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Macroeconomic shocks and income inequality: An empirical investigation on the distributional channels of monetary policy and oil price news shocks
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis thesis provides a deeper insight into the connection between macroeconomic
shocks and income inequality across various economies. Through empirical analysis of
the distributional channels of propagation, the study reveals that macroeconomic shocks,
such as monetary policy and oil price news, impact the income distribution asymmetrically.
This observation is consistent across both economies studied, the US and the UK.
Particularly, a contractionary monetary policy shock in the UK is found to produce an
increase in inequality through the earnings heterogeneity and income composition channel.
While low-income households are mainly left unaffected due to their high exposure
to social benefits, high-income households race away because of the higher proportion of
capital income components.
These asymmetric effects also exist in the US, an economy investigated by looking at
the time-varying effects of a contractionary monetary policy shock. In line with previous
findings in the UK, the study finds that the same channels are in place. However, by
adding another layer of complexity to the model, the study shows that the US income
distribution became more responsive to monetary contractions in the more recent periods
of the sample. This is mainly rooted in the dominant effects of the capital income
components leading to a stronger effect of the income composition channel.
Finally, the thesis examines a different macroeconomic shock: oil price news shocks.
When including all deciles of the income distribution in the modelling approach the
asymmetric effects of these shocks again are detected. The overall picture i.e. capital
income components make up a significant part of rich households and hence, are the main
drivers for the different reactions of this group, is confirmed
Solar panels for Qatar homes: Challenges, feasibility, and a data-driven framework for deployment, performance monitoring and energy management
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDespite Qatar’s obvious massive latent solar power potential, panel deployment has primarily occurred in major plants, with negligible residential deployment in homes, despite inherent advantages and government subsidies. Therefore, this research explores reasons behind the low deployment of solar panels, conducts a feasibility study, and recommends a deployment framework to encourage solar panel adoption within Qatar. This initiative aims to contribute to the country’s transformation towards clean energy, and align with Qatar’s National Vision 2030, as well as global efforts to reduce carbon emissions.
Following an initial review of related literature, challenges facing solar panel deployment in Qatar were identified, followed by an analytical feasibility study to assess different scenarios involving varying numbers of panels with different efficiencies. This analysis compared the generated energy against typical home consumption and explored the feasibility of selling surplus energy locally or internationally during periods of low consumption. Subsequently, a field survey was conducted in Qatari homes to determine the practicability of solar panel rooftop installation. This led to the development of a data-driven model to enable dynamic decision-making for monitoring and efficiently managing and maintaining deployed solar panels, ensuring sustainable energy generation.
The research outcomes demonstrated a high potential for deploying solar panels within Qatar using medium or high-efficiency panels, which could meet local energy needs and potentially export surplus energy. Furthermore, Qatari house roofs were found to have approximately 50 percent of available space, with good accessibility and orientation for maximising energy generation.
The data-driven model proved to be instrumental in monitoring dust accumulation, planning cleaning operations, and tracking energy degradation due to panel aging, among other data requirements for analysis, forecasting, and management purposes. Integration with the proposed deployment framework provides stakeholders with a clear roadmap to achieve their clean energy goals and facilitate the transition towards cleaner energy sources
The Impact of ‘Posted’ Information on Informal Learning in a Service Supply Chain- A Case Study in UK Insurance
No full text......European Commission ref: GA 324408 (KNOWNET - Engaging in knowledge networking via an interactive 3D social supplier network)
Convolutional Versus Large Language Models for Software Log Classification in Edge-Deployable Cellular Network Testing
Get PDFSoftware logs generated by sophisticated network emulators in the telecommunications industry, such as VIAVI TM500, are extremely complex, often comprising tens of thousands of text lines with minimal resemblance to natural language. Only specialised expert engineers can decipher such logs and troubleshoot defects in test runs. While AI offers a promising solution for automating defect triage, potentially leading to massive revenue savings for companies, state-of-the-art large language models (LLMs) suffer from significant drawbacks in this specialised domain. These include a constrained context window, limited applicability to text beyond natural language, and high inference costs. To address these limitations, we propose a compact convolutional neural network (CNN) architecture that offers a context window spanning up to 200,000 characters and achieves over 96% accuracy (F1>0.9) in classifying multifaceted software logs into various layers in the telecommunications protocol stack. Specifically, the proposed model is capable of identifying defects in test runs and triaging them to the relevant department, formerly a manual engineering process that required expert knowledge. We evaluate several LLMs; LLaMA2-7B, Mixtral_8 × 7B, Flan-T5, BERT and BigBird, and experimentally demonstrate their shortcomings in our specialized application. Despite being lightweight, our CNN achieves strong performance compared to LLM-based approaches in telecommunications log classification while minimizing the cost of production. Our defect triaging AI model is deployable on edge devices without dedicated hardware and is applicable across software logs in various industries.The authors acknowledge the support of VIAVI Solutions Inc., for their provision of data, funding, and MLOps infrastructure, including GPUs, which contributed significantly to the completion of this project
