Streamflow is the flow of water in streams, rivers, and other channels. Changes in streamflow can influence the amount of water available for crops, the generation of electricity, fishery, many plants, and animals. The target of my research project is to predict daily streamflow using streamflow and precipitation data of six locations of Lower Cosumnes in California. We are using deep learning models as Recurrent Neural Network, and Machine Learning models such as Support Vector Regression, Boosting, ARIMA, and Linear Regression. We are using these methods with different windows of 7, 10, 14, and 30 days and two preprocessing methods (Z-score and normalization) for each of the models. Our ultimate goal is to find the model with the least error, the most accurate prediction, and at the same time predicting the peaks correctly.
We participated in a research project dedicated to analyzing the interactions of contrast media with the molecular components of fruits to compare the interactions with the human brain. Microscopic properties of various radiologic contrast materials are studied for weak to strong surface interactions in model fruits. Low and high X-ray energies may show different imaging noise reflective of scattered radiation from iron, manganese, and other metal ions in fruits. The results in the first couple of days of exposing the fruits showed that the contrast was very bright and the background SD would have a lot of scatter from the injection sites. As the fruit gets drier the contrast movement gets slower mimicking the residual Gd or Iodine in a patient’s brain who had multiple contrast Rad exams. Contrasts that are demonstrated to break down slower are more preferable contrasts to be used in humans undergoing medical exams relying on contrast media.
Radiographic imaging was done using low and high energy radiography equipment. The test hypothesis that macromolecular aggregation changes sample noise in imaging samples for optical imaging methods. Inorganic complexes scatter radiation at the molecular level and may increase the sample noise locally. At high and low photon energies in various x-ray machines, sample and background noise were gathered and compared with those from mammography systems from mammography researchers. This Project is based on model biosystems (chicken eggs) where the protein composition is well known, it is virtually free of heavy metals and protein degeneration due to metal toxicity could be experimentally modeled.
This project involves the interruption of nutrient transport in plants and animal tissues with a small amount of radiopaque compounds and subsequent imaging of diffusion and molecular interaction using x-ray, CT, and near infra-red spectroscopy (NIRS). Time series analysis of affected protein layers due to “toxic” interaction with Gadolinium and Iodine moieties in naturally occurring proteins in Aloe Vera plants and chicken egg yolk are observed at various x-rays conditions. Both of these groups of nanomaterials may offer new insights in nanomedicine where tissue transport is gaining importance.
