The mining industry relies on civil structures for ground support, safety and continuation of production through rock fragmentation by rock blasting activities involving drilling, charging and detonation. Drill and blast operations are the lever of measure for productivity but can also cause blast-induced ground vibration issues in sensitive catchment communities. Rising global production targets correspond to increased ground vibration and a solution to retain social licenses must be developed employing civil and geotechnical mitigation strategies that consider rock structure and behavior under cyclic loading conditions. The key physics in cyclic dynamic wave propagation in rock masses provide fundamental knowledge for effective mitigation of blast-induced ground vibration. Specifically, conduct laboratory studies of dynamic wave propagation and attenuation through meta-sedimentary (phyllite) and igneous (granite) rocks under field-representative cyclic loading conditions, coupled measurements of loading-unloading cycles, deformation, damage accumulation, active ultrasonic wave propagation, and passive acoustic emission characterization are used. Also, porosity measurement and X-ray diffraction (XRD) analysis are used to quantify rock mineral composition. Thin-section and scanning electron microscope (SEM) imaging is completed on rock samples before and after the cyclic loading to understand the microscopic locales of damage and cracks. Our study addresses the key knowledge gap of coupling between rock damage accumulation and dynamic wave attenuation, which feeds into the thread to design an impending structure to effectively retain blast-induced ground vibrations. The study will also suggest advancements in building designs and sustainable material solutions for local structures to remain resilient throughout the life of the mine. 

This research investigates the effectiveness of current prevention techniques for Black Lung Disease (pneumoconiosis) among miners, challenging the validity of prevalent assumptions such as simplified lung geometrical models and the neglect of inhalation mechanisms in predicting lung dust deposition. Utilizing a multifaceted approach that combines Computational Fluid Dynamics (CFD) with innovative experimental setups, including a dust wind tunnel and artificial 3D printed lung models, this study scrutinizes the impact of these assumptions on the accuracy of dust deposition predictions in the lungs. Central to our investigation is the deployment of a dust wind tunnel, equipped with an artificial lung model created through 3D printing, and Particle Image Velocimetry (PIV) alongside a transparent silicon lung model. These experimental setups are designed to simulate real-life exposure scenarios within a controlled environment, enabling precise measurements of dust particle trajectories and deposition patterns in the lungs. By integrating CFD simulations, our research further explores the dynamics of airborne dust particles and their interaction with the lung models. This dual approach allows for a comprehensive analysis of how simplified models and overlooked inhalation mechanisms can lead to misconceptions in the estimation of dust deposition in the lung's complex structures. The aim is to reveal the discrepancies between current preventive strategies and the actual deposition rates of dust particles within the lung tissue. By shedding light on these critical gaps, our study seeks to pave the way for more accurate and effective prevention techniques against Black Lung Disease. 

Allopolyploidy – a hybridization-induced whole-genome duplication event – has been a major driver of plant diversification. The extent to which chromosomes pair with their proper homolog vs. with their homoeolog in allopolyploids varies across taxa, and methods to detect homoeologous gene flow (HGF) are needed to understand how HGF has shaped polyploid lineages. The ABBA-BABA test represents a classic method for detecting introgression between closely related species, but here we developed a modified use of the ABBA-BABA test to characterize the extent and direction of HGF in allotetraploid Coffea arabica L. We found that HGF is abundant in the C. arabica genome, with both subgenomes serving as donors and recipients of variation. We also found that HGF is highly maternally biased in plastid-targeted – but not mitochondrial-targeted – genes, as would be expected if plastid-nuclear incompatibilities exist between the two parent species. Together our analyses provide a simple framework for detecting HGF and new evidence consistent with selection favoring overwriting of paternally derived alleles by maternally derived alleles to ameliorate plastid-nuclear incompatibilities. Natural selection therefore appears to shape the direction and intensity of HGF in allopolyploid coffee, indicating that cytoplasmic inheritance has long-term consequences for polyploid lineages.

