12508 research outputs found
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Development of novel tumour-activated peptide prodrugs of ATR/ATM inhibitor, AZD6738
No full textThe full text will be available at the end of the embargo period: 3rd April 2026The author's name as given on this thesis is Francis MPRAH BARNIEH. His publications use the name format Francis M. Barnieh
A Study On Employee’s Intention To Adopt Green Practices At The Workplace In The Context Of The Hotel Industry
Full text linkThis study aims to examine the effect of organisational commitment and
employee’s pro-environmental behaviour at home on their intention to adopt
green practices at the workplace in the context of hotel industry, by taking the
theory of planned behaviour as a conceptual framework. Hotel employees play
a critical role that affects customers' experiences, which then affects the overall
hotel performance. However, the mechanism that affects their behavioural
intention has yet to be investigated properly. Thus, a survey was conducted to
collect the data from employees working in green and non-green hotels in
Malaysia. Overall, there were 407 responses received, which represented a
response rate of 55.75 percent. Then, a set of hypotheses was tested using
the structural equation modelling. The empirical results indicate that
organisational commitments have a positive effect on the attitude for engaging
in a green behaviour and subjective norm, which in turn influenced employees’
intention to adopt green practices at work. Meanwhile, employees’ pro environmental behaviour at home has an indirect impact on employee’s
intention to adopt green practices in the workplace through their attitude for
engaging in a green behaviour, subjective norms, and perceived behavioural
control. The findings lead to a theoretical contribution by incorporating another
theory into the theory of planned behaviour, which is the social bond theory through organisational commitment and spill-over effect through pro environmental behaviour at home. Subsequently, a practical recommendation
from this research is attainable to policy makers and hotel providers in order
for them to understand and increase employees’ willingness to adopt green
practices at the workplace.The full text will be available at the end of the embargo: 15th Dec 202
The role of value-based leadership in multi-agency safeguarding responses to child sexual exploitation
No full textYesThis chapter examines the development of value-based leadership (VBL) within multi-agency safeguarding and how complex problems are identified and managed as a collective. Theories of VBL have been chosen to help identify how common components associated with this form of leadership can inform leadership practices in a multi-agency partnership environment. To bridge the gap between VBL and multi-agency safeguarding, a case study relating to a multi-agency response to Child Sexual Exploitation (CSE) is presented, with links to evolving leadership structures, practices, and processes. The case study exemplifies the benefits of flattening traditional hierarchical leadership structures, alongside the importance of allowing the voices of all involved to be heard and listened to. The need to balance collective decision making, with accountability across and within organisations, is also focused upon. Subsequently, the chapter considers the role of VBL in complex safeguarding situations and offers novel empirical contributions to best practice in social work.The full-text of this book chapter will be released for public view at the end of the publisher embargo in September 2026
Keratoconus Severity Staging Using Random Forest and Gradient Boosting Ensemble Techniques
No full textYesAccurate keratoconus (KC) staging is vital for improving patient care and guiding clinical decision making. Choosing the right Machine Learning (ML) algorithm is key to effectively tackling this challenge and ensuring optimal performance. This paper presents a detailed comparison of eight ML algorithms commonly used in KC detection, based on a clinical dataset collected by the authors over the past decade. The study investigates each algorithm's effectiveness in distinguishing KC severity stages. Results showed that ensemble learning algorithms outperformed others, with Random Forest (RF) achieving the highest accuracy at 98.82%, followed closely by Gradient Boosting (GB) at 98.24%. These models also had the highest classification quality scores, with RF at 0.985 and GB at 0.978. These findings underscore the strength and effectiveness of ensemble classifiers for KC severity staging. Furthermore, the top-performing model (RF) exceeded results from recent studies on KC severity, highlighting its potential for clinical application.The full text will be available at the end of the publisher's embargo: 20th May 202
Design, synthesis and biological evaluation of duocarmycin compounds for tumourselective therapy
No full textThe full text will be available at the end of the embargo: 21st Dec 202
Consumer Willingness to Repair Electronics: Machine Learning-Based Analysis of Consumer Survey
Full text linkYesWith the introduction of Right to Repair legislation, manufacturers are required to provide repair services.
