Engineering

Wicked Solutions Domain – Sustainability

The Johns Hopkins University COVID-19 Tracking Map has provided a way in which coronavirus statistics can be visualized and understood by researchers and individuals worldwide, allowing for across the board comprehension of the scope of the pandemic. This platform has proved that design thinking and mapping can be an effective way to convey data surrounding problems facing our society to be analyzed in many different ways. In our study, we are investigating the ways in which this model of design can be applied to other wicked problems, including sustainability, obesity, poverty, and food. A large portion of the project thus far has involved literature gathering in which the full extent of the wicked problems being studied are present in our society, within varying sectors including economic, social, and environmental. Annotated bibliographies of academic literature concerning each of the focused issues are being developed to evaluate existing design interventions that exist, which can be eventually compiled into the WISDOM Solutions Domain, to be accessed and interacted with by communities everywhere.

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Calibration of a system of measuring indoor radon concentrations using charcoal canisters.

kennings

Radon-222 is a radionuclide present in elevated concentrations in many indoor areas. Radon can pose a health risk to building inhabitants, so accurate and affordable radon screening is needed. Canisters filled with activated charcoal are a common and inexpensive method for testing, with different charcoal batches and canister designs requiring specific calibration. This is typically performed in large volume commercial environmental chambers with controlled and constant temperature, pressure, humidity, and radon concentration. If canisters could be calibrated in less controlled environments both calibration and quality control could be more accessible. The objective of this study was to compare calibrations of charcoal canisters from a controlled radon chamber and a less controlled basement storage room. A group of canisters was sent to a commercial environmental chamber with constant and known radon levels. Another group of canisters manufactured from the same charcoal batch was placed in a basement laboratory with high, naturally occurring radon concentration. Following exposure, each canister was counted using the same gamma-ray spectroscopy system. The data were processed in several different ways before being used to estimate the signal corresponding to a given radon concentration. Certain spectroscopic data processing techniques improved the accuracy of calibrations. Mathematical models were needed to account for radon uptake on the charcoal and the decay of radon and its decay products. Initial results suggest that commercial-grade chambers are not necessary to accurately calibrate charcoal canisters as long as the change in radon as a function of time in the calibration space is measured.

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Glow Curve Analysis Software with Background Removal, Data Smoothing, and Automated Peak Identification for Thermoluminescent Dosimeter Data

yuwenjin

Thermoluminescent dosimeters integrate incoming ionizing radiation dose and release photons proportional to it. When heat is applied to the material, peaks in the emitted light occur at different temperatures corresponding to characteristic quantum states. Separating these peaks may be done through fitting the data to thermoluminescence models, in a process called glow curve analysis. This analysis reveals information about signal fading and has the potential to improve dose quantitation. Dosimeter readings can have discontinuities due to experimental phenomena such as incineration of dust particles, which can result in imprecise glow curve fits. Additionally, the light emitted as a result of the readout process makes the initial fit inaccurate. As manual removal of the noise and the curve fitting process itself is time-consuming, an automated program is more efficient. This research creates software using C++ that automatically subtracts the background noise from raw data, removes any discontinuous spikes, and further smoothes the data. Peaks are identified by implementing custom functions and existing algorithms such as the Savitzky-Golay algorithm and the method of gradient descent. The final software fits the approximate curve for each peak according to first-order kinetic physics and outputs a figure of merit for the fit. An executable compatible with different computer systems allows greater portability. The software can help other researchers clean raw data and analyze peaks with efficiency and accuracy. The resulting software should result not only in greater insights into the thermoluminescent process and better radiation dosimetry, but may lend itself to applications to other fields.

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Design of cancer proteins

imerem

The goal of this research project is to develop a form of gene therapy for someone who has been diagnosed with cancer. In order to do so, it is essential that we change a protein protein interaction (PPI) between a cancerous cell and the protein it is interacting with so that the cancerous cell can cease to both mutate and produce even more cancerous proteins.

