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ASSESSING ELK BEHAVIOR IN RELATION TO THE SPATIAL AND TEMPORAL PATTERNS OF WILDFIRES IN WESTERN MONTANA
Get PDFAs wildfire activity increases in the western United States, the effect of wildfire on wildlife habitat is an important yet understudied area of research. Wildfires are becoming more frequent in the western US and fire events influence cover, forage quantity, and forage quality for ungulate species such as elk. To evaluate the influence that fire history has on elk behavior, we created resource selection functions (RSFs) to assess habitat selection of female elk within the Bitterroot Valley of southwestern Montana, USA. We assessed habitat selection at both the landscape scale (2nd order) and home range scale (3rd order) and habitat selection across seasons (summer and winter) of 92 female elk collared from 2011-2014. We used the following landscape predictors: slope, aspect, elevation, canopy cover, and distance to water and the following predictors for wildfire dynamics: distance to burn, distance to unburned, time since fire, dNBR, and distance to high severity burn. RSFs were fit using a generalized linear mixed model (GLMM) with a logit link function using the glmmTMB package in R. At the landscape scale, elk selected for home ranges that were closer to high-severity fire but avoided actual high-severity burn areas, indicating elk selection of edge habitat around high-severity burn areas. Within home ranges, elk avoided burned areas and selected for unburned areas. At both scales, fire covariates were comparably predictive as commonly used landscape covariates. Additionally, relative effect sizes were generally greater in the winter than the summer, indicating stronger selection for the habitat features included in RSF models during the winter season. These results show that fire history significantly influences elk habitat selection and demonstrates the potential benefit of heterogeneity in the time since fire, burn intensities, and the resultant edge habitat across the larger landscape
HR Source Newsletter, July 2025
No full textIn this issue of HR Source (a newsletter for Washington State University employees): Visible and Invisible: Rethinking Disability in the Workplace; Classified Staff Salary Range Updates Effective July 1
CloverGram, June 2025
No full textIn this issue: June 30th Ribbon Cutting; Jacket Award FAQs, Youth Jackets; Children’s Entrepreneur Markets; 4-H Teen Leader of the Year Award; Archery & Riflery Instructor Training; Fishing Project Kicks Of
Ag Sounder, July 2025
Get PDFIncluded in this issue of Ag Sounder:Thurston County Fair - July 31st - August 3rdSW Washington Grasslands & Grazing Photography Competition - Submissionsaccepted now through Nov 15thExplore Your Farm Dreams - August 18thNominations open for USDA Farm Service Agency County CommitteesWhole Farm Planning - Sept 22nd - Nov 3r
A QUALITATIVE STUDY OF PRINCIPALS’ CHARACTERIZATIONS OF SCHOOL IMPROVEMENT
Get PDFA QUALITATIVE STUDY OF PRINCIPALS’ CHARACTERIZATIONS OF SCHOOL IMPROVEMENTAbstractby Sylvia Diahanne CampbellWashington State University
December, 2025Chair: Katherine RodelaTo address evolving societal demands, schools must implement improvement initiatives to enhance students’ academic, social, and emotional growth. Effective leadership is essential for the successful planning and implementation of school improvement initiatives. While we understand the critical importance of effective leadership, there remains a lack of insights from experienced principals on the necessary skills required to lead school improvement efforts effectively. The purpose of this study was to investigate how effectively (as measured by external outcomes) principals characterize their role in school improvement efforts and describe barriers and facilitators to school improvement. Findings highlight four key principal roles: (a) team building, (b) capacity building, (c) facilitating professional development, and (d) monitoring and improving implementation. Additionally, findings indicated that student-centric approaches and data-driven decision-making are critical areas for school improvement. Major barriers identified include lack of staff buy-in, high costs, staff turnover, and time constraints, while family and community support emerged as key facilitators. These insights provide actionable strategies for principals to enhance school improvement efforts by building effective teams, fostering teacher capacity, leveraging professional development, and addressing implementation challenges through ongoing monitoring and data-informed practices
RNA VARIATION AS A DRIVER OF GENOMIC FUNCTION AND COMPLEXITY: INSIGHTS FROM ALTERNATIVE POLYADENYLATION SITE USAGE AND SEXUAL DIMORPHISM IN KARAKUL SHEEP
