31825 research outputs found
Sort by
Phase separation dynamics of ternary system with and without shear: a Lattice Boltzmann Method approach
No full textSchiller, UlfTernary phase separation plays a crucial role in understanding the complex behavior of multi-component fluid mixtures, particularly under varying flow conditions. This study investigates the phase separation dynamics of ternary fluid mixtures under both quiescent and sheared conditions using a free-energy Lattice Boltzmann Method. In the absence of shear, domain growth and final morphologies are characterized for different volume fraction configurations across a range of values of the intrinsic fluidity parameter, defined as the ratio between peclet and capillary number. ☐ When a ternary mixture subjected to shear flow undergoes phase separation, the resulting morphology is governed by two competing effects: the natural coarsening of domains and shear-induced deformation. While domains tend to grow over time, the applied shear stretches them along the flow direction, leading to unique morphologies. Our results for an intermediate shear regime (applied shear is insufficient to induce strongly anisotropic or fully aligned structures), reveal that at low capillary numbers, the system reaches a periodic steady state featuring complex droplet morphologies such as double emulsions and worm-like structures. In contrast, at high capillary numbers, phase separation results in banded structures extended along the shear direction. Under inertialess conditions, we find that the phase separation dynamics are primarily governed by capillary number, and appear to be largely independent of the components' volume fractions. This contrasts with the no-shear case, where the final morphologies are strongly dependent on the volume fractions. ☐ The range of morphologies predicted under both quiescent and sheared conditions demonstrates the potential of this research for applications in the design and manufacturing of polymeric and soft materials.University of Delaware, Department of Materials Science and EngineeringM.M.S.E
Building an affordable self-driving lab: Practical machine learning experiments for physics education using Internet-of-Things
No full textThis article was originally published in APL Machine Learning . The version of record is available at: https://doi.org/10.1063/5.0283529
© 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Machine learning (ML) is transforming modern physics research, but practical, hands-on experience with ML techniques remains limited
due to cost and complexity barriers. To address this gap, we introduce an affordable, autonomous, Internet-of-Things (IoT)-enabled experimental
platform designed specifically for applied physics education. Utilizing an Arduino microcontroller, a customizable multi-wavelength
light emitting diode array, and photosensors, our setup generates diverse, real-time optical datasets ideal for training and evaluating foundational
ML algorithms, including traversal methods, Bayesian inference, and deep learning. The platform facilitates a closed-loop, self-driving
experimental workflow, encompassing automated data collection, preprocessing, model training, and validation. Through systematic performance
comparisons, we demonstrate the superior ability of deep learning to capture complex nonlinear relationships compared to traversal
and Bayesian methods. At ∼$60, this open-source IoT platform provides an accessible, practical pathway for students to master advanced
ML concepts, promoting deeper conceptual insights and essential technical skills required for the next generation of physicists and engineers.This study received funding from the National Natural Science Foundation of China (NSFC) (Grant Nos. 62011530438 and 61704129). This study was partially supported by the Fundamental Research Funds for the Central Universities (Grant No. QTZX23026), the fund of the State Key Laboratory of Solidification Processing in Northwestern Polytechnical University (Grant No. SKLSP201612), and the Open Fund of the State Key Laboratory of Infrared Physics (Grant No. SITP-NLIST-ZD-2024-01). Y.X. acknowledges the European Research Council through the ERC-2024-PoC StEnSo (Grant Agreement No. 101185235) and the ERC-2024-SyG SKIN2DTRONICS (Grant Agreement No. 101167218). We would also like to acknowledge the Severo Ochoa Centers of Excellence program through Grant No. CEX2024-001445-S. This work was also supported by the 2024 Textbook Development Grant of Xidian University (Project No. AJA2412). The authors also acknowledge Dr. Eduardo R. Hernandez (ICMM, CSIC) and Dr. Andres Castellanos-Gomez (ICMM, CSIC) for the careful reading of the paper
The aunts of Anglo-American literature
