Modern radio interferometers such as the LOw Frequency ARray (LOFAR) are capable of producing data at hundreds of gigabits to terabits per second. This high data rate makes the analysis of radio data cumbersome and computationally expensive. While high performance computing facilities exist for large national and international facilities, that may not be the case for instruments operated by a single institution or a small consortium. Data rates for next generation radio telescopes are set to eclipse those currently in operation, hence local processing of data will become all the more important. Here, we introduce the REAL-time Transient Acquisition backend (REALTA), a computing backend at the Irish LOFAR station (I-LOFAR) which facilitates the recording of data in near real-time and post-processing. We also present first searches and scientific results of a number of radio phenomena observed by I-LOFAR and REALTA, including pulsars, fast radio bursts, rotating radio transients, the search for extraterrestrial intelligence, Jupiter, and the Sun.

Poly (N-vinylcaprolactam) (PNVCL) and poly (N-isopropylacrylamide) (PNIPAm) are two popular negatively temperature-responsive hydrogels, due to their biocompatibility, softness, hydrophilicity, superabsorbency, viscoelasticity, and near-physiological lower critical solution temperature (LCST). These characteristics make them ideal for biomedical applications. When combined with other materials, hydrogel expansion induces the morphing of the assembly due to internal stress differences. Our recent developments in NVCL hydrogel, enhanced by nanoclay incorporation, have driven us to the creation of a bilayer structure to study its shapeshifting response across various temperatures. This study focused on the bending behaviour of bilayer samples composed of an active hydrogel layer and a passive non-swellable layer. Using photopolymerisation, circular discs and rectangular bilayer samples of varying sizes were fabricated. Homogeneous circular samples demonstrated that hydrogel density increased proportionally with temperature, with the swelling ratio exhibiting two distinct rates of change below and above its LCST. In bilayer samples, the volume of the passive layer influenced bending, and its optimal volume was identified. The investigation revealed that geometry affected the overall bending effect due to changes in the passive layer stiffness. Lastly, a temperature-responsive gripper capable of picking up objects several times its own weight was demonstrated, highlighting the potential of NVCL hydrogels as bioactuators for minimally invasive surgery.

Objective quantification of tremor remains a challenge in Parkinson’s disease (PD) assessment, with current clinical assessments relying largely on subjective scale ratings. This study evaluates the feasibility and signal behaviour of integrating surface electromyography (sEMG) with MDS-UPDRS-aligned tasks in a healthy adult cohort, with the aim of establishing normative low-frequency muscle activation profiles. Thirty-two healthy participants (mean age 27.6 ± 5.3 years) completed seven upper-limb tasks derived from the MDS-UPDRS while sEMG data were recorded from antagonistic forearm muscles. Signals were normalised using maximum voluntary contraction, filtered at 14 Hz, and analysed using frequency-domain (FFT) and time-frequency (STFT) methods. Significant task-dependent differences were observed in both frequency occurrence and magnitude (p < 0.05), particularly within the 3.5–9 Hz range. Finger tapping elicited increased low-frequency activity compared to baseline, while pronation–supination produced the most stable and consistent muscle activation across participants. Frequencies above 12 Hz showed minimal task discrimination. These findings demonstrate that low-frequency tremor-like activity can occur during specific MDS-UPDRS tasks in healthy individuals and may require further validation before being considered suitable for PD staging. This work establishes normative sEMG benchmarks to support future clinical validation and PD cohort comparisons.

The Sun periodically emits intense bursts of radio emission called solar radio bursts (SRBs). These bursts can disrupt radio communications and be indicative of large solar events that can disrupt technological infrastructure on Earth and in space. The risks posed by these events highlight the need for automated SRB classification, providing the potential to improve event detection and real-time monitoring, thereby enhancing techniques used to research space weather and related events. Using data recorded by the e-Callisto network, a dataset containing images of radio spectra was created. This dataset consists of three classes; Empty spectrograms, spectrograms containing Type II SRBs, and spectrograms containing Type III SRBs. This dataset was used to fine-tune several popular pre-trained deep learning models to classify Type II and Type III SRBs, including; VGGnet-19, MobileNet, ResNet-152, DenseNet-201 and YOLOv8. The results obtained from testing the models on the testing set ranged between an F1-score of 87% and 92%. The best performing model, YOLOv8, demonstrates that utilising pre-trained models for event classification can provide automated approaches to classying SRBs and provide a practical solution to the limited amount of data samples available for Type II SRBs.