Karina R. Liles

Abstract 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 p K a values, and an improved web‐based visualization tool for viewing electrostatics.

Music therapy is often used for children with autism to improve social interaction, communication, and imitation skills through music. Increasingly, music therapists have incorporated technology into therapy sessions as therapeutic tools. Robotics has shown potential to encourage interaction with children with autism. This research utilizes a case study approach (N=4), with children diagnosed with autism in music therapy sessions. A NAO robot was used in therapy for a minimum of 6 weeks, designed to instruct children to imitate dance movements though a therapist-selected song. Behavioral data analysis showed that the children increased imitation over the course of the study. Therapists benefited from using the robot tool to decrease their multitasking, and allow them to observe child.

In this paper, we describe the social perceptions and emotions of 5th grade students (N=22) studying multiplication with a social robot by measuring students' multiplication knowledge, their perceptions of the technologies, emotions after interaction, willingness to continue, and preference for technology use. The goal of this study was to investigate specific social traits that lead to increasing students' motivation to learn math with a robot and promoting robot acceptance. Our results show that students' positive perceptions and attitudes toward a robot tutor leads to student motivation, as well as intentional acceptance of the technology.

We present a socially interactive robotic teaching assistant to engage 5 th 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.