The Impact of the Covid-19 Pandemic on Interior Design Students' Performance in Jordanian Universities

This research study compared the perceived performance of interior design students participating in Classroom Learning Versus Online Learning due to the COVID-19 pandemic lockdown in Jordan. The survey results suggest that first-year students had higher satisfaction ratings than third-year students. Second-year students had a medium level of expectations and perceptions regarding both online and classroom learning. The reasons for this difference in perceived performance could be attributed to a variety of factors, such as the novelty of online learning, the more complex coursework of third-year students, and the difficulty of conveying concepts in an online learning environment. In order to ensure that all students are receiving an equitable education, regardless of their year level, it is important to understand the root causes of the difference in satisfaction between first-and third-year students and to develop strategies to address any issues that arise. The findings of this study provide insight into the factors affecting student satisfaction with online learning and can inform the development of strategies to support students in their learning during the pandemic.

RAPTOR: Ravenous Throughput Computing

We describe the design, implementation and performance of the RADICAL-Pilot task overlay (RAPTOR). RAPTOR enables the execution of heterogeneous tasks-i.e., functions and executables with arbitrary duration-on HPC platforms, pro-viding high throughput and high resource utilization. RAPTOR supports the high throughput virtual screening requirements of DOE's National Virtual Biotechnology Laboratory effort to find therapeutic solutions for COVID-19. RAPTOR has been used on 8300 compute nodes to sustain 144M/hour docking hits, and to screen 1011 ligands. To the best of our knowledge, both the throughput rate and aggregated number of executed tasks are a factor of two greater than previously reported in literature. RAPTOR represents important progress towards improvement of computational drug discovery, in terms of size of libraries screened, and for the possibility of generating training data fast enough to serve the last generation of docking surrogate models. © 2022 IEEE.