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UK-China Cultural and Creative Industries Collaboration: Opportunities, Challenges and Mechanisms
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Advertising Against the Government: Nationalised Industry Advocacy Advertising 1970-1985
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This paper draws upon archival research conducted with the records of a mix of Government and Business archives. It foremost uses Government records from the National Archives in Kew, London to explore government responses to advertising, nationalised industries and social responsibility. It also draws upon records from the UK Parliamentary Papers materials to observe political reactions to such campaigns in parliament. Similarly, it draws upon a number of business archives of previously nationalised industries. The primary business archives in focus for this particular paper are the National Gas Archive located in Warrington – which represents the archive of previously nationalised gas and energy - and the British Airways Heritage Centre in Harmondsworth – which represents the previously nationalised airways in Britain. This project also draws upon materials in the History of Advertising Trust in Raveningham, Norfolk.
These materials will be analysed primarily using organizational source criticism (Heller 2023), which draws a distinction between “performative” and “reportative” organisational sources, and between “narrative” and “documentary” organisational sources. In turn, it will examine how the organisation sought to enact political change, and examine the perceptions of that attempt to enact political change.From 1970, slogans such as “British Gas is a national asset”, “Why you can take coal for granted”, “British industry deserves better lighting” and “How BOAC takes good care of you and Britain” headlined advertisements in a number of national newspapers. Such taglines were designed to advocate for its position of nationalised industries in the national economy. Using the case of the nationalised energy sector (and drawing upon materials from other nationalised industries) this paper shows how advertising was used to talk directly to the British public about political issues. In so doing, this paper seeks to fill an important gap in both the literature of nationalised industries and marketing scholarship. The wider public sector has rarely been historicised, and where they have tend to argue that the sector had no marketing culture. This paper seeks to challenge the literature and argue that nationalised industries did have marketing cultures. However, those cultures were subject to unique pressures felt only by nationalised industries and therefore had to evolve in different ways to respond to those pressures. This paper examines the broader tensions between the many stakeholders of the nationalised industries and their relationship to the marketing process, including the owners (the general public), the Government(s) and the staff. As a managerial process, marketing was often thought of by these stakeholders as a secondary endeavour
The detrimental effect of institutional forces on green innovations and customer cooperation
Get PDFSupplementary data are available online at: https://www.emerald.com/apjml/article-abstract/doi/10.1108/APJML-06-2025-1110/1300305/The-detrimental-effect-of-institutional-forces-on?redirectedFrom=fulltext#supplementary-data .Purpose:
This study examines how macro (unpredictable government interventions) and micro (guanxi) institutional forces moderate the effect of firms' green innovations on green customer cooperation (GCC) practices.
Design/methodology/approach:
Using a randomized experimental vignette method, the research collected data from 240 managers in China's electronics sector, each presented with realistic supply chain scenarios. We used Multiple regression analyses to analyze data and test our hypotheses.
Findings:
The findings indicate that government intervention has a negative influence on the relationship between green innovations and the adoption of GCC (when government intervention is high, the impact of green innovations on the adoption is weaker). Guanxi negatively moderates this relationship as well: at a high level of guanxi, the impact of green innovations on the adoption of GCC is significantly weaker.
Originality/value:
Our results highlight the detrimental effects of both macro and micro institutional forces by recognizing their resource-demanding nature. Acknowledging the importance of green customer cooperation practices from a customer-oriented approach, this study extends green innovation and supply chain management literature
Towards Reliable Adhesive Bonding: A Comprehensive Review of Mechanisms, Defects, and Design Considerations
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Data sharing is not applicable. No new data were created or analyzed in this study.Adhesive bonding has emerged as a transformative joining method across multiple industries, offering lightweight, durable, and versatile alternatives to traditional fastening techniques. This review provides a comprehensive exploration of adhesive bonding, from fundamental adhesion mechanisms, mechanical and molecular, to application-specific criteria and the characteristics of common adhesive types. Emphasis is placed on challenges affecting bond quality and longevity, including defects such as kissing bonds, porosity, voids, poor cure, and substrate failures. Critical aspects of surface preparation, bond line thickness, and adhesive ageing under environmental stressors are analysed. Furthermore, this paper highlights the pressing need for sustainable solutions, including the disassembly and recyclability of bonded joints, particularly within the automotive and aerospace sectors. A key insight from this review is the lack of a unified framework to assess defect interaction, stochastic variability, and failure prediction, which is mainly due complexity of multi-defect interactions, the compositional expense of digital simulations, or the difficulty in obtaining sufficient statistical data needed for the stochastic models. This study underscores the necessity for multi-method detection approaches, advanced modelling techniques (i.e., debond-on-demand and bio-based formulations), and future research into defect correlation and sustainable adhesive technologies to improve reliability and support a circular materials economy.This research received no external funding
Robotic Systems for Cochlear Implant Surgeries: A Review of Robotic Design and Clinical Outcomes
