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The role of metacognitive beliefs in generalised anxiety disorder in men who have sex with men living with HIV in Nigeria
No full textMen who have sex with men (MSM) living with HIV tend to experience a range of mental health issues, in particular generalised anxiety disorder (GAD), often caused and maintained by psychosocial variables including HIV stigma, discrimination, self-esteem issues, substance abuse and loneliness. This is particularly problematic in countries like Nigeria where same sex activity is illegal and can result in up to 14 years imprisonment. An important psychological variable that may contribute to the experience of GAD are metacognitive beliefs. Participants ( = 311) completed measures to examine the relationship between these variables. Results indicated that metacognition was associated with, and significantly predicted, GAD in this population. Moderation analysis showed that the effect of HIV stigma on GAD was explained by the proposed interaction with metacognition. Findings suggest that metacognition may be an important variable in explaining GAD symptoms in MSM living with HIV in Nigeria
Enhanced Intrusion Detection in IoT Networks Using Hybrid Machine Learning Technique
No full textThis study introduces a unique framework for an Intrusion Detection System (IDS) that employs an advanced machine learning approach to improve the Internet of Things (IoT) networks. IoT devices, now increasingly prevalent, rely on data which is a subject of interest to hackers/attackers who explore the present rise in network security vulnerabilities. There is therefore the need for a more robust and accurate intrusion detection system. The integration of Random Forest (RF) algorithms with Deep Neural Networks (DNNs) provides a significant increase in model evaluation metrics and robustness. A comprehensive CICIoT2023 dataset was adopted and used to meticulously train and evaluate the IDS model, resulting in an exceptional and effective system of identifying and preventing potential threats. Also, the study analysis highlights areas of improvement, particularly in detecting specific attack types such as SQL injection. Whilst these findings push the boundaries of IoT security using state-of-the-art machine learning techniques, they have also underlined the need for further studies to address the obvious gaps
EADCN-BCSR: A novel framework for accurate and real-time waste detection and classification
No full textWaste detection and classification are critical processes in modern waste management systems, as they enable the efficient sorting and processing of various waste types. The significance of effective waste detection lies in its potential to minimize environmental impact and promote sustainability. With the growing volumes of urban waste, implementing advanced detection technologies is essential to facilitate timely interventions and optimize waste handling practices. For effective waste detection, several Deep Learning-based techniques have been implemented. Yet, they are limited by several drawbacks including poor accuracy, generalizability, computational efficiency, and class imbalance issues. This research work develops an automated methodology that incorporates Deep Convolutional Neural Networks with Adaptive Boosting for accurate waste detection and categorization. This study deployed a data augmentation step to enhance the quantity of images and resolve class imbalance problems. Deep Convolutional Neural Networks automatically identify and learn relevant features from raw image data, such as edges, textures, and shapes, which are crucial for distinguishing between different types of waste. The proposed model introduces an Adaptive Boosting ensemble learning technique for enhancing the classification performance by combining the outputs of several weak classifiers. Then, the proposed technique adopted a Binomial Crossover Ship rescue algorithm that incorporates the Ship Rescue Optimization algorithm with the Binomial Crossover Strategy that fine-tunes the hyperparameters of the proposed technique and improved the overall effectiveness of the model. In addition, the effectiveness of this study is evaluated using several waste detection datasets with distinct performance measures and the model attains superior detection results such as accuracy of 98.82% and precision of 98.56%. The simulation findings show that the proposed method provides an excellent contribution to early and accurate waste detection systems
Attitudes of lecturers and students towards disability and inclusion of Higher Education disabled students and the impact on disabled students’ lived experiences
