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Disrupting gender-related violence through youth work: A feminist new materialist enquiry
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis thesis explores gender-related violence in youth work settings. Using a
critical sociomaterial approach (Murris, 2020), the project investigated how
gender-related violence affects young people, youth workers, and youth
settings and how youth work disrupts gender-related violence as a form of
primary educational prevention (Ellis and Thiara, 2014). The methodology used
in this investigation was based on Critical Participatory Action Research
(Kemmis et al., 2014). It unfolded in three phases in four youth work settings.
Phase 1 focused on identifying gender-related violence and planning youth
work interventions to tackle gender-related violence. Phase 2 involved
implementing practice changes. During phase 3, these changes were
sustained and evaluated. The data collected included critical incidents of
practice (Fook and Gardner 2007), in-depth interviews and observations
(Kemmis et al., 2014). The analysis comprises a Feminist New Materialist
cartographic mapping of gender-related violence-youth work-assemblages.
This draws from an ethological approach espoused by Fox and Alldred (2022)
and Feely’s (2020) assemblage analysis to identify and describe how the sociomaterial
components of the gender-related violence-youth work-assemblage
affect one another through processes of de/re/territorialisation (Fox and Alldred,
2022). The findings show that gender-related violence has emotional, material,
and social affects that regulate (territorialise) young people, youth workers,
youth workplaces, and youth work things. The thesis also demonstrates how
youth work that is founded on norm-critical, feminist, and queer pedagogy
disrupts gender-related violence by producing opportunities for resistance and
change. This thesis, therefore, builds on the GAP WORK Project (Alldred,
David et al., 2014, Cooper-Levitan and Alldred, 2022) by demonstrating how
youth workers put feminist, queer, and norm-critical praxis into practice. The
significance of the vitality of the non-human as part of this critical praxis is
illuminated. This is significant for youth work as it challenges the humanist
foundations of both research and practice
Integrated Programme Assessment: 10 years on
Get PDFThis paper explores the implementation and impact of Integrated Programme Assessment (IPA) across diverse disciplines in English higher education institutions. IPA is advocated as a framework that focuses on programme outcomes, aligns learning outcomes with assessment practices, and enhances the authenticity and relevance of assessments, thereby reducing over-assessment and improving student learning experiences and outcomes. Drawing on case studies from four institutions—University of Nottingham, University of Surrey, Brunel University London, and Sheffield University—the paper examines how IPA has been integrated into various undergraduate programmes to foster interdisciplinary learning, promote student engagement, and prepare graduates for professional challenges. Key to the success of IPA initiatives is institutional support that accommodates disciplinary identities and addresses operational challenges, ensuring sustainable and meaningful curriculum change. The study discusses the difficulty of balancing pedagogical innovation against operational realities, emphasising the need for a collective institutional approach to enhance educational quality while accommodating disciplinary diversity. This research contributes insights into the transformative potential of programme-level assessment approaches, informing discussions on enhancing educational practices in universities
Mutual Monitoring: How TMT Faultlines Affect Corporate Financial Fraud
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The data used for this study are available at https://data.csmar.comWe develop and test a theoretical model to investigate the effects of faultlines within the top management team (TMT) on corporate financial fraud. We propose that TMT faultlines can generate mutual monitoring among factional subgroups in the executive suite, which reduces fraudulent behavior. We also examine the contingent roles of subgroup configuration and the TMT members’ tenure overlap in shaping the relationship between TMT faultlines and financial fraud. The mutual monitoring effect is likely to be stronger when the TMT has a balanced subgroup configuration and shorter TMT members’ tenure overlap. We test our argument in the context of publicly listed firms in China. This article extends the mutual monitoring perspective of corporate governance and has important research implications for the corporate financial fraud literature.摘要
本文建构并检验了高管团队(TMT)内部存在的断裂带对公司财务舞弊行为影响的理论模型。我们提出高管团队内部的断裂带能够促进高管群体内形成派系间的相互监督, 从而降低财务舞弊的发生。我们进一步研究了高管团队内部存在的小群体构成和其成员任期重叠程度在断裂带和财务舞弊关系中的调节作用。当高管团队具有更加平衡的子群体构成以及更短的成员任期重叠时, 这种相互监督的作用会更加明显。我们使用中国上市公司的数据对上述观点进行了实证检验。本研究拓展了公司治理中的相互监督理论视角, 并对公司财务舞弊相关文献具有重要的研究启示。This work was supported by the Beijing Municipal Social Science Funds (Grant No. 23GLB016)
Electron Beam Bonding: a novel method for joining additively manufactured carbon fiber thermoplastic composites with aluminum to produce multi-material joints for lightweight applications
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No datasets were generated or analysed during the current study.In recent years, new solutions have been explored to reduce the weight of components for the automotive, railway, and aerospace industries. For this reason, Carbon Fiber Composites (CFCs) have increasingly replaced metals in products that need to be lightweight. However, due to their poor thermal conductivity, CFCs have limited use in applications requiring efficient heat dissipation. In such applications, conventionally manufactured metal alloys are typically utilized. To address these limitations, a novel approach using a combination of additively manufactured aluminum and CFCs is proposed to exploit the distinct advantages of both materials. These innovative hybrid structures aim to combine good structural and thermal management properties with reduced weight compared to conventionally produced metal products. In this study, additively manufactured aluminum alloy (AlSi10Mg) and short carbon fiber Polyamide 6 composite (sCF-PA6) are utilized to produce metal–polymer pairs using electron beam energy to bond the two materials. Direct irradiation of short CFCs with electron beam leads to polymer degradation. Thus, a novel method “Electron Beam Bonding” for joining CFCs with aluminum alloy in various joint configurations using electron beam technology is demonstrated. This innovative approach presents a promising solution for creating metal–polymer multi-materials for lightweight applications.This project was funded by the European Union’s Horizon Europe Research & Innovation Programme 2021 -2027 under grant agreement number 10109149
Low cholesterol and risk of violence in forensic inpatients with schizophrenia, personality disorder or dual diagnosis: same or different?