Ambient IoT: Backscatter-Based Connectivity Topologies and Outage Behavior
Get PDFAdvancements in low-power and cost-effective backscatter communications are enhancing connectivity for battery-constrained devices, as highlighted by 3GPP’s recent ambient Internet-of-things (A-IoT) study. This paper presents a comprehensive analysis of the outage behavior exhibited by various A-IoT backscatter communication topologies, including direct connection with the base station (Topology 1), relay-assisted backscattering (Topology 2), and user equipment-assisted backscattering (Topology 3). For each topology, the outage event is characterized and the outage probability expression is derived. Additionally, asymptotic analysis is performed to study the outage behavior of each topology at high transmit SNR γo. The results show that at high SNR, the outage probability scales as (1/γo) for Topology 1 with symmetric channel, (1/γo3/2) for Topology 2, and (1/γo) for Topology 3 and Topology 1 with frequency division duplex (FDD) transmission. Through analytical modeling and extensive simulations, our study provides insights into the outage behaviors of these topologies. It is found that the distance of the backscatter link significantly affects outage performance across these topologies. Specifically, Topology 2 shows superior performance at shorter distances, while Topology 3 is more effective at longer distances. Moreover, the impact of backscatter coefficient and target transmission rate on performance is investigated.in part by the European Project Hexa-X II, and in part by the Business Finland Project 6G-eMTC
Neural correlates of implicit emotion regulation in mood and anxiety disorders: an fMRI meta-analytic review
Get PDFData availability:
Raw and generated data, as well as data analysed during this review, are available within this published article and its supplementary materials.Electronic supplementary material is available online at: https://www.nature.com/articles/s41598-025-03828-5#Sec21 .Maladaptive implicit emotion regulation has been highlighted as a transdiagnostic characteristic of mood and anxiety disorders. Whilst clinical diagnosis has relied on signs and symptoms, the integration of clinical neurosciences is becoming more important as a means of enhancing assessment, diagnosis, and treatment. Thus, activation likelihood estimation (ALE) meta-analysis was conducted for whole-brain foci comparing implicit emotion regulation in a large sample of patients with mood and anxiety disorders and healthy controls. Twenty-four clinical studies were identified based on established criteria (e.g., DSM-5). ALE meta-analysis reported convergence of hypoactivation in patients (n = 432) in the right medial frontal gyrus (BA9), spreading to the right anterior cingulate gyrus (BA32); and in the left middle temporal gyrus (BA21), spreading to the left superior temporal gyrus (BA22). Convergence of hyperactivation was reported in patients (n = 536) in the left medial frontal gyrus (BA9), spreading to the left superior frontal gyrus and the left middle frontal gyrus. Separate analysis of the mood disorders subgroup further highlighted convergence of hyperactivation in the insula and claustrum. The implications of the current findings are discussed within the context of the Research Domain Criteria (RDoC) framework of developing diagnostic systems that are more predictive of treatment outcomes.This research was done as part of a PhD thesis and did not receive any specific funding from agencies in the public, commercial, or not-for-profit sectors. Stefan Daniel Paul Dalton is supported by the Brunel University London, College of Health, Medicine and Life Sciences Doctoral Research Fund and no other financial support was received during the research and/or the preparation of the manuscript
Collecting real-time infant feeding and support experience: co-participatory pilot study of mobile health methodology
Get PDFData availability:
The quantitative dataset supporting the conclusions of this article is available in the OSF project https://osf.io/yqsnd/ [https://doi.org/10.17605/OSF.IO/YQSND].Supplementary Information is available online at: https://internationalbreastfeedingjournal.biomedcentral.com/articles/10.1186/s13006-025-00707-7#Sec31 .Background:
Breastfeeding rates in the UK have remained stubbornly low despite long-term intervention efforts. Social support is a key, theoretically grounded intervention method, yet social support has been inconsistently related to improved breastfeeding. Understanding of the dynamics between infant feeding and social support is currently limited by retrospective collection of quantitative data, which prohibits causal inferences, and by unrepresentative sampling of mothers. In this paper, we present a case-study presenting the development of a data collection methodology designed to address these challenges.
Methods:
In April–May 2022 we co-produced and piloted a mobile health (mHealth) data collection methodology linked to a pre-existing pregnancy and parenting app in the UK (Baby Buddy), prioritising real-time daily data collection about women's postnatal experiences. To explore the potential of mHealth in-app surveys, here we report the iterative design process and the results from a mixed-method (explorative data analysis of usage data and content analysis of interview data) four-week pilot.
Results:
Participants (n = 14) appreciated the feature’s simplicity and its easy integration into their daily routines, particularly valuing the reflective aspect akin to journaling. As a result, participants used the feature regularly and looked forward to doing so. We find no evidence that key sociodemographic metrics were associated with women’s enjoyment or engagement. Based on participant feedback, important next steps are to design in-feature feedback and tracking systems to help maintain motivation.