No full textDisabled university students face barriers and are among those likelier to withdraw and have lower degree outcomes (OfS 2023). One potential barrier impacting disabled students’ university experience and outcomes is lecturers and students’ attitudes. Phase one of this two-phase study which adopts a critical realist framework, employs Q-Methodology to investigate lecturers and students’ attitudes towards disability and inclusion of disabled higher education (HE) students. Phase two Interpretive Phenomenological Analysis (IPA) of semi-structured interviews with disabled students explores the impact of attitudes on their lived experience.Using Q Method Software, thirty-one lecturers sorted forty-five statements describing the spectrum of attitudes towards disability and inclusion and provided optional post-sort survey and interview data. Two stances about inclusion emerged from factor analysis and interpretation: cautiously committed with concerns and confidently committed with concerns. Both groups are committed to inclusion of disabled students. However, the majority group is more cautious and concerned about expertise. The second group is more ableism aware and confident implementing inclusion but shares group one’s concerns about training. Fifty-two students sorted thirty-nine Q-sort statements. Analysis of thirty-three non-disabled students’ data revealed two views: pro-inclusion and confidently proactive, and pro-inclusion but cautious. Both groups share concerns relating to discrimination faced by disabled students. However, the minority group are more cautious about disability inclusion. The second group are more empathetic, ableism aware and confident to challenge discrimination. Analysis of nineteen disabled students’ data uncovered one perspective: pro-inclusion but concerned about ableist barriers.Phase two IPA of eight semi-structured interviews with disabled students revealed four Group Experiential Themes: Diagnosis, disclosure and identity issues; Reasonable adjustments and knowledgeable, empathetic lecturers- for some; Supportive, empathetic peers and sense of belonging- for some; Facing ableism beyond university.The findings contribute to academic discourse in this sparsely researched area and highlight future research implications. The thesis recommendations for HE policy and practice will positively impact disabled students’ university experience and outcomes
Growing Forward: Exploring Post-Traumatic Growth and Trait Resilience Following the COVID-19 Pandemic in England
No full textThe COVID-19 pandemic presented many potentially traumatic circumstances. Research continues to investigate pandemic-related Post-traumatic Growth (PTG). However, most studies fail to fulfil the parameters of PTG whereby a triggering event must be of seismic intensity and have ceased before PTG can manifest, producing significant validity and reliability issues. The relationships between PTG, trait resilience and fear are also under-researched, particularly in circumstances where the parameters of PTG are met. This study examined the relationship between PTG, COVID-19-related fear and trait resilience. Participants (n = 229) completed an online questionnaire incorporating the Post-Traumatic Growth Inventory and the Connor–Davidson Resilience Scale. The sample participants were moderately traumatised with moderate–low PTG (M = 50.85). Participants reported greater levels of PTG compared to participants from pre-COVID studies, notably in relation to the constructs of Relating to Other (d = 0.29), New Possibilities (d = 0.47), Personal Strength (d = 0.39), and Spiritual Change (d = 0.29). Higher levels of resilience (B = 0.48) and COVID-19-related fear (B = 0.16) were associated with greater overall PTG. Younger participants also reported greater levels of PTG (B = −0.29). The findings advance current knowledge regarding the potential relationship between fear and PTG and demonstrate that trait resilience is a promotional factor, presenting opportunity for future intervention formulation. However, reform is required within the PTG literature pool. Future research investigating PTG must reach both parameters. In circumstances where this is impossible, research concerning newfound positive cognition during adverse circumstances should be re-explored as Post-Adversarial Appreciation (PAA) to maintain validity
Next-Generation Bio-Composites: Alkali-Treated Borassus Husk Fiber for Structural and Thermal Efficiency
No full textNatural fibers from renewable resources provide a sustainable alternative to synthetic reinforcements. This study examines the thermal and mechanical properties of Borassus husk fiber/epoxy composites, fabricated using untreated and alkali-treated fibers through the hand layup process. Fibers were treated with sodium hydroxide (NaOH) for 0.25–2 hours, and their thermal and thermo-mechanical properties were analyzed through thermogravimetric analysis (TGA) according to ASTM E2550, and dynamic mechanical analysis (DMA) was conducted adhering ASTM D5418–01 followed by scanning electron microscopy (SEM) analysis. Alkali treatment significantly enhanced thermal stability, as indicated by increased char content (11.5%) and higher integral process decomposition temperature (IPDT) values, with the 0.75-hour treated fiber/epoxy achieving the highest value (580°C). The composites exhibited superior mechanical stiffness and energy dissipation compared to neat epoxy (NE) and other bio-fiber composites. The glass transition temperature (Tg) increased significantly for 0.5TBHFE (94.6°C). Additionally, storage modulus and tanδ improved, with 0.5TBHFE offering the best stiffness–damping balance. A 34% reduction in total mass loss clearly indicates improved thermal stability, which is further supported by SEM images showing enhanced fiber–matrix interlocking. These findings highlight alkali-treated Borassus husk fiber composites can be promising structural materials for aerospace and automotive applications, contributing to eco-friendly and sustainable development
Modelling a Reliable Multimedia Transmission Approach for Medical Wireless Sensor Networks
