SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environments.

The rapid growth of Internet of Things (IoT) devices presents significant cybersecurity challenges due to their diverse and resource-constrained nature. Existing security solutions often fall short in addressing the dynamic and distributed environments of IoT systems. This study aims to propose a novel deep learning framework, SecEdge, designed to enhance real-time cybersecurity in mobile IoT environments.

Dataset linking women's maternity care experiences with hospital environment and governance in Ireland.

Many scholars argue that there is a deepening crisis of trust in healthcare systems. What is not contested is the centrality of public trust in building reputational value in healthcare organisations. However, there is a dearth of research focused on better understanding how trust in healthcare institutions, and the healthcare workforce, can be sustainably cultivated.

Temporal Changes and Between-Competition Differences in the Activity Profile of Elite Hurling Referees.

This study examined the activity profile of elite hurling referees during games in the National Hurling League (NHL) and All-Ireland Championship (AIC) and across all divisions of the NHL and phases of the AIC. Temporal changes between the first and second half and across the four quarters were also examined. Data were collected from 36 referees using 10-Hz global positioning system technology during 106 NHL and 85 AIC games and analyzed for duration, total distance, very low-speed movement (<0.

Adapting physics-informed neural networks to improve ODE optimization in mosquito population dynamics.

Physics informed neural networks have been gaining popularity due to their unique ability to incorporate physics laws into data-driven models, ensuring that the predictions are not only consistent with empirical data but also align with domain-specific knowledge in the form of physics equations. The integration of physics principles enables the method to require less data while maintaining the robustness of deep learning in modelling complex dynamical systems. However, current PINN frameworks are not sufficiently mature for real-world ODE systems, especially those with extreme multi-scale behavior such as mosquito population dynamical modelling.

A multidimensional database of in-game player movements (Actions and events) in gaelic football.

Research in field sports often measures the performance of players during competitive games with individual and time-based descriptive statistics. Data is generated using GPS technologies, capturing simple data such as time (seconds) and position (latitude and longitude). While the data capture is highly granular and in relatively high volumes, the raw data are unsuited to any form of analysis or machine learning functions.

A global analysis of portion size recommendations in food-based dietary guidelines.

Objective: Since large food portion sizes (PS) lead to overconsumption, our objective was to review PS recommendations for commonly consumed food groups reported in Food-Based Dietary Guidelines (FBDGs) globally and to assess variation in PS across countries and regions.

Methods: Consumer-oriented FBDGs from the Food and Agriculture Organization (FAO) online repository were used to evaluate dietary recommendations, PS and number of portions for common food groups. Guidelines were classified for each group as qualitative, quantitative, or missing.

Smart Specialisation Strategies and regional knowledge spaces: how to bridge vision and reality.

Smart Specialisation Strategies (S3) are implemented across European regions. However, investigations into whether S3 initiatives adequately match local knowledge capabilities are very scarce. This work analyses to what extent S3 policies are coherent with the local knowledge space of 164 European regions, respectively.

The Training Intensity Distribution of Marathon Runners Across Performance Levels.

Background: The training characteristics and training intensity distribution (TID) of elite athletes have been extensively studied, but a comprehensive analysis of the TID across runners from different performance levels is lacking.

Methods: Training sessions from the 16 weeks preceding 151,813 marathons completed by 119,452 runners were analysed. The TID was quantified using a three-zone approach (Z1, Z2 and Z3), where critical speed defined the boundary between Z2 and Z3, and the transition between Z1 and Z2 was assumed to occur at 82.

Setting targets for antibiotic use in general practice in Europe: A scoping review.

Background: National Action Plans (NAPs) aim to address antimicrobial resistance (AMR) understanding and awareness but struggle to translate targets into clinically relevant guidance for general practice.

Objective: To identify and map antibiotic use targets in European general practice and explore if and how these targets are linked to NAPs.

Methods: A systematic search was carried out in MEDLINE (OVID), EMBASE and SCOPUS, with additional manual searches.

Correlation between proprioception, functionality, patient-reported knee condition and joint acoustic emissions.

Non-invasive assessment of joint status using acoustic emissions (AE) is a growing research area that has the potential to translate into clinical practice. The purpose of this study is to investigate the correlation of the knee's AE with measures of proprioception, self-assessment, and performance, as it can be hypothesised that, AE parameters will correlate with joint function metrics due to AE being recorded during interaction of the articular surfaces. Threshold to detect passive motion (TTDPM), Knee Osteoarthritis Outcome Scores (KOOS) and 5 times sit-to-stand test (5STS) were collected from 51 participant.

Vector time series modelling of turbidity in Dublin Bay.

Turbidity is commonly monitored as an important water quality index. Human activities, such as dredging and dumping operations, can disrupt turbidity levels and should be monitored and analysed for possible effects. In this paper, we model the variations of turbidity in Dublin Bay over space and time to investigate the effects of dumping and dredging while controlling for the effect of wind speed as a common atmospheric effect.

