Diagnostic tests to determine analyte concentration can be repetitive and require extensive training for proper analysis. To address these limitations, we developed two ratiometric nanosensors (calcium (Ca2+) and oxygen (O2)) which could be implemented in-vivo to give insight into biological functions such as nerve signaling and cellular respiration. The Ca2+ nanosensors’ optical properties (fluorescence and absorbance) vary to reflect the surrounding Ca2+ concentration. These sensors are selective to Ca2+ over other biologically relevant cations (Mg2+, Na+, K+) and show a sensitivity to Ca2+ at concentrations as low as 100 µM. The O2 nanosensor is composed of two dyes encapsulated in a hydrophobic PVC matrix. The O2 sensitive dye, platinum octaethylporphyrin (PtOEP), shows a decrease in luminescence with increasing oxygen concentrations. Whereas, the reference dye, DiA, has no O2 sensitivity. These O2 sensors are reversible and have a detection range that spans from anoxic (0% O2) to atmospheric conditions (21% O2). While the Ca2+ and O2 sensors showed functionality in in-vitro studies, testing these sensors in-vivo will determine their effectiveness as a long-term diagnostic aid.

Musculoskeletal injury to the spine and lower back resulting from heavy load carriage (30–40 kg) is common among military service members. Static peak pressure is a reliable parameter for predicting discomfort. The effect of using a hip belt on shoulder pressure is not well understood. This study aimed to quantify the pressure under shoulder straps when carrying a backpack with and without a hip belt. Three military service members wore a helmet and body armor (~6.5kg) and carried a backpack in two attachment conditions: (1) entirely shoulder borne, and (2) with a hip-belt engaged, all totaling 40% body weight. Participants walked at three different slope conditions (10° downhill, level, and 10° uphill) at 1.15 m/s for each backpack condition. Peak pressure across both shoulders was extracted from each condition. Shoulder borne peak pressure (down: 36.33 kPa; level: 37.67 kPa; up: 36.67 kPa) was greater than the hip belt (down: 29.67 kPa; level: 24.67 kPa; up: 29.67 kPa). Walking with the hip belt engaged compared with the shoulder borne-only backpack resulted in ~9 kPa smaller peak shoulder pressure on average across all three slopes, indicating that peak pressure is reduced when using a hip belt, although greater participant numbers are needed to confirm these results.

Military backpacks are equipped with hip belts, which have been shown to support 30% of the vertical force from the backpack. These belts do not offload enough force to prevent tissue strain on the shoulders, which can lead to discomfort and injury. Stiffness and damping in a backpack’s straps and connections can affect backpack movement, which may be related to tissue strain, when walking. However, the effects of using a hip belt on relative motion between the backpack and the wearer are unclear. Four active duty military participants walked at 1.15 m/s on uphill (+10¬∞), downhill (‚àí10¬∞), and level (0¬∞) slopes. They wore body armor with helmet and backpack using only the shoulder straps (Shoulder) or with the hip belt secured (Hip Belt), carrying 40% body weight total. 3D motion of the backpack and torso was obtained with optical motion capture. Maxima and minima of backpack-torso displacement were calculated in each walking condition. The difference between sequential maxima and minima were used to obtain ranges of vertical backpack-torso displacement during each walking condition for each participant. The hip-belt did not influence relative vertical motion of the backpack. The pack and slope did affect the horizontal relative displacement (p = .086). The greatest relative displacement for anterior/posterior direction was 7.7mm, and vertical direction was 19.9mm. Horizontal displacement was not affected by slope alone during Hip Belt, but was affected during Shoulder (p = .056). The vertical relative displacement was affected by slope, p = .007.

