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Distributing Quantum Circuits Using Formal Methods
No full textThis paper presents a vision for generating formal specifications of the problems involved in the mapping of quantum algorithms to quantum networks, called the Sub-problems of Distribution (SpD). This is a significant challenge due to the importance of distributed quantum computing and the intertwined nature of SpD. Examples of SpD include teleportation minimization, qubit routing and load balancing. As such, formal specifications can help in providing a rigorous way for specifying and solving SpD. We instantiate the proposed vision in the context of Alloy, called qcAlloy, and for two of the most important SpD problems, namely teleportation minimization and load balancing. Part of the Alloy specifications that specify the constraints of SpD are reusable for any quantum circuit and any network. qcAlloy is also compositional in that it partitions the input circuit into sub-circuits, solves the SpD for each sub-circuit, and then combines the results towards generating a near-optimal solution. qcAlloy competes, and in some cases outperforms, the state-of-the-art for minimizing the number of teleportations for the quantum circuits in the RevLib and RLSB benchmarks
First-Principles Study of the Interaction of Atomic and Molecular Chlorine with Graphene, Silicene, Phosphorene, and h-BN Monolayer
Get PDFThe environmental stability of 2D monolayers is critical for their applications across various technology-related fields. These monolayers can degrade when exposed to gaseous components in the environment, so minimizing these degrading effects is essential. In this paper, chlorine exposure to the 2D monolayers, specifically graphene, silicene, phosphorene, and h-BN monolayer, is investigated using van der Waals corrected density functional theory. The results find that atomic chlorine chemisorbs on graphene, h-BN, silicene, and phosphorene with adsorption energies of −1.09, −0.65, −3.10, and −1.74 eV/atom, and bond distances of 3.0, 2.6, 2.2, and 2.1 Å, respectively. In contrast, molecular Cl2 exhibits physisorption with adsorption energies around −0.22 eV and bond distances ranging from 3.3 to 3.6 Å. NEB calculations show that Cl2 dissociative chemisorption is exothermic on buckled monolayers (silicene and phosphorene) and endothermic on planar monolayers (graphene and h-BN). On buckled surfaces, Cl2 dissociates after overcoming energy barriers of 2.0 eV for silicene and 3.2 eV for phosphorene, forming a stable chemisorbed state that is 0.9 eV lower than the physisorbed state. However, on planar monolayers, Cl2 remains in the physisorbed state because the dissociated chemisorbed state is ≈ 1.5 eV higher in energy. These differences are due to the weaker π-bonds in buckled monolayers, which make dissociation easier, while planar monolayers stabilize the molecular form
Challenges and artificial intelligence solutions for clinically optimal hepatic venous vessel segmentation
Get PDFBackground:: Liver vessel identification is crucial for clinical disease assessment and treatment planning, especially concerning local treatment of liver tumors. As artificial intelligence (AI) develops in radiology, opportunities arise to craft models adept at hepatic venous vessel segmentation, opening possibilities for creating patient-specific models of the liver anatomy quickly, despite the diverse features of CT images encountered in clinical settings. Objective: This research evaluates the performance of AI models combined with various pre-processing filters for liver vessel segmentation, emphasizing clinically relevant results. A novel evaluation method was introduced to offer more anatomically accurate assessments, moving beyond traditional metrics like the Dice score. Methods: Using open-source and proprietary datasets, we implemented residual UNet and Dense UNet in combination with smoothness and vesselness filters. We used a clinical evaluation approach focused on major and minor liver vessels, thereby underscoring the precision of AI outcomes. Results: The Dense UNet model with a specific pre-processing filter produced an average Dice score of 0.8144 in our internal dataset. For the public test dataset, the score was 0.7859. Both scores were higher than those not using pre-processing filters, 0.8052 and 0.7765. Clinical assessments showed 85% of AI predictions accurately identified all wanted vessel structures, though segmentation beyond the vessel borders did occur in half the predictions. Conclusion: This study highlights the effectiveness of AI in liver vessel segmentation, with the Dense UNet model combined with pre-processing filters showing high Dice scores and clinical accuracy
Assessing Power and Water Network Resilience When Water Pumps Provide Frequency Regulation
No full textPumps in drinking water distribution networks can be operated as flexible, controllable loads to help support the electric power grid, e.g., by providing frequency regulation. However, departures from conventional water network operation should not degrade the ability of the water and power networks to respond to high impact low frequency events. In this paper, we evaluate the resilience of water and power distribution networks surrounding a storm-induced power outage given an optimal pumping strategy that minimizes electricity costs and is capable of offering frequency regulation. The water network resilience under optimal water pumping strategies is compared with its resilience under a conventional rule-based water pumping strategy. In a case study, we consider an extreme wind event that causes power outages in the power distribution network impacting pumps in the water network. We found that the optimal control strategies are significantly less expensive than the traditional rule-based strategy but the water tanks levels are lower within the optimal pumping strategies, potentially reducing water service availability during long power outages. However, we also observed that the tank levels remain further from their limits when the optimal pumping strategy provides frequency regulation in addition to minimizing electricity costs, resulting in improved resilience metrics
Risk factors identification and injury severity classification in Alaska’s mining industry using statistical and machine learning approaches
