/library/oar/handle/123456789/9973 2025-11-05T04:20:29Z /library/oar/handle/123456789/140734 Title: Life cycle assessment of smart and sustainable food packaging solutions Abstract: The scope of this dissertation is to analyse the environmental impact associated with smart and sustainable food packaging solutions. This was conducted by understanding the state-of-the-art in novel takeaway packaging, including smart and sustainable features. The study aims to develop guidelines and conclusions on what should be considered when designing such innovative packaging solutions while identifying the most significant factors influencing their environmental performance. As a case study, this dissertation examines the SMARTSPACK (R&I-2021-005-T) project, delegated by the University of Malta, which proposes a novel packaging solution integrating smart features to enhance user experience while maintaining a holistically sustainable design. Through comparative Life Cycle Assessment using SimaPro and the ReCiPe framework, this research evaluates the environmental performance of novel takeaway packaging against conventional takeaway packaging. The study also considers the environmental effect associated with the integrated sanitisation features proposed in SMARTSPACK, in comparison to conventional hand cleaning methods. The analysis spans raw material extraction, manufacturing, transportation, use phase, and end-of-life disposal. This research contributes to bridging existing literature gaps on the Life Cycle Assessment of smart food packaging, particularly regarding tamper-evidence, sanitisation integration, and recyclability. By highlighting critical impact areas, this study provides valuable insights to inform the development of more sustainable, user-friendly food packaging solutions. Description: M.Sc.(Melit.) 2025-01-01T00:00:00Z /library/oar/handle/123456789/140669 Title: Design, manufacture, and testing of an integrated pH-potential measuring device for subsea metallic structures Abstract: This study addresses a cultural heritage conservation challenge for underwater metallic wrecks. Left unattended, shipwrecks will eventually be consumed by corrosion, creating a cultural heritage conservation issue, and also an environmental problem, if the wrecks still carry payloads of oil or unexploded ordnance. This study used a structured approach to the design, fabrication, and testing of a handheld device that can be operated by underwater archaeologists and conservators for the in-situ corrosion monitoring of metallic underwater cultural heritage. By measuring the pH and the corrosion potential at various points around the surface of the wreck, the state of corrosion that the metal is in was assessed. The device was equipped with a pH measuring system comprised of a pH meter and probe, along with a corrosion potential measuring system, comprised of a multi-meter, a silver/silver chloride reference electrode, and a platinum electrode. While several examples of such a device are found in literature, there exists no off-the-shelf systems available for purchase nor any documentation which provides a structured approach to the design of such a device. The design methodology adopted for this study was the Pahl and Beitz systematic approach, with an implemented verification validation, and testing framework. The pressure vessel portion of the device was designed using a design by formulae approach that was complemented with Finite Element Analysis exercises conducted on each of the constituent parts of the vessel. Following the fabrication of the device, a hydrostatic pressure test was conducted to ensure it would withstand the 100 m maximum depth required by the stakeholders of the project (Heritage Malta). Tests were conducted to validate the effectiveness of the method of corrosion assessment, following which a pilot implementation of the device was conducted on the wreck of the X127 at the Lazzaretto Wharf on Manoel Island. The device was successfully able to take measurements of pH and corrosion potential, allowing the determination of the state of corrosion of the hull of the X127 by plotting the data on a eh-pH (Pourbaix) diagram of iron in seawater. Description: M.Sc.(Melit.) 2025-01-01T00:00:00Z /library/oar/handle/123456789/140563 Title: Towards a secure urban traffic network Abstract: Traditional Intelligent Transport Systems (ITS) face several critical limitations such as single points of failure due to centralized control and data. These limitations result in ITS solutions that are often inefficient and unreliable in meeting modern transportation demands. Recent research highlights blockchain technology as a promising solution to these challenges. By decentralizing control, securing data immutably, and enabling transparent, distributed decision-making, blockchain can strengthen ITS against failures and manipulation while improving responsiveness to dynamic traffic conditions. This dissertation presents the design, development, and implementation of a decentralized traffic management system that integrates blockchain technology with a traffic simulation. A locally hosted Ethereum Virtual Machine (EVM)-compatible blockchain (via Ganache) is connected to the Aimsun Next 23 simulation platform using Python APIs, enabling real-time, bidirectional communication between the simulation and the blockchain network. The system uses three smart contracts: one to log vehicle counts using event emissions, another to manage actuated traffic light logic based on real-time traffic data and another to establish priority to emergency vehicles. This decentralized approach enables tamperproof data logging, distributed control, and programmable traffic responses. It also supports the logging and management of emergency events, such as simulated lane closures, without relying on centralized control. Four simulations were conducted to evaluate the system’s functionality, ranging from basic traffic data logging to full actuated signal control under emergency conditions. The results demonstrate that integrating blockchain technology into ITS frameworks is favourable, leading to several benefits such as transparency, resilience, and dynamic traffic management capabilities. The dissertation concludes that the prototype meets its objectives. Future work could explore deployment on scalable platforms like Polygon and the application of this work to other traffic networks. Description: B.Eng. (Hons)(Melit.) 2025-01-01T00:00:00Z /library/oar/handle/123456789/140561 Title: Stock market prediction using ensemble learning methods Abstract: The stock market is complex and stochastic, which presents a significant challenge to accurately predict movements and prices. This study focuses on leveraging decision tree ensemble methods, specifically Random Forests and Extreme Gradient Boosting (XGBoost), for predicting future stock market movements. The primary focus of this study is to extract valuable stock market information whilst making the necessary changes to establish the best balance between investment risk and portfolio returns. The Random Forest and XGBoost models were trained and evaluated using historical stock data from five leading technology companies, namely Apple, Microsoft, Google, Tesla and Amazon. The results proved that both models were capable of delivering a reliable performance, generating substantial annual returns. The Random Forest model achieved a mean annual return of 47.8%, whilst the XGBoost model showed slightly lower performance, with a mean annual return of 32.0%. However, Random Forests achieved a maximum annual return of 85.2%, whereas XGBoost reached a higher maximum of 101.3%. These findings also revealed that Random Forests produced more consistent and conservative results, while the XGBoost model was more volatile and risky, occasionally achieving higher returns but with less stability. Overall, the results demonstrate that both ensemble models, Random Forests and XGBoost, were able to generalize effectively across unseen data, capturing informative patterns and relationships within the stock market data. Description: B.Eng. (Hons)(Melit.) 2025-01-01T00:00:00Z