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Using Known Relation Between Quantities to Make Measurements More Accurate and More Reliable
No full textMost of our knowledge comes, ultimately, from measurements and from processing measurement results. In this, metrology is very valuable: it teaches us how to gauge the accuracy of the measurement results and of the results of data processing, and how to calibrate the measuring instruments so as to reach the maximum accuracy. However, traditional metrology mostly concentrates on individual measurements. In practice, often, there are also relations between the current values of different quantities. For example, there is usually an known upper bound on the difference between the values of the same quantity at close moments of time or at nearby locations. It is known that taking such relation into account can lead to more accurate estimates for physical quantities. In this paper, we describe a general methodology for taking these relations into account. We also show how this methodology can help to detect faulty measuring instruments -- thus increasing the reliability of the measurement results
Why Decisions Based on the Results of Worst-Case, Most Realistic, and Best-Case Scenarios Work Well?
No full textOften, to make an appropriate decision, people try three scenarios: the worst case, the most realistic case, and the best case. This three-scenarios approach often leads to reasonable decisions. A natural question is: why worst case and best case? These extreme cases mean that all numerous independent random factors work in the same direction: either are all stacked for or are all stacked against. Such stacking of random factors is highly improbable. So, at first glance, it would be more beneficial to use more realistic scenarios than the worst case and the best case. However, empirically, decisions based on the worst-case and the best-case scenarios work well -- better than other three-scenarios alternatives. In this paper, we provide a theoretical explanation for this empirical phenomenon
Redefining Approaches For Measuring Landscape Subsidence And Permafrost Degradation In Arctic Tundra Environments
No full textAs climate change accelerates in the Arctic, the degradation of permafrost is leading to significant landscape transformation in tundra landscapes. This dissertation investigates the multifaceted responses of permafrost systems to warming, focusing on the dynamics of surface elevation changes and active layer thickness (ALT) across the North Slope of Alaska. In this study, I explore the capacity of repeat Terrestrial Laser Scanning (TLS) technology for modeling tundra features and detecting surface subsidence, specifically how different climate and landscape conditions during scanning impact TLS model precision. We also compare TLS model precision estimates to TLS model accuracy by comparing elevation values derived from TLS models to DGPS models. The findings indicate that adverse climate and landscape conditions can significantly reduce TLS precision. In general model precision decreased by 45% when transitiong from ideal (clear skies, dry tundra, no wind) conditions to less-than-ideal (windy and wet) conditions. Under both conditions TLS precision most under preformed in trough features, but this effect was significantly impacted by less-than-ideal conditions. We also found that individual less-than-ideal conditions (windy vs wet) impacted TLS precision differently, emphasizing the necessity for optimized survey protocols to enhance change detection between repeat surveys. Using our understanding of TLS model precision, we analyzed over a decade of high-resolution TLS data, along with differential GPS (DGPS) data across the North Slope to access landscape elevation change over time. This analysis documents landscape elevation changes at multiple spatial and temporal scales, revealing that broad-scale landscape subsidence is mostly driven by solid earth dynamics (related to tectonic movement and changes in earth crust). The magnitude of solid earth movement often surpassed surface elevation changes (due to isotropic subsidence and thermokarst development), revealing the impact that solid earth movement may have on our understanding of landscape elevation change in the Arctic. The study also highlights the significant spatial and temporal variability of surface elevation change across the Arctic, that became apparent once we separated solid earth dynamics. These results suggest that decadal trends in vulnerable areas may not be changing as previously assumed, as five out of our seven sites had linear surface elevation gain trends, with only two having surface subsidence trends. These insights challenge conventional understanding of surface subsidence in permafrost landscapes impacted by global warming and highlight the need for long-term monitoring of permafrost regions and the separation of solid earth movement from surface elevation change calculations. This dissertation uses our understanding of surface elevation change in the North Slope to then examine the influence of surface elevation changes on ALT and ecosystem processes. Contrary to prevailing assumptions that ALT is uniformly increasing, my analysis demonstrates potential decreases in ALT in some areas, influenced by local geomorphic conditions and soil moisture dynamics. Overall, this dissertation underscores the complex interplay between atmospheric warming, surface changes, and permafrost dynamics, calling for a reevaluation of existing paradigms regarding permafrost evolution and its implications for global climate systems. By refining our understanding of these processes, this work contributes valuable insights to the field of permafrost research and underscores the urgency of long-term ecological monitoring in the Arctic. These findings underscore the intricate interplay between atmospheric warming, surface dynamics, and permafrost processes. This work provides critical insights into how landscape-scale changes interact with permafrost thaw, with implications for carbon release, hydrology, and ecosystem stability. By refining our understanding of the patterns and processes of permafrost evolution, this study can inform the development of strategies for monitoring and mitigating the impacts of Arctic climate change on global systems related to fate of carbon, vegetation change, sea level rise, and infrastructure, improving resilience planning for Arctic communities, and guiding international climate policy focused on the preservation of permafrost