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All data supporting this work will be made freely available via Brunel University London research repository.Correction: This article was originally published with a spelling error in the name of author Alexandra I. Blakemore. This has now been rectified and this corrigendum published.
European Psychiatry , Volume 68 , Issue 1 , 2025 , e135
DOI: https://doi.org/10.1192/j.eurpsy.2025.10078 .Background:
Violence and suicidality are common in forensic inpatients, most commonly with schizophrenia (SZ), personality disorder (PD), or comorbid SZ and PD (dual diagnosis, DD). There are no biological markers used in risk assessment tools. Lipids may provide a useful biomarker to aid violence prediction, but the roles of diagnosis and sex remain unclear. We therefore investigated lipids in adult forensic inpatients in association with the risk of violence and suicidality by primary diagnosis and sex.
Method:
Anonymized data were obtained for all eligible inpatients [n = 230; 114 SZ (75 males), 77 PD (40 males), 39 DD (20 males)] who had been admitted (2002–2021) to Elysium Healthcare (UK-wide) medium/low-secure facilities on lipids, age, sex, diagnosis, medication, risk of violence and suicidality, as well as days in seclusion and on high observations due to violence.
Results:
Mean total cholesterol (TC) in the patient sample (4.57, s.d. = 1.09) was lower, relative to the age- and sex-corrected UK population norm (4.91 mmol/l). PD (4.46 ± 1.08 mmol/l) and DD (4.24 ± 0.82 mmol/l), compared to SZ patients (4.77 ± 1.14 mmol/l), had significantly lower TC (not explained by statin use; no effect or interaction involving sex). Lower TC had significant though small associations with more days in seclusion or high observation levels due to violence across all patients, and marginally with suicidality in females.
Conclusions:
A low TC-violence (towards others) link exists not only for SZ but also for PD and DD and for males and females, encouraging further enquiry into lipids as a biomarker to aid violence prediction in secure care.This research received no specific grant from any funding agency, commercial or not-for-profit sectors
Energy-Aware Optimization for Off-Grid ORAN with RIS and Edge Computing
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The data supporting the findings of this study are simulation data generated within a MATLAB experimental environment. These data do not originate from real-world measurements. The simulation data are available from the corresponding author upon reasonable request for research and verification purposes.This paper proposes a strategy for designing Open Radio Access Networks (ORAN) to maximise their energy efficiency using solar power, supplemented by Reconfigurable Intelligent Surfaces (RIS) and Mobile Edge Computing (MEC). Because grid power is not always available where these ORAN systems are built, our approach manages the difficulties created by dynamic energy and timing issues found in isolated environments. The approach concentrates on allocating energy to all transmitters, CPU speed and RIS phases in real time, subject to strict rules on power use, latency issues and heat. The primal-dual algorithm we propose reacts to queue and energy changes to update the dual variables and control policies without access to every channel parameter. Our combined (composite) cost function measures energy use, delays encountered by users, reliability of the SINR and fairness. Results from the simulation indicate that using the proposed method lowers energy usage by 25% and average delay by 18%, outperforming baseline models under varying solar and traffic patterns. Robustness is further validated through sensitivity and ablation analyses. This work demonstrates the feasibility of deploying sustainable, intelligent ORAN infrastructures in remote 6G scenarios where conventional power and connectivity are unavailable.This work was partially supported by Brunel University of London
Physical activity and favourable adiposity genetic liability reduce the risk of hypertension among high body mass individuals
Get PDFPhysical Activity was defined as METs for moderate activity ≥ 150 OR METs for vigorous activity ≥ 75 OR summed METs for all activity ≥ 600.Objective:
Hypertension is a global health issue, and the risk factors include genetics, physical inactivity, and excess body fat (adiposity). Genetic predisposition to adiposity generally increases risk of hypertension. Several genetic variants have been identified to increase adiposity but unexpectedly reduce hypertension (favourable adiposity genes). Here, we tested the effect of these genetic variants on risk of hypertension in European ancestry participants under various scenarios of physical activity and body mass index.