No full textAdvancement in wireless and communication technologies has remarkably boosted healthcare services such as Medical WSN. Protecting the patient's health‐related data against malicious activities is also essential. It is mandatory to ensure its dependability and reliability. The reliability of the proposed model in Secure Multimedia Transmission for medical wireless sensor network (IRSMT) system considers the need for authentication and confidentiality in security. Additionally, it enhances the transmission reliability during multimedia data transmission compared to the prevailing methods. To provide reliable multi-media (MM) data transmission, an improved energy‐efficient protocol is considered where the protocol differentiates MM and non‐MM data to enhance routing methodology for MM transmission. The proposed IRSMT enhances adaptability by balancing media quality with prompt delivery and loss tolerance. It is achieved through the anonymous routing method, which maintains the node secrecy using the SHA 256 method. It reduces the probability of data retransmission and provides less processing delay to acquire routing reliability. The simulation results demonstrate the advantages of IRSMT in comparison with the prevailing protocols in performance metrics such as throughput, packet delivery ratio, jitter etc
Proceedings of the 4th International Conference on Advances in Communication Technology and Computer Engineering (ICACTCE'24) Transforming Industries: Harnessing the Power of Artificial Intelligence and the Internet of Things. Volume 1
No full textThis proceedings book offers a refined and comprehensive exploration of cutting-edge advancements in communication networks, computational intelligence, and smart applications, seamlessly blending theoretical insights with practical solutions. Each paper outlines objectives, challenges, proposed solutions, and key findings, enabling swift comprehension of complex topics. By adopting a problem-solving approach and including case studies, the book effectively demonstrates the application of advanced techniques in domains such as industry, healthcare, and smart cities. Addressing the demands of an evolving digital landscape, it highlights emerging technologies like artificial intelligence (AI), the Internet of Things (IoT), and autonomous systems, ensuring its relevance to both current challenges and future innovations. Covering a wide spectrum of topics, including network security, AI applications, IoT ecosystems, and smart technologies, the book serves as a comprehensive resource for understanding the innovations shaping the future of communication and computing. Targeted at graduate students, researchers, professors, and industry professionals, it functions as both an educational tool and a reference guide for those seeking to remain at the forefront of technological advancements. Featuring state-of-the-art research contributions, the book introduces new techniques, algorithms, and solutions to real-world challenges, complemented by structured insights into objectives, problems, and results. Practical applications are brought to life through successful case studies in key areas like smart cities and healthcare, illustrating the tangible impact of these innovations. With contributions reviewed by a distinguished editorial team of leading researchers, engineers, and academics, the book ensures credibility, academic rigor, and relevance. By blending theoretical depth, practical utility, and expert validation, this proceedings book is an indispensable resource for navigating the rapidly evolving fields of computing and communication technologies, equipping readers with the knowledge and tools to excel in an increasingly digital and interconnected world
Investigating factors affecting apprenticeship retention and achievement in the Northwest of England Training Providers’ Perspectives
No full textClassification of self-induced and externally induced laughter using EEG and Artificial Intelligence to improve mental health
No full textLaughter is known to improve mental well-being. Laughter is generally categorized into self-induced and externally induced laughter, and there is a lack of empirical evidence differentiating the two. There are limited studies on the use of brain signals to differentiate frequency patterns related to these two types of laughter, and to explore their role in mental health and well-being. This study aims to address this gap using brain frequency wave responses. Brain frequency data were collected from fifty participants using a Muse headband. MNE-Python, Independent Component Analysis, and time-frequency were used for exploratory analysis. Machine learning and deep learning techniques (Random Forest, Gradient Boosting, LSTM, and Logistic Regression) were used to classify EEG trends. Random Forest revealed greater accuracy of 74%. Brainwave trends differed notably between the two types of laughter. Brain signals during Self-induced displayed prominent beta and gamma responses, while externally induced showed significant alpha and theta values. Thus, the self-induced laughter has a stronger impact on brainwaves connected to cognitive engagement and mental health compared to externally induced laughter. The research provides evidence that laughter can be prescribed to improve mental health and well-being. This research aids the utilization of EEG data for laughter analysis and unlocks paths for future studies into the therapeutic use of laughter for mental health advancements