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Non-Intrusive Monitoring of Machining Processes for In-Process Product Health Prediction based on Machine Learning
No full textPeer-review under responsibility of the scientific committee of the 18th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME ‘24)Intelligent monitoring systems for machining processes have been gradually developed for various scenarios, such as tool condition monitoring, chatter detection and product health prediction. Existing approaches of monitoring machining processes for quality assurance typically rely on intrusive sensor systems, such as dynamometers and spindle accelerometers, to obtain informative signals for training an algorithm, thus limiting their widespread adoption in industry. This paper presents a non-intrusive machining process monitoring method for in-process product health prediction with uncertainty information using Gaussian Process Regression (GPR). The performance of the proposed method is demonstrated on the prediction of dimensional deviations of a milling process.Royal Society for the grant RGS\R2\22209
Technological Transformation in Human-Robot and Human-AI Ensembles in Marketing and Consumer Services: Activity Theory and a Socio-Cultural Framework
No full text........
Gender and equality in economics and finance journals
Full text linkJEL: A11, J16, J24.∗ This is a revised version of earlier papers titled “Gender differences in citations at top economics journals” (January 2018) and “Gender and equality at top economics journals” (May 2020). We especially thank Lunna Ai for excellent research assistance, Olga Gorelkina for help with the proof of Theorem 3.1 and Marco Scarsini for suggesting several references. We are also grateful for valuable feedback from Stefano DellaVigna, Jeremy Edwards, Guido Imbens, Balázs Muraközy, Stephen Ross, Anne Winkler, audience members at the 2019 American Economic Association and European Economic Association Annual Meetings and seminar participants at the Universities of Liverpool, Nottingham, Portsmouth and Duisburg-Essen. All errors are definitely our own.Using (asinh) citations as a proxy for quality, we show that female-authored papers published in a wide array of economics and finance journals are, on average, higher quality than male-authored papers; however, we find no evidence that women’s manuscripts are accepted at higher rates. Conditional on publishing in the very top journals, we also find that men’s and women’s papers are higher quality when they co-author with women instead of men: for example, the same senior male economist receives almost 80 log points more citations when he co-authors with a junior woman as opposed to a junior man. Under strong—but we believe reasonable—assumptions, we argue that these findings imply that economics and finance journals hold female-authored papers to higher standards and, consequently, do not publish the highest quality research. They also suggest that popular proxies of academic impact discount women’s contributions, and that existing co-authoring relationships in economics under-exploit the capacity of female researchers.
Trust, risk and organic food: Evidence from the UK and Japan
No full text......This research is funded by the Japan Society for the Promotion of Science (JSPS, grant reference JPJSJRP 20211704) and the UK Research and Innovation's Economic and Social Research Council (UKRI-ESRC, grant reference ES/W011913/1)
User experience and usability requirements of a physical activity smartphone application for wheelchair users with spinal cord injury
No full textData availability statement:
The quantitative datasets generated and/or analysed during the current study are available at https://doi.org/10.17633/rd.brunel.28524245.v1. The qualitative datasets generated and/or analysed during the current study are not publicly available because they contain information that could compromise participant privacy and/or consent.Supplemental material is available online at: https://www.tandfonline.com/doi/full/10.1080/17483107.2026.2628898# .Purpose:
Usability considerations for wheelchair users remain underexplored. This study evaluated usability requirements of a smartphone App (MvBii) for monitoring physical activity and sedentary behaviour in manual wheelchair users with spinal cord injury (SCI).
Materials and methods:
A mixed-methods design was adopted. Manual wheelchair users with SCI completed System Usability Scale, e-loyalty and user experience questionnaires, think-aloud sessions and scenario-based workshops. Six design and research evaluators undertook think-aloud sessions. Qualitative data was analysed thematically and mapped against heuristics.
Results:
Ten participants with SCI (C5-L1; three females) with a mean age of 51 ± 9 years took part. The App received positive ratings on e-loyalty (mean scores, 5.6 ± 1.51 to 6.10 ± 0.99 across items) and user experience (4.3 ± 1.03 to 5.93 ± 0.78) from participants with SCI. A novel heuristics principle was developed to explore “accessibility and inclusion” usability issues. Thematic analysis captured patterned meanings across tasks and heuristics including “Navigating with autonomy” (e.g., challenges with interface clarity and understanding terminology), “Language and representation” (e.g., simplifying using inclusive language and icons), and “Seeing progress not noise” (e.g., physical activity notifications that encouraged self-competition without external pressure).
Conclusions:
This study demonstrates the value of a mixed-methods approach to usability and heuristic evaluation for identifying effective, accessible and inclusive tailoring of physical activity Apps universally and for wheelchair users specifically. These findings can inform refinements to the MvBii app and provide broader insights for designing inclusive and effective mobile health Apps across diverse populations.
