These authors mathematically deduce a model that explains the interesting (and unintuitive) physical phenomenon that occurs when water falls.
Read More...Estimating the liquid jet breakdown height using dimensional analysis with experimental evidence
These authors mathematically deduce a model that explains the interesting (and unintuitive) physical phenomenon that occurs when water falls.
Read More...Effects of different synthetic training data on real test data for semantic segmentation
Semantic segmentation - labelling each pixel in an image to a specific class- models require large amounts of manually labeled and collected data to train.
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...The characterization of quorum sensing trajectories of Vibrio fischeri using longitudinal data analytics
Quorum sensing (QS) is the process in which bacteria recognize and respond to the surrounding cell density, and it can be inhibited by certain antimicrobial substances. This study showed that illumination intensity data is insufficient for evaluating QS activity without proper statistical modeling. It concluded that modeling illumination intensity through time provides a more accurate evaluation of QS activity than conventional cross-sectional analysis.
Read More...Optimizing airfoil shape for small, low speed, unmanned gliders: A homemade investigation
Here, the authors sought to identify a method to optimize the lift generated by an airfoil based solely on its shape. By beginning with a Bernoullian model to predict an optimized wing shape, the authors then tested their model against other possible shapes by constructing them from Styrofoam and testing them in a small wind tunnel. Contrary to their hypothesis, they found their expected optimal airfoil shape did not result in the greatest lift generation. They attributed this to a variety of confounding variables and concluded that their results pointed to a correlation between airfoil shape and lift generation.
Read More...Transfer learning and data augmentation in osteosarcoma cancer detection
Osteosarcoma is a type of bone cancer that affects young adults and children. Early diagnosis of osteosarcoma is crucial to successful treatment. The current methods of diagnosis, which include imaging tests and biopsy, are time consuming and prone to human error. Hence, we used deep learning to extract patterns and detect osteosarcoma from histological images. We hypothesized that the combination of two different technologies (transfer learning and data augmentation) would improve the efficacy of osteosarcoma detection in histological images. The dataset used for the study consisted of histological images for osteosarcoma and was quite imbalanced as it contained very few images with tumors. Since transfer learning uses existing knowledge for the purpose of classification and detection, we hypothesized it would be proficient on such an imbalanced dataset. To further improve our learning, we used data augmentation to include variations in the dataset. We further evaluated the efficacy of different convolutional neural network models on this task. We obtained an accuracy of 91.18% using the transfer learning model MobileNetV2 as the base model with various geometric transformations, outperforming the state-of-the-art convolutional neural network based approach.
Read More...Sex differences in confidence and memory
In this work, the authors sought to provide an original experiment to investigate the conflict over whether males or females tend to exhibit greater accuracy or confidence in their memories. By using an online portal to obtain a convenience sample, the authors found that their results suggest that though males tend to be more confident regarding their memories, they may in fact remember fewer details. The authors suggest that these findings merit further research before making systematic changes regarding crime scene recall settings.
Read More...Enhancing marine debris identification with convolutional neural networks
Plastic pollution in the ocean is a major global concern. Remotely Operated Vehicles (ROVs) have promise for removing debris from the ocean, but more research is needed to achieve full effectiveness of the ROV technology. Wahlig and Gonzales tackle this issue by developing a deep learning model to distinguish trash from the environment in ROV images.
Read More...Characterizing the evolution of antibiotic resistance in commercial Lactobacillus strains
In this study, the authors studied the ability for bacteria to develop antibiotic resistance over successive generations and modeled the trajectory to predict how antibiotic resistance is developed.
Read More...Examining the relationship between screen time and achievement motivation in an adolescent population
In this study, the authors conduct a survey of high school students to evaluate the effects of screen time and habits on motivation.
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