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Browsing by Author "Simske, Steven, advisor"

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    A novel design methodology for osseointegrated implants and the effects of heat-treatment on shape setting nitinol foil
    (Colorado State University. Libraries, 2020) Morrone, Adam, author; Simske, Steven, advisor; Popat, Ketul, committee member; Kawcak, Christopher, committee member
    Nitinol, approximately equiatomic nickel and titanium and a popular shape memory alloy, has been used extensively in modern, implantable medical devices due to its natural biocompatibility, remarkable shape memory properties, and superelasticity. Much of the current literature on processing and handling this material focuses on thin wires, as this is what has historically been of most interest (e.g. for orthopedics, orthodontia, and orthognathics); however, as this technology advances, there are emerging applications of nitinol that require other form factors such as films and foils. In addition, although many manufacturers can produce three-dimensional nitinol structures, much of the information on shaping techniques is still proprietary. In an effort to fill these gaps in the literature and add to the knowledge of nitinol shaping techniques, this study compares the effects of various heat-treatments on the shape-setting of nitinol foil. Foils of two different NiTi compositions (50.2 and 50.8 percent Ni by atomic mole fraction) were rigidly fixed into a cylindrical shape and heat-treated at five different temperatures (400, 450, 500, 550, and 600 degrees C) and for five different durations (5, 10, 15, 20, and 25 minutes). The morphological rebound of these samples was evaluated, and a model was developed to described this shape setting behavior. In addition, the Austenite finishing temperature (AÆ’), and fatigue effects of all samples were evaluated to further quantify the effects of heat-treatment. The results from this materials study were then used in part to develop a novel design methodology for osseointegrated implants. Devices using this methodology have anchors that deploy from the main body to lock the implant in place. The contact points act as "active sacrificial zones" which can experience bone resorption without losing rigidity, while the remainder of the implant body undergoes normal loading conditions. This methodology aims to improve the quality and speed of bone ingrowth.
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    Artificial intelligence powered personalized agriculture
    (Colorado State University. Libraries, 2023) Tetala, Satya Surya Dattatreya Reddy, author; Simske, Steven, advisor; Conrad, Steve, committee member; Gaines, Todd, committee member; Nalam, Vamsi, committee member
    The integration of Artificial Intelligence (AI) in agriculture has shown the potential to improve crop selection and enhance sustainability practices. In this study, we aim to investigate the benefits and feasibility of using AI-powered personalized recommendations for crop selection and sustainability practices in the context of agroecology. We propose to lay the foundation for an agricultural recommendation engine that considers several parameters that influence yield and presents the best crop(s) to sow based on the model's output. We aim to examine this recommendation engine's impact on agriculture's sustainability and to evaluate its effectiveness and accuracy. Our ultimate goal is to provide a comprehensive understanding of the potential benefits and challenges of using AI-powered recommendations in agriculture and to lay the foundation for the development of a practical, effective, and user-friendly recommendation engine that can help farmers make informed decisions about their crops and improve the long-term sustainability of agriculture.
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    Hybrid MBSE-DevOps model for implementation in very small enterprises
    (Colorado State University. Libraries, 2024) Simpson, Cailin R., author; Simske, Steven, advisor; Miller, Erika, committee member; Reisfeld, Brad, committee member; Sega, Ronald, committee member
    This work highlights the challenge of implementing digital engineering (DE) practices, specifically model-based systems engineering (MBSE) and DevOps, in very small entities (VSEs) that deliver software products. VSEs often face unique challenges due to their limited resources and project scale. Various organizations have authored strategies for DE advancement, such as the Department of Defense's Digital Engineering Strategy and INCOSE's System Engineering 2035 that highlight the need for improved DE practices across the engineering fields. This work proposes a hybrid methodology named FlexOps, combining MBSE and DevOps, to address these challenges. The authors highlight the challenges faced by VSEs and emphasize that MBSE and DevOps adoption in VSEs requires careful consideration of factors like cost, skill availability, and customer needs. The motivation for the research stems from the difficulties faced by VSEs in implementing processes designed for larger companies. The authors aim to provide a stepping stone for VSEs to adopt DE practices through the hybrid FlexOps methodology, leveraging existing MBSE and DevOps practices while accommodating smaller project scales. This work emphasizes that VSEs supporting government contracts must also adopt DE practices to meet industry directives. The implementation of FlexOps in two case studies highlights its benefits, such as offering a stepping stone to DE practices, combining Agile, MBSE, and DevOps strategies, and addressing VSE-specific challenges. The challenges faced by VSEs in adopting DE practices may be incrementally improved by adopting a hybrid method: FlexOps. FlexOps was designed to bridge the gap between traditional practices and DE for VSEs delivering software products.
