CFD Analysis of Aerodynamic Drag Effects on Vacuum Tube Trains

Aerodynamic aspects of train shapes suitable for Vacuum Tube Train System are investigated in this paper. Three feasible geometries for the vacuum tube train system have been considered and modelled in three dimensions and have been computationally studied using the commercial software Ansys Fluent. Aerodynamic drag loads on these geometries have been calculated under different tube pressures and speeds of the train, which provide insight on various operating parameters that need to be considered while designing the vacuum tube train system. The present computational research shows that, the suitable vacuum pressure, and different shapes of head and tail of the train have significantly effects the drag force of the vacuum train in the tunnel. Overall, the elliptical train shape with a height to base ratio of 2:1 is more efficient for aerodynamic drag reduction of the vacuum tube train at the vacuum tube pressure of 1013.25 Pa

The Role of Auditory and Visual Stimuli in Stress Perception and Sensory Preference within Virtual Environments

The purpose of this study is to explore the individual and combined effects of auditory and visual stimuli on stress perception within Virtual Reality (VR). The exploration utilized physiological measures as Blood Volume Pulse (BVP), psychological assessments like the State-Trait Anxiety Inventory-Short Form (STAI-S), and NASA Task Load Index (NASA-TLX). Participants were immersed in two contrasting VR environments. The first environment was a tranquil forest, whereas the second scene was a chaotic city. Participants’ stress levels under different sensory conditions were assessed methodically by switching between congruent and incongruent audio-visual experiences. The findings of this study contribute to our understanding of sensory impacts on stress perception in VR environments and to the development of individualized VR experiences specific to individuals’ sensory preferences. While the findings suggest some individual variability in stress responses, particularly in audio versus visual stimulus dominance, these observations were not statistically significant, indicating a need for further exploration into personalized sensory experiences in VR

Merging Brain-Computer Interface P300 speller datasets: Perspectives and pitfalls

BackgroundIn the last decades, the P300 Speller paradigm was replicated in many experiments, and collected data were released to the public domain to allow research groups, particularly those in the field of machine learning, to test and improve their algorithms for higher performances of brain-computer interface (BCI) systems. Training data is needed to learn the identification of brain activity. The more training data are available, the better the algorithms will perform. The availability of larger datasets is highly desirable, eventually obtained by merging datasets from different repositories. The main obstacle to such merging is that all public datasets are released in various file formats because no standard way is established to share these data. Additionally, all datasets necessitate reading documents or scientific papers to retrieve relevant information, which prevents automating the processing. In this study, we thus adopted a unique file format to demonstrate the importance of having a standard and to propose which information should be stored and why.MethodsWe described our process to convert a dozen of P300 Speller datasets and reported the main encountered problems while converting them into the same file format. All the datasets are characterized by the same 6 × 6 matrix of alphanumeric symbols (characters and numbers or symbols) and by the same subset of acquired signals (8 EEG sensors at the same recording sites).Results and discussionNearly a million stimuli were converted, relative to about 7000 spelled characters and belonging to 127 subjects. The converted stimuli represent the most extensively available platform for training and testing new algorithms on the specific paradigm – the P300 Speller. The platform could potentially allow exploring transfer learning procedures to reduce or eliminate the time needed for training a classifier to improve the performance and accuracy of such BCI systems.</jats:sec

A unified ensemble soil moisture dataset across the continental United States

Abstract A unified ensemble soil moisture (SM) package has been developed over the Continental United States (CONUS). The data package includes 19 products from land surface models, remote sensing, reanalysis, and machine learning models. All datasets are unified to a 0.25-degree and monthly spatiotemporal resolution, providing a comprehensive view of surface SM dynamics. The statistical analysis of the datasets leverages the Koppen-Geiger Climate Classification to explore surface SM’s spatiotemporal variabilities. The extracted SM characteristics highlight distinct patterns, with the western CONUS showing larger coefficient of variation values and the eastern CONUS exhibiting higher SM values. Remote sensing datasets tend to be drier, while reanalysis products present wetter conditions. In-situ SM observations serve as the basis for wavelet power spectrum analyses to explain discrepancies in temporal scales across datasets facilitating daily SM records. This study provides a comprehensive soil moisture data package and an analysis framework that can be used for Earth system model evaluations and uncertainty quantification, quantifying drought impacts and land–atmosphere interactions and making recommendations for drought response planning