Instituto Politécnico Nacional

About: Instituto Politécnico Nacional is a education organization based out in Mexico City, Mexico. It is known for research contribution in the topics: Population & Control theory. The organization has 43351 authors who have published 63315 publications receiving 938532 citations. The organization is also known as: Instituto Politécnico Nacional & Instituto Politecnico Nacional.

Exposure to arsenic in drinking water is associated with increased prevalence of diabetes: A cross-sectional study in the Zimapán and Lagunera regions in Mexico

Abstract: Human exposures to inorganic arsenic (iAs) have been linked to an increased risk of diabetes mellitus. Recent laboratory studies showed that methylated trivalent metabolites of iAs may play key roles in the diabetogenic effects of iAs. Our study examined associations between chronic exposure to iAs in drinking water, metabolism of iAs, and prevalence of diabetes in arsenicosis-endemic areas of Mexico. We used fasting blood glucose (FBG), fasting plasma insulin (FPI), oral glucose tolerance test (OGTT), glycated hemoglobin (HbA1c), and insulin resistance (HOMA-IR) to characterize diabetic individuals. Arsenic levels in drinking water and urine were determined to estimate exposure to iAs. Urinary concentrations of iAs and its trivalent and pentavalent methylated metabolites were measured to assess iAs metabolism. Associations between diabetes and iAs exposure or urinary metabolites of iAs were estimated by logistic regression with adjustment for age, sex, hypertension and obesity. The prevalence of diabetes was positively associated with iAs in drinking water (OR 1.13 per 10 ppb, p < 0.01) and with the concentration of dimethylarsinite (DMAsIII) in urine (OR 1.24 per inter-quartile range, p = 0.05). Notably, FPI and HOMA-IR were negatively associated with iAs exposure (β -2.08 and -1.64, respectively, p < 0.01), suggesting that the mechanisms of iAs-induced diabetes differ from those underlying type-2 diabetes, which is typically characterized by insulin resistance. Our study confirms a previously reported, but frequently questioned, association between exposure to iAs and diabetes, and is the first to link the risk of diabetes to the production of one of the most toxic metabolites of iAs, DMAsIII.

Observer-Based Adaptive SMC for Nonlinear Uncertain Singular Semi-Markov Jump Systems With Applications to DC Motor

Abstract: This paper deals with the observer-based adaptive sliding mode control (OBASMC) design for nonlinear uncertain singular semi-Markov jump systems. The system satisfies the singular property and follows a stochastic semi-Markov process related to Weibull distribution. Due to the influence of sensor factors in practical systems, the state vectors are not always known. Additionally, the unavoidable measurement errors in the actual system always lead to the model uncertainties and the unknown nonlinearity. Our attention is to design the OBASMC law for such a class of complex systems. First, by the use of the Lyapunov–Krasovskii functional, sufficient conditions are given, such that the sliding mode dynamics are stochastically admissible. Then, the OBASMC law is proposed to guarantee the reachability in a finite-time region. Finally, the practical system about dc motor model is given to verify the validity.

Implementing a Digital Strategy: Learning from the Experience of Three Digital Transformation Projects

Abstract: The rapid growth of digital technologies and the extraordinary amount of data that devices and applications collect each day are increasingly driving companies to radically transform the business a

Dissecting Video Server Selection Strategies in the YouTube CDN

Abstract: In this paper, we conduct a detailed study of the YouTube CDN with a view to understanding the mechanisms and policies used to determine which data centers users download video from. Our analysis is conducted using week-long datasets simultaneously collected from the edge of five networks - two university campuses and three ISP networks - located in three different countries. We employ state-of-the-art delay-based geolocation techniques to find the geographical location of YouTube servers. A unique aspect of our work is that we perform our analysis on groups of related YouTube flows. This enables us to infer key aspects of the system design that would be difficult to glean by considering individual flows in isolation. Our results reveal that while the RTT between users and data centers plays a role in the video server selection process, a variety of other factors may influence this selection including load-balancing, diurnal effects, variations across DNS servers within a network, limited availability of rarely accessed video, and the need to alleviate hot-spots that may arise due to popular video content.