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1. Individual differences (or the lack of them) in comprehension of singular they.

   Arnold, J. E. ; Mayo, H. ; Dong, L. ( January 2020 , UNC Language Processing Lab Technical Report #3)

   Arnold, J. E., Mayo, H., & Dong, L. (2020). Individual differences (or the lack of them) in comprehension of singular they. Technical Report #3. UNC Language Processing Lab, Department of Psychology & Neuroscience, University of North Carolina – Chapel Hill, Chapel Hill, North Carolina. The pronoun “they” can refer to an individual who identifies as nonbinary, but it also is commonly used as a plural pronoun. How do listeners identify whether “they” is being used in a singular or plural sense? Arnold, Mayo, & Dong (in press) report three experiments in that test the role of explicitly introducing gender identity via pronouns, e.g. “This is Alex, and they use they/them pronouns.” Participants read short stories like “Alex went running with Liz and they fell down.” Answers to “Who fell down” indicated whether participants interpreted they as Alex or Alex-and-Liz. Singular interpretations of they were more likely when participants hear an explicit statement that Alex uses they/them pronouns, and in supporting discourse contexts. This paper is a companion to the main article, and reports analyses of individual difference measures. Participants self-reported familiarity with individuals who identify as nonbinary, which was expected to increase singular interpretations, but mostly it did not. In experiment 2 we also measured print exposure, but we found that it did not affect interpretation of singular they. In short, we saw virtually no effects of individual difference predictors. 

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2. Does SES affect pronoun comprehension and prediction in implicit causality scenarios?

   Johnson, E. ; Arnold, J.E. ( January 2021 , UNC Processing Lab Technical Report #4)

   Johnson, E. & Arnold, J. E. (2021). Does SES affect pronoun comprehension and prediction in implicit causality scenarios? Technical Report #4. UNC Language Processing Lab, Department of Psychology & Neuroscience, University of North Carolina – Chapel Hill, Chapel Hill, North Carolina. This paper examines whether individuals’ pronoun resolution varies with respect to their socioeconomic status (SES). It uses the data from the Johnson and Arnold (2021) paper to determine whether the author recognition task (ART) effect that was found could instead be explained by participants’ SES. Both socioeconomic status and the author recognition task have been shown to correlate with measures of reading skill, so as a secondary analysis, SES was included as a possible predictor of individual differences. 

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3. A high proportion of red snapper sold in North Carolina is mislabeled

   https://doi.org/10.7717/peerj.9218  

   Spencer, Erin T. ; Richards, Emilie ; Steinwand, Blaire ; Clemons, Juliette ; Dahringer, Jessica ; Desai, Priya ; Fisher, Morgan ; Fussell, Sloane ; Gorman, Olivia ; Jones, Diamond ; et al ( June 2020 , PeerJ)

   Seafood mislabeling occurs when a market label is inaccurate, primarily in terms of species identity, but also regarding weight, geographic origin, or other characteristics. This widespread problem allows cheaper or illegally-caught species to be marketed as species desirable to consumers. Previous studies have identified red snapper (Lutjanus campechanus) as one of the most frequently mislabeled seafood species in the United States. To quantify how common mislabeling of red snapper is across North Carolina, the Seafood Forensics class at the University of North Carolina at Chapel Hill used DNA barcoding to analyze samples sold as “red snapper” from restaurants, seafood markets, and grocery stores purchased in ten counties. Of 43 samples successfully sequenced and identified, 90.7% were mislabeled. Only one grocery store chain (of four chains tested) accurately labeled red snapper. The mislabeling rate for restaurants and seafood markets was 100%. Vermilion snapper (Rhomboplites aurorubens) and tilapia (Oreochromis aureusandO. niloticus) were the species most frequently substituted for red snapper (13 of 39 mislabeled samples for both taxa, or 26 of 39 mislabeled total). This study builds on previous mislabeling research by collecting samples of a specific species in a confined geographic region, allowing local vendors and policy makers to better understand the scope of red snapper mislabeling in North Carolina. This methodology is also a model for other academic institutions to engage undergraduate researchers in mislabeling data collection, sample processing, and analysis.

    

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4. Seabed grain-size data from Harrison Bay (Beaufort Sea continental shelf), Alaska 2021

   https://doi.org/10.18739/A2JW86P50  

   Eidam, Emily ( January 2023 , NSF Arctic Data Center)

   This file contains grain-size data from seabed shipek grab samples collected from R/V Ukpik in summer 2021 as part of NSF project 1913195 (Arctic Shelf sediment fate – an observational and modeling study of sediment pathways and morphodynamic feedbacks in a changing polar environment). Samples were collected from across Harrison Bay on the Alaskan Beaufort Shelf, north of the Colville River and between Oliktok Point and Cape Halkett. Samples were bagged in the field and returned to the University of North Carolina at Chapel Hill where grain-size analyses were performed using an Escitec Bettersizer S3Plus laser diffraction sensor. Samples were sonicated for two minutes prior to analyses. Samples ranged from well sorted sands (typically medium sand or fine) to poorly sorted bimodal sands and muds to unimodal muds. In the field, samples exhibited diverse textures including mud clasts and very stiff muds. 

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5. Enabling Opportunistic Low-cost Smart Cities By Using Tactical Edge Node Placement

   https://doi.org/10.23919/WONS51326.2021.9415579  

   Madamori, Oluwashina ; Max-Onakpoya, Esther ; Erhardt, Gregory D. ; Baker, Corey E. ( January 2021 , 2021 16th Annual Conference on Wireless On-demand Network Systems and Services Conference (WONS))

   null (Ed.)

   Smart city projects aim to enhance the management of city infrastructure by enabling government entities to monitor, control and maintain infrastructure efficiently through the deployment of Internet-of-things (IoT) devices. However, the financial burden associated with smart city projects is a detriment to prospective smart cities. A noteworthy factor that impacts the cost and sustainability of smart city projects is providing cellular Internet connectivity to IoT devices. In response to this problem, this paper explores the use of public transportation network nodes and mules, such as bus-stops as buses, to facilitate connectivity via device-to-device communication in order to reduce cellular connectivity costs within a smart city. The data mules convey non-urgent data from IoT devices to edge computing hardware, where data can be processed or sent to the cloud. Consequently, this paper focuses on edge node placement in smart cities that opportunistically leverage public transit networks for reducing reliance on and thus costs of cellular connectivity. We introduce an algorithm that selects a set of edge nodes that provides maximal sensor coverage and explore another that selects a set of edge nodes that provide minimal delivery delay within a budget. The algorithms are evaluated for two public transit network data-sets: Chapel Hill, North Carolina and Louisville, Kentucky. Results show that our algorithms consistently outperform edge node placement strategies that rely on traditional centrality metrics (betweenness and in-degree centrality) by over 77% reduction in coverage budget and over 20 minutes reduction in latency. 

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