Conductive polymers are materials with conjugated organic backbones that as their name suggests conduct electricity. For their discovery the Nobel in chemistry was awarded in 2000 and these materials continue to be of interest for use in organic semiconductors, electrochromic devices and many other applications. Electrosynthesis is one of the primary methodologies used to generate conductive polymers. In Thiophenes, and other monomers, high oxidation potentials in most organic solvent-electrolyte systems leads to defects and the so-called thiophene paradox caused by the overpotential requirement of the thiophene monomer oxidation. To overcome this electrochemists utilize the solvent, electrolyte and lewis acid catalyst boron trifluoride diethyl etherate(BFEE). BFEE is a hygroscopic and reactive liquid which restricts its use to inert atmospheres, but as a result of its catalysis lowers the onset of oxidation of the monomer and addresses the the issues associated with other solvent electrolyte systems. This study uses in-situ ultraviolet-visible-near infrared(UV/vis/NIR) spectroscopy to examine the film formation and electrochromism of polymers generated from thiophene and its 3,4-substituted derivatives in BFEE and Acetonitrile. UV/vis/NIR spectroscopy provides insight into the polymer formation on the surface of the electrode and oligomer formation at the surface and in solution during the oxidation. In this study the formation of the polymers and oligomers by oxidative electropolymerization of a variety of thiophenes is examined and the contributions of the solution state oligomerization is decoupled from the solid state polymerization through solution phase versus solid phase In-situ UV-vis/NIR Spectroelectrochemistry.

Redox flow batteries (RFBs) offer promising solutions for large-scale energy storage, boasting high-power density, scalability, and safety. Despite their potential, current RFBs face challenges like resource constraints and high costs. Aqueous organic RFBs could address these issues if expenses are reduced. This study focuses on optimizing a promising total aqueous organic RFB chemistry using cost-effective and sustainable materials. Specifically, utilizing (SPr)2V as an anolyte and 4-HO-TEMPO as a catholyte, with benign KCl as the supporting electrolyte functioning through an anion exchange mechanism. (SPr)2V, derived from the viologen class, shows promise due to its high solubility, negative redox potential, and ability to accept 2e- reversibly. The objectives include understanding viologen species degradation, characterization of the degraded products, exploring oxygen and pH susceptibility, and enhancing its performance through modifications like alternative supporting salt anions. This study aims to double the compound's storage capacity by leveraging its 2nd electron. The findings will reveal low-capacity fade rates under certain parameters, with insights into the effects of oxygen, concentration, heating, and pH on dimerization and capacity loss. This study assesses the electrochemical properties using cyclic voltammetry and rotating disk electrode voltammetry. The (Spr)2V/4-HO-TEMPO ARFB has an exceptionally high cell voltage, 1.25 V (1e-) and 1.9 V (2e-). Prototypes of the organic ARFB can be operated at high current densities ranging from 20 to 100 mA cm-2 and deliver stable capacity for 100 cycles with 99+% Coulombic efficiency. This research could pave the way for cost-effective organic-organometallic RFBs, facilitating grid-scale electricity storage and renewable energy integration.

Any missions to clean up space debris by removing it from orbit will encounter multiple obstacles from international space laws. In order to assess both immediate and long-term challenges a mission might encounter, a hypothetical scenario was devised wherein NASA is the sole proprietor of a meticulously tested and fully operational space debris removal device, intended for a single use. All of NASA’s missions must comply with regulation for space exploration enforced by the U.S., as well as regulations enforced by international organizations the U.S. belongs to. Following an analysis of each challenge identified, possible solutions are discussed and evaluated. Articles VI, VII, and VIII of the Outer Space Treaty were found to create the most challenges for a debris removal mission due to lack of details included in the regulations. Long-term challenges primarily include mitigating future space debris by implementing new laws and creating innovative designs, such as the LignoSat satellite mission currently in progress.

In mine catastrophes, mine rescue teams must effectively locate and extract trapped miners, while operating with their safety in mind despite their time-sensitive mission. Thus, mine rescuers must often wait for hazardous conditions to subside. It is, therefore, necessary to expedite mine rescue operations to evacuate miners in a timely manner. The aim of this project is to create an autonomous multi- agent robotic mine rescue system to assist search and rescue (S&R) operations in underground mines. The proposed approach is comprised of a UGV base capable of housing and deploying a custom drone. The UGV-drone system will be able to map the environment, collect gas and environmental data, and detect humans. In operation, the UGV will map the environment and relay sensor readings to the rescuers. The drone will be deployed if the UGV cannot traverse a path. The design of the system involves software and hardware selection, drone housing fabrication, mine permissibility considerations, communication nodes dropping, and precision drone landing. This system could significantly expedite S&R operations, minimize risks for the rescuers, and save lives. 

