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Developing and Testing Low-Cost Air Cleaners for Safer Spaces During Wildfires
Get PDFAir cleaning reduces indoor exposure to fine particulate matter (PM2.5) during wildfire smoke events. However, resource and cost constraints may limit access to air cleaning during such an event, as both commercial devices and the higher-rated MERV filters that do-it-yourself (DIY) assemblies typically rely upon tend to be expensive and in short supply. With these constraints in mind, we developed and evaluated several configurations of a novel, DIY air cleaner that uses common household fabrics as filtration media. Clean air delivery rates (CADRs) of the devices were experimentally evaluated in two ways: first, with independent measurements of flowrates and single pass removal efficiencies, and second, via pull-down testing in a large chamber. With two layers of cotton batting fabric and a flowrate-increasing cardboard shroud attached, the device achieved particulate matter CADRs of 162, 134, and 206 m3/h in 0.02–0.3, 0.3–1, and 1–2.5 µm particle diameter bins, respectively, during chamber testing. Results indicate that these simple, inexpensive, fabric configurations can meaningfully reduce PM2.5 levels in smaller zones of a home, and thus represent a viable option for improving indoor air quality during rapid-onset air pollution events, such as wildfires
Modelling Postfire Recovery of Snow Albedo and Forest Structure to Understand Drivers of Decades of Reduced Snow Water Storage and Advanced Snowmelt Timing
Get PDFForest fires darken snow albedo and degrade forest structure, ultimately reducing peak snow–water storage, and advancing snowmelt timing for up to 15 years following fire. To date, no volumetric estimates of watershed-scale postfire effects on snow–water storage and snowmelt timing have been quantified over decades of postfire recovery. Using postfire parameterizations in a spatially-distributed snow mass and energy balance model, SnowModel, we estimated postfire recovery of forest fire effects on snow–water equivalent (SWE) and snowmelt timing over decades following fire. Using this model, we quantified volumetric recovery of forest fire effects on snow hydrology across a chronosequence of eight sub-alpine forests burned between 2000 and 2019 in the Triple Divide of western Wyoming. We found that immediately following fire, forest fire effects reduced snow–water storage by 6.8% (SD = 11.2%) and advanced the snow disappearance date by 31 days (SD = 9 days). Across the 15-year recovery following fire, forest fire effects reduced snow–water storage by 4.5% (SD = 11.4%). Postfire effects on snow hydrology generally recovered over time, but still persisted beyond 15-years following fire due to the observed postfire shift from forest to open meadow. Estimates of postfire reductions on peak SWE summed over the entire 15-year postfire recovery period were 18 times greater than the immediate losses in the first winter following fire alone. These lasting effects of forest fires on snow hydrology decades following fire highlight the importance of postfire parameterizations for more accurate watershed-scale volumetric estimates of forest fire effects on snow–water resources
Navigating Identity Through Social Media: Twitter Use of Muslim-American Nonprofits
Get PDFSocial media enables nonprofits to communicate with stakeholders. Literature has primarily focused on the social media communication of mostly large and secular nonprofit organisations. This study contributes to this literature by looking at nonprofits’ social media activity belonging to religious minorities, specifically the Muslim-American nonprofit sector. Using Lovejoy and Saxton’s (2012) Hierarchy of Engagement framework, we find that Muslim-American nonprofits conventionally use social media for information, community, and action messaging. However, these nonprofits also utilise social media to encourage religious practices and strongly advocate for Muslim rights. We also find that organisational identity affects social media framing as organisational type affects the type of topics these organisations pick to advocate on social media. Moreover, organisational size also affects social media activity, as smaller organisations are more likely to use social media for fundraising purposes
McNair Scholars Program Summer Symposium (Video Recording) Part One
No full textThis is part one of the 2024 McNair Symposium
Exploration of Large-Scale Vegetation Transition in Wet Ecosystems: a Comparison of Conifer Seedling Abundance Across Burned Vs. Unburned Forest-Peatland Ecotones in Western Patagonia