Integrating Responsible and Ethical Conduct of Research into CURES: A Longitudinal Study Observing Professional Development of Fellows within the Ethics Network for Course-Based Opportunities in Undergraduate Research
No full textProtective And Risk Exposures Of Cancers Disproportionately Affecting Hispanics In A U.S. - Mexico Border Population: A Clinical Informatics Approach
No full textBACKGROUND: Cancer is the second leading cause of death in the U.S. with over 8 million new cases between 2016 and 2020. A variety of protective and risk exposures are associated with multiple cancers and disparities and inequalities exist in the health outcomes of minority populations. Hispanic populations in the U.S. and Texas have some of the highest rates of cancer incidence and mortality when compared to other populations. The use of Population and Public Health Informatics approaches to address these issues have shown promise when employed in healthcare institutions METHODS: A case-control study design examined associations between multiple cancers and protective and risk exposures in a primarily Hispanic U.S. border population. The HealtheIntent population health system was used to extract patient data from a multitude of data sources within a U.S.-Mexico border healthcare system. Participants were patients at TTUHSC-EP between 2011 and 2023, 18 years or older, and from a west Texas or southern New Mexico county. RESULTS: A total of 7,070 patients (M age = 63.17, SD=14.34) were included in this study and the majority of the sample was Hispanic (79.7%). Cancer patients were found to have a higher comorbidity burden than non-cancer patients and Hispanic cancer patients were found to have poorer health outcomes than their non-Hispanic counterparts. Several logistic regression analyses examining different cancer types revealed similar predictors including insurance coverage, preferred language (English/Spanish), and several comorbidities. CONCLUSION: The results of this study describe the importance that health information systems play in identifying and addressing risk and protective exposures, health disparities, health determinants, and health outcomes, as well as show the role they play in developing interventions and policies to improve patient outcomes within a health system
Uncovering the Light Network Load Performance Penalty of the Network Link Outlier Factor (NLOF)
No full textThis thesis evaluates the effectiveness of the Network Link Outlier Factor with Most Likely Link (NLOF: MLL) algorithm under varying network load conditions. Repeated simulation experiments using Mininet were conducted for four different network-wide load levels: 100 Mbps, 500 Mbps, 1 Gbps, and 5 Gbps. Using statistical inference, our experimental results indicate that NLOF: MLL is ineffective under light load conditions (i.e., 100Mbps load) due to the limited network flow data available for its learning process. This limitation highlights a key challenge in applying the algorithm to lightly loaded networks. A preliminary algorithm was proposed to address this light-load performance penalty using synthetic traffic generation. This algorithm demonstrates promising results toward improving the performance of NLOF: MLL. Future work will continue to explore this use of synthetic traffic
Development Of Electric Vehicle Charging Station Microgrid With Simulation Modelling
No full textThe rapid adoption of electric vehicles (EVs) necessitates innovative solutions to overcome challenges in energy management, sustainability, and grid reliance. This research develops a hybrid simulation model for EV charging stations, integrating renewable energy sources, such as solar photovoltaic (PV) systems, with energy storage systems (ESS). Leveraging discrete-event simulation (DES) and agent-based modeling (ABM) in AnyLogic, the model offers a comprehensive analysis of charging station operations, incorporating stochastic EV arrivals, dynamic energy allocation, and user-defined customization.Key contributions include the development of hybrid simulation model integrating solar PV, energy storage, and energy management systems (EMS) that dynamically optimizes energy distribution from PV, ESS, and grid power, reducing operational costs and enhancing sustainability. The simulation model also incorporates interactive dashboards, enabling stakeholders to test scenarios and analyze performance metrics, fostering decision-making and stakeholder engagement. Optimization experiments utilizing genetic algorithms were conducted to evaluate charging station utilization, renewable energy efficiency, and system scalability. Results highlight critical trade-offs between infrastructure configurations and performance metrics, providing actionable insights for enhancing EV charging infrastructure. This thesis contributes to the scientific advancement of sustainable transportation, aligning with the mission of the ASPIRE Engineering Research Center. The findings lay the groundwork for future research in renewable-integrated EV charging systems, emphasizing the potential for scalable and adaptive infrastructure solutions
Pretrial Release Decision-Making: Understanding The Factors Contributing To Pretrial Release Bond Information
No full textAt initial appearance, judges and magistrates must strike a balance between a defendantâ??s likelihood of returning to court and the safety of the community when determining pretrial release bonds, using limited available information in short periods of time. Across two complementary studies, factorsâ??both established by literature and novelâ??are assessed to further understand judicial decision-making in pretrial settings. Study 1 was a secondary data analysis to determine whether established factors considered at release are replicated in El Paso, Texas. Study 2 used observational data to review characteristics specific to defendants and the initial hearing interaction, while similarly analyzing established factors considered at pretrial release. Both of these studies assessed these factors on bond type (financial, personal recognizance) and bail amount independently. Study 1 results suggest defendantsâ?? age, length of time living at their place of residence, and supervision status influence the bond given, though not for the bail amount associated with their release. Study 2 found that defendants\u27 appearance during their initial hearings and the charge type (felony, misdemeanor) were predictive of bond type and bail amount. These two studies further illustrate how release decisions are made at an understudied point of the legal system, while identifying critical gaps in the literature for future work
