University of Washington Bothell

Invasive plant species threaten forest ecosystems by outcompeting native flora for essential resources such as sunlight, water, and nutrients. One such invader, English holly (Ilex aquifolium), a shade-tolerant evergreen shrub or tree native to Eurasia and northern Africa, has become increasingly problematic in the Pacific Northwest of the United States. This study investigates the environmental factors influencing I. aquifolium distribution and spread using species distribution modeling (maximum entropy) and spatial analysis (Ripley’s K function) in an urban forest near Seattle, Washington. Results indicate that evergreen forests support holly establishment more than deciduous vegetation. Temperature emerged as a strong positive predictor of holly presence, suggesting that climate change could accelerate its invasion. In contrast, soil nutrient levels showed a slight negative association with holly occurrence, potentially indicating either holly’s tolerance for nutrient-poor soils or a disadvantage in nutrient-rich environments. Additionally, holly may deplete soil nutrients through uptake or by altering soil chemistry. Further, the clustered pattern of holly dispersion is likely driven by a combination of short-distance seed dispersal and vegetative spread. By identifying the ecological conditions that favor holly’s spread, this study provides insights to inform management strategies aimed at mitigating its impact on native biodiversity, forest succession, and ecosystem services in urban forests in the Pacific Northwest of the United States.

The carbon footprint of AI inference is determined first by the physical characteristics of the electricity grid rather than by scheduling or software optimization. This study quantifies the best physically achievable emissions across six European power systems by identifying the cleanest strictly contiguous multi-day windows from 2022 to 2024 using legally reported hourly data from ENTSO-E. In 2024, feasible emissions vary by a factor of 19.2, from 0.084 gCO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> per inference in Sweden to 1.615 g in Poland, under fixed model energy and power usage effectiveness (PUE) assumptions. This gap persisted across window durations (3 to 30 days), model tiers, and PUE values, and was statistically validated using non-overlapping bootstrap confidence intervals (95% CI, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> = 1000). All six countries show steady improvement from 2022 to 2024, yet their relative rankings remain unchanged, indicating a structural rather than temporary disparity. The resulting feasibility benchmark provides a reproducible foundation for siting AI infrastructure, sustainability reporting, and grid-aligned policy design.

In this section, we feature reports from conferences, symposia, workshops, and similar events, focusing on discussions where the boundaries of HCI and UX are being challenged and where debate is lively and ongoing.

ABSTRACT The confluence of economic conditions and emerging evidence of the advantages of a neurodiverse workforce has sparked dozens of high‐profile neurodiversity employment programs. Despite some success, the impact of these initiatives pales in comparison to the scope of employment challenges facing neurodivergent job seekers. In this article, we argue that the limited impact of neurodiversity employment initiatives is a function of at least three key challenges: (1) an emphasis on the strengths of neurodivergent employees without a simultaneous focus on substantial organizational change; (2) unrecognized demands placed on neurodivergent employees to navigate their work and careers; and (3) a focus on prototypical forms of neurodiversity without considering intersectional identities and their implications for inclusion and belonging. Acknowledging these limitations, we build on the foundation of these programs and provide suggestions for fostering cultural changes aimed toward improving and broadening the impact of neurodiversity employment programs to create truly neuroinclusive organizations.

