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Harnessing Coconut Shell Carbon Micro Particles For Enhanced Optical Absorbance And Sustainable Energy Storage In Organic Phase Change Materials
No full textInitiation of heat energy absorb, and release depends on the surrounding temperature and phase transition temperature of PCM. Nevertheless, the commonly hindered practical issue of PCMs is poor optical absorbance, fluctuation in energy storage and low thermal conductance. The current research aimed to assess the chemical constancy, improve the optical absorbance, decrease the optical transmittance & to analyse the variation in melting enthalpy of organic PCM functioning at 50 degrees C via dispersion of coconut shell biochar-based carbon micro particles (CSCMPs). The synthesized CSCMPs are dispersed within the PCM matrix at weight fraction of 0.2%, 0.4%, 0.6%, 0.8% and 1.0% via meltingblending-sonication process. The developed composite organic PCMs are analysed with a series of material characterization experiments by employing Fourier Transform Infra-Red spectroscopy (FTIR), Ultraviolet visible spectroscopy (UV-Vis), and differential scanning calorimeter (DSC). Results ensure, chemical stability, improved optical absorbance by 63.6%, decreased optical transmissibility by 64.3%; and surge in energy storage ability from 158.2 J/g to 190.1 J/g. The findings also provide valuables information towards design and improvement of composite PCM materials for various thermal regulation application from buildings to electronic devices
System automation and organisation for intelligent electricity networks
No full textThe transition from conventional energy generation to clean energy generation based onrenewable energies is leading to a rapidly growing share of decentralised energy sources in theelectricity supply. As a result, fundamental changes in the electricity supply structure are takingplace, creating new challenges for the decentralised operation of future electricity grids. TheClustering Power Systems Approach (CPSA) provides a solution in terms of the organisationand subdivision of the electricity grid by allocating cluster areas for its structured automationand control. This research focuses on providing a suitable software system for decentralisedautomation and control systems based on the CPSA to meet the rapid changes and futurechallenges in electrical power networks.Using this approach, a developed software architecture design for automation and controlsystems, the so-called Smart Grid Cluster Controller (SGCC), was developed and is presentedin this doctoral thesis. A suitable method for digitally describing the structure of powernetworks and the data organisation of clustered power system status was researched, developedand validated under real grid operating conditions. The topology of decentralised power gridsis mapped by graph-based fundamental structures and enhanced by a novel Neighbour ClusterOverlapping Method (NCOM). In addition, a time-series database was used for decentralisedprocess data mapping, whereby a direct reference to the topology description was realised.Decentralised neighbouring grid cluster areas can be coordinated concerning the necessaryprocess data exchange.The results of the validated software architecture design, the graph-based cluster topologydescription using NCOM, and the organisation for decentralised process data exchange show asignificant contribution to conventional industrial automation systems for the application ofdecentralised automation and control. The results developed based on the research discussed inthis thesis provide the possibility of an organised and structured operation of increasinglydecentralised power networks
Development Of A Conceptual Framework For Strategic Implementation Of Zero Carbon Initiatives In The UAE Construction Industry; Methodological Choices
No full textCarbon reduction initiatives cannot be implemented unless and until, main workplace processes have been dealt with to similar standards. This study critically evaluates the strategic implementation of environmental management systems in the United Arab Emirates (UAE) construction industry and their contribution to zero carbon goals. As part of robust management systems, it is argued that generic best practice simultaneously promotes optimum outcomes in both - they are mutual and not independent of each other. The study adopted a quantitative research method approach. A questionnaire survey was conducted, with responses from 106 UAE construction professionals. There is emphasis in the research on the value of soft-systems research methodologies (non-technical) as a tool to investigate environmental problems rather than scientific or technical approaches. The findings indicate that reviews, audits, and evaluations are essential for projects to sustain their specialist competence. These mechanisms assist project managers in navigating the evolving landscape of industry by leveraging accumulated expertise. Common challenges include a lack of clarity and executing tasks in an unordered manner, which can impede construction involves refining operations and side-stepping mistakes. Technical and nonand zero carbon outcomes
Adoption of Human Resource Analytics to Improve Business Outcomes and to Solve Workplace Needs A Case of the Nigerian Oil and Gas Sector