Get PDFAltered fire regimes, combined with a warmer and drier climate, have been eroding the resilience of temperate rainforests and peatlands worldwide and leading to alternative post-fire vegetation communities. Chronic anthropogenic burning of temperate rainforests at the forest-peatland ecotone in western Patagonia appears to have shifted vegetation communities in poorly-drained sites from forests dominated by the threatened conifer, Pilgerodendron uviferum, to peat-accumulating wetlands covered by Sphagnum mosses. We collected and modeled post-reburn field data using ordinations and hierarchical Bayesian regressions to examine mechanisms through which P. uviferum forests may recover following fire or become locked into alternative development pathways by comparing biophysical factors of a reburned ecotone to those of an unburned (control) ecotone. We found that, (1) the significantly higher densities of P. uviferum trees and seedlings in the forested patches at both the reburned and control sites were associated with significantly lower seasonal water tables, lower cover of Sphagnum mosses and higher cover of other mosses (i.e., not in the Sphagnum or Dicranaloma genera); (2) despite abrupt boundaries in vegetation at both sites, successive fires homogenized the environment at the reburned site; and (3) the distinct life forms and individual species that characterized the understory plant communities across the ecotones affected seedling abundance by shaping microtopography and the substrates available for establishment. Together, our results suggest that fire can push edaphically wet P. uviferum-dominated sites towards a non-forested state by reducing the diversity of microsite structure and composition, thereby placing P. uviferum seedlings in direct competition with Sphagnum mosses and potentially limiting the availability of microsites that are protected from both seasonal inundation and seasonal drought. If wildfires continue under increasingly warmer and drier conditions, the forest-peatland ecotone of western Patagonia may be susceptible to large-scale transformation towards a non-forested state
Exploring the Social Processes Influencing the Well-Being and Social Integration of Systemically Marginalized Students in Higher Education: A Mixed-Methods Approach
Get PDFComprising two manuscripts, this dissertation employs a mixed-methods approach to comprehensively examine the intrapersonal and interpersonal processes influencing the socioemotional well-being and social integration of systemically marginalized students. The first manuscript quantitatively explored the relationship between ethnic identity commitment and indices of well-being, the mediating role of social relationships, and the moderating roles of gender and immigrant generation status among Latinx college students (N = 707). Results suggested that ethnic identity commitment was positively associated with socioemotional well-being. Although both types of relationships were significant mediators on their own, maternal (vs. peer) relationship quality was the stronger mediational influence. Furthermore, gender and immigrant generation status were not significant moderators of these indirect effects. The second manuscript qualitatively explored how students (N = 10) navigate university experiences in relation to various intersecting marginalized identities (e.g., ethnic-racial identity, sexual identity, first-generation college student status, socioeconomic status, geographical identity, gender identity, and religious identity). Analyzing discussions that occurred within the context of a social integration support program, this study employed a strength-based risk and resilience framework to shed light on the unique challenges and strengths that stem from systemically marginalized identities in the university setting. Together, the studies provide a comprehensive exploration of the experiences and needs of systemically marginalized students and contribute to our understanding of how to effectively address educational disparities. Implications for the development of effective resources on university campuses are discussed
Competition Between Long- and Short-Range Order in Size-Mismatched Medium-Entropy Alloys
No full textChemical short-range order (SRO) is known to alter a wide array of alloy properties. Here, we investigate the SRO of binary and ternary alloys in the Cr–Mo–W system using density functional theory (DFT) calculations, coupled with Monte Carlo simulations and two distinct approaches: the real-space cluster expansion (CE) and a machine learned interatomic potential (MLIP) based on the moment tensor potential (MTP) form. The size-mismatched binary, Cr–W, exhibits phase-separating long-range order (LRO) in the phase diagram, but surprisingly, the SRO is predicted to be ordering-type from both CE and MTP. We rationalize this apparent discrepancy by accounting for the large coherency strain present in this system, which significantly suppresses the coherent phase-separating tendency relative to the incoherent phase diagram. This competition between LRO and SRO persists in the ternary Cr–Mo–W system, where we find that SRO tendencies can qualitatively differ from those in the corresponding binary alloy. The real-space CE can efficiently capture the correct nearest neighbor SRO tendencies in this system, despite failing to account for long-range strain effects, provided it is trained on the energies of sufficiently large structures to capture short-range strain contributions over the nearest neighbor ranges; however, we suggest MTP, or more generally MLIP, may provide a more general approach for comprehensive studies in disordered alloy systems, especially in medium- and high-entropy alloys exhibiting large lattice mismatch
Customer Involvement in Co-Development: Problem-Solving and Decision-Making in New Product Development
