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This study focuses on identifying pilots' mental states linked to attention-related human performance-limiting states (AHPLS) using a publicly released, imbalanced physiological dataset. The research integrates electroencephalography (EEG) with non-brain signals, such as electrocardiogram (ECG), galvanic skin response (GSR), and respiration, to create a deep learning architecture that combines one-dimensional Convolutional Neural Network (1D-CNN) and Long Short-Term Memory (LSTM) models. Addressing the data imbalance challenge, the study employs resampling techniques, specifically downsampling with cosine similarity and oversampling using Synthetic Minority Over-sampling Technique (SMOTE), to produce balanced datasets for enhanced model performance. An extensive evaluation of various machine learning and deep learning models, including XGBoost, AdaBoost, Random Forest (RF), Feed-Forward Neural Network (FFNN), standalone 1D-CNN, and standalone LSTM, is conducted to determine their efficacy in detecting pilots' mental states. The results contribute to the development of efficient mental state detection systems, highlighting the XGBoost algorithm and the proposed 1D-CNN+LSTM model as the most promising solutions for improving safety and performance in aviation and other industries where monitoring mental states is essential.

Airport service quality is considered to be an indicator of passenger satisfaction. However, assessing this by conventional methods requires continuous observation and monitoring. Therefore, during the past few years, the use of machine learning techniques for this purpose has attracted considerable attention for analysing the sentiment of the air traveller. A sentiment analysis system for textual data analytics leverages the natural language processing and machine learning techniques in order to determine whether a piece of writing is positive, negative or neutral. Numerous methods exist for estimating sentiments which include lexical-based methodologies and directed artificial intelligence strategies. Despite the wide use and ubiquity of certain strategies, it remains unclear which is the best strategy for recognising the intensity of the sentiments of a message. It is necessary to compare these techniques in order to understand their advantages, disadvantages and limitations. In this paper, we compared the Valence Aware Dictionary and sentiment Reasoner, a sentiment analysis technique specifically attuned and well known for performing good on social media data, with the conventional machine learning techniques of handling the textual data by converting it into numerical form. We used the review data obtained from the SKYTRAX website for each airport. The machine learning algorithms evaluated in this paper are VADER sentiment and logistic regression. The term frequency-inverse document frequency is used in order to convert the textual review data into the resulting numerical columns. This was formulated as a classification problem, whereby the prediction of the algorithm was compared with the actual recommendation of the passenger in the dataset. The results were analysed according to the accuracy, precision, recall and F1-score. From the analysis of the results, we observed that logistic regression outperformed the VADER sentiment analysis. Homaid M, Bisandu D, Moulitsas I, Jenkins K. (2022) Analysing the sentiment of air-traveller: a comparative analysis, International Journal of Computer Theory and Engineering, Volume 14, Issue 2, May 2022, pp. 48-53

We present in this work shock-/interface-capturing numerical methods in the finitevolume central-weighted essentially non-oscillatory (CWENO) reconstruction scheme on unstructured grids for the simulation of multi-component or multiphase compressible flows. Using the five-equation interface capturing models of Allaire et al. and Kapila et al. in the open-source unstructured compressible flow solver UCNS3D, we will demonstrate the capabilities and robustness of the CWENO in capturing and resolving the material interface in multicomponent/multiphase flows in the presence of strong gradients and material discontinuities, with oscillation free solutions and reduced numerical diffusion. To test our numerical methods, a simple one-dimensional test case and a more sophisticated 2d underwater test case with cavitation are considered. The numerical results of our study are compared with results from existing high-order methods. The results show that the CWENO is less dissipative without the spurious oscillations that typically develop at material boundaries and also gives a high-resolution description of the moving material interface with less artificial smearing than other high other schemes.

In this paper, the Implicit Large-Eddy Simulation (ILES) is investigated on the flow around a square cylinder incorporating an unstructured Weighted Essential Non-Oscillatory (WENO) reconstruction method for a Reynolds number of 22,000. Simulations are undertaken in the framework of open-source package OpenFOAM and additional implicit 2nd/3rd-order WENO scheme on the convective term of the viscous incompressible Navier-Stokes Equations. A 2nd-order Euler implicit time integration and Pressure-Implicit Splitting-Operator (PISO) algorithm is used to for the pressure-velocity coupling. Conventional LES with Wall Adapting Local Eddy Viscosity (WALE) model is also carried out as a baseline. The results are compared to high fidelity experiment, DNS data and conventional LES with dynamic Smagorinsky model from previous work. Results show favorable performance for ILES with 3rd-order WENO scheme compared with the conventional LES with dynamic Smagorinsky model and similar performance against LES with WALE model. Results also show acceptable predictions over time-averaged statistics with less computational effort for the ILES of 2nd-order WENO scheme. Shear layer flow analysis suggests that both ILES and LES face similar challenges with small quantities, such as shear stress. Finally, simulations are capturing Von Krmn vortex, Kelvin-Helmholtz instability and induced frequency changes.