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Integration of Autonomous Equipment in the Heavy Civil Industry
Get PDFThe heavy civil industry plays a pivotal role in shaping the infrastructure landscape, with construction equipment serving as the backbone of any project. In an industry that is constantly looking for ways to improve efficiency, productivity, and safety, autonomous construction equipment has emerged as an innovative solution to help improve these factors on a heavy civil project. The purpose of this paper is to present an in-depth look at the current state of new technology involving autonomous construction equipment, and how prevalent it is in today\u27s heavy civil industry. Qualitative interviews that were completed with a project manager, superintendent, and a foreman that work for Toro Enterprises were used to help emphasize where autonomous equipment is at related to a smaller heavy civil company, and if it\u27s worth it for them to implement this equipment into their everyday work. With autonomous equipment being a relatively new technology, future research that can be studied is cost-benefit analysis, initial investment costs, maintenance expenses, potential productivity gains, and how it can work side by side with BIM
Nail Polish Epiphany
Get PDFDo they really smell of marshmallow Peeps? I nervously sniffed my brightly polished fingernails as I walked to my on-campus video interview, where I would talk about what it was like being a gender non-conforming person at my workplace. My university wanted individuals’ experiences, mine included, to create a webpage full of written and video stories of a group of employees to show how inclusive we were. But back to my nails
Office Building in Moffett Park District
Get PDFThe objective of this review is to analyze and evaluate the fire safety of a 5-story office building located in the Moffett Park District in Sunnyvale, California. The building is constructed largely of mass timber and classified as a Type IIIA building used primarily as office space. The first section of the report focuses on the building’s defining characteristics and use cases. The second portion of the report presents a comprehensive review of egress design, fire alarm systems, fire suppression systems, structural design, implementation of mass timber elements, and flammability requirements. The results of the analysis show that the building complies with current prescriptive requirements.
The ensuing section of the report provides a performance-based analysis that reviews two design fire scenarios. The first design fire scenario involves a fire lobby, which is an intervening space along a primary egress path in the building. The analysis showed that the space reaches untenable conditions within 40 seconds. The RSET for the building is 26 minutes. Since this fire opens occupants to significant hazards, it is recommended that the fuel loading in the space is reduced. The second design fire scenario explored sprinkler activation within a team work-pod on the second floor. This space restricted the size of a fire within an otherwise open environment with high ceilings. Sprinkler activation in this area occurred at 351 seconds and provides coverage within the space
Binary Ever After: Gender Representation of Non-Human & Non-Animal Characters in Disney/Pixar’s Inside Out
Get PDFMMF-DRL: Multimodal Fusion-Deep Reinforcement Learning Approach with Domain-Specific Features for Classifying Time Series Data
Get PDFThis research focuses on addressing two pertinent problems in machine learning (ML) which are (a) the supervised classification of time series and (b) the need for large amounts of labeled images for training supervised classifiers. The novel contributions are two-fold. The first problem of time series classification is addressed by proposing to transform time series into domain-specific 2D features such as scalograms and recurrence plot (RP) images. The second problem which is the need for large amounts of labeled image data, is tackled by proposing a new way of using a reinforcement learning (RL) technique as a supervised classifier by using multimodal (joint representation) scalograms and RP images. The motivation for using such domain-specific features is that they provide additional information to the ML models by capturing domain-specific features (patterns) and also help in taking advantage of state-of-the-art image classifiers for learning the patterns from these textured images. Thus, this research proposes a multimodal fusion (MMF) - deep reinforcement learning (DRL) approach as an alternative technique to traditional supervised image classifiers for the classification of time series. The proposed MMF-DRL approach produces improved accuracy over state-of-the-art supervised learning models while needing fewer training data. Results show the merit of using multiple modalities and RL in achieving improved performance than training on a single modality. Moreover, the proposed approach yields the highest accuracy of 90.20% and 89.63% respectively for two physiological time series datasets with fewer training data in contrast to the state-of-the-art supervised learning model ChronoNet which gave 87.62% and 88.02% accuracy respectively for the two datasets with more training data
Mountain Lion Resource Selection in the California Central Coast: Modeling Habitat Suitability for a Large Carnivore in a Rapidly Changing Environment
Get PDFLand use conversion toward agriculture such as orchards and vineyards can have severe negative impacts on habitat and wildlife, particularly large carnivores, globally through habitat fragmentation and loss. The mountain lion (Puma concolor) population in the California Central Coast is thought to provide “stepping-stone” connectivity between several severely genetically compromised coastal populations throughout the Santa Cruz Mountains and several mountain ranges in Southern California; however, the California Central Coast is one of the fastest-developing regions of California with little protection against future land use conversion. Conserving areas of and corridors between high-quality mountain lion habitat through conservation easements should be prioritized. Our results showed that this is especially important in areas currently zoned for agriculture and residential but not fully developed yet. Conserving quality habitat is not only beneficial to mountain lions, but also many species underneath their ecological “umbrella.”