A Blue Hydrogen Framework for Regional Energy Resiliency
No full textHigh-pressure gasification is gaining attention as an emerging solution for sustainable energy production due to its potential to achieve higher efficiencies, improved syngas quality and smaller equipment footprints. High operating pressures and temperatures can increase the synergistic reactivity of biomass and organic materials, consequently enabling higher in situ tar reformation and ultimately reducing the number of gas treatment requirements. Additionally, higher operating pressures result in a more concentrated CO2 stream and reduce the amount of post-gasification compression requirements in the carbon capture and storage (CCS) unit, improving overall system efficiency. Plus, pressurized CO2 streams from the gasifier also provides routes for direct conversion to valuable products via reactive carbon conversion (RCC) pathways and provide options for incentivizing the blue hydrogen production process. This approach also saves energy by eliminating the need for additional pressurization of hydrogen for storage, making the process more environmentally friendly and cost-effective.The realization of a hydrogen economy in the US requires significant end-to-end infrastructure developments from production to consumers. Significant investments are being made to establish new hydrogen plants and hubs to meet the growing demands. However, most of the investments are concentrated in the coastal areas, which face significant challenges in supply and delivery to far inland regions and require thousands of miles of pipeline infrastructure development, coupled with existing challenges in hydrogen storage can create potential resiliency risks and increase the levelized costs of per unit hydrogen. This research explores a high-pressure gasification system integrated into an IGCC framework to produce both hydrogen and electricity utilizing locally available resources (pecan shells, cotton gin waste, and municipal solid waste (MSW)) to create regional energy resiliency and improve energy security. A unique strategy of recirculating CO2 and steam from IGCC gas turbine exhaust gas is utilized, which has improved system thermal efficiency. Additionally, a trade-off study has been performed to identify the optimum hydrogen extraction amount from syngas while balancing the required power generation from the IGCC cycle, which creates a chance for sustainable hydrogen production with power generation. Through experiments and simulations, the study identified optimal operating conditions, such as an equivalence ratio (ER) of 0.18â??0.22 and temperatures of 850â??950°C, to maximize syngas quality and hydrogen production. A blend of 60% MSW and 40% pecan shells was found to minimize emissions while achieving a plant efficiency of 41% with a 50 v/v% hydrogen extraction from syngas for end-use purposes while other 50 vol% being utilized in gas turbine to power auxiliary units and meet additional 50 MWe demand of the community. Compared to that, an 80 v/v% extraction of Hydrogen from the syngas with the same IGCC power output has improved the combined efficiency of the plant and hydrogen generation to 61%, mainly due to higher volumetric energy density of methane in the syngas, allowing greater hydrogen extraction. A Life Cycle Assessment (LCA) was conducted to measure greenhouse gas emissions from feedstock collection to CO2 capture. A cradle to grave approach with relevant system boundaries have been developed for the LCA. Local sourcing of feedstock kept transportation emissions low, while the choice of feedstock blend and operating parameters influenced the overall environmental impact. Aspen Plus and LCA software were used to analyze emissions data and ensure accuracy, highlighting the potential for MSW and biomass blends to reduce waste and emissions. This study demonstrates how high-pressure gasification can provide a reliable and scalable solution for regional energy systems. By optimizing operating conditions and integrating efficient processes, this thesis provides a roadmap for producing clean energy while reducing emissions and advancing sustainability, energy independence and energy security
Is Energy Local? Counterintuitive Non-Locality of Energy in General Relativity Can Be Naturally Explained on the Newtonian Level
No full textFrom the physics viewpoint, energy is the ability to perform work. To estimate how much work we can perform, physicists developed several formalisms. For example, for the fields, once we know the Lagrangian, we can find the energy density and, by integrating it, estimate the overall energy of the field. Usually, this adequately describe how much work this field can perform. However, there is an exception -- gravitational field in General Relativity. The known formalism to compute its energy density leads to 0 -- and by integrating this 0, we get a counterintuitive conclusion that the overall energy of the gravitational field is 0 -- while hydroelectric power stations that produce a significant portion of world\u27s energy show that gravity {\it can} perform a lot of work and thus, has non-pzero energy. The usual solution to this puzzle is that for gravity, energy is not localized. In this paper, we show: (1) that non-locality of energy can be explained already on the Newtonian level, (2) that the discrepancy between energy as ability to perform work and energy as described by the Lagrangian-based formalism is ubiquitous even in the Newtonian case, and (3) that there may be a possible positive side to this non-locality: it may lead to faster computations