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Taking Boys Seriously:Empowering Voices Exhibition
Get PDFThis exhibition presents images and voices of boys and young men as they wish to be seen and heard. It is the result of a Taking Boys Seriously collaborative project involving St Joseph's Boys' School, Oakgrove Integrated College, Ulster University Belfast School of Art, Nerve Centre and Translink.A series of compelling portraits are accompanied by OR codes, inviting you to listen to the authentic voices of boys and young men as they talk about themes including belonging, community, school future, roles models, strength, and being a boy.Launch EventFriday 19th September 2025, 11am - 1pmUlster University Derry~Londonderry CampusExhibition on display at North West Transport Hub from 20th Sep - 3rd October 202
Fitting soil extracellular enzyme activity into the complex network of abiotic and biotic soil properties often associated with soil health
Get PDFIn this mini review we examine how soil extracellular enzymes play a key role in nutrient cycling, but stress that their activity alone does not fully represent ecosystem processes. We emphasize the need for more contextual environmental data—such as pH, temperature, moisture and nutrient availability—for accurate interpretation of the significance of enzyme activity in carbon and nutrient (N, P) cycling in soil ecosystems. The importance of enzymes within the soil microbiome determines its inherent capacity to support crop growth and often reflects soil quality and soil health, which are in turn governed by multiple different soil properties. Soil enzymes (e.g., phosphatase, glucosidases, glycosaminidases) activity have been used as key soil health bio indicators for monitoring soil nutrient transformations in overgeneralized statements. Although soil enzymes constitute important attributes that are closely linked to the dynamics of soil nutrient transformation and make nutrients available to plants, we suggest a multi-factor assessment for soil health measurement. We propose that this can give a pulse reading of soil nutrient health at crucial times of soil, land use, and crop management practices but that care is required to incorporate temporal soil and land use properties for correct interpretation
Deep Learning-Based Secure Tag Selection in BackCom Network With RIS-Induced Interference
Get PDFThis article investigates the secrecy performance of a non-linear energy-harvesting backscatter communication (BackCom) network in the presence of direct link and reconfigurable intelligent surface (RIS) interference. The network comprises a source, multiple passive tags, an RIS, and a legitimate reader, with an eavesdropper attempting to intercept the communication. We analyze a tag selection scheme based on long-short-term memory (LSTM) to address the challenge of selecting tags under the influence of direct link and the RIS interference. The nonideal behavior of the RIS is exploited to enhance secrecy performance by modeling RIS phase errors using Von Mises and uniform distributions. Because of interference from the direct link and the RIS being common to all tags, the secrecy rates of different tags are correlated. The LSTM-based scheme effectively captures this correlation and perfectly matches the conventional selection scheme on low and high tag counts. The secrecy outage probability (SOP) achieved using the LSTM outperforms other machine learning techniques, such as k -nearest neighbors ( k -NN), decision trees (DT), and support vector machines (SVM). We also demonstrate the impact of RIS elements, phase error parameters, and the number of tags on the SOP in the considered RIS-aided BackCom network
Alphaenhancer: A Resource-Aware Game Agent for Single Image Super Resolution for Next-Generation Edge Communication Networks
Get PDFEmbedded resources have been becoming part of the Internet of Things networks, where they are increasingly taking part in various kinds of decision-making using Tiny Machine Learning (TinyML) models. Although offloading the TinyML model for these devices includes removing many layers that have less impact on the overall performance, they often lead to a sacrifice on the overall performance of the model. In this paper, we propose a novel device-aware training strategy to customize the training based on the resources on which the model will be applied. We proposed AlphaEnhancer, a resource-aware game agent for medical image super-resolution. We baseline our approach on the Residual Feature Distillation Model (RFDN) and propose a device efficacy metrics, which is based on the learned actions of the agent. The model with the highest efficacy is deemed appropriate for that particular device. Our preliminary results show that our methods performed significantly well with respect to the baseline and other recent state-of-the-art
Acoustic Fingerprinting and Nanoslip Dynamics of Biofilms