No full textCarroll, SiobhanThe Aunts of Anglo-American Literature theorizes how aunts complicate the so-called “cult of domesticity” within nineteenth-century domestic print culture. As part of the family but outside its nucleus, the aunt is a tangential but crucial nexus through which cultural anxieties about gender, class, and race are not only exposed, but also negotiated. Indeed, her liminality evokes the wider, often racially diverse kinship networks that concurrently shaped Anglo-American domesticity. Domestic labor has always depended on collective structures of support, whether from (enslaved) servants, extended relatives, close friends, or broader communities of care. The aunt’s presence cracks the façade that domestic work is a solitary pursuit performed by the idealized “angel of the house” who purportedly regulates the family and culture itself via her expert domestic management. As I argue, the term aunt becomes a rhetorical shorthand to account for women’s labor that falls outside the heteronormative ideal, and aunting emerges as a capacious literary mode that paradoxically enacts and bends the boundaries that the cult of domesticity imposes on “women’s work.” This project therefore contributes to ongoing feminist reappraisals of the angel in the house and the cult of domesticity as dominant critical frameworks within nineteenth-century studies. Moreover, it reclaims the aunt as a central, though curiously overlooked, agent in this deconstruction. By examining these tensions within the everyday media of periodicals, this dissertation illuminates not only the diversity of nineteenth-century kinship formations, but also their enduring legacies.University of Delaware, Department of EnglishPh.D
Evidence of racial differences in peripheral blood pressure, central haemodynamics and arterial stiffness between young Black and White women
No full textThis article was originally published in Experimental Physiology. The version of record is available at: DOI https://doi.org/10.1113/EP092929
©2025 The Author(s). Experimental Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.
This is an open access article under the terms of the Creative Commons Attribution License 4.0, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Hypertension diagnosed via peripheral (brachial) blood pressure (pBP) is a strong independent predictor of overt cardiovascular disease (CVD). However, central (aortic) blood pressure (cBP), which is influenced by arterial stiffness, may be more strongly associated with CVD risk. Young Black women (BLW) demonstrate higher pBP than White women (WHW), but investigations of racial differences in central haemodynamics and arterial stiffness in young women are lacking. We assessed pBP, central haemodynamics and arterial stiffness in young, non-hypertensive BLW and WHW. We hypothesized that pBP, central haemodynamics (cBP, augmentation pressure (AP), augmentation index normalized to a heart rate of 75 beats per minute (AIx75), arterial wave reflections), and arterial stiffness (carotid–femoral pulse wave velocity (cf-PWV)) would be higher in BLW. Under standardized resting conditions, supine brachial pBP was measured, and central haemodynamics were estimated via pulse wave analysis using partial cuff inflation. cf-PWV was assessed via simultaneous carotid artery applanation tonometry and partial cuff inflation over the femoral artery. Participants were young, apparently healthy women who self-identified their race as Black (BLW: n = 44) or White (WHW: n = 40). Systolic pBP (P = 0.04) and diastolic pBP (P < 0.01) were higher among BLW. Systolic cBP (P < 0.01), diastolic cBP (P < 0.01), heart rate (P < 0.001), AP (P = 0.02), AIx75 (P < 0.001), arterial wave reflection magnitude (P = 0.40) and cf-PWV (P = 0.04) were all higher among BLW. Findings demonstrate elevations in pBP, central haemodynamics and arterial stiffness in young BLW versus WHW. Central haemodynamics and arterial stiffness may be promising targets in the early assessment of CVD risk in young BLW.Research reported in this publication was supported, in part, by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health (2P20GM113125), the American Heart Association (Award #831488), a University of Delaware Research Foundation—Strategic Initiative Award, and the University of Delaware Graduate College through the Doctoral Fellowship for Excellence
SYMMETRIC GENERALIZED CP TENSOR DECOMPOSITION
No full textenterCanonical Polyadic (CP) tensor decomposition is an emerging workhorse algo rithm in data science for fnding underlying low-dimensional structure in tensor data
(i.e., N-way arrays). Generalized CP (GCP) decompositions generalize conventional
CP by allowing general loss functions that can be more appropriate for data such as bi nary and count data, or that can allow desired statistical properties such as robustness
to outliers. In this thesis, we develop a new Symmetric GCP (SymGCP) decomposition
for data tensors that exhibit symmetry across some of their dimensions, which arises
in applications such as dynamic social networks and higher-order statistical moments.