No full textPurpose Customers can participate in new product development (NPD) in many ways. Drawing on the knowledge-based view (KBV) and innovation literature, this study aims to contrast two main product development activity types, i.e. problem-solving and decision-making. It proposes customers play distinct roles if they get involved in these activities, which influence NPD outcomes differently. It also explores customer need specificity as a boundary condition for the above-mentioned relationships. Design/methodology/approach The authors collected survey data from 308 managers in the innovation domain. Findings Customer involvement in problem-solving and decision-making distinctively influences new product innovativeness and development speed. Customer need specificity interacts with the two co-development types differently to impact these NPD outcomes further. Research limitations/implications This research extends the KBV and addresses the inconsistent findings in the literature regarding customer involvement as co-developers in innovation. It also provides novel insights into how knowledge characteristics like customer need specificity can direct co-developing activities to generate distinct NPD results. Practical implications This paper offers practical implications for firms on how to involve customers in developing innovative new products while managing development speed. Originality/value Prior research has yet to distinguish customer responsibilities related to co-development activities. This research fills this gap and offers novel insights that problem-solving and decision-making have opposite impacts on different NPD outcomes. This research demonstrates that finer knowledge about customer involvement responsibilities is needed for critical NPD outcomes
The Delicate Situation of Childhood Vaccination: on the Dispreferredness of Soliciting Parents\u27 Intent to Vaccinate
Get PDFIn the Netherlands, parents of newborns typically participate in two-, four-, and eight-week medical consultations to monitor their children’s development and discuss vaccinations, which will not be administered before eight weeks. During these visits, healthcare professionals routinely ask parents if they intend to vaccinate their children (i.e. to participate in the National Immunization Program). Using Conversation Analysis, we examine 62 videotaped consultations and present two lines of evidence to argue that the sequence initiated by professionals wherein they solicit parents’ intent to vaccinate is dispreferred. First, this action is routinely deferred by preliminary sequences. Second, when professionals eventually initiate this action (i.e. if it is not preempted by parents during pre-sequences), they orient to its dispreferred status, for example by highlighting benefactive details of vaccination. We discuss the possible existence of asymmetrical (initiator-sided) pre-sequences, why soliciting parents’ vaccination intent might be dispreferred, and implications for the design of communication interventions
Machine Learning Framework for Predicting Reliability of Solder Joints
Get PDFThis paper presents the techniques and framework to produce an explainable machine learning model for predicting the reliability of solder joints exposed to thermal cycle testing. While machine learning techniques have become prevalent for many engineering and data analysis tasks, the complexity of the model occludes valuable information which can be used to refine the data, tune model parameters, and aid in scientific discovery. By exposing the relationships within the model, users can have greater confidence in the use and employment of it. The purpose of this study is to show how useful relationships can be obtained from a traditional ”black-box” machine learning model which can be utilized to investigate the model’s ability to extract and learn fundamental physical processes.
Various machine learning techniques are explored with an emphasis on feature engineering, framework selection, and parameter optimization. Various model explanation methods are employed in conjunction with a generated dataset to highlight the relationships between the independent variable inputs and dependent variable output. The independent variable marginal contributions are derived from mean observations of surrogate model behavior, which can be compared to the mean observations of the machine learning model to determine relevant and exceptional behavior.
The application of machine learning techniques with experimental datasets certainly enables rapid evaluation of novel combinations in the problem space. While the end result is useful in itself, the ability to extract the influence of individual variable perturbations is quite challenging. The methods described herein provide this opportunity. The limitations of the model are determined by the quality and breadth of the data used to train the model parameters, in conjunction with model design specifications. While the accuracy of the determined relationships can be verified for some independent variables is possible, sparsely populated variables are less likely to generate meaningful relationships that correlate to the expected behavior of physical phenomena. This creates an opportunity for researchers to determine which data is needed to improve the model behavior in accordance with known processes. Originality/value: The ability to predict thermal fatigue life accurately is extremely valuable to the industry because it saves time and cost for product development and optimization. This ability is improved when the model can be examined critically through the methods described herein