University of Delaware

Autistic girls are often diagnosed late, missed, or misdiagnosed, which can negatively impact quality of life and mental health. Although research shows the social profiles of autistic girls differ from boys in systematic ways that might explain gaps in diagnosis, little is known about how autistic girls' social language compares to their same-sex non-autistic peers. This study investigated social words-words that make reference to other people-produced by 138 age- and IQ-matched autistic and non-autistic youth (ages 6-15) during one Autism Diagnostic Observation Schedule, Second Edition task. Girls used significantly more social words than boys across both diagnostic groups. There was a larger gap in social word production between autistic girls and non-autistic girls than autistic boys and non-autistic boys, with non-autistic girls using the most social words. Non-autistic girls' social language behavior-including their social word production-sets an especially high bar for autistic girls, who often report trying to blend in with other girls. Growing evidence of the distinct social language profiles of autistic and non-autistic girls versus boys should guide researchers and clinicians to assess autism in ways that are sensitive to sex-associated differences and develop interventions that consider the norms of youth's target social circles.Lay AbstractAutism is often diagnosed later in girls and women as compared to boys and men. More research is needed to understand how autism presents differently in girls. This study investigates how autistic and non-autistic youth aged 6 to 15 years use social words (e.g. "friend," "mom," "help," "talk") during an interview about friends, relationships, and marriage as part of an autism diagnostic assessment. Overall, girls used more social words compared to boys and talked more about friends. Specifically, non-autistic girls used the most social words in comparison with other groups. Highly social language produced by non-autistic girls may make it especially hard for autistic girls to blend in with other girls and could lead them to engage in more camouflaging behaviors to hide their autistic characteristics. With such different average social language behavior from girls and boys, researchers should consider adapting autism assessments and interventions to support the unique needs of autistic girls.

Conformational asymmetry, a difference in Kuhn length between two monomers, is known to shift the order-order transition (OOT) boundaries of block copolymer (BCP) melts and stabilize complex morphologies (e.g., Frank-Kasper phases) with enticing optical and transport properties. In this work, we investigate the influence of conformational asymmetry on the self-assembly of AxByAzByAx pentablock copolymers (pentaBCPs) by adopting our previously developed workflow involving self-consistent field theory (SCFT) calculations and coarse-grained (CG) molecular dynamics (MD) simulations aided by the RAPSIDY protocol. For varying conformational asymmetry ratios, CAR ≡ βA/βB, where βi is related to the Kuhn length of each block, SCFT shows shifts in the OOTs as CAR changes. For example, as CAR increases, the stability window widths of the hexagonally closed-packed cylinder and body-centered cubic spherical phases expand. To further understand the effect of CAR on chain conformations, we conduct MD simulations using two CG models - the 'semiflexible' chain model and the 'unequal-bead-diameter' (UBD) model. To vary CAR, in the 'semiflexible' chain model we vary the stiffness of A and B blocks, and in the UBD chain model we vary the A bead diameter with respect to the B bead diameter. The simulated phase behavior is qualitatively similar with both models and agrees to a large extent with the predicted phase diagram from SCFT. The normalized chain end-to-end distance and A-B interface width are consistent between both models and generally insensitive to CAR. However, the normalized lamellae periodicity expands with increasing conformational asymmetry (i.e., as CAR moves away from 1) for the semiflexible chain model, and the opposite is observed with the UBD chain model.

Thin-film lithium niobate (TFLN) electro-optic modulators have been widely explored at 1550 nm, achieving ultra-low driving voltages, broad bandwidths, and compact footprints. Yet, development at 1064 nm has remained comparatively limited, despite its growing importance for applications that demand high power and cost-effective integration with CMOS-compatible silicon photodetectors such as free-space optics, frequency combs and biophotonics. In this work, we demonstrate a high-efficiency TFLN modulator operating at 1064 nm contributing to fulfilling this gap. The device employs a 1 cm-long interaction region with capacitively loaded traveling-wave electrodes designed to minimize the velocity mismatch between RF and optical modes, achieving an index mismatch as low as 0.05. This careful optimization enables a high frequency half-wave voltage of 1.45 V and a 40 GHz 3-dB bandwidth, representing the best performance reported to date for 1064 nm operation. These results highlight the potential of TFLN modulators to extend beyond traditional telecom wavelengths leveraging lower Vπ, high quality lasers and easy integration with Si photodetectors.

