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Modeling uncertainties in primary zone soot predictions for a rich-quench-lean combustion system
Get PDFA comprehensive uncertainty quantification (UQ) of soot volume fraction (SVF) predictions in the primary zone of a Rich-Quench-Lean (RQL) combustor is presented, with particular emphasis on the modeling uncertainties in the rates of nucleation, growth, oxidation, condensation, and coagulation. Large-eddy simulations (LES) of soot formation in a realistic single-sector RQL combustor are first performed, and the local thermochemical data, along with the volumes of the zones containing the respective finite volume cells, are then used to construct a chemical reactor network (CRN) model. The CRN model, coupled with the UQ tool DAKOTA, is used to conduct forward UQ, sensitivity analysis, and inverse UQ. The forward UQ indicates variability ranging from 28 % to 89% around the mean soot prediction, depending on the location within the combustor. The local and global sensitivity analyses highlight the contributions of nucleation and condensation processes near the fuel injection zone, while growth and oxidation processes predominantly influence soot predictions in the primary zone. Since the baseline model underpredicts soot compared to experimental measurements, a Bayesian inference-based inverse UQ analysis is performed to identify sensitive input rate uncertainties that can improve the quantitative agreement with experimental soot levels. Thus, the overall strategy identifies the most influential aspects of the soot model, their relevant sensitivity to local zones within the combustor, and further refinements to the baseline rates that can provide valuable insights for future model developments
A hobbyist guide to growing shitake mushrooms on logs for woodland owners
Get PDFShiitake (Lentinula edodes) is a well-known specialty mushroom that is prized for its flavor and nutrition. It is native to East Asia, but shiitake is now cultivated around the world. Most shiitakes are grown indoors using bags of sterilized sawdust in climate-controlled facilities. However, shiitake can also be cultivated outdoors on freshly cut logs in forested settings. Growing shiitake mushrooms on logs is an enjoyable hobby for woodland owners and can also generate income for growers who want to develop a mushroom enterprise. It requires few inputs and little space, so it can be practiced on properties with minimal forested acreage. This publication describes best practices for hobby-scale, forest-cultivated shiitake in the Pacific Northwest (PNW) based on research conducted by Washington State University Extension. For farmers and forest owners interested in developing a commercial shiitake enterprise, visit the Resources section of the Pacific Northwest Forest-Cultivated Mushroom Growers Network website
ADVANCING BIOFUEL CATALYSIS DECIPHERING HOW SURFACE INTERACTIONS AND ELECTRIC FIELDS SHAPE HYDRODEOXYGENATION FROM FIRST PRINCIPLES
Get PDFTo optimize hydrodeoxygenation for biofuel production, it is essential to understand the behavior of molecules in the fuel and the catalysts used to develop the fuel. Modeling these systems requires the inclusion of lateral interactions which influence reaction pathways. These are affected by other factors, such as the metal catalyst type, the coverage of adsorbed species, and reaction conditions. This dissertation provides a foundation for modeling biofuel adsorbates and potential catalysts for hydrodeoxygenation under different reaction conditions.The first-principles-based results reveal that the adsorption of aromatics is greatly influenced by the aromatic's complexity and the coverage. The lateral interactions can be parsed out into the surface-mediated and through-space interactions. Surface-mediated interactions, stronger on noble metals like platinum, govern the behavior of closed-shell aromatics, while through-space interactions become more dominant for open-shell and complex aromatics. Using simple descriptors for the mean-field parameters, parity plots are developed to predict the coverage-dependent adsorption energies for all aromatic/metal combinations studied, enabling the inclusion of experimentally-relevant coverages in reaction studies with significantly reduced computational cost. Further work explored the use of electric fields to mitigate iron catalyst oxidation. The findings show that electric fields weaken oxygen adsorption on iron, regardless of coverage or iron facet. Comparison with experimental work highlights the value of combining theory and experiment to understand catalytic systems. The results indicate that the different iron facets have varied responses to changes in reaction conditions, emphasizing the importance of modeling catalytic systems to optimize reactions and enhance catalyst effectiveness. The role of alkali metals on the hydrodeoxygenation of phenol in the presence of water over an iron catalyst is also explored. Alkali metals are found to attract water molecules, reducing interactions between water and iron and