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Global trends in vitamin D-fortified food and drink product launches (2019–2023)
Get PDFGlobal vitamin D deficiency drives the need for fortification. This study explored vitamin D-fortified products launched 2019–2023 using Mintel’s Global New Product Database. From 18,923 products identified, data on vitamin D content (µg/100g/ml) and product details (e.g. category/year/region) were extracted. Product launches increased over time, with +178% reported in 2020 vs 2019. Most were introduced in Asia and Europe (∼60%), with a ∼3.5-fold higher vitamin D in those launched in Asia vs Europe (11.3 ± 161.0 vs 3.3 ± 5.3 µg/100g, p < 0.001). “Dairy/dairy alternatives” were the most common fortification vehicle, but with relatively low levels of vitamin D. “Foods high in sugar/fat” and “hot beverages” had the highest levels. Most did not specify the use of vitamin D2/D3, and few used health claims. Global inconsistencies observed in vitamin D fortification supports the need for regional-specific guidance that is cognisant of the population’s vitamin D-need and dietary patterns, to help consumers meet vitamin D requirements
A zero-shot framework for cross-project vulnerability detection in source code
Get PDFThe growing prevalence of software vulnerabilities has increased the need for effective detection methods, particularly in cross-project settings where domain differences create significant challenges. Existing vulnerability detection models often struggle to generalise across projects due to variations in coding styles, feature distributions, and the absence of labelled target data. This paper presents ZSVulD, a zero-shot, cross-project vulnerability detection framework designed to operate without target-domain labels. ZSVulD uses domain-agnostic CodeBERT embeddings to capture both syntactic and semantic features of source code, enabling knowledge transfer between projects. The framework applies an iterative pseudo-labelling process in which a neural network and XGBoost classifier collaboratively refine predictions for the target domain. Feature alignment is incorporated as a diagnostic technique to assess and visualise distributional differences between source and target datasets. Experiments on the Devign and REVEAL datasets show that ZSVulD achieves higher recall, F1, and F2 scores compared to existing methods, with an emphasis on reducing false negatives. These findings indicate that ZSVulD can support automated vulnerability detection pipelines, contributing to more reliable security assessments across different software projects
Colours of Rathlin:Colour Palette
Get PDFColours of Rathlin formed part of the Pathways to Rathlin Wool engagement series during Island Insights: Rathlin Community Engagement Days, delivered through the AHRC Future Island–Island project (18–20 April). Using participatory action research methods, the event engaged residents in creative exploration and dialogue to reflect on Rathlin’s visual and cultural identity through colour.The day began with a talk, The Importance of Colour in Design, Textiles, and Identity, introducing colour theory and the ways in which colour can express place and belonging. In preparation, a collaboration with a local crafter produced two initial palettes drawn from her photographs of Rathlin’s land and sea. These examples illustrated how colour can be extracted from imagery, translated into yarn, and applied in textile design.During the workshop, participants identified colours of personal and collective significance, describing “Rathlin as a Pantone colour.” Each group’s selections and accompanying narratives were documented and later collated into a digital community colour palette that captures shared memories and landscape identity.The final Colours of Rathlin palette now features in the Future Observatory: Tools for Transition exhibition at the Design Museum, London (Sept 2025–Sept 2026). It has also informed several project outputs, including the illustrated publication The Story of Rathlin Wool, and will be part of Ulster University's Textile Roof Garden.This participatory, design-led approach demonstrates how co-creative methods can strengthen community connections and embed local knowledge within bioregional design and material research.<br/
Survodutide for treatment of obesity: Baseline characteristics of participants in a randomized, double‐blind, placebo‐controlled, phase 3 trial ( SYNCHRONIZE ™‐1)
