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1. Self-Regulation of Cognition and Self-Regulation of Motivation in Problem Solving

   https://doi.org/10.18260/1-2--43079  

   Lawanto, Oenardi; Minichiello, Angela; Ul_abideen, Zain; Naqash, Talha; Iqbal, Assad (June 2023, American Society for Engineering Education)

   This work-in-progress paper shares findings of the early stage of a 3-year research funded by the National Science Foundation. The major aim of the project is to advance engineering and mathematics (EM) education theory and practice related to students’ self-regulation of cognition and motivation skills during problem-solving activities. The self-regulation includes students’ metacognitive knowledge about task (MKT) and self-regulation of cognition (SRC). The motivational component of self-regulation (SRM) includes self-control of the motivation needed to maintain the level of engagement and deliberate practice necessary for scientific thinking and reasoning. To be effective problem-solvers, students must understand the relationship between the MKT, SRC and SRM throughout the problem-solving activities. Four research questions will guide the research: (1) How do students perceive their self-regulation of cognition (SRC) and motivation (SRM) skills for generic problem-solving activities in EM courses; (2) How does students’ metacognitive knowledge about problem-solving tasks (MKT) inform their Task interpretation?; (3) How do students’ SRC and SRM dynamically evolve?; and (4) How do students’ SRC and SRM reflect their perceptions of self-regulation of cognition and motivation for generic EM problemsolving activities? A sequential mixed-methods research design involving quantitative and qualitative methods are used to develop complementary coarse- and fine-grained understandings of undergraduate students’ SRC and SRM during academic problem-solving activities. Two 2nd year EM courses: Engineering Statics, and Ordinary Differential Equations were purposefully selected for the contexts of the study. One hundred forty two students from both courses were invited and participated in quantitative data collection using two validated surveys during spring 2022 semester. Later in the semester, qualitative data will be generated with twenty students in both courses through one-on-one interviews with students and course instructors, think-aloud protocols with students, and classroom observations. Coarse-grained understandings of students’ SRC and SRM are currently developed through analysis of quantitative data collected using self-report surveys (i.e., BRoMS and PMI). Fine-grained understandings of students’ SRC and SRM will be developed through analysis of qualitative data gathered via one-on-one interviews, think-aloud protocols, classroom observations, and course artifacts gathered as students engage in EM problem-solving activities. 

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2. Self-oscillating, self-stabilizing, and self-referenced electro-optic comb

   https://doi.org/10.1364/OE.535014  

   Trask, Lawrence_Robert; Pericherla, Srinivas_Varma; Delfyett, Peter_J (January 2025, Optics Express)

   We demonstrate a widely spaced, stabilized, and self-referenced opto-electronic oscillator driven electro-optic modulator based optical frequency comb. Using an ultra-stable Fabry-Perot etalon as a stable reference, we simultaneously stabilize a CW laser and generate a low noise and stable RF oscillation used to drive an electro-optic comb. In such a manner, the Fabry-Perot etalon pins both the carrier-envelope-offset frequency (f_ceo) and the repetition rate of the comb in place (f_rep), eliminating the need for an external RF oscillator. Usage of the ultra-stable Fabry-Perot etalon as both an optical and RF reference allows the removal of an external RF oscillator. Additionally, we determined the key parameters in producing high contrast ultrashort pulses necessary for coherent octave spanning supercontinuum generation using long and weak pulses associated with electro-optic modulator based combs. By using a monolithically fiber based pulse compression scheme, we produced ultrashort pulses to facilitate measuring the carrier-envelope-offset frequency, allowing for the first self-starting, self-stabilized, and self-referenced opto-electronic oscillator driven electro-optic modulator based optical frequency comb. 

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3. Geometric self-centering and force self-balancing of railroad-vehicle hunting oscillations

   https://doi.org/10.1007/s00707-021-02983-w  

   Shabana, Ahmed A. (August 2021, Acta Mechanica)

   null (Ed.)
4. Molecular Geometry‐Directed Self‐Recognition in the Self‐Assembly of Giant Amphiphiles

   https://doi.org/10.1002/marc.202200216  

   Zhou, Yifan; Luo, Jiancheng; Liu, Tong; Wen, Tao; Williams‐Pavlantos, Kayla; Wesdemiotis, Chrys; Cheng, Stephen Z. D.; Liu, Tianbo (May 2022, Macromolecular Rapid Communications)

   Abstract Three sets of polyoxometalate (POM)‐based amphiphilic hybrid macromolecules with different rigidity in their organic tails are used as models to understand the effect of molecular rigidity on their possible self‐recognition feature during self‐assembly processes. Self‐recognition is achieved in the mixed solution of two structurally similar, sphere‐rigid T‐shape‐linked oligofluorene(TOF₄) rod amphiphiles, with the hydrophilic clusters being Anderson (Anderson‐TOF₄) and Dawson (Dawson‐TOF₄), respectively. Anderson‐TOF₄is observed to self‐assemble into onion‐like multilayer structures and Dawson‐TOF₄forms multilayer vesicles. The self‐assembly is controlled by the interdigitation of hydrophobic rods and the counterion‐mediated attraction among charged hydrophilic inorganic clusters. When the hydrophobic blocks are less rigid, e.g., partially rigid polystyrene and fully flexible alkyl chains, self‐recognition is not observed, attributing to the flexible conformation of hydrophobic molecules in the solvophobic domain. This study reveals that the self‐recognition among amphiphiles can be achieved by the geometrical limitation of the supramolecular structure due to the rigidity of solvophobic domains. 

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5. Self-Supervised Camera Self-Calibration from Video

   https://doi.org/10.1109/ICRA46639.2022.9811784  

   Fang, Jiading; Vasiljevic, Igor; Guizilini, Vitor; Ambrus, Rares; Shakhnarovich, Greg; Gaidon, Adrien; Walter, Matthew R (May 2022, IEEE)

   Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams. In practice, calibration is a laborious procedure requiring specialized data collection and careful tuning. This process must be repeated whenever the parameters of the camera change, which can be a frequent occurrence for mobile robots and autonomous vehicles. In contrast, self-supervised depth and ego-motion estimation approaches can bypass explicit calibration by in-ferring per-frame projection models that optimize a view-synthesis objective. In this paper, we extend this approach to explicitly calibrate a wide range of cameras from raw videos in the wild. We propose a learning algorithm to regress per-sequence calibration parameters using an efficient family of general camera models. Our procedure achieves self-calibration results with sub-pixel reprojection error, outperforming other learning-based methods. We validate our approach on a wide variety of camera geometries, including perspective, fisheye, and catadioptric. Finally, we show that our approach leads to improvements in the downstream task of depth estimation, achieving state-of-the-art results on the EuRoC dataset with greater computational efficiency than contemporary methods. 

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