The influence of environmental factors and human infrastructure on shorebird pre-incubation movements in Alaska

Arctic-breeding shorebirds have experienced widespread population declines in recent decades, largely driven by the loss and degradation of migratory stopover habitats, contaminants, and unsustainable harvest. These declines are occurring even though Arctic nesting grounds are relatively undisturbed by human infrastructure and fragmentation, although future infrastructure expansion poses a threat to breeding shorebirds. Additionally, climate change effects are more pronounced in the Arctic, leading to variable spring environmental conditions and the phenological advancement of key shorebird food resources. However, it is unknown how shorebirds respond to more variable environmental conditions due to climate change or to what extent human infrastructure influences shorebird movements on the breeding grounds. To address this knowledge gap, I monitored Dunlin movements using high-frequency GPS tracking devices during the pre-incubation period (i.e., the period between arrival on the breeding grounds and the first day of incubation) near the town of Utqiaġvik in northern Alaska. My overall objectives were to 1) evaluate and describe Dunlin movements and behavior states during the pre-incubation period, 2) assess the influence of snow cover and temperature on Dunlin movements, 3) identify hotspots of Dunlin occurrence across years, and 4) assess Dunlin space-use relative to human infrastructure, particularly the roads. To address objectives 1 and 2, I monitored the movements of 93 Dunlin captured during the pre-incubation period from 2021-2024, and used hidden Markov models and generalized linear mixed models to identify behavior states and assess the influence of snow cover and temperature on the movements and behaviors of male and female Dunlin. Dunlin spent >90% of the time engaged in small-scale movements (i.e., foraging, roosting, courtship). Dunlin engaged in larger transitory movements (i.e., territory scouting or moving between foraging patches) when snow cover was highest early in the season, and males engaged in more long-distance movements than females. As incubation approached, males and females adopted similar movement distances. We found limited evidence that temperature influenced Dunlin movements. To address objectives 3 and 4, I used a spatial kernel density estimator to identify areas of high use across years when Dunlin engaged in small-scale behaviors. Dunlin used primary (74 ha) and secondary (99 ha) core foraging areas along gravel roads and other developed areas where snow had been removed through regular road maintenance activities. Dunlin spent more time in these core areas and were closer to roads when snow cover was high and temperatures were low. My findings indicate that Dunlin respond strongly to annual environmental conditions and that increasing snowmelt variability caused by climate change influence Dunlin movements and behaviors prior to nesting. Dunlin also use anthropogenically-modified areas during the most physiologically challenging portion of the breeding season, likely due to the availability of food resources in these areas. My work highlights the need to evaluate the consequences, positive or negative, of shorebird use of human-modified areas as infrastructure increases and the effects of climate change in the Arctic become more pronounced in future years

Advancing the shifted boundary method: scalable multi-physics simulations on octree meshes for complex geometries

This dissertation advances the field of computational mechanics by developing, optimizing, and applying the Shifted Boundary Method (SBM) integrated with octree-based meshes for accurate and efficient simulation of large-scale problems involving complex geometries. The research is structured into four key contributions: First, the SBM framework is extended and optimized to improve the simulation of Partial Differential Equations (PDEs) on irregular domains. By shifting boundary conditions to surrogate boundaries on Cartesian meshes and correcting them with Taylor expansions, we achieve high accuracy without requiring boundary-fitted meshes. We demonstrate that the numerical error of SBM can be significantly reduced by an optimal choice of surrogate boundaries, with mathematical proofs validating optimal convergence. Second, the dissertation introduces a robust Octree-SBM framework for simulating incompressible Navier-Stokes equations. Leveraging efficient surrogate boundary construction on incomplete and adaptive octree meshes, we address the computational challenges of fluid dynamics in complex geometries. Benchmark simulations confirm the framework's scalability, accuracy, and efficiency across various flow regimes. Third, the SBM is applied to multiphysics thermal-flow simulations, coupling incompressible flow with heat transfer. Using a linearized Navier-Stokes RB-VMS formulation, the framework captures diverse thermal-flow phenomena with precision, enabling accurate enforcement of Dirichlet and Neumann boundary conditions on non-conformal meshes. Benchmark studies over a wide range of Rayleigh and Reynolds numbers validate the approach for laminar to turbulent regimes. Fourthly, the dissertation integrates SBM with adaptive mesh refinement (AMR) for incompressible flow and coupled thermal-flow problems. By employing vorticity-based adaptivity on hierarchical octree meshes, the framework dynamically resolves fine-scale features such as complex vorticity structures and steep thermal gradients while reducing computational costs. This integration enhances the capability to handle non-trivial geometries and evolving flow patterns in distributed-memory environments. Finally, the SBM framework is extended to simulate flow past open-interface geometries. By integrating SBM with octree meshes and leveraging Nitsche's method for weak enforcement of boundary conditions, this extension achieves numerical stability and accurate flow blockage without requiring boundary-fitted or excessively refined meshes. Applications to engineering scenarios confirm the framework's scalability and computational efficiency in addressing large-scale problems involving open-interface geometries. The methodologies and applications presented in this dissertation establish the Octree-SBM framework as a practical and effective tool for solving computational fluid dynamics and multiphysics problems. The framework is shown to be robust, scalable, and adaptable, addressing specific challenges such as complex geometries, diverse flow regimes, and efficient handling of large-scale simulations with accuracy and computational efficiency

