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Widespread Phenological Shifts With Temperature in Alaska's Marine Fishes
No full textThis article was originally published in Global Change Biology. The version of record is available at: https://doi.org/10.1111/gcb.70708
This is an open access article under the terms of the Creative Commons Attribution License https://creativecommons.org/licenses/by/4.0/ , which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Published 2026. This article is a U.S. Government work and is in the public domain in the USA. Global Change Biology published by John Wiley & Sons Ltd.Changes in the timing of fish spawning and early life stage development can affect the temporal match or mismatch of larvae with production of preferred prey as well as their availability to predators, with potential consequences for recruitment success, food- web dynamics, and fisheries. Using > 370,000 observations from over four decades of spring ichthyoplankton surveys in the Gulf of Alaska and Bering Sea, we investigated long- term changes in the phenology of 29 fish species, including commercially important taxa such as Pacific cod, walleye pollock, and Pacific halibut. Larval size on a standardized date (size- at- date) was used as a proxy for larval developmental timing in spring, and reflects a combination of hatch timing (larval age), growth, and mortality. Spatiotemporal generalized linear mixed models were used to account for variable sampling effort in space and time in order to isolate long- term trends and thermal effects on larval size. For a majority of species, interannual variation in mean size- at- date was significantly and positively related to temperature, demonstrating widespread thermal effects on the phenology of fish early life stages. Despite the wide diversity of life history traits exhibited by the 29 species examined, patterns in size- at- date over time were similar across most species within each ecosystem, reflecting the common effect of temperature on phenology. While temperature affected size- at- date, there was little evidence of long- term trends, likely due to the lack of a linear trend in winter–spring temperatures observed in recent decades. We demonstrate a novel analytical method to assess changes in phenology from larval size observations sampled at variable locations and times, and detect phenological shifts that were not necessarily identifiable from larval abundance data alone. Our results suggest that earlier spring phenology due to warming will be a common response among fishes to projected future climate change in high- latitude ecosystems.We gratefully acknowledge the decades of field work, painstaking laboratory work, and diligent data stewardship by NOAA EcoFOCI scientists past and present that made this study possible. We also thank the Plankton Sorting and Identification Center in Szczecin, Poland, for processing the ichthyoplankton samples. Feedback from Margaret Siple and Will Fennie improved the clarity of the manuscript. The findings and conclusions in this paper are those of the authors and do not necessarily represent the views of NOAA or the US Department of Commerce. This paper is contribution EcoFOCI- 1074 to NOAA's Ecosystems and Fisheries- Oceanography Coordinated Investigations Program
Improving learning under data scarcity constraints: application in brain MRI, sonar, and natural images
No full textBrockmeier, Austin J.Lack of data significantly hampers machine learning approaches in domains with limited data. This shortage impedes the effective use of deep learning models, which are prone to overfitting and often perform poorly when processing data not seen during their training. The nature of this problem varies by application, necessitating tailored solutions. We focus on using machine learning to achieve different computer vision tasks on various imaging modalities: structural MRI from brain scans, sonar images, and natural images. ☐ To achieve abnormal tissue segmentation (brain lesion detection) from structural MRI, we propose a self-supervised task that exploits the intricate spatial structure in the brain. We take a patch taken from an MRI slice and attempt to learn the mapping to its location relative within a brain. We add to this task an estimation for the uncertainty of the predicted location. Then, for the downstream tasks of abnormality detection and segmentation, we use a combination of two scores, namely the estimated location error and the uncertainty, as an unsupervised abnormality score for the input patch. ☐ While this approach focuses on leveraging spatial context within available structural images, in many clinical scenarios, some MRI modalities may be missing or unavailable due to limited resources, acquisition time, or patient-specific constraints. To address this complementary challenge of modality scarcity, we propose a 3D two-stage model for many-to-many modality translation. This model achieves state-of-the-art performance in both reconstruction quality and inference time, making it a practical solution for completing missing modalities in multi-modal MRI pipelines. ☐ For natural images, we utilize the fact that they are composed of two parts: background and foreground objects, where the latter is defined as the salient parts of the images, in training a masking network to separate the two. In sonar images of the sea floor, this can separate objects from the background sea floor. To do this we propose a weakly-unsupervised training scheme to train a masking network that takes an input image and generates a mask for the foreground objects in the input image. This mask is used to generate a synthetic image with the foreground superimposed