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Time to Write: A necessity, not a nicety
No full textIn this critical reflection we will explore our Time to Write project, providing a brief overview of our work, followed by discussion of expected and unexpected benefits, and the knowledge and skills leveraged in design and delivery. We also consider the remaining challenges as our funding from the UKRI Enhancing Research Culture allocation ends in July 2025 and we move to a ‘business as usual’ model
Exploring the role of serious leisure in shaping entrepreneurial intention: Insights from higher education students in China
No full textOur study examines how serious leisure (SL) shapes entrepreneurial intention (EI) among Chinese university students. Grounded in Social Cognitive Career Theory (SCCT), we test a model in which SL enhances career adaptability (CA), which in turn fosters EI, with social support (SS) moderating the CA–EI link. A two-study design integrates external and internal validity: Study 1, a cross-sectional survey of 779 undergraduates from 28 Chinese universities, establishes construct validity and tests the hypothesised associations; Study 2, a 2 × 2 vignette experiment with 308 postgraduates, confirms causality and examines second-stage moderation. Findings consistently support the model: SL boosts EI both directly and indirectly via CA, and SS strengthens the CA–EI pathway. Theoretically, this research broadens SCCT by identifying SL participation as an informal yet significant adaptive career resource, especially when situated within enabling environments like China. Practically, it suggests that higher education institutions should recognise skill-based leisure as a scaffold for EI development. Methodologically, the sequential survey-experiment design provides a robust template for future research seeking ecological and causal validity in leisure, higher education, and entrepreneurship studies
Explainable AI-aided feature selection and model reduction for DRL-based V2X resource allocation
No full textArtificial intelligence (AI) is expected to significantly enhance radio resource management (RRM) in sixth-generation (6G) networks. However, the lack of explainability in complex deep learning (DL) models poses a challenge for practical implementation. This paper proposes a novel explainable AI (XAI)-based framework for feature selection and model complexity reduction in a model-agnostic manner. Applied to a multi-agent deep reinforcement learning (MADRL) setting, our approach addresses the joint sub-band assignment and power allocation problem in cellular vehicle-to-everything (V2X) communications. We propose a novel two-stage systematic explainability framework leveraging feature relevance-oriented XAI to simplify the DRL agents. While the former stage generates a state feature importance ranking of the trained models using Shapley additive explanations (SHAP)-based importance scores, the latter stage exploits these importance-based rankings to simplify the state space of the agents by removing the least important features from the model’s input. Simulation results demonstrate that the XAI-assisted methodology achieves ~97% of the original MADRL sum-rate performance while reducing optimal state features by ~28%, average training time by ~11%, and trainable weight parameters by ~46% in a network with eight vehicular pairs.</p
End-to-end learning of beam probing and RSSI-based multi-user hybrid precoding design
No full textThis paper presents an end-to-end (E2E) autoencoder learning framework that relies on unsupervised deep learning for the joint design of millimeter wave (mmWave) probing beams and hybrid precoding matrices in multi-user communication systems. Our model utilizes prior channel observations to achieve two main objectives: designing a compact set of probing beams and predicting off-grid radio frequency (RF) beamforming vectors. The E2E learning framework optimizes probing beams in an unsupervised manner, concentrating sensing power on promising spatial directions based on the environment. To this aim, we develop a neural network architecture respecting RF chain constraints and model received signal strength (RSS) using complex-valued convolutional layers. The autoencoder is trained to directly produce RF beamforming vectors for hybrid architectures based on projected RSS indicators (RSSIs). Once RF beamforming vectors for multi-users are predicted, baseband digital precoders are designed by accounting for multi-user interference. The autoencoder neural network is trained E2E in an unsupervised manner with a customized loss function aimed at maximizing RSS. In a system with 64 antennas, 4 RF chains, and 4 users, our approach requires only 8 probing beams to design RF beamforming vectors, compared to the conventional predefined codebooks with 64 or 128 beams.</p
Men’s partner-objectification vs. women’s perceived partner-objectification in heterosexual couples: outcomes for women’s self-objectification, sexual self-consciousness, and orgasm frequency
No full textBelow I discuss my MSc in Health Psychology dissertation which explored partner-objectification in the context of heterosexual couples and received the 2024 DHP MSc Research Award
Estimates of irrigation water volume by assimilation of satellite land surface temperature or soil moisture into a water-energy balance model in Morocco
No full textThe agricultural sector is the biggest and least efficient water user, accounting for around 80% of total water use in North Africa, which is already strongly impacted by climate change with prolonged drought periods, imposing limitations on irrigation water availability. The objective of this study was to estimate irrigation water use for the irrigation district of Doukkala in Morocco from 2017 to 2022 at daily resolution. The approach is based on the energy-water balance model FEST-EWB, which computes continuously in time on a pixel basis the main processes of the hydrological cycle and models evapotranspiration and soil moisture (SM) dynamics in the agricultural soil layer by solving the energy and water mass balance equations. Three different approaches were implemented to quantify actual irrigation volumes: (a) FAO-approach with the irrigation scheduling based on soil moisture and crop stress thresholds, (b) assimilation of satellite land surface temperature (LST) (downscaled Sentinel-3 data) and (c) assimilation of satellite soil moisture (SMAP-Sentinel-1 data). The model was first calibrated over non-irrigated areas, against LST from LANDSAT and Sentinel-3. The three irrigation approaches were then validated against soil moisture and evapotranspiration from reference models (MOD16 and WaPOR). The assimilation of LST gave the best estimates of total irrigation volumes compared to observed water allocation data (relative error = 1.5%). The FAO approach also performed well but slightly overestimated the observed data by 15%. On the other hand, coarse pixel resolution and low revisit time affected the performance of the satellite SM assimilation (relative error of −80%)
