Mountain Scholar (Digital Collections of Colorado and Wyoming)
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Geologic mapping and kinematic analysis of the Independence Mine shear zone in the Sangre de Cristo Range, southern Colorado: extensional reactivation of a Laramide reverse fault
Full text link2023 Spring.Includes bibliographical references.The Sangre de Cristo Range in southern Colorado records some of the deepest Cenozoic structural levels in the Rocky Mountain region. Exposures of Laramide-age contractional mylonites show evidence for brittle-plastic extensional overprinting associated with the Rio Grande rift. This study examines the relation between Laramide contraction and Rio Grande rift extension by detailed geologic mapping and kinematic, geochronological, and geochemical analyses in a 50 km2 area centered on the Independence Mine shear zone (IMSZ), formerly called the Independence Mine thrust. The IMSZ is a 15- to 100-meter-thick, shallow-to-moderately (25°–62°), WSW-dipping brittle-plastic shear zone near the topographic base of the western flank of the range. It displays microstructural evidence for initiation as a top-NE contractional mylonite zone consistent with Laramide kinematics but is pervasively overprinted by deformation fabrics indicating top-SW extensional reactivation. Top-SW microstructures are characterized by phyllosilicate-lined C- and C'-shear bands and mixed brittle-plastic deformation of quartz. Mapping shows that the IMSZ is the thickest member of a system of mylonitic shear zones that dip shallowly to moderately (25°–67°) to the WSW and are hosted primarily within Proterozoic gneiss. Shear zones in amphibole-rich gneiss are commonly dominated by chlorite whereas those in quartzo-feldspathic gneiss have abundant white mica. Many of the thinner shear zones also record top-SW overprinting of top-NE fabrics. Though both top-NE and top-SW shear fabrics involve cataclasis and quartz dislocation creep, extensional overprinting appears to be mostly restricted to mylonites where secondary phyllosilicates form an interconnected weak phase. These relations are interpreted as fluid-mediated, reaction-weakening gradients where lithologically controlled rheological contrasts were variably sensitive to extensional reactivation. One top-SW shear zone adjacent to the IMSZ cuts a gabbro stock that was dated at 25.7 ± 0.7 Ma using LA-ICP-MS zircon U-Pb geochronometry. Synkinematic monazite grains in two samples of the IMSZ yield LA-ICP-MS U-Pb and U-Th-Pb ages of 24.9 ± 3.0 Ma and 22.2 ± 0.7Ma, respectively. These data are consistent with extensional reactivation occurring during Late Oligocene to Early Miocene time. The IMSZ and associated reactivated shear zones may represent mid-crustal extension that was widespread in the earliest stages of Rio Grande rifting before extension shifted to high-angle brittle-regime normal faults along the range front
Simulating cut to length forest treatment effects on fire behavior over steep slopes
Full text link2023 Spring.Includes bibliographical references.The increase of wildfire size and behavior in many western U.S. forests is due to increased fuel loads resulting from the past century's fire suppression, logging, and grazing policies of the 20th century, along with compounding climactic changes including increased drought and temperatures. Fuel hazard treatments are the key land management tool used to reduce fire intensity and severity however these treatments are often not possible on steep terrain of over 30% slope. Cable tethered cut to length machinery opens new avenues for managers to treat forests in steep slopes, but there is limited data on how effective the treatments will be. I conducted a numerical experiment using the wildfire model, FIRETEC, coupled with the atmospheric dynamics model, HIGRAD, to understand the complex interactions of wind, topography, and fire behaviors of two cut to length forest treatments on slopes of up to 60%. Results show that treatments can effectively reduce some fire behaviors such as heat release and canopy consumption when compared to untreated forests on slopes. However, increased sub canopy wind penetration along the slopes following treatments results in marginal fire severity reduction regarding biomass consumption and variable results on rates of spread. The results of these numerical experiments indicate that CTL treatment can effectively reduce some fire behavior and severity, however the effects were marginal and additional research is needed to better understand treatment's effects
Single pixel computational imaging
