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Hardware and Software Design for a Portable Surgical Training Simulator
No full textMore than 51 million surgeries are performed on patients annually in the United States. That number grows to 310 million surgeries performed globally each year. Most surgeons go through a minimum of five years of residency – where they train and hone their surgical skills. It is important during this time, and throughout their careers, that they get plenty of practice. Surgical training is time intensive, expensive, and frequently under-resourced. There are finite amounts of surgical training modules for students to share at their facilities. The problem of getting adequate surgical training time continues to exist after surgeons begin their careers too. It is still important for surgeons to practice procedures they may not commonly perform, especially rural surgeons practicing across multiple surgical specialties. Continued surgical training is also important to allow surgeons to practice with variations in anatomy and keep updated on new advances in techniques and technology. Current surgical training models on the market are large and heavy, requiring that they must be used in a dedicated space such as a training facility. They also frequently feature single-use training parts that need to be replaced, meaning facilities must keep numerous extras on-hand and order more when needed. A portable, cost-effective surgical training simulator was developed to address these problems. This device features a compact, lightweight enclosure with a visual tracking system, a Raspberry Pi single-board computer, and a realistic virtual simulator that runs on a user’s computer. This device is easy to transport and can withstand the rigors of travel. The device is designed to work with a user’s preferred laparoscopic instruments, allowing them to develop muscle memory with their tools. Currently this device is constructed to train surgeons for laparoscopic cholecystectomy (LC), the most common gallbladder removal surgery. Over 750,000 LC surgeries are performed each year in the United States. Unfortunately, with the rise of laparoscopic cholecystectomies, the amount of bile duct injuries (BDI) during LCs have increased. The simulation created focuses on teaching surgeons to identify the CVS, though it can be easily adapted to other surgical trainings in the future. The current prototype of this device provides a small, portable, realistic surgical training simulation with plans to implement performance metrics and feedback for trainee improvement.
Advisor: Carl A. Nelso
Data-driven Methods for Signal Analysis and Nonlinear System Identification
No full textIn recent years, data-driven modeling has gained considerable attention in nonlinear system identification, offering a powerful alternative to traditional physics-based methods. This has been possible by advances in machine learning, sparse regression, and differential equations in a weak form, which allow the accurate capture of complex nonlinear dynamics, even when the measurements are in strong background noise. Moreover, integrating these models with physical knowledge further enhances their reliability and interpretability, making them suitable for a wide range of science and engineering applications.
In this dissertation, we investigate algorithms for data-driven nonlinear system identification to obtain ordinary differential equations that govern the system dynamics. This task requires data from dynamical systems, theoretical techniques such as Lagrangian mechanics, conservation of mechanical energy, the weak form of differential equations, and computational approaches such as automatic differentiation, sparse regression, and neural networks.
We start with a signal decomposition method that deals with when the signal components have crossover frequencies. We then move to a structural system identification method that novelly leverages the system’s energy to achieve its aim for the case of smooth nonlinearities. Then, we extend this method to a more complex scenario where clearance nonlinearities are present. We then combine two system identification methods to create a robust approach to obtain governing equations of motion for different dynamical systems when the data is under strong noise. Finally, we introduce an approach that simultaneously performs parametric system identification, validation, and uncertainty quantification.
Advisor: Keegan J. Moor
Educational Attainment Patterns Among MENA Adults: A Comparative Study of Native- and Foreign-born Groups in the US
No full textThis study examines educational attainment patterns among Middle Eastern and North African (MENA) adults in the United States, focusing on how nativity status, residential patterns, and gender shape these outcomes. Drawing on segmented assimilation and spatial assimilation theories, I analyze data from the 2018-2022 American Community Survey using ordered logistic regression models. Results indicate that educational attainment varies by gender, with MENA women consistently achieving higher education levels than men across all nativity groups, though this advantage varies considerably in magnitude between native-born and foreign-born subgroups. Residence in ethnic enclaves demonstrates a strong negative association with educational attainment among foreign-born MENA adults, while showing no significant effect for native-born individuals. Contrary to classic assimilation theories, longer duration of U.S. residence correlates with lower educational attainment among all foreign-born groups. Substantial variation exists by country of origin, with Iraqi immigrants exhibiting significantly lower educational attainment and Iranian immigrants showing higher attainment compared to Egyptians. These findings challenge linear models of immigrant integration and highlight how complex selection processes and historical contexts shape educational outcomes. The study highlights the heterogeneity within the MENA population\u27s educational trajectories, supporting segmented assimilation theory\u27s emphasis on diverse incorporation pathways.
Advisor: Regina Werum, Julia McQuillan, and Seulki Ki
Erratum of Temporal variability in grassland leaf water content is strongly and similarly dependent on soil moisture among dominant grasses
No full textIn this erratum, we correct a typographical error in “Temporal variability in grassland leaf water content is strongly and similarly dependent on soil moisture among dominant grasses.” In this correction, we strengthen our findings that leaf water content in the dominant grasses of grasslands in the Northern Great Plains is directly tied to changes in soil moisture. Consequently, this soil moisture is non-linearly correlated with time since rainfall events, showcasing a relationship between precipitation and leaf water content. Our results indicate a strong and nearly identical vulnerability of two key forage grasses to fine-scale fluctuations in soil water availability, suggesting high vulnerability of semi-arid rangeland ecosystems of the western US to climate-driven changes in water availability
Digitization, Diversity, and Discovery: A Review of the 2025 Potsdam Digital Humanities Spring School
No full textThe case of Chigozie Obioma, Chimamanda Ngozi Adichie, and NoViolet Bulawayo.
The Akan of Ghana have a proverb that translates, “When a thing is not placed in its rightful place, it will fall down,” and “Respect must be accorded to whom respect is due.” They also say, “The one behind walks in the footsteps of the one in front.” These Akan proverbs capture the literary journey encountered in the works of Chigozie Obioma, Chimamanda Ngozi Adichie, and NoViolet Bulawayo in The Fishermen, Purple Hibiscus, and We Need New Names, respectively. Their narratives reflect an undeniable acknowledgment of the literary path cleared by one man often hailed as the father of African literature: Chinua Achebe
The Structure of U.S. Agriculture
No full textIncludes a brief historical overview of the the structure of the agriculture sector in the United States