Ongoing Progress on Pervaporation Membranes for Ethanol Separation.

Ethanol, a versatile chemical extensively employed in several fields, including fuel production, food and beverage, pharmaceutical and healthcare industries, and chemical manufacturing, continues to witness expanding applications. Consequently, there is an ongoing need for cost-effective and environmentally friendly purification technologies for this organic compound in both diluted (ethanol-water-) and concentrated solutions (water-ethanol-). Pervaporation (PV), as a membrane technology, has emerged as a promising solution offering significant reductions in energy and resource consumption during the production of high-purity components.

Deep Learning-Based NMPC for Local Motion Planning of Last-Mile Delivery Robot.

Feasible local motion planning for autonomous mobile robots in dynamic environments requires predicting how the scene evolves. Conventional navigation stakes rely on a local map to represent how a dynamic scene changes over time. However, these navigation stakes depend highly on the accuracy of the environmental map and the number of obstacles.

Moving towards More Diverse and Welcoming Conference Spaces: Data-Driven Perspectives from Biology Education Research Scholars.

Academic conferences are integral to the dissemination of novel research findings and discussion of pioneering ideas across all postsecondary disciplines. For some participants, these environments are spaces to develop new collaborations, research projects, and social bonds; however, for others, conferences can be a place of marginalization and outright hostility. To assess how diverse individuals experience conference spaces, we interpreted results from a conference climate survey filled out by 198 of 482 registrants of the Society for the Advancement of Biology Education Research (SABER) West 2021 conference.

Teaching to Empower: Leveraging the Neuroscience of Now to Help Students Become Self-Regulated Learners.

In his book Descartes' Error, neurologist Antonio Damasio argues that humans do not make decisions by relying exclusively on the rational or reason-oriented parts of their brain (2008). Evidence from patients with brain damage reveal that our abilities to reason and make decisions are greatly influenced by our emotions (Damasio et al., 1990; Saver and Damasio, 1991).

Transfer Learning Based Semantic Segmentation for 3D Object Detection from Point Cloud.

Three-dimensional object detection utilizing LiDAR point cloud data is an indispensable part of autonomous driving perception systems. Point cloud-based 3D object detection has been a better replacement for higher accuracy than cameras during nighttime. However, most LiDAR-based 3D object methods work in a supervised manner, which means their state-of-the-art performance relies heavily on a large-scale and well-labeled dataset, while these annotated datasets could be expensive to obtain and only accessible in the limited scenario.