SymGCP accounts for the symmetry in the data by producing a decomposition with
matching symmetry, which involves developing a new corresponding optimization algo rithm. To enable SymGCP to scale to large tensors, we develop an effcient stochastic
approach for computing SymGCP decompositions. Finally, we demonstrate the utility
of SymGCP on a variety of experiments with real and synthetic data.ente
Elevated LDL-C induces T-cell metabolic dysfunction and increases inflammation and oxidative stress in mid-life adults
No full textThis article was originally published in Journal of Applied Physiology. The version of record is available at: https://doi.org/10.1152/japplphysiol.00226.2025
Copyright © 2025 The Authors. Licensed under Creative Commons Attribution CC-BY-NC-ND 4.0. Published by the American Physiological Society.T-cells may contribute to chronic, low-grade, sustained inflammation and oxidative stress commonly observed with aging and chronic disease. T-cell metabolic alterations impact T-cell differentiation, inflammation, and oxidative stress in animal models. Low-density lipoprotein cholesterol (LDL-C) has been identified as a novel antigen that activates T-cells via a canonical pathway. However, in humans, little is known about the direct effect of LDL-C on T-cells. Endogenous LDL-C concentration peaks during mid-life in humans and may contribute to midlife chronic disease risk by inducing T-cell dysfunction. Thus, this study investigated the effects of exogenous LDL-C exposure on CD4+ and CD8+ T-cells from mid-life adults. Compared to a physiologically “low” LDL-C concentration, we hypothesized that exposure to “borderline high” LDL-C would induce activation, alter metabolism, and increase mitochondrial and inflammation and mitochondrial reactive oxygen species production in T-cells from mid-life adults. T-cell metabolism was assessed using extracellular flux analysis and all other outcomes were assessed using flow cytometry. Our findings indicate that exposure to a borderline high concentration of LDL-C induced CD4+ and CD8+ T-cell activation, impaired mitochondrial respiration, and increased glycolytic metabolism. Further, we observed exogenous LDL-C exposure induced T-cell differentiation towards activated effector memory and effector memory re-expressing CD45RA subpopulations and increased inflammatory cytokine and mitochondrial reactive oxygen species production. These data suggest that borderline high LDL-C induces T-cell dysfunction that may increase the risk for age-related diseases. Future observational and clinical research should investigate the effects of endogenous LDL-C and other blood lipids on in vivo T-cell function and the implications for disease risk.Research reported in this publication was supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under award number 2P20GM113125
DIVERSITY, MOVEMENT AND SURVIVAL OF JUVENILE FISHES AROUND AQUACULTURE GEAR IN NEARSHORE ENVIRONMENTS OF DELAWARE BAY, USA
No full textenterAs aquaculture continues to expand in coastal systems worldwide, evaluating
ecological impacts on wild fish communities is essential for sustainable marine
resource management. This study examined the influence of rack-and-bag (RB) oyster
aquaculture structures on the diversity, movement, and apparent survival of juvenile
fishes in nearshore environments of Delaware Bay, USA. Using a mark-recapture
framework, Passive Integrated Transponder (PIT) tagging, and environmental
monitoring, we investigated the habitat use of two focal species—American Eel
(Anguilla rostrata) and Black Sea Bass (Centropristis striata)—across two
ecologically distinct sites: Port Mahon and Lewes.