Queer joy on social media: exploring the expression and facilitation of queer joy in online platforms
No full textQueer Joy is conceptualised as a form of resistance to oppression by celebrating queerness in the face of adversity. This research aimed to centre queer joy and understand how it is expressed and may be facilitated in online spaces. To do this we conducted a survey with 100 UK participants who indicated they identified as LGBTQ+ on the online recruitment platform Prolific. We asked a series of open and closed questions in an online survey to investigate 1) what queer joy looks like on social media 2) how queer joy content is engaged with on social media 3) which platforms are perceived to facilitate queer joy and 4) how queer people protect their privacy online. The results suggested that to facilitate queer joy online, platforms should allow flexible self expression and community engagement, while allowing for granular control over privacy and the audience such content is shown to
Novel core-pumped multicore fiber amplifier integrated with energy-efficient pump light distributor
No full textComparative analysis of protein expression between oesophageal adenocarcinoma and normal adjacent tissue
No full textOesophageal adenocarcinoma (OAC) is the 7th most common cancer in the United Kingdom (UK) and remains a significant health challenge. This study presents a proteomic analysis of seven OAC donors complementing our previous neoantigen identification study of their human leukocyte antigen (HLA) immunopeptidomes. Our small UK cohort were selected from donors undergoing treatment for OAC. We used label-free mass spectrometry proteomics to compare OAC tumour tissue to matched normal adjacent tissue (NAT) to quantify expression of 3552 proteins. We identified differential expression of a number of proteins previously linked to OAC and other cancers including common markers of tumourigenesis and immunohistological markers, as well as enrichment of processes and pathways relating to RNA processing and the immune system. Our findings also offer insight into the role of the protein stability in the generation of an OAC neoantigen we previously identified. These results provide independent corroboration of existing oesophageal adenocarcinoma biomarker studies that may inform future diagnostic and therapeutic research.</p
The role of airway tissue-resident memory T Cells in severe asthma
No full textThe growing realisation that severe asthma encompasses a collection of clinical phenotypes and endotypes, plus the variable response to current asthma therapies suggest underlying pathophysiological heterogeneity and complex immune molecular pathways.T cells are critical orchestrators of airway inflammation. However, the role of tissue-resident memory T (TRM) cells, localised to sites of inflammation in the airway epithelium, in the pathogenesis of severe asthma remains unknown. Therefore, this thesis aims to investigate the molecular heterogeneity of airway CD4+ and CD8+ TRM cells in severe asthma pathogenesis and extend this characterisation to the underlying clinical phenotypic nature of severe asthma compared to mild asthma.In the first section of this thesis, I undertook extensive clinical phenotypic characterisation of participants with difficult/severe and mild asthma (Chapter 3). Subsequent separate K-means clustering analysis of the difficult/severe and mild asthma cohorts identified 6 clinically relevant difficult/severe asthma clusters and 2 mild asthma clusters, reflecting severe disease heterogeneity (Chapter 4).For the second section of this thesis, bronchoalveolar (BAL) fluid samples collected from a proportion of severe and mild asthma participants were immunophenotyped using flow cytometry. This analysis suggested that CD103+CD4+ TRM and CD103+CD8+ TRM cells represented the dominant population in asthma. Subsequent bulk and single-cell RNA-seq of BAL memory CD4+ (Chapter 5) and CD8+ (Chapter 6) T cell populations were completed to investigate the molecular profiles of these cells in relation to asthma severity. The transcriptional profiling of BAL CD4+ T cells highlighted a novel population of cytotoxic CD103+CD4+ TRM cells enriched for transcripts linked to TCR activation, TH1-like cytotoxicity and pro-inflammatory molecular features, which was associated with increasing asthma severity in the male adult-onset severe asthma phenotype. In contrast, the transcriptional profiling of BAL CD8+ T cells revealed 9 transcriptionally distinct putative airway CD103+CD8+ TRM cell states across the spectrum of asthma severity, thus highlighting significant molecular heterogeneity. Strikingly, 3 airway CD8+ TRM cell states were unique to severe asthma and appeared to be highly proliferative with enhanced cytotoxicity, glucocorticoid insensitivity and pro-inflammatory molecular properties. Such superior functional properties of cytotoxic CD103+CD4+ TRM and CD103+CD8+ TRM cells suggest their role as key drivers of persistent airway inflammation, remodelling and glucocorticoid insensitivity in severe asthma.In conclusion, these novel findings indicate the need to look beyond the traditional T2 model of severe asthma to better understand disease heterogeneity. Future work will aim towards completing functional studies in vivo to better understand the molecular role of airway CD4+ and CD8+ TRM cell populations and their interactions in severe asthma
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