IMPLICATIONS FOR REHABILITATION:
• Wheelchair users with spinal cord injury demonstrated high intention to use the physical activity smartphone App.
• Key usability issues were identified that should be considered in physical activity Apps include interface clarity, terminology, and visual accessibility.
• A novel heuristic principle was proposed that will aid in effective design for accessible digital experiences.
• Recommendations for physical Apps for wheelchair users include enhanced customisation, inclusivity and simplified language.The author(s) reported there is no funding associated with the work featured in this article
Understanding the eating behaviours of low-income families during the cost-of-living crisis: Informing future interventions
Full text linkThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonEstablishing healthy dietary behaviours in childhood is critical for long-term growth and development. However, the Cost-of-Living Crisis in the UK has substantially increased food insecurity and diet quality, with food prices rising 25% from 2022 to 2024. This left 4.3 million children in relative poverty in 2024. This has increased demands on food charities supporting low-income families.
This PhD thesis addresses key gaps in the literature, including (1) A lack of behaviour change interventions for low-income families, (2) Limited understanding on the impacts of the Cost-of-Living Crisis on dietary interventions, (3) Lack of real-time perspectives and experiences on children’s eating behaviours from charity stakeholders and parents in low-income families. Therefore, this thesis aimed to identify evidence-based proposals for future interventions to promote healthy eating behaviours in children from low-income families during the Cost-of-Living Crisis, through application of the Behaviour Change Wheel and the Theoretical Domains Framework.
Study 1 comprised of a systematic review of existing digital interventions targeting child dietary behaviours in low-income families. Using the Behaviour Change Intervention Ontology, five eligible studies were synthesised, identifying key characteristics. The most common Behaviour Change Techniques used amongst the five studies were Goal Setting (k=4), Problem Solving (k=3), Instruction on how to perform a Behaviour (k=3) and Prompts and Cues (k=3). This review highlighted core characteristics and gaps within existing child dietary interventions.
Study 2 and 3 explored experiences of stakeholders in 12 food charities (n=22) and parents in low-income families (n=23) using qualitative interviews and dyads. Following an interpretivist methodological approach, a combination of inductive and deductive coding and using reflective thematic analysis was conducted. Findings were mapped to the Theoretical Domains Framework to identify behavioural determinants influencing charity support and parental feeding practices.
Across stakeholder and parent perspectives, key barriers and facilitators centered on
‘Physical Opportunity’; resources and environmental constraints, ‘Social Opportunity’; community networks and reducing stigma, ‘Psychological Capability’; knowledge, child involvement and adaptive strategies, and ‘Reflective Motivation’; communication and reaching populations directly, are all required to support child healthy eating during economic crises.
Integrating findings across all three studies, the Behaviour Change Wheel was applied to identify intervention functions, policy categories, Behaviour Change Techniques, and mode of deliveries, which inform future intervention design. This thesis demonstrates how combining enablement and service provision with practical social support and flexible delivery modes may strengthen both parental capacity and food support systems.
This research contributes a novel, theory-driven, multi-level approach to designing dietary interventions for children in low-income families, highlighting the importance of behavioural and systemic adaptation during periods of economic crisis in the UK.Give.Help.Share, The Mayor’s Fund for London, The Felix Project, The Food Foundation, Citizens UK, School Food Matters, Impact on Urban Health, Taste Education, and the Childhood Trust, Feeding Britian, Borough Food Cooperative, Pecan Charity, and Middle Park Community Center in Greenwich
Molecular dynamics study on the mechanisms of ultrafine bubbles in CO₂ hydrate formation
Full text linkHighlights:
• CO₂ hydrate-based carbon capture was studied using molecular dynamics in the presence of H₂, N₂, O₂, and CO₂ nanobubbles.
• The mechanism and kinetics of nanobubble-assisted CO₂ hydrate growth were elucidated.
• Structural ordering and cage characteristics of CO₂ hydrates were systematically investigated.
• The interplay between thermodynamic conditions and nanobubbles in hydrate formation was demonstrated.