on a different background-only image, yielding a counterfactual image. We use the cluster assignments of background content of images to define a conditional statistical divergence between the generated counterfactual images and the real ones for each target background cluster. The trained model that minimizes this divergence can be used in downstream tasks such as foreground segmentation and classification. Additionally, counterfactual images composed of foreground objects overlaid onto different backgrounds that are not present in the training data are useful for data augmentation. ☐ While the proposed methods address core aspects of learning under data scarcity, they also reveal new directions for future work. First, finer-grained localization in Patch2Loc could be achieved by applying out-of-distribution detection techniques to spatially organized latent spaces, particularly to overcome the limitations imposed by fixed patch sizes. In the context of weakly supervised segmentation, the background clustering mechanism could be extended with dynamic or adaptive clustering methods to handle more complex, real-world backgrounds. Additionally, to mitigate hallucinations such as partial object removal, a discriminator could be employed. For modality translation, incorporating uncertainty modeling would help identify when a translation is ill-posed due to missing modality-specific content, thereby improving reliability in clinical settings. We also plan to extend the approach to other modalities such as stiffness maps estimated from MRE images. Furthermore, we observed that dynamic models can better estimate missing information during translation (e.g., the contrast of T1CE), but they may alter the structural integrity of the brain. Introducing structural regularization into these generative models could preserve anatomical fidelity and enhance translation performance.University of Delaware, Department of Electrical and Computer EngineeringPh.D
Getting Back to "Traditional" Education: Racialized Norms in School Governance
No full text© The Author(s) 2026.
Creative Commons License (CC BY-NC 4.0)
This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
This article was originally published in AERA Open. The version of record is available at: https://doi.org/10.1177/2332858425140400Previous research has demonstrated how policies and procedures governing community engagement within K–12 school districts can function as barriers to advancing racial equity. Much of this work suggests that K–12 school board meetings, purported to be democratic spaces of deliberation and public engagement to inform district-level policymaking, do not always serve that purpose. This qualitative study builds on this existing literature by exploring how racialized norms within K–12 school boards can shape the ways in which board members manage community engagement in contentious times. Drawing on insights from the theory of White Institutional Spaces, we describe how existing norms of order, balance, neutrality, and adherence to traditional hierarchies in school boards primarily function as ways to uphold the status quo and continue to operate to the detriment of marginalized communities.The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research reported here was supported by a National Academy of Education/Spencer Foundation Dissertation Fellowship and a USC Rossier School of Education Dean’s Research Grant. The views expressed are those of the authors and do not necessarily reflect the views of the National Academy of Education, the Spencer Foundation, or the USC Rossier School of Education
From the shore to the screen: how virtual reality and collective vulnerability influence perceptions of sea-level rise in Bowers, Delaware
No full textBruck, JulesBowers, Delaware, is a small, tight-knit coastal community that has lived with periodic flooding for generations. Rising sea levels and increasingly severe storms are now pushing the limits of the town’s infrastructure and testing its capacity to adapt. Despite these growing risks—exacerbated in Delaware by its low elevation—public engagement with climate adaptation remains limited. One key barrier is the psychological distance many feel toward climate change; its abstract nature often makes it seem distant or irrelevant to their daily lives. This study examines the impact of a virtual reality (VR) simulation of localized future sea-level rise (SLR) projections on participants’ perceptions of psychological distance to SLR. It also explores how residents perceive their vulnerability to SLR and the barriers identified in planning for the future. Using a quasi-experimental, mixed-methods single-case study design, the research collected quantitative data through Likert-scale surveys and qualitative data through semi-structured interviews. Three theoretical frameworks guided the analysis: Construal-Level Theory (CLT), Collective Vulnerability Theory (CVT), and the Social-Ecological Systems Framework (SESF). Integrating these frameworks enabled a transition from individual cognition to collective experience to systems-level insight. Findings reveal that the VR simulation significantly altered participants’ perceptions, on personal items, with statistically significant changes in 7 of the 12 measured items, which formed a Personal Impact Scale. This suggests that immersive, localized visualizations can reduce psychological distance, fostering engagement with climate risks. Findings highlight a sense of collective vulnerability within the Bowers community, despite perceived limited governmental influence. Despite a strong sense of social capital and collective action, Bowers still experiences vulnerability. Ultimately, this work sheds light on how perceptions of collective vulnerability, rooted in socioeconomic inequalities and institutional failures, influence individuals’ psychological distance to environmental threats. Findings demonstrate that psychological distance is not solely a cognitive construct but is deeply embedded within socio-political realities.University of Delaware, Department of Plant and Soil SciencesPh.D
