Here, recognizing the difficulty associated with tracking the progression of dementia, the authors used machine learning models to predict between the presence of cognitive normalcy, mild cognitive impairment, and Alzheimer's Disease, based on blood DNA methylation levels, sex, and age. With four machine learning models and two dataset dimensionality reduction methods they achieved an accuracy of 53.33%.
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A cost-effective IoT-based intelligent indoor air quality monitoring
Poor air quality is associated with negative effects on human health but can be difficult to measure in an accurate and cost-effective manner. The authors design and test a monitor for measuring indoor air quality using low-cost components.
Read More...A colorimetric investigation of copper(II) solutions
In this study, the authors investigate the effects of acetone on the color of copper chloride (CuCl2) solution, which has important implications for detecting copper in the environment.
Read More...A comparison of use of the mobile electronic health record by medical providers based on clinical setting
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.
Read More...A spatiotemporal analysis of OECD member countries on sugar consumption and labor force participation
In this article the authors look at sugar consumption and the relationship to productivity in the work/labor force.
Read More...A novel encoding technique to improve non-weather-based models for solar photovoltaic forecasting
Several studies have applied different machine learning (ML) techniques to the area of forecasting solar photovoltaic power production. Most of these studies use weather data as inputs to predict power production; however, there are numerous practical issues with the procurement of this data. This study proposes models that do not use weather data as inputs, but rather use past power production data as a more practical substitute to weather-based models. Our proposed models demonstrate a better, cheaper, and more reliable alternatives to current weather models.
Read More...A chemical and overwintering honey bee apiary field study comparing new and expired amitraz miticide
In this study, the authors test the longevity of a anti-mite compound, amitraz, in commercially-sold strips and the age-dependent efficacy of these strips in preventing honey bee colony collapse by ectoparasitic mite Varroa destructor.
Read More...A comparative analysis of synthetic and natural fabrics
The authors test the durability of synthetic versus non-synthetic fabrics though loose thread counts, color fade over time, and shrinkage tests.
Read More...A novel in vitro blood-brain barrier model using 3D bioprinter: A pilot study
The authors looked at how a 3D bioprinter could be used to model the blood brain barrier.
Read More...A land use regression model to predict emissions from oil and gas production using machine learning
Emissions from oil and natural gas (O&G) wells such as nitrogen dioxide (NO2), volatile organic compounds (VOCs), and ozone (O3) can severely impact the health of communities located near wells. In this study, we used O&G activity and wind-carried emissions to quantify the extent to which O&G wells affect the air quality of nearby communities, revealing that NO2, NOx, and NO are correlated to O&G activity. We then developed a novel land use regression (LUR) model using machine learning based on O&G prevalence to predict emissions.
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