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Peroxidase-catalyzed Coloration for Fabric Design with Color Patterns
Full text linkThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Biotechnology using enzymes has been explored in textile wet processing for the potential of reducing energy and water consumption, due to the use of the highly specific biocatalysts that can operate under mild temperature and neutral pH conditions. The current research study contributes to an understanding of the use of the enzyme peroxidase for textile coloration of wool fabrics as an alternative coloration method to using conventional dyestuffs. Peroxidases, belonging to the enzyme group of oxidoreductases, can catalyze oxidation of a wide range of colorless simple aromatic compounds as precursors to form polymeric colorants. This enzymatic coloration can be successfully applied to in-situ dyeing of wool fabrics at a low temperature through peroxidase catalysis of various precursors over a broad range of pH values to achieve a diverse color palette. To explore the potential of enzymatic coloration for fabric design, a woven wool base fabric was embroidered using computer-controlled embroidery machines with embroidery yarns of different fiber types and subsequently enzymatically dyed to create color patterns. Peroxidase-catalyzed coloration has the potential not only as an alternative coloration process to create design patterns of fabrics, but also for saving energy and preventing fiber damage during the dyeing process
Betwixt and Between II
No full textAn intermedia performance artwork for live solo movement and fixed media: single screen moving image, eight-channel fixed medium electraocoustic music, theatrical lighting.
First performance (peer-reviewed) 07.11.2025 at Sound/Image 2025 festival, University of Greenwich, Bathway Theatre, London.This exposition presents the next iteration of the authors’ long-standing collaborative explorations into the delicate and complex relationships between live-digital dance performance and acousmatic sound. Building on previous work, Betwixt & Between II seeks to ‘reflect forwards’ on the interplay between the body, media and sound. Many of the authors’ underlying concerns still resonate today, including ideas of intimacy, fragility, connection, and imagination. In our increasingly technologized world; opportunities to reconnect with bodies, images, and sounds reflectively and deliberately feel ever more significant. By challenging some of the normative traditions of combining movement, media, and music, particularly in terms of working generatively and spontaneously, this interactive and intuitive work presents bodies, media and sounds simultaneously as mutual sensuous entities
Disentangling the Effects of Firm‐Level Climate Risk and Capital Market Signalling: Evidence From Stock Price Informativeness
Full text linkopen access articleThis study examines the impact of firm-level climate risk on stock price informativeness (SPI) through the integrated lens of stakeholder–shareholder theory. Using a global unbalanced panel of 73,770 firm-year observations across 38 countries (2000–2020), we find that higher carbon emissions significantly reduce SPI, reflecting increased information asymmetry. Governance mechanisms, specifically board size, independence, tenure and nationality mix, consistently moderate this effect by enhancing disclosure and mitigating opacity. The negative relationship between emissions and SPI is strongest in common law countries and those with high institutional quality, where stricter enforcement and disclosure regimes heighten investor sensitivity to environmental risks. Additionally, we document that transparency in emission disclosure, financial risks and environmental liabilities is identified as a key channel through which firm-level climate risk affects market informativeness. Furthermore, higher SPI is associated with lower cost of capital, more efficient capital allocation and reduced crash risk. This study contributes novel insights to the climate finance literature by integrating firm-level governance factors with cross-jurisdictional analysis. Robustness checks, including placebo tests, alternative SPI measures and system GMM estimation, confirm the validity of our results and underscore the importance of institutional context in pricing environmental risk
Facilitators and barriers to early diagnosis of malignant plural mesothelioma (FILMM): Patients journey towards mesothelioma diagnosis, in England.
Full text linkPoster presentationBackground:
Prognosis with malignant plural mesothelioma (MPM), which is a rare form of cancer, is poor, yet evidence indicates a better chance of survival if earlier diagnosis is provided. Partly due to late presentation and diagnosis of malignant plural mesothelioma (MPM), the
survival rate in the United Kingdom is below the European average. Furthermore, there has been little attention to MPM patients’ experiences prior to diagnosis as available studies have focused on their lived experiences after diagnosis. This study therefore aims to identify the barriers and facilitators to MPM diagnosis and looks to understand the reasons for any variability in patients’ experiences of the pathway to diagnosis and proposed treatment plans.
Methods:
The theoretical basis for this study is the Model of pathway to treatment (MPT), which highlights four intervals (Appraisal; Help-seeking; Diagnostic; and Pre-treatment) along the pathway to diagnosis where a patient can experience ‘delay’ in obtaining a cancer diagnosis. This model was used to develop the interview topic guide.
Patients with confirmed MPM diagnosis were invited to take part in an in-depth, semi structured interview about their pathway to diagnosis. To ensure the recruitment of the targeted number of participants, participants were purposively recruited from two specialist
MPM outpatient clinics in England, of which, one of these clinics manages the second largest number of MPM patients in the country. A total of seventeen participants took part in the study.