No full textAs organisations continue to search for ways to increase efficiency, there has been a tendency to rely on Human Resource Analytics (HR Analytics). HR Analytics has revamped the decision-making processes of Human Resource Management (HRM). It provides valuable insights that can help optimise HRM practices. By leveraging analytical tools, HR practitioners now have the ability to assess retention strategies and identify potential attrition issues based on empirical data. Such data-driven approaches also enable the recruitment of skilled job candidates that align with organisational needs. However, there is a scarcity of research on HR Analytics in developing nations, specifically in Nigeria. Most organisations in Nigeria have yet to embrace this practice, particularly in the oil and gas sector, which possesses the necessary resources for investment. This research examines the adoption of HR Analytics as a strategic approach to improve business outcomes and solve workplace needs.The study employed mixed methods research to answer the research questions. The qualitative insight was obtained from HR practitioners in the sector regarding their perceptions of the application of HR Analytics in their organisations. The qualitative data generated from twelve semi-structured interviews was analysed thematically. In the quantitative phase, the study employed 80 HR practitioners within the oil and gas sector as participants. Using a survey, insights about HR Analytics adoption were analysed with the help of IBM SPSS v21.0.The result suggests that there is a limited level of HR Analytics application in the sector. It emphasises crucial fundamentals about the challenges and potentials arising from the use of HR Analytics in the sector. The study established the importance of the relationship between HR Analytics and performance indicators, for example, productivity and organisational efficiency. The strategic role of the application of HR Analytics in the decision-making process in the sector was also identified
Identifying illegal waste dumps scenes using deep learning on aerial and satellite images
No full textIllegal waste dumping is a great threat to the environment and the health of people worldwide. Through the application of a deep learning concept, this study introduces an innovative discovery of identifying scenes of illegal waste dump (IWD) utilizing aerial and satellite imagery (ASI). The study combines an advanced feature pyramid network (FPN) and the robust ResNet101 architecture to optimize the model to manage the complicated, variable data that is characteristic of remote sensing (RS) data. Significantly, this model attained a remarkable recall of 0.95 and AUC of 0.95, showing the model's superiority in recognising actual waste scenes, which is of great essence in the effort to conserve the environment. Although the accuracy was relatively low (0.39), the high recall level will make sure that the model will have minimum false alarms in illegal dumping areas, which is needed to effectively manage waste and sustain the environment. Such findings not only encourage the technological opportunities in waste management but also provide significant knowledge regarding the future enhancement and implementation of the sources in environmental observation systems
Provision of medical same day emergency care services within the UK: analysis from the Society for Acute Medicine Benchmarking Audit
No full textTo evaluate the current provision of medical same day emergency care (SDEC) services within the UK, and the current utilisation of these pathways in the assessment of unplanned medical attendances.Survey data was used from the Society for Acute Medicine Benchmarking Audit (SAMBA), including anonymised patient-level data collected annually using a day of care survey.Hospitals accepting unplanned medical attendances within the UK, 2019-2023.34 948 unplanned and 4342 planned attendances across 188 hospital sites.29.8% of unplanned medical attendances received their initial medical assessment within SDEC services (2403 patients in SAMBA23), with the proportion increasing over time. 82.4% of patients assessed in SDEC services were discharged without overnight admission. Assessment in SDEC services was less likely in male patients, patients with frailty and older adults (all p<0.005).Selected operational standards for SDEC delivery, set by the Society for Acute Medicine, were met in 64%-91% of hospitals. Most hospitals (82%) accepted referrals from emergency department triage and 63% accepted referrals directly from the paramedic team. 38% of hospitals did not use a recognised selection criteria to identify suitable patients for SDEC and only 8% used a criteria designed to identify patients suitable for discharge. Overall, 34.7% of medical attendances discharged without overnight admission received their medical assessment in locations other than SDEC.Medical SDEC provides assessment for one-third of patients seen through acute medicine services. Although the proportion of patients assessed within SDEC is increasing, further innovation and improvements are needed to ensure appropriate patients access this service
Barriers Experienced by Visually Impaired Rugby Players When Undertaking Concussion Assessment: A Qualitative Investigation
