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Framing the Films by Yorgos Lanthimos
No full textThis book examines the Greek filmmaker Yorgos Lanthimos’s feature films. Lanthimos’s films have been linked to the so called Greek weird wave in Greek cinema, which is commonly agreed to have started with his Dogtooth (2009), and so they have largely been discussed as national and localised cinematic phenomena allegorically commenting on the Greek economic crisis or society. Lanthimos’s distinctive style is discussed in this book in terms of both national and European cinematic traditions, in the latter case specifically Brecht and the aesthetics of the uncanny. The author provides an in-depth, thorough and systematic analysis of the ‘weird’ mixture of uncanniness and Brecht in Lanthimos’s cinema as performing an uncanny transformation of Brechtian aesthetics within the context of Greek and international cinema. As the author proposes, the Brechtian aesthetics, that we also find in the modernist cinema of Theo Angelopoulos, in combination with the aesthetics of the uncanny and the unnatural narratives, is what marks, and links, formally the feature films by Yorgos Lanthimos. The filmmaker’s radical film form and subversive thematics are linked to the uncanny here, and this Brechtian uncanny, as the author calls it, redefines Brechtian aesthetics. This unique book will interest scholars and students of film studies, media studies, modern Greek studies, cultural studies, theatre studies, psychology, literary studies, philosophy
Motor competence explains the variance in biomechanical variables related to anterior cruciate ligament injury risk, with distinct predictors for male and female athletes
Full text linkPurpose: Anterior cruciate ligament (ACL) injuries have consistently been linked to specific kinetic and kinematic patterns, including elevated vertical ground reaction forces, increased knee abduction angle and moment (dynamic valgus), reduced knee flexion during landing and excessive hip adduction/internal rotation. However, the relationship between motor competence as a factor affecting athletes' performance and kinetic and kinematic variables has not yet been investigated. Methods: A total of 112 elite athletes (66 males and 46 females; mean age = 19.4 ± 1.1 years) from basketball, volleyball and handball were assessed. Motor competence was evaluated using the short form of the Bruininks–Oseretsky Test of Motor Proficiency. Biomechanical data were collected during a single‐leg drop landing task using a three‐dimensional motion analysis system (Vicon) and a force plate. Pearson correlation coefficients and multiple linear regression (Enter method) were used to analyse relationships between motor competence and kinetic/kinematic variables. Results: All biomechanical variables showed significant correlations with motor competence (p < 0.001). Notably, knee flexion angle (r = 0.613) and knee abduction angle (r = −0.576) demonstrated strong associations. Regression analysis identified several biomechanical variables that were statistically associated with motor competence, explaining 69.6% of its variance. Conclusion: Motor competence was related to several kinetic and kinematic variables previously linked to ACL injury risk. However, due to the cross‐sectional design, these associations should not be interpreted as causal, and further longitudinal or interventional studies are warranted. Level of Evidence: Level IV, cross‐sectional study
A Novel EEG-ECG Based Fusion Model for Multimodal Emotional Classification
Full text linkThe identification of emotional states through physiological signals is vital in human-computer interaction, mental health monitoring, and management. In contrast to existing models, which use either a combination of visual and Electroencephalogram (EEG) features or individual features, the present study introduces a hybrid multimodal machine learning framework that fuses EEG and ECG (Electrocardiogram) features for emotion classification. The EEG indeed captures the brain's neural activity associated with emotional states, while the ECG monitors the heart's electrical signals, reflecting autonomic responses that are highly responsive to emotional fluctuations. Integrating these physiological signals enhances the representation of emotional states by capturing complementary information from both the cortical and autonomic nervous systems. To exploit this observation, we explore the combination of a Temporal Convolutional Network (TCN) and a Long Short-Term Memory (LSTM) network. This work considers four distinct emotion levels based on valence and arousal dimensions. Experiments on two benchmark datasets, namely, DREAMER and Multimodal, demonstrate the superiority of the proposed method over state-of-the-art methods
Health librarian involvement in systematic reviews scoping review protocol
No full textThis is a protocol and search strategy for a scoping review on the contribution of librarians to systematic reviews in evidence based healthcare
GUIDELINES for the Sexism-Free and Gender-Sensitive Use of AI in the Film and Media Industries
No full textThis is a report that Power to Transform compiled, to which I contributed as an expert, aimed at filmmaking practitioners to provide guidelines for the ethical, fair, and gender-inclusive use of AI tools in film production.It is not a peer reviewed publication, and is freely and openly accessible to the public at https://power-to-transform.org/
The Cadenza lyric intelligibility prediction (CLIP) dataset.
