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Machine learning driven cyber resilience framework for mobile tactical networks with graph-based threat detection and adversarial security engineering in cyber physical systems
No full textAdvanced mobile network connectivity is being fueled by the rapid evolution of 5G and the forthcoming 6G technologies. This however has exposed mobile networks to new cyber threats and security vulnerabilities. Consequently, cyber resilience, that is, the cope to prepare, identify, respond and recover from cyber-related incidents has become crucial. This paper focuses on a cyber resilience framework for mobile networks utilizing machine learning (ML) aiming at emerging threats. Machine Learning supervised, unsupervised and deep learning algorithms can perform anomaly detection, intrusion detection, prediction and automated threat response systems. Major ones like IDS and anomaly detection are discussed and analyzed with practical instances. The study examines and proposes federated learning, reinforcement learning and explainable AI (XAI) suffice in addressing issues of scarcity of data, time-sensitive processing, and emerging cyber threats. Integrating IoT, edge computers and 6G networks can also improve resilience. It is evident that there is great potential for cyber resilience through machine learning however it has been suggested that standardization, benchmarking and effective test frameworks are put in place
The Interpretation of Vital Signs and Other Vital Bedside Information: Expanding the Paradigm
No full textThis chapter reviews the symptoms, signs, and other vital information that can and should be obtained at the bedside of the sick; this information should be recorded, vetted for accuracy, and if it changes responded to promptly and appropriately. Some vital information, such as age, sex, and body weight, are stable, whereas others are dynamic and include the traditional vital signs, breathlessness, other subjective patient feelings, changes in breathing, weakness, mobility, and mental status; changes in these are associated with higher in-hospital mortality, higher resource use, longer length of stay, and higher long-term mortality.The five vital signs of respiratory rate, temperature, pulse rate, blood pressure, and oxygen saturation are indicators of hypoperfusion and hypoxemia, which are the final common pathways of clinical deterioration and death. Little else is known about the changes and trends of individual vital signs during the entire course of acute illness in hospital. Therefore, the best judge as to whether a vital sign value is appropriate for a clinical situation is how patients feel and their mental and physical functions. It is unclear if routinely measuring vital signs is effective at promptly detecting adverse events, and to date, there are no high-quality, large, well-controlled studies of continuous vital monitoring that show that it is of benefit. Although patients with three or more seriously abnormal vital signs will require prompt intervention to restore circulatory and respiratory stability, for less sick patients, the situation is unclear. It is possible that simply observing these patients, asking them how they “feel”, and “worrying about them” may detect life-threatening illness earlier than frequent routine vital sign measurements
Effect of pandemic and lockdown on the performance and operations of farmers’ markets in Southwest, Nigeria
No full textThe study critically examined the effect of the pandemic and lockdown on the performance and operations of farmers' markets in Southwest, Nigeria. Primary data were used and the information was sourced using questionnaires. A multistage sampling technique was used to randomly select farmers for the study. Descriptive statistics, budgetary technique and two-stage least squares regression were used for the analysis. The results showed that age, revenue, perceived COVID-19 effect, household size, experience, market space acquisition, and frequent visits to farmers markets were the significant factors that influenced the performance of the farmers in the study area. Challenges faced by the farmers after lockdown on farmers markets were increased price, high cost of farm input, reduced quantity of farm products and high transportation cost. Therefore, there should be more government intervention/assistance programs as a way of assisting the farmers to boost food production and alleviate poverty in the area
The impact of emerging cloud security threats : a focus on advanced persistent threats
