The Adaptive Poisson-Boltzmann Solver (APBS) software was developed to solve the equations of continuum electrostatics for large biomolecular assemblages that have provided impact in the study of a broad range of chemical, biological, and biomedical applications. APBS addresses the three key technology challenges for understanding solvation and electrostatics in biomedical applications: accurate and efficient models for biomolecular solvation and electrostatics, robust and scalable software for applying those theories to biomolecular systems, and mechanisms for sharing and analyzing biomolecular electrostatics data in the scientific community. To address new research applications and advancing computational capabilities, we have continually updated APBS and its suite of accompanying software since its release in 2001. In this article, we discuss the models and capabilities that have recently been implemented within the APBS software package including a Poisson-Boltzmann analytical and a semi-analytical solver, an optimized boundary element solver, a geometry-based geometric flow solvation model, a graph theory-based algorithm for determining pK values, and an improved web-based visualization tool for viewing electrostatics.
We present a socially interactive robotic teaching assistant to engage 5th grade rural minority students in practicing multiplication. In this research we use a NAO humanoid robot as a robotic teaching assistant, Ms. An (Meeting Student’s Academic Needs). We have programmed this robot to ask a student multiplication questions based on the Common Core State Standards. We measured, via questionnaires, the students’ perceptions of the robot’s sociability and explored the students’ preference for using the robot as a study tool. We discovered that students perceived the robot as a sociable agent, for 8 of the 10 social ability questionnaire items. Furthermore, perceptions of social ability significantly increased between pre- and post-interaction. Students also indicated, via questionnaire, that they preferred their interaction with the robot assistant over other kinds of study support: peers, computer programs, teachers, other adults. Results from this study provide insight toward the design of a social robot teaching aid.
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