maternal age (35–39 years, OR: 4.83 95% CI 4.47%-5.22%; ≥40 years, OR: 6.36 95%, CI 5.80-6.98%), with lower educational status (less than high school, OR: 1.15, 95% CI 1.10%-1.20%; high school graduate, OR: 1.10, 95% CI 1.06%-1.13%), Medicaid users (OR: 1.10, 95% CI 1.06%-1.13%), or with an overweight (OR: 1.78, 95% CI 1.72%-1.84%) or obese (OR: 3.61, 95% CI 3.50%-3.72%) pre-pregnancy BMI were found to be at an increased risk of developing GDM. Conclusions There are persistent rural-urban and racial/ethnic disparities present from 2005 to 2019 among pregnant women in Kansas with or at-risk of GDM. There are several socioeconomic factors that contribute to these health disparities affecting GDM development. These findings, alongside with prominent rising maternal obesity trends, highlight the need to expand GDM services in a predominantly rural state, and implement culturally-responsive interventions for at-risk women.

Childhood and adolescent obesity is a serious health problem that is on the rise at the global level. Earlier, certain diseases such as Type 2 diabetes, high blood pressure, and heart disease affected only adults, but now they are being detected in young children as well. Several studies based on machine learning have been proposed to develop obesity prediction models or to determine key determinants of obesity for designing intervention tools. Despite having a rich and diverse set of literature on obesity prediction models, obesity rates are at an all-time high for both children and adolescents. There is a need of proper understanding and critical analysis of existing machine learning models in order to design effective strategies for curbing obesity at childhood and adolescent level. This paper surveys the growing body of recent literature on machine and deep learning models for obesity prediction by providing a coherent view (critical analysis) of the limitations of the existing systems. The taxonomy of the existing literature on obesity prediction into methods used, predicted outcome, factors used, type of datasets, and the associated purpose, is discussed for analysis of the state-of-the-art. This analysis revealed that a) prediction-focused models do not use variables from as many domains as predictor-focused models do, b) very few studies proposed gender-specific and race-specific obesity prediction models, c) lack of large-scale multimodal datasets and d) existing predictor-focused models obtain an accuracy range of [53.7%, 96%] with an optimum set of predictors. Further, computer vision-based methods for obesity prediction and interpretable techniques for understanding the outcome of the models are discussed as well. In addition, we have also identified novel research directions. The overall aim is to advance the state-of-the-art and improve the quality of discourse in this field.

The progress of logistics in a company has been meaningful during the last decades due to the possibilities of achieving a competitive advantage trough it. Nowadays, there is a chance to recover and take economical advantage of those products that no longer satisfy the needs of the customer. This generates a flow directed to the manufacturer, and it is called Reverse Logistics. The aim of this paper is to relate some reverse logistics applications with the firm's competitive strategies, by means of a thorough review of the associated literature, and the results of a study conducted among a sample of companies from Barranquilla, Colombia (including newspapers, bakeries, chemicals, and drug distributors industries). This sample showed no evidence of Reverse Logistics significant use as a driving force that makes possible for the firms to stay in the market, and suggests that local market conditions do not meet competitive levels requiring this kind of competencies as a differentiator, or furthermore, the generalized absence of knowledge on Reverse Logistics topics by the local firms keeps them from taking advantage of its potentials benefits. [ABSTRACT FROM AUTHOR]

The performance of a production line will always be an important subject of analysis. In this document, a progressive analysis is experimentally made, based on the simulation of several scenarios That consist of a combination of some variables in different levels. In order to appreciate their interaction, stochasticity in processing times are taken into account for the definition of each scenario, with the objective of observing and inquiring into the relation of the variation coefficient of processing times with the one previously obtained in prior research. [ABSTRACT FROM AUTHOR]