Numerous specialty treatments claim to reduce swelling and scarring; however, it is unknown if these treatments are more effective than less expensive treatments. In an attempt to determine if one outperforms the other, treatments were applied to the same subject following bilateral orthopedic foot surgery. No difference was found the specialty treatments compared to more cost-effective treatments.
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The Effect of Sunglass Price on Ocular Exposure to Ultraviolet Radiation
Wearing sunglasses may offer protection against the negative health outcomes linked to exposure to ultraviolet waves. In this study, the authors test whether more expensive sunglasses offer better UV protection.
Read More...Transfer Learning with Convolutional Neural Network-Based Models for Skin Cancer Classification
Skin cancer is a common and potentially deadly form of cancer. This study’s purpose was to develop an automated approach for early detection for skin cancer. We hypothesized that convolutional neural network-based models using transfer learning could accurately differentiate between benign and malignant moles using natural images of human skin.
Read More...The effect of molecular weights of chitosan on the synthesis and antifungal effect of copper chitosan
Pathogenic fungi such as Alternaria alternata (A. alternata) can decimate crop yields and severely limit food supplies when left untreated. Copper chitosan (CuCts) is a promising alternative fungicide for developing agricultural areas due to being inexpensive and nontoxic. We hypothesized that LMWc CuCts would exhibit greater fungal inhibition due to the beneficial properties of LMWc.
Read More...Heavy metal and bacterial water filtration using Moringa oleifera and coconut shell-activated carbon
One-third of the world's people do not have access to clean drinking water. Nadella and Nadella tackle this issue by testing a low-cost filtration system for removing heavy metal and bacteria from water.
Read More...Entropy-based subset selection principal component analysis for diabetes risk factor identification
In this article, the authors looked at developing a strategy that would allow for earlier diagnosis of Diabetes as that improves long-term outcomes. They were able to find that BMI, tricep skin fold thickness, and blood pressure are the risk factors with the highest accuracy in predicting diabetes risk.
Read More...Pressure and temperature influence the efficacy of metal-organic frameworks for carbon capture and conversion
Metal-organic frameworks (MOFs) are promising new nanomaterials for use in the fight against climate change that can efficiently capture and convert CO2 to other useful carbon products. This research used computational models to determine the reaction conditions under which MOFs can more efficiently capture and convert CO2. In a cost-efficient manner, this analysis tested the hypothesis that pressure and temperature affect the efficacy of carbon capture and conversion, and contribute to understanding the optimal conditions for MOF performance to improve the use of MOFs for controlling greenhouse CO2 emissions.
Read More...Managing CO2 levels through precipitation-based capture from seawater and electrochemical conversion
The authors set out to develop an electrochemical device that would have efficient and sustained carbon dioxide capture.
Read More...An efficient approach to automated geometry diagram parsing
Here, beginning from an initial interest in the possibility to use a computer to automatically solve a geometry diagram parser, the authors developed their own Fast Geometry Diagram Parser (FastGDP) that uses clustering and corner information. They compared their own methods to a more widely available, method, GeoSolver, finding their own to be an order of magnitude faster in most cases that they considered.
Read More...Prediction of molecular energy using Coulomb matrix and Graph Neural Network
With molecular energy being an integral element to the study of molecules and molecular interactions, computational methods to determine molecular energy are used for the preservation of time and resources. However, these computational methods have high demand for computer resources, limiting their widespread feasibility. The authors of this study employed machine learning to address this disadvantage, utilizing neural networks trained on different representations of molecules to predict molecular properties without the requirement of computationally-intensive processing. In their findings, the authors determined the Feedforward Neural Network, trained by two separate models, as capable of predicting molecular energy with limited prediction error.
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