A Chemical Biostimulant Enhances Growth of Greenhouse Lettuce, But Not Some Cruciferous Vegetables, in Aerated Hydroponics
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This article was originally published in HortScience. The version of record is available at: https://doi.org/10.21273/HORTSCI19170-25A calcium-mobilizing chemical biostimulant has been developed to improve crop growth and quality by promoting calcium uptake and mobilization. Although designed to be applied as a foliar spray, it can potentially be added to the nutrient solution in controlled-environment hydroponic systems. Although it has been shown to mitigate calcium deficiency–induced tipburn while maintaining biomass in hydroponic lettuce (Lactuca sativa), its efficacy in other emerging hydroponic leafy greens remains unclear. The objective of our study was to determine the influence of this biostimulant, when added to the nutrient solution, on the growth traits of four hydroponic leafy greens: arugula (Eruca sativa), ‘Astro’; kale (Brassica oleracea var. sabellica), ‘Starbor’; lettuce ‘Rex’; and pac choi (Brassica rapa var. chinensis), ‘Win-Win Choi’. After 11 days of germination and seedling propagation under indoor sole-source lighting, we transplanted seedlings of all crops into actively aerated deep-water culture trays in a summer greenhouse environment. The trays had the same nutrient solution with and without the added biostimulant at a concentration of 0.25 mL·L–1 in three blocks of a randomized complete block design. Plant growth data were collected 21 and 28 days after transplanting (DAT). At 21 DAT, the added biostimulant decreased shoot fresh and dry mass of arugula, kale, and pac choi by 21% to 31%, but increased that of lettuce by 23% to 25%. At 28 DAT, the added biostimulant also increased shoot fresh and dry mass of lettuce by 24% to 29%, did not influence shoot fresh mass of the other crops, and decreased shoot dry mass of kale and pac choi by 14% to 21%. Tipburn incidence was minimal without or with the added biostimulant at 21 and 28 DAT, although tipburn reduction was observed in arugula and pac choi with the added biostimulant. In general, the chlorophyll concentration index was unaffected by the added biostimulant, except for an 11% increase in lettuce with the added biostimulant at 28 DAT. Extension growth of all crops except lettuce had a 9% to 15% reduction with the added biostimulant at 21 DAT, but was unaffected by the added biostimulant at 28 DAT. We conclude that the added biostimulant boosted the growth of lettuce, but not the cruciferous vegetables tested, in summer greenhouse aerated hydroponics.This work was supported by the Urban, Indoor, and Emerging Agriculture Program (project award no. 2023-70019-39371) from the US Department of Agriculture (USDA) National Institute of Food and Agriculture. This work was supported in part by the University of Delaware College of Agriculture and Natural Resources Envision Program, which was funded by the USDA National Institute of Food and Agriculture (award no. 2020-67037-31077. This program provided the stipend for summer undergraduate research.
We thank Grodan and JR Peters for supply donations, and the University of Delaware Indoor Ag Laboratory students and greenhouse staff for experimental assistance.
Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and should not be construed to represent any official USDA or US government determination or policy
Electronic and structural variations of 2D metal-chalcogenide and 3D oxide semiconductors
No full textJanotti, AndersonElectronic structure methods based on the hybrid density functional theory (DFT) are powerful tools for studying the electronic and structural properties of materials. Starting from only the atomic numbers and a guess for the atomic positions and crystal structure, DFT calculations predict lattice parameters typically within 1% of the experimental values. It can be used to determine enthalpies of formation, defect formation energies, surface and interface energies, migration energy barriers, phonon spectra, and electronic band structure. The screened hybrid functional, as proposed by Heyd, Scuseria, and Enzerhof (HSE06), can describe most metals, semiconductors, and insulators quite accurately, allowing the study of a wide range of materials, covering almost all the Periodic Table. Here, we use hybrid DFT to study two classes of materials: 2D metal dichalcogenides and metal oxides. The sensitivity of these materials to external stimuli, such as strain or doping, provides a powerful tool for engineering their properties. Besides, these are important materials in semiconductor research due to their exotic electronic, optical, and structural properties that enable a wide range of applications, including transistors, photonic memory devices, and solar cells. ☐ In this dissertation, the effects of uniaxial stress and biaxial strain on the electronic structure and optical properties of the metal dichalcogenides, and the effects of doping on lattice expansion and band alignments in the metal oxides are explored.Transition-metal dichalcogenides (TMDs) are promising materials for nano- and optoelectronics due to their tunable electronic properties, especially when strained. We employed the HSE06 hybrid functional to study the effects of uniaxial