Middlesex University, known primarily for its medical and veterinary schools, operated from 1914 until 1947, first in Cambridge, Massachusetts, later in Waltham, Massachusetts..
The advent of unmanned vehicles integrated with Vehicle-to-Everything (V2X) communication technology has revolutionized various sectors by enabling enhanced operational coordination and real-time data exchange. However, this integration has also introduced a plethora of cyber threats that pose significant risks to the safety, functionality, and security of these systems. This paper presents a comprehensive taxonomy of cyber threats targeting V2X-enabled unmanned vehicles and the corresponding countermeasures necessary for their mitigation. Through a detailed review, this study explores the technological foundations of V2X communication, its integration with different classes of unmanned vehicles, and the multifaceted cyber threats that arise from such integration. The paper further discusses the defense strategies across various domains—ground, aerial, and underwater—and emphasizes the need for robust encryption, secure authentication, and privacy-preserving techniques.
With the increasingly busy lifestyles of people, advanced maintenance systems such as automated feeding has become of utmost importance to aquarium owners. Despite existing technologies, there remains a gap in smart feeding solutions tailored to the specific dietary requirements of different fish species and population sizes. This paper investigates smart fish feeding through automated detection, counting, classification and feeding of fishes within aquariums. As part of this paper, a smart population-driven aquarium maintenance system, named AquaTrack, is proposed that integrates Internet of Things (IoT), computer vision, machine learning, and sensor technologies. The system detects, classifies, and counts fish, adjusting feeding amounts based on species and population to mitigate the risks of overfeeding and underfeeding, which can significantly affect water quality. The system was evaluated through four research questions, where a general accuracy of 76.9% in fish species identification and 77.1% in fish counting were found. While promising outcomes were observed under specific conditions, variability and limitations in performance across different scenarios were also noted. Environmental factors such as lighting, reflections, shadows, and particulate matter affected the accuracies, particularly in scenarios involving smaller fish quantities. Based on these findings, the study recommends further refinement of the system to enhance its capabilities.
This study compares the performance of the ESP-r building energy modeling tool in containerized environments (Docker, LXD, LXC) versus traditional virtual machines (VMs, VirtualBox, VMware), focusing on execution time, resource utilization, and scalability. Given the increasing adoption of cloud computing and virtualization, it's crucial to identify the most efficient platform for ESP-r simulations. The hypothesis suggests that containers, due to their lightweight architecture, may offer superior performance compared to VMs. The research involves setting up ESP-r in both environments, conducting simulations, and recording execution times to determine the optimal virtualization technology. Additionally, the study examines the role of these technologies in supporting digital twins, dynamic virtual representations of physical systems used for performance monitoring and decision-making in the construction industry.
With the rise of the internet usage and web applications, Reflected Cross-Site Scripting (RXSS) attacks have become increasingly prevalent, accounting for over 90% of recent XSS incidents. This paper proposes a novel defense mechanism against RXSS through a browser extension, called RXSS Protect, integrated with a machine learning (ML) algorithm. This extension, compatible with Google Chrome, Microsoft Edge, and Mozilla Firefox, employs a Support Vector Machine (SVM) model to detect and block malicious scripts in real-time. The system’s architecture includes a Flask server for running the ML model, a browser extension for client-side operations, and an SQLite database for storing URL data. This approach aims to enhance web browsing security by providing an effective tool against RXSS attacks, with potential for future extensions to other types of cyber threats. The SVM model is trained on a dataset of benign and malicious URLs and XSS payloads. Evaluation focused on answering two key research questions, related to detection accuracy and performance across browsers. Results showed that RXSS Protect achieved a high accuracy of 97.53% in identifying RXSS payloads and relatively good overall performance across browsers.
The global population is going through a significant demographic shift and the percentage of individuals aged 65 and over is projected to increase globally. This demographic transition brings forth challenges associated with aging, including an increased prevalence of non-communicable diseases and disabilities. Fall is one of the most common cause of serious injury for the elderly. There is a growing need for comprehensive care and support systems encompassing primary, acute, and end-of-life care, along with assistance in daily activities. Abnormal Activities of Daily Living (AADL) among elderly individuals present significant health risks and challenges. This study explores an innovative approach to detecting such incidents through the integration of artificial intelligence (AI). By analyzing acoustic distress signals, particularly screams, AI algorithms can promptly identify and respond to emergencies. The AI algorithms achieved an average success rate of 80.5% in identifying emergency situations. This method offers a proactive solution to enhance the safety and well-being of the elderly, potentially reducing the time to provide assistance and mitigating the consequences of falls and other abnormal ADL incidents. The implementation of AI in this context underscores its transformative potential in elder care, paving the way for smarter, more responsive health care technologies.