The low-intensity ground-level fires necessary for forest health have increasingly been replaced by uncontrolled, crowning, and stand-replacing fires due to a warming climate and accumulated fuel loads. This not only has led to a substantial loss of land and livelihoods, but has also raised serious air quality concerns. Wildland fire events emit significant amounts of particulate matter less than 2.5 micrometers in aerodynamic diameter (PM2.5) including black carbon (BC) and brown carbon (BrC) particles. Particulate within this size range is known to be hazardous to human health and specifically BC and BrC are notorious climate forcing agents due to their low albedo. Using a customizable unmanned aerial vehicle (UAV) to carry a multiwavelength light absorption instrument (AethLabs MA200) and a mass concentration sensor (PurpleAir) allows three-dimensional freedom when observing lofted pollutants from wildfire plumes. Laboratory burn experiments were conducted to validate the Purple Air sensor. It was found that two Purple Air sensors were well correlated to two QuantAQ modules during both flaming (R2 > 0.97) and smoldering (R2 > 0.99) burns. Initial UAV flights were conducted at the Socorro Fire Training Center (SFTC) to sample emissions from burning buildings and diesel fuel spills (both representative of fuels in the wildland-urban interface). In both events, the UAV system was able to detect key absorptive properties as well as detect significant amounts of lofted PM2.5 concentrations.

Quantitative schlieren is an optical imaging method used to analyze refractive index changes in gases which can be related to the local gas density. Here quantitative schlieren is used to measure the density of air inside of the shock wave surrounding a supersonic conical projectile. The challenge with this technique is that schlieren imaging records a projection of the refractive index field, requiring a geometric reconstruction to return a planar density field. This research seeks to maximize the precision of the density reconstruction by exploring several three-dimensional deconvolution techniques. The technique is used to reconstruct the density fields around projectiles with different velocities. The results are compared to the theoretical Taylor-Maccoll profile.

Bacteriophages are some of the most underexplored biological entities on the planet. Despite their high abundance and importance in biogeochemical processes, there has been little study into the viromes in most of the Earth’s environments, especially in the deep subsurface. We investigated phages in deep groundwater from the Moab Khotsong mine in South Africa using a two-pronged strategy. First, a culture-dependent approach was applied to both a 1.2-kilometer-deep groundwater and a 3-kilometer-deep,1.2-billion-year-old brine. A bacterial host was isolated from the samples and lytic phages infecting the host were isolated. These phages were visualized using electron microscopy and their genomes were sequenced using Illumina technology. Second, we created and analyzed metagenomic libraries from the groundwater. Viruses were concentrated from 0.2 micrometer-filtered water by both sorption to iron oxide and tangential flow filtration with a 100 kilodalton membrane; DNA was then extracted and shotgun sequenced. In addition, viral-like particle and cell-counting analyses are being performed on both sets of samples. In the culture-dependent approach, Halalkalibacterium halodurans was isolated from the deep, ancient brine, and phages lytic for the host were isolated. These phages have siphovirus morphology; their genomes are assembled and annotated. The metagenomic sequences are currently being assembled and analyzed but based on preliminary analyses, the groundwater sample contains many novel phages infecting a variety of hosts which also appear to harbor some auxiliary metabolic genes. Future work includes more analysis of metagenome-assembled genomes from both bacterial and viral sources and further isolation against other cultured bacteria from both samples.

Breast cancer is a complex disease requiring an early detection and evaluation of tumors in an early stage. In this study, we employed machine learning (ML) algorithms including Support Vector Machine (SVM), random Forest (RF), Dense Neural Network (DNN) and 1-dimensional CNN (1-DCNN) to classify four pivotal breast cancer biomarkers- estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), progesterone receptor (PgR), Nottingham histologic grade (NHG), and the nuclear protein Ki67. The analysis leveraged a dataset of 13,804 gene expressions, sourced from the NCBI’s gene expression database. Additionally, the survival outcomes of 2,599 breast cancer patients were investigated to compare the accuracy of these ML approaches and identify the most effective approach on this specific database. We particularly wish to contrast our findings with the results presented in the studies by [Brueffer et al., 2018] and [Chen et al., 2021], both of which also drew insights from similar datasets, to assess the efficacy of our ML methodologies against existing standards. The results indicate the potential of machine learning models in predicting ER and HER2 biomarkers with high accuracy. In particular, the 1-DCNN model achieved an impressive 97.80% accuracy for ER prediction. However, it’s important to note that gene ordering has a substantial impact on model performance, highlighting alphabetic gene order as the best method for PgR (95.26%), HER2 (95.61%), NGH (75.26%) and survival status (92.72%) of patients with breast cancer. However, difficulties continue to arise in precisely projecting NHG and survival results, indicating the necessity for further model improvement.