and biaxial strain on monolayer TMDs (MX2 , with M = Mo, W, and X=S, Se, Te). The study focuses on how strain can modulate the band structures of these materials, providing valuable insights into strain engineered devices. The results show that uniaxial tensile stress breaks the ideal hexagonal symmetry and strongly affects the conduction band, lowering it with respect to the vacuum level. For strains greater than 2%, the conduction band minimum and valence-band maximum are shifted from the high symmetry K points, leading to significant distortion and anisotropy of the band structure near K, with important implications to luminescence efficiency and, thus, optoelectronic device applications. ☐ These results emphasize the potential of TMDs for hosting defects that act as single-photon emitters. The ability to change TMDs’ electronic properties using stress opens up new opportunities for controlling their emission properties. Fine-tuning the strain levels allows for the optimization of emitting wavelengths and increased photon generation rates. Furthermore, the inclusion of controlled defects within TMDs can result in confined states that allow the emission of single photons. This dual approach, which combines strain and defect engineering, provides an effective approach for creating efficient, scalable single-photon sources, which are important for the advancement of quantum information and communication technologies. ☐ In the case of In2Se3 , a phase-change material suitable for photonic memories, experiments explored the reversible transitions between its α and β crystalline phases. These transitions, triggered by a single nanosecond pulse, enable low-energy switching, which is essential for non-volatile memory applications. We investigated the atomic mechanisms driving these phase transitions using DFT calculations in combination with experimental observations. The results show that the low configurational entropy and distinct optical properties of In2Se3 make it an excellent candidate for reconfigurable photonic devices. The study also reveals how phase stability and refractive index variations contribute to the performance of the material in optical systems. ☐ In the study of oxides, we investigate the effects of high doping levels on the lattice expansion of CaSnO3 , which is a promising wide bandgap oxide with high electron mobility at room temperature. CaSnO3 has long been considered undopable due to previous challenges in achieving effective n-type doping. In this study, our collaborators have successfully demonstrated n-type doping with lanthanum (La) using hybrid molecular beam epitaxy. These results open new possibilities for the use of CaSnO3 in high-performance electronic devices. Although the ionic radii of La and Ca are comparable, increasing the doping concentration led to an expansion of the out-of-plane lattice parameters, a phenomenon attributed to an electronic effect, as revealed by our DFT calculations. In addition, CdTe-related oxides are studied to understand the role of surface and grain boundary oxidation on solar cell efficiency, as CdTe is an important technology for photovoltaic applications. We are investigating how different CdTe-related oxides, such as CdO and CdTeO3 , affect the band alignment with that of CdTe. ☐ Finally, we systematically evaluated the performance of different DFT functionals, including DFT+U and hybrid functionals, to compare their accuracy in predicting important properties such as band gaps, ionization potentials (IP), and electron affinities (EA) for metal oxides. While DFT+U tends to improve band gap predictions in many cases, it often falls short in accurately predicting IP and EA. On the other hand, hybrid functionals, particularly the Heyd-Scuseria-Ernzerhof (HSE) functional with the right mix of parameters, provide more reliable predictions for a wider range of materials. This emphasizes the need for careful selection of computational methods, especially for high-throughput material screening in fields such as catalysis, photovoltaics, and batteries, where accurate description of band gaps, IP, and EA is critical to designing devices.University of Delaware, Department of Materials Science and EngineeringPh.D
Innovative Methods for Secondary Material Development in Mechanical Textile Recycling
No full textThis article was originally published in International Textile and Apparel Association Annual Conference Proceedings. The version of record is available at: https://doi.org/10.31274/itaa.18903.
© 2024 The author(s). Published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.The textile and apparel industry generates significant waste, with only 14.7% of the 17 million tons produced in 2018 being recycled. Current mechanical recycling efforts often result in downcycled products and lack scalability for commercial viability. This research explored innovative methods for developing yarns and nonwoven fabrics using mechanically recycled fibers ("Respool fibers") to address these challenges. End-of-use 100% denim cotton and polyester fabrics were shredded into fibers and blended with new fibers at 65% and 85% recycled-to-new ratios. The fibers were processed into yarns and nonwoven fabrics, and their durability (tensile strength, elongation) and comfort (thickness, air permeability) properties were analyzed. Results showed that yarns with 65%, 85%, and 100% recycled polyester exhibited comparable tenacity, demonstrating potential for high recycled fiber content without sacrificing strength. Nonwoven fabrics with higher recycled content were more breathable, suggesting suitability for applications prioritizing air permeability. These findings advance circularity in textile production
