Dr. Thomas Donlon
Physics and Astronomy | tjd0021@uah.edu
Accelerations from Fast Radio Bursts and White Dwarfs
Fast Radio Bursts (FRBs) are a recently-discovered astrophysical phenomenon, which still lack a theoretical explanation. One popular idea is that FRBs may be caused by neutron stars in binary systems. There are currently two known repeating periodic FRBs, which are thought to be located in other galaxies. Currently, observations of neutron stars (pulsars) in binary systems can be used to measure accelerations throughout our Galaxy -- however, if FRBs are due to binary neutron star systems, it may be possible to use FRB data in this way to measure the cosmological acceleration directly.
The student will gather data for FRBs from the CHIME collaboration, as well as any other relevant publications. The student will analyze this data in order to calculate accelerations for the two known periodic FRB sources, assuming they are binary neutron stars. These observed accelerations will then be compared to the cosmic acceleration expected from the standard model of cosmology. . This would represent the first ever direct measurement of the redshift drift. The student will also forecast how many periodic FRBs would be required to discriminate between different cosmological models.
Prerequisites/Requirements: The student is expected to have a sophomore-level or higher foundation in physics, astrophysics, and mathematics. Successful completion of AST106 and AST107 is required. Successful completion of PH301 and/or PH305 would be beneficial, but is not required. The student should be enrolled in the College of Science (Physics, Math, and CS majors are encouraged to apply), although applicants from the College of Engineering will also be considered. A basic ability to write code, preferably Python, is required.
Prof. John Mecikalski
Atmosphere and Earth Science | mecikaj@uah.edu
Analysis of Growing and Evolving Cumulus Clouds using combined Ground and Satellite Observations over the DoE AMF3
The project will address research questions surrounding use of Department of Energy-Bankhead National Forest (BNF) ARM Mobile Facility-3 (AMF3) and GOES-16 satellite observations to study cumulus cloud characteristics, specifically how cloud base characteristics change over the course of the morning and early afternoon, and how these characteristics relate to the area (60 km2) cloud field (coverage, cloud type), as well as to cumulus cloud growth rates. The main datasets will be collected over and surrounding the BNF AMF3 on ~7-12 humid summer (June-July) days when cumulus clouds are present. AMF3 Doppler Lidar and ceilometer data will characterize cumulus cloud-base and lifted condensation level (LCL) heights, which will be compared to cloud-base vertical motion (wCB) estimates from the AMF3 Vertical-Scanning Radar/KAZR. The study will also rely on GOES-16 satellite cumulus cloud top cooling rate (CTCR) product data (2 km resolution, every 5 minutes), and 13 km Rapid Refresh (RAP) model LCL and boundary layer thermodynamic information. The paralax corrected CTCR data will be matched to the cumulus clouds observed over and near the AMF3, and several methods will relate CTCR to in-cloud updraft velocities. Statistical analyses will be done to compare ground- and satellite cumulus cloud observations. In addition, cloud type information will determine if more continuous fields of low clouds break up as the cumulus cloud growth rates change/increase, or if any other relationships exist between cloud growth rates and cloud type, with respect to lower tropospheric temperature, dew point, LCL and boundary layer height. The study will also determine if more rapidly growing convective clouds have stronger wCB values, and what if any relationship that may have to thunderstorm development later in a given day.
Prerequisites/Requirements: Strong programming skills are highly desired.
Dr. Tanya Sysoeva
Biological Sciences | lmj0020@uah.edu
Analysis of inhibition mechanisms by Lactobacillaceae isolates from human urinary bladder
Lactobacilli are known commensals in the human urogenital tract (UT) and possibly important for the prevention of urinary tract infections (UTIs). Lactobacilli have innate defense factors that enable them to outcompete other bacteria, including uropathogenic E. coli (UPEC) through production of toxic components, such as bacteriocins, peroxide, surfactants, and lactic acid. In an earlier project we isolated a set of 29 commensal lactobacillus strains from the human urinary bladder and performed whole genome sequencing on 3 of those isolates. Preliminary analysis of the sequenced lactobacilli genomes shows presence of several bacteriocin genes. In follow-up projects, we worked with 2 of these isolates and observed their ability to inhibit the growth of model UPEC isolates/strains in vitro in artificial media by a combination of whole-cell and acid factors. Comprehensive understanding of the specific mechanism(s) underlying these patterns of inhibition is currently lacking. Therefore, the goal of this project is to determine the mechanisms of UPEC inhibition by these isolates. We also plan to sequence representative lactobacillus isolates, proceed with the bioinformatic analyses, and attempt to do genetic modifications of the strains. Overall, we will use a combination of bioinformatic analyses, sequencing, and microbiological techniques such as anaerobic growth, well diffusion inhibition assays, CFU counting, and others.