No full textThe rapid advancement in cloud computing technology is continually evolving, with threat actors refining their tactics, exploiting new vulnerabilities, and expanding their influence. This dynamic environment exposes cloud infrastructure to emerging cyber-attacks, including Advanced Persistent Threats (APT), impacting both customers and service providers. Understanding the gap in APT detection literature is crucial for researchers. The research aims to comprehensively understand APTs' influence on cloud security, analyse existing approaches, emulate adversary plans, simulate attacks using Mitre Caldera, employ Snort for detection, and utilise the Nessus vulnerability scanning tool. The study addresses critical questions about APTs' exploitation of cloud environments, strengths and weaknesses of mitigation methods, impacts of successful APT attacks, vulnerabilities in cloud infrastructures, and techniques for detecting APTs. The findings underscore the intricate interplay between APT activities and cloud environments, emphasising the need for robust detection and mitigation strategies. The combination of APT simulation, vulnerability assessment, and detection mechanism analysis yields invaluable insights into the evolving threat landscape within cloud ecosystems. As organisations increasingly embrace cloud technologies, the lessons from this study contribute substantially to the ongoing discourse on fortifying cloud security against persistent and evolving cyber threats
Energy Efficient Routing Algorithms for Wireless Sensor Networks
No full textAbstractWireless Sensor Networks (WSNs) are crucial in applications that require minimal human intervention, such as remote habitat monitoring, environmental monitoring, healthcare, agriculture, and surveillance. They are also vital in disaster management, industrial automation, smart cities, and military operations. The increasing adoption of Internet of Things (IoT) and the evolution of Industry 4.0, WSNs have attracted increasing research attention. These networks often employ sensor nodes with limited energy resources, deployed in environments where recharging is not feasible, making energy efficiency a key concern. Consequently, energy depletion frequently leads to node and network failures. To address this challenge, numerous studies have focused on enhancing the energy efficiency of WSNs through clustering mechanisms and energy-efficient routing protocols. Clustering, especially through the application of fuzzy logic to select the CHs, has been shown to extend network longevity and decrease total energy usage. This research aims to optimize existing energy-efficient routing algorithms for clustered WSNs, incorporating fuzzy logic and network coding techniques to rise data transmission rates and extend the operational lifespan of the network. The objectives include conducting a comprehensive literature review to understand the impact of energy-consuming elements, studying clustering functions and protocols, extracting parameters from state-of-the-art approaches, and designing an optimized routing algorithm that increases energy efficiency and network lifetime. The proposed algorithm will be implemented and simulated in a WSN environment, and its performance will be analysed and evaluated against existing algorithms. Initial results from simulations indicate that the E--FL-NC-EEC/D protocol outperforms existing protocols like FL-NC-EEC/D, FL-EEC/D, K-LEACH, FL and LEACH relative to throughput, energy consumption, and network lifetime. The long-term implications of these results suggest that the optimized algorithm can appreciably improve the sustainability and effectiveness of WSNs in numerous applications by minimizing energy intake and extending operational lifespan of nodes, thus contributing to more reliable and efficient monitoring and data collection in remote and critical environments
Exploring student perspectives on AI-generated feedback using a Socratic method chatbot
No full textThe integration of Artificial Intelligence (AI) in educational settings has opened new avenues for enhancing student learning. This study investigated the use of a generative AI chatbot, trained to provide feedback using the Socratic Method, in a Business Management programme. Recent literature highlights the transformative potential of AI in education, particularly in fostering personalised learning experiences and supporting critical thinking (Gökçearslan et al., 2024; Lee and Moore, 2024; Mustafa et al., 2024). Understanding student perspectives on AI-generated feedback is crucial for optimising its use in learning development. This study aimed to evaluate the effectiveness of AI feedback in promoting critical thinking and its acceptance among students. Previous research has shown that AI chatbots can enhance learning by providing timely and relevant feedback, though challenges such as limited interaction and potential for misleading guidance remain (Banihashem et al., 2024; Gökçearslan et al., 2024; Guo et al., 2024). A qualitative approach was employed, utilising a focus group with n=14 final-year undergraduate students on a Business Management pathway. The generative AI tool was piloted to provide feedback on student drafts for summative coursework. The quality of feedback was assessed based on its accuracy, relevance, timeliness, and effectiveness in fostering critical thinking. Data was analysed using thematic analysis, a method well-suited for identifying and interpreting patterns within qualitative data (Nowell et al., 2017; Braun and Clarke, 2022). The Socratic Method, known for its effectiveness in promoting critical thinking through questioning, was employed as the feedback mechanism (Buckingham Shum, 2024). The study revealed that students found AI-generated feedback useful and relevant for improving their work and identifying knowledge gaps, thereby promoting deep learning. The Socratic Method used by the AI encouraged deeper engagement with their work, unlike the straightforward answers typically provided by other chatbots. However, students preferred tutor feedback