Get PDFIt is reported that bacteria can generate nanomotion, but understanding the complex dynamics of bacterial colony gliding on solid interfaces has remained unresolved. Here, this work captures the real-time development and gliding of bacterial biofilms on vibrating solids made of piezoelectric quartz. The gliding, characterized by liquid slips, is measured in form of frequency and dissipation changes of the vibrating solid. These vibrations enable the generation of distinct acoustic fingerprints (sound/ music) of the three phases of biofilm development: viscoelastic strengthening, biofilm growth and biofilm stability. In adition, the effect of extracellular matrix secretion on the rigidity of the film and its nanoslip in each of the distinct biofilm developmental phases is quantified. This work provides a real-time, label-free method of quantifying bacteria biofilm dynamics and paves the way for developing libraries of acoustic signatures of bacteria and their metabolic products.</p
Identifying comorbidity patterns of mental health disorders in community-dwelling older adults: A cluster analysis
Get PDFAs global life expectancy increases, understanding mental health patterns and their associated risk factors in older adults becomes increasingly critical. Using data from the cross-sectional Trinity Ulster Department of Agriculture study (TUDA, 2008-2012; n=5186; mean age 74.0 years) and a subset of participants followed-up longitudinally (TUDA 5+, 2014-2018; n=953), we perform a multi-view co-clustering analysis to identify distinct mental health profiles and their relationships with potential risk factors. The TUDA multi-view dataset consists of five views: (1) mental health, measured with Center for Epidemiologic Studies Depression Scale [CES-D] and Hospital Anxiety and Depression Scale [HADS], (2) cognitive and neuropsychological function, (3) illness diagnoses and medical prescription history, (4) lifestyle and nutritional attainment, and (5) physical well-being. That is, each participant is described by five distinct sets of features. The mental health view serves as the target feature set, while the other four views are analyzed as potential contributors to mental health risks. Under the multi-view co-clustering framework, for each view data, the participants (rows) are partitioned into different row-clusters, and the features (columns) are partitioned into different column-clusters. Each row-cluster is most effectively explained by the features in one or two column-clusters. Notably, the row-clusterings across views are dependent. By analyzing the associations between row clusters in the mental health view and those in each of the other four views, we can identify which risk factors co-occur and contribute to an increased risk of poor mental health. We identify five distinct row-clusters in the mental-health view data, characterized by varying levels of depression and anxiety: Group 1, mild depressive symptoms and no symptoms of anxiety; Group 2, acute depression and anxiety; Group 3, less severe but persistent depression and anxiety symptoms; Group 4, symptoms of anxiety with no depressive symptoms; and Group 5, no symptoms of either depression or anxiety. Cross-view association analysis revealed the following key insights: Participants in Group 3 exhibit lower neuropsychological function, are older, more likely to live alone, come from more deprived regions, and have reduced physical independence. Contrasting Group 3, participants in Group 2 show better neuropsychological function, greater physical independence, and higher socioeconomic status. Participants in Group 5 report fewer medical diagnoses and prescriptions, more affluent backgrounds, less solitary living, and stronger physical independence. A significant portion of this group aligns with cognitive health row-clusters 1 and 3, suggesting a strong link between cognitive and mental health in older age. Participants with only depressive (Group 1) or anxiety symptoms (Group 4) exhibit notable differences. Those with anxiety symptoms are associated with healthier clusters across other views. The co-clustering methodology also categorizes the questions in the CES-D and HADS scales into meaningful clusters, providing valuable insights into the underlying dimensions of mental health assessment. In the CES-D scale, the questions are divided into four clusters: those related to loneliness and energy, those addressing feelings of insecurity, worthlessness, and fear, those concerning concentration and effort, and those focused on sleep disturbances. Similarly, the HADS questions are grouped into clusters that reflect themes such as a strong sense of impending doom, nervousness or unease, and feelings of tension or restlessness. By organizing the questions from both scales into these smaller groups, the methodology highlights distinct symptom patterns and their varying severity among participants. This approach could be leveraged to develop abridged versions of the assessment scales, enabling faster and more efficient triage in clinical practice.</p