Full text linkThis paper presents CLIP, a dataset of 11,072 popular western music signals sourced from independent artists, accompanied by ground truth lyrics, and lyric intelligibility scores from listening tests. The dataset is designed to facilitate music information retrieval (MIR) research using machine learning. It was created to allow the development of algorithms to predict lyric intelligibility for the Cadenza ICASSP 2026 Signal Processing Grand Challenge. Currently, it is the only publicly available large-scale dataset for such a task. The music was sourced from the Free Music Archive (FMA) dataset and is unlikely to be familiar to listeners. We excluded tracks whose license did not allow derivative works and those that did not have English singing. Ground truth transcriptions were generated by seven native English speakers, resulting in 3700 excerpts of 5 to 10 words each from 1452 different songs. A hearing loss simulation was also applied to the stereo audio. This resulted in 11,100 music signals with no, mild or moderate hearing loss. This was done so more diverse hearing is represented in the dataset. Human transcriptions were then collected via an online listening experiment. Participants self-reported as having normal-hearing and being native English speakers. They listened to each music signal twice before transcribing each line. Final intelligibility scores were the ratio of matching words between the listening test responses and the ground truth transcriptions. The final dataset consists of audio, ground truth lyrics, intelligibility scores and associated metadata. [Abstract copyright: © 2026 The Authors.
Performing Jerome Rothenberg's 'Twelve Lunar Meditations'
Full text linkAccount of a performance delivered at the Anthology as Manifesto symposium 21-22 March 2025, University of Glasgo
Evaluation of Marine Polysaccharides as Novel Antiproliferative Agents
No full textA class of polysaccharides, known as glycosaminoglycans, have long been studied tounderstand their vast and diverse biological functions in cellular biology; their vital role inthe extracellular matrices of cells, interactions with growth factors and cytokines, as well asthe growth and maintenance of cartilage and axons in neurons. In more recent years, as moreknowledge has been gathered on this class of sugars, questions have arisen regarding theirroles in pathology, and whether these sugars could have pharmacological potential fortreating multiple diseases, such as osteoarthritis and multiple cancers. Alternative research inmarine glycosaminoglycans has demonstrated that these compounds display anti-proliferativeeffects.In this study, glycosaminoglycans were extracted from King Prawns (Litopennaeusvannamei) using a cetylpyridinium chloride extraction and used for structural analysis,including nuclear magnetic resonance spectroscopy and saccharide analysis. The extractedglycosaminoglycans were also tested in vitro using an MTT assay and flow cytometry toassess the extract's ability to inhibit cell proliferation.Through this process, GAG isolated from L. vannamei proved effective in preventingcell proliferation, presenting a reduction in cell viability similarly to cisplatin, but sufficientlyreached IC50. These results highlight the potential for marine glycosaminoglycans to beanalysed further as possible antiproliferative agents. NMR techniques were utilised to gain amore extensive understanding of the structures present in the GAG samples, and multiplefamiliar monosaccharides were identified, including those from heparan sulphate andchondroitin sulphate. Comparisons of marine polysaccharides by NMR spectroscopy indicatethat the prawn extracts from this study have considerably simpler spectra in comparison tobioactive marine polysaccharides from other shellfish, previously subjected to thiscetylpyridinium chloride extraction process. NMR and disaccharide analysis will make theidentification of key structures within the marine polysaccharide mixtures that are linked totheir antiproliferative effects much easier. Anion-exchange chromatography was conducted,and NMR spectra were taken of the fractions in an attempt to further simplify the structure.The complexity of the structures of these marine polysaccharide extracts has been a hurdle inthe past; however, the findings of this study are encouraging in regard to identifying activepolysaccharide sequences
Measurement and Analysis of Volatile Organic Compounds (VOC) Emissions from Major Pressurised Cosmetic Aerosol Sprays
Full text linkThis thesis investigates volatile organic compound (VOC) emissions from widely useddomestic aerosol sprays, focusing on both personal care (body sprays, antiperspirants, hairsprays) and household products (air fresheners, wood polishes, insecticides). Aerosols are anestablished contributor to indoor air pollution through the release of VOCs, particularly fromliquefied petroleum gas (LPG) propellants. These emissions are linked to tropospheric ozoneformation, secondary organic aerosol production, and global warming potential. Despite theirsignificance, product-level, source-resolved measurements have been limited. This studytherefore aimed to quantify VOC emissions directly at source, identify their chemicalcomposition, and assess the potential reductions achievable by replacing LPG propellants withcompressed gases such as nitrogen.A purpose-built experimental setup was developed, consisting of a sealed volumetric samplingchamber, precision weighing system, and gas chromatography/mass spectrometry (GC/MS) forqualitative