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. 

R-loops are RNA-DNA hybrids with a displaced single strand of DNA that arise naturally during transcription and are a potent source of DNA damage. Unresolved R-loops promote replication stress and DNA breaks that can lead to pathological conditions such as cancer,

neurodegenerative diseases, and autoimmune disorders. At the cellular level, R-loops are tightly regulated by a large number of proteins and enzymes. DNA topoisomerase 1 (TOP1) is one of the major enzymes involved in R-loop metabolism. TOP1 prevents R-loop formation and consequent DNA damage by restoring the DNA topology. Our previous work identified a novel interaction of TOP1 with transcription termination factor Kub5-Hera (K-H) that is also known to regulate R-loops and preserve genomic stability. Based on the potential interaction of TOP1 with K-H and their involvement in R-loop metabolism, we hypothesized that these proteins work in an epistatic manner within the cell to prevent aberrant R-loops and genomic instability. To test this hypothesis, we used a wide range of biochemical and cellular approaches. At the cellular level, we found that K-H cooperates with TOP1 to prevent R-loops and DNA damage. At the biochemical level, our data strongly suggest that K-H modulates the enzymatic activity of TOP1. Altogether, our data provide a novel insight into cellular strategies to prevent R-loops and genomic stability. 

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. 

Ensuring structural integrity of satellites in space is critical for maintaining longevity and preventing catastrophic failure. Structural health monitoring (SHM) could improve spacecraft safety and reduce operational costs. In SHM, various parameters from sensors are used to compare the current conditions to the initial or failure condition. Sound speed is one of the key parameters considered in SHM. This contribution studies the soundpeed of acoustic guided waves in aluminum structures in space by measuring piezoelectric actuators. An active sensor sends an acoustic guided wave which is received by other sensors. Sound speed is calculated from the distance between sensors and the time for the pulse to be received. There are many factors that influence these measurements such as noise, structural conditions, reflections, etc. Since such measurements will be done in orbit with little intervention and large amounts of data, development of an automated algorithm for calculating sound speeds is extremely important. In this work, an algorithm is developed that takes into consideration structural geometry and different guided wave modes. An approach to calculate sound speed for multiple types of guided waves from noisy data is implemented. Examples of application of the algorithm to laboratory specimens and actual space structures are presented. It is suggested that such algorithm will be further implemented in portable hardware. 

During an underground mine fire, the presence of smoke and toxic gasses, low visibility, and unfavorable changes in the airflow quantity in the ventilation system can significantly impede the identification of the optimal evacuation measures as well as the optimum path to safety. This study presents a framework that couples data from mine fire simulator software with a graph-based optimized algorithms for solving fire evacuation routes considering the distribution of toxic gasses inside the mine in real time. The proposed algorithm identifies the viable evacuation paths considering the cost of exposure to unhealthy conditions. The algorithm quantifies safety parameters on nodes and edges of a graph which model the escape routes through the mine layout. By accumulating the quantified effect of the safety values in an iterative manner and updating the network depending on the mine conditions, different escape routes can be computed and evaluated in real-time. The algorithm is demonstrated through fire simulation rig data acquired from a model developed in New Mexico Tech. Data such as air quantity flow, heat, gas concentrations, around an incident zone are acquired from the device and input into the proposed algorithm. Based on the spatiotemporal distribution of the quantified hazards, the algorithm computes optimal evacuation paths. The computed evacuation routes minimize the exposure to dangerous toxic gas concentrations, as the algorithm prioritizes the health of the trapped miners, even at the expense, at times, of evacuation time. 

The widespread integration of electronic control units (ECUs) into vehicles has highlighted the security vulnerabilities in the Controller Area Network (CAN) protocol. The protocol's limitations expose them to cyberattacks. The conventional anomaly detection approaches, such as Recurrent Neural Networks (RNNs), rely on sequential data processing. However, the limited scope of feature extraction may overlook critical contextual information. To address these challenges, we introduce a hybrid approach that combines an anomaly detection system that harnesses graph and temporal features through a Transformer-based Attention Network (TAN). Unlike RNNs, this approach uses the self-attention mechanism of transformers to achieve comprehensive attack detection. The experimental findings reveal that this model significantly exceeds existing state-of-the-art methods, achieving an impressive 97.43% effectiveness in detecting the anomalies. To identify the source of the attacks, our method exploits the unique clock skew deviations caused by manufacturing imperfections in ECU oscillators. We compute the clock skew of the sender ECU by comparing the actual length of a single CAN frame, derived from its electrical signal, to its nominal length. The nominal length is calculated as the product of nominal bit time and the number of bits. Our preliminary findings appear to be quite encouraging. This research has profound implications for automotive security, offering a dual-layered defense mechanism against cyber threats. Integrating graph-based anomaly detection with clock skew-driven ECU identification sets a new benchmark for securing in-vehicle networks against increasingly sophisticated cyber-attacks.