Prerequisites/Requirements: Applicants (ranging from freshmen - juniors) should have completed some basic biology courses at UAH (i.e. BYS 119/120) or an AP Biology/Chemistry course. While a good academic standing is not a requirement, the applicant has to have a sincere interest in the general topic of microbiology or, in particular, an understanding of the implication of the microbiome on human health.
Dr. Thomas Donlon
Physics and Astronomy | tjd0021@uah.edu
Calibration of Binary Pulsar Spindown Rates
Historically, studies of the Milky Way have relied on the positions and motions of stars to determine properties of our Galaxy. Pulsars periodically emit radio waves with temporal stability rivaling that of atomic clocks; this allows us to directly measure accelerations of these sources. These accelerations allow us to model the gravitational potential, and therefore the dark matter content, of the Milky Way in a novel way that does not rely on the assumptions of other kinematic methods. This could shed light on unknown properties of dark matter, which is a main goal of modern astrophysics. However, we are currently only able to use pulsars in binary systems (pulsars that are orbiting some other object) to measure accelerations. This is because a pulsar's spin rate slows down over time due to a poorly-understood magnetic braking term, while the orbital period of the binary does not depend on the magnetic braking term.
The student will gather observed data for binary pulsars from online catalogs and publications. The student will then compare the accelerations measured from the binary orbital period and the spin rate of binary pulsars in order to determine the contribution of the magnetic spin-down term for each binary pulsar. By comparing these spin-down rates to theoretical models and other observed properties of the binary pulsars, it may be possible to determine the spin-down term for any given pulsar. This would allow us to use solitary pulsars to measure accelerations, which could potentially double the number of pulsars we can use to measure accelerations. The student may also calibrate dispersion-measured distances to pulsars, which could lead to a further increase in the number of usable pulsars. The student will produce a list of all pulsars with new acceleration measurements, which may be included in a future publication if the project is successful.
Prerequisites/Requirements: The student is expected to have a sophomore-level or higher foundation in physics, astrophysics, and mathematics. Successful completion of AST106 and AST107 is required. Successful completion of PH301 and/or PH305 would be beneficial, but is not required. The student should be enrolled in the College of Science (Physics, Math, and CS majors are encouraged to apply), although applicants from the College of Engineering will also be considered. A basic ability to write code, preferably Python, is required.
Prof. Shanhu Lee
AES | sl0056@uah.edu
Chemical Analysis of Atmospheric Species
To use state-of-the-art mass spectrometers to analyze aerosol chemical composition.
Prerequisites/Requirements: Major in Chemistry or Chemical Engineering or Civil Engineering, with excellent GPA and work ethics.
Dr. Jie Ling
Chemistry | jl0243@uah.edu
Development of Novel Ultraviolet Nonlinear Optical Crystals
Nonlinear optical materials (NLO), which can alter the frequency of light, have a wide application in military and civil fields, such as telecommunication, quantum computing, and biodiagnostics, etc. Depending on the chemical and physical construct of the materials, NLO materials can combine multiple photons to generate shorter wavelength photons or split one photon into several new photons of longer wavelengths. To be NLO active, materials must adopt noncentrosymmetric (NCS) crystal structures, and simultaneously meet other requirements, including transparency, phasematchability, high optical quality, optical damage threshold and availability in bulk form. Currently, only limited amount of them is practical and their application is narrowed in the visible light range, while IR and UV NLO materials are much underdeveloped.