Design and method:
Favourable adiposity genetic liability was estimated using previously identified genetic variants and their effect sizes. The study analysed data from 230,027 unrelated participants in the UK Biobank. Logistic regression was used to examine the association between this genetic liability and hypertension (defined as systolic blood pressure greater than or equal to 140 mmHg, diastolic blood pressure greater than or equal to 90 mmHg, or the use of anti-hypertensive medications). The analyses were conducted separately based on physical activity status (physically active and inactive) within low and high body mass groups.
Results:
Individuals with high body mass, could reduce their risk of hypertension by up to 16% depending on the favourable adiposity genetic liability and physical activity status (P adjusted = 1.32 x10-8). In high body mass individuals, physical activity alone contributes to 6-9% reduction in risk of hypertension.
Conclusions:
The study provides evidence that the protective effect of favourable adiposity on hypertension risk varies according to body mass composition and physical activity status
Stochastic co-optimisation of energy, frequency and carbon services considering flexible nuclear power plants
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No data was used for the research described in the article.Integration of high levels of non-synchronous Renewable Energy Sources (RES) and nuclear power plant will play a vital role in decarbonising the electricity grid of Great Britain (GB). However, the uncertainties associated with RES may increase the risk of grid frequency deterioration, which will increase the requirement for the provision of ancillary services such as inertia and Frequency Response (FR). Furthermore, nuclear power plants typically have lower operational flexibility due to limited load following capabilities and the ability to provide FR services, which will not only lead to high system operation cost but also present a potential barrier to reach the net-zero emissions target cost-effectively by reducing the utilisation of RES. A potential solution to mitigate these challenges consists of incorporating thermal energy storage and secondary steam rankine cycle into the nuclear power plant, effectively resulting in a Flexible Nuclear Power Plant (FNPP) configuration. Therefore, this paper proposes a novel ancillary services constrained stochastic unit commitment model, which optimises the simultaneous provision of energy production, synchronised inertia and primary FR from conventional power plants and FNPP, enhanced FR from wind, whilst explicitly considering the uncertainties associated with wind generation using the quantile-based scenario tree method. The effectiveness of the proposed model is demonstrated through several case studies conducted on the 2030 GB power system. The results explore the economic savings and carbon emissions cost reductions obtained from simultaneous co-optimisation of FNPP and the provision of FR services provided by FNPP and wind.This work was supported by the UK EPSRC project: ‘Integrated Development of Low-Carbon Energy Systems (IDLES): A Whole-System Paradigm for Creating a National Strategy’ (project code: EP/R045518/ 1)
Graph signal representations for EEG analysis and machine learning classification
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis thesis investigates the dynamic characteristics of electroencephalography (EEG)
signals using graph signal processing (GSP), focusing on modeling spatial and temporal
variations in brain connectivity. Conventional approaches often rely on static graph structures,
which do not adequately capture time-varying, task-specific, and subject-specific
relationships among brain regions. To address these limitations, this thesis proposes a
dynamic graph representation framework for EEG signals. This framework allows EEG
signals to be represented with a broader set of spatial frequency components derived from
multiple graphs.
A unified spatiotemporal frequency representation is developed to reflect how spatial
patterns evolve over time by combining temporal frequency components with graph-based
spatial frequencies. The proposed methods are evaluated in the contexts of imagined
speech classification and biometric identification. The effects of clustering strategies and
eigenvector selection are systematically examined, and their influence on classification
performance is demonstrated.
The thesis also introduces a graph-based EEG data augmentation method designed to
preserve channel-wise correlations while generating artificial EEG trials. The feasibility of
using dynamic graph representations for biometric identification is further evaluated. Experimental
results demonstrate that individual differences in EEG graph representations
enable reliable subject identification, achieving 100% classification accuracy
Multi-Attribute Data-Driven Flight Departure Delay Prediction for Airport System Using Deep Learning Method
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The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.Complex and diverse multi-attribute flight data can provide data-driven opportunities for airport flight delay prediction. However, it is a challenge to effectively and efficiently process multi-attribute flight data. This paper proposes a hybrid dynamic spatial-temporal long short-term memory network (LSTM) with 3D-directional multi-attribute features (3DF-DSCL) for departure flight delay prediction. The model is based on a 3D convolutional neural network (3D-CNN), graph convolutional network (GCN) and long short-term memory networks (LSTM) model. Firstly, the dataset divides the state and environment of departure flight delay into three situations, including the dynamic operation link, which integrates the trajectory system of aircraft movement in the terminal area, the network congestion link caused by aircraft multi-area movement in the air and ground, and other delay factors determined by the airport take-off and landing requirements. Multi-attribute data are divided into time series, spatial-temporal network and dynamic moving trajectory grid input variables. Among them, the spatial network and dynamic moving trajectory grid data are the inputs of GCN and 3D CNN models, which aim to extract spatial-temporal features. The time series input variables are fed into LSTM. These features are then integrated and fed into LSTM for flight delay prediction, where the flight delay of airport outbound flights is taken as the output variable. The case study shows that the proposed method can significantly improve the accuracy of flight prediction delay. The Mean Absolute Error (MAE) can reach 0.26, which is a 14.47% reduction compared with 2D CNN+GCN+LSTM.This research was funded by Yujie Yuan grant number 3122024QD18 and by basic scientific research of central universities
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