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The role of government initiatives in sustainability practice and performance in the UK hotel sector
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonSustainability has become increasingly important in today’s business world, with companies recognising the need to integrate ethical, social and environmental considerations into their operations. The hotel sector is no exception, and interest in promoting sustainable practices within the industry continues to grow. Although the UK government has introduced several initiatives to advance sustainability in hotels, a comprehensive understanding of their effectiveness as well as the challenges and opportunities that accompany their implementation remains limited.
The main objectives and contributions of this research are to assess the impact of government initiatives on sustainability practices and performances in the UK hotel industry and to identify effective strategies and mechanisms to encourage sustainable practices. There is a lack of a holistic framework that not only explains how government initiatives drive sustainability practices and performance, but also how the various elements of government initiatives, sustainability practices, and performance influence one another, including the interrelationships among these elements. This study adopts institutional theory to examine how external drivers and pressures, i.e. government sustainability initiatives, shape sustainability practices and performance in organisations. Semi-structured interviews with hospitality managers and employees provide a fresh empirical perspective and add a new dimension to the existing body of knowledge. The findings of this research demonstrate that there are four key government initiatives that encourage sustainability practices in the hotel industry in the UK i.e. norms and culture, set of rules, decision-making process and innovative initiative entrepreneurship. The research outcome was the development of the new holistic framework based on empirical results, institutional theory and the previous literature discussion, which resulted in the final research framework, as being a significant theoretical contribution of this research. Policymakers, hotel‐industry stakeholders, and researchers can apply this framework to devise government strategies that advance sustainability practices and performance across the hospitality sector and the wider service economy
Understanding stigma through camera-based mobile apps: studies on visually impaired users
No full textAcknowledgements:
We want to express our sincere gratitude to the DASH charity organisation in London for their invaluable support in participant recruitment and for providing the venue for the workshops. We also thank all the visually impaired participants, staff, and volunteers who generously shared their time and experiences with us.This study used multiple methods to investigate the stigma experienced by visually impaired people (VIP) when using a camera-based assistive mobile application. Initial investigations, including semi-structured interviews with VIP and a formalised expert conversation with academics and designers, highlighted the role of design and cooperation in mitigating stigma issues. The semi-structured interviews, formalised expert conversation, and co-design workshop were conducted to explore further the multifaceted nature of stigma and its impact on technology use. The findings reveal the complexity of stigma and its effects on the attitudes and abilities of VIP to use technology. Four key themes were identified as the design considerations, namely i) prioritising user autonomy, ii) fostering trust in communication, iii) designing for daily tasks, and iv) involving users in the design process. The developed design guidelines as a result of the study provide references for creating smart assistive mobile apps.DASH charity organisation
Optimal Set of Features for Leukaemia Images with Extracted Areas of Interest
Get PDFFeature extraction was found effective in image classification across various studies. From a dataset containing leukemia images of four main categories 17 features were extracted including Haralick texture features, the size and number of white blood cells, and average colours. The process of feature extraction produced high accuracy when evaluated with some machine learning classifiers. Nonetheless, selective application of features was implemented on the modified dataset by extracting regions of interest (ROI). This approach resulted in 131,071 different combinations of features; some configurations achieved superior accuracy. This research aimed to identify the optimal combination of features within an expanded leukemia dataset through ROI extractions. Notably, cell size and count emerged as significant factors contributing to enhanced accuracy
Effect of alkali treatment on mechanical properties of concrete reinforced with phragmites australis fibres
No full textThis study evaluates the influence of untreated and alkali-treated Phragmites australis fibres (PAF) on the fresh and hardened properties of concrete. Concrete mixes with fibre contents ranging from 0% to 2% were tested for workability, density, compressive strength, splitting tensile strength, flexural strength, and ultrasonic pulse velocity (UPV) at 7, 14, and 28 days of curing. Results showed that the addition of PAF reduced workability and density, though treated fibres maintained slightly higher slump (about 3% at 1% fibre) and density (up to 2% higher at 28 days) compared to untreated fibres. Mechanical performance improved significantly at low fibre dosages (0.5-1%), particularly with treated fibres. At 28 days, compressive strength increased by 6% (34 MPa vs. 32 MPa) at 1% fibre, splitting tensile strength improved by 12% (3.14 MPa vs. 2.81 MPa for the control), and flexural strength improved by 32% (10.35 MPa vs. 7.85 MPa for the control). Beyond 1% fibre content, mechanical properties declined due to fibre clustering and increased porosity. UPV results confirmed that alkali treatment enhanced internal homogeneity, with treated mixes achieving up to 3% higher velocities compared to untreated counterparts at the same fibre volume. Overall, the findings demonstrate that alkali-treated Phragmites australis fibres, when used at controlled dosages (0.5-1%), can improve concrete’s mechanical performance while providing an eco-friendly alternative to synthetic fibres.This research was supported by the British Council UK under the Research Environments grant (Grant No: RE-2023-129)
Machine learning for alloy design
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe optimisation of alloy compositions and processing has always been a goal in aluminium alloy design, and the recent increase in volume of data generated has enabled the use of data driven approaches to attempt to aid in this area. The potential for accurate predictions of mechanical properties without the need for lengthy and costly experiments hails as a means to quickly evaluate large areas of the design space. Machine learning models coupled with optimisation methods pose a method of finding sets of input parameters to achieve tailored properties. These methods however are highly data-dependent and in the domain of alloy design, obtaining data detailing alloy compositions, processing conditions and properties can be a costly affair. This work introduces 3 novel datasets to attempt to complete such work on and contributes to the availability of data in the domain for further research. An opensourced based dataset and synthetic dataset were curated and curated, and can be used by all for further research, a proprietary dataset was also curated with the support of an industry partner but is not publicly available. These three datasets all include compositional and thermomechanical processing variance and property values of corresponding alloy yield tensile strength values. Yield strength was used
as a target property for the majority of this work due to its relative simplicity
as a tensile property, less impacted by plastic deformation behaviour than other properties, but also its value as a property used to define material use. These datasets include an open-source dataset consisting of low quality, highly varied data, a proprietary dataset consisting of accurate measurements of a small part of the domain, and a synthetic dataset generated from a physics-based model, used in later experiments.