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    Novel assessments of country pandemic vulnerability based on non-pandemic predictors, pandemic predictors, and country primary and secondary vaccination inflection points
    (Colorado State University. Libraries, 2024) Vlajnic, Marco M., author; Simske, Steven, advisor; Cale, James, committee member; Conrad, Steven, committee member; Reisfeld, Bradley, committee member
    The devastating worldwide impact of the COVID-19 pandemic created a need to better understand the predictors of pandemic vulnerability and the effects of vaccination on case fatality rates in a pandemic setting at a country level. The non-pandemic predictors were assessed relative to COVID-19 case fatality rates in 26 countries and grouped into two novel public health indices. The predictors were analyzed and ranked utilizing machine learning methodologies (Random Forest Regressor and Extreme Gradient Boosting models, both with distribution lags, and a novel K-means-Coefficient of Variance sensitivity analysis approach and Ordinary Least Squares Multifactor Regression). Foundational time series forecasting models (ARIMA, Prophet, LSTM) and novel hybrid models (SARIMA-Bidirectional LSTM and SARIMA-Prophet-Bidirectional LSTM) were compared to determine the best performing and accurate model to forecast vaccination inflection points. XGBoost methodology demonstrated higher sensitivity and accuracy across all performance metrics relative to RFR, proving that cardiovascular death rate was the most dominant predictive feature for 46% of countries (Population Health Index), and hospital beds per thousand people for 46% of countries (Country Health Index). The novel K-means-COV sensitivity analysis approach performed with high accuracy and was successfully validated across all three methods, demonstrating that female smokers was the most common predictive feature across different analysis sets. The new model was also validated with the Calinski-Harabasz methodology. Every machine learning technique that was evaluated showed great predictive value and high accuracy. At a vaccination rate of 13.1%, the primary vaccination inflection point was achieved at 83.27 days. The secondary vaccination inflection point was reached at 339.31 days at the cumulative vaccination rate of 67.8%. All assessed machine and deep learning methodologies performed with high accuracy relative to COVID-19 historical data, demonstrated strong forecasting value, and were validated by anomaly and volatility detection analyses. The novel triple hybrid model performed the best and had the highest accuracy across all performance metrics. To be better prepared for future pandemics, countries should utilize sophisticated machine and deep learning methodologies and prioritize the health of elderly, frail and patients with comorbidities.
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    The selective de-identification of ECGs
    (Colorado State University. Libraries, 2022) Akhtar, Musamma, author; Simske, Steven, advisor; Wang, Zhijie, committee member; Vans, Marie, committee member
    Biometrics are often used for immigration control, business applications, civil identity, and healthcare. Biometrics can also be used for authentication, monitoring (e.g., subtle changes in biometrics may have health implications), and personalized medical concerns. Increased use of biometrics creates identity vulnerability through the exposure of personal identifiable information (PII). Hence an increasing need to not only validate but secure a patient's biometric data and identity. The latter is achieved by anonymization, or de-identification, of the PII. Using Python in collaboration with the PTB-XL ECG database from Physionet, the goal of this thesis is to create "selective de-identification." When dealing with data and de-identification, clusters, or groupings, of data with similarity of content and location in feature space are created. Classes are groupings of data with content matching that of a class definition within a given tolerance and are assigned metadata. Clusters start without derived information, i.e., metadata, that is created by intelligent algorithms, and are thus considered unstructured. Clusters are then assigned to pre-defined classes based on the features they exhibit. The goal is to focus on features that identify pathology without compromising PII. Methods to classify different pathologies are explored, and the effect on PII classification is measured. The classification scheme with the highest "gain," or (improvement in pathology classification)/ (improvement in PII classification), is deemed the preferred approach. Importantly, the process outlined can be used in many other systems involving patient recordings and diagnostic-relevant data collection.