https://www.um.edu.mt/library/oar/handle/123456789/519 The Faculty of Engineering is located at the University's main campus and offers tuition and supervision to about 477 students at both undergraduate and postgraduate levels while conducting research in all fields covered by its departments. 2026-06-10T11:11:58Z https://www.um.edu.mt/library/oar/handle/123456789/147132 Title: Wave-dependent predictability of floating offshore wind turbine responses : a BiLSTM study based on fully coupled CFD simulations Authors: Haider, Rizwan; Shi, Wei; Lin, Zaibin; Tran, Tien Anh; Wu, Ji; Li, Xin Abstract: Reliable short-term prediction of floating offshore wind turbine (FOWT) responses under complex wave conditions remains challenging due to nonlinear aero–hydro–mooring interactions and transient wave-induced effects. This study evaluates the predictability of coupled FOWT responses using a Bidirectional Long Short-Term Memory (BiLSTM) framework trained on high-fidelity datasets generated from a fully coupled aero–hydro–mooring computational fluid dynamics (CFD) model of the National Renewable Energy Laboratory (NREL) 5 MW OC4 semi-submersible system. Two excitation conditions are examined: regular waves representing periodic steady-state behavior and focused waves representing transient amplified responses. The model simultaneously predicts platform motions, mooring-line tensions, aerodynamic power, and total thrust. Hyperparameter optimization is performed to ensure stable convergence and robust model performance. Predictability is assessed across multiple prediction-ahead times (PATs). Results show that regular-wave responses maintain high accuracy at longer horizons (R² > 96% at 2.5 s and 5.0 s), whereas focused-wave cases exhibit decreasing accuracy with increasing PAT, achieving R² values above 95%, 90%, and 85% at 0.5 s, 1.0 s, and 1.5 s, respectively. These findings demonstrate that forecasting performance strongly depends on wave type, emphasizing the need to consider wave conditions when predicting coupled FOWT dynamic responses. 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/147124 Title: Physicochemical characterisation of difluprednate and 6α9α-difluoroprednisolone : determination of solubility, pKa and LogP Authors: Baluci, Giulia; Buhagiar, Paul Immanuel; Vella Szijj, Janis; Attard, Everaldo; Sammut Bartolo, Nicolette Abstract: Steroids are used in the treatment of diverse pathological conditions for their anti-inflammatory and immunosuppressive effects, with the mode of action being affected by their physicochemical characteristics. To date, difluprednate (DFBA) has been characterised using computational methods which rely on the selected computational model and disregard external factors. The aim of the study was to determine the solubility, pKa and LogP of DFBA and its metabolite, 6α9α-difluoroprednisolone (DFP) using experimental methods. Methods using UV–Visible spectroscopy and High-Performance Liquid Chromatography (HPLC) were developed to determine the solubility and the pKa, LogD and LogP of the selected steroids. The steroids are soluble in acetonitrile and methanol, and are insoluble in water. DFBA achieved a solubility of 5.76 mg/mL and 0.81 mg/mL in acetonitrile and methanol, while DFP achieved solubility of 2.29 mg/mL and 0.16 mg/mL, respectively. DFBA had an average LogP value of 3.2, and DFP had an average LogP of 1.5. The pKa values for DFBA were 1.5 and 7.2 and for DFP, were 5.9 and 10.8. The characterisation of the physicochemical properties of DFBA and DFP can help support efficient formulation development. 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/147102 Title: The PQ8 architecture : deploying picosatellite constellations from a single launch Abstract: Over the past decade, demand for nano- and pico-class satellites has surged, driving up costs and competition for launch opportunities. CubeSat launches, once easily accessible, have become prohibitively expensive for small institutions, especially when considering constellation deployment. Although the PocketQube standard offers a lower cost alternative, its adoption has been limited and its cost benefits modest, primarily due to launch integration limitations, debris mitigation and trackability concerns. To address these challenges, this work proposes the PQ8 Architecture: a novel deployment model for pico-scale satellite constellations that reduces launch costs by up to 87 %, simplifies integration, and enables the simultaneous deployment of multiple satellites. The research is divided into three key components. First, the structural design is developed to accommodate eight PocketQube-sized satellites within a 1U CubeSat frame, while remaining scalable. The design is evaluated using finite element analysis and mechanical testing, including modal analysis, vibration, shock, and static load tests, all in accordance with ECSS launch qualification guidelines.. Second, constellation dispersal is addressed through tailored differential drag control algorithms. This approach calculates separation velocities and timing to achieve in-plane phasing, accounting for the operational parameters introduced by the PQ8 form factor. Two case studies with orbital simulations validate the method’s effectiveness and scalability. Third, a novel disengagement mechanism is presented, in which magnetorquer coils are reconfigured to act as synchronized electromagnetic actuators. The circuitry is validated through simulation and bench-top testing, and actuator forces are confirmed via finite element analysis. Overall separation dynamics are then demonstrated using a pendulum testbed to emulate near-free-body translational and rotational disengagement behaviour. Together, these contributions, structural innovation, coordinated dispersal, and integrated separation, form a robust and cost-effective platform for small-satellite constellations. The PQ8 Architecture significantly lowers the barriers to entry and enables missions that would otherwise be financially or logistically infeasible. Description: Ph.D.(Melit.) 2025-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/147080 Title: Intelligent systems and sustainable solutions for AIOT-powered environmental monitoring Authors: Shaheen, Momina; Pandey, Jay Kumar; Tran, Tien Anh Abstract: The integration of artificial intelligence (AI) and the Internet of Things (IoT), collectively known as AIoT, revolutionizes environmental monitoring by enabling intelligent, data-driven, and sustainable solutions. AIoT-powered systems combine real-time data collection from sensors with advanced analytics and machine learning algorithms to monitor, predict, and manage environmental changes. These intelligent systems detect pollution levels, track climate patterns, optimize resource usage, and support early warning systems for natural disasters, promoting environmental sustainability. By harnessing the connection between AI and IoT, researchers and policymakers may develop smarter, more adaptive frameworks that enhance environmental protection while contributing to global sustainability goals. Intelligent Systems and Sustainable Solutions for AIOT-Powered Environmental Monitoring provides a comprehensive overview of the integration of AI and IoT in environmental monitoring and sustainability. It explores how intelligent, connected systems transform monitoring, analysis, and response to environmental changes. This book covers topics such as data science, smart technology, and sustainable development, and is a useful resource for engineers, academicians, researchers, and scientists. 2026-01-01T00:00:00Z