Conclusions:
Reflecting on future opportunities, this case-study underscores that mHealth in-app surveys may be an effective way to collect prospective real-time data on complex infant feeding behaviours and experiences during the postnatal period, with important implications for public health and social science research.We acknowledge the funding by the BA/Wellcome Trust small grants for supporting this project (reference SRG2021/210128)
Hippocampal subfield volumes and memory deficits in schizophrenia
Get PDFData Availability: The data that support the findings of this study are available from the corresponding author upon reasonable request.Supplementary materials are available online at: https://www.sciencedirect.com/science/article/pii/S0925492725000952?via%3Dihub#sec0020 .Background:
Schizophrenia is a debilitating disorder commonly associated with significant cognitive impairment, particularly in memory. Reduced gray matter volume in various brain regions, including hippocampus and its subfields, is also well-documented in individuals with schizophrenia (SZH). However, few studies have investigated how memory deficits relate to hippocampal subfield volume loss.
Methods:
In this study, we examined hippocampal subfield volumes and their associations with immediate and delayed memory performance (using the WMS-III battery), comparing 57 individuals with SZH to 32 well-matched controls.
Results:
Compared to controls, SZH exhibited lower memory performance, and lower hippocampal volumes, particularly in the left hippocampus and parasubiculum, CA1 subfields specifically. Both Immediate and Delayed Free Recall memory performance was seen to be positively correlated with left CA1 volume in SZH only, and not in controls. Positive associations were also observed between Thematic Recall scores and volumes in the left CA1, CA3, and CA4/DG subfields in SZH only, but only at an uncorrected threshold.
Conclusion:
These findings support the notion that hippocampal volumetric alteration contributes to memory impairment in SZH. In particular, findings point to the left CA1 subfield as being particularly important in this regard, informing potential targeted intervention strategies to address memory impairment and functional recovery in SZH.The study was supported by funds from the Wellcome Trust, UK (067427/z/02/z)
An investigation of the use of the solid-state microwave technology as an energy-efficient method to improve heating uniformity and moisture retention during the baking process
Get PDFThis study investigates heating uniformity and moisture retention in bread baked using industrial microwave in combination with conventional oven. Twelve samples have been prepared and temperature and humidity were measured at multiple zones using thermocouples and fibre optic sensors. Additionally, hardness and shelf if of the products was observed during the eight days of period. This research also analyses the environmental and economic implications of these baking methods, offering valuable understandings into advancements in baking technologies that promote energy efficiency and sustainability to the baking industry. Results showed that the involvement of the industrial solid-state microwave in the baking processes have a potential to develop more uniform temperature changes and water retention trough minimized surface drying, internal temperature gradients, and overall moisture loss which reduced the hardness of the products and improved the shelf life. Furthermore, industrial solid-state microwave baking showed as the most energy-efficient and cost-effective method, with lower emissions compared to conventional and other microwave baking modes, making it a more sustainable alternative.This research was funded by UKRI - BBSRC FoodBioSystems Doctoral Training Partnership (DTP), grant number BB/T008776/1
Circularity and sustainability measurement and assessment of water and resource systems integration
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDecoupling resource consumption from economic growth and development is essential for long-term sustainability. Water, being a critical resource for sustaining ecosystems and supporting human health and well-being, holds significant social and economic value. However, due to linear consumption practices, water stress is becoming increasingly prevalent, leading to disruptions in essential services. Desalination has emerged as a prominent solution to address water scarcity and meet the growing demand for water across various sectors. Despite its potential, desalination faces significant environmental and economic challenges. While assessment methodologies have been widely employed to evaluate the environmental, economic, and social impacts of desalination systems, they often focus primarily on consequential effects. As the desalination industry embraces circular strategies like Minimal Liquid Discharge (MLD) and Zero Liquid Discharge (ZLD), there is an increasing need to evaluate the intrinsic circularity of these systems. Integrating this assessment is crucial for ensuring that desalination aligns effectively with Circular Economy (CE) principles, promoting long-term sustainability. To address this need, a systematic and comprehensive methodological approach was developed to measure the intrinsic circularity of desalination systems. This approach incorporates CE principles, such as resource flow traceability, which assigns circular and linear properties to flows associated with the desalination process, and assesses the circular value created by actions implemented in the system. The method identifies benefits and hotspots in various system configurations, including conventional desalination, MLD, and ZLD systems. Furthermore, by adopting MLD and ZLD strategies to reduce brine discharge and improve water recovery, the desalination sector is transforming into multifunctional product systems. A criterion-based Life Cycle Assessment framework was developed and applied to evaluate these multifunctional desalination systems. The results revealed that different assessment approaches (e.g., global vs. individual co-production) yield varying outcomes. However, the analysis demonstrated that brine, as a secondary product, can alleviate environmental pressures associated with conventional systems, such as those in the mining and chemical industries. Additionally, a circularity assessment conducted on the integration of desalination systems into the ceramic industry highlighted optimisation opportunities through scenario analysis. Ultimately, this research provides valuable insights into the performance and impact of water and resource recovery systems, like desalination, in contributing to sustainability
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