Mass Transfer and Radiative Heat Transfer Flow of MHD Casson Fluid with Temperature Gradient Dependent Heat Sink and Internal Mass Diffusion in a Vertical Channel with Stretching Porous Walls

In the present paper an investigation of the flow, heat and mass transfer characteristics of a MHD Casson fluid in a parallel plate channel with stretching walls with respect to a uniform magnetic field and porous media is carried out. Similarity transformation technique is used to transform the system of partial differential equations into ordinary differential equations. The governing non-linear differential equations are solved numerically using Runge-Kutta method of fourth order via shooting technique. Graphs for the velocity, temperature and concentration are plotted to analyze the behavior of the flow with different physical parameters. The skin friction, Nusselt number and Sherwood numbers are computed and tabulated Keywords: Casson Fluid, Porous Media, MHD, Heat and Mass Transfer, Stretching Walls

Probabilistic boundary element method

The purpose of the Probabilistic Structural Analysis Method (PSAM) project is to develop structural analysis capabilities for the design analysis of advanced space propulsion system hardware. The boundary element method (BEM) is used as the basis of the Probabilistic Advanced Analysis Methods (PADAM) which is discussed. The probabilistic BEM code (PBEM) is used to obtain the structural response and sensitivity results to a set of random variables. As such, PBEM performs analogous to other structural analysis codes such as finite elements in the PSAM system. For linear problems, unlike the finite element method (FEM), the BEM governing equations are written at the boundary of the body only, thus, the method eliminates the need to model the volume of the body. However, for general body force problems, a direct condensation of the governing equations to the boundary of the body is not possible and therefore volume modeling is generally required

Evaluating the Performance of Using Speaker Diarization for Speech Separation of In-Person Role-Play Dialogues

Development of professional communication skills, such as motivational interviewing, often requires experiential learning through expert instructor-guided role-plays between the trainee and a standard patient/actor. Due to the growing demand for such skills in practices, e.g., for health care providers in the management of mental health challenges, chronic conditions, substance misuse disorders, etc., there is an urgent need to improve the efficacy and scalability of such role-play based experiential learning, which are often bottlenecked by the time-consuming performance assessment process. WSU is developing ReadMI (Real-time Assessment of Dialogue in Motivational Interviewing) to address this challenge, a mobile AI solution aiming to provide automated performance assessment based on ASR and NLP. The main goal of this thesis research is to investigate current commercially available speaker diarization capabilities and evaluate their performance in separating the speeches between the trainee and the standard patient/actor in an in-person role-play training environment where the crosstalk could interfere with the operation and performance of ReadMI. Specifically, this thesis research has: 1.) identified the major commercially-available speaker diarization systems, such as those from Google, Amazon, IBM, and Rev.ai; 2.) designed and implemented corresponding evaluation systems that integrate these commercially available cloud services for operating in the in-person role-play training environments; and, 3.) completed an experimental study that evaluated and compared the performance of the speaker diarization services from Google and Amazon. The main finding of this thesis is that the current speaker diarization capabilities alone are not able to provide sufficient performance for our particular use case when integrating them into ReadMI for operating in in-person role-play training environments. But this thesis research potentially provides a clear baseline reference to future developers for integrating future speaker diarization capabilities into similar applications