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Faculty of Engineering and Built Environment

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    Geopolymer cement in pavement applications : bridging sustainability and performance
    (MDPI AG, 2024-06) Ikotun, Jacob O.; Aderinto, Gbenga E.; Madirisha, Makungu M.; Katte, Valentine Y.
    Sustainability and the quest for a more robust construction material cannot be divorced from each other. While Portland cement has revolutionized the construction sector, its environmental toll, particularly in greenhouse gas emissions and global warming, cannot be ignored. Addressing this dilemma requires embracing alternatives like geopolymer cement/geopolymer binder (GPC/GPB). Over the last few decades, considerable strides have been achieved in advancing GPC as a sustainable construction material, including its utilization in pavement construction. Despite these advances, gaps still exist in GPC optimal potential in pavement construction, as most studies have concentrated on specific attributes rather than on a comprehensive evaluation. To bridge this gap, this review adopts a novel, holistic approach by integrating environmental impacts with performance metrics. To set the stage, this review first delves into the geopolymer concept from a chemistry perspective, providing an essential broad overview for exploring GPC’s innovations and implications in pavement applications. The findings reveal that GPC not only significantly reduces greenhouse gas emissions and energy consumption compared to Portland cement but also enhances pavement performance. Further, GPC concrete pavement exhibits superior mechanical, durability, and thermal properties to ensure its long-term performance in pavement applications. However, challenges to GPC utilization as a pavement material include the variability of raw materials, the need for suitable hardeners, the lack of standardized codes and procedures, cost competitiveness, and limited field data. Despite these challenges, the process of geopolymerization presents GPC as a sustainable material for pavement construction, aligning with Sustainable Development Goals (SDGs) 3, 9, 11, and 12.
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    Machine learning for road traffic accident improvement and environmental resource management in the transportation sector
    (MDPI AG, 2023-02) Megnidio-Tchoukouegno, Mireille; Adedeji, Jacob Adedayo
    Despite the measures put in place in different countries, road traffic fatalities are still considered one of the leading causes of death worldwide. Thus, the reduction of traffic fatalities or accidents is one of the contributing factors to attaining sustainability goals. Different factors such as the geometric structure of the road, a non-signalized road network, the mechanical failure of vehicles, inexperienced drivers, a lack of communication skills, distraction and the visual or cognitive impairment of road users have led to this increase in traffic accidents. These factors can be categorized under four headings that are: human, road, vehicle factors and environmental road conditions. The advent of machine learning algorithms is of great importance in analysing the data, extracting hidden patterns, predicting the severity level of accidents and summarizing the information in a useful format. In this study, three machine learning algorithms for classification, such as Decision Tree, LightGBM and XGBoost, were used to model the accuracy of road traffic accidents in the UK for the year 2020 using their default and hyper-tuning parameters. The results show that the high performance of the Decision Tree algorithm with default parameters can predict traffic accident severity and provide reference to the critical variables that need to be monitored to reduce accidents on the roads. This study suggests that preventative strategies such as regular vehicle technical inspection, traffic policy strengthening and the redesign of vehicle protective equipment be implemented to reduce the severity of road accidents caused by vehicle characteristics.
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    Ransomware detection, avoidance, and mitigation scheme : a review and future directions
    (MDPI AG, 2021) Kapoor, Adhirath; Gupta, Ankur; Gupta, Rajesh; Tanwar, Sudeep; Sharma, Gulshan; Davidson, Innocent E.
    Ransomware attacks have emerged as a major cyber-security threat wherein user data is encrypted upon system infection. Latest Ransomware strands using advanced obfuscation techniques along with offline C2 Server capabilities are hitting Individual users and big corporations alike. This problem has caused business disruption and, of course, financial loss. Since there is no such consolidated framework that can classify, detect and mitigate Ransomware attacks in one go, we are motivated to present Detection Avoidance Mitigation (DAM), a theoretical framework to review and classify techniques, tools, and strategies to detect, avoid and mitigate Ransomware. We have thoroughly investigated different scenarios and compared already existing state of the art review research against ours. The case study of the infamous Djvu Ransomware is incorporated to illustrate the modus-operandi of the latest Ransomware strands, including some suggestions to contain its spread.