Salt marshes in the northeastern United States support several specialized breeding bird species that are threatened by sea level rise (SLR) and coastal development, processes that drive habitat change and fragmentation. There have been rapid, widespread declines in some species, but mechanisms driving population change and whether declines continue remain unclear. We examined the influence of phenomena expected to modify salt marshes, including SLR, sediment delivery rates, and land use, on the population trajectories of saltmarsh breeding birds. We modeled population trajectories of 5 species with spatially extensive point count surveys conducted from Maine to Virginia from 2011 to 2022. We used Bayesian hierarchical abundance models and model selection to identify phenomena that had the strongest effect on population change. Clapper rails (Rallus crepitans) continued their long-term decline (-4.1%/year). Willets (Tringa semipalmata semipalmata) (2.6%/year) and saltmarsh sparrows (Ammospiza caudacuta) (4.1%/year) increased, and seaside (Ammospiza maritima) and Nelson's (Ammospiza nelsoni subvirgatus) sparrows exhibited no clear change in abundance. The estimated increase for saltmarsh sparrow was not consistent with trends over the previous 25years but aligned with prior demographic modeling, which predicted a short-term stabilization during the study years before an expected return to a long-term decline. Road density and other tidal restrictions near marshes were generally good predictors of abundance over the study period, as was marsh habitat composition. Local rates of SLR and sediment delivery were not as good predictors. During periods of relatively low rates of realized SLR, local-scale drivers of population trends had relatively stronger effects than global drivers on the persistence of several saltmarsh breeding birds. Conservation practitioners, however, should be attentive to global drivers, especially as rates of SLR accelerate in the future.

Bridge deck deterioration remains a major concern for owners because of its direct impact on road safety and usability. Existing deterioration models predict future conditions of the deck by linking deterioration to various factors (or features) identified through different engineering and statistical techniques. This led to a lack of a unified feature set, which mostly influences the deterioration of the bridge deck. Additionally, traditional deterioration models (e.g., linear regression) are unable to accurately predict the discretized values of the deck condition ratings, resulting in inaccuracies. For instance, a predicted value of 6.51 was approximated to be a condition rating of 7, which is inappropriate when using discrete data. This paper uniquely combines the bridge features identified in the literature and applies principal component analysis (PCA) to capture the relevant information in the feature set needed to predict the condition of the bridge deck. This case study analyzed 53,000 observations from 24,240 unique bridges in Maryland and Virginia, categorizing the bridges into five condition rating groups. Feedforward artificial neural network (ANN) models were developed using different combinations of principal components derived from the dataset. The performances of these models were compared to a base model that used 14 features collected from the literature. Analyses revealed that incorporating at least nine principal components resulted in a deterioration model with a prediction accuracy of 76%, surpassing the base model’s accuracy of 75%. The results demonstrate a lower prediction error compared to previous studies. Utilizing nine principal components reduces the feature-set’s dimensionality from 14 to nine, thereby minimizing the subjectivity associated with visual inspection data and enhancing model performance for bridge condition assessment.

This study develops and validates a strain rate dependent constitutive model for uncured highly aligned discontinuous fiber (ADF) prepregs in the fiber direction. Uniaxial tensile and stress-relaxation experiments were conducted at room temperature on 3 mm carbon fiber/epoxy prepreg specimens over three orders of magnitude in strain rate, revealing nonlinear hardening up to a rate-dependent peak stress followed by pronounced strain softening and rapid viscous relaxation. Based on these experimental observations, a phenomenological continuum model was formulated with separate hardening and softening regimes together with a time-dependent relaxation behavior. Model parameters were calibrated against normalized experimental data and shown to accurately reproduce the measured stress-strain response across all tested strain rates. An incremental formulation was derived to accommodate arbitrary strain rate histories, enabling direct implementation in finite element forming simulations. Implementation in Abaqus/Explicit reproduced the measured stress-strain response for constant and variable strain rate tensile loading with high fidelity. Experimental validation under variable strain rate histories confirms the high predictive accuracy of the proposed phenomenological continuum model. The proposed framework provides a practical methodology for converting experimental data into a constitutive model that can be used in forming simulations of ADF composites. The method can be applied to other ADF composites and establishes a foundation for future extensions to multiaxial and non-isothermal conditions.

The “Golden Penny” demonstration appeals to many audiences given its visual links to alchemical lore. In the demonstration, a copper coin is plated with zinc, giving it a silver appearance; the zinc plated coin is then heated, generating an alloyed coating of brass (as the zinc and copper mix), making the coin appear gold. Despite the visual appeal of this demo, the methodology utilized for the traditional “Golden Penny” process faces issues relating to safety and obscures the underlying electrochemical nature of the zinc-plating step for most onlookers. Given these limitations, we have worked to develop an electrochemical method to deposit lustrous zinc coatings on copper pennies to improve the demonstration’s safety profile. The updated methodology is versatile and may be used in the classroom as a traditional demonstration or as a laboratory activity to illustrate introductory electrochemical concepts. In addition to the electrochemical methods developed, the straightforward construction and use of an inexpensive trim potential circuit highlight the interconnection of electric and chemical potential for students. Additionally, slight modifications to the base demonstration show students the role of each component of the electrochemical cell (i.e., electrode material, pH, electrolyte, etc.). The effectiveness of this augmented Golden Penny demo in improving both high-school and introductory university students’ understanding of electrochemical terminology and conceptual difficulties was assessed using multiple choice pre/post-test questions and self-reported postactivity evaluations. Students’ scores dramatically increased from the preactivity test (average score: 31.6%) to the postactivity test (average score: 56.1%) representing a 24.6% improvement in score.