Get PDFAims: Survodutide, a novel glucagon receptor and glucagon‐like peptide‐1 (GLP‐1) receptor dual agonist, elicited significant weight loss in a phase 2 trial in individuals with obesity without type 2 diabetes (T2D). Two multinational phase 3 trials are investigating survodutide for obesity management in individuals with or without T2D. We report the baseline characteristics of participants in the SYNCHRONIZE‐1 trial in adults with obesity without T2D (ClinicalTrials.gov: NCT06066515). Materials and methods: Participants aged ≥18 years with BMI ≥30 or ≥27 kg/m2 with ≥1 obesity complication without T2D were randomized 1:1:1 to double‐blind, once‐weekly, subcutaneous injections of survodutide (up‐titrated to 3.6 or 6.0 mg) or placebo for 76 weeks. The primary endpoints are percent body weight change and achievement of body weight reduction ≥5% from baseline to Week 76. Efficacy and safety analyses will include all randomized and treated participants. Results: At baseline, participants (n = 725 from 14 countries) had a mean age of 47.1 years, BMI 37.9 kg/m2, and waist circumference 115.2 cm. Most participants (59.4%) were female; 47.3% were from North America, 21.0% from Europe, and 20.0% from East Asia. Obesity complications included hypertension (40.0%), dyslipidaemia (33.7%), and prediabetes (30.2%). Mean haemoglobin A1c was 5.5%, estimated glomerular filtration rate 93.0 mL/min/1.73 m2, systolic/diastolic blood pressure 127.0/82.7 mmHg, and low‐density lipoprotein cholesterol 116.4 mg/dL; 21.8% were taking lipid‐lowering drugs. Conclusions: SYNCHRONIZE‐1 will determine the efficacy, safety, and tolerability of survodutide, a glucagon receptor/GLP‐1 receptor dual agonist, for weight loss in a representative cohort of people with obesity without T2D
Do athletes and coaches do what they know? A mixed-method survey on beliefs and attitudes on drafting in endurance sports
Get PDFDrafting occurs when athletes follow closely behind another to reduce aerodynamic resistance, lowering their biomechanical, physiological, and psychobiological load. However, it is unclear how aware athletes and coaches are of this advantage and how it is applied in training and competition. The aim of this study was to investigate the beliefs and attitudes of athletes and coaches towards drafting. An online survey was conducted with athletes (N = 236) and coaches (N = 79) active in cycling, skating and running. They answered five items on a Likert scale and answered one open question (“what is needed for optimal drafting?”). Thematic analysis revealed six themes, emphasizing the complexity and interpersonal nature of the drafting skill. Overall, 91.3% of respondents agreed that drafting can improve sports performance (60.5% strongly agree), while only 1.7% strongly disagreed. Yet, few indicated that they regularly worked on improving their drafting skills in training (16.4%). There was a moderate correlation between awareness and training on drafting, for athletes (rs = 0.31, p < 0.001) and coaches (rs = 0.35, p < 0.05). The findings demonstrate that athletes and coaches are generally aware of the advantages of drafting and recognise it as an important, complex and multifaceted skill. Yet, paradoxically, only few see value in exploring and improving this skill during training. This study highlights a critical gap between theoretical understanding and practical application, revealing a missed opportunity in athlete development
High frequency DOC proxy sensor assessment for peatland streams and across hydrological continua
Get PDFFluvial dissolved organic carbon (DOC) can only be quantified through laboratory measurements which, at low frequency intervals, may lead to unreliable carbon load estimates. As a solution, studies have implemented high frequency DOC proxy sensors, which are generally fluorescence or absorbance based. These sensors, however, have rarely been tested in an upland peatland environment with very high DOC concentrations or across hydrological continua. Therefore, to test their suitability and fill this knowledge gap, fluorescence and absorbance sensors were used to take measurements along varying environmental gradients and a hydrological continuum at eleven sites. These ranged from a first-order stream in an open moor peatland, down the main channel, and up to 126 km2 during seven separate campaigns (August 2023–August 2024). Results showed that the absorbance-based sensor provided a linear relationship with DOC concentrations across the hydrological continuum and during different flow conditions. However, the fluorescence-based sensor experienced signal issues when taking measurements in the upland portion of the catchment, when DOC concentrations exceeded ∼15 mg L−1, and struggled during high flow events. Through a series of post hoc experiments, uncorrectable signal quenching was identified to be caused by a higher level of humification present in the water, measured through the E4:E6 ratio (r = 0.282, p = 0.024). Therefore, it is recommended that future peatland fluvial carbon studies implementing high frequency proxy monitoring (with higher DOC concentrations >15 mg L−1) should use absorbance based sensors rather than fluorescence based, and which offer transferability to other fluvial environments and flow conditions
Does the Common-Sense Model of Illness Representations Predict Parent Help-Seeking for Adolescent Mental Health Distress?