To this work, the targeted materials include metal borates, iodates, and tellurites with NCS structures which are essential for nonlinear behavior and they will be synthesized using a suite of complementary crystal growth methods. And to increase the transparency and facilitate their application in UV, small halide ions, i.e., F- will be introduced into the structure by replacing partial of oxygen atoms on these NSC oxoanions which can blue-shift the absorption edge. This proposed work will expand the family of NLO materials, advance the understanding and knowledge on the correlation between crystal structure and optical property, and may develop novel practical optical crystalline products. By participating in this work, students will learn to design the synthesis of NCS compounds and grow high-quality crystals through hydrothermal and salt fluxes routes. They will also gain hands-on experience on single crystal X-ray diffraction, powder XRD, SEM-EDS, FTIR, UV-Vis-NIR which can be used to determine the crystal structure, composition and optical property.
Prerequisites/Requirements: None
Prof. Sean Freeman
Atmospheric and Earth Science | swf0006@uah.edu
Evaluating Weather Models with Cloud Tracking
For us to trust our weather prediction models, we must be able to evaluate them. This is particularly important as we move into the era of high-resolution global models, which can now forecast the local impacts of individual clouds and storms globally. Traditional evaluation methods for weather models have primarily focused on broad-scale weather conditions rather than local-scale or have relied on incomplete local-scale surface observation datasets for comparison. In this project, we will use the existing operational weather radar dataset to evaluate today's high-resolution regional-scale operational weather models, such as the High Resolution Rapid Refresh (HRRR) model, to prepare for future high-resolution global-scale models.
We will use the Tracking and Object-Based Analysis of Clouds (tobac) python-based cloud tracking package to track individual clouds and storms in numerical models and observations to compare the radar and model datasets. tobac has the unique advantage of allowing the tracking of storms on any input dataset, meaning that we can produce comparable output when tracking across both radar observations and weather models. With our two tracked datasets, we will evaluate, on a statistical scale, how storm-scale weather models perform on the local scale. This project will give us a unique look into how current weather models are performing on storm scales, provide us with tools to evaluate new weather forecasting models, and have significant consequences for weather forecasting today and in the future.
Prerequisites/Requirements: Some Python background, some knowledge of weather (AES majors preferred)
Dr. Mohamad Nassar
Computer Science | men0012@uah.edu
Explainable IP Flow Classification
In the 21st century, botnets are becoming one of the most critical cybersecurity threats. Botnets are used to initiate Denial-of-Service (DDoS) attacks, credential stuffing, spamming, and malware spreading. This motivated cybersecurity researchers to develop novel approaches that analyze botnets, detect them, and mitigate the attacks they perform. IP flows are sometimes the only and most important data source to identify botnets' IP addresses. However, most IP flow analysis techniques are still based on signature-based intrusion detection. This approach is ineffective since botnet signatures have become more complex and associated with evolving behavior. Researchers are experimenting with machine learning-based intrusion detection that can embed IP addresses in a hyperspace based on their behavior in the IP flow data. Embedding allows measuring similarities between IP addresses and clustering them according to their nature (Normal or Botnet). This project aims to provide an explainability layer on top of IP2Vec model. We plan to study and experiment with several explainability algorithms and report on their utility, expressibility, and performance.
Prerequisites/Requirements: Major: Computer Science, Computer Engineering or Mathematics. Required Courses: Intro to AI or intro to Machine Learning, Intermediate Programming, and Computer Networks. In addition, mathematical maturity is required.