Balance regulation during daily activities is a key component of movement performance and fall risk as we age. There is a well-defined correlation between lower limb muscle strength and hand grip strength (HGS) and lower limb muscle strength is critical for balance regulation. The Five Times Sit-To-Stand test (5xSTS) is an evaluation of muscle strength and mobility during transitions that helps identify individuals at risk of fall. Thus, we aimed to determine if mediolateral movement biomechanics during the 5xSTS test, which are important for balance performance, were correlated with HGS. Ten young and healthy participants completed a 5xSTS trial where they rose from the seat to a standing position and returned to the seat five consecutive times as quickly as possible. We performed a Pearson correlation analysis (α ± < 0.05) between range of mediolateral torso center of mass (COM) displacement, dominant HGS and time to completion of 5xSTS. There was not a significant correlation between time to completion and HGS (rho (ρ) = -0.515, p = 0.127). A moderate negative correlation that approached significance (0.05 < p < 0.10) was found between mediolateral torso COM displacement and HGS (rho (ρ) = -0.558, p = 0.094). There was no correlation between mediolateral torso COM displacement and time to completion (rho (ρ) = 0.143, p = 0.693). HGS could help indicate mediolateral balance performance during 5xSTS and improve the lower limb strength assessment due to its correlation with torso motion. Recording dominant HGS alongside 5xSTS completion time may provide insight into mediolateral dynamic balance performance.

Intelligent Transportation Systems (ITS) are gaining popularity among governments, businesses, and individuals due to their potential to make travel safer and more efficient. Machine learning for traffic prediction has emerged as a promising subfield of ITS, with the potential to aid in routing planning, congestion management, and urban development. Traffic infrastructure and mobile devices collect large amounts of heterogeneous data that can be used to predict traffic conditions, including real-time traffic data such as traffic camera images, speed measurements, and volume counts, as well as long-term static data such as speed limits, road conditions, and surrounding geography and infrastructure. Despite the availability of traffic data, many current machine learning models struggle to handle the wide variety of data types and to address both temporal aspects of real-time data and spatial aspects of long-term static data. To address this, we propose a new enrichment learning model that integrates dynamic data containing varying numbers of instances with static data to create an enriched fixed-length vector which can be used with other machine learning methods to improve performance and identify regions important for prediction. Results show that this novel enrichment learning model can improve the performance of traditional machine learning methods in the task of predicting future traffic speeds.

Rising sea levels have disproportionately large impacts on Southern coastal regions of the United States, and to a greater degree among communities therein affected by socioeconomic inequalities. Despite this, research in one of the leading causes of global sea level rise, melting ice (i.e., the cryosphere), is largely left behind in the curricula of public middle and high school institutions throughout these southern states. As rates of sea level rise continue to increase, an assessment of educational capacities within the context of present-day access to climate and cryosphere education in the United States becomes increasingly important. In this study, we apply an inductive coding method to current public-school curricula at the "8th to 12th" grade levels to assess the capacities of Southern schools to support strengthened cryosphere and sea level rise education. This developed framework can be applied to school systems across the United States and ranks availability of access, capacities form and connections to cryosphere and sea level rise education. The results of this study indicate a great potential to connect sea level rise and cryosphere education to the impacts and experiences of coastal communities in the United States, as well as capacities to forge new collaborations between funded university-level research centers and local communities to support educational access. Future work will quantify the identified capacities within southern public school science curricula in an actionable manner for use by Boards of Education, politicians, and scientists in sea level rise and cryospheric science spaces.

Quantum Computing poses a considerable threat in the world of cyber security. Policy makers are largely unprepared for a post-quantum world, significantly due to a lack of understanding and awareness. The goal of this paper is to improve understanding and provide a new and effective way to analyze post-quantum cryptography, for researchers and security engineers alike. This is done by providing a background of quantum computing, a survey of the state of technologies and relevant policies, and a novel application of machine learning to perform analysis of quantum-ready encryption. The machine learning research will provide a Multinominal Naïve Bayes for discrete analysis of the RSA and CRYSTAL-Kyber encryptions.

