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Pierce County Resource Guide - Washington
Get PDFCounty level and region-specific resource guides focused on mental health and substance use
Howling of a model-scale nozzle due to shock-induced boundary-layer separation at its exit
No full textThe jet from a model-scale, internally mixed nozzle produced a loud howling when operated at jet Mach numbers between 0.80 and 1.00. Discrete tones dominated the noise radiated to the far field and powerful oscillations were present in the jet. To explain these observations, this paper leverages a blend of experimental acoustic and flow measurements and modal analyses thereof via the spectral proper orthogonal decomposition, computational fluid dynamics simulations and local, linear stability analyses of vortex-sheet models for the flow inside the nozzle. This blend of experiments, computations and theory makes clear the cause of the howling, what sets its characteristic frequency and how it may be suppressed. The flow around a small-radius, convex bend just upstream of the final-nozzle exit led to a pocket of locally supersonic flow that was terminated by a shock. The shock was strong enough to separate the boundary layer, but neither the attached nor separated states were stable. A periodic, shock-induced separation of the boundary layer resulted, and this shock-wave/boundary-layer interaction coupled with a natural acoustic mode of the nozzle’s interior in a feedback phenomenon of sorts. Acoustic tones and large flow oscillations were produced at the associated natural frequency of the nozzle’s interior
How Can Building Materials Be Designed to Minimize Environmental Harm if Left Behind After Natural Disasters?
No full text"How can building materials be designed to minimize environmental harm if left behind after natural disasters? This question reflects a wicked problem because it extends beyond the physical destruction of buildings and into long-term social, ecological, and material consequences. Natural disasters leave behind large quantities of debris that contaminate soil, pollute waterways, harm wildlife, and create unsafe constitutions for affected communities and recovery workers. Toxic, non-renewable, or easily fragmented materials intensify pollution, while resilient, low-toxicity, and biodegradable options can reduce harm. Because rebuilding often happens quickly and under financial and policy constraints, the issue becomes deeply intertwined with questions of equity, access, and environmental responsibility.Design theory provides the conceptual tools needed to navigate these complexities. It helps designers understand how materials function not only in buildings, but within broader social and ecological systems.Theories that emphasize sustainability, circular design, and regenerative approaches encourage designers to consider the entire lifecycle of a material, from extraction and manufacturing, to use, destruction, and decomposition. This matters in the context of natural disasters, because it shifts the focus from simply replacing what was lost to creating systems of rebuilding that reduce pollution, strengthen resilience, and support both human and environmental recovery. By grounding reconstruction in design theory, designers can make intentional decisions that minimize future debris, prioritize safer material choices, and create built environments that are better prepared to withstand natural disasters.
Evaluating the Performance of Novel TPMS-Based Acoustic Liner Designs Suitable for Additive Manufacturing
No full textAcoustic liners are integral to the noise management of aircraft engines. As such, acoustic liner designs and configurations are the subject of research and development to address the acoustic requirements of next generation engine layouts. Perforate sheet over honeycomb core, or single degree of freedom (SDOF) liners and double degree of freedom (DDOF) liners are traditional designs that have been used in turbofan engines for many years to reduce noise. Leveraging the design freedom of additive manufacturing, our current research explores the use of triply periodic minimal surface (TPMS) lattices as an advanced core structure for acoustic liners. In this study, previously developed design exploration methods are applied to identify and compare the performance of novel TPMS acoustic liners to SDOF and DDOF liners. The comparisons are used to assess the viability of TPMS-based novel acoustic liner designs and justify the need to further explore through future research efforts
Investigating the role of cranberry products on gut and vaginal microbial modulation in healthy women for urinary tract infection prevention
Get PDFCranberry phytochemicals have historically been associated with urinary tract health, yet their role in the gut-urogenital axis in women for urinary tract infection prevention remains
