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  1. Free, publicly-accessible full text available December 1, 2024
  2. Abstract

    We report the formation of minerals from the tochilinite-valleriite group (TVG) during laboratory serpentinization experiments conducted at 300 and 328 °C. Minerals in the TVG are composed of a mixture of sulfide and hydroxide layers that can contain variable proportions of Fe, Mg, Cu, Ni, and other cations in both layers. Members of this group have been observed as accessory minerals in several serpentinites, and have also been observed in association with serpentine minerals in meteorites. To our knowledge, however, TVG minerals have not previously been identified as reaction products during laboratory simulation of serpentinization. The serpentinization experiments reacted olivine with artificial seawater containing 34S-labeled sulfate, with a small amount of solid FeS also added to the 300 °C experiment. In both experiments, the predominant reaction products were chrysotile serpentine, brucite, and magnetite. At 300 °C, these major products were accompanied by trace amounts of the Ni-bearing TVG member haapalaite, Ni,Fe-sulfide (likely pentlandite), and anhydrite. At 328 °C, valleriite occurs rather than haapalaite and the accompanying Ni,Fe-sulfide is proportionally more enriched in Ni. Reduction of sulfate by H2 produced during serpentinization evidently provided a source of reduced S that contributed to formation of the TVG minerals and Ni,Fe-sulfides. The results provide new constraints on the conditions that allow precipitation of tochilinite-valleriite group minerals in natural serpentinites.

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    Free, publicly-accessible full text available January 2, 2025
  3. Abstract

    We present eVscope observations of comets 12P/Pons-Brooks and C/2023 A3 (Tsuchinshan-ATLAS) when their magnitudes were greater than 16th magnitude. From a set of two observations of 12P taken on 2023 June 19 and 20, we measure an apparent magnitude ofG = 16.59 ± 0.31. From four sets of observations of C/2023 A3 taken on 2023 April 14, 25, 26, and May 9, we measure apparent magnitudes ofG = 16.78 ± 0.41, 16.55 ± 0.29, 16.71 ± 0.28, 16.59 ± 0.18 respectively. These images were taken from Unistellar telescope models: eVscope 1 and 2. We find an average background value ofG = 17.35 ± 0.21.

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  4. Abstract

    Iron–sulfur (Fe–S) proteins are essential for the ability of methanogens to carry out methanogenesis and biological nitrogen fixation (diazotrophy). Nonetheless, the factors involved in Fe–S cluster biogenesis in methanogens remain largely unknown. The minimal SUF Fe–S cluster biogenesis system (i.e., SufBC) is postulated to serve as the primary system in methanogens. Here, the role of SufBC inMethanosarcina acetivorans, which contains twosufCBgene clusters, was investigated. The CRISPRi-dCas9 and CRISPR-Cas9 systems were utilized to repress or deletesufC1B1andsufC2B2, respectively. Neither the dual repression ofsufC1B1andsufC2B2nor the deletion of bothsufC1B1andsufC2B2affected the growth ofM. acetivoransunder any conditions tested, including diazotrophy. Interestingly, deletion of onlysufC1B1led to a delayed-growth phenotype under all growth conditions, suggesting that the deletion ofsufC2B2acts as a suppressor mutation in the absence ofsufC1B1. In addition, the deletion ofsufC1B1and/orsufC2B2did not affect the total Fe–S cluster content inM. acetivoranscells. Overall, these results reveal that the minimal SUF system is not required for Fe–S cluster biogenesis inM. acetivoransand challenge the universal role of SufBC in Fe–S cluster biogenesis in methanogens.

