Dundalk Institute of Technology

As cyber threats escalate globally, understanding public attitudes towards data protection and institutional trust is crucial for effective reform. This exploratory study investigates cybersecurity perceptions in Ireland after the 2021 Health Service Executive (HSE) ransomware attack, using an anonymous survey of 106 participants. Statistical analysis showed that confidence in government platforms strongly predicts trust in healthcare organisations, while increased awareness of data security is associated with slightly lower trust in healthcare technology. Strong support (68.9%) for stricter data protection laws further emphasised the public demand for systemic change. These findings illustrate how major breaches influence public sentiment and highlight the need for transparent communication, robust regulation, and shared responsibility to protect the digital infrastructure.

This research introduces a Reinforcement Learning (RL) based testbed designed for real-time coordination among multiple Unmanned Aerial Vehicles (UAVs) using actor-critic models to control a randomly manoeuvring target UAV, and a swarm of tracking UAVs. The system simulates realistic multi-agent interactions within a scalable and distributed architecture built on FlightGear and JSBSim. A key feature is the ability of multiple agents to learn independently yet collaboratively, enabling synchronized behaviors and adaptive tracking in dynamic environments. Collision avoidance, swarm cohesion, and target tracking efficiency are ensured through a 3D ellipsoidal spatial model and a Gaussian reward function, which guide agents to maintain optimal relative positions. The integration of an Intrinsic Curiosity Module (ICM) enhances exploration in sparse-reward settings. Experimental results show that the multi-agent swarm UAVs effectively tracks complex and evasive target paths, demonstrating robustness, scalability, and real-time coordination capabilities essential for advanced UAV operations.

In people living with HIV (PLWH), surveillance for Leishmania infection is crucial for identify those at risk of developing visceral leishmaniasis (VL). Leishmania-HIV coinfection worsens patient outcomes and increases mortality and relapse rates. We conducted a prospective study (2017–2023) in Northeast Brazil to assess the long-term outcomes of asymptomatic Leishmania-HIV coinfected outpatients and Leishmania-negative PLWH. Participants were drawn from a cross-sectional study performed in 2017, which identified both Leishmania-HIV coinfected and Leishmania-negative PLWH. Epidemiological, clinical, and laboratory data were collected from medical records (2017–2023). In 2023, these individuals were retested for Leishmania using serological tests and conventional polymerase chain reaction (PCR). Categorical variables were compared using the chi-square test, and non-parametric tests were used for quantitative variables. During follow-up, six individuals developed VL: five from the coinfected group and one from the non-coinfected PLWH group (OR 10.4; 95% CI 1.2–94.2; p = 0.023). Three patients experienced relapse: one from the PLWH group and two from the coinfected group. There was one death in the Leishmania-HIV group. In 2023, 80 patients were retested; five coinfected patients remained positive for VL by one or more tests, and two PLWH patients seroconverted for VL. Our findings underscore the critical need for long-term follow-up of asymptomatic Leishmania-HIV patients to mitigate disease progression and associated complications.

Frontline high-dose therapy (HDT) is, in 2025, the standard of care for patients with multiple myeloma (MM) who are eligible for autologous stem cell transplantation (ASCT). Prior to high-dose melphalan, induction therapy with quadruplet combinations is also proposed systematically when possible. Lenalidomide maintenance until progression is also recommended per international guidelines. This strategy has been developed over recent decades, based on results of phase 3 trials designed and conducted by different academic groups. With these therapeutic advances, the median overall survival (OS) for patients with MM has increased from 5 years in the 1990s, to over 15 years at present. Here, we present the contribution of the French myeloma cooperative group Intergroupe Francophone du Myélome (IFM) to these advances in the newly diagnosed transplant-eligible (NDTE) setting.

The concerns over global warming and energy security have made the development and implementation of renewable energy a global priority. One notable renewable energy target is the 3X renewable energy program which aims to triple the renewable energy capacity before 2030 and almost 200 governments are committed to this program. Floating offshore wind turbines are one of a number of important sectors of this program with a set target of increasing 9% wind turbine installations annually over this period. Furthermore, the recent progress in this field shows the ability and ambitions of this industry to deliver on this long-term target. Due to the complex dynamics of floating offshore wind turbines, achieving this goal needs placing a huge focus on development, accurate simulations, and analysis of the operational performance of these structures in real conditions. Floating offshore wind turbines are frequently exposed to huge environmental loads which are wave, wind, and tidal/ocean current loads and the effect of the loads induce different motions with large amplitudes in 6-DOF. Among all the six degrees of freedom, surge and pitch motions have a significant effect on wind turbine rotor displacement and consequently on aerodynamic performance and power generation of floating offshore wind turbines. In this paper, the NREL 5-MW floating wind turbine supported by a spar platform is selected under wave-wind loading. Additionally, a fully-coupled analysis tool which is called F2A is utilised to provide a fully-coupled aero-hydro-servo-elastic simulation at a site along the Irish coastline. The F2A tool is open-source, efficient, and it provides a coupled simulation of hydrodynamic and aerodynamic loads with high degree of accuracy. The main aim of this paper is to present the finding of the analysis on the effects of surge and pitch motion on the aerodynamic performance and power generation, considering different wave-wind case simulations, and also to provide a detailed proposal for optimising the performance of these complicated platforms.