Full text linkIncludes bibliographical references.2023 Spring.Microscopy has a long rich history of peering into life's smallest mysteries. Ever since the first microscope, the ability to see objects that would otherwise be impossible to see with the naked eye have allowed new discoveries and modern technology has benefited tremendously. There have been many improvements on microscopes over the centuries with each improvement unlocking more knowledge as we go. Some of these advancements are the modern objective lens correcting for numerous optical aberrations, phase contrast imaging allowing nearly transparent samples to have high contrast, the confocal pinhole allowing an easy method to get optical sectioning, and super resolution microscopy surpassing the diffraction limit by several orders of magnitude. One of the most amazing things about all these discoveries is that they all rely on the same fundamental concepts. This work focuses on expanding the capabilities of single pixel imaging. Single pixel imaging is a class of imaging that encodes spatial information on a temporal signal using a single element detector; having knowledge of the encoding allows the time signal to be reconstructed to generate a spatial image. A canonical example of single pixel imaging is laser scanning microscopy (LSM). More complicated encoding systems have been developed but the basic idea for reconstruction remains the same. There are several advantages conferred to single pixel imaging such as image formation is resistant to scattering, very fast temporal response, flexibility in detector selection at a given wavelength, and exotic imaging information. My research primarily utilizes two techniques, SPatIal Frequency modulated Imaging (SPIFI) and Coherent Holographic Image Reconstruction by Phase Transfer (CHIRPT), both are explained in detail. My research aims to expand the capability's of SPIFI by providing a method for homogenizing the anisotropic resolution observed in the higher orders, additionally, I present a method of solving the inverse problem that allows the measurement matrix to more accurately represent to true image formation process there by increasing the performance of the reconstruction. I present research for CHIRPT which takes advantage of the encoded coherent phase information of two interfering beams to measure the quantitative phase of an object. I also present a new technique utilizing CHIRPT's holographic phase information to extend optical diffraction tomography to incoherent emitters which has long been an illusive task
Visible & thermal imaging and deep learning based approach for automated robust detection of potholes to prioritize highway maintenance
Full text link2023 Spring.Includes bibliographical references.Potholes are a primary pavement distress that can compromise safety and cause expensive damage claims. Potholes are results of deterioration of pavements due to aging, weather and traffic overloads and are common problems across the U.S. Potholes are even more common in the Mountain Plains region due to the snow and freeze/thaw effect. Identifying and repairing potholes is one critical aspect of highway maintenance. Accurate, robust and fast detection of potholes is critical to enabling timely and cost-effective pavement maintenance. Recently, there has been growing interest and research in using machine learning techniques for pothole detection using different views of visible images. However, quality of potholes detection using only visible images may be significantly compromised due to poor lighting, weather conditions, low contract to surrounding pavement. On the other hand, thermal images are more robust to lighting and weather conditions. Although thermal images may lack the texture details of visible images, they can offer additional unique features compared to visible images, e.g., temperature difference between pothole and surrounding pavement, which can be potentially used for pothole detection. However, so far, the great potential and effectiveness of integrating both visible and thermal images as well as using fused images to enable accurate and robust pothole detection have not been investigated. This research aims to develop an automated deep learning based pothole detection and mapping tool for highway maintenance using the fusion of visible and thermal images. First, a unique and valuable database of geotagged and labeled trios of visible, thermal and fused images is established for training pothole detection algorithms. This is done through collecting pothole images using a low-cost FLIR ONE thermal camera connected to a smart phone. These data are used to train the machine learning algorithms for pothole detection. To establish an accurate pothole detection algorithm, we proposed and compared the performances of three machine learning algorithms, i.e., Anisotropic Diffusion Fusion (ADF) + Mask R-CNN, RTFNet, and RTFNet with Enhancement Parameters (EPs). These algorithms differ in how the visible and thermal images are fused and used for pothole detection. We achieved the best F1-score of 93.7% in the daytime scenario by the RTFNet method and 90.9% in the nighttime scenario by the RTFNet with EPs method. To best leverage the information from the thermal images, in the end we developed a Bright-Dark detector to determine the lighting conditions of candidate testing images, and then feed the images to the respective algorithms for pothole detection. For images with potholes detected, we also developed a mapping tool to map the location of the pothole using GPS information of the images. In the end, the trained overall algorithm is packaged as a tool with graphical user interface (GUI) to facilitate its adoption by highway maintenance team. As more images are collected, the overall algorithm can be continuously improved to further increase the pothole detection accuracy