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  5. Free, publicly-accessible full text available September 12, 2024
  6. This paper reports on the initial implementation of a two student “tiger team” in an engineering capstone design class. A tiger team is a small group of individuals that covers a range of expertise and is assigned when challenges arise that helps address the root issues causing the challenge. The term was coined in the 1960’s in the Cold War; tiger teams are used in industry, government, and military organizations. While tiger teams in these situations are usually formed around an issue then disbanded, in the capstone class the tiger team was formed for the duration of the two semester long class; details on formation and the larger context and organization of the class are discussed in the paper. The rationale for the tiger team was the observation over many years of a capstone class that as projects are functionally decomposed and subsystems assigned to individual students, a not insignificant fraction of students become “stuck” at some point in time – the concept of “stuckness” is further derived in the full paper. The result is that if delays accumulate on critical parts of the project, teams often struggle to get the project back on track and end up with a cascading series of missed deadlines. The rationale for the tiger team is to help teams identify when parts of the project are getting behind schedule and to have additional, short-term help available. In the initial implementation described here, the tiger team was two students—one from electrical and one from computer engineering—who volunteered for the position and were confirmed in that role by the other students in the class. Initial data shows that during the problem identification phase of the project the tiger team attended team meetings, helped evaluation project milestone reviews, worked to solve individual and team issues, and regularly met with the faculty. Early in the semester the two tiger team students described their role as unclear and worried their technical exposure would be limited. Later, as the teams developed technical representations, the tiger team provided independent feedback and addressed multiple technical challenges. Finally, as teams started to build technical prototypes the tiger team role again shifted to helping individuals with specific aspects of their project; this role continued throughout the remainder of the year-long course. This in-depth case-study of the experience of implementing a tiger team draws on observations from students, faculty, the tiger team members, and an external ethnographer. This work may help other capstone instructors who may be considering similar interventions. 
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    Free, publicly-accessible full text available July 1, 2024
  7. Grewe, Lynne L. ; Blasch, Erik P. ; Kadar, Ivan (Ed.)
    Sensor fusion combines data from a suite of sensors into an integrated solution that represents the target environment more accurately than that produced by individual sensors. New developments in Machine Learning (ML) algorithms are leading to increased accuracy, precision, and reliability in sensor fusion performance. However, these increases are accompanied by increases in system costs. Aircraft sensor systems have limited computing, storage, and bandwidth resources, which must balance monetary, computational, and throughput costs, sensor fusion performance, aircraft safety, data security, robustness, and modularity system objectives while meeting strict timing requirements. Performing trade studies of these system objectives should come before incorporating new ML models into the sensor fusion software. A scalable and automated solution is needed to quickly analyze the effects on the system’s objectives of providing additional resources to the new inference models. Given that model-based systems engineering (MBSE) is a focus of the majority of the aerospace industry for designing aircraft mission systems, it follows that leveraging these system models can provide scalability to the system analyses needed. This paper proposes adding empirically derived sensor fusion RNN performance and cost measurement data to machine-readable Model Cards. Furthermore, this paper proposes a scalable and automated sensor fusion system analysis process for ingesting SysML system model information and RNN Model Cards for system analyses. The value of this process is the integration of data analysis and system design that enables rapid enhancements of sensor system development. 
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    Free, publicly-accessible full text available June 14, 2024
  8. This paper focuses on how former collegiate student entrepreneurs define failure and compares their definitions with how academic literature has traditionally defined entrepreneurial failure. The article examines the context by which collegiate student entrepreneurs, and more specifically student entrepreneurs who studied an engineering discipline, start their venture, and how that influences their perceptions of what entrepreneurial failure is. Entrepreneurial failure and its importance to the field of entrepreneurship is discussed almost as frequently as entrepreneurial success. In fact, learning from failure and learning to fail quickly as a means to assist in advancing toward success are often discussed as fundamental key attributes of successful entrepreneurs. Despite this, factors that influence and contribute to entrepreneurial success and how to increase entrepreneurial success through support mechanisms are far more understood than methods that would help support entrepreneurs in learning from failure, or finding ways to fail early and often in a way that helps them as opposed to discouraging or demoralizing them. Given the rapid increase and interest within colleges of engineering in introducing and exposing students to entrepreneurial experiences, and also in developing programs that help students start entrepreneurial ventures, it is timely to better understand the experiences of these student entrepreneurs, particularly the largest percentage of them who started ventures that failed. While the importance of learning from failure is often repeated in the literature, this paper highlights distinct differences between how collegiate entrepreneurs define failure, compared with more traditionally researched non-collegiate entrepreneurs, and also outlines how the various contexts by which students become involved in an entrepreneurial endeavor influences their perception of how failure is defined. 
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    Free, publicly-accessible full text available June 25, 2024
  9. Colloidal suspensions are an ideal model for studying crystallization, nucleation, and glass transition mechanisms, due to the precise control of interparticle interactions by changing the shape, charge, or volume fraction of particles. However, these tuning parameters offer insufficient active control over interparticle interactions and reconfigurability of assembled structures. Dynamic control over the interparticle interactions can be obtained through the application of external magnetic fields that are contactless and chemically inert. In this work, we demonstrate the dual nature of magnetic nanoparticle dispersions to program interactions between suspended nonmagnetic microspheres using an external magnetic field. The nanoparticle dispersion simultaneously behaves as a continuous magnetic medium at the microscale and a discrete medium composed of individual particles at the nanoscale. This enables control over a depletion attractive potential and the introduction of a magnetic repulsive potential, allowing a reversible transition of colloidal structures within a rich phase diagram by applying an external magnetic field. Active control over competing interactions allows us to create a model system encompassing a range of states, from large fractal clusters to low-density Wigner glass states. Monitoring the dynamics of colloidal particles reveals dynamic heterogeneity and a marked slowdown associated with approaching the Wigner glass state. 
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    Free, publicly-accessible full text available June 21, 2024
  10. Sensor fusion approaches combine data from a suite of sensors into an integrated solution that represents the target environment more accurately than that produced by an individual sensor. Deep learning (DL) based approaches can address challenges with sensor fusion more accurately than classical approaches. However, the accuracy of the selected approach can change when sensors are modified, upgraded or swapped out within the system of sensors. Historically, this can require an expensive manual refactor of the sensor fusion solution.This paper develops 12 DL-based sensor fusion approaches and proposes a systematic and iterative methodology for selecting an optimal DL approach and hyperparameter settings simultaneously. The Gradient Descent Multi-Algorithm Grid Search (GD-MAGS) methodology is an iterative grid search technique enhanced by gradient descent predictions and expanded to exchange performance measure information across concurrently running DL-based approaches. Additionally, at each iteration, the worst two performing DL approaches are pruned to reduce the resource usage as computational expense increases from hyperparameter tuning. We evaluate this methodology using an open source, time-series aircraft data set trained on the aircraft’s altitude using multi-modal sensors that measure variables such as velocities, accelerations, pressures, temperatures, and aircraft orientation and position. We demonstrate the selection of an optimal DL model and an increase of 88% in model accuracy compared to the other 11 DL approaches analyzed. Verification of the model selected shows that it outperforms pruned models on data from other aircraft with the same system of sensors. 
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    Free, publicly-accessible full text available April 17, 2024