The interview data were analysed using framework analysis. The MPT was used for the initial coding framework of the interview transcriptions. Common themes were then identified within each MPT interval.
Results:
Our findings identified barriers and facilitators within every interval along the MPM patients’ journey to diagnosis. Within the Appraisal and Diagnostic intervals, the presentation of vague symptoms that were mistaken for a less serious illness were found to
be a barrier. Health literacy regarding MPM appears to have an impact on how soon a patient sought help regarding their symptoms. Good health literacy by healthcare professionals (HCPs) appeared to facilitate placement on an MPM diagnostic pathway which
reassured patients about better potential outcomes.
Conclusion:
Earlier symptom recognition by both patient and HCPs including General Practitioners (GPs) can be used to target significant and avoidable delays along patients’ MPM diagnosis pathway, thereby promoting earlier diagnosis and treatment options
Region-aware prediction strategy based on shared points and multiple scales for dynamic multi-objective optimization
No full textThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In dynamic multi-objective optimization problems, effectively predicting and tracking the Pareto optimal front (POF) under environmental changes has been one of the core challenges. In this paper, we propose a region-aware prediction strategy based on shared points and multiple scales (RADMOEA) that combines global and local characteristics, aiming to enhance the algorithm’s ability to sense and adapt to POF. Firstly, the center-point movement strategy is used to move the non-dominated solution set from the previous moment to obtain the non-dominated solution set after the movement. The actual non-dominated solution set at the current moment and the non-dominated solution set after the movement share points in the objective space, and these shared points divide the non-dominated solution set at the current moment into several subregions. Within each region, all individuals are appropriately rescaled, and a local coordinate system is established. Then, within the local coordinate system, each individual is associated with the nearest post-movement non-dominated individual. Finally, new populations adapted to environmental changes are generated by combining centroid movement directions, Gaussian perturbations, and multi-scale individual association relationships. The proposed strategy is compared with six advanced algorithms, and the experimental results demonstrate that RADMOEA is effective in tracking the POF under dynamic environments
Improving Access and Recruitment to Clinical Trials for Lung Cancer Patients: A Multi-Phase, Qualitative Focus Group and Co-Production Study
No full textopen access articleAim
To design and develop a novel co-produced intervention tool aimed at facilitating discussions that lung cancer nurses have with lung cancer patients about clinical trial opportunities; and promote trial recruitment.
Design
A multi-phase qualitative focus group (phase 1) and co-production (phase 2) study.
Methods
The rigorous design and content of the intervention tool was informed by qualitative data from seven focus groups with lung cancer healthcare professionals (n = 38) and patients and their carers (n = 22) to establish barriers and facilitators to clinical trial participation. Data collection took place across England and Scotland between October and December 2023. Findings from a previously published systematic review were also incorporated to inform intervention tool design. The tool was developed through an extended co-production workshop comprising lung cancer nurses (n = 7), lung cancer patients (n = 2) and health researchers (n = 4). The COM-B model of behavioural change underpinned both phases of the project to guide tool development.
Results
Phase 1 focus groups identified the need for a tool to provide basic trial information to patients, and to support lung cancer nurses in discussing trials with patients, thus improving nurses' knowledge, confidence, and awareness of trials. The phase 2 coproduction workshop identified that the tool should consist of two elements: a patient-facing information pamphlet and a large poster for nurses to assist them in discussing trial opportunities.
Conclusion
The study results demonstrate how nurses can be supported to discuss clinical trial opportunities with patients, with the potential to increase long-term recruitment to clinical trials
Integrating Personalized Individual Semantics and Consistency Control to Support Consensus Reaching in 2-Rank Group Decision Making
Full text linkThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Traditional group decision making (GDM) problems typically aim to obtain a complete ranking of all considered alternatives from best to worst. However, in numerous real-life scenarios, there are instances where it is imperative to assign each alternative into one of two rank levels, creating a ranking where one subset of alternatives is prioritized above the other subset of alternatives. These scenarios are known as 2-rank GDM problems. While a range of methods exist for addressing 2-rank GDM problems, most are specifically tailored to multiattribute decision making situations, thereby limiting their applicability in scenarios involving preference relations. The linguistic preference relation (LPR) is an effective representation tool of decision makers’ (DMs’) preferences for pairwise comparisons of alternatives using linguistic terms. Since words may have different meanings for different DMs, a phenomenon known as personalized individual semantics (PISs), the modeling of linguistic PISs in 2-rank GDM problems with LPRs is worth investigating and challenging to address. Consequently, this article develops models to support consensus reaching for 2-rank linguistic GDM problems with PISs and consistency of DMs. Specifically, PIS consistency-driven models are initially employed to measure and improve the consistency of the LPRs of the individual DMs with unacceptable consistency level. Based on this foundation, the 2-rank vectors for both individuals and the group are determined. Subsequently, a 2-rank consensus measurement method is proposed on which a 2-rank consensus reaching process is designed to support DMs in improving their consensus levels. This involves the development of a PISs-based minimum adjustment consensus optimization model and a PISs-based individual consensus level maximization model. An algorithm to implement the proposed consensus reaching framework is also provided. Finally, numerical experiments and simulation results are reported to demonstrate the effectiveness of the proposed method
The PMDWell framework: A confirmatory factor analysis of video game players’ wellbeing
Full text linkopen access articleDespite the video game moral panics that have sprung up since the early 1990s, videogames remain a popular medium, increasing in capacity and market value every year. With the growth in the number of digital game players came the growth of uncertainty over the impacts of video games on wellbeing. The new generations are growing upsurrounded by ubiquitous, always-available digital technology and increasingly practice digitally mediated socialisation. The cultural shift suggests a change in the conceptualisation of wellbeing that can explain the phenomena of video game playing deaths.