Get PDFBackground Parents can be slow to recognise that an adolescent needs help from a mental health professional, yet the factors affecting their help-seeking intentions are not well understood. The aim of this study was to test the application of the Common-Sense Model (CSM) of Illness Representations to parents’ perception of adolescent distress and intentions to seek help. Method The study employed an experimental design using video vignettes. Parents ( N = 1,176; female N = 993) of adolescents (10–19 years) were asked to self-report key demographic information, an illness perceptions questionnaire, and a measure of stigma. Results Results demonstrated that the CSM model explained 38% of the variance in help-seeking intentions. Parents were more likely to report intentions to seek help if they believed that treatment could control the adolescent’s problem (OR = 1.39), or if they believed the problem would have negative consequences (OR = 1.41). Parents who believed the problem was in the control of the adolescent, had lower help-seeking intentions (OR = .87). Conclusion The CSM provides a useful model of help-seeking intentions to guide parental education. Perceiving treatment as controlling distress or that distress would have negative consequences for an adolescent, were key predictors of parental help-seeking intentions
A hybrid spiking neural network - quantum framework for spatio-temporal data classification: a case study on EEG data
Get PDFThe study introduces a hybrid computational framework that combines neuro-inspired information processing using spiking neural networks (SNNs) and quantum information processing using quantum kernels to develop quantum-enhanced machine learning models for spatio-temporal data, demonstrated through the classification of EEG data as a case study. In the proposed SNN-quantum computation (SNN-QC) framework, SNN with spike time information representation is employed to learn spatio-temporal interactions (EEG recorded from multiple channels over time). Frequency-based (rate-based) information as spike frequency state vectors are extracted from the SNN and classified using a quantum classifier. In the latter part, we use the quantum kernel approach utilising feature maps for classification tasks. The proposed SNN-QC is demonstrated on a benchmark EEG dataset to classify three distinct wrist movement tasks in six binary classification setups as a proof of concept. We introduce a novel high-order nonlinear feature map that demonstrates improved performance over state-of-the-art feature maps and several machine learning methods across most of the tasks studied. Furthermore, the role of hyperparameters for enhanced feature maps is also highlighted. The performance of SNN-QC is evaluated using statistical metrics and cross-validation techniques, demonstrating its efficacy across multiple binary classifiers. Quantum hardware validation is conducted using both a superconducting IBM-QPU and a high-fidelity noisy simulation that replicates a real QPU. Furthermore, the results demonstrate that the SNN-QC outperforms models that use statistical features rather than features extracted from the SNN, as the SNN accounts for the temporal interaction between the spatio-temporal input variables. Finally, we conclude that the SNN-QC offers a potential pathway for developing more accurate neuromorphic-quantum enhanced systems that are both energy-efficient and biologically-inspired, well-suited for dealing with spatio-temporal data
An Experimental EACI-Based Localization Framework Using LQI and CNN for Consumer IoT
Get PDFPrecise indoor localization remains a challenge in wireless sensor networks (WSNs) due to multipath fading, interference, and signal fluctuations in different environments. Traditional methods depend on Received Signal Strength (RSS) also often struggle with accuracy in indoor scenario. This study presents an experimental localization framework that utilizes Link Quality Indicator (LQI) values and Convolutional Neural Networks (CNNs) within an Edge Computing-Assisted Consumer IoT (EACI) model. The proposed approach segments the network using a pyramid-loop algorithm and employs LQI-based measurements for more stable and accurate distance estimation. A CNN classifier is trained on normalized LQI data, including statistical features such as kurtosis, to predict node locations. The system is authenticated by a real-world testbed using Zigbee XB24C nodes. The experimental results show an overall localization error of 0.12m at zone 1 with a standard deviation of 0.89m. This reflects an improved localization accuracy and reduced error compared to RSS-based and existing CNN-based methods. The proposed technique effectiveness is observed for indoor localization in consumer IoT environments
Exploring access to community neurorehabilitation for people with progressive neurological conditions: a qualitative study
Get PDFPurpose: Community neurorehabilitation enables people with progressive neurological conditions (PNCs) to manage their symptoms to live an active, fulfilling life; however, it is not accessible to all. This study explored the factors influencing access to community neurorehabilitation in Northern Ireland from the perspective of people with PNCs and their carers. Methods: Eleven people living with a PNC and three carers took part in virtual focus groups. Data was thematically analysed using the framework method. Results: Access to neurorehabilitation was described as a staged journey, driven by people with PNCs, and impacted by interactions with others. Four themes were identified: the person in the driving seat, describing the value of person-centred care and the need for proactivity; the traffic lights, depicting the role and influence of health care professionals (HCPs); the need for direction; and roadworks and roadblocks, identifying additional barriers to access. In addition, six fundamentals of good access were identified. Conclusions: This study adds depth to our understanding of the complexity, and the roles and needs of people with PNCs and HCPs, in accessing community neurorehabilitation. Further research is needed to determine how best to empower people to access rehabilitation.</p