This study emphasises the growing relevance of hydrogen as a green energy source in meeting the growing need for sustainable energy solutions. It foregrounds the importance of assessing the environmental consequences of hydrogen-generating processes for their long-term viability. The article compares several hydrogen production processes in terms of scalability, cost-effectiveness, and technical improvements. It also investigates the environmental effects of each approach, considering crucial elements such as greenhouse gas emissions, water use, land needs, and waste creation. Different industrial techniques have distinct environmental consequences. While steam methane reforming is cost-effective and has a high production capacity, it is coupled with large carbon emissions. Electrolysis, a technology that uses renewable resources, is appealing but requires a lot of energy. Thermochemical and biomass gasification processes show promise for long-term hydrogen generation, but further technological advancement is required. The research investigates techniques for improving the environmental friendliness of hydrogen generation through the use of renewable energy sources. Its ultimate purpose is to offer readers a thorough awareness of the environmental effects of various hydrogen generation strategies, allowing them to make educated judgements about ecologically friendly ways. It can ease the transition to a cleaner hydrogen-powered economy by considering both technological feasibility and environmental issues, enabling a more ecologically conscious and climate-friendly energy landscape.

The safety of flight operations depends on the cognitive abilities of pilots. In recent years, there has been growing concern about potential accidents caused by the declining mental states of pilots. We have developed a novel multimodal approach for mental state detection in pilots using electroencephalography (EEG) signals. Our approach includes an advanced automated preprocessing pipeline to remove artefacts from the EEG data, a feature extraction method based on Riemannian geometry analysis of the cleaned EEG data, and a hybrid ensemble learning technique that combines the results of several machine learning classifiers. The proposed approach provides improved accuracy compared to existing methods, achieving an accuracy of 86% when tested on cleaned EEG data. The EEG dataset was collected from 18 pilots who participated in flight experiments and publicly released at NASA’s open portal. This study presents a reliable and efficient solution for detecting mental states in pilots and highlights the potential of EEG signals and ensemble learning algorithms in developing cognitive cockpit systems. The use of an automated preprocessing pipeline, feature extraction method based on Riemannian geometry analysis, and hybrid ensemble learning technique set this work apart from previous efforts in the field and demonstrates the innovative nature of the proposed approach.

This work focuses on the review of particle-based multiscale and hybrid methods that have surfaced in the field of fluid mechanics over the last 20 years. We consider five established particle methods: molecular dynamics, direct simulation Monte Carlo, lattice Boltzmann method, dissipative particle dynamics and smoothed-particle hydrodynamics. A general description is given on each particle method in conjunction with multiscale and hybrid applications. An analysis on the length scale separation revealed that current multiscale methods only bridge across scales which are of the order of \({\mathcal {O}}(10^2) {-} {\mathcal {O}}(10^3)\) and that further work on complex geometries and parallel code optimisation is needed to increase the separation. Similarities between methods are highlighted and combinations discussed. Advantages, disadvantages and applications of each particle method have been tabulated as a reference.

This study leverages approximately 7500 reviews from Skytrax to explore the determinants of airport service quality and their influence on passenger recommendations. The dataset includes various features such as terminal cleanliness, terminal seating, terminal signs, food and beverages, airport shopping, WiFi connectivity, and airport staff. The research employs a comprehensive methodology encompassing statistical data analysis, predictive modelling, and interaction effects analysis. The descriptive analysis of time-series data highlighted trends and fluctuations in service quality and recommendations, providing insights into temporal dynamics. Multiple machine learning models, including logistic regression, Random Forest, SVM, KNN, Gradient Boosting, and Neural Networks, were developed in this study and cross-validated for airport recommendation based on Skytrax’s online reviews. Among others, Gradient Boosting emerged as the most accurate model with an 88.15% mean accuracy. Interaction effects revealed significant combined influences, such as terminal cleanliness and terminal seating, on passenger recommendations. This multifaceted approach offers robust insights into factors influencing airport recommendations and guides improvements in airport management to enhance passenger satisfaction. Future work will focus on a general-purpose machine learning framework and its toolbox development for airport service quality analysis based on online reviews from various sources.

This paper introduces a multi-fidelity computational framework for the analysis of aerodynamic performance of flight formation. The Vortex Lattice and Reynolds Averaged Navier–Stokes methods form the basis of the framework, as low- and high-fidelity, respectively. Initially, the computational framework is validated for an isolated wing, and then two rectangular NACA23012 wings are considered for assessing the aerodynamic performance of this formation; the optimal relative position is through the multi-fidelity framework based on the total drag reduction. The performance estimates are in good agreement with experimental measurements of the same configuration. Total aerodynamic performance of formation flight is also assessed with respect to attitude variations of the lifting bodies involved. The framework is also employed to determine the optimal position of blended-wing-body unmanned aerial vehicles in tandem formation flight.