A mini review of transforming dementia care in China with data-driven insights: overcoming diagnostic and time-delayed barriers
Get PDFIntroduction: Inadequate primary care infrastructure and training in China and misconceptions about aging lead to high mis−/under-diagnoses and serious time delays for dementia patients, imposing significant burdens on family members and medical carers.Main body: A flowchart integrating rural and urban areas of China dementia care pathway is proposed, especially spotting the obstacles of mis/under- diagnoses and time delays that can be alleviated by data-driven computational strategies. Artificial intelligence (AI) and machine learning models built on dementia data are succinctly reviewed in terms of the roadmap of dementia care from home, community to hospital settings. Challenges and corresponding recommendations to clinical transformation are then reported from the viewpoint of diverse dementia data integrity and accessibility, as well as models’ interpretability, reliability, and transparency.Discussion: Dementia cohort study along with developing a center-crossed dementia data platform in China should be strongly encouraged, also data should be publicly accessible where appropriate. Only be doing so can the challenges be overcome and can AI-enabled dementia research be enhanced, leading to an optimized pathway of dementia care in China. Future policy- guided cooperation between researchers and multi-stakeholders are urgently called for dementia 4E (early-screening, early-assessment, early-diagnosis, and early-intervention)
Tackling Poverty Across the United Kingdom. Devolution, Difference and Discourse
Get PDFThe UK welfare state is often considered as being highly centralised, yet the design and administration of UK social security involves significant spatial variations in law, policy, and practice. As such, where you live in the UK can affect the value of benefits and cash transfers you are entitled to, as well as how you experience benefit administration. In this article we advocate for greater consideration of spatial variations in social security and draw attention to existing policy differences in the devolved nations and across localities. The article explores policy discourse and design differences to identify competing narratives and to encourage greater consideration of spatial policy developments in social security. Drawing attention to the Safety Nets research project, it argues that a better understanding of the causes and outcomes of spatial variation in social security provision is necessary in the context of governance reforms to increase devolution and decentralistion including the rise of mayoral regions in England.<br/
Context-Mechanism-Outcome configurations from a realist evaluation exploring digital interventions for children with speech sound disorder [dataset]
Get PDFThe data is comprised of context-mechanism-outcome configurations (CMOs) from a realist evaluation. The CMOs depict what works, for whom, why, in which circumstances when supporting children with speech sound disorder to engage in intensive home-practice using digital health. The CMO data is presented within a table, saved as a PDF document. For each identified CMO, the table columns presents the Context, Mechanism resource, Mechanism response, Outcome, Supporting literature, and Example quotations from participants. These are presented across five programme areas of interest: Intervention Intensity; Partnership and Collaboration; Parent-training; The child, parent/carer and SLT dynamic; Child Participation
A Python-Based Automation Script to Mark Computer-Aided Design Assessments
Get PDFThe integration of automated tools in engineering education has the potential to improve student assessments, ensuring consistency and reducing instructor workload. This study introduces a Python-based automation tool designed to evaluate student Computer-Aided Design (CAD) submissions. The tool utilises software API and Open Cascade library to calculate model parameters. These parameters are compared against expected values from a solution file and marks are assigned based on deviations relative to the solution file. As a use case, seventy-five Solid Edge CAD files were assessed for geometric properties such as volume, surface area, and centre of gravity location to evaluate inter- and intra-marker reliability. The results showed perfect agreement, with a Cohen kappa of 1.0 for both metrics. Furthermore, the automated tool reduced grading time by 89.7% compared to manual evaluation. The potential of automation in improving marking efficiency, consistency, and objectivity in engineering education has been shown, providing a foundation for further integration of software. The python-based automation script is openly available on GitHub