Exponential Adoption of Battery Electric Cars

The adoption of battery electric vehicles (BEVs) may significantly reduce greenhouse gas emissions caused by road transport. However, there is wide disagreement as to how soon battery electric vehicles will play a major role in overall transportation. Focusing on battery electric passenger cars, we here analyze BEV adoption across 17 individual countries, Europe, and the World, and consistently find exponential growth trends. Modeling-based estimates of future adoption given past trends suggests system-wide adoption substantially faster than typical economic analyses have proposed so far. For instance, we estimate the majority of passenger cars in Europe to be electric by about 2031. Within regions, the predicted times of mass adoption are largely insensitive to model details. Despite significant differences in current electric fleet sizes across regions, their growth rates consistently indicate fast doubling times of approximately 15 months, hinting at radical economic and infrastructural consequences in the near future

Operating at a Distance-How a Teleoperated Surgical Robot Reconfigures Teamwork in the Operating Room

This paper investigates how a teleoperated surgical robot reconfigures teamwork in the operating room by spatially redistributing team members. We report on findings from two years of fieldwork at two hospitals, including interviews and video data. We find that while in non-robotic cases team members huddle together, physically touching, introduction of a surgical robot increases physical and sensory distance between team members. This spatial rearrangement has implications for both cognitive and affective dimensions of collaborative surgical work. Cognitive distance is increased, necessitating new efforts to maintain situation awareness and common ground. Moreover, affective distance is introduced, decreasing sensitivity to shared and non-shared affective states and leading to new practices aimed at restoring affective connection within the team. We describe new forms of physical, cognitive, and affective distance associated with teleoperated robotic surgery, and the effects these have on power distribution, practice, and collaborative experience within the surgical team

A reflective approach to learning in a global design project

This paper describes a three-week project run jointly between the University of Strathclyde, Scotland, Franklin W. Olin College of Engineering, MA and Stanford University, CA. The purpose of this class was to provide students with an understanding of the technological and organisational issues involved in global product development teams, and to provide an experience which would prepare them for work in such environments. Reflective learning techniques were applied, including reviews of relevant literature, analyses of case studies, and a critical review of the completed project. The main result of this approach was that students had a more considered attitude towards the project process than in typical, more output-focussed student design assignments. This was crucial given the cultural and pedagogical variations across institutions. The Global Team Design Project was successful, particularly for the first year of implementation, and provides a potential framework that other institutions could employ in similar project classe

"How Did They Come Across?" Lessons Learned from Continuous Affective Ratings

Social distance, or perception of the other, is recognized as a dynamic dimension of an interaction, but yet to be widely explored or understood. Through CORAE, a novel web-based open-source tool for COntinuous Retrospective Affect Evaluation, we collected retrospective ratings of interpersonal perceptions between 12 participant dyads. In this work, we explore how different aspects of these interactions reflect on the ratings collected, through a discourse analysis of individual and social behavior of the interactants. We found that different events observed in the ratings can be mapped to complex interaction phenomena, shedding light on relevant interaction features that may play a role in interpersonal understanding and grounding. This paves the way for better, more seamless human-robot interactions, where affect is interpreted as highly dynamic and contingent on interaction history.Comment: arXiv admin note: substantial text overlap with arXiv:2306.1662

Identifying the threshold to sustainable ridepooling

Ridepooling combines trips of multiple passengers in the same vehicle and may thereby provide a more sustainable option than transport by private cars. The efficiency and sustainability of ridepooling is typically quantified by key performance indicators such as the average vehicle occupancy or the total distance driven by all ridepooling vehicles relative to individual transport. However, even if the average occupancy is high and rides are shared, ridepooling services may increase the total distance driven due to additional detours and deadheading. Moreover, these key performance indicators are difficult to predict without large-scale simulations or actual ridepooling operation. Here, we propose a dimensionless parameter to estimate the sustainability of ridepooling by quantifying the load on a ridepooling service, relating characteristic timescales of demand and supply. The load bounds the relative distance driven and uniquely marks the break-even point above which the total distance driven by all vehicles of a ridepooling service falls below that of motorized individual transport. Detailed event-based simulations and a comparison with empirical observations from a ridepooling pilot project in a rural area of Germany validate the theoretical prediction. Importantly, the load follows directly from a small set of aggregate parameters of the service setting and is thus predictable a priori. The load may thus complement standard key performance indicators and simplify planning, operation and evaluation of ridepooling services

CORAE: A Tool for Intuitive and Continuous Retrospective Evaluation of Interactions

This paper introduces CORAE, a novel web-based open-source tool for COntinuous Retrospective Affect Evaluation, designed to capture continuous affect data about interpersonal perceptions in dyadic interactions. Grounded in behavioral ecology perspectives of emotion, this approach replaces valence as the relevant rating dimension with approach and withdrawal, reflecting the degree to which behavior is perceived as increasing or decreasing social distance. We conducted a study to experimentally validate the efficacy of our platform with 24 participants. The tool's effectiveness was tested in the context of dyadic negotiation, revealing insights about how interpersonal dynamics evolve over time. We find that the continuous affect rating method is consistent with individuals' perception of the overall interaction. This paper contributes to the growing body of research on affective computing and offers a valuable tool for researchers interested in investigating the temporal dynamics of affect and emotion in social interactions