Advancements in Measuring and Modeling the Mechanical and Hydrological Properties of Snow and Firn: Multi-sensor Analysis, Integration, and Algorithm Development

Estimating snow mechanical properties – such as elastic modulus, stiffness, and strength – is important for understanding how effectively a vehicle can travel over snow-covered terrain. Vehicle instrumentation data and observations of the snowpack are valuable for improving the estimates of winter vehicle performance. Combining in-situ and remotely-sensed snow observations, driver input, and vehicle performance sensors requires several techniques of data integration. I explored correlations between measurements spanning from millimeter to meter scales, beginning with the SnowMicroPenetrometer (SMP) and instruments applied to snow that were designed for measuring the load bearing capacity and the compressive and shear strengths of roads and soils. The spatial distribution of snow’s mechanical properties is still largely unknown. From this initial work, I determined that snow density remains a useful proxy for snowpack strength. To measure snow density, I applied multi-sensor electromagnetic methods. Using spatially distributed snowpack, terrain, and vegetation information developed in the subsequent chapters, I developed an over-snow vehicle performance model. To measure the vehicle performance, I joined driver and vehicle data in the coined Normalized Difference Mobility Index (NDMI). Then, I applied regression methods to distribute NDMI from spatial snow, terrain, and vegetation properties. Mobility prediction is useful for the strategic advancement of warfighting in cold regions. The security of water resources is climatologically inequitable and water stress causes international conflict. Water resources derived from snow are essential for modern societies in climates where snow is the predominant source of precipitation, such as the western United States. Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. In this work, I combined high-spatial resolution light detection and ranging (LiDAR) measured snow depths with ground-penetrating radar (GPR) measurements of two-way travel-time (TWT) to solve for snow density. Then using LiDAR derived terrain and vegetation features as predictors in a multiple linear regression, the density observations are distributed across the SnowEx 2020 study area at Grand Mesa, Colorado. The modeled density resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation. The integration of radar and LiDAR sensors shows promise as a technique for estimating SWE across entire river basins and evaluating observational- or physics-based snow-density models. Accurate estimation of SWE is a means of water security. In our changing climate, snow and ice mass are being permanently lost from the cryosphere. Mass balance is an indicator of the (in)stability of glaciers and ice sheets. Surface mass balance (SMB) may be estimated by multiplying the thickness of any annual snowpack layer by its density. Though, unlike applications in seasonal snowpack, the ages of annual firn layers are unknown. To estimate SMB, I modeled the firn depth, density, and age using empirical and numerical approaches. The annual SMB history shows cyclical patterns representing the combination of atmospheric, oceanic, and anthropogenic climate forcing, which may serve as evaluation or assimilation data in climate model retrievals of SMB. The advancements made using the SMP, multi-channel GPR arrays, and airborne LiDAR and radar within this dissertation have made it possible to spatially estimate the snow depth, density, and water equivalent in seasonal snow, glaciers, and ice sheets. Open access, process automation, repeatability, and accuracy were key design parameters of the analyses and algorithms developed within this work. The many different campaigns, objectives, and outcomes composing this research documented the successes and limitations of multi-sensor estimation techniques for a broad range of cryosphere applications

LAST-MILE POINTS OF DISTRIBUTION: OPTIMAL DISASTER RELIEF FOR WINDWARD OAHU AND MARINE CORPS BASE HAWAII

The island of Oahu is the most populous island in the State of Hawaii. If Oahu were to be struck by a natural disaster, its citizens and strategic assets, including military installations located on the island and service members living in Oahu communities, would be vulnerable to disruptions to the island’s central supply chain. To support disaster relief, last-mile points of distribution (POD) to act as the handoff points for people seeking food and water are needed. Predetermining POD locations helps planners pre-position supplies before a disaster and get supplies to affected communities quickly afterward. We developed a data set and model to determine optimal POD locations for windward Oahu communities for both resupply and pre-covery situations. We studied idealized, manpower-constrained, and optimistic scenarios to determine which PODs are chosen given different model constraints. Looking across 87 possible PODs for windward Oahu, we found different subsets that serve each scenario studied. Overall, five locations near Marine Corps Base Hawaii and in windward communities are identified as optimal for both resupply and pre-covery, including four schools and one park. Federal and state plans should highlight these locations for future distribution management planning.4800 Mark Center Drive, Suite 16F16, Alexandria, VA 22350-3605Captain, United States ArmyApproved for public release. Distribution is unlimited

The Berry-Keating Hamiltonian and the Local Riemann Hypothesis

The local Riemann hypothesis states that the zeros of the Mellin transform of a harmonic-oscillator eigenfunction (on a real or p-adic configuration space) have real part 1/2. For the real case, we show that the imaginary parts of these zeros are the eigenvalues of the Berry-Keating hamiltonian H=(xp+px)/2 projected onto the subspace of oscillator eigenfunctions of lower level. This gives a spectral proof of the local Riemann hypothesis for the reals, in the spirit of the Hilbert-Polya conjecture. The p-adic case is also discussed.Comment: 9 pages, no figures; v2 included more mathematical background, v3 has minor edits for clarit

Engineering a minimal G protein to facilitate crystallisation of G protein-coupled receptors in their active conformation