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No new data were generated or analyzed in this study; therefore, data sharing is not applicable.Sensorineural hearing loss occurs when cochlear hair cells fail to convert mechanical sound waves into electrical signals transmitted via the auditory nerve. Cochlear implants (CIs) restore hearing by directly stimulating the auditory nerve with electrical impulses, often while preserving residual hearing. Over the past two decades, robotic-assisted techniques in otologic surgery have gained prominence for improving precision and safety. Robotic systems support critical procedures such as mastoidectomy, cochleostomy drilling, and electrode array (EA) insertion. These technologies aim to minimize trauma and enhance hearing preservation. Despite the outpatient nature of most CI surgeries, surgeons still face challenges, including anatomical complexity, imaging demands, and rising costs. Robotic systems help address these issues by streamlining workflows, reducing variability, and improving electrode placement accuracy. This review evaluates robotic systems developed for cochlear implantation, focusing on their design, surgical integration, and clinical outcomes. This review concludes that robotic systems offer low insertion speed, which leads to reduced insertion forces and lower intracochlear pressure. However, their impact on trauma, long-term hearing preservation, and speech outcome remains uncertain. Further research is needed to assess clinical durability, cost-effectiveness, and patient-reported outcomes.Oneeba Ahmed’s PhD is funded by the Royal National Institute of Deaf People (R.N.I.D) under grand number S64 to Brunel University of London, UB8 3PH, United Kingdom
Design of MIMO Planar Sparse Array and Improved Back-Projection Algorithm for High-Performance 3-D Microwave Imaging
No full textA novel two-step iterative optimization method could be used in microwave and mm-wave bands for optimal topology of 2-D sparse multiple input multiple output (MIMO) arrays and an improved back-projection algorithm (IBPA) for near-range scenarios have been proposed to achieve high-resolution three-dimensional (3D) imaging. According to point spread functions (PSF), the array topology results from the proposed design method has superior performance on sidelobe level both in the interference region (IR) and non-interference region (NIR), and the obtained peak sidelobe levels (PSLs) are lower than the conventional topologies over 2 dB. The IBPA has weighting factors incorporated into its forward physical wave equations to improve accuracy of the model. The imaging capabilities of the proposed array and IBPA have been experimentally verified. The proposed array could further mitigate the artifact distribution in target image than conventional arrays which is consistent to theoretical analysis. The proposed IBPA could provide higher resolution and lower PSLs without increasing the computational complexity.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62293493)
Evaluating physician associate students’ perceptions of an online team-based learning session on stroke medicine
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Data are available upon reasonable request. Further data for this study including the teaching material is available upon reasonable request.Correction notice: This article has been corrected since it was first published. License type has been updated from 'CC-BY-NC' to 'CC-BY'.Supplementary files are available online at: https://bmjopenquality.bmj.com/content/14/1/e002966#supplementary-materials .Background and aims: Team-based learning (TBL) is an effective, active learning strategy that has been validated and used in medical schools. It consists of three phases; preparation, readiness assurance tests and application exercise. It follows a ‘flipped classroom’ model where assessment takes place at the beginning and encourages team discussions that emulate clinical practice. TBL has been used in medical education; however, there is a lack of literature on its use specifically in physician associate (PA) education. We therefore explored the perceptions of a Stroke TBL session among PA students in a UK PA Programme.
Methods: The study took place during the COVID-19 pandemic; therefore, TBL was implemented virtually using online video conferencing platforms. The students’ perceptions were then analysed using anonymous online questionnaires sent to them shortly after the session. The questionnaire included specific questions comparing TBL to other teaching methods such as problem-based learning (PBL).
Results: Overall, the students felt that TBL was an effective teaching method that was better than other methods such as lectures and PBL.
Conclusions: This was a small study of a single TBL session that provided rich qualitative data around students’ perceptions. It is a good foundation for developing TBL further in UK PA Programmes. We encourage further use of this strategy with further studies in this area.The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors
Green AI for industry 4.0: Energy-efficient generalised deep learning approaches to induction motor condition monitoring
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe field of Industry 4.0 has seen a significant increase in demand for efficient and
effective methods of data-driven analysis, particularly in the domain of condition
monitoring for machinery. This thesis explores the use of deep learning techniques
to address the challenges faced in this field, focusing on the development of energyefficient
and generalised approaches for Induction Motor fault detection and classification.
The first part of the thesis introduces the Multi-Channel LSTM-Capsule Autoencoder,
a novel Neural Network (NN) architecture designed to tackle issues such as
generalisation ability, the need for large volumes of labelled data, and understanding
spatial context in multivariate time series data from a single data source. Experimental
results demonstrate the architecture’s resilience to overfitting, improved
training efficiency, and state-of-the-art performance in outlier detection.
Building upon the LSTM-Capsule Autoencoder, the second part presents the
Dataset Fusion algorithm, a novel dataset composition method for fusing periodic
signals from multiple homogeneous datasets into a single dataset while retaining
unique features. The proposed approach, tested on a case study of 3-phase current
data from Induction Motor fault datasets, significantly outperforms conventional
training approaches and effectively generalises across all datasets. The algorithm’s
effectiveness under non-ideal conditions and its computational efficiency, in line with
the principles of Green AI, highlight its potential for practical use in real-world
applications.
