Bradford Scholars
Bradford Scholars is the University of Bradford online research archive. Access is free to anyone interested in research being conducted at Bradford. In the repository you will find a range of materials from journal articles and conference papers to research reports and theses.
Contact the repository team via openaccess@bradford.ac.uk with any queries about Open Access or how to deposit your research papers.
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Publication Optimal Operation of Multi-Energy Systems in Integrated Energy Networks with Distributed Peer-to-Peer Energy Transactive FrameworkMulti-carrier energy systems (MES) and peer-to-peer (P2P) energy sharing have gained research attention for their sustainability benefits to energy systems. As a result, this thesis proposes optimal operation models for integrated energy distribution networks with interconnected MES operating in a P2P market. A stochastic bi-level optimization model is formulated for interconnected energy hubs in an integrated gas/electrical distribution network with P2P trading to simulate the interaction between the distribution network operator (DNO) and energy hubs. A new P2G energy hub is then modelled to capture the strategic interactions and dependencies of the energy networks, enabling more efficient resource allocation and cost minimisation. Furthermore, to improve energy management across multiple energy networks, a novel model is developed to optimally size and site an innovative design for a multi-energy storage system (MESS). Lastly, to investigate the coordination of energy hubs in a fully integrated energy network of electricity/heat/gas, a novel optimisation model is proposed for the multi-node sizing and siting of multiple interconnected MES. The effectiveness of the proposed models was demonstrated through case studies. The results show that by participating in the P2P market, the energy hubs can reduce trade with DNOs, reducing their daily operating cost. Furthermore, the combined effect of P2P and the energy hubs reduced the planning cost of the MESS by 11% and the energy hubs' operating cost by 7%. The results also show that optimally configured MES could support the operation of integrated energy networks with renewable energy. Moreover, the thesis offers valuable strategic decision-making tools for energy system operators.Publication Exploring Player Frustration: A Comparative Study of One vs. Multi-Level Difficulty in Video Games and the Short-Term Impact on PlayersWith the rise in popularity of challenging video games such as Elden Ring, belonging to the Souls genre known for its high difficulty level, the question arises regarding the immediate consequences players experience after frustrating encounters. Specifically, this study investigates the short-term consequences of frustration after playing action RPG video games with one difficulty level vs multiple difficulty levels and whether this affects the day-to-day life of the players. This research provides insights for players and game developers, shedding light on the potential consequences of frustration during gameplay and offering less frustrating game design alternatives. Based on the literature review of difficult video games, two versions of the same experimental video game were created to conduct a comparative analysis, version A with one level of difficulty and version B offering multiple difficulty options. An online survey was distributed to the players who played the experimental video game to gather data about the consequences of frustration after playing. The survey analysis demonstrated that playing video games with one difficulty level caused significantly more consequences on players than games with multi-level difficulty. The results indicate that players experienced significant consequences like laziness, less productivity, discomfort, and unwellness, which affected their day-to-day lives shortly after playing a game with one difficulty level. On this basis, it is recommended that players who don’t want to experience frustration should play games with multiple difficulty levels which cause less frustration. Further research is needed to identify the long-term consequences of playing games with one difficulty level.Publication Fluid flow measurement using correlation techniquesThe applicability of the temperature cross correlation method to measurement of flow under steady and pulsating flow conditions is investigated. The factors to be considered in applying this technique to different flow systems are discussed and its advantages over the conventional techniques are highlighted. The method involves monitoring random temperature variations in the flow at two points along the pipe and determining the transit time between the two points using the cross correlation technique. The velocity of flow is thus determined by the time and distance between the two points. The method is proved in the laboratory to be an absolute, reliable and accurate method of volumetric flow measurement in laminar flow of highly viscous liquids (e.g. oils) and for the turbulent flow of liquids under steady and pulsating flow conditions. For low viscosity liquids (e.g. water) under laminar flow conditions the measurement is dependent on the flow characteristics and fluid properties and hence sensitive to upstream effects and pulsations. The flow characteristics of laminar and turbulent flow under steady and pulsating flow conditions and their influence on the heat transport mechanism are examined. Theoretical models are presented for developed and developing flow conditions and the experimental results show fair agreement with theory. Errors in the measurement technique are discussed and design conditions are recommended so as to minimise these errors. It is shown theoretically and experimentally that an important