Get PDFSexual dimorphism in mammals is often attributed to differential gene expression between males and females, despite both sexes sharing nearly identical autosomal genomes. One of the key mechanisms contributing to these expression differences lies in post-transcriptional regulation. Among these, Alternative Polyadenylation (APA), a process that alters the length of the 3′ untranslated regions (3′UTRs) of mRNA transcripts, has emerged as a pivotal contributor to transcriptome complexity, mRNA stability, localization, and translational efficiency. Through differential selection of polyadenylation sites, APA modulates the inclusion of regulatory elements such as microRNA binding sites and RNA-binding protein motifs, influencing gene expression outcomes without changing the underlying DNA sequence.This thesis investigates the role of APA in generating sexually dimorphic patterns of gene expression across multiple tissues in male and female Karakul sheep (Ovis aries). Using high-throughput RNA sequencing and bioinformatic analyses, mRNA expression profiles and APA site usage were quantified across several somatic and reproductive tissues. By comparing APA landscapes between the sexes, this study aims to determine whether sex-specific APA patterns contribute to differential gene regulation in the absence of genomic divergence on autosomes. Understanding these dynamics will not only deepen our knowledge of transcriptomic complexity in livestock species but may also shed light on broader principles of sexual dimorphism and post-transcriptional gene regulation in mammals
NUMERICALLY DETERMINING ACOUSTIC MINOR LOSSES IN CRYOGENIC HYDROGEN USING REAL FLUID PROPERTIES
Get PDFAcoustic oscillations in cryogenic systems fall into one of two categories: naturally occurring, such as self-excited Taconis oscillations, or intentionally induced, as is the case of thermoacoustic cryocoolers. An accurate analysis of these systems requires a confident way to model minor losses at junctions between segments. Present modeling of minor losses in acoustic systems tends to be based on correlations established for steady flow, which may not fully apply when used in oscillatory flows. Additionally, fluids under cryogenic conditions may have significantly different properties than those near ambient conditions, particularly near the saturation curve, where some cryogenic hydrogen systems operate. High-fidelity numerical simulations using computational fluid dynamics provide a detailed and relatively rapid means to obtain parametric results. System properties of interest include pressure and temperature of the fluid, acoustic wave phase, and geometric parameters. This research aims to produce useful correlations for acoustic minor losses in systems with cryogenic fluids.The specific setups considered are an abrupt expansion at a junction between two pipes, which can be more broadly applied to other situations, such as expansion out of a stack, and minor losses at a pipe exit, which are important to modeling the behavior of effects like Taconis oscillations. Correlations are established for both of these systems with cryogenic hydrogen, demonstrating strong inverse dependence on Reynolds number and a lesser dependence on other properties, like temperature. Frequency is observed to have little effect on minor losses in the explored range. Steady flow correlations available for common fluids are found to be inaccurate even in the asymptotic region, and variously overpredict or underpredict, depending on geometry. Two further applications of this line of research are examined. Geometric modifications to a pulse tube section in a thermoacoustic cryocooler are examined for their effects on losses, and how they depend on flow velocity amplitude. One particular modification to the tube wall is found to significantly reduce losses, decreasing them by more than half. Additionally, phase change and multiphase flow effects are explored in the open pipe configuration modified from a prior study. Phase change in a saturated liquid is found to appreciably reduce minor losses, while phase change in a saturated mixture with 50% vapor has a negligible effect
Nanoporous Materials Discovery via Search Bias-Guided Surrogate Modeling
