A Coupled-Oscillator Model of Human Attachment Dynamics Evaluated in a Robot Dyadic Interaction

A better understanding of the nature of human relationships can aid the design of effective and appropriate social behaviour for robots. The investigation of human bonding via robotic modelling can also serve to test psychological theories in an embodied setting. In this work we present a robotic model of “attachment”—the primary bond between child and caregiver that shapes relationship behaviour throughout our lives. Following a dynamical systems approach, we model attachment as a behavioural coupling between motivational oscillators and show, by means of a dynamical analysis, that coupled robot dyads generate dynamical patterns that resemble caregiver-child interactions. By demonstrating coupling in an embodied model, we also show that measures of physical and emotional distance (a psychological variable), inferred from sensory data, can serve as effective control parameters for attachment behaviour. We find that this oscillator framework generates rich patterns of robot behaviours that can be associated with quantitative and qualitative observations of the “strange situation” procedure, an experimental paradigm that is widely studied in human relationship science, and of human avoidant and ambivalent attachment styles. The ability to estimate human attachment style and to generate appropriately-matched robot behaviours could be useful in social and companion robotics

A national assessment of the drinking water infrastructure deficit in New Zealand by territorial authority and sociodemographic characteristics

The quality of drinking water reticulation networks is central to ensuring the provision of safe water. We conducted a national assessment of public drinking water reticulation condition in New Zealand (NZ) derived from information on pipe material and age and investigated regional and sociodemographic variations in the reticulation network. In total, 30.7% of the 57,174 km of drinking water pipes in NZ were in poor or very poor condition, while 18.5% were past their life expectancy. We identified wide variation in the proportion of pipes in poor or very poor condition amongst Territorial Authorities (TAs) and between areas of varying socioeconomic deprivation within TAs. Using nationally consistent data, our findings suggest that the current drinking water infrastructure deficit in NZ may be larger than previously estimated. Our results also highlight potential challenges to TA-based amalgamation of water services under the new legislation

The ecological dynamics of cognizant action in sport

The widespread inferential understanding of human action attributes to the brain the power of modelling actions and predicting immediate changes in environmental circumstances. However, an ecological rationale proposes that sport performance is founded on coupled perception and action, avoiding the need for the brain, as a mediator, to be lagging behind immediate corporeal contact with the sport environment. Here, a theory of cognizant action is presented where behaviour is understood in terms of self-organized action, shaped by a performer's complex skills, directed towards perceived affordances. Cognizant action is defined as the conservation of intentionality by coupled perception and action. Being oriented towards action possibilities (affordances), cognizant action self-organizes in every performance environment, and at the same time it is constrained by performers' skills. Accordingly, the study of cognizant action demands representative experimental designs and analysis of eco-physical variables to understand sport performance. Current debates include the role of knowledge, the symmetry between performer and environment, and team cognition. Future research might be directed to test tensegrity as well as ‘strong’ anticipation in individual and team sport tasks

Raman gas sensing technology: A new horizon?

The question in the title alludes to the importance of comprehending the relevance and manner of operation in the field of gas sensors, which is undeniably one of the most important scientific and economic interests. Despite being superior to several commonly used techniques, such as infrared (IR) spectroscopy, Nondispersive IR (NDIR) and gas chromatography coupled with mass spectrometry (GC-MS), Raman spectroscopy-based gas sensors are yet to be widely explored for real-world applications. Given the weak Raman effect, numerous innovative strategies have emerged to improve its utility in chemical sensing, biological imaging, and material characterization, among other applications. This review covers five important approaches with a high potential for use in Raman-based gas sensors: spontaneous (SRS), stimulated (StRS), coherent anti-Stokes (CARS), surface-enhanced (SERS), and tip-enhanced (TERS) Raman scattering spectroscopy. The initial strategy of this review is to provide the in-depth foundational knowledge necessary for the reader to grasp several types of Raman techniques, their advantages and limitations. This is followed by an overview of current competing technologies and their applications. The remainder of the paper focuses on recent major experimental findings based on the Raman techniques and their practical applications. As a comprehensive introduction to Raman spectroscopy, this review article also serves as a knowledge base for future developments in the field of gas sensors