parameter called the ‘frequency parameter’ (KR) governs the applicability of the technique to measurement of pulsating laminar flow. Only if KR<1 can the flowmeter, calibrated for steady flow measurement, meter pulsating flow accurately. This condition is satisfied easily by highly viscous liquids and hence this method is an accurate method for metering highly viscous liquids under pulsating flow conditions. The measurement of developing laminar flow with moderate amplitude pulses (<50% of mean) and turbulent flow with small and large amplitude pulses, is independent of the frequency parameter. This technique of flow measurement offers minimum pressure drop, no mechanical wear, no blockage of pipe lines and can be used under adverse environmental conditions using a simple and robust sensing element. The flowmeter gives a linear output and accuracy better than +/- 2% can be achieved. The temperature signals used have rather narrow bandwidths and therefore averaging times of between 2 and 4 mins are required. However a better heat injection technique is described which will provide wider band width signals which should give a quicker response flowmeter.Publication Integrated fuzzy decision-making methodology with intuitionistic fuzzy numbers: An application for disaster preparedness in clinical laboratories(Elsevier, 2025-03)Society is on constant alert due to the increasing frequency and severity of Seasonal Respiratory Diseases (SRDs), posing significant challenges from both a humanitarian and public health perspective. The recent COVID-19 pandemic has tested the capacity of clinical laboratories to address seasonal infections, epidemic outbreaks, and critical emergencies. This scenario has led to operational burdens, primarily from resource limitations, a lack of proactive planning, and the low adaptation to unforeseen circumstances. Coupling different data-driven approaches considering multi-criteria weighting, interdependence assessment, and outranking are critical for devising effective interventions upgrading the operability of clinical labs during SRDs. Nonetheless, a deep literature review revealed there are no studies using these hybridized approaches when addressing this problem. Consequently, this article proposes the application of an innovative hybrid Multicriteria Decision-Making (MCDM) methodology that integrates the Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP), Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL), and Combined Compromise Solution (CoCoSo) to assess the disaster preparedness of clinical laboratories during SRDs. Initially, we applied IF-AHP to assign the relative weights to criteria and sub-criteria, considering the inherent hesitation and uncertainty in decision-making. Subsequently, IF-DEMATEL was utilized to analyze the interrelationships between criteria, providing insights into the interrelations among clinical lab disaster management drivers. Finally, the CoCoSo method was applied to estimate each lab’s Preparedness Index (PI) and detect response gaps when coping with SRDs. The suggested methodology was validated across nine clinical laboratories in Colombia during the most recent respiratory pandemic. This study contributes to the healthcare sector authorities by identifying key criteria and sub-criteria affecting the response of clinical labs, the elicitation of main response drivers in clinical labs when facing SRDs, and the calculation of a multidimensional indicator representing the preparedness of the labs. This work also enriches the literature by applying the IF-AHP, IF-DEMATEL, and CoCoSo approach to a challenging case study requiring a multi-method data-driven application. Furthermore, it suggests future directions to improve the proposed framework in other related contexts.Publication Examining the Influence of Sustainable Development Indicators on Economic Development: A Machine Learning Approach with Evidence from Africa(Emerald, 2025-11-12)Purpose Government interventions for economic growth often fail to enhance citizen well-being, highlighting the need for research on effective strategies. Integrating sustainability into economic planning holds potential, yet there is a dearth of research on the interplay between sustainable development indicators (SDIs) and economic development (ED). This study addresses this gap by examining the influence of SDIs on ED through machine learning (ML), specifically the random forest (RF) algorithm, analyzing 408 SDIs across six African countries: Algeria, Egypt, Kenya, Morocco, Nigeria and South Africa. Design/methodology/approach A quantitative approach was adopted, drawing data from two prominent databases: the World Bank’s Sustainable Development Goals database and the United Nations Development Program’s (UNDP) all-composite indices time series dataset. The random forest algorithm was employed to investigate the relationship between SDIs and ED. Findings This study found no negative relationship between the identified SDIs and Human Development Index metrics. The findings reveal that indicators such as gross national income, CO2 emissions, and mortality rates significantly impact ED, while others (e.g. forest area and school enrolment) vary by country. The findings suggest that tailored policies leveraging country-specific resources and capabilities can drive sustainable economic growth and enhance performance management for optimized development outcomes. Originality/value Unlike prior studies on ED, this study shifts the focus from traditional economic variables to non-economic indicators such as health (e.g. under-5 mortality rates), environment (e.g. CO2 emissions) and society (e.g. urban population). The research expands ED metrics, provides country-specific insights and proposes an integrated performance management approach incorporating underexplored variables in the literature.