A Player Multidimensional Wellbeing scale (PMDWell) is presented. The scale was derived from a conceptual framework drawn from existing literature on video game specific influences on wellbeing, and tested of 443 participants aged 13 to 65 worldwide. Teenagers were included due to the prevalence of gamers in the younger population. The scale constructs were validated using confirmatory factor analyses, ranging from good to excellent model fits, validity and reliability. We concluded that player wellbeing is a multidimensional construct with internal (social functioning, mental health) and external (physical health, life circumstances) dimensions.
Compared to other measures of wellbeing, PMDWell offers a broader understanding of wellbeing in the digital era that can be used to promote and maintain good health and perhaps highlight the lifestyle changes needed to optimise wellbeing and improve mental health. Future research could seek to replicate our validation in wider populations to enable demographic comparisons, especially comparing adolescents and young adults
An enhanced spatial-temporal graph convolution network with high order features for skeleton-based action recognition
Full text linkopen access articleSkeleton-based action recognition has emerged as a promising field within computer vision, offering structured representations of human motion. While existing Graph Convolutional Network (GCN)-based approaches primarily rely on raw 3D joint coordinates, these representations fail to capture higher-order spatial and temporal dependencies critical for distinguishing fine-grained actions. In this study, we introduce novel geometric features for joints, bones, and motion streams, including multi-level spatial normalization, higher-order temporal derivatives, and bone-structure encoding through lengths, angles, and anatomical distances. These enriched features explicitly model kinematic and structural relationships, enabling the capture of subtle motion dynamics and discriminative patterns. Building on this, we propose two architectures: (i) an Enhanced Multi-Stream AGCN (EMS-AGCN) that integrates joint, bone, and motion features via a weighted fusion at the final layer, and (ii) a Multi-Branch AGCN (MB-AGCN) where features are processed in independent branches and fused adaptively at an early layer. Comprehensive experiments on the NTU-RGB+D 60 benchmark demonstrate the effectiveness of our approach: EMS-AGCN achieves 96.2% accuracy and MB-AGCN attains 95.5%, both surpassing state-of-the-art methods. These findings confirm that incorporating higher-order geometric features alongside adaptive fusion mechanisms substantially improves skeleton-based action recognition
PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport
Full text linkThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Point cloud compression significantly reduces data volume but sacrifices reconstruction quality, highlighting the need for advanced quality enhancement techniques. Most existing
approaches focus primarily on point-to-point fidelity, often neglecting the importance of perceptual quality as interpreted by the human visual system. To address this issue, we propose a generative adversarial network for point cloud quality enhancement (PCE-GAN), grounded in optimal transport theory, with the goal of simultaneously optimizing both data fidelity and perceptual quality. The generator consists of a local feature extraction (LFE) unit, a global spatial correlation (GSC) unit and a feature squeeze unit. The LFE unit uses dynamic graph construction and a graph attention mechanism to efficiently extract local features, placing greater emphasis on points with severe distortion. The GSC unit uses the geometry information of neighboring patches to construct an extended local neighborhood and introduces a transformer-style structure to capture long-range global correlations. The discriminator computes the deviation between the probability distributions of the enhanced point cloud and the original point cloud, guiding the generator to achieve high quality
reconstruction. Experimental results show that the proposed method achieves state-of-the-art performance. Specifically, when applying PCE-GAN to the latest geometry-based point cloud compression (G-PCC) test model, it achieves an average BD-rate of -19.2% compared with the PredLift coding configuration and -18.3% compared with the RAHT coding configuration. Subjective comparisons show a significant improvement in texture clarity and color transitions, revealing finer details and more natural color gradients
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