Dr. Satyaki Roy
Mathematical Sciences | sr0215@uah.edu
Game Theoretic Strategies to Optimize Global Healthcare Resource Distribution
In the wake of the unprecedented health emergency of COVID-19, the necessity of efficient healthcare resource distribution cannot be overstated. Any global allocation strategy presents a multifaceted optimization challenge, encompassing an interplay of socioeconomic, demographic, and political dimensions. This complex task is further constrained by factors, such as budgetary considerations, clinical and epidemiological need assessment, and international relations. To tackle this complex problem, this study outlines four primary goals. First, it aims to optimize the supply chains for medical equipment and pharmaceuticals, with a keen focus on minimizing logistical costs. Second, it endeavors to utilize principles from game theoretic approaches to design multi-objective optimization strategies to incentivize the players to adhere to practices that are likely to result in equitable allocation based on long-term epidemiological and sociodemographic factors. Third, it aims to leverage behavioral economics to shape public health messaging and interventions, recognizing the pivotal role of human behavior in crisis management. Lastly, the study delves into international relations, analyzing vaccine diplomacy strategies to gauge how countries, organizations, and non-state actors utilize vaccines and drugs as tools for geopolitical influence. Validation of these approaches will be conducted through large-scale discrete event modeling and simulation experiments, capturing the effects of the policy recommendation. The goal will be to ensure that the optimization of supply chains, cooperative game theory, and behavioral economics models are not merely theoretical constructs but practical solutions that can enhance resource allocation, particularly during a major disease outbreak. In summary, this research project addresses the pressing issue of resources. It emphasizes the importance of needed resource distribution to overcome the time-critical and global humanitarian challenge.
Prerequisites/Requirements: Preliminary knowledge of optimization, game theory, and programming
Dr. Themis Chronis
Physics | tc0025@uah.edu
The student will develop a curriculum for pre-college physics for Newtonian mechanics on the Simphy platform. They will design clever and unique experiments while working on the analytical solution (if one exists). Examples among others: Atwood machine, variable friction and forces, oscillating springs etc. The goal of the project is make physics problems more visually enhanced and comprehensible to a student of minimal or no background
Prerequisites/Requirements: Very strong background on PH-111
Dr. Leiqiu Hu
Atmospheric and Earth Science | lh0063@uah.edu
How to cool Huntsville in the summer
Urban areas often experience higher temperatures during the summer compared to their surrounding rural areas, a phenomenon known as the "urban heat island" effect. Extensive research has demonstrated the detrimental consequences of this phenomenon on urban environments, particularly during the summer months. These consequences include heightened photochemical reactions, leading to reduced air quality, increased human discomfort, and elevated energy and water consumption. Various mitigation strategies, such as expanding urban forest coverage and employing lighter building surfaces, have been explored and tested in several cities.
One effective mitigation strategy involves strategically planting trees and shrubs around buildings to provide shade, which directly reduces direct solar radiation and lowers daytime building and ground surface temperatures. Additionally, the evapotranspiration process of these plants can potentially cool the surrounding environment. However, the nighttime impact of urban forests remains inconclusive, particularly when the urban heat island effect is pronounced. Some studies suggest that tree canopies may trap longwave radiation in the urban canopy layer, thereby slowing the cooling rate of urban surfaces at night. Lighter-colored building exterior surfaces contribute to reduced solar radiation absorption, resulting in lower surface temperatures.
The primary objective of this project is to assess the potential benefits of these mitigation strategies at a microclimate scale in Huntsville. To achieve this, a combination of thermal cameras and weather sensors will be deployed to monitor thermal environment changes in various urban settings within the city, including the UAH campus, greenways, downtown areas, and shopping plazas. The data collected from these observations will be used for statistical analysis, aiming to identify effective methods to enhance microclimate thermal comfort in Huntsville.
Prerequisites/Requirements: Students who are interested in this topic and have basic knowledge of math and image processing are encouraged to apply.
Dr. Bradley Kraemer
Biological Sciences | brk0006@uah.edu
Investigating p75NTR Signaling Mechanisms and Associated Effects on Neurodegeneration Associated with Parkinson's Disease
The proposed research project will investigate the signaling mechanisms through which a transmembrane protein termed the p75 Neurotrophin (p75NTR) influences neurodegeneration associated with Parkinson's Disease. The specific project details will be determined collaboratively with the RCEU student, but options include 1.) assessing whether disruption of p75NTR signaling protects dopaminergic neurons from oxidative stress-induced neurodegeneration 2.) exploring the effects of the p75NTR coreceptors sortilin and TrkA on oxidative stress-induced p75NTR activation or 3.) evaluating the role of JNK in promoting oxidative stress-induced p75NTR internalization. The student working on this project will use cell culture and/or mouse models, and the student will learn a variety of molecular biology research assays. However, due to the time required to learn the assays associated with this project, this project is best suited for a student who can commit to working on the project over multiple semesters. Thus, preference will be given to applicants who can work on this project over multiple terms (a one-year commitment is preferred), with the RCEU program serving as a funding source and training program for one term of a multi-term project.