G protein-coupled receptors (GPCRs) modulate cytoplasmic signalling in response to extracellular stimuli, and are important therapeutic targets in a wide range of diseases. Structure determination of GPCRs in all activation states is important to elucidate the precise mechanism of signal transduction and to facilitate optimal drug design. However, due to their inherent instability, crystallisation of GPCRs in complex with cytoplasmic signalling proteins, such as heterotrimeric G proteins and β-arrestins, has proved challenging. Here, we describe the design of a minimal G protein, mini-Gs, which is composed solely of the GTPase domain from the adenylate cyclase stimulating G protein Gs. Mini-Gs is a small, soluble protein, which efficiently couples GPCRs in the absence of Gβγ subunits. We engineered mini-Gs, using rational design mutagenesis, to form a stable complex with detergent-solubilised β1-adrenergic receptor (β1AR). Mini G proteins induce similar pharmacological and structural changes in GPCRs as heterotrimeric G proteins, but eliminate many of the problems associated with crystallisation of these complexes, specifically their large size, conformational dynamics and instability in detergent. They are therefore novel tools, which will facilitate the biochemical and structural characterisation of GPCRs in their active conformation

The Effect of Ambient Temperature on Cold Start Urban Traffic Emissions for a Real World SI Car

The influence of ambient temperature on exhaust emissions for an instrumented Euro 1 SI car was determined. A real world test cycle was used, based on an urban drive cycle that was similar to the ECE urban drive cycle. It was based on four laps of a street circuit and an emissions sample bag was taken for each lap. The bag for the first lap was for the cold start emissions. An in-vehicle direct exhaust dual bag sampling technique was used to simultaneously collect exhaust samples upstream and downstream of the three-way catalyst (TWC). The cold start tests were conducted over a year, with ambient temperatures ranging from – 2°C to 32°C. The exhaust system was instrumented with thermocouples so that the catalyst light off temperature could be determined. The results showed that CO emissions for the cold start were reduced by a factor of 8 downstream of catalyst when ambient temperature rose from -2°C to 32°C, the corresponding hydrocarbon emissions were reduced by a factor of 4. There was no clear relationship between NOx emissions and ambient temperature. For subsequent laps of the test circuit the reduction of CO and HC emissions as a function of ambient temperature was lower. The time for catalyst light off increased by 50% as the ambient temperature was reduced. The results show that the vehicle used is unlikely to meet the new – 7oC cold start CO emission regulations

Active state structures of G protein-coupled receptors highlight the similarities and differences in the G protein and arrestin coupling interfaces

G protein-coupled receptors (GPCRs) regulate cellular signalling through heterotrimeric G proteins and arrestins in response to an array of extracellular stimuli. Structure determination of GPCRs in an active conformation bound to intracellular signalling proteins has proved to be highly challenging. Nonetheless, three new structures of GPCRs in an active state have been published during the last year, namely the adenosine A2A receptor (A2AR) bound to an engineered G protein, opsin bound to visual arrestin and the m opioid receptor (mOR) bound to a G protein-mimicking nanobody. These structures have provided novel insight into the sequence of events leading to GPCR activation, and have highlighted both similarities and differences in the structure of the interface between GPCRs and different signalling proteins

Real-world comparison of probe vehicle emissions and fuel consumption using diesel and 5 % biodiesel (B5) blend.

An instrumented EURO I Ford Mondeo was used to perform a real-world comparison of vehicle exhaust (carbon dioxide, carbon monoxide, hydrocarbons and oxides of nitrogen) emissions and fuel consumption for diesel and 5% biodiesel in diesel blend (B5) fuels. Data were collected on multiple replicates of three standardised on-road journeys: (1) A simple urban route; (2) A combined urban/inter-urban route; and, (3) An urban route subject to significant traffic management. At the total journey measurement level, data collected here indicate that replacing diesel with a B5 substitute could result in significant increases in both NOx emissions (8-13%) and fuel consumption (7-8%). However, statistical analysis of probe vehicle data demonstrated the limitations of comparisons based on such total journey measurements, i.e., methods analogous to those used in conventional dynamometer/drive cycle fuel comparison studies. Here, methods based on the comparison of speed/acceleration emissions and fuel consumption maps are presented. Significant variations across the speed/acceleration surface indicated that direct emission and fuel consumption impacts were highly dependent on the journey/drive cycle employed. The emission and fuel consumption maps were used both as descriptive tools to characterise impacts and predictive tools to estimate journey-specific emission and fuel consumption effects

The effect of ambient temperature on cold start urban traffic emissions for a real world SI car

The influence of ambient temperature on exhaust emissions for an instrumented Euro 1 SI car was determined. A real world test cycle was used, based on an urban drive cycle that was similar to the ECE urban drive cycle. It was based on four laps of a street circuit and an emissions sample bag was taken for each lap. The bag for the first lap was for the cold start emissions. An in-vehicle direct exhaust dual bag sampling technique was used to simultaneously collect exhaust samples upstream and downstream of the three-way catalyst (TWC). The cold start tests were conducted over a year, with ambient temperatures ranging from – 2°C to 32°C. The exhaust system was instrumented with thermocouples so that the catalyst light off temperature could be determined. The results showed that CO emissions for the cold start were reduced by a factor of 8 downstream of catalyst when ambient temperature rose from -2°C to 32°C, the corresponding hydrocarbon emissions were reduced by a factor of 4. There was no clear relationship between NOx emissions and ambient temperature. For subsequent laps of the test circuit the reduction of CO and HC emissions as a function of ambient temperature was lower. The time for catalyst light off increased by 50% as the ambient temperature was reduced. The results show that the vehicle used is unlikely to meet the new – 7oC cold start CO emission regulations