The problem of ethnicity identification from names has a variety of important applications, including biomedical research, demographic studies, and marketing. Here we report on the development of an ethnicity classifier where all training data is extracted from public, non-confidential (and hence somewhat unreliable) sources. Our classifier uses hidden Markov models (HMMs) and decision trees to classify names into 13 cultural/ethnic groups with individual group accuracy comparable accuracy to earlier binary (e.g., Spanish/non-Spanish) classifiers. We have applied this classifier to over 20 million names from a large-scale news corpus, identifying interesting temporal and spatial trends on the representation of particular cultural/ethnic groups.

Introduction: The objective of this study was to examine changes in hemoglobin A1c (HbA1c), anti-diabetic medication use, insulin resistance, and other ambulatory glucose profile metrics between baseline and after 90 days of participation in the Twin Precision Nutrition (TPN) Program enabled by Digital Twin Technology. Methods: This was a retrospective study of patients with type 2 diabetes who participated in the TPN Program and had at least 3 months Digital Features To view enhanced digital features for this article go to https://doi.org/10.6084/m9.figshare. 12943226.

Obesity and overweight are considered a health threat globally. Saudi Arabia is a country that has a high percentage of people suffering from obesity. These people can be helped to lose weight through the usage of mobile apps as these apps can collect users' personal information. These collected data is used to provide precise and personalized weight loss advices. However, weight loss apps must be user friendly, provide data security and user privacy protection. In this paper, we analyze the usability, security, and privacy of a weight loss app. Our main aim to clarify the data privacy and security procedure and test the usability level of the new Arabic weight loss app 'Akser Waznk' that is developed considering the social and cultural norms of Saudi users.

This paper proposes a new pattern of two dimensional cellular automata linear rules that are used for efficient edge detection of an image. Since cellular automata is inherently parallel in nature, it has produced desired output within a unit time interval. We have observed four linear rules among 2 9 total linear rules of a rectangular cellular automata in adiabatic or reflexive boundary condition that produces an optimal result. These four rules are directly applied once to the images and produced edge detected output. We compare our results with the existing edge detection algorithms and found that our results shows better edge detection with an enhancement of edges.

s-Obesity is considered as the main health issue worldwide. The obesity rate within Saudi's citizens is rising alarmingly. The Internet of Things (IoT)-enabled mobile apps can assist obese Saudi users in losing weight via collecting sensitive personal information and then providing accurate and personalized weight loss advice. These data can be collected using embedded IoT devices in a smartphone. However, these IoTenabled apps should be usable and able to provide data security and user privacy protection. This paper aims to continue our usability study for two Arabic weight loss IoT-enabled apps by performing a qualitative analysis for them. It discusses users' and health professionals' feedbacks, concerns and suggestions. Based on the analysis, a comprehensive usability guideline for developing a new Arabic weight loss IoT-enabled app for obese Saudi users is provided.