No full textThis study examines safety-related factors influencing injury severity in Alaska’s mining industry using workers’ compensation data. The injury severity was characterized by claim type (medical only and lost time). Statistical analyses, including chi-square tests and logistic regression, identified significant associations between claim type and factors such as age group and cause of injury, while mine type, gender, body part injured, and nature of injury were found to be non-significant. Logistic regression revealed that older adults (OR = 2.76) and young adults (OR = 1.78) had higher odds of severe injuries, with strain injuries as the most frequent cause. Machine learning models were developed to classify injury severity using these factors, with logistic regression demonstrating the most consistent performance on test data (average score: 0.62). The findings emphasize the importance of targeted safety interventions for high-risk groups and prevalent injury causes to enhance workplace safety. While this study provides actionable insights for safety management, it is constrained by the limited scope of six safety-related factors and a relatively small dataset. The approach adopted in this study offers a framework for developing safety management models applicable to other mining operations and industrial contexts
Microstructural control of Zn alloy by melt spinning - A novel approach towards fabrication of advanced biodegradable biomedical materials
No full textBiodegradable metallic stents that dissolve over time are essential for treating vascular artery disease. Previous designs made from polymers and magnesium have not achieved the required mechanical properties and degradation patterns. Here, we report a novel zinc alloy that possesses a combination of high strength, good ductility, and uniform degradation behavior. The Zn-0.9Cu-0.4Mn-0.01 Mg alloy is produced using melt spinning (a rapid solidification technique), compaction, and extrusion to enhance the synergy between strength and ductility. The melt-spun extruded alloy exhibits an elongation to failure of nearly 30 % and a tensile strength exceeding 320 MPa, meeting the mechanical performance criteria required for vascular stenting materials. Melt spinning results in weak texture facilitating basal slip dislocations, and promoting ductility, while maintaining high strength. The microstructure of the melt-spun alloy displays a more uniform and finer microstructure as compared to the extruded alloy. The fine grain size and the uniform dispersion of secondary phases contribute to the uniform degradation behavior of the melt-spun extruded alloy, with a corrosion rate of ∼0.6 mm/year and low corrosion current density of ∼40 μA/cm2. The findings suggest that rapid solidification of zinc alloys through melt spinning is a promising approach for developing biodegradable medical implants of predictable degradation
ThermalTrack Dataset- Training Images- Fused RGB LWIR- sequence 5
Get PDFWe present a wheel track detection system that leverages RGB- Thermal (RGB-T) imaging, where thermal channels reveal critical temperature differentials between compacted tracks and loose snow- tracks exhibit higher thermal inertia and lower reflectivity, emitting stronger radiation signatures even in visually homogeneous conditions. By fusing these distinctive thermal patterns with RGB spatial information, our method reliably identifies navigable tracks, enabling robust path-following in complete white-out conditions where snow textures and terrain features become indistinguishable
FROM CONSCIENTIOUS FRENCH CANADIAN TO ENTERPRISING AMERICAN: MAKING MEMORY & MEASURING SOCIAL MOBILITY OF FRENCH CANADIANS 1880-1940
Get PDFThis thesis studies the social mobility of French-Canadian immigrants to the Keweenaw Peninsula 1880-1940 and memory among their descendants to explain how French-Canadian culture in the Keweenaw faded over time. Oral histories were conducted to gain an understanding of the current French-Canadian culture in the Keweenaw, understand how the current generation perceives changes in culture over time, and provide possible explanations for why French-Canadians thrived in the Keweenaw. Tax records from the Michigan Technological University Archives and social mobility scores, mother tongue, and homeownership information from the United States census was analyzed to measure language loss, and the social mobility of French Canadians and the general population in the Keweenaw. This analysis was used to compare the social mobility of French Canadians to the general population in the Keweenaw and to conduct three case studies of individuals during the study period. Combining oral histories and historical data analysis allowed for a clearer view of the difference between mobility and the perception of mobility as was parsed out during oral histories. Keweenaw French Canadians were found to have had upward mobility during the period of study while continuing to hold onto their language and culture into the early twentieth century
DEVELOPING A GRAPHQL MESH FEDERATED API GATEWAY: RAPID INTEGRATION OF NEW ENDPOINTS INTO A PREDEFINED SCHEMA
Get PDFThe Navy’s Undersea Warfare Decision Support System (USW-DSS) uses data from an ever growing number of sensors, accessible through an equally growing number of Application Programming Interfaces (APIs). Due to the lack of standardization among these sensors and APIs, as the system has continued to grow, the challenge of collecting and using these data has become increasingly prevalent. Previous work at Michigan Tech, in collaboration with engineers at ARiA (Applied Research in Acoustics LLC), introduced a GraphQL Mesh federated API gateway. The gateway would enable the combination of diverse API sources into a predefined hierarchical structure. This report follows the continued implementation of this gateway, focusing on its structure, the implementation of its Graphical User Interface to facilitate development utilizing the gateway, and the challenges faced along the way. Ultimately, we successfully implemented the GraphQL Mesh federated API gateway, though we note that there still exists room for future work
BIOMEDICAL APPLICATIONS OF REACTIVE OXYGEN SPECIES FROM CATECHOL OXIDATION
Get PDFCatechol, a key functional moiety found in mussel adhesive proteins, undergoes oxidation to generate reactive oxygen species (ROS), which can be leveraged for biomedical and environmental applications. By modifying catechol-based polymers, ROS generation can be controlled and applied toward organic compound degradation, antimicrobial treatments, and polymer crosslinking. This dissertation focuses on the design and application of catechol-based materials that leverage ROS generation for biomedical and environmental applications