A Marr-Inspired Framework for Raising “Good” Robots.-

Our current computer and AI systems are built on Neuroscience principles from almost a century ago. Recent advances in our understanding of biological computation have not crossed into computer science to catalyse advancements. We outline a multidimensional blueprint for a form of bio-inspired agent leveraging modern Neuroscience principles (including the co-localisation of memory and compute, plasticity, embodiment, active inference, and neurodevelopmental principles). We discuss how combining these core features could theoretically lead to cognitive agents that are aligned to our prosocial values, transparent, explainable, and energy efficient (i.e., “good” robots). In particular, we leverage Marr’s tri-level framework and advocate for an “Implementation Level” consisting of embodied neuromorphic hardware, an “Algorithmic Level” consisting of Active Inference, and a “Computational Level” consisting of prosocial goals (supported by evidence of prosociality catalysing the development of our own complex cognitive abilities). A developmental process scaffolds different prosocial computations over time. Supporting our perspective, we include simulation data demonstrating the transfer of priors between two different prosocial behaviours (Computational Level) via Active Inference (Algorithmic Level), supported by an embodied process (Implementation Level). Agent behaviour is transparent and explainable throughout. We advocate for this blueprint as a guide in creating capable, ethical, and sustainable machine intelligence

Haunted Spaces and Unsettling Predicaments: An Interrogation of (Capitalist) Sport via the Work of Mark Fisher

In utilising the work of Mark Fisher, this article critically interrogates contemporary manifestations of capitalist sport. Specifically, it examines how the notion of capitalist realism, as well as the related concepts of hauntology and the weird and the eerie, might serve to resist anthropocentricism and challenge political impotence in the face of multiple existential crises. Emphasising Fisher’s focus on temporality and temporal aberration, the article explores how certain aspects of sports culture, such as the media coverage of the COVID-19 pandemic, mountain bike trail building, GPS tracking and landscape photography—experiences that are both fascinating and terrifying in equal measure—might unsettle us in ways that provoke a reconfiguration of existing sociopolitical frames of reference. In doing so, the paper urges scholars and practitioners of sport to accept that things are never what they seem (or feel), and to embrace the ghosts of our, as-yet, unrealised (sporting) futures

OhmNet: Advanced neural network-based viscosity prediction of sauces for efficient Ohmic heating processing.

Industrial food processes such as Ohmic Heating (OH) are gaining popularity due to their lower carbon emissions and improved energy efficiency. The effectiveness of OH largely depends on the electrical conductivity, physical properties, and rheological characteristics of the food product, with dynamic viscosity directly influencing the fluid flow, residence time, and heating rate in a Continuous Flow Ohmic Heating (CFOH) system. Therefore, accurate prediction of viscosity during CFOH processing is crucial for optimising heating efficiency and maintaining the desired output temperature, ultimately reducing energy consumption and operational costs. To address this challenge, this study introduces OhmNet - an advanced Neural Network (NN)-based predictive model designed to accurately estimate the dynamic viscosity of tikka sauce during OH, offering a robust solution for viscosity prediction in CFOH applications. The predictive model has been developed using real-time data obtained from heating experiments, where viscosity measurements were recorded using a rheometer at varying target temperatures. To achieve the optimal configuration of OhmNet, three different approaches were explored: separate network development for each target temperature, a transfer learning-based neural network, and a one-hot encoding-based unified neural network model. These approaches were systematically evaluated through a grid search for hyperparameter tuning to identify the most accurate and robust dynamic viscosity predictive model during Continuous Flow Ohmic Heating. The resulting OhmNet model demonstrates high performance and reliability, achieving a Mean Squared Error (MSE) of 0.002, a Mean Absolute Error (MAE) of 0.025, and a coefficient of determination (R2) equal to 0.99. This optimal configuration of OhmNet offers a powerful tool for enhancing process efficiency and control in industrial food processing applications. In the future, the model can be seamlessly integrated with advanced process controllers for precise temperature control and power consumption optimisation, driving sustainable and energy-efficient food processing applications

Focus on community energy and infrastructure resilience

This focus issue on community energy and infrastructure resilience compiles six contribution that seek to put into context projects that aim to put people at the core of energy development. The promises of community energy are multifold, from facilitating the democratization of energy systems to allowing for the development of decentralized and off-grid networks, which may accelerate the adoption of renewables. However, community energy also faces challenges. In this case, the special issue focused on challenges related to infrastructure resilience, whether they are linked to building resilience in community energy projects or to the contributions that community energy projects make to advance sustainability transitions. This focus issue makes five key thematic contributions: the importance of community engagement in building resilience, analytical tools to understand the multi-dimensional nature of infrastructure resilience, the need to incorporate place-based concerns into policy, the development of tools to evaluate different aspects of resilience, and the explicit consideration of gender equality and social inclusion to facilitate project sustainability