No full textPrevious work has conjectured that visually impaired athletes may face barriers when attempting concussion assessments because they can present with signs of concussion as part of their condition. The present study aimed to explore the qualitative experiences of visually impaired players undertaking the Sport Concussion Assessment Tool 5 (SCAT5). Four visually impaired Physical Disability Rugby League players completed the SCAT5 neurological assessment (i.e. read aloud and visual tracking sections) prior to attending an online focus group discussion. Thematic analysis was performed, revealing numerous barriers and consequent additional needs experienced by the athletes. The present results support the removal of the read aloud section from the SCAT5 and suggest that the SCAT6 may thus be a more appropriate assessment tool for visually impaired athletes. Clinicians using the SCAT6 may want to make adjustments to meet the additional needs of visually impaired athletes when completing the visual tracking section
Developing a Data-Driven Personalized Fitness Web Application for Obese and Sedentary Individuals with Django
No full textThis paper presents the development of a data-driven personalized fitness web application aimed at combating obesity and sedentary behavior.The web application leverages machine learning algorithms, particularlyGradient Boosting, to provide individualized fitness and dietary recommendations based on user input, such as BMI, activity levels, and healthdata. Built on the Django framework, the application ensures scalability, security, and ease of use. Key features include real-time adaptiverecommendations, a user-friendly interface, and a feedback loop that personalizes fitness plans according to user progress. The machine learningmodels were trained on a large dataset and tested against several models, with Gradient Boosting achieving the highest prediction accuracy (R²= 0.9975). Initial user feedback indicated high satisfaction, particularlydue to the system’s adaptability to evolving health conditions. Futureresearch directions include enhancing algorithm performance, expandingdata sources, and incorporating wearable devices for more precise realtime recommendations
An adaptive approach to reconceptualizing corporate social responsibility and corruption in Nigeria’s oil-rich Niger Delta
No full textSignaling the fundamental tensions in the conceptualization of corporate social responsibility (CSR) and corruption has simply lost its capacity to inspire. Like an emperor without clothes, both concepts are estranged from comprehension. This paper therefore examines these deeply contested conceptions of corruption and CSR frameworks as they relate to Nigeria’s oil-rich Niger Delta. It seeks to test the competing notions within institutional and operational corruption on the one hand and CSR frameworks on the other hand. The idea is to establish a fundamental nexus between the inconsistent narrative conception of the above forms of corruption and the incoherent framing of CSR within institutional settings in Nigeria. This paper maintains the view against the voluntarist conception that sees corruption as the offshoot of cultural disposition wrapped into the logical frames of CSR. As a result, the study seeks to resolve the question of whether corruption is incidental to or a function of framework and systems design. The aspects of relativist, nonrelativist, and communalist analytical methods provide a context for an examination of the competing notions of corruption and its relationship with the incoherent CSR framework in Nigeria’s oil and gas sector. It argues that the intentionality of gaps created within the CSR framework provides the basis for corrupt activities. Initial findings reveal a strong connection between defective systems design and a high tendency for institutional and operational corruption within the CSR framework in Nigeria’s oil and gas sector. This has implications for associated and connected institutional systems in Nigeria, Africa, and across the world
Developing and evaluating an artificial intelligent framework for sustainable urban transformation and climate resilience in low-income cities
No full textThis doctoral study presents a novel, AI-powered framework designed to facilitate the sustainable transformation of low-income cities into climate-resilient, smart urban environments. Central to this research is the imperative to address the mounting challenges faced by resource-constrained urban areas in achieving sustainability goals, amidst increasing climate vulnerabilities, governance limitations, infrastructural deficiencies, and economic constraints. Unlike prevailing smart city models—predominantly constructed for high-income, technologically advanced contexts—this research advances a new paradigm that democratises AI technologies for application in low-income urban settings. The framework is purposefully aligned with the United Nations Sustainable Development Goals (UNSDGs), particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 9 (Industry, Innovation and Infrastructure), ensuring its theoretical grounding in globally recognised sustainability standards.The core contribution of this study lies in the development, validation, and simulation of an AI-centric framework that is both modular and adaptable to the varied realities of low-income cities. The research highlights specific AI applications capable of transforming urban