Get PDFNanoporous materials (NPMs)—such as MOFs and COFs—offer a path toward cleaner energy and environmental resilience, enabling carbon capture, gas storage, separations, and sensing. However, the pace of discovery remains constrained by expensive and labor-intensive synthesis and characterization processes, which limits iteration and constrains exploration toa narrow slice of a vast design space. This thesis addresses the offline discovery setting where only historical experimental measurements are available, and the goal is to recommend new NPMs that improve upon the best materials observed to date. A common baseline is to train a surrogate model that fits measured properties and then ranks candidates. While effective for prediction, this strategy can be misaligned with discovery: a model optimized to reduce regression error is not necessarily biased toward better-than-seen materials. We propose an optimization-regularized surrogate that augments
standard fitting with an explicit optimization bias. The central idea is to learn from the data’s improvement structure: we algorithmically identify and emulate monotonically improving sequences of materials and introduce a regularizer that encourages the surrogate’s score field to be consistent with these improvement directions. This couples predictive accuracy with a principled search bias suited to offline optimization. Across multiple NPM discovery tasks, the proposed surrogate recommends candidates that consistently outperform existing baselines, including regression-only surrogates, one-step
and batch Bayesian optimization, and generative-model-based approaches, in best-of-batch outcomes. Overall, this work shows that embedding an optimization bias into surrogate learning materially strengthens offline materials discovery
CHINESE AMERICAN GRAPHIC NOVELS: PROMOTING AGENCY FOR K-8 Grade STUDENTS
Get PDFThe purpose of this dissertation is using qualitative content analysis to explore what and how agency is represented in five Chinese American graphic novels. The theoretical framework employs critical multicultural analysis of children’s literature (Botelho & Rudman, 2009) and transactional theory of literacy (Rosenblatt, 1978). The results showed that the Chinese American graphic novels socio-political factors influenced the Chinese American characters’ decision-making, and they were able to overcome challenges in making choices. Several strategies for teaching agency through graphic novels will also be discussed in this dissertation. Keywords: Chinese American, graphic novels, content analysis, children’s literature, agency
Blend Prediction Model for Vapor Pressure of Jet Fuel Range Hydrocarbons
Get PDFThe ability to predict the vapor pressure and vapor-phase composition of hydrocarbon mixtures (such as jet fuel, sustainable aviation fuel or its un-refined precursors) and partially vaporized hydrocarbon mixtures is important to simulations of processes that involve vaporization such as distillations, flash points, combustion properties of partially vaporized fuels, etc. Raoult's Law provides a simple algebraic formula relating liquid composition and temperature to vapor composition and pressure. However, Raoult's Law is not accurate at low mole fractions, which is typical for complex mixtures such as fuels. A common approach to correcting Raoult's Law is to apply a scale factor, a so-called activity coefficient. Numerous models exist for predicting activity coefficients. Here we benchmark against the UNIFAC model, which predicts activity coefficients based on mole fractions, group fractions, Van der Waals volume and surface area and temperature-dependent interaction terms between groups. While this approach is truly predictive, its accuracy at very low mole fractions has not been validated, and it is computationally intensive, particularly for simulations (especially optimizations) that require vapor composition or pressure within the inner-most loop. Here we present an alternative correction to Raoult's law, where the vapor pressure of the ith component is represented by a modified form of the Clausius-Clapeyron equation. The reference temperature (Tref) is replaced by a simple algebraic function that converges to Tref as xi approaches 1 while smoothly increasing from this value as xi decreases. Simultaneously, the heat of vaporization (Delta Hvap,i(T)) term is replaced by another simple algebraic expression that converges to Delta Hvap,iT as xi approaches 1 while smoothly decreasing as xi decreases. In this model, the temperature-dependent heat of vaporization is tuned at each temperature such that the Clausius-Clapeyron equation reproduces the correct vapor pressure of the neat material, while the parameterized algebraic corrections are tuned to vapor pressure data of mixtures involving n-pentane, toluene, and dodecane, where the mole fractions of n-pentane and toluene are maintained below 10%mol. Validation of the resulting model is accomplished by comparing modeled vapor-liquid equilibrium systems with experimental measurements. This approach improves the accuracy and computational efficiency of volatility predictions, thereby supporting the development, certification, and adoption of sustainable aviation fuel