potentially extending the catalyst lifespan by preventing oxidation. Cesium is shown to inhibit phenol tautomerization in favor of direct oxygen cleavage. The interactions between the catalyst, phenol, water molecules, and alkali metal alter the reaction pathways, complementing the findings from the other studies. The impact of coverage on phenol and hydroquinone decomposition products is further examined. The study shows that phenol and hydroquinone follow similar thermal decomposition pathways. High coverage is found to introduce complexity in the catalytic systems, likely due to increased through-space interactions, making thermal X-ray photoelectron spectroscopy spectra difficult to deconvolute, but highlighting the importance of lateral interactions in affecting reaction pathways and energetics.The studies in this dissertation emphasize the importance of incorporating lateral interactions for accurately modeling catalytic hydrodeoxygenation systems. High coverage, solvent environments, electric fields, and other adsorbates are shown to alter both adsorption energies and reaction pathways, affecting catalyst performance. By modeling and understanding these systems, we can optimize catalyst properties and reaction conditions, ultimately improving reaction efficiency and extending catalyst lifespans, laying the foundation for optimizing biofuel production
LARGE SCALE LEARNING ON TINY DEVICES OPTIMIZATION TECHNIQUES FOR ADAPTIVE AUTONOMOUS DRIVING SYSTEMS
Get PDFWith recent advances in computing and sensing technologies, autonomous driving has gained increasing attention and emerged as a promising platform for the next generation of intelligent transportation systems. A key requirement for autonomous driving systems is the ability to utilize artificial intelligence and machine learning techniques to make reliable, real-time decisions on edge devices. However, deploying robust machine learning models on resource-constrained hardware in real-time environments remains a challenging task. In this work, we explore two different techniques for efficient AI inference on such devices and analyze their effectiveness through two distinct projects.First, we conducted a comprehensive study on quantization and pruning techniques to enable efficient and accurate decision-making for autonomous driving applications, such as traffic sign detection. We implemented channel and fine-grained pruning on a custom-trained YOLOv8 model using the German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains 50,000 traffic sign images across 40 classes. We further optimized the detection layers and applied INT8 quantization using NVIDIA TensorRT along with k-means quantization. These methods collectively improved computational efficiency by 4.4x, reduced the model size by 90%, and resulted in only a 3% drop in accuracy. Experimental evaluations were performed on two devices: the Jetson Nano and the Jetson Orin Nano.Second, we investigated the integration of large language models (LLMs) into autonomous driving systems, addressing the associated challenges and potential for optimization. Running LLMs directly on resource-constrained devices is impractical without quantization. To address this, we applied AWQ and Q4_0 quantization techniques to the Mistral 7B model. We used the Gymnasium simulation environment by OpenAI, specifically the Highway-env environment from the Farama Foundation, which simulates the ego vehicle and surrounding traffic. The extracted simulation data was converted into structured JSON format and pro- vided to the Mistral model via an agent prompt for classification tasks. This experiment was conducted on a Lambda system and the Jetson Orin Nano. While the model ran efficiently within the hardware limitations, the results were less accurate due to difficulties in handling complex, large-scale JSON data
Machine Learning for Hour-Ahead Solar Radiation Prediction in Seattle, WA
Get PDFSolar energy is rapidly becoming a major source of electricity generating capacity worldwide. However, unlike traditional sources of electricity, solar generation is subject to the diurnal cycle of the sun as well as transitory changes in weather. As a result, there is a significant need to predict solar generating capacity on both an hour-ahead and day-ahead schedule so utilities can dispatch appropriate resources to meet demand. This paper explores the hour-ahead prediction of solar radiation and makes two contributions to the field. First, we develop a suite of machine learning based models for predicting hour-ahead ground level solar global irradiance in Seattle, WA, an area known for dense cloud cover and long periods of rain. The best of these models is implemented using XGBoost with a Root Mean Squared Error (RMSE) of 49.843 W/m2 and a Normalized Root Mean Squared Error (NRMSE) of 0.334. We demonstrate that this model improves upon alternative architectures in terms of the trade-off between test performance, training efficiency, and prediction efficiency. Additionally, we identify several properties of past work that make it difficult to fully evaluate the current landscape. These include critical differences in how training and testing sets are produced and how error is measured. Based on this analysis, we provide a set of recommendations for future work that we believe will help to improve understanding of the field at large
