Using RFID/NFC and QR-Code in Mobile Phones to Link the Physical and the Digital World | InTechOpen, Published on: 2012-03-07. Authors: Mabel Vazquez-Briseno, Francisco I. Hirata, Juan de Dios Sanchez-Lopez, et

Childhood obesity has become a major problem around the world. In many cases children develop unhealthy eating patterns, resulting in excess energy intake, because they eat alone without their parents or teachers advice. In this paper we propose a mobile health (mhealth) platform intended to increase children's health awareness by tracking their food intake and sending proper notifications and messages based on their food choices. The platform is based on the Internet of Things concept in order to facilitate capturing food information using intelligent tags instead of doing manual entries.

Waveguides are structures that can be used as sensors. After adding a cladding of a proper material, waveguides can be used for detecting specific chemical species. In particular, high band gap oxide semiconductors can be used as cladding for optical waveguides. In these materials, the synthesis processes are expensive or contaminant generators, for this reason novel approaches are required. This work addresses a simple synthesis method for the preparation of zinc oxide nanoparticles using Citrus reticulata extract. XRD presented only the wurtzite structure for the zinc oxide nanoparticles, while TEM showed size and shape homogeneity. The ZnO band gap was lower than commercial ZnO. Using a computer simulation and frequency dependent electrical characteristics, a waveguide model was tested. The resulting characteristics presented great parameters for using in optoelectronic sensors. Resulting ZnO NPs have negligible amounts of green matter afterwards, making this a non-toxic method. As a result, herein presented synthesis is a promising method for developing new optoelectronic sensors. These devices can be used for automatic detection of chemical species by automated systems.

For the last decade, several studies on mid-IR spectroscopy for blood glucose quantification have not considered the compounds involved in the glucose regulation mechanism, in which insulin plays an important role. This work shows how insulin overlaps in the same mid-IR region in which glucose is quantified. This optical absorption interference is an important factor to be considered for this type of studies, in the scope of whole blood modeling for spectroscopy applications and the possible use of computer based metrics. Keywords: Biomedical Optics, Glucose quantification, Whole blood, Insulin, FTIR Spectroscop

Coronavirus disease (COVID-19) represents one of the greatest challenges to public health in modern history. As the disease continues to spread globally, medical and allied healthcare professionals have become one of the most affected sectors. Stress and anxiety are indirect effects of the COVID-19 pandemic. Therefore, it is paramount to understand and categorize their perceived levels of stress, as it can be a detonating factor leading to mental illness. Here, we propose a computer-based method to better understand stress in healthcare workers facing COVID-19 at the beginning of the pandemic. We based our study on a representative sample of healthcare professionals attending to COVID-19 patients in the northeast region of Mexico, at the beginning of the pandemic. We used a machine learning classification algorithm to obtain a visualization model to analyze perceived stress. The C5.0 decision tree algorithm was used to study datasets. We carried out an initial preprocessing statistical analysis for a group of 101 participants. We performed chi-square tests for all questions, individually, in order to validate stress level calculation (p < 0.05) and a calculated Cronbach’s alpha of 0.94 and McDonald’s omega of 0.95, demonstrating good internal consistency in the dataset. The obtained model failed to classify only 6 out of the 101, missing two cases for mild, three for moderate and one for severe (accuracy of 94.1%). We performed statistical correlation analysis to ensure integrity of the method. In addition, based on the decision tree model, we concluded that severe stress cases can be related mostly to high levels of xenophobia and compulsive stress. Thus, showing that applied machine learning algorithms represent valuable tools in the assessment of perceived stress, which can potentially be adapted to other areas of the medical field.