In this study, the authors determine which house model is most resistant to high winds by building smaller prototypes that could be tested with a handheld source of wind.
Read More...An accessible experiment to assess the impact of shapes of buildings and roofs on wind resistance
In this study, the authors determine which house model is most resistant to high winds by building smaller prototypes that could be tested with a handheld source of wind.
Read More...Hybrid Quantum-Classical Generative Adversarial Network for synthesizing chemically feasible molecules
Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules.
Read More...Quantitative definition of chemical synthetic pathway complexity of organic compounds
Irrespective of the final application of a molecule, synthetic accessibility is the rate-determining step in discovering and developing novel entities. However, synthetic complexity is challenging to quantify as a single metric, since it is a composite of several measurable metrics, some of which include cost, safety, and availability. Moreover, defining a single synthetic accessibility metric for both natural products and non-natural products poses yet another challenge given the structural distinctions between these two classes of compounds. Here, we propose a model for synthetic accessibility of all chemical compounds, inspired by the Central Limit Theorem, and devise a novel synthetic accessibility metric assessing the overall feasibility of making chemical compounds that has been fitted to a Gaussian distribution.
Read More...Building a video classifier to improve the accuracy of depth-aware frame interpolation
In this study, the authors share their work on improving the frame rate of videos to reduce data sent to users with both 2D and 3D footage. This work helps improve the experience for both types of footage!
Read More...An optimal pacing approach for track distance events
In this study, the authors use existing mathematical models to how high school athletes pace 800 m, 1600 m, and 3200 m distance track events compared to elite athletes.
Read More...DNA-SEnet: A convolutional neural network for classifying DNA-asthma associations
In this study, the authors developed a model named DNA Sequence Embedding Network (DNA-SEnet) to classify DNA-asthma associations using their genomic patterns.
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...Predicting the factors involved in orthopedic patient hospital stay
Long hospital stays can be stressful for the patient for many reasons. We hypothesized that age would be the greatest predictor of hospital stay among patients who underwent orthopedic surgery. Through our models, we found that severity of illness was indeed the highest factor that contributed to determining patient length of stay. The other two factors that followed were the facility that the patient was staying in and the type of procedure that they underwent.
Read More...A novel approach for early detection of Alzheimer’s disease using deep neural networks with magnetic resonance imaging
In the battle against Alzheimer's disease, early detection is critical to mitigating symptoms in patients. Here, the authors use a collection of MRI scans, layering with deep learning computer modeling, to investigate early stages of AD which can be hard to catch by human eye. Their model is successful, able to outperform previous models, and detected regions of interest in the brain for further consideration.
Read More...A novel approach for predicting Alzheimer’s disease using machine learning on DNA methylation in blood
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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