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1. MODELING OF OIL SPREAD ON ICE WITH SURFACE HEATING

   Nair Sharanya; Hayashi, T.; Farahani, H. F.; Sakaue, H.; Rangwala, A.S. (June 2022, Arctic Marine Oil Spill Program)

   NA (Ed.)

   Oil spill in oceans is identified as a key environmental issue resulting in water contamination and major harm to marine life. These spills in ice-infested waters can be even more catastrophic as the process of ice melting is non-trivial and adds an additional complexity in determining the extent of the oil spread from the initial spill zone. The prediction of the impact and extent of the spill assists in employing the required clean-up countermeasures. A validated numerical model that simulates the oil spread is reported in this study, where the spread of an oil layer in ice is analyzed. Experiments in literature have shown that for solar radiation flux higher than 0.5 kW/m2, the oil temperatures can be around 5 - 6 ºC even if the ambient is at sub-zero temperature. This surface heating is simulated in the numerical model to study the effect of in-depth heating of oil on the ice melting to further analyze the spreading of oil in the melt zone. 

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2. How the Hidden Curriculum Reveals the Enculturation Experience of Underrepresented Students in Engineering

   https://doi.org/10.1109/FIE58773.2023.10343275  

   Shiekh, Kylee; Nieusma, Dean; Zhu, Qin; Rea, Stephen C (October 2023, IEEE)

   In engineering education in the United States (as elsewhere), it is widely recognized that the percentage of women and minorities who acquire engineering degrees is significantly lower than their representation in the general population. Many studies have investigated the cause of this lack of representation in engineering and other STEM (science, technology, engineering, and mathematics) degree programs. It is widely recognized that the percentage of women and minorities who acquire engineering degrees is significantly lower than their representation in the general population. Adolescents' occupational identity development depends in large part on their internalized mental models of what a given type of professional “looks like,” their subjective sense of their own capacity to be successful at certain tasks and with certain types of knowledge, and the degree to which they feel as if they belong to a community of practice. This paper considers how the concept of “hidden curriculum” can be applied to how underrepresented students experience engineering education uniquely. The concept of the “hidden curriculum” is used to describe the set of structured learning experiences or conditions that occur beyond the design intent of the learning journey established by the explicit curriculum. The hidden curriculum is typically unintentional, unplanned, and less “controllable” than the explicit curriculum. Despite the difficulty in assessing hidden learning expectations, hidden curriculum consistently places expectations on students beyond the explicit curriculum. It is critical to understand not just what variables prevent underrepresented students from persisting, but also what factors encourage their persistence, as such persistence is critical to ensuring a more diverse engineering workforce. This work focuses on how minoritized groups specifically develop professional identity through the hidden curriculum. We consider their perception of belonging in engineering, their experiences of exclusion in various forms, and the mechanisms by which exclusion transpires. By better understanding the cultural dimensions of exclusion, we hope to advance efforts toward inclusion. 

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3. Studying the Interactions Between Science, Engineering, and Computational Thinking in a Learning-by-Modeling Environment.

   https://doi.org/10.1007/978-3-030-52237-7_48  

   Zhang, N.; Biswas, G.; McElhaney, K.; Basu, S.; McBride, E.; Chiu, J. (June 2020, In: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science.)

   Computational Thinking (CT) can play a central role in fostering students' integrated learning of science and engineering. We adopt this framework to design and develop the Water Runoff Challenge (WRC) curriculum for lower middle school students in the USA. This paper presents (1) the WRC curriculum implemented in an integrated computational modeling and engineering design environment and (2) formative and summative assessments used to evaluate learner’s science, engineering, and CT skills as they progress through the curriculum. We derived a series of performance measures associated with student learning from system log data and the assessments. By applying Path Analysis we found significant relations between measures of science, engineering, and CT learning, indicating that they are mutually supportive of learning across these disciplines. 

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4. Estimates of Occupational Inhalation Exposures to Six Oil-Related Compounds on the Four Rig Vessels Responding to the Deepwater Horizon Oil Spill

   https://doi.org/10.1093/annweh/wxaa072  

   Huynh, Tran B; Groth, Caroline P; Ramachandran, Gurumurthy; Banerjee, Sudipto; Stenzel, Mark; Quick, Harrison; Blair, Aaron; Engel, Lawrence S; Kwok, Richard K; Sandler, Dale P; et al (October 2020, Annals of Work Exposures and Health)

   null (Ed.)