This paper introduces an Intrinsic Curiosity Module (ICM) based Reinforcement Learning (RL) framework for swarm Unmanned Aerial Vehicles (UAVs) target tracking, leveraging the actor–critic architecture to control the roll, pitch, yaw, and throttle motions of UAVs. A key challenge in RL-based UAV coordination is the delayed reward problem, which hinders effective learning in dynamic environments. Existing UAV testbeds rely primarily on extrinsic rewards and lack mechanisms for adaptive exploration and efficient UAV coordination. To address these limitations, we propose a testbed that integrates an ICM with the Asynchronous Advantage Actor-Critic (A3C) algorithm for tracking UAVs. It incorporates the Self-Reflective Curiosity-Weighted (SRCW) hyperparameter tuning mechanism for the ICM, which adaptively modifies hyperparameters based on the ongoing RL agent’s performance. In this testbed, the target UAV is guided by the Advantage Actor-Critic (A2C) model, while a swarm of two tracking UAVs is controlled by using the A3C-ICM approach. The proposed framework facilitates real-time autonomous coordination among UAVs within a simulated environment. This system is developed using the FlightGear flight simulator and the JSBSim Flight Dynamics Model (FDM), which enables dynamic simulations and continuous interaction between UAVs. Experimental results demonstrate that the tracking UAVs can effectively coordinate and maintain precise paths even under complex conditions.

Abstract Collecting lymphatic vessels (cLVs) exhibit spontaneous contractions with a pressure-dependent frequency, but the identity of the lymphatic pacemaker cell is still debated. Here we combined immunofluorescence and scRNAseq analyses with electrophysiological methods to examine the cellular constituents of the mouse cLV wall and assess whether any cell type exhibited morphological and functional processes characteristic of pacemaker cells. We employed inducible Cre (iCre) mouse models to target specific cell populations including c-kitCreERT2 to target interstitial cells of Cajal like cells; PdgfrβCreERT2 to target pericyte-like cells; PdgfrαCreERTMto target CD34+ adventitial cells; and Myh11CreERT2to target lymphatic muscle cells (LMCs) directly. These inducible Cre lines were crossed to the fluorescent reporter ROSA26mT/mG, the genetically encoded Ca2+ sensor GCaMP6f, and the light-activated cation channel rhodopsin2 (ChR2). Only LMCs consistently, but heterogeneously, displayed spontaneous Ca2+ events during the diastolic period of the contraction cycle, and whose frequency was modulated in a pressure-dependent manner. Further, optogenetic depolarization with ChR2 only induced propagated contractions in LMCs. Membrane potential recordings in LMCs demonstrated that the rate of diastolic depolarization significantly correlated with contraction frequency. These findings support the conclusion that LMCs, or a subset of LMCs, are responsible for mouse cLV pacemaking.

Abstract Aim Improving nitrogen use efficiency (NUE) is critical for enhancing the sustainability of agricultural grasslands. However, there is limited research assessing NUE of grasslands managed with different nitrogen (N) fertilization inputs. We tested the hypothesis that multispecies swards managed with the combination of chemical and organic fertilizer would perform best with respect to herbage N yield and NUE. Methods A grassland field experiment was conducted over two years with four sward types: permanent pasture (baseline sward), perennial ryegrass monoculture, 6-species and 12-species swards; and three N fertilizer treatments: chemical, organic and a combination of chemical and organic fertilizer. Perennial ryegrass received 170 kg N/ha/yr while the other swards received 70 kg N/ha/yr. Results We found significant main effects of sward types and the N fertilizer treatments on herbage N yield and NUE indices. The multispecies swards (i.e., 6- and 12-species) had higher NUE, more negative partial N balance and higher partial factor productivity than perennial ryegrass sward across both years (P < 0.05). The proportion of legumes in the multispecies swards explained 55% of the herbage N yield. All N fertilizer treatments had NUE > 1 and negative partial N balance with the highest value in chemical fertilizer. NUE > 1 and negative partial N balance indicate greater herbage N yield relative to total N input. Conclusion Multispecies swards consistently outperformed perennial ryegrass across the three NUE indices. This highlights the potential of multispecies swards as a suitable option for improving NUE and minimizing the risk of N surplus without compromising yields.

This article continues One Health discussions from earlier EMWA journal articles. [1][2]2][3] Earlier articles highlighted farmers' important role in One Health concerning the health of our planet.This article illustrates how biotechnology aligns with One Health and how definitions might apply.It should help many see

This study explores the application of predictive analytics in evaluating player performance in the National Basketball Association (NBA), focusing on rebounds per game (REB), an essential component for better performance and results in basketball. The research employs a comparative analysis of machine learning (ML) models by leveraging a detailed NBA dataset. A key novelty lies in integrating advanced hyperparameter tuning and feature selection, enabling these models to capture complex relationships within the dataset. The Gradient Boosting Regressor demonstrated superior predictive performance, achieving an R² score of 0.8749 after tuning, with Linear Regression following closely at 0.8668. This study also highlights the importance of model interpretability and scalability, emphasizing the balance between predictive accuracy and usability for real-world decision-making. By offering actionable insights for optimizing player strategies and team performance, this research contributes to the growing body of knowledge in data-driven sports analytics and paves the way for more advanced applications in professional basketball management.