Mountain Scholar
Mountain Scholar is an open access repository service that collects, preserves, and provides access to digitized library collections and other scholarly and creative works from Colorado State University and the University Press of Colorado. It also serves as a dark archive for the Open Textbook Library.
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- Explore the Colorado State University community’s scholarly output as well as items from the University at large and the CSU Libraries.
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Recent Submissions
Nature and health researcher needs assessment: investigating the state of evidence among researchers to better inform practitioners
(Colorado State University. Libraries, 2025) Merfeld, Julia, author; Tamlyn, Kaiya, author; Johnson, Sharde, author; Terol, Silvia, author; Collins, Sara Ashley, author; Farwell, Josie, author; Zarestky, Jill, author; Walker, Sarah, author; LoTemplio, Sara, author
Comparison of Agile scaling frameworks
(Colorado State University. Libraries, 2023-08) Yeman, Robin J., author; Malaiya, Yashwant K., author; ACM, publisher
21st century software development approaches, such as Agile has benefitted small initiatives with a single team building software in their ability to respond to change, reduce product delivery schedules, reduce product cost, increase product quality, and Increase employee morale. The industry has begun to question what the benefits and challenges could be if we use those same practices at scale for large Initiatives with multiple teams. There have been multiple studies on the benefits of Agile at Scale including quality, productivity, and shorter time to market. Over the last two decades dozens of Scaling frameworks have been created. However, given the large number of frameworks available, how do companies choose the right scaling framework? In this paper we review what the difference between the frameworks are by comparing their core principles. This paper began by analyzing results Digital.ai's 16th Annual state of Agile Survey which identifies the 10 most utilized frameworks according to their respondents. The paper presents a detailed analysis of the principles to assess the degree of differences among the frameworks and determine based on this which framework is the best for large scale organizations to choose.
Research on predictive algorithms for cardiovascular disease
(Colorado State University. Libraries, 2023-10) Wang, Yingzhen, author; ACM, publisher
Cardiovascular disease (CVD) is a global disease with acute and chronic complications. It is primarily responsible for the vast majority of deaths worldwide, which account for 17.9 million deaths annually. In terms of CVDs, illnesses like rheumatic heart disease and coronary heart disease are included, of which coronary heart disease (CHD) accounts for more than 50% of all these cases. In this research, principal component analysis (PCA) and backward stepwise elimination are used to identify the relevant predictors and avoid overfitting models for random forest analysis and logistic regression analysis. Moreover, for assessing the effectiveness of the models, the confusion matrix and the receiver operating characteristics (ROC) curve with AUC (area under the ROC curve) value are produced for model comparison. The outcomes demonstrate that the random forest model performs better at categorizing high-dimensional data. Thus, the techniques discussed in this paper give medical researchers better ways to handle coronary heart disease data statistically and provide a new statistical procedure for coronary heart disease prediction and prevention.
Partition Crossover can linearize local optima lattices of k-bounded Pseudo-Boolean functions
(Colorado State University. Libraries, 2023) Whitley, Darrell, author; Ochoa, Gabriela, author; Chicanl, Francisco, author; ACM, publisher
When Partition Crossover is used to recombine two parents which are local optima, the offspring are all local optima in the smallest hyperplane subspace that contains the two parents. The offspring can also be organized into a non-planar hypercube "lattice." Furthermore, all of the offspring can be evaluated using a simple linear equation. When a child of Partition Crossover is a local optimum in the full search space, the linear equation exactly determines its evaluation. When a child of Partition Crossover can be improved by local search, the linear equation is an upper bound on the evaluation of the associated local optimum when minimizing. This theoretical result holds for all k-bounded Pseudo-Boolean optimization problems, including MAX-kSAT, QUBO problems, as well as random and adjacent NK landscapes. These linear equations provide a stronger explanation as to why the "Big Valley" distribution of local optima exists. We fully enumerate a sample of NK landscapes to collect frequency information to complement our theoretical results. We also introduce new algorithmic contributions that can 1) expand smaller lattices in order to find larger lattices that contain additional local optima, and 2) introduce an efficient method to find new improving moves in lattices using score vectors.
Next generation genetic algorithms: efficient Crossover and local search and new results on Crossover lattices
(Colorado State University. Libraries, 2024-07) Whitley, Darrell, author; ACM, publisher