Brian Pena Garcia: capstone
No full text2023 Spring.Colorado State University Art and Art History Department capstone project.Capstone contains the artist's statement, a list of works, and images of works.The artist's statement: As a Mexican immigrant I've had to witness and experience a lot of mistreatment while living in the U.S. Feeling the pressure of being the absolute perfect model citizen in a country that doesn't respect or care for my existence is exhausting. With no financial sustainability or support medically, I've seen my family and other immigrants struggling year after year. Even though Undocumented Immigrants pay Billions of dollars in taxes every year, we are still not accepted as citizens. For my project I would like to explore the feelings and sentiments I have felt through my life in growing up in the U.S. as a Mexican immigrant. Throughout my years of watching the political climate in constant fear, to my personal experience of racial discrimination, I want to create illustrations in ink that demonstrate the aspects of being an immigrant in the United States. Some examples of concepts I'd like to explore are the experiences in the work environment. Being underpaid, working more hours, and having no choice in where to work are a few points that describe the situation of work for undocumented immigrants. Another aspect is the ability to see family from their home country. Undocumented immigrants usually don't have the money to travel, and if they did it would be impossible to leave the U.S. because they would no longer be able to come back. This causes them to not be able to see their family. This is an experience I had to go through in my family and witness this pain in my parents. Pieces like "Thinking of Mexico" explore this feeling of diaspora that Mexican immigrants deal with when it comes to being in the United States. Only experience my birthplace through memories while experiencing the unjust systematic discrimination and oppression in the U.S
Singer / songwriter night: Saja Butler & Bevin Luna: performing with CSU Music Therapy students
No full textLinking organismal physiology and the landscape to predict vulnerability to climate change
Full text linkIncludes bibliographical references.2023 Spring.Global temperatures continue to increase at unprecedented rates, both in mean and in variance. Thus, a major challenge for scientists of the 21st century is to predict whether species will persist through these changes. One way to partly assess vulnerability to climate change is to investigate the relationships between the environment and traits that are either particularly sensitive to temperature or may confer resilience against thermal changes. In ectotherms, external temperatures dictate their physiology, thus thermal physiological traits may be key to understanding ectothermic persistence. Although population variation is integral to the evolvability of thermal physiological traits, most studies using these traits to infer vulnerability extrapolate data from one or few populations to represent the species. Furthermore, many studies also use coarse metrics of environmental temperatures which may not fully capture the variation experienced by the organism. Here, using a cold-water frog system, I demonstrate the relationships between thermal physiological traits and local environmental temperatures among populations. In my first chapter, I provide a brief overview of ectothermic physiology, environmental thermal landscapes, and the ecology of the two species of tailed frogs that I investigated. In my second chapter, I show that populations of tailed frogs vary in their critical thermal limit (CTmax) plasticity, which impacts species-level assessments of vulnerability. I also demonstrate the methodological impacts of ignoring acute responses to temperature when estimating plasticity in this trait. For my third chapter, I demonstrate relationships between CTmax and local thermal environments, including temporal and spatial variability in temperature, among populations of tailed frogs. These results show that tailed frogs have limited opportunity for behavioural avoidance of warm temperatures, and that populations of one tailed frog species show a positive relationship between CTmax and maximum stream temperature while populations of the other species does not. In my fourth chapter, I test the critical assumption that CTmax is related to fitness, specifically mortality in ecologically relevant temperatures. My results show that populations with higher estimates of CTmax experience less mortality from thermal stress in temperatures experienced in nature, demonstrating the link between CTmax and fitness. Lastly, in my fifth chapter, I return to the plasticity in CTmax results and demonstrate the relationship between this trait and local thermal environments, showing that populations experiencing greater temperature fluctuations have greater estimates of plasticity in CTmax. Overall, these results underscore the importance of sampling widely among populations when inferring vulnerability to climate changes from physiological traits. The population variation in CTmax and its plasticity that I uncovered demonstrate the differing trends in vulnerability to climate change for the two species investigated. This work also highlights the importance of quantifying local thermalscapes and highlight how similar environments can differentially shape physiological tolerance and patterns of vulnerability among populations, in turn impacting vulnerability to future warming