   Abstract Background The 2010 Deepwater Horizon (DWH) oil spill involved thousands of workers and volunteers to mitigate the oil release and clean-up after the spill. Health concerns for these participants led to the initiation of a prospective epidemiological study (GuLF STUDY) to investigate potential adverse health outcomes associated with the oil spill response and clean-up (OSRC). Characterizing the chemical exposures of the OSRC workers was an essential component of the study. Workers on the four oil rig vessels mitigating the spill and located within a 1852 m (1 nautical mile) radius of the damaged wellhead [the Discoverer Enterprise (Enterprise), the Development Driller II (DDII), the Development Driller III (DDIII), and the Helix Q4000] had some of the greatest potential for chemical exposures. Objectives The aim of this paper is to characterize potential personal chemical exposures via the inhalation route for workers on those four rig vessels. Specifically, we presented our methodology and descriptive statistics of exposure estimates for total hydrocarbons (THCs), benzene, toluene, ethylbenzene, xylene, and n-hexane (BTEX-H) for various job groups to develop exposure groups for the GuLF STUDY cohort. Methods Using descriptive information associated with the measurements taken on various jobs on these rig vessels and with job titles from study participant responses to the study questionnaire, job groups [unique job/rig/time period (TP) combinations] were developed to describe groups of workers with the same or closely related job titles. A total of 500 job groups were considered for estimation using the available 8139 personal measurements. We used a univariate Bayesian model to analyze the THC measurements and a bivariate Bayesian regression framework to jointly model the measurements of THC and each of the BTEX-H chemicals separately, both models taking into account the many measurements that were below the analytic limit of detection. Results Highest THC exposures occurred in TP1a and TP1b, which was before the well was mechanically capped. The posterior medians of the arithmetic mean (AM) ranged from 0.11 ppm (‘Inside/Other’, TP1b, DDII; and ‘Driller’, TP3, DDII) to 14.67 ppm (‘Methanol Operations’, TP1b, Enterprise). There were statistical differences between the THC AMs by broad job groups, rigs, and time periods. The AMs for BTEX-H were generally about two to three orders of magnitude lower than the THC AMs, with benzene and ethylbenzene measurements being highly censored. Conclusions Our results add new insights to the limited literature on exposures associated with oil spill responses and support the current epidemiologic investigation of potential adverse health effects of the oil spill. 

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5. Predicting the impact of future oil-spill closures on fishery-dependent communities—a spatially explicit approach

   https://doi.org/10.1093/icesjms/fsz138  

   Berenshtein, Igal; O’Farrell, Shay; Perlin, Natalie; Sanchirico, James N.; Murawski, Steven A.; Perruso, Larry; Paris, Claire B.; Gibbs, ed., Mark (July 2019, ICES Journal of Marine Science)

   Abstract Major oil spills immensely impact the environment and society. Coastal fishery-dependent communities are especially at risk as their fishing grounds are susceptible to closure because of seafood contamination threat. During the Deepwater Horizon (DWH) disaster for example, vast areas of the Gulf of Mexico (GoM) were closed for fishing, resulting in coastal states losing up to a half of their fishery revenues. To predict the effect of future oil spills on fishery-dependent communities in the GoM, we develop a novel framework that combines a state-of-the-art three-dimensional oil-transport model with high-resolution spatial and temporal data for two fishing fleets—bottom longline and bandit-reel—along with data on the social vulnerability of coastal communities. We demonstrate our approach by simulating spills in the eastern and western GoM, calibrated to characteristics of the DWH spill. We find that the impacts of the eastern and western spills are strongest in the Florida and Texas Gulf coast counties respectively both for the bandit-reel and the bottom longline fleets. We conclude that this multimodal spatially explicit quantitative framework is a valuable management tool for predicting the consequences of oil spills at locations throughout the Gulf, facilitating preparedness and efficient resource allocation for future oil-spill events. 

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