Currently there are many orphaned wells emitting methane which is detrimental to the environment and a vital reason for global warming. The objective is to build a fully sustainable isolated autonomous vertiport system allowing drones to detect sources of methane due to these wells. The vertiport system will be divided into two major parts, the first being the power grid of the system and the second will be the autonomous system that controls both the vertiport and drones. Currently the power will be supplied by solar panels, the energy created will be used or sent to the main power bank. The main goal of the grid is to both support multiple drones and the vertiport itself. There are multiple different components of the vertiport that needs to be supported, most importantly is the computing system and the contact based charger for the drones. The major concern is the balancing act of both consumption and production of energy. Currently the method of charging for the drones will be via a contact based charger that will require the drone to have a high precision landing capability as well. The reason for the high precision landing is due to the small size of both contacts and contact pins. The drones will also need to have a capacity of heading out for tasks and returning to the vertiport unharmed. Traveling to point A then back to the origin of the drone.

We consider the Cahn-Hilliard equation, a nonlinear partial differential equation with applications in materials science. A convexity-splitting approach is used to efficiently solve the problem. This approach also provides the desired energy decreasing property and numerical stability. We discretize the space domain using spectral methods and perform a forward Euler approximation in the time derivative. We investigate the acceleration by performing the computations on a GPU. Preliminary results from MATLAB have shown a speed increase by a factor of seven when compared with the CPU only approach. Future work includes deriving higher order methods and CUDA implementations.

Privacy Risk Assessment of any information is a measure to evaluate the possibility of occurring unwanted events where the privacy of any user can be harmed. Existing risk assessment methodologies either do not consider the uncertainties of an event or assigns a approximate risk value to it. In this paper, we propose a privacy assessment framework using Evidential Reasoning Model and Dampster-Shafer theory to assess privacy risk of the smart grid system. Dempster-Shafer theory is one of the popular theories used in intelligent systems which can integrate evidence from multiple sources to assess the likelihood and impact of a particular risk event. It can help to account for uncertainty in the risk assessment process, especially when dealing with incomplete or conflicting information. Evidential Reasoning Model is a general multiple attribute decision analysis model that systematically analyzes and combines information about various evidences to make a decision. Our research focuses on the smart grid system as a case study to demonstrate the integration of a privacy assessment framework.

The goal of this project is to create a thermite based energetic initiator ink made from a polymer binder, a metal fuel, and a metal oxide for use in additive manufacturing. The ink will be produced with tailorable burn properties in a wide range of geometries in order to ignite and alter the burn profile of other energetic materials. An important aspect in creating an energetic ink for these applications is characterizing and understanding its combustion properties, including the burn rate and energy threshold for ignition. This work focuses on the analysis of the combustion properties as a function of the percentage of iron sulfur (Fe-S), which undergoes a high heat producing redox reaction but is more controllable compared to pure thermite, and the percent solids loading for strontium nitrate (Sr(NO3)2), barium nitrate (Ba(NO3)2), and manganese oxide (MnO2) with aluminum thermite systems. Burn rate testing showed that the Sr(NO3)2 and Ba(NO3)2 systems produced the highest burn rates, while ignition testing found that the Ba(NO3)2 system was the most sensitive. Ongoing work is focused on characterizing how the energetic ink can be used to alter the burn profile of samples of solid rocket propellant.

The nature of the task performed by quadrotors in underground mining operations places them in proximity to other objects and surfaces, generating external forces due to aerodynamic effects. Flying a quadrotor at a lower altitude in an underground mine autonomous navigation risks its efficiency being affected by ground effects. Flying at a high altitude poses a threat of crashing into the ceiling due to ceiling effects. Flying a quadrotor at optimal height in underground mining will maximize efficiency and get activities done quickly and safely. Therefore, this research investigates the interactions of Unmanned Aerial Vehicles UAVs using a single motor dynamometer mounted on a stepper motor and operated using an Arduino microcontroller to evaluate the expected aerodynamic impact on the motor performance in the close proximity to the floor, roof, and walls of an underground mine. Hence, the impacts of the proximity of all these obstacles were evaluated at different altitudes to the ground, walls, ceiling, and ventilation systems at different fan speeds. The results show the region of influence in the presence of obstacles magnified when the ventilation is blowing at a high air speed compared to the low airspeed or complete absence of a ventilation system. Also, the correction factor table was developed based on the result obtained using the Cheeseman-Bennett model. Finally, this approach was also validated experimentally with flow visualization and DJI drone reaction in hovering, and the optimum altitudes are then recommended.