Over a 12-week sampling period, eel traps were deployed at fixed distances (0,
3.5, 7, and 14 meters) from aquaculture gear to detect spatial gradients in fish
distribution. Biodiversity metrics revealed higher species richness and abundance at
Port Mahon, while Lewes exhibited greater species evenness. A pronounced halo
effect was observed at both sites, characterized by elevated capture rates and species
richness nearest to RB structures, suggesting that aquaculture installations enhance
localized habitat complexity and biological activity. Cormack-Jolly-Seber models
estimated an apparent survival rate (Φ) of 64.6% for American Eels, with a detection
probability (p) of 15% at Port Mahon. In Lewes, Black Sea Bass exhibited a higher
survival estimate under a time-variant detection model (Φ = 90.6%), though with wide
uncertainty. For comparison, a time-invariant model for Black Sea Bass yielded a
lower survival estimate (Φ = 43.88%; 95% CI: 19.98–70.99%) and a detection
probability of 13.42% (95% CI: 3.52–39.68%). These results reflect the episodic use
of aquaculture gear by American Eel and Black Sea Bass. My findings indicate that
RB oyster aquaculture provides valuable refuge and foraging habitat for juvenile
fishes and may function as a habitat enhancement tool in estuarine systems. The
spatial patterns observed underscore the ecological role of aquaculture gear in shaping
fish community structure and movement, with implications for the design and
management of sustainable aquaculture operations.ente
Effects of an Early Home Visiting Program on Maternal Depression
No full textThis article was originally published in Administration and Policy in Mental Health and Mental Health Services Research. The version of record is available at: https://doi.org/10.1007/s10488-025-01440-3.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Maternal depression has been associated with negative parenting behaviors and poor developmental outcomes in children. Home visiting programs have positively impacted parenting behaviors and child outcomes; however, such programs often require specialized, highly trained professionals, resulting in a limited number of home visiting providers. One home visiting parenting program, Attachment and Biobehavioral Catch-up (ABC), does not have requirements regarding experience or background to become an ABC parent coach and deliver the intervention. ABC consists of ten 1-hour weekly sessions for parents of children between 0 and 6 months (ABC-Newborn), 6–24 months (ABC-Infant) or 24–48 months (ABC-Early Childhood). ABC has demonstrated efficacy in improving parental sensitivity and children’s developmental outcomes. A randomized clinical trial in one community implementation setting showed that ABC decreased maternal depressive symptoms. The current study aimed to replicate this finding across multiple implementation sites and expand on it by exploring if the effect differed by ABC model. Data included a community sample of 163 families from six countries who completed ABC. Maternal reports of depressive symptoms were collected prior to and after receiving ABC. Results showed a significant decrease in maternal depressive symptoms scores from pre-intervention to post-intervention regardless of ABC model. Findings demonstrate that a home visiting parenting intervention program can successfully leverage non-traditional mental health providers to ensure that mothers and children receive necessary resources and support
Development of ErAs-embedded III-V semiconductor structures for terahertz photoconductive devices
No full textZide, Joshua M. O.Terahertz (THz) technology has emerged as a promising platform for applications in spectroscopy, imaging, and high-speed communications. However, the development of compact, efficient, and telecom-compatible THz sources and detectors remains a significant challenge. To address this challenge, this dissertation focuses on the growth, fabrication, and characterization of ErAs-embedded III-V semiconductor heterostructures for photoconductive antennas (PCAs) designed to operate under 1550 nm optical excitation. ☐ Molecular beam epitaxy (MBE) was employed to synthesize ErAs:GaAs and digital alloys of [ErAs:(InGaBiAs)x(InAlBiAs)1−x], with precise control of fluxes and growth temperature to optimize bulk resistivity, carrier lifetime, and bandgap compatibility with telecom wavelengths. The structural, optical, and electrical properties of the films were characterized using high-resolution X-ray diffraction (HRXRD), atomic force microscopy (AFM), scanning electron microscopy (SEM), spectrophotometry, Hall effect measurements, and optical pump THz probe spectroscopy (OPTHzP). ☐ Devices were fabricated using maskless laser photolithography and lift-off metallization techniques. A PCA detector of THz based on ErAs:InGaAlBiAs demonstrated measurable response at 1550 nm excitation, while ErAs:GaAs was integrated with a spintronic emitter architecture to explore THz pulse shaping and chirality control. Functional testing using THz time-domain spectroscopy (THz-TDS) was used to evaluate device performance. ☐ Overall, this work establishes a fabrication and characterization approach for ErAs-based THz PCAs and highlights the potential of digital alloy heterostructures for applications requiring compatibility with telecom-band excitation.University of Delaware, Department of Materials Science and EngineeringPh.D
Synthetic Aperture Radar information extraction and phase characterization via complex-valued neural networks