In my first chapter, I performed a literature review detailing what ecologists currently understand about human impacts on wildlife, with an emphasis on large carnivores, through habitat fragmentation and loss, land conversion, and human-carnivore conflict. I also reflected on mountain lion ecology and management in California and North America as a whole, before reviewing analytical methods most commonly used to study their home ranges and resource selection.
In my second chapter, I used GPS collar data from seven GPS-collared mountain lions on the Fort Hunter Liggett Army Base in Monterey County, California to compare minimum convex polygon, kernel density isopleth, and adaptive-local convex hull methods to elucidate the strengths and weaknesses of each when estimating wildlife home ranges and utilization distributions. Following this, I used the GPS data to create a resource selection function to model predicted resource selection patterns of the mountain lions on the Army Base before projecting my model out to the counties comprising the greater California Central Coast. I then overlaid this habitat suitability map with zoning and land protection status maps from each county. My results provide a clear visual representation of not only mountain lion habitat suitability throughout the Central Coast, but areas wildlife and land managers should prioritize for conservation in relation to adjacent areas of varying zoning and protection statuses
Static Vascular Modeling of Diabetes Progression
Get PDFCardiovascular disease is the leading cause of mortality in diabetic patients, and diabetes is one of the main causes of cardiovascular disease. Risk factors for cardiovascular disease result in structural and functional changes in the vascular wall. Arterial stiffness is a prominent structural change observed in the arterial wall that can be measured in clinical settings. The purpose of this thesis was to create a static model of the changes in arterial stiffness seen in diabetes. Elastic tubes with varying wall thicknesses were used to create artificial arteries for this purpose. Compliance (inverse of stiffness) of the arteries was determined using a pressurevolume model and a mathematical model. The compliance curves generated using the pressurevolume model exhibited trends predicted by the mathematical model. These trends were comparable to arterial stiffness changes seen in diabetes. Compliance obtained from pressurevolume measurements of elastic tubes with varying wall thickness can therefore be used to model the general trends of arterial stiffness in diabetes
Establishing a Monitoring Framework to Evaluate Impacts of Grazing Exclusion and Inform Restoration at Santa Rita Ranch
Get PDFSanta Rita Ranch is a 1,750-acre cattle ranch in Templeton, California, that was privately owned and continuously grazed for the past 70 years. In 2021, The Land Conservancy of San Luis Obispo (LCSLO) acquired the property, and in the winter of 2022, they built a fence that bisects the ranch, excluding the cattle from 800 acres to preserve the riparian habitat. The LCSLO is currently defining the ecological baseline for the ranch to inform future management decisions. Given that cattle grazing has many environmental tradeoffs, they wanted to establish a long-term monitoring study to understand how relieving the pressures of grazing impacts plant community composition within the grasslands of Santa Rita Ranch. We established monitoring sites with comparable soil types, topography, and vegetation on the grazed and ungrazed sides of the ranch. We collected the first year of data in the spring of 2023 using the California Native Plant Society’s Relevé vegetation sampling technique. In addition, we sampled above and belowground biomass from the grazed and ungrazed sites to study the impacts cattle have on community plant biomass, particularly root biomass and percent moisture. The early findings revealed no significant differences between the plant community composition of the grazed and ungrazed monitoring sites, likely because the cattle had only been excluded for six months, and monitoring studies have extended time frames before any correlation between variables can be detected. We did find a statistical difference between grazed aboveground and belowground plant biomass. However, the two treatments had no significant difference between the average belowground biomass. Site-specific ecological data is often limited; therefore, another objective of this project was to bridge the communication gap between stakeholders by making the data from this study and other student research projects at Santa Rita Ranch accessible online at www.srr.land. The hope behind this project is that researchers will continue monitoring and land managers will use the information to help them develop land management strategies that enhance plant diversity, increase soil health, and maintain ecosystem multi-functionality