Ministry of Higher Education and Scientific Research (UAE)

Ensuring secure and energy-efficient connectivity in wireless networks aided by a low-altitude autonomous aerial vehicle (AAV)-mounted active reconfigurable intelligent surface (ARIS) is a major challenge, owing to rapid channel variations, mobility-induced uncertainty, and the heightened risk of adversarial eavesdropping inherent to open-air communication. To address these challenges, we propose a digital twin (DT)-empowered Transformer-based policy learning framework that combines accurate real-time virtual modeling with attention-driven sequential decision-making for AAV–ARIS-assisted communications. The DT enables efficient synchronization with physical channels in dynamic environments, significantly improving data-efficient training. Meanwhile, a Transformer-based actor–critic architecture captures long-range temporal dependencies to jointly optimize ARIS configuration and AAV mobility control across diverse scenarios. A multi-objective reward function is formulated to maximize both secrecy rate and energy efficiency while ensuring robustness against channel uncertainty and adversarial threats. The formulated problem is solved using reinforcement learning with Transformer-enhanced policy optimization. Simulation results demonstrate consistent performance gains over strong baselines, including DT-deep deterministic policy gradient (DDPG), DT-twin delayed DDPG (TD3), and RIS-soft actor-critic (SAC). The proposed framework achieves up to a 12.5% improvement in secrecy rate, a 14.8% enhancement in energy efficiency, and a 33% faster convergence rate. Furthermore, ablation studies and complexity analyses confirm the contribution of each component and demonstrate the scalability of the framework with acceptable latency. These findings highlight the potential of integrating DT technology with Transformer-based reinforcement learning to enable secure, adaptive, and resource-efficient ARIS-assisted communications in next-generation wireless networks.

This paper explores the evolving role of engagement within Irish Higher Education Institutions (HEIs) since the introduction of the National Strategy for Higher Education to 2030. Using Ernest Boyer’s framework of scholarship of discovery, integration, application, and teaching as a lens, the research investigates executive perspectives on engagement implementation, evaluation, and strategic relevance. A national survey of executive management across publicly funded HEIs reveals that while engagement is widely acknowledged and increasingly integrated into institutional strategy, challenges persist in its definition, measurement, and resourcing. The findings highlight a preference for application-based engagement, limited recognition of integration, and varied interpretations of Boyer’s relevance. The study underscores the need for clearer strategic planning, institutional coordination, and inclusive models that reflect both academic and non-academic contributions to engagement. Findings from this study will have significance for HEIs, both nationally and internationally

Human Activity Recognition (HAR) is a promising, rapidly advancing research field that has significant contribution to various real-life applications such as healthcare monitoring, fall detection, sports, and smart home systems. Yet smartphone-based sensors generate high-dimensional data, necessitating effective feature selection methods to maintain recognition performance without the computational burden of redundant information. This paper proposes a novel three-stage filter-wrapper feature selection approach to optimize the recognition of human physical activities. In the first stage, the Pearson Correlation Coefficient (PCC) is calculated to identify and eliminate redundant features, retaining only the most informative ones based on a specified threshold. In the second stage, the remaining features are re-evaluated using the Symmetrical Uncertainty (SU) measure to identify those most relevant to the target classes. The third stage involves refining the SU-selected features through a Genetic Algorithm (GA) wrapper to obtain the final optimal subset. To assess the effectiveness of the proposed method, six classification techniques: Decision Trees (DT), Naive Bayes with Kernel Density Estimation (NB-KDE), Random Forest (RF), Gradient Boosting Trees (GBT), Generalized Linear Models (GLM), and Support Vector Machines (SVM) were employed to discriminate human activities at the first two stages. For the third stage, a SVM-based GA-wrapper was employed to find the optimal feature subset. Extensive experiments were conducted on three benchmark HAR datasets, namely, UCI-HAR, UCI-HAPT, and UCI-AAL, comprising 561 features, and the results demonstrate that the proposed approach significantly reduces the original feature space by 66.31

In this work, we present a detailed study of argon plasma treated effects on the structural, morphological, optical and electrical properties of thermally evaporated CuPc thin film. The plasma–induced changes were observed to remarkably improve the molecular orientation and crystallinity, consistent with the increasing intensity and sharpening of the (312) reflection in XRD observations. Lowering surface roughness and grain-height fluctuations were observed by AFM, suggesting that efficient surface smoothing was achieved based on the energetic Ar⁺ ion bombardment with a partial molecular rearrangement. Concomitantly, UV–Vis analysis showed evident modifi cation of the absorption spectrum with a small increase in the band gap, which implied change π − π electronic transitions and reduced density of localized states in organic semiconductor. Electrical measurements confirmed that the plasma-treated ZnO layers underwent a p–n transition, which was ascribed to the alteration of the energy levels and improvement of electron conduction away routes. Most significantly, such a plasma-treated CuPc based photodetector showed marked enhancement of the photoresponse. The responsivity increased from 54.92 to 313.48 µA/mW and the EQE from 10.89