Prerequisites/Requirements: Preference will be given to applicants who have achieved a grade of B or higher in a cell biology-related course (BYS 119, or a higher-level cell biology course) prior to the start of the RCEU project.
Prof. Don Gregory
physics | gregoryd@uah.edu
Laser Light for Propellant Ignition
The research will experimentally determine what parameters are required for using an optical fiber (illuminated with intense laser light) to ignite solid rocket motor propellants. These fibers are inert and can be cast inside the propellant when it is formed. Initial testing has shown that scattered light from a properly etched fiber may be enough to ignite the propellant, but there is another possible method. With intensities easily achieved with a laboratory laser, it is possible to effectively melt the end of the fiber--and the melt propagates back toward the source, igniting the propellant in a controlled manner (by controlling the laser power incident on the fiber). This sort of control is not possible with conventional fuses and should lead to an improvement in rocket motor performance throughout the burn stage. The goal is to demonstrate ignition using the two methods above and to quantify the parameters required.
Prerequisites/Requirements: The ideal student would be experimentally oriented with a basic knowledge of optics gained in the classroom or in practical situations. The student should have completed basic courses in physics and/or mechanical engineering and be competent in the analysis of experimental data.
Prof. Sivaguru Ravindran
Mathematical Sciences | ravinds@uah.edu
Prediction and Feedback Control of Epidemics
The ongoing COVID-19 pandemic has renewed interest in the mathematical modeling, analysis and control of epidemic spread. One of the most successful mathematical models of epidemics is the so-called susceptive-infective-recovered (SIR) models. In this project we study SIR epidemic models with feedback control to reduce contact rate, to reduce the cost of facilities etc. Feedback control will be designed using linear quadratic regulator approach. Computed feedback control will be employed in the nonlinear model to close the loop. By constructing a suitable Lyapunov function global stability of the disease free equilibrium and endemic equilibrium of the model will be investigated. We will investigate to find out by choosing suitable values of feedback control variables, we can make the desease endemic or extinct. The results from this project are expected to provide insights in understanding and controlling the epidemic.
Prerequisites/Requirements: The student applications need to know differential equations (MA238 or MA465) and programming proficiency (e.g., MATLAB).
Dr. Roderick Davidson
Physics and Astronomy | rbd0001@uah.edu
Stimulated emission for generating quantum photonic states
To develop a room-temperature, on-demand single photon source for quantum information processing using nitrogen vacancy centers in diamond that will be robust to realistic environmental conditions. This source will be able to optically control the wavelength and polarization of individual photons.
Absorption events in an excited quantum system can trigger the premature decay of that system to a lower energy level and result in stimulated emission. Such emission from a singular defect site will have no two photons closer to each other than the duty cycle of the stimulating event. To separate the stimulating and stimulated photon, we propose to use intense femtosecond laser pulses to drive this transition into a virtual state instead of the ground state. The virtual state is not an eigenstate of the system and can only exist for a small amount of time before the defect finishes relaxing all the way down to the ground state. This intermediate state allows for single photon emission that is both non-degenerate and well correlated in time with the incoming laser pulse. Additionally, the conservation of momentum still dictates that the polarization state of the resulting photon must be determined by the state of the laser pulse. Therefore, we can control the phase, wavelength, and polarization of this single photon source at room temperature. NV centers in diamond will be the primary material for this research due to the well-known quantum optical properties and the large two-photon cross section of the system. The large bandgap of diamond (5.5 eV) shields the system from free electron interference and allows for efficient coupling of the system to an external cavity. The cavity will then allow for tunability across the broad range of emission of NV centers, 600-800 nm.