poorly understood. This dissertation investigated how various cranberry products modulate
microbial composition across complementary in vitro and in vivo models. The studies herein
establish a microbiome-mediated framework through which cranberry consumption may
reinforce mucosal homeostasis and indirectly reduce urinary tract infection (UTI) risk.In vitro fermentation in an anaerobic chamber of cranberry press cake and seed fractions revealed distinct yet complementary microbial trajectories over 32 hours in an incubator shaker
at 37℃ and 120 RPM. Both substrates stimulated saccharolytic fermentation, where press cake
and seeds selectively enriched Bacteroides and secondary butyrate producers such as
Clostridium XIVa and Subdoligranulum (p < 0.05). These findings support prebiotic potential of
cranberry byproducts for promoting short-chain fatty acid and phenolic-acid metabolism as
indirect mechanisms beneficial for urinary tract health.A randomized, double-blind, placebo-controlled, crossover dietary intervention (45 days) with daily low-calorie cranberry juice (LCCJ, 8 fl oz) examined the effects on fecal and vaginal
microbial modulation in healthy women (n = 47). Overall fecal microbial stability was
maintained while eliciting subtle, compositionally favorable shifts. Phylum-level analysis
revealed a significant increase (p < 0.05) in Bacteroidetes following LCCJ intake, aligning with
enrichment of saccharolytic taxa observed across both in vitro and in vivo models. The α- and β
diversity indices remained stable (p > 0.05), yet SIMPER analysis revealed enrichment of
saccharolytic and butyrogenic taxa including Faecalibacterium and Anaerostipes alongside
minor declines in proinflammatory genera such as Blautia and Collinsella. Enterotype modeling
confirmed microbial stability, suggesting that cranberry phytochemicals support intestinal
balance rather than restructuring.Parallel vaginal microbiome analyses following the same methodology from the gut microbiome with RStudio® indicated modest but biologically meaningful changes during the
LCCJ phase. Richness and Shannon diversity decreased (p < 0.05) while evenness increased,
reflecting consolidation toward Lactobacillus-dominant profiles. Phylum-level shifts showed
increased Firmicutes and decreased Actinobacteria consistent with reduced dysbiotic anaerobes
and enhanced mucosal resilience. Further genus-level analyses in directional relative abundance
shifts revealed a reduction (p < 0.05) in anaerobes associated with vaginal infections, thus
supporting a homeostatic vaginal microbiome potentially protective against UTIs.Integrative comparisons across systems revealed directionally consistent microbial modulation favoring saccharolytic, butyrogenic, and lactic acid–producing taxa. These patterns
suggest that cranberry phytochemicals act through metabolite-mediated crosstalk along the gut
urogenital axis, generating SCFAs and phenolic intermediates that reinforce epithelial integrity while discouraging uropathogen colonization. Collectively, the findings expand the classical urinary anti-adhesion paradigm to a broader, microbiome-centered mechanism in which cranberry consumption stabilizes mucosal microbial ecosystems, thereby promoting systemic homeostasis conducive to UTI prevention
A MULTISCALE MODELING FRAMEWORK TO PREDICT TRITIUM TRANSPORT IN IRRADIATED γ-LITHIUM ALUMINATE
Get PDFGamma-phase lithium aluminate (γ-LiAlO₂) is an essential ceramic breeder material for applications such as Tritium-Producing Burnable Absorber Rods (TPBARs), where its performance is governed by tritium transport. Current engineering models rely on empirical correlations, which lack the predictive, mechanistic foundation necessary to account for variations in microstructure, irradiation conditions, and defects. Limited understanding of tritium diffusion pathways, trapping mechanisms, and chemistry in irradiated γ-LiAlO₂ hinders the accurate prediction of diffusivity as a function of radiation damage, composition, impurities, and microstructural diversity. This research addresses these challenges by establishing and validating a physics-based, multiscale modeling framework for tritium transport in irradiated γ-LiAlO₂.The developed framework bridges atomistic-level phenomena and engineering-scale transport behavior to address key modeling gaps. Density Functional Theory (DFT)-computed data provides parameters for hopping energetics and trapping mechanisms, while graph-theoretical Kinetic Monte Carlo (KMC) simulations are used to compute intrinsic diffusivity in defect-free γ-LiAlO₂. To capture the effects of radiation-induced defects, the framework incorporates a semi-analytical model based on the Grand Canonical Ensemble (GCE) and Fermi-Dirac statistics to predict effective diffusivity considering multi-occupancy trapping at lithium vacancies (VLi).
