In order to address current water challenges, scientific research on water-related issues is crucial. However, traditional techniques for selecting research topics, such as literature reviews and expert opinions, can be time-consuming and may not provide a comprehensive overview of available information. We propose using Natural Language Processing (NLP) techniques in this study to extract, align, and compare water research topics from different corpora. We apply these techniques to the research paper abstracts from the New Mexico Water Resources Research Institute (NMWRRI) and the U.S. Geological Survey (USGS) to assess these institutions’ current research interests and identify potential new research directions. We use a Latent Dirichlet Allocation (LDA) model for topic extraction and a Word2Vec model for topic alignment. This study highlights the benefits of using NLP techniques to analyze trends and identify novel research directions in water studies.

Local Memory Store (LMStore) is a novel scratchpad memory (SPM) design, with recent research evaluation showing its capability for improving program performance. However, the performance of LMStore depends on its memory layout decided by its allocation scheme. In this paper, we evaluate the impact of SPM allocation on LMStore performance. Our experimental results, using benchmarks from the Malardalen WCET benchmark suite executing on LMStore architecture modeled in the PyCacheSim simulator, demonstrate that LMStore with a stack distance-based SPM allocation scheme significantly improves data movement by an average of 44.46% compared to a Cache-only architecture, and by an average of 23.89% compared to LMStore with a frequency-based SPM allocation scheme.

Distance learning has dramatically increased in recent years because of advanced technology. In addition, numerous universities had to offer courses in online mode in 2020 and 2021 because of the COVID-19 pandemic. However, there are more challenges in distance learning than in the traditional learning method (e.g., feedback and interaction). Recently, researchers started using simple EEG headsets to identify confused students during online courses based on machine learning approaches. However, they faced unpleasant accuracy using traditional machine learning algorithms or nondeep neural networks. In this paper, we present a data-driven approach based on a multi-view deep learning technique called CSDLEEG to identify confused students. We employ the students' demographic information and EEG signals to feed our novel neural networks. The results show that our proposed approach is superior to state-of-the-art methods for 98% accuracy and 98% F1-score.