Stories of Mental Health, Resilience and Recovery
No full textStories of Mental Health, Resilience and Recovery is a compelling exploration of personal strength in the face of adversity. This volume brings together seven deeply moving first-person accounts, highlighting the resilience of individuals who have faced trauma, discrimination, and profound psychological struggles
New Labour's new deal for socially excluded young people: a critical retrospective
No full textThis paper critically examines New Labour's New Deal for Young People (NDYP) and its intended role in reducing the number of socially excluded young people classified as NEET (not in education, employment or training). It focuses on New Deal as an example of Third Way policy design aimed to address social exclusion, including the promotion of an 'evidence-based policy' which sought to impose the New Labour orthodoxy that any labour-market participation was preferable to none - and that this, in turn, would improve the life chances of NEET young people and combat social exclusion. The paper critically considers the policy design of NDYP, and its underlying assumptions, including the importance of global economic competition as justification for a range of education and employment initiatives expected to deliver improved social inclusion and a resulting improvement in socio-economic standards. Whilst New Deal was presented as a fresh, evidence-based approach to (re)engaging NEET young people in education and work, the central argument of this paper is that New Labour's focus on social exclusion at the expense of poverty meant that its approach can, in many ways, be regarded as a missed opportunity to enact meaningful change for those most in need
Maternal health inequalities focusing on Black pregnant women
No full textThe gap between maternal mortality for Black and White women in the UK continues to widen. Deprivation significantly increases the risk of maternal morbidity, mortality and adverse birth outcomes, affecting access to nutritious foods and antenatal care as well as increasing the likelihood of negative health behaviours such as smoking and substance use. However, ethnic health disparities exist regardless of social or economic status, meaning social disadvantage fails to explain these differences alone. Studies have identified racial discrimination and bias as important factors fueling the disparities in pregnancy outcomes among Black women. Black women report dismissal of concerns, assumptions and stereotypes among other negative experiences of their maternity care. This ultimately fosters fear and mistrust in maternity services, causing Black women to report health concerns later and avoid attending for care. Acknowledging that racism exists in maternity systems is a crucial step in addressing inequalities in maternal outcomes
Early Diagnosis of Alzheimer's Disease using Adaptive Neuro K-means Clustering Technique
No full textAlzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by memory loss, behavioral changes, and impaired self-care, often preceded by Mild Cognitive Impairment (MCI). Not all MCI cases progress to AD, creating a diagnostic challenge. This study proposes a novel framework for early AD diagnosis using T1-weighted Magnetic Resonance Imaging (MRI). The approach integrates the Adaptive Moving Self-Organizing Map (AMSOM), a neural network technique for unsupervised training and tissue segmentation, with K-means clustering and Principal Component Analysis (PCA) for feature selection. AMSOM dynamically updates neuron weights to improve segmentation accuracy. Classification is performed using various algorithms, evaluated on sensitivity, accuracy, precision, and similarity metrics. Compared to existing techniques such as Fuzzy C-means (FCM) and hybrid Self-Organizing Mapping-K-means (SOM-FKM), the proposed method demonstrates statistically significant improvements in tissue segmentation and classification. It achieved a mean accuracy of 99.8%, reducing the Mean Squared Error (MSE) from 2.3 to 0.44 and improving the Discriminative Overlap Index (DOI) and Tissue Clarity (TC) values to 0.435105 and 0.282381, respectively. Implemented in MATLAB, this method provides a robust, efficient framework for early AD detection, surpassing existing approaches in precision and reliability