and quantitative analysis. The method distinguished VOCs originating from thepropellant and those from the bulk formulation, with repeatability confirmed through replicatetesting and an overall uncertainty of approximately 1.5%. Thirty-four commercial aerosolproducts were tested under controlled laboratory conditions, and results were scaled to estimateregional and global emission inventories. In addition, ozone forming potential (OFP) andglobal warming potential (GWP) were assessed for the principal hydrocarbon species. Asubstitution scenario was also evaluated using a matched pair of LPG- and nitrogen-propelledair fresheners to explore the benefits of compressed gas propellants.The results show that cosmetics contained higher VOC propellant masses than householdproducts. Antiperspirants exhibited the highest values at around 98g per can, while body spraysand hair sprays averaged 51g and 42g respectively. In household products, insecticidescontained the highest VOC mass at 85 g per can, followed by air fresheners (63g) and woodpolish (41g). Scaling these findings, total VOC emissions were estimated at 1,089 kt globallyby 2027. The nitrogen-based air freshener showed the lowest emissions at 27g per can,confirming the potential for large reductions through propellant substitution.The analysis of chemical composition identified hydrocarbons (ethane, propane, butane,isobutane, isopentane, pentane) and ethanol as the dominant VOCs, with LPG-derivedhydrocarbons accounting for over half of total emissions. OFP analysis highlightedantiperspirants (80 g O3 per can) and insecticides (62 g O3 per can) as the most ozone-intensiveproducts, while hair sprays showed the lowest values. GWP assessments also indicated strongdependence on LPG content, with antiperspirants and insecticides carrying the highest CO2-equivalent burdens. Importantly, replacing LPG with nitrogen reduced VOC emissions byaround 64%, equivalent to nearly 700 kt annually on a global scale.The findings demonstrate that domestic aerosols are a substantial and under-recognised sourceof VOCs with significant implications for air quality, climate, and public health. Emissions areoverwhelmingly driven by propellant choice, meaning that policy and industrial efforts focusedon alternative propellants are likely to deliver the greatest reductions
Developing a tailored framework for digital transformation in UK small, micro and medium construction enterprises: evidence from Greater Manchester
Full text linkThis thesis investigates digital transformation (DT) adoption among construction small and medium-sized enterprises (CSMEs) in Greater Manchester, with the aim of developing a tailored framework that reflects the size-specific challenges, enablers, and strategic needs of these enterprises. While DT has received considerable attention in large construction organisations, CSMEs remain underrepresented in both scholarly literature and practice, limiting their capacity to respond to economic volatility, labour shortages, and uncertainties. This digital exclusion has contributed to escalating insolvency rates, with one-third of CSMEs entering administration in the first quarter of 2023, highlighting the urgent need for inclusive transformation strategies.This study employs a sequential mixed-methods design grounded in the Technology-Organisation-Environment (TOE) framework, Dynamic Capabilities (DC) and the Resource-Based View (RBV) theory. It begins with a systematic literature review (SLR) to synthesise key determinants of DT adoption, followed by a quantitative survey of 307 CSMEs categorised into micro (0-9 employees, n=173), small (10-49, n=58), and medium (50-249, n=76) enterprises. Qualitative validation was conducted through a focus group with five key experts. Findings reveal significant variations in DT adoption across CSME sizes. Micro-enterprises report the lowest engagement with 84.5% using no digital tools, citing insufficient capital (100%) and poor digital skills (73%) as key barriers. Small enterprises demonstrate moderate adoption (47%), while medium enterprises exhibit the highest integration (78%), supported by strategic leadership and investment in advanced technologies. Six critical determinants also emerged, namely digital skills, digital technologies, digital leadership, organizational culture, perceived relative advantage, and government support.The developed framework addresses size-specific needs through targeted interventions. Micro enterprises require foundational digital literacy programs and cost-effective solutions, small enterprises benefit from scalable technologies with peer learning networks, and medium enterprises need strategic digital leadership alongside comprehensive transformation strategies. Through focus group session, experts validated and confirmed the framework’s clarity and practical value, this research contributes by empirically identifying barriers and determinants of digital DT adoption in CSMEs, developing a novel tailored framework and extending established theories into CSMEs’ contexts. Findings also provide actionable insights for policymakers, industry leaders, and technology providers to create inclusive digital transformation initiatives addressing distinct size-specific needs. Future research should explore implementation strategies for each CSME category, investigating how size-specific interventions can be scaled across different regional and economic contexts to ensure sustainable digital transformation outcomes
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