Relations Between Individual Differences in Emotion Differentiation and the Brain
Get PDFThis thesis examined the individual differences and underlying brain processes of emotion differentiation. Emotion differentiation (ED) is the ability to accurately identify and label distinct emotions. ED is linked to more effective emotion regulatio strategies and emotional well-being. This thesis explores the following research questions: (1) whether individuals high in emotion differentiation are more sensitive to the emotions expressed by others through correlation analysis, (2) positive emotionality is linked to greater relative left frontal alpha activity also reflecting more sensitive emotion differentiation abilities, and (3) the possibility that frontal alpha asymmetry (FAA) moderates the hypothesized association between emotion differentiation and emotion discrimination. The results did not reveal evidence for these hypothesized relations. The implications for this research include focusing on cognitive and neural pathways to understanding emotional competence beyond emotion differentiation and discrimination. Acknowledging the limitations, this study extends the discussion of how best to measure emotion differentiation behaviorally and neurally
3D-PRINTED WEARABLE DEVICES FOR HEALTH MONITORING
Get PDFDiabetes is a chronic disease associated with common complications, including heart disease, kidney failure, and adult blindness. People with diabetes cannot produce enough insulin to regulate their blood glucose, making glucose monitoring essential for managing the disease and reducing the risk of these complications. Traditional finger stick is the simplest and most convenient method to monitor blood glucose, but invasive and painful. In addition to blood, alternative non-invasive and minimally invasive biofluids, such as sweat and interstitial fluids have shown strong correlations with blood glucose levels, making them promising candidates for non-invasive or minimally invasive glucose monitoring. Wearable biosensors have gained significant attention in recent years for their potential to enable continuous and non-invasive health monitoring. The integration of 3D printing technologies has further revolutionized the fabrication of these sensors, facilitating miniaturization, flexibility, and customization. Despite these advancements, challenges such as sensitivity, material limitations, and integration approaches for wearable devices remain. In this dissertation, wearable biosensors for continuous glucose monitoring were developed by leveraging extrusion-based direct ink writing and resin-based 3D printing technologies. By combining nanomaterials with 3D printing techniques, my work addresses key challenges in sensor fabrication, enhances device performance, and contributes to the advancement of next-generation wearable healthcare technologies. Through interdisciplinary collaboration and innovation, these efforts not only pave the way for improved glucose monitoring solutions but also offer a versatile platform adaptable for detecting multiple biomarkers, such as lactate and uric acid, as well as body fluid dynamics (e.g., sweat rate), enabling deeper physiological insights for personalized healthcare
A HOT TOPIC EXAMINING PERSONALITY AND AMBIENT TEMPERATURE IN PREDICTING AFFECT
Get PDFAs world temperatures hit a record high for the year 2024, there is increasing concern about heat waves and how to identify individuals who might be vulnerable to temperature-based phenomena. While there is extensive documentation on how physical health is impacted by heat stress, there is much less research documenting the psychological and mental components of how people respond and react to heat. As an example, personality traits might function as an indicator of why some individuals respond to heat-related stressors more strongly, especially in the case of manifesting emotions. However, this has previously been underexplored. Based on the preliminary results from an experience sampling study, the Big Five personality trait neuroticism shows potential as a person-stable predictor of increased negative affective states in response to heat. Using the Emotion Construction Theory, this study explores the conceptual mechanisms through which personality might predict affect in uncomfortably hot environments from an experimental basis (N = 78). Results from this study show the trait of neuroticism predicted changes in positive affect over time when experiencing heat stress compared to individuals lower in this personality trait. Neuroticism was also a predictor of baseline positive and negative affect but found no relationship with reported comfort levels. This work provides further information on other populations that may be particularly vulnerable to heat stress (beyond physical states) and provides useful information for future interventions and public service announcements to increase awareness of personality and emotional states in the presence of ambient temperature-related phenomena