Prerequisites/Requirements: 1. Become familiar with the theory of single photon generation and sub-poissonian statistics 2. Operate an HBT interferometer to establish single photon statistics from test samples 3. Test diamond samples to isolate single defect sites 4. Demonstrate stimulated emission based gain from NV centers in diamond
Prof. Ming Sun
Physics & Astronomy | ms0071@uah.edu
Studying galaxy evolution with the data from the Hubble Space Telescope
Galaxy clusters are the gravitationally bound structure of hundreds or even thousands of galaxies. Most baryons (or ordinary matter) in clusters are in the hot intracluster medium (ICM) with temperatures of ~ 10^8 K. Cluster galaxies soar through the ICM and the interaction with the ICM plays a vital role in galaxy evolution, through ram pressure stripping (RPS) of the galactic cold gas (ram pressure is a pressure exerted on a body moving through a surrounding medium. Think about the pressure you feel inside a swimming pool when you try to run). As the cold interstellar medium is depleted by stripping, the galactic star formation will eventually be shut down and blue disk galaxies can turn into red galaxies. Thus, RPS is an important process in galaxy evolution. Observational evidence of stripping in cluster galaxies has only started to emerge in the last 15 years and is growing fast. We are recently granted a large research program to study 28 galaxies and their stripped tails to study galaxy evolution. An important component of this multi-wavelength campaign is the studies on galaxies and the young star clusters in the stripped tails with the data from the Hubble Space Telescope (HST). The HST data, with superior angular resolution, can map the dust distribution, the detailed galaxy morphology, the nucleus and resolve young star clusters in the stripped tail of galaxies. All these results provide important information on the evolution of galaxies during stripping. Our science goals are:
1) Study the galaxy morphology and fit the HST images with GALFIT to search for any asymmetric features.
2) Study the dust distribution as it provides important constraints on stripping.
3) Study the nuclear region to examine the activity of the central super-massive black hole --- is it enhanced or suppressed by the ongoing RPS?
4) Study young star clusters in the stripped tails.
Our group has rich experience with the HST data and have in-house codes for the proposed research.
Prerequisites/Requirements: The successful applicant should have a good academic record (GPA > 3.4) and have finished introductory math & computer classes. Introductory Physics and Astronomy classes are preferred but not required. The successful applicant should also have experience with python and programming.
Dr. Satyaki Roy
Mathematical Sciences | sr0215@uah.edu
Temporal Disease Subtype Interactions: A Deep Machine Learning-based Investigation
There is an increasing prevalence of diagnosed and undiagnosed comorbidities among hospital admissions. Late detection or undetected comorbidities results in delayed treatment and worsening health outcomes. It is imperative to identify and manage comorbidities as they directly impact treatment strategies, cost and resource allocation, and clinical outcomes. Existing models, which map the clinical features of patients to their observable phenotypic presentations, frequently prove inadequate in capturing the relationships between diseases and their many subtypes. This shortfall results in patient profiles that are, at best, incomplete and, at worst, inaccurate. This project aims to harness the expressive power of machine learning (ML) to enhance the clinical features with the interdependency among diseases and their subtypes in predictive analysis. Specifically, the project revolves around graph-based deep ML techniques to combine the physiological features at a patient level with interdependency information to improve inference. Such models have proven to be effective in tracking and visualizing complex interactions between disease entities. They scale to large datasets while being transferrable to myriad healthcare scenarios. They even take into consideration the temporal aspects of disease progression by modeling the evolution of the interaction among diseases and their phenotypic presentations. The ability to predict missing and future comorbidities promises to have a profound impact on the healthcare ecosystem, particularly in improving risk assessment, resource allocation, and cost management. Finally, a key takeaway from this study will be new ontologies, where diseases are classified by organ systems, symptoms, or pathological characteristics, leading to the identification of disease-specific biological pathways and drug targets. This could be a new stride in personalized medicine, where treatment will be tailored to a person’s physiological profile and risks.
