This article discusses Alopecia areata, an autoimmune disorder causing sudden hair loss due to the immune system mistakenly attacking hair follicles. The article introduces the use of deep learning (DL) techniques, particularly convolutional neural networks (CNN), for classifying images of healthy and alopecia-affected hair. The study presents a comparative analysis of newly optimized CNN models with existing ones, trained on datasets containing images of healthy and alopecia-affected hair. The Inception-Resnet-v2 model emerged as the most effective for classifying Alopecia Areata.

The electronic health record (EHR), along with its mobile application, has demonstrated the ability to improve the efficiency and accuracy of health care delivery. This study included data from 874 health care providers over a 12-month period regarding their usage of mobile phone (EPIC® Haiku) and tablet (EPIC® Canto) mEHR. Ambulatory and inpatient care providers had the greatest usage levels over the 12-month period. Awareness of workflow allows for optimization of mEHR design and implementation, which should increase mEHR adoption and usage, leading to better health outcomes for patients.

Levy & Levy tackle the optimization of the coronary artery bypass graft, a life-saving surgical technique that treats artery blockage due to coronary heart disease. The authors develop a novel auto-suturing method that saves time, allows for an increased number of sutures, and improves graft quality over hand suturing. The authors also show that increasing the number of sutures from four to five with their new method significantly improves graft quality. These promising findings may help improve outcomes for patients undergoing surgery to treat coronary heart disease.

Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.

In this paper, we measured the privacy budgets and utilities of different differentially private mechanisms combined with different machine learning models that forecast traffic congestion at future timestamps. We expected the ANNs combined with the Staircase mechanism to perform the best with every value in the privacy budget range, especially with the medium high values of the privacy budget. In this study, we used the Autoregressive Integrated Moving Average (ARIMA) and neural network models to forecast and then added differentially private Laplacian, Gaussian, and Staircase noise to our datasets. We tested two real traffic congestion datasets, experimented with the different models, and examined their utility for different privacy budgets. We found that a favorable combination for this application was neural networks with the Staircase mechanism. Our findings identify the optimal models when dealing with tricky time series forecasting and can be used in non-traffic applications like disease tracking and population growth.

Buildings, which are responsible for the majority of electricity consumption in cities like Dubai, are often exclusively reliant on electrical lighting even in the presence of daylight to meet the illumination requirements of the building. This inefficient use of lighting creates potential to further optimize the energy efficiency of buildings by complementing natural light with electrical lighting. Prior research has mostly used ballasts (variable resistors) to regulate the brightness of bulbs. There has been limited research pertaining to the use of pulse width modulation (PWM) and the use of ‘triodes for alternating current’ (TRIACs). PWM and TRIACs rapidly stop and restart the flow of current to the bulb thus saving energy whilst maintaining a constant illumination level of a space. We conducted experiments to investigate the feasibility of using TRIACs and PWM in regulating the brightness of bulbs. We also established the relationship between power and brightness within the experimental setups. Our results indicate that lighting systems can be regulated through these alternate methods and that there is potential to save up to 16% of energy used without affecting the overall lighting of a given space. Since most energy used in buildings is still produced through fossil fuels, energy savings from lighting systems could contribute towards a lower carbon footprint. Our study provides an innovative solution to conserve light energy in buildings during daytime.

The sugar-rich modern diet underlies a suite of metabolic disorders, most common of which is diabetes. Accurately reporting the sugar content of pre-packaged food and drink items can help consumers track their sugar intake better, facilitating more cognisant and, eventually, moderate consumption of high-sugar items. In this article, the authors examine the effect of several variables on the accuracy of Fehling's reaction, a colorimetric reaction used to estimate sugar content.

The authors analyzed the heat transfer of different containers in the microwave aiming to identify the most optimal material of container to reduce heating time.

Manufacturers that produce products using fused filament fabrication (FFF) 3D printing technologies have control of numerous build parameters. This includes the number of solid layers on the exterior of the product, the percentage of material filling the interior volume, and the many different types of infill patterns used to fill their interior.This study investigates the hypothesis that as the density of the part increases, the mechanical properties will improve at the expense of build time and the amount of material required.

Digital compasses are essential in technology that we use in our everyday lives: phones, vehicles, and more. Li and Liu address the accuracy of these devices by presenting a new algorithm for accurately calibrating low-cost magnetometers.