Stochastic Black Start Resource Allocation to Enable Dynamic Formation of Networked Microgrids and DER-aided Restoration

Extended outages in distributed systems (DSs) dominated by distributed energy resources (DERs) require innovative strategies to efficiently and securely deploy black start (BS) resources. To address the need, this paper proposes a two-stage stochastic resource allocation method within synchronizing dynamic microgrids (MGs) for black start (SDMG-BS), enabling risk-averse and adaptive restoration across various scenarios while ensuring frequency security. Virtual synchronous generator (VSG)-controlled grid-forming inverters (GFMIs) equipped with primary frequency governors (PFGs) are modeled as BS resources. Their frequency response is characterized by three transient indices, which are deployed as frequency dynamic constraints on load pick-up events to ensure frequency stability during the BS process. SDMG-BS framework facilitates location-independent synchronization among restored MGs and with the transmission grid (TG) with the help of smart switches (SSWs). The model incorporates scenario-based stochastic programming to address multi-source uncertainties, including season-dependent operational conditions and unpredictable TG outage durations, ensuring a resilient allocation plan. The proposed approach is validated on a modified IEEE 123-node feeder with three study cases designed across sixteen uncertainty scenarios.This is a preprint from Bai, Cong, Salish Maharjan, Han Wang, and Zhaoyu Wang. "Stochastic Black Start Resource Allocation to Enable Dynamic Formation of Networked Microgrids and DER-aided Restoration." arXiv preprint arXiv:2508.13306 (2025). doi: https://doi.org/10.48550/arXiv.2508.13306

Lost in Space: Exploring and Mitigating the Effects of Packet Loss and Volatility in LEO Satellite Networks

In recent years, operators such as Starlink and Eutelsat OneWeb have launched mega-constellations of low-Earth orbit (LEO) satellites to support their LEO satellite networks (LSNs). These networks have offered a new source of broadband internet, a source which can reach rural or un-serviced areas that do not contain apt terrestrial infrastructure. However, these LSNs are not without idiosyncrasies; relatively high latency and jitter accompany throughput variations. Most notably, though, are frequent packet losses that hinder link usage and consistent traffic flows. This work overviews existing studies and surveys of LSNs to better understand the complexities put forth by satellite dynamics. Then, a measurement study of the OneWeb LSN is presented, documenting and detailing an under-studied network with a focus on reliability. Finally, we examine the potential benefits of erasure codes for maintaining a high TCP cwnd and reducing retransmissions, drastically minimizing the effects of packet loss on traffic flows. We find that with the correct selection of LSN and end-to-end coding technique, LEO satellite networks can function as reliable gateways to the internet for agricultural, maritime, and aerial applications