No full textMirotznik, Mark S.Deep learning techniques based on optical imagery have demonstrated recent success with improving Synthetic Aperture Radar (SAR) image quality compared to traditional post-processing techniques. However, the capabilities and limitations of these neural network designs, specifically as applied to diverse SAR datasets, is lesser known. Furthermore, unlike optical imagery, SAR coherent collection also include phase information captured within the complex domain. This additional phase information is traditionally disregarded due to is random appearance and the need to fit real-valued neural network designs. However, this research demonstrates, that preserving and enhancing this phase information through single-channel complex-valued neural network can significantly improve SAR image enhancement, target characterization, and Moving Target Identification (MTI). ☐ To improve the dataset diversity and relevance for improved neural network training, a new high-resolution Sensor Independent Complex Dataset (SICD) was compiled and processed using Capella Space’s commercial SAR satellites operating in spotlight-mode [1]. Lower resolution training images were obtained through sub-aperture captures within the Fourier domain – creating a diverse and accurate training set with multiple lower resolution frequency and angular captures capable of being mapped to a single high-resolution complex scene. This sup-aperture sampling, along with pre-processing algorithms for reducing dataset noise, was shown to notably improve network performance. ☐ Initial amplitude domain research focused on comparing super-resolution deep learning architectures, based on Convolutional Neural Networks (CNNs), Residual Networks (ResNets), and Generative Adversarial Networks (GANs). These architectures were optimized to support SAR image enhancement using new performance metric evaluations, patch-wise statistics, 1D feature extractions, and detailed visual inspection. A real-valued Residual Regression CNN (RR-CNN) achieved greater speckle reduction, smoothing, and feature contrast, with residual blocks offering improvements for network expansion. In comparison, a real-valued Conditional Cycle GAN (CC-GAN), with the key addition of L1 loss and cycle-consistency loss, significantly improved scattering point separations and reduced ringing artifacts. This research provides direct comparisons for neural network designs, loss-functions, and hyperparameter selections, and establishes amplitude-domain deep learning recommendations for super-resolving SAR imagery. While promising image enhancement improvements for SAR images were achieved through these amplitude-domain deep learning techniques, these studies were further extended into the complex-domain to fully exploit the additional phase information available with SAR coherent collections. ☐ This phase information was found to provide important insight on scattering behavior that can further improve SAR deep learning analysis and automatic feature identification, as shown through phase derivative calculations. Novel single-channel complex-valued neural networks (CVNNs) were designed and optimized to preserve and enhance SAR phase characterization. These evaluations focused on extending the amplitude-domain CNNs, ResNets, and GANs into the complex-domain to support complex-valued regression outputs, convolutions, weights, normalization layers, and activation functions. Loss functions for complex-valued inputs were also evaluated for phase preservation. The optimized CVNNs were directly compared to the similar Real-Valued Neural Networks (RVNNs), with the CVNNs achieving greater speckle reduction and higher peak signal strengths. This resulted from a reduction in phase interference effects, as the CVNNs learned the structured phase response shared between the low-resolution and high-resolution captures, effectively reducing the random background interference. This was further demonstrated through improved phase derivative separations, in which the target feature phase sensitivity even surpassed the original high-resolution captures. These learnings provide a new complex-valued approach for super-resolving SAR imagery and improving target phase characterization. ☐ To further explore this, CVNNs were compared to RVNNs for moving target characterization and identification within high clutter scenes. SAR coherent collections traditionally focus on stationary artifacts, resulting in the distortion of non-stationary processes and limiting the identification of moving targets within background clutter. To provide automated MTI for traditional single-channel SAR collection modes, a new CVNN was developed that improves detections within high clutter environments. To support this analysis, a new labeled complex-valued dataset was created using synthetically injected moving targets within the Compensated Phase History Data, allowing for various target-to-clutter signatures, target headings, and target speeds. Additionally, CVNN comparisons were provided for both the complex spatial and Fourier domains. Phase derivative analysis visualized the enhanced moving target characterizations achievable within the complex domain, while CVNN MTI analysis quantified these results, achieving a target detection accuracy of 81.4%, compared to 77.0% for a similar RVNN. Furthermore, for the lowermost 10% of target-to-clutter signatures, the CVNN correctly classified 57.7% more targets compared to the RVNN. The CVNN also detected 10.1% more targets with speeds exceeding 20 knots and 11.1% more targets with predominately cross-range velocities. These results highlight the ability for CVNNs to also improve MTI sensitivities beyond those achievable in only the amplitude domain. The results from this research establish neural network processing recommendations, as extended into the complex-domain, for improving the information extraction from SAR collections.University of Delaware, Department of Electrical and Computer EngineeringPh.D