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The sea surface microlayer (SSML) contains light-absorbing organic chromophores known to initiate daytime aqueous-phase chemistry in the environment. 

When a consumer is finished using an electronic device (End-of- First-Use), they might recycle, resell, donate/give away, trade-in or throw it in the trash. There are security threats if a hostile party obtains the device and extracts data. Data wiping at End- of-First-Use is thus an important security behavior, one that has received scant analytical attention. To explore consumer behavior and reasoning behind data wiping practices, we undertake a survey of the U.S. population. One key result is that 31% of the population did not wipe data when dispositioning a device. When asked why not, 44% replied that they did not find data wiping important or that it did not occur to them. 33% replied the device was broken and data could not be wiped, 12% reported difficulty in wiping and 11% could not find a way to wipe. The 44% who thought data wiping was not important showed lower awareness of the security threat, 23% had heard that data can be recovered from discarded devices, versus 44% for the general population. The most prevalent device types for which data wiping was reported as unimportant are smart TVs, kitchen appliances, streaming, and gaming devices, suggesting that consumers may not be aware that private information is being stored on these devices. To inform future interventions that aim to raise awareness, we queried respondents where they obtained security knowledge. 47% replied that they learned about security threats from a single venue; social media was this single venue 43% of the time. This suggests that social media is a key channel for security education 

Abstract Parent–child communication is an essential part of developing and sustaining career interests. Parents’ involvement in the process of career interest development and persistence of women in STEM is critical to understand to marshal support for women entering high-barrier fields. This manuscript answers 3 questions about how parents are involved in the processes of career interest development and persistence: What do parents do? What do parents say? And what should parents say? Models of interest development, studies of parental vocational anticipatory socialization, and communication theories such as the support framework and confirmation theory answer these questions. We propose paths for future research that use communication theory to predict effective parental communication while embracing an intersectional lens and considering support gaps. 

Abstract Twisted bilayer graphene (TBG) has drawn significant interest due to recent experiments which show that TBG can exhibit strongly correlated behavior such as the superconducting and correlated insulator phases. Much of the theoretical work on TBG has been based on analysis of the Bistritzer-MacDonald model which includes a phenomenological parameter to account for lattice relaxation. In this work, we use a newly developed continuum model which systematically accounts for the effects of structural relaxation. In particular, we model structural relaxation by coupling linear elasticity to a stacking energy that penalizes disregistry. We compare the impact of the two relaxation models on the corresponding many-body model by defining an interacting model projected to the flat bands. We perform tests at charge neutrality at both the Hartree-Fock and Coupled Cluster Singles and Doubles (CCSD) level of theory and find the systematic relaxation model gives quantitative differences from the simplified relaxation model. 

Cosmological moduli generically come to dominate the energy density of the early universe, and thereby trigger an early matter dominated era. Such non-standard cosmological histories are expected to have profound effects on the evolution and production of axion cold dark matter and dark radiation, as well as their prospects for detection. We consider moduli-axion couplings and investigate the early history of the coupled system, considering closely the evolution of the homogeneous modulus field, the back-reaction from the axion, and the energy densities of the two fields. A particular point of interest is the enhancement of axion production from modulus decay, due to tachyonic and parametric resonant instabilities, and the implications of such production on the cosmological moduli problem, axion dark radiation, and the available parameter space for axion dark matter. Using an effective field theory approach, WKB-based semi-analytical analysis, and detailed numerical estimates of the co-evolution of the system, we evaluate the expected decay efficiency of the modulus to axions. The effects of higher-order operators are studied and implications for UV-complete frameworks such as the Large Volume Scenarios in Type IIB string theory are considered in detail. 

This paper explores the addition of AI into ChangeMaker K-12, a set of experiences and materials designed to support teacher candidates (TC) in learning to teach engineering design. The ChangeMaker K-12 model and materials consist of four progressive stages: design awareness, design for function, design with empathy, and design for change. This new research explores the introduction of AI assistance into these four stages. 

Since their creation, displays have used the top-to-bottom raster scan. In today's interactive applications, this scan is a liability, forcing users to choose between complete frames with synchronization delay; or "torn" frames without this delay. We propose a stochastic scan that enables low-latency, unsynchronized display without tearing. We also discuss an interactive display simulator that allows us to investigate the effects of stochastic and other scans on interaction and imagery. 

The latest light-field displays have improved greatly, but continue to be based on the approximate pinhole model. For every frame, our real-time technique evaluates a full optical model, and then renders an image predistorted at the sub-pixel level to the current pixel-to-eye light flow, reducing cross-talk and increasing viewing angle. 

The goal of this study is to find patterns in how consumers disposition electronic devices at End-of-First-Use, i.e. store, recycle, resell, trade in, donate/give or and throw in the trash. K-means clustering was used on survey data from 3,747 U.S. respondents across 10 device categories to divide the population into three clusters of consumers based on stated attitudes and knowledge of data privacy, environmental benefits, convenience and other aspects of End-of-First-Use options. We then measure the reported intended disposition of devices for each cluster and compare with the general population. Cluster 1 has higher data security concerns when recycling, reselling or donating, and less knowledge and trust in End-of-First-Use options overall. The intended behavior of cluster 1 shows higher than average uncertainty in what to do at End-of-First-Use and more intent to store (lower values for other options - recycling, reselling and donating). Cluster 2 shows higher knowledge and trust in recycling, reselling, and donation, and slightly higher than average concern about data security of these options. The intended behavior of cluster 2 shows higher intent to resell, trade-in or donate, and lower levels of being uncertain of what to do and of storing. Cluster 3 expresses much less concern about data security, and lower utility of a stored device. Their intended behavior shows less storage and higher levels of other End-of- First-Use" options. While cluster analysis does not yield causal connections, the groups show consistent trends in stated knowledge and attitudes towards different End-of-First-Use options and corresponding planned behaviors. These results indicate there are subgroups of the general population with similar reported attitudes, knowledge and behaviors. The three subgroups do not have distinct demographic characteristics, i.e. knowledge and attitudes regarding disposition of electronics does not depend strongly on age, education level, income and similar factors. Understanding segmentation is useful to investigate more effective interventions to influence behavior for better sustainability outcomes.