• Nanobubbles were shown to play a dual role in CO₂ hydrate formation.Data availability:
Data will be made available on request.The accelerating rise in atmospheric CO₂, driven by anthropogenic emissions, necessitates urgent mitigation strategies. Among carbon capture and storage (CCS) technologies, CO₂ hydrate-based methods offer a promising pathway for efficient sequestration, storage, and utilization. However, the inherently slow kinetics of hydrate nucleation and growth limit their practical application. This study explores the use of various nanobubbles (NBs), including hydrogen, nitrogen, oxygen, and carbon dioxide, as stable, nanoscale gas cavities that act as novel promoters to enhance CO₂ hydrate formation, using molecular dynamics (MD) simulations. The results demonstrate that under optimal thermodynamic conditions, the presence of NBs significantly enhances hydrate formation. This enhancement is attributed to the hydrophobic NB surfaces acting as nucleation spots, promoting local concentration gradients and accelerating clathrate formation kinetics, while reducing the likelihood of random nucleation events in the bulk phase. Due to their smaller molecular sizes, hydrogen and nitrogen NBs further facilitate hydrate formation by diffusing into the solution from the NB core. However, lower temperature, as a primary sub-optimal thermal condition, reduce molecular mobility and suppress these mechanisms, thereby hindering hydrate growth. At elevated pressures, NBs exhibit a dual role, both promoting and inhibiting hydrate formation, and the comparison with non-nanobubbled samples reveals a pressure-dependent shift in the dominant nucleation mechanism from NB-induced interfacial ordering to bulk-phase interactions.This work was supported by a UKRI Future Leaders Fellowship (MR/T042915/1 and UKRI1057) and EPSRC DTP (EP/T518116/1–2688449). MD simulations were run on MMM Hub Young, the UK's National Supercomputing Service
Secrecy Performance Analysis of RIS-Aided Wireless Systems over Correlated Extended α-η-F Composite Fading Channels
Full text linkIn this paper, the secrecy of the physical layer of an reconfigurable intelligent surface (RIS)-assisted wireless communications system over extended α-η-F composite fading channels is analyzed. The extended α-η-F composite fading model is introduced in this work as a generalization of the versatile extended η-F and α-μ distributions. Accordingly, this model encompasses as special cases most of the generalized fading distributions, such as extended α-η-μ, extended η-F, and α-η-F. To this effect, the probability density functions (PDFs) of a single random variable (RV) and product of two non-identically distributed variates of extended α-η-F composite fading model are provided first. Thereafter, we derive mathematically tractable expressions of the average secrecy capacity (ASC), secure outage probability (SOP), lower bound of SOP (SOPL), and effective secrecy throughput (EST) of RIS-assisted wireless communications system over correlated extended α-η-F composite fading channels. In addition, the asymptotic expressions of the ASC, SOP, and SOPL at high average SNR regime are also presented to explain the impact of both the number of the RIS elements and fading parameters on the secrecy performance metrics. The validity of our analytical results is confirmed through Monte Carlo (MC) simulations as well as a comparison with some special cases of the extended α-η-F composite fading channels
EQARO-ECS: Efficient Quantum ARO-Based Edge Computing and SDN Routing Protocol for IoT Communication to Avoid Desertification
Full text linkData Availability Statement:
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.Desertification is the impoverishment of fertile land, caused by various factors and environmental effects, such as temperature and humidity. An appropriate Internet of Things (IoT) architecture, routing algorithms based on artificial intelligence (AI), and emerging technologies are essential to monitor and avoid desertification. However, the classical AI algorithms usually suffer from falling into local optimum issues and consuming more energy. This research proposed an improved multi-objective routing protocol, namely, the efficient quantum (EQ) artificial rabbit optimisation (ARO) based on edge computing (EC) and a software-defined network (SDN) concept (EQARO-ECS), which provides the best cluster table for the IoT network to avoid desertification. The methodology of the proposed EQARO-ECS protocol reduces energy consumption and improves data analysis speed by deploying new technologies, such as the Cloud, SDN, EC, and quantum technique-based ARO. This protocol increases the data analysis speed because of the suggested iterated quantum gates with the ARO, which can rapidly penetrate from the local to the global optimum. The protocol avoids desertification because of a new effective objective function that considers energy consumption, communication cost, and desertification parameters. The simulation results established that the suggested EQARO-ECS procedure increases accuracy and improves network lifetime by reducing energy depletion compared to other algorithms.This research received no external funding
Advancing river water quality prediction: a comparative assessment of deep learning models for dissolved oxygen forecasting
Full text linkAccurate forecasting of dissolved oxygen (DO) is crucial for monitoring river water quality and protecting aquatic ecosystems. This study compares the performance of four deep learning models – Temporal Fusion Transformer (TFT), Informer, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) – for forecasting DO concentrations in the River Lee (London, UK) across 7- and 30-day time frames. A multivariate time-series dataset was employed, with temperature, turbidity, pH, conductivity, chlorophyll, and river flow as predictors. Model skills were evaluated using RMSE, MAE, R2, and SMAPE. Over the 7-day period, TFT had the lowest RMSE (0.06) and SMAPE (8.86%), while LSTM had the greatest R2 (0.77). TFT outperformed Informer, LSTM, and GRU at the 30-day horizon, with R2 = 0.79 and SMAPE of 8.23%, despite significant accuracy losses. According to the variable contribution study, temperature and river flow were the most significant factors, particularly for short-term projections. Overall, the results show that transformer-based structures, particularly TFT, can successfully represent nonlinear temporal dependencies and multivariate interactions, making them ideal for multi-horizon DO forecasting in river systems. These models have the ability to supplement normal monitoring by offering short-term predictions about probable oxygen conditions.This study is partially funded by the UK Research and Innovation UKRI project 10063665
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