Interpolation has been shown to be a relatively straightforward task in literature time and time again, with accurate predictions from decision tree based models dominating this type of problem. This work demonstrates their use can effectively predict yield strength values within the domain of existing data to an error of 19.5MPa on proprietary datasets detailing 6000 series aluminium alloys, with typical yield strength values of 350MPa. Similar models are able to achieve close results. These models however lack insight into the aluminium domain, and are frequently subject to the biases of the data which trained them. Qualitative results go further and
show that the responses of these models can be unphysical. This work shows that the use of physical metallurgy variables through averaging methods, commonly used to attempt to aid with domain knowledge inclusion in literature, demonstrates no improvement to model accuracies, contributing to the discussion of this method’s use in similar work. Investigations into the extrapolation capabilities showed that typical models used in the domain demonstrate poor extrapolation performances, a metric not commonly highlighted currently in the domain despite the common purpose of models to guide experiment to outperforming alloys. The best performing typical model for extrapolation being linear regression, achieving errors of 57MPa on 6000 series aluminium alloys. The introduction of Delta modelling techniques taken from other machine learning domains and applied to this property prediction was able to reduce these extrapolation errors to 51MPa however a significant margin, the best performing method for improving extrapolation capability was a transfer learning derivative,
domain adaptation. Applying domain adaptation techniques to the datasets compiled and curated in this work coupled with a novel implementation of delta modelling via the introduced ’Composite model’ further reduced these extrapolation errors by 31% from previous best models. Multiple datasets, with varying quality and differing input feature sets, were used in a composite model leveraging domain adaption. This model was able to make extrapolation predictions of the same sets to an error of 35Mpa for the same 6000 series aluminium alloys.EPSC
Application of factor models to risk premium estimation
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWe provide a rigorous mathematical approach to the beta-pricing model, starting from the
standard two-step cross-sectional regression, through Nonlinear Seemingly Unrelated Regression
(NSUR) and Generalized Method of Moments (GMM), and finally compare the results with several
linear approximation methods. The use of the linear approximation applied to a single-factor
nonlinear system of equations is new in the literature and is one of the major contributions of this
work. Our results show that, in the presence of heavy-tailed distributions, the L1-norm methods
proposed in this study are more appropriate (exhibiting lower bias and variance) for risk price
estimation than traditional L2-norm approaches.
It is also the first time that the Capital Asset Pricing Model (CAPM) is applied systematically to
compare the integration and segmentation between different markets and a given portfolio set.
Our study, Penco and Lucas (2024), applies a two-factor integration model to the economies of
Asia, Europe, Japan and North America, showing integration between the European and North
American economies.
We also extend the integration model to commodity markets. To capture more accurately the
cross-sectional pricing of commodity risk we use the Cochrane factor mimicking approach and
compare the results with alternative dependence-based integration measures using copulas. We
show how the copula correlation between the Stochastic Discount Factor (SDF) and returns
differentiates the contribution of joint dependence from the contribution of the risk prices.
Finally, we introduce a penalised p-value Fama-MacBeth Generalized Least Squares (GLS) regularisation,
which provides several advantages over other methods as it ensures that retained
factors contribute not only to statistical fit but also to risk pricing. Unlike other approaches, this
method regularises the pricing kernel directly. Factors that lack significance or explanatory power
are penalised and removed, while priced and relevant sources of risk are preserved
Wireless Single-Camera Markerless Motion Capture System for Healthcare Applications
Get PDFSingle-camera markerless systems have emerged as a robust methodology for human motion capture and rehabilitation applications. Traditional methodologies typically necessitate multiple strategically positioned cameras or special equipment, including sensors to capture patient ambulatory motion, requiring preliminary calibration and synchronization procedures, which may incur significant costs. This paper presents a wireless single-camera markerless framework for rehabilitation applications that leverages advanced deep learning (DL) architectures to estimate and extract three-dimensional skeletal coordinates from monocular camera views of ambulatory patients. The extracted skeletal representation is subsequently transmitted across wireless communication channels. Then, the rendering technique has been applied for displaying virtual movement of the patient for privacy enhancement. Simulations demonstrate the effectiveness of the framework while maintaining motion assessment capabilities, presenting opportunities for deployment in remote healthcare monitoring scenarios
Friends or foes: Can large-scale mining companies partner with small-scale miners? Yes, they can?