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Mapping Ptolemy’s Star Catalogue

moonjoy

Almagest is the most important and complete text of Astronomy of antiquity, written by the Greek mathematician and astronomer, Claudius Ptolemy in the second century AD. Although the book served as an important guide for astronomers for centuries and even today, there are still some uncertainties regarding how Ptolemy mapped the constellations mentioned in his book into the sky. In this project, we reconstruct Ptolemy’s constellations according to his descriptions and also as seen in the sky during the time the book was being written. We work using Toomer’s English translation of Almagest as a basis and create digital drawings of all 48 of Ptolemy’s constellations with computer graphic programs; these reconstructions will be displayed on a free website to be easily accessible to the public. Ptolemy’s account of astronomy and mathematics in his book provide a deep insight into ancient Greek astronomy. These constellations are also a product of human imagination when people connect the stars in the sky to form shapes and figures that may or may not have been real. Comprehending the meaning behind these descriptions of constellations also allow us to look into their lives through a different perspective and the visual reconstructions also aid us to better understand them.

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A Predictive Machine Learning Model for [18F] SnAr Radiofluorination

chengkev

Positron Emission Tomography, commonly known as PET imaging, is a noninvasive, rapid, and accurate imaging system that tracks the distribution of a radioactive tracer. There are many strategies to incorporate radioactive nuclides into tracers for PET imaging, with the most prevalent approach being radiofluorination”” the incorporation of the radioactive nuclide 18F into a bioactive molecule. One of the most common ways of incorporating 18F into substrates is nucleophilic aromatic substitution (SNAr); however, this approach suffers from harsh conditions, exotic leaving groups, and unpredictability, thereby limiting the ease and broad use of this procedure. Herein, a predictive machine learning model will be developed for SNAr using nucleophilic 18F, allowing for prediction of radiochemical yields given a certain set of reaction conditions. An appropriate data structure for machine learning was constructed by encoding literature substrates and conditions. This involved DFT (density function theory) calculations using Gaussian09 to create an optimized structure and determine quantifiable properties, such as HOMO and LUMO energies, steric hindrance, and NMR data. However, due to the time requirements needed to assemble the data set, no concrete results have been produced as of yet. Using this data set, regression models will be generated via several machine learning algorithms in Python, ranging from simple linear models to more complex decision tree and neural network models. The project hopes to build a successful model that will allow for the prediction of radiochemical incorporation given a substrate and reaction conditions. This model may then be validated through further experimentation and generalized to a wider variety of substrates via strategic experiment selection.

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Implementation of a virtual reality radiation protection game in Unity for Oculus Quest

angelz

At its core, virtual reality (VR) technology seeks to create virtual experiences that reflect as closely as possible the movements and responses users would have to reality. While commonly used for entertainment, it has also found use in virtual training for jobs or tasks that require hands-on experience. This is especially relevant for radiation protection training, which requires familiarity with a variety of equipment, sources, and emergency procedures. The main question is how to implement this VR training effectively. On the most basic level, the core physics and user movement must be as realistic as possible. These objectives can be achieved by choosing a game engine that supports VR development, and a target VR device with a wide range of user mobility. For this project, Unity was chosen for its versatile VR support, including support for specific devices (Oculus Quest) and more generalized support systems that allow the game to be run on any device with the proper movement controls. Care was also taken in making in-game detector models and radiation physics scripts as realistic as possible. A well-designed VR game recreates and even exceeds the limits of hands-on training, as the player can be placed in complex and potentially dangerous situations without worries of cost or potential harm. A game which fully takes advantage of the VR platform teaches players proper handling of equipment and procedures while also providing more open-ended scenes (i.e. emergencies, high-risk scenarios) that are difficult to simulate in real life.

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Innovating Innovation: Unpacking URM Engagement in NSF I-CORPS

jessegs

NSF Innovation Corps (I-Corps) is a 7-week entrepreneurship education program that brings academics and industry experts together for an opportunity recognition project and business model creation. While the program has met with success since its inception, it has had challenges attracting and engaging racially minoritized populations (i.e., people who identify as Black, Latinx, or Native American). Diversity is not only critical in entrepreneurship, but beneficial in problem solving and innovation. This study aims to better understand how I-Corps can better attract and engage racially minoritized populations and unpack how to help foster an inclusive environment for racially minoritized participants in I-Corps. To address this challenge a series of interviews were conducted with I-Corps participants who identified as racially minoritized and a subset of those interviews were analyzed. Two themes emerged: 1) I-Corps must improve on recruiting racially minoritized populations and 2) racially minoritized participants experienced additional barriers while participating in the program. To combat these issues several implementable policies and guidelines are proposed.

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