Predictive analysis of ratings of perceived exertion in elite Gaelic football.

This study aimed to compare the predictive accuracy of absolute and relative external load indices (ELI) across three machine learning models, and predict the rating of perceived exertion (RPE) of elite Gaelic football players using ELI, personal characteristics, wellness scores, and training workloads. ELI and related variables were collected from 49 elite Gaelic football players over three competitive seasons resulting in 1617 observations. ELI included total distance, high speed running distance (≥ 4.

Bayesian clustering of spatially distributed compositional data with application to the Great Barrier Reef.

The relative abundance of groups of species is often used in ecological surveys to estimate community composition, a metric that reflects patterns of commonness and rarity of biological assemblages. The focus of this paper is measurements of the abundances of four benthic groups (that live on the seafloor) at several reefs on Australia's Great Barrier Reef (GBR) gathered between 2012 and 2017. In this paper we develop a statistical model to find clusters of locations with similar composition.

Composite Ensemble Learning Framework for Passive Drone Radio Frequency Fingerprinting in Sixth-Generation Networks.

The rapid evolution of drone technology has introduced unprecedented challenges in security, particularly concerning the threat of unconventional drone and swarm attacks. In order to deal with threats, drones need to be classified by intercepting their Radio Frequency (RF) signals. With the arrival of Sixth Generation (6G) networks, it is required to develop sophisticated methods to properly categorize drone signals in order to achieve optimal resource sharing, high-security levels, and mobility management.

Usability of the BigO system in pediatric obesity treatment: A mixed-methods evaluation of clinical end-users.

Objective: To assess technical usability of the BigO app and clinical portal among diverse participants and explore the overall user experiences of both.

Methods: Methods included technical usability testing by measuring the relative user efficiency score (RUS) for the app and measuring Relative User Efficiency (RUE) using the 'think aloud' method with the clinical portal. Qualitative approaches involved focus groups with adolescent app users and semi-structured one-to-one interviews with clinician participants.

Dataset on comprehensive assessment & classification of upper & lower limb pain in athletes.

Data were charted as part of a scoping review which followed the Joanna Briggs Institute (JBI) evidence synthesis guidelines and the Preferred Reporting Items for Systematic Reviews and Meta Analysis Scoping Review extension (PRISMA-SCr) guidelines. Data was extracted from 470 articles that met the inclusion criteria for the scoping review; primary research articles of athletes where upper and/or lower limb pain since database inception. A draft data charting tool was developed by the research team and piloted for feasibility, accuracy and agreement.

How strongly does diet variation explain variation in isotope values of animal consumers?

Analysis of stable isotopes in consumers is used commonly to study their ecological and/or environmental niche. There is, however, considerable debate regarding how isotopic values relate to diet and how other sources of variation confound this link, which can undermine the utility. From the analysis of a simple, but general, model of isotopic incorporation in consumer organisms, we examine the relationship between isotopic variance among individuals, and diet variability within a consumer population.

Breaking down the Digital Fortress: The Unseen Challenges in Healthcare Technology-Lessons Learned from 10 Years of Research.

Healthcare is undergoing a fundamental shift in which digital health tools are becoming ubiquitous, with the promise of improved outcomes, reduced costs, and greater efficiency. Healthcare professionals, patients, and the wider public are faced with a paradox of choice regarding technologies across multiple domains. Research is continuing to look for methods and tools to further revolutionise all aspects of health from prediction, diagnosis, treatment, and monitoring.

Machine learning and deep learning for classifying the justification of brain CT referrals.

Objectives: To train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iGuide categorisation, and to determine if prediction models can generalise across multiple clinical sites and outperform human experts.

Methods: Adult brain computed tomography (CT) referrals from scans performed in three CT centres in Ireland in 2020 and 2021 were retrospectively collected. Two radiographers analysed the justification of 3000 randomly selected referrals using iGuide, with two consultant radiologists analysing the referrals with disagreement.

Test-Retest Reliability of Running Economy and Metabolic and Cardiorespiratory Parameters During a Multistage Incremental Treadmill Test in Male Middle- and Long-Distance Runners.

This study investigated the test-retest reliability of running economy (RE) and metabolic and cardiorespiratory parameters related to endurance running performance using a multistage incremental treadmill test. On two occasions separated by 21-28 days, 12 male middle- and long-distance runners ran at 10, 11, 12, 13, and 14 km/hr for 8 min each stage, immediately followed by a ramp test to volitional exhaustion. Carbohydrate (10% maltodextrin solution) was consumed before and during the test to provide ∼1 g/min of exercise.

How group structure impacts the numbers at risk for coronary artery disease: polygenic risk scores and nongenetic risk factors in the UK Biobank cohort.

The UK Biobank (UKB) is a large cohort study that recruited over 500,000 British participants aged 40-69 in 2006-2010 at 22 assessment centers from across the United Kingdom. Self-reported health outcomes and hospital admission data are 2 types of records that include participants' disease status. Coronary artery disease (CAD) is the most common cause of death in the UKB cohort.