Composite structures are susceptible to transverse loading due to their inherent layered structure, particularly when under impact. Under low energy repetitive impacts (LERI), little is understood regarding damage mechanisms, damage accumulation, and the post-mortem global response of the composite material under different loading configurations. In this study, we examine carbon fiber-reinforced composite (CFRC) plates subjected to low energy repetitive impacts of 2 J to investigate their behavior under impact fatigue. Post-mortem specimens are then investigated using three main methods: digital image correlation (DIC), the virtual fields method (VFM) and compression-after-impact (CAI) tests. DIC is used to extract surface kinematics under an applied static load to observe the evolution of strain fields under bending as the number of impacts accumulate. This data is then used as input for VFM analysis which is used to reveal local gaps in mechanical equilibrium, allowing it to be used as an indicator of damage. Next, ASTM standard CAI tests are performed on the impacted specimens to compare ultimate compression strength values. X-ray computed tomography (XCT) is also used to corroborate damage detection provided by the equilibrium gap method and identify catastrophic microstructural damage pre-cursors. Using quasi-isotropic 8-ply CFRC plates, XCT results showed that interlaminar cracking appeared in as little as 10 impacts. At 100 impacts, extensive matrix cracking and delaminations were observed. After 300 impacts, severe delaminations were imaged using XCT, while barely visible surface cracks were imaged on the rear face of the specimen.

Ancient lakes on Mars and the river deltas which occur along their shorelines offer attractive targets for mission landing sites due to their habitability and high biosignature preservation potential. Deltas are promising targets for finding organic molecules and other signatures of life because on Earth deltas have biodiverse and rich ecosystems. Furthermore, the presence of deltas are used to map paleoshorelines for ancient oceans and lakes on Mars. For instance, Jezero Crater was chosen as the NASA Perseverance landing site because the fan-shaped channel network at the edge of the crater was interpreted as a delta. However, on Earth, fan-shaped channel networks may also form in fluvial fans that are inland terrestrial landforms that can form 1000s of kilometers from shorelines. We demonstrate that morphometric criteria are needed to accurately identify fan-shaped landforms for potential future landing sites. The goal of this research project is to differentiate deltas and fluvial fans on Mars by quantifying fan-shaped paleochannel network morphometrics. To accomplish this, we map Martian fan-shaped paleochannel networks using images from the Mars Reconnaissance Orbiter (MRO) High Resolution Imaging Science Experience (HiRISE) and Contex Camera (CTX) photographs in combination with ArcGIS. Morphometric data is statistically analyzed using python and other open-source data visualization libraries. The outcomes of this project will improve our ability to choose appropriate landing sites in search of life, and to map paleo-shorelines on Mars. Preliminary results suggest the channel network at Jezero resembles a fluvial fan, while the landform at Eberswalde crater resembles a delta. Fluvial fan formation has been linked to large sediment and water discharges, and to fluctuations in discharge as a result of highly seasonal precipitation in climatic settings that promote marked seasonal and interannual hydrological changes, leading to variable discharge regimes and exceptional flood events. Alternative evidence is required to identify paleo-shorelines as fluvial fans may also form along shorelines. On Earth, fluvial fans are less sensitive to sea-level rise and coastal hazards than deltas and react differently from deltas due to changing sea levels.

The Colca River valley in southern Peru is the longest waterway of the Pacific Peruvian hydrologic basins, starting in the Perú Altiplano, and crossing the western Andean Cordillera to the Pacific Ocean. The area has been regularly impacted by large landslides of the valley slopes, and geologic evidence documents intense, recurrent, and catastrophic events, like landslides, debris avalanches, and floods. On June 18, 2020, more than 5,400,000 m3 of soil and weak rock slid into the Colca River valley near the town of Achoma. The rotational slide involved 40 hectares of land that was displaced 500 meters. The event destroyed the agricultural land, impacting the economy of many families, and the displaced material over the Colca River created a dam that increased the risk of flooding for the towns upstream. The exact factors that led to the landslide in Achoma, including triggering factors, are uncertain. The activity of farms, most of which are currently irrigated or have been irrigated in the past, and the presence of a large water transportation canal upslope of the landslide are the most likely causes of the increase in ground-water levels leading to failure. The purpose of this work is test various groundwater and infiltration scenarios to estimate the amount of water involved in the destabilization and triggering of the Achoma landslide, using numerical simulation of changing groundwater conditions. While not definitive, our early work indicates that, even though the landslide occurred during the dry season and before the irrigation began for the year, we cannot yet rule out irrigation as a contributing factor. On the other hand, increasing ground-water levels from leakage from the water conveyance canal appears to be a necessary component to cause slope failure.