Probabilistic variations of zero forcing and power domination

Zero forcing is a graph propagation process in which white vertices are colored blue by neighboring blue vertices. Probabilistic zero forcing is a generalization of zero forcing where a blue vertex $v$ colors a white neighbor blue with probability proportional to the number of blue neighbors of $v$. In this dissertation we introduce two probabilistic variations on zero forcing and probabilistic zero forcing with connections to disease spread and power grid modeling. The first variation is reversion probabilistic zero forcing (RPZF), which generalizes probabilistic zero forcing so that blue vertices now "recover" (revert to being white) at some fixed rate. We formalize the study of RPZF through Markov chain theory, which provides methods of calculating any graph's probability of becoming all white or all blue from a given starting configuration as well as the time at which this is expected to occur. Moreover, the long-term behavior of the process is studied in-depth on the complete graph and some related structures, developing a threshold number of blue vertices such that with high probability the graph is entirely blue in the next round. The second variation is fragile power domination. The power domination problem seeks to minimize the number of phasor measurement units (PMUs) necessary to monitor the power grid. This is formalized in a graph theoretic process consisting of a domination step (sensors observe neighboring vertices) and a zero forcing step (observation propagates throughout the graph according to the zero forcing rule). Fragile power domination introduces random sensor failure before the domination step, and the goal of fragile power domination is to understand optimal sensor placements under different probabilities of sensor failure. In particular, we study the expected number of observed vertices and relate this to fault-tolerant and PMU-defect-robust power domination

Forensic Analysis of Google Fit App on WearOS

This presentation is from the 77th Annual Conference of the American Academy of Forensic Sciences (AAFS), Baltimore, Maryland, February 17-22, 2025

Cover crops have positive and negative effects on soil properties and crop yield over a 15-year timespan

Winter cover crops (WCC) have received much attention due to their environmental benefits, particularly improvements to soil health. However, most studies are made less than 5 years after implementation, and there is no consensus about when to soil sample to best quantify a WCC effect. We used a paired, chronosequence approach with 1–15 years since implementation of cereal rye (Secale cereale) as a WCC, and analyzed soils collected in spring and autumn. We measured soil bulk density, maximum water-holding capacity, penetration resistance, pH, total carbon (C) and nitrogen (N), permanganate oxidizable carbon, microbial biomass carbon (MBC), and microbial biomass N, potentially mineralizable carbon (PMC), and potentially mineralizable nitrogen (PMN). We also analyzed maize (Zea mays) and soybean (Glycine max) grain yield. We found that WCC increased MBC and PMC by 8% each and increased PMN by 11%, regardless of time-since-implementation. Furthermore, sampling biological soil health indicators in the spring resulted in more positive, significant treatment effects (12%–19%) compared to sampling in the autumn, where we found no effect. WCC increased soybean yields by 7% after 8–9 years but decreased maize yield by 23% after 15 years. WCC reduced soil penetration resistance by 10% after 8–9 years but increased it by 20% after 15 years. These later contrasting results may be due to management nuances or biophysical changes in cropping systems with time. Overall, WCC have many environmental benefits, and in our study, WCC increase biological soil health indicators quickly, but yield drag and increased soil penetration resistance may occur later in WCC adoption.This article is published as Dutter, Cole R., Marshall D. McDaniel, Morgan P. Davis, Teresa A. Middleton, Stefan Gailans, and Sarah Carlson. "Cover crops have positive and negative effects on soil properties and crop yield over a 15‐year timespan." Soil Science Society of America Journal 89, no. 2 (2025): e70032. doi:10.1002/saj2.70032.Iowa Nutrient Research Center, College of Agriculture and Life Sciences, Iowa State University, Grant/Award Number: Grant #: E2017-10. Open access funding provided by the Iowa State University Library

Predictive Design of Sustainable Biobased Packaging via Machine Intelligence for Improved Postharvest Preservation