Prerequisites/Requirements: Basic understanding of machine learning and biology
Dr. Phillip Bitzer
Atmospheric and Earth Science | bitzerp@uah.edu
The Effect of Lightning on Tropical Forests
We currently are exploring how lightning affects tropical forests, particularly how different lightning strikes affect different tree types. Work done so far has included in-field investigations of trees struck by lightning, flying drones to assess lightning damage, and analysis of a camera network to identify the lightning that struck a tree. Our study area is in Panama, where we have several instruments that detect lightning in place. In this project, we will explore how different lightning parameters relate to how trees respond to the impulse. For example, we have found that lightning can kill anywhere from 3-7 trees from a single discharge, and damage around 20-30. One outstanding question from this work is how different types of lightning affect the tree mortality. Work to be done includes analysis of lightning data correlated with known struck trees. The ultimate goal is to produce a relationship between lightning attributes and the response of the trees in the forest. Besides the main goal, there is also an opportunity to conduct field work to collect more lightning and forest data, as well as opportunities in the laboratory to work with the lightning instrumentation.
Prerequisites/Requirements: Some experience with coding is preferred
Prof. Xiaomin Chen
Atmospheric and Earth Science | xc0011@uah.edu
Thermodynamic Processes during Hurricane Intensification under Strong Vertical Wind Shear
Strong vertical wind shear (VWS) is typically considered detrimental to hurricane intensification, as it can disrupt the core structure and introduce dry air into the hurricane circulation. Nevertheless, unexpected rapid intensification events were occasionally observed under strong VWS, sometimes just before landfall. These events underscore the critical need to comprehend the physical processes that counteract the impact of VWS and contribute to hurricane intensification. Gaining insight into these processes is imperative for advancing hurricane intensity forecasts.
The proposed research aims to identify the critical physical processes that lead to rapid hurricane intensification under conditions of strong VWS. This will be accomplished by analyzing output data from NOAA's state-of-the-art hurricane forecast model, the Hurricane Analysis and Forecast Model (HAFS). The participating student will have the opportunity to become proficient in the data analysis of this cutting-edge hurricane forecast model and make meaningful contributions to the forefront of hurricane science.
Prerequisites/Requirements: Majors of Atmospheric Science or Meteorology are welcome to apply, and preference will be given to students with a strong interest in analyzing the hurricane model output. Experience of using Python or other data analysis softwares is a plus. The minimum GPA requirement is 3.0 but GPA is only one of the criteria used to evaluate applicants.
Dr. Marc Pusey
Chemistry | mlp0041@uah.edu
Thermofluor Screening for Protein Crystallization
Crystallization requires stable protein in the solution conditions of interest. This project is to develop a protein melting curve (aka thermofluor) assay for protein stability determinations under crystallization conditions. The first step is the developing of assay parameters, specifically the protein, fluorescent probe, and precipitant concentrations. Model proteins, having previously determined crystallization conditions, will be used for this purpose.
The thermofluor assay is where a protein is mixed with the solution conditions of interest, plus a fluorescent indicator dye. This mixture is then heated at a fixed rate, from ambient to ~100 °C, with the fluorescence intensity being measured at specific time or temperature intervals. As the protein denatures the dye partitions to the exposed hydrophobic interior regions, resulting in an increase in the fluorescence intensity. Analysis of this signal is used to determine the protein melting temperature under the assay conditions. This assay is typically carried out in 96 well plates using a rtPCR instrument, and usually takes ~2 hours to run.
Once the assay parameters have been determined thermofluor measurements will be made with several proteins using up to four standard sets of crystallization conditions. The melting curves will be correlated with crystallization screening results for those proteins and sets of conditions. It is anticipated that the crystallization conditions are those that have higher temperature melting points. This will be tested with one or two new blocks of screening conditions, where the crystallization conditions testing will be made based on the outcome of the thermofluor assay.
Prerequisites/Requirements: Chemistry or Biology majors.
Prof. Ming Sun
Physics & Astronomy | ms0071@uah.edu
Understanding 3D data cubes from the Very Large Telescope
We often say "a picture is worth a thousand words". In Astronomy, we also say "a spectrum is worth a thousand pictures". Nowadays with the advance of observational technology, we can further say "a data cube is worth a thousand spectra". Data cubes from spatial spectroscopy have become more and more common in Astronomy, from radio, sub-mm, IR, optical to X-rays. Data cubes provide spectroscopic information on every pixel of a picture. A world-leading telescope/instrument combo to produce data cubes with a high spectral resolution is the Very Large Telescope (VLT)/MUSE in optical, which is one of the most sought-after instruments in the world (typically only one of ten proposals submitted can be approved). The rich amount of information in data cubes has also brought a lot of challenge on data analysis and visualization, as source detection becomes a three-dimensional problem and kinematics often needs to be studied together with morphology.