However, successful implementation depends not only on product design and repair infrastructure but also
on consumer willingness to repair rather than replace. This study investigates consumer attitudes and
behaviours to repair electronics through a machine learning–based analysis of survey data from 840
respondents across 51 countries. Five consumer profiles were examined: Self Repair Oriented, Professional
Reliant, Replace Oriented, Throwaway Oriented, and Procrastinators, capturing the distinct reasons,
motivations, barriers, and interventions to repair. Results show that 73% of participants are willing to repair,
with cost savings and the enjoyment of do-it-yourself activities as major drivers, while lack of skills, tools,
knowledge, and time remain the most significant barriers. Our findings outline the steps and interventions
needed for each consumer profile to facilitate the repair of electronics
Digital Technology Readiness: Conceptualisation And Empirical Validation
No full textYesThis study aims to conceptualize and introduce a new formative construct called ‘Digital Technology Readiness’ (DTR), to develop a scale to operationalize this new construct and to test an integrative conceptual model for the influence of Digital Readiness on key service outcomes
Linking the Essence of a Place Brand to Motivations
No full textYesCurrent methods in destination branding seldom capture the essence of a brand in operationalizable terms. This research aims to apply causal maps to establish of the essence of a destination. Brand essence describes the cognitive features that cause affective attributes. The study applies cognitive causal modelling by employing a qualitative pre-study based on face to face personal interviews, Study 1 survey utilising a face to face questionnaire and Study 2 based on an online survey. Respondents for the pre-study and Study 1 were recruited using intercept sampling whereas the online sample was drawn with the help of an online consumer panel. The context (the Fens) has a difficult-to-define, almost ephemeral brand. For the Fens, countryside is the most causal attribute of affective outcomes of relaxed and friendly. The dimensions of brand essence reflect attributes that are liked by visitors, considered unique to the destination and have always been there, qualities that are highly useful in defining and promoting the differentiating features of the destination. The project will be developed to extend attention restoration and motivation theories by correlating motivations with causalities post-Covid. This future development will explore recovery from psychological distress during periods of extreme contexts and explore the influence of the changing context on motivations in choices of location to visit
Automotive IVHM: a framework for intelligent health management of powertrain systems. Development of a framework and methodology based on the fusion of knowledge-based and data-driven modelling approaches for diagnostics and prognostics of complex systems with application to automotive powertrain systems
No full textThe full text will be available at the the end of the embargo period: 29th Jul 202
Decarbonisation in Supply Chain Management with Blockchain Technology: Using Multi-Criteria Decision-Making in Industry 4.0
No full textYesThis study explores the role of blockchain technology (BT) in enabling decarbonisation within healthcare supply chain management (SCM) in the context of Industry 4.0. A novel hybrid methodological framework combining the fuzzy spherical analytical hierarchy process (FS-AHP) and the Z-number logarithm methodology of additive weights (ZLMAW) is developed to address the complexities and uncertainties inherent in this domain. The research identifies critical factors influencing the adoption of BT in decarbonised SCM and ranks healthcare companies based on their suitability for implementation. The results highlight the lack of a robust scope framework as a priority concern and demonstrate the efficacy of the proposed model in advancing sustainable practices. This work contributes to theoretical advancements, by integrating multi-criteria decision-making (MCDM) techniques with blockchain in SCM, and practical insights, by offering a scalable approach to supply chain decarbonisation. The findings bridge gaps in the literature by addressing the intersection of blockchain, Industry 4.0, and sustainability in healthcare, presenting actionable strategies for supply chain optimisation.The full text will be available at the end of the publisher's embargo 12 months after publication