The findings reveal distinct behaviors in tritium transport. KMC simulations indicate that tritium in pristine γ-LiAlO₂ exhibits rapid diffusion via interstitial hopping, with a low activation energy of approximately 0.02 eV. However, parameterization of the GCE model with DFT data reveals the complex binding behavior of lithium vacancies (VLi). When benchmarked against the TMIST-3A dataset, the diffusion-only model significantly overestimates tritium release. In contrast, the trapping-inclusive framework demonstrates that lithium vacancies act as near-perfect sinks for tritium, mechanistically explaining the experimentally observed levels of tritium retention. This analysis establishes that irradiation-induced defect trapping, and not intrinsic diffusivity, is the dominant rate-limiting mechanism governing tritium transport in γ-LiAlO₂.
The multiscale framework developed in this work establishes a physics-based foundation for understanding tritium transport in γ-LiAlO₂ as a defect-driven, kinetically-limited process. By transitioning tritium transport modeling from empirical approaches to mechanistically-grounded science, this framework enables a deeper understanding of tritium breeding materials and provides a critical basis for improving the design and optimization of ceramic breeder systems in nuclear applications
Understanding and Detecting Cross-Language Defects
Get PDFThe contemporary software landscape increasingly integrates multiple programming languages within a single system to leverage complementary strengths (e.g., the efficiency of C with the programmability of Python). This multilingual paradigm underpins platforms such as Android and machine-learning frameworks like PyTorch, but it also introduces nontrivial security and reliability risks. Heterogeneous language semantics, foreign-function interfaces, and cross-boundary data transformations complicate reasoning about control and data flows, elevating the likelihood of defects that are difficult to detect and diagnose.Traditional techniques—program analysis and fuzzing—were largely devised for single-language settings and struggle to scale across language boundaries. Static analyses often lose precision or soundness when flows traverse heterogeneous runtimes, while greybox fuzzing faces incomplete and misleading coverage when other language units are treated as opaque. Moreover, the intricacy of runtime ecosystems for multilingual software (frequently themselves multi-language) further exacerbates these limitations, underscoring the need for analysis methods that explicitly model cross-language interactions.This dissertation addresses these challenges through a comprehensive program of empirical study and tool design. First, it establishes a practitioner-grounded problem space via a large-scale analysis of Stack Overflow discussions on multilingual development, revealing recurring issues in interfacing, data representation, and tooling. Next, it constructs empirical foundations by systematically characterizing cross-language bugs in real-world Python–C and Java–C projects and by conducting a focused study of native (C/C++) bugs within Python applications, yielding curated datasets and taxonomies of symptoms, root causes, and fixes. Building on these insights, the dissertation introduces two complementary techniques: xLoc, a deep-learning approach for detecting and localizing bugs near cross-language boundaries using control-flow–aware encodings; and PolyFlow, a neuro-symbolic static information-flow framework that combines traditional taint analysis with LLM-guided semantic reasoning to track data flows across heterogeneous languages. Together with released benchmarks and artifacts, these contributions advance the empirical and methodological foundations for understanding and detecting cross-language defects in modern multilingual software
EFFECTIVENESS OF CHATBOTS AS A SOURCE OF SEXUAL HEALTH INFORMATION: A STUDY ON HUMAN PAPILLOMAVIRUS VACCINATION
Get PDFHuman Papillomavirus (HPV) is the most common sexually transmitted disease in the U.S., which is the cause of cervical, anal, and oropharyngeal cancers. AI-based chatbots offer a promising solution for addressing knowledge gaps and improving HPV vaccine communication. This study examines the effectiveness of AI-based chatbots in delivering sexual health information to young adults (aged 18-30) by utilizing an extended version of the DeLone and McLean Information Systems Success Model. The study employed an online survey (N = 700) to assess participants’ experiences using chatbots, which included a structured questionnaire administered through Prolific. After analyzing the data using Partial Least Squares-based Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0, the results showed that Compatibility is the strongest driver of use and a significant predictor of satisfaction. Information and service quality significantly enhance satisfaction. Satisfaction is the key determinant of performance impact, even more than actual use. Theoretical contributions and practical implications were discussed
College catalog, 2025
Get PDFSchool catalog delineating classes offered, credits, requirements, and other academic information believed to be necessary for incoming students at Washington State University