We are recently granted a large amount of the VLT/MUSE data to study galaxy evolution and feedback from super-massive black holes. While we have been using an IDL code to generate maps from the cube data, the code needs to be updated to python with more features, like adaptive binning with voronoi tessellation, parallel processing and more choices of available spectral models. Our group has started a project to have a new python code for the required cube analysis. In this RCEU project, the student, ideally already with python experience, will contribute to the project to have a new python code to generate maps of emission lines, gas kinematics etc. The student will also use the results to study the feedback of super-massive black holes on the surrounding medium and the accretion of materials to the super-massive black holes.
Prerequisites/Requirements: The successful applicant should have a good academic record (GPA > 3.4) and have finished introductory math & computer classes. Introductory Physics and Astronomy classes are preferred but not required. The successful applicant should also have experience with python and programming.
Dr. Lavanya Ashokkumar
Atmospheric and Earth Science | la0057@uah.edu
Understanding climate change from glaciers
The Intergovernmental panel on climate change has reported that the melting glaciers and thermal expansion of the oceans are causes of sea-level rise (IPCC, 2022). Globally, around 200 billion people are likely to be affected by increasing sea-level rise by the end of this century, with direct consequence for humans in the coastal regions. Excluding the ice sheets, glaciers play a prominent role in the increasing sea-level rates, which is about 10-30% of the total contribution from glacier melt. Even though this is a smaller proportion, mountain glaciers have significant importance in regional hydrology, and are considered as the most sensitive climate indicators in our warming planet. This is particularly relevant in the Arctic region since the environment is highly vulnerable to the effects of global climate change. Also, this region suffers from lack of field measurements due to inaccessibility in the polar (cold and arid) environments. Therefore, there is a need for better understanding on how much these glaciers are melting at spatial (geographical location) and temporal resolution (continuous time interval over the last 20 years).
For this project, we will be using the techniques in remote sensing and geospatial methods to explore, analyze and interpret the glacier change. We will be utilizing the remote sensing datasets from NASA satellites, which are available from several open-source archives (listed in the background section). These datasets can be readily downloaded from the public repositories. In this way, we will systematically be considering glaciers in the Arctic region (Alaska, Northern Canada, Iceland) to understand how glacier melt in terms of ice thickness and/or volume from remote sensing and geospatial techniques. The project will also address the challenges in big data of remote sensing for climate change studies.
Prerequisites/Requirements: For this course, it is important for the student to understand climate change and its effects to the environment. So, you should have taken a basic 100 level course in Environmental Earth Science or Climate in your Freshman or sophomore. It is preferred that the student has a background in the Atmospheric and Earth Science, but not a prerequisite for this project. If the student is not having a major or minor in AES, then the student should have taken programming course (Computer science or Mathematics) at 300 or 400 level. For AES Majors, you should have taken either two of these courses listed: AES 301 Introduction to Remote sensing, AES 313 Geographic Information System, or a programming course at 300 level.
Dr. Olaf Nachtigall
Chemistry | on0012@uah.edu
Use of Pulsed Laser Ablation in Metal Cluster Synthesis
Clusters of ferromagnetic metals with zero-valent kernels are expected to possess unique electronic and magnetic properties and could be used in catalysis or quantum information science. In this project, intense laser pulses will be focused onto metallic targets in anhydrous liquids to ablate metal atoms and to form such clusters. In contrast to the synthetic procedures commonly used for laser ablation, all experiments will be conducted under inert reaction conditions as known from modern organometallic chemistry. Therefore, the water- and air-free samples will be handled in a dinitrogen-filled glovebox and Schlenk techniques will be applied.
Prerequisites/Requirements: There are no requirements or prerequisites for this project. However, knowledge in the field of organic, inorganic, and/or electrochemistry chemistry, as well as spectroscopy and/or laser optics is preferred.