The widespread use of petrochemical-based plastics in food packaging raises environmental concerns and lacks antimicrobial properties, limiting protection against microbial contamination. While biobased nanocomposites offer a sustainable alternative, optimizing their formulations remains challenging due to a vast materials library, inefficient trial-and-error experimentation, and complex multi-property requirements. Herein, a data-driven workflow integrating robotic automation, machine learning predictions, density functional theory (DFT) simulations, and life cycle assessment (LCA) is developed to accelerate the discovery of sustainable biobased packaging materials, enabling enhanced postharvest preservation with a reduced environmental footprint. An automated pipetting robot formulates 2,420 biobased nanocomposites, and their film quality data train an artificial neural network classifier, defining a design space. Within this space, 16 active learning loops iteratively fabricate and characterize 343 biobased nanocomposites, generating a high-quality experimental dataset. Leveraging this dataset and DFT simulations, a prediction model is constructed to explore ~1 billion formulations, identifying biobased nanocomposites with superior mechanical resilience and tunable transparency. Among them, a Cu2+-incorporated, chitosan-rich film further demonstrates moisture absorption, oxygen impermeability, and antimicrobial performance, outperforming conventional plastic wraps and extending the shelf life of postharvest produce. To further enhance sustainability, LCA-informed feedback is integrated into predictive modeling, refining nanocomposite formulations to minimize environmental impact. Additionally, a data-sharing platform is created, featuring forward prediction and inverse design capabilities to promote community adoption. This integrative approach significantly accelerates the development of high-performance biobased packaging and paves the way for a more sustainable and antimicrobial alternative to conventional plastics.This preprint is published as Po-Yen Chen, Yang Li, Tianle Chen et al. Predictive Design of Sustainable Biobased Packaging via Machine Intelligence for Improved Postharvest Preservation, 26 May 2025, PREPRINT (Version 1);https://doi.org/10.21203/rs.3.rs-6215151/v1. Supplementary Files - Supplementary Information (chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://assets-eu.researchsquare.com/files/rs-6215151/v1/79e3c0438b940c1222092bdb.pdf); Supplementary Video 1- https://assets-eu.researchsquare.com/files/rs-6215151/v1/af6f8ef1afbcf5e94c819037.mp4.; Supplementary Video 2 - https://assets-eu.researchsquare.com/files/rs-6215151/v1/36d567b4b2c3427076874964.mp

Comparison of Different Transverse Rumble Strip Patterns at Rural Stop-Controlled Intersections in Minnesota

Transverse rumble strips (TRS) provide a tactile and audible warning for drivers approaching an intersection and are used primarily to decrease crashes resulting from failure to yield. The objective of this study was to evaluate how TRS patterns affect behavior at rural stop-controlled intersections. A TRS design was selected based on the Minnesota (state in the midwestern U.S.) Department of Transportation’s current rumble strip depth/shape and panel locations. Four patterns representing variations of this design were developed and installed at rural intersections with stopping behavior concerns in St. Louis County, Minnesota. The patterns included two panels with six rumble strips each (two sites), two panels with 12 rumble strips each (two sites), three panels with six rumble strips each (one site), and three panels with 12 rumble strips each (two sites). Traffic volume, speed, and video data were collected 1 week before TRS installation and then 1 month and 9 months after installation. Evaluation metrics included average speed, percentage of vehicles traveling 45 mph or more upstream of the intersection, full/rolling stops, and late braking. Each pattern was assigned a qualitative score based on points assigned for each metric, and differences in metrics among the patterns were compared between the 1-month and 9-month-after periods. The three-panel/12-rumble strip pattern had the highest qualitative score (1.33). The second highest score was for the three-panel/six-rumble strip pattern (0.88). However, since this pattern was only installed at one site, this result should be interpreted with caution. The two-panel/12-rumble strip pattern had the lowest score (0.44).This is a manuscript of an article published as Hallmark, Shauna, Nicole Oneyear, David Veneziano, Hossein Naraghi, and Victor Lund. "Comparison of Different Transverse Rumble Strip Patterns at Rural Stop-Controlled Intersections in Minnesota." Transportation Research Record (2025): 03611981251320397. doi: https://doi.org/10.1177/03611981251320397

Making the Best of Problematic HCI Readings: Moving Licklider from Man- to Human-Computer Symbiosis

This paper addresses the challenge of discussing HCI readings in class that are somehow problematic. Specifically, we describe the motivation for changing Licklider's influential 1960 article, Man-Computer Symbiosis to Human-Computer Symbiosis, removing all gender-specific language from it, and using that version in the classroom. We then generalize this discussion to other forms of problems – for example, when articles contain outdated technology or ideas that have fallen into disregard or controversy. This research offers HCI educators concrete teaching suggestions for handling problematic readings.This proceeding is published as Gilbert, Stephen B., Amanda K. Newendorp, and Joanne M. Marshall. "Making the Best of Problematic HCI Readings: Moving Licklider from Man-to Human-Computer Symbiosis." In Proceedings of the 7th Annual Symposium on HCI Education, pp. 1-6. 2025. doi: https://doi.org/10.1145/3742901.3742906