FORGING TRUSTFUL CONNECTIONS A CASE STUDY ON THE ROLE OF TRUST IN FORMING INTER-ORGANIZATIONAL RELATIONSHIPS FOR YOUTH APPRENTICESHIP PROGRAMS IN CAREER AND TECHNICAL EDUCATION IN WASHINGTON STATE
Get PDFThis dissertation examines the role of trust in shaping inter-organizational relationships (IORs) among school districts, employers, and intermediary organizations in developing a Youth Apprenticeship in Washington State. Using the framework developed by Mayer, Davis, and Schoorman (1995), the study explores trust through three dimensions—Benevolence, Competence, and Integrity—to analyze how these factors influence stakeholder collaboration, decision-making authority, and program sustainability.While research on apprenticeship systems in countries such as Switzerland and Germany highlights structured governance models, the role of trust in emerging apprenticeship ecosystems in the United States remains underexamined. This study addresses that gap by investigating how trust facilitates or constrains the formation of learning benchmarks, power distribution, and governance structures in youth apprenticeship programs. Through a qualitative case study approach, data were collected via stakeholder interviews, project reports, meeting minutes, and CTE frameworks, with analysis conducted using NVivo coding.Findings reveal that trust is not automatic but must be actively cultivated through transparency, shared decision-making, and structured competency alignment. Competence-based trust was strongest when employer-driven competency frameworks directly informed learning outcomes, ensuring alignment with workforce expectations. Benevolence-based trust emerged when stakeholders demonstrated a mutual investment in student success, prioritizing long-term career pathways over short-term labor needs. Integrity-based trust was reinforced through equitable decision-making structures and accountability mechanisms, preventing power imbalances from undermining collaboration.This study contributes to the fields of Career and Technical Education (CTE), workforce development, and apprenticeship policy by providing a trust-based framework for designing and sustaining youth apprenticeship programs. The findings offer actionable recommendations for improving employer-educator collaboration, embedding structured governance models, and ensuring that apprenticeship programs serve as effective career and postsecondary education pathways. By strengthening trust-driven partnerships, this research informs best practices for expanding youth apprenticeship initiatives in Washington State and beyond
ETHICAL BOUNDARIES AND SOCIAL RELATIONSHIPS UNDERSTANDING OUR LIMITS WITH ZOONOTIC SPILLOVER AND DISEASE MITIGATION STRATEGIES
Get PDFThe complex and ever-evolving social relationship between humans and nonhuman animals began thousands of years ago, with evidence of domestication and companionship between the two species. Although human and nonhuman animal relationships have benefits for each species, such as adaptation to the environment, husbandry, and partnership, a negative consequence is the spread of zoonotic diseases. The recent and ongoing outbreak of COVID-19, as well as the numerous other zoonotic outbreaks within the past decade and the instances of spillover within our historical record, have served as a clear demonstration of the interconnectedness of human and nonhuman animal health and the difficulties that can arise from zoonotic spillover between various species. However, the approach to eliminate the spread of zoonotic diseases has not been simple. While there have been ample methods proposed to stop the transmission of zoonotic diseases, such as philosophical ideologies suggesting a new relationship between human and nonhuman animals, ecological approaches like vaccine creation and distribution, policy changes to protect specific species, and implementation of vaccine mandates to prevent spillover, it has become increasingly obvious that disease mitigation strategies to limit the transmission of zoonotic diseases cannot succeed throughout one singular discipline. Effectively implementing disease mitigation strategies to limit zoonotic spillovers must be approached from a comprehensive interdisciplinary perspective that considers a variety of factors and perspectives from multiple disciplines and their potential contributions. Such perspectives can bridge the gap between several fields of research, provide the most concise approach to the potential of limiting the spread of zoonotic diseases, and further demonstrate why some diseases are better left to take their course in nature. However, to reduce zoonotic spillover and improve disease mitigation strategies, the social relationship between human and nonhuman animals must improve, as the health of all species has become inherently interconnected