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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 

Broadcasting emergency notifications during disasters is crucial, particularly in Monroe County, NY, which is home to one of the largest per capita Deaf and Hard of Hearing (DHH) populations in the United States. However, text alerts may not effectively reach DHH individuals who are in a state of reduced responsiveness, like sleep, placing them at great risk. This paper presents cloud-based platform designed to deliver emergency alerts with visual and haptic feedback. A prototype utilizing an off-the-shelf IoT device demonstrates how alerts can be received via vibration and light-based feedback. The platform aims to be accessible to DHH community, providing its own solutions to maintain haptic devices and receive critical alerts in real time. This work contributes to the literature on IT solutions for bridging the communication gap between text-based alerts and intuitive visual/haptic communication, enhancing emergency response readiness for the DHH community, ultimately improving safety and potentially saving lives. 

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. 

Discovery of new materials plays a critical role in developing advanced high-temperature thermoelectric (TE) applications. Transition metal oxides (TMOs) are one of the attractive candidates for hightemperature TE applications due to their thermal and chemical stability. However, the trade-off relationship between thermopower (S) and electrical conductivity (s) limits the maximum attainable power factor (PF), thereby hindering improvements in TE conversion efficiency. To overcome this tradeoff relationship, the emerging approach of the redox-driven metal exsolution in TMOs shows promise in improving both S and s. However, the effect of metal exsolution with different particle sizes and densities on S and s is still largely unexplored. This study demonstrates an unusually large enhancement in PF through the exsolution of Ni nanoparticles in epitaxial La0.7Ca0.2Ni0.25Ti0.75O3 (LCNTO) thin films. Metal exsolution leads to a decrease in the carrier concentration while increasing the carrier mobility due to energy filtering effects. In addition, the exsolved metal particles introduce high-mobility electron carriers into the low-mobility LCNTO matrix. Consequently, the exsolution of metal particles results in a significant enhancement in S along with a substantial increase in s, compared to the pristine film. Overall, the TE power factor of LCNTO is dramatically enhanced by up to 8 orders of magnitude owing to the presence of exsolved metal particles. This enhancement is attributed to the selective filtering of carriers caused by energy band bending at the metal–oxide interfaces and the high-mobility carriers from the exsolved Ni particles with a high Ni0 fraction. This study unequivocally demonstrates the impact of metal exsolution on oxide TE properties and provides a novel route to tailor the interconnected physical and chemical properties of oxides, leading to enhanced TE power output. 

Abstract The rapid growth of uncharacterized enzymes and their functional diversity urge accurate and trustworthy computational functional annotation tools. However, current state-of-the-art models lack trustworthiness on the prediction of the multilabel classification problem with thousands of classes. Here, we demonstrate that a novel evidential deep learning model (named ECPICK) makes trustworthy predictions of enzyme commission (EC) numbers with data-driven domain-relevant evidence, which results in significantly enhanced predictive power and the capability to discover potential new motif sites. ECPICK learns complex sequential patterns of amino acids and their hierarchical structures from 20 million enzyme data. ECPICK identifies significant amino acids that contribute to the prediction without multiple sequence alignment. Our intensive assessment showed not only outstanding enhancement of predictive performance on the largest databases of Uniprot, Protein Data Bank (PDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG), but also a capability to discover new motif sites in microorganisms. ECPICK is a reliable EC number prediction tool to identify protein functions of an increasing number of uncharacterized enzymes.