Co-design methodology for rapid prototyping of modular robots in care settings

This paper introduces a structured co-design methodology for developing modular robotic solutions for the care sector. Despite the widespread adoption of co-design in robotics, existing frameworks often lack clear and systematic processes to effectively incorporate user requirements into tangible robotic designs. Method: To address this gap, the present work proposes an iterative, modular co-design methodology that captures, organises, and translates user insights into practical robotic modules. The methodology employs Design Research (DR) methods combined with Design for Additive Manufacturing (DfAM) principles, enabling rapid prototyping and iterative refinement based on continuous user feedback. The proposed approach was applied in the development of Robobrico, a modular robot created collaboratively with care home users. Results: Outcomes from this study demonstrate that this structured process effectively aligns robot functionality with user expectations, enhances adaptability, and facilitates practical integration of modular robotic platforms in real-world care environments. Discussion: This paper details the proposed methodology, the tools developed to support it, and key insights derived from its implementation

Asymmetry of Cyclonic Sea Surface Wind and Wave Observed by SAR

CyclObs-derived wind and SWH field are extracted from over 600 dual-polarized Sentinel-1 (S-1) images of around 300 tropical cyclones (TCs) over the past eight years to investigate asymmetry of wind and wave fields during TCs. Fetch analysis and machine learning technique, eXtreme Gradient Boosting (XGBoost), is used to establish a relationship between TC wind speed and significant wave height (SWH). It was found that TC wind and SWH radii become asymmetric as sea states intensify. Notably, wind radii correlations (CORs) increase on the left-right and left-back quadrants for wind speeds larger than 20 m/s, while SWH radii exhibit the opposite trend. XGBoost is employed to obtain the improved relationship between wind fetch and SWH (COR < 0.17). Validation against buoys and Haiyang-2 (HY-2) observations of 20 TCs indicates that the root mean squared error (RMSE) in SWH predictions is reduced by up to 1.1 m using XGBoost instead of empirical model. The new TC wave model by XGBoost is particularly robust under high-wind conditions, therefore vital for warning and mitigation of extreme storms and improved parameterizations of air-sea interaction

fMRI of the Central Auditory System

Over the years, blood oxygen level-dependent (BOLD) fMRI has made important contributions to the understanding of central auditory processing in humans. Although there are significant technical challenges to overcome in the case of auditory fMRI, the unique methodological advantage of fMRI as an indicator ofpopulation neural activity lies in its spatial precision. It can be used to examine the neural basis of auditory representation at a number of spatial scales, from the micro-anatomical scale of population assemblies to the macro-anatomical scale of cortico-cortical circuits. The spatial resolution of fMRI is maximized in the case of mapping individual brain activity, and here it has been possible to demonstrate known organizational features of the auditory system that have hitherto been possible only using invasive electrophysiological recording methods. Frequency coding in the primary auditory cortex is one such example that we shalldiscuss in this chapter. Of course, non-invasive procedures for neuroscience are preferable. As the field moves toward this goal by recording in awake, behaving animals, human neuroimaging techniques will be increasingly relied upon to provide an interpretive link between animal neurophysiology at the multi-unit level and the operation of larger neuronal assemblies. This comparative approach is crucial for the ultimate aim to understand the mechanisms of auditory perception. For example, the neural effects of intentional behavior on stimulus-driven coding have been explored both in animals, using electrophysiological techniques, and in humans, using fMRI. While the feature-specific effects of selective attention are well established in the visual cortex, the effects of auditory attention in the auditory cortex are only just starting to be revealed. Each section considers some future directions for auditory fMRI research