Nicole Hines: capstone
No full text2023 Spring.Colorado State University Art and Art History Department capstone project.Capstone contains the artist's statement, a list of works, and images of works.The artist's statement: In my designs, I strive to create visual imagery and use typography to inform and describe the world around us in an imaginative and creative way. I strive to create designs that can be used as a universal language that is accessible and comprehensible for all while still using an innovative touch. My grandmother was an artist from Argentina, who showed me how to create imagery that can reflect a visual language that people from all walks of life can understand and be inspired by. Creating art with my grandmother taught me the invaluable meaning of creation as an expression of the self and understanding the world around me. Drawing and painting with her brought me to see that my passion for creative outlet would be fundamental for myself to view life in a completely creative way. This vision allows me to create unique work for anybody who requires an original and insightful message through the impact of my design. My work in graphic design aims to convey messages about the environment around us through creative visual imagery that includes bold colors and unique compositions that are inclusive for anybody to understand. My process for creating design starts where art began for me, through pencil and paper. I find my most valuable insights for design come by exploring all possibilities for an idea by drawing. Then I take my best ideas from my sketches and transform them through digital programs. Digitally my work can be accessible for clients to spread their message through my designs. Through the process of creating digital work, my designs can be made for anyone with any necessity for visual imagery and language to excite and inspire innovation
Machine learning and deep learning applications in neuroimaging for brain age prediction
No full textIncludes bibliographical references.2023 Spring.Machine Learning (ML) and Deep Learning (DL) are now considered as state-of-the-art assistive AI technologies that help neuroscientists, neurologists and medical professionals with early diagnosis of neurodegenerative diseases and cognitive decline as a consequence of unhealthy brain aging. Brain Age Prediction (BAP) is the process of estimating a person's biological age using Neuroimaging data, and the difference between the predicted age and the subject's chronological age, known as Delta, is regarded as a biomarker for healthy versus unhealthy brain aging. Accurate and efficient BAP is an important research topic, and hence ML/DL methods have been developed for this task. There are different modalities of Neuroimaging such as Magnetic Resonance Imaging (MRI) that have been used for BAP in the past. Diffusion Tensor Imaging (DTI) is an advanced quantitative Neuroimaging technology that gives insight into microstructure of White Matter tracts that connect different parts of the brain to function properly. DTI data is high-dimensional, and age-related microstructural changes in White Matter include non-linear patterns. In this study, we perform a series of analytical experiments using ML and DL methods to investigate the applicability of DTI data for BAP. We also investigate which Diffusivity Parameters, which are DTI metrics that reflect direction and magnitude of diffusion of water molecules in the brain, are relevant for BAP as a Supervised Learning task. Moreover, we propose, implement, and analyze a novel methodology that can detect age-related anomalies (high Deltas), and can overcome some of the major and fundamental limitations of the current supervised approach for BAP, such as "Chronological Age Label Inconsistency". Our proposed methodology, which combines Unsupervised Anomaly Detection (UAD) and supervised BAP, focuses on addressing a fundamental challenge in BAP which is how to interpret a model's error. Should a researcher interpret a model's error as an indication of unhealthy brain aging or the model's poor performance that should be eliminated? We argue that the underlying cause of this problem is the inconsistency of chronological age labels as the ground truth of the Supervised Learning task, which is the common basis of training ML/DL models. Our Unsupervised Learning methods and findings open a new possibility to detect irregularities and abnormalities in the aging brain using DTI scans, independent of inconsistent chronological age labels. The results of our proposed methodology show that combining label-independent UAD and supervised BAP provides a more reliable and methodical way for error analysis than the current supervised BAP approach when it is used in isolation. We also provide visualization and explanations on how our ML/DL methods make their decisions for BAP. Explainability and generalization of our ML/DL models are two important aspects of our study