governance, infrastructure, and service delivery systems, with empirical focus areas including traffic management, energy optimisation, predictive infrastructure maintenance, environmental monitoring, and water resource management. Through the integration of real-time sensor data, AI-driven traffic management systems were found to significantly reduce congestion and vehicular emissions in pilot simulations. Similarly, AI-enabled energy management applications, based on predictive analytics and machine learning, demonstrated measurable gains in energy efficiency across building systems—especially in scenarios where traditional energy monitoring mechanisms were absent or unreliable.Another notable finding relates to the implementation of predictive maintenance algorithms, which enable city authorities to detect and address infrastructure weaknesses before critical failures occur. This capability is particularly valuable for cities with chronic underinvestment in infrastructure and limited operational budgets. The framework also includes AI-driven environmental monitoring tools capable of conducting continuous real-time assessment of air and water quality, thereby providing early warning systems for pollution and enhancing the city's capacity for public health interventions. Across these application areas, the study identified and applied 165 KPIs, systematically extracted from literature, case studies, and scenario modelling, to evaluate the efficacy and scalability of AI tools within climate action frameworks.Methodologically, this research employs a rigorously designed mixed-methods approach, integrating elements of pragmatism and critical realism to ensure theoretical depth and empirical applicability. The study triangulates findings from a systematic literature review, comparative case study analysis, simulation modelling, and quantitative correlation and factor analysis. Four cities—Sana’a, Baghdad, Dubai, and London—were selected as comparative case studies to capture a spectrum of urban development contexts, ranging from highly resource-constrained environments to advanced digital economies. Case analysis revealed that cities such as Sana’a and Baghdad are impeded by institutional fragmentation, political instability, and infrastructural decay, which constrain the adoption of AI technologies. In contrast, Dubai and London provided models of best practice, particularly in the domains of AI-enabled mobility, decentralised energy systems, and integrated urban planning.Simulation models were utilised to validate the framework under different climate change and governance scenarios. These simulations enabled the identification of high-priority variables—such as traffic flow reduction, CO₂ emission levels, energy usage, and predictive maintenance frequency—thus offering quantifiable projections of AI’s effectiveness. The outputs indicate that phased, KPI-guided deployment of AI systems can significantly enhance urban resilience and operational efficiency, even in contexts where digital infrastructure and data availability are limited. Moreover, the study’s use of open-source simulation environments and replicable data structures ensures that the framework is both scalable and transferable across diverse urban contexts.This research contributes to theory by advancing the understanding of AI's role in socio-technical urban transformations, particularly in low-income cities. It challenges the epistemological bias inherent in much of the smart city literature, which tends to exclude informal urbanism and decentralised governance models. Theoretically, the study aligns AI-driven urban innovation with systemic change models, ethical AI governance, and climate adaptation theory. It also integrates frameworks such as the Circular Economy Model (CEM), the Inclusive Smart Framework (ISF), and the Triple Bottom Line (TBL), ensuring that technological development is embedded within broader social, economic, and environmental considerations.Practically, the research offers actionable recommendations for urban policymakers, planners, and technology developers. It proposes a phased AI adoption roadmap that begins with low-cost, high-impact interventions and progressively integrates more complex. The study also highlights the need for participatory governance structures, transparent algorithmic oversight, and targeted capacity-building initiatives to ensure that AI technologies are deployed ethically and equitably. By identifying both enabling factors and systemic barriers, the research provides a roadmap for overcoming institutional inertia and financial constraints in the AI implementation process.In conclusion, this study makes a substantial theoretical, methodological, and applied contribution to the growing body of knowledge on AI and urban sustainability. It establishes a new research trajectory that centres the needs of low-income cities in global smart city discourse and demonstrates that artificial intelligence, when contextualised and deployed responsibly, can serve as a powerful catalyst for climate-resilient, inclusive, and data-driven urban development. The framework proposed herein offers both a blueprint and a policy toolkit for cities seeking to align technological innovation with sustainable development imperatives, ensuring that no city is left behind in the global transition toward smart, resilient urban futures