The final part introduces the Order Domain Transformer (ODT), a pre-processing
algorithm designed to standardise and align the frequency components of signals
from different motors, enabling the fusion of multiple heterogeneous datasets in
the frequency domain. Experimental results indicate that using ODT maintains
performance on data from the same motors but results in a substantial improvement
in cross-motor generalisation and model performance. The ODT approach
demonstrates the potential to train a single model for multiple motors, optimising
the utilisation of available labelled data and reducing the computational resources
required for training.
The proposed methods in this thesis progressively address the challenges of working
with single data sources, multiple homogeneous data sources, and multiple heterogeneous
datasets, providing a comprehensive framework for data-driven fault
detection and classification in industrial settings
Genomics assessment of the impact of ionising radiation on the human brain
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIonising radiation (IR) is a pervasive environmental factor with known biological effects,
yet its long-term impact on brain function and cognitive processes remains insufficiently understood.
In recent years, evidence has emerged purporting IR as a possible risk factor for
cognitive impairment, characterised by deficits in learning, memory, and information processing
ability. While consequences of prenatal exposure has been more extensively studied,
there is much more uncertainty about the effects of IR when exposure occurs in adolescents
and adults, particularly at low to moderate doses. High-throughput technologies have highlighted
changes in genes and pathways related to brain function secondary to radiation exposure.
Consequently, individuals exposed through medical therapy, nuclear disasters, and
occupation might be subject to such a risk.
The aim of this thesis was to investigate the relationship between gene expression, genetic
variation and IR exposure, with a specific focus on the molecular mechanisms that
underlie the impact on the brain’s function and activity. A multi-tiered approach was employed
integrating data from healthy and disease brain tissues, IR-exposed cell lines and a
unique population of potentially exposed British nuclear test veterans (NTVs). Leveraging
publicly available data, the gene expression profiles in normal brains, brain cancers and neurological
disorders were investigated. Furthermore, the impact of exposure to IR at various
levels was studied in human cell lines. Whole genome sequencing data from the NTVs were
analysed and compared with a matched military control group, to reveal the potential impact
of radiation in the context of brain function and cognition. The present work includes
critical analysis of phenotypic, clinical, genomic and transcriptomic data to identify patterns
of gene expression and variation.
The findings revealed that the healthy brain is tightly regulated by gene specific expression
patterns involved in synaptic transmission, cellular stress responses and metabolic
processes. Brain cancers revealed upregulation in the cell cycle and proliferation pathways,
whilst highlighting dysregulation of neuroinflammation and metabolic processes in
the Alzheimer’s brain. Limited differential expression was noted in psychiatric disorders,
suggesting complex, multifactorial origins in cognitive symptoms.
Transcriptomics landscape analysis of IR-exposed human cells identified dose-responsive
genes, with the number of differentially expressed genes increasing tenfold from low to high
doses of exposure. While the effects of IR did not show a linear dependence on dose, the
key pathways related to DNA repair, oxidative stress response and inflammatory pathways
affected were observed across all exposure levels. These pathways were also found to be
implicated in cognitive dysfunction and neurodegenerative diseases, suggesting a potential
link between IR and CNS diseases. Moreover, the results pinpointed to several novel
radiation exposure and dose-specific biomarkers, that showed good discriminatory power
between irradiated and control samples.
Genomic analyses in the military veteran population revealed no significant differences
in the frequency of single nucleotide polymorphisms, indels, and the incidence of clustered
mutations between the two cohorts. The lack of genetic variation between the groups appeared
to be influenced by ageing. Several shared and cohort-specific mutations pinpointed
genes potentially associated with ageing, underpinning some of the self-reported health
challenges of the NTVs have and some known associations to CNS and cognitive functions. The findings elucidate the biological responses, mutations, genetic, and the adverse
pathways that can lead to cognitive impairments. Additionally, it illustrates the limited
association between the NTVs and potential IR exposure.Centre for health effects of radiological and chemical agents via nuclear community charity fund and Brunel University of London
Updatable Online Learning Successive Difference Mode Decomposition for Rotating Machine Fault Diagnosis
Get PDFSignal processing methods are widely used in fault diagnosis and are known for their strong interpretability. Among them, signal adaptive decomposition algorithms are used to extract the features of fault signals. As an effective adaptive decomposition algorithm, difference mode decomposition divides the signals into three components using spectrum weighting. However, it can only separate mixed fault components and is not suitable for multi-class fault diagnosis tasks. This paper presents a successive difference mode decomposition method. The reference component and concerned components (fault features) are defined based on the differences in faults. Then, the filters corresponding to different components are obtained through iterative convex optimization at each layer. Finally, using these filters, signals are decomposed into multiple fault components corresponding to different fault sources. Furthermore, the white noise replacement module is proposed to solve the gradient vanishing problem introduced by successive decompositions. Also, an updatable online learning framework is proposed for the incremental demand scenario, providing data efficiency and interpretability. The effectiveness of this method is validated on real datasets.Science Center for Gas Turbine Project (Grant Number: P2022-DC-I-003-001)
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