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The data that has been used is confidential.This paper (re)examines the nexus between large-scale mining (LSM) activities and artisanal and small-scale mining (ASM) operations. Broadly speaking, the existing combative resource politics between the two mining entities has created room for the emergence of divergent opinions on sustainable solutions encapsulated in standpoints of ‘cohabitation’ vis-à-vis ‘autonomy’. Employing ‘partnership’ as an analytical lens, this study provides a refreshing perspective of the ‘cohabitation’ of LSM and ASM where they develop and flourish together. Firmly rooted at the base of this success, however, is the formalisation canon that has long ignored the partnership opportunities for ASM operations in many resource-rich countries. We, therefore, argue for formalisation policies to design cohabitation agreements that focus on creating synergies devoid of resource conflicts. Further, we discuss ways through which resources that cannot be fully enclosed by LSM companies can become sources of compromise and negotiation rather than of conflict and violence
A green approach to milkmaid wastewater: Evaluating up-flow anaerobic sludge blanket-biofilter effluent for fodder shrubs cultivation and drought resistant
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The data that has been used is confidential.The agricultural industry is highly dependent on using water quality due to challenging soil and climatic conditions. Hence, this study investigated the potential reuse of treated effluents from a pilot-scale coupled UASB-Biofilter system for the cultivation of drought-resistant fodder shrubs using wastewater generated from the buffalo milkmaid process. The milkmaid wastewater was treated in a sedimentation tank, with the aim of removing large suspended solids and settleable organic matter, and then in an up-flow anaerobic sludge blanket (UASB). The UASB effluent was treated in a biofilter. A field experiment was carried out at the Experimental Farm of Veterinary Faculty, Suez Canal University, from March to October 2023 to study the effect of different quality of treated buffalo milkmaid wastewater with COD (Null, 1000 ± 28, 130 ± 33, 520 ± 27, 100 ± 30 mg.L−1, respectively)on the growth of Acacia saligna and Moringa oleifera shrubs. Split plot designed with five Treatments: Control (freshwater) (T1), sedimentation tank effluent (T2), Up-flow Anaerobic Sludge Blanket (modified UASB with rice straw biochar) effluent unit (T3), conventional UASB effluent unit (T4), and biofilter effluent unit (T5), of each species. Vegetative growth, the biomass of shrubs, and the soil NPK, organic matter, and organic carbon were determined. Moringa oleifera shrubs showed higher biomass (2330.3 g)and nitrogen content (62.02 mg kg−1) in soil compared to A. saligna. UASB treatments (T3 and T4) promoted the best growth, with treated buffalo milkmaid wastewater increasing nitrogen concentrations in the soil. Overall, UASB proved to be the most effective treatment for enhancing growth in A. saligna and M. oleifera shrubs in sandy loam soil. This study evaluates the performance of a pilot-scale UASB-Biofilter system for treated effluent reuse in drought-resistant fodder shrubs cultivation. It also conducts a bibliometric analysis using VOSviewer and RStudio 4.4.1, analyzing trends in wastewater reuse research from 1999 to 2024 based on data from the Web of Science. The review provides insights into the evolution of wastewater reuse in sustainable agro-hydrology and highlights emerging research areas for further investigation.This research was funded by The Science, Technology &Innovation Funding Authority (STDF). Grant supported this work from The STDF Fund within the framework of the "Egyptian American Cooperation Grant" call 20, Egypt (Project ID 45898, C1130, 2021)
Disconnected Platforms, Networked Lives. Social bridging across fragmented payment systems in China
Get PDFMoney is increasingly accessed through multifunctional digital platforms via mobile payment and banking apps that offer various financial services, several of which integrate social media connectivity. In China, Alipay and WeChat Pay handle billions of transactions annually to the extent that physical cards and cash are rarely carried or used. These platforms have been described as ‘walled gardens’ because financial transfers cannot be made directly between them. While Alipay and WeChat Pay share similar transactional components, they differ in their financial products, constraints, interdependencies, and social media integrations, and each offers different interactional possibilities for users to meet their everyday financial needs. We examine how users navigate infrastructural payment problems, perform financial management across different platforms and accounts, deal with ‘trouble’ in making payments, and weave their social and financial lives across platforms to create interoperability between these otherwise disconnected services. Our analysis suggests these apps do not just initiate, record and track payments, but are actively configured by users through interconnected social, transactional, and money-management practices, and that user interactions and digital payment practices are shaped by complex socio-financial arrangements. We discuss the findings, drawing implications for designing social-financial interactions in bridging disconnected services.Mark Perry was supported by a Royal Society Kan Tong Po International Fellowship [KTP\R1\181004]. Part of this work was supported by an APSS Research Fund 2023 grant [P0046004] and APSS Research Cluster grant [P0052636] from the Hong Kong Polytechnic University
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