The longstanding nonnegative inverse eigenvalue problem (NIEP) is to determine which multisets of complex numbers occur as the spectrum of an entry-wise nonnegative matrix. Although there are some well-known necessary conditions, a solution to the NIEP is far from known. An invertible matrix is called a Perron similarity if it diagonalizes an irreducible, nonnegative matrix. Johnson and Paparella [Linear Algebra Appl. 493 (2016), pp. 281–300] developed the theory of real Perron similarities. Here, we fully develop the theory of complex Perron similarities. Each Perron similarity gives a nontrivial polyhedral cone and convex polytope of realizable spectra (thought of as vectors in complex Euclidean space). The extremals of these convex sets are finite in number, and their determination for each Perron similarity would solve the diagonalizable NIEP, a major portion of the entire problem. By considering Perron similarities of certain realizing matrices of Type I Karpelevič arcs, large portions of realizable spectra are generated for a given positive integer. This is demonstrated by producing a nearly complete geometrical representation of the spectra of four-by-four stochastic matrices. Similar to the Karpelevič region, it is shown that the subset of complex Euclidean space comprising the spectra of stochastic matrices is compact and star-shaped. Extremal elements of the set are defined and shown to be on the boundary. It is shown that the polyhedral cone and convex polytope of the discrete Fourier transform (DFT) matrix corresponds to the conical hull and convex hull of its rows, respectively. Similar results are established for multifold Kronecker products of DFT matrices and multifold Kronecker products of DFT matrices and Walsh matrices. These polytopes are of great significance with respect to the NIEP because they are extremal in the region comprising the spectra of stochastic matrices. Implications for further inquiry are also given.

The proliferation of the Internet of Things (IoT) has elevated the demand for energy-efficient gateway solutions, particularly for battery-powered Wi-Fi devices functioning as gateways in IoT data collection. Traditional approaches either neglect gateway energy consumption or struggle to balance energy efficiency with delay requirements. To address this, we introduce the Dual-interface Gateway Activation (DGA) scheme, a significant advancement enhancing IoT gateway energy management via interactive cooperation between ZigBee and Wi-Fi. DGA’s core innovation lies in its dual-interface design and heterogeneity-aware activation strategy. By leveraging coexisting low-power ZigBee communications, DGA dynamically activates the gateway’s high-power Wi-Fi interface only when necessary for data reception, effectively decoupling power consumption from continuous Wi-Fi operation. Meanwhile, DGA conservatively schedules high-reliability Wi-Fi sessions to augment unreliable ZigBee communications, thereby ensuring prompt gateway activation for delay-bounded data delivery under varying network conditions. This innovative approach not only guarantees high energy efficiency but also low latency, overcoming the limitations of single-interface gateways. Experimental validation of our prototype system demonstrates DGA’s superior performance, achieving up to 62.1% and 45.9% reductions in gateway energy consumption compared to state-of-the-art single-interface and dual-interface solutions, respectively. Additionally, DGA attains lower node energy consumption, further highlighting its potential to enable more energy-efficient IoT networks.

As the science and engineering disciplines push toward more culturally sustaining and justice-centered approaches, there is a growing need to support educators in designing more expansive learning experiences. In this chapter, we provide a Justice-Centered Design Matrix to support formal and informal science and engineering (SE) educators in (re)designing their curriculum and pedagogy to weave in the sociopolitical and sociocultural contexts of SE with the edges of disciplinary knowledge and practices toward expansive learning experiences. Through an overview of the design matrix and illustrative examples of how the matrix was used to design learning experiences for a pre-service teacher SE content course and in-service professional learning course, we describe how this framework can be used to design culturally sustaining and justice-centered SE learning experiences. In creating a framework for educators to critically reflect on their design and practices, we propose a model for how to make culturally responsive and sustaining pedagogies and curriculum in SE more accessible.

Drawing on a case study of TikTok’s operations in the United States, this paper examines state-capital relations in an era of globalized digital platforms. Specifically, it analyzes how ByteDance, TikTok’s parent company, grapples with being a global digital platform while navigating intensifying entanglements between state interests and capitalist expansion. This study reveals that despite ByteDance’s attempts to downplay Chinese state connections and align with U.S. market and political practices, TikTok’s identity remains contested due to its failure to integrate fully into the U.S. military-digital complex. It argues that the core of TikTok’s predicament lies in evolving state-capital dynamics, in which the traditional Silicon Valley model’s reliance on venture capital and neoliberal operations is being tested against rising geopolitical rivalries and the resurgence of state influence in digital capitalism.