Assessing the Acceptability of Human-Robot Co-Presence on Assembly Lines: A Comparison Between Actual Situations and their Virtual Reality Counterparts

International audienceThis paper focuses on the acceptability of human-robot collaboration in industrial environments. A use case was designed in which an operator and a robot had to work side-by-side on automotive assembly lines, with different levels of co-presence. This use case was implemented both in a physical and in a virtual situation using virtual reality. A user study was conducted with operators from the automotive industry. The operators were asked to assess the acceptability to work side-by-side with the robot through questionnaires, and physiological measures (heart rate and skin conductance) were taken during the user study. The results showed that working close to the robot imposed more constraints on the operators and required them to adapt to the robot. Moreover, an increase in skin conductance level was observed after working close to the robot. Although no significant difference was found in the questionnaires results between the physical and virtual situations, the increase in physiological measures was significant only in the physical situation. This suggests that virtual reality may be a good tool to assess the acceptability of human-robot collaboration and draw preliminary results through questionnaires, but that physical experiments are still necessary to a complete study, especially when dealing with physiological measures

Correlation between mechanical properties and cross-linking degree of ethyl lactate plasma polymer films

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In vitro and in vivo effects of several antivirals against Aujesky's disease virus

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Diagnosis and endovascular management of vasospasm after aneurysmal subarachnoid hemorrhage — survey of real-life practices

International audienceBackground Vasospasm and delayed cerebral ischemia (DCI) are the leading causes of morbidity and mortality after intracranial aneurysmal subarachnoid hemorrhage (aSAH). Vasospasm detection, prevention and management, especially endovascular management varies from center to center and lacks standardization. We aimed to evaluate this variability via an international survey of how neurointerventionalists approach vasospasm diagnosis and endovascular management. Methods We designed an anonymous online survey with 100 questions to evaluate practice patterns between December 2021 and September 2022. We contacted endovascular neurosurgeons, neuroradiologists and neurologists via email and via two professional societies – the Society of NeuroInterventional Surgery (SNIS) and the European Society of Minimally Invasive Neurological Therapy (ESMINT). We recorded the physicians’ responses to the survey questions. Results A total of 201 physicians (25% [50/201] USA and 75% non-USA) completed the survey over 10 months, 42% had >7 years of experience, 92% were male, median age was 40 (IQR 35–46). Both high-volume and low-volume centers were represented. Daily transcranial Doppler was the most common screening method (75%) for vasospasm. In cases of symptomatic vasospasm despite optimal medical management, endovascular treatment was directly considered by 58% of physicians. The most common reason to initiate endovascular treatment was clinical deficits associated with proven vasospasm/DCI in 89%. The choice of endovascular treatment and its efficacy was highly variable. Nimodipine was the most common first-line intra-arterial therapy (40%). Mechanical angioplasty was considered the most effective endovascular treatment by 65% of neurointerventionalists. Conclusion Our study highlights the considerable heterogeneity among the neurointerventional community regarding vasospasm diagnosis and endovascular management. Randomized trials and guidelines are needed to improve standard of care, determine optimal management approaches and track outcomes

Pan-tropical prediction of forest structure from the largest trees

Aim: Large tropical trees form the interface between ground and airborne observations, offering a unique opportunity to capture forest properties remotely and to investigate their variations on broad scales. However, despite rapid development of metrics to characterize the forest canopy from remotely sensed data, a gap remains between aerial and field inventories. To close this gap, we propose a new pan‐tropical model to predict plot-level forest structure properties and biomass from only the largest trees. Location: Pan‐tropical. Time period: Early 21st century. Major taxa studied: Woody plants. Methods: Using a dataset of 867 plots distributed among 118 sites across the tropics, we tested the prediction of the quadratic mean diameter, basal area, Lorey’s height, community wood density and above ground biomass (AGB) from the ith largest trees. Results: Measuring the largest trees in tropical forests enables unbiased predictions of plot‐ and site‐level forest structure. The 20 largest trees per hectare predicted quadratic mean diameter, basal area, Lorey’s height, community wood density and AGB with 12, 16, 4, 4 and 17.7% of relative error, respectively. Most of the remaining error in biomass prediction is driven by differences in the proportion of total biomass held in medium‐sized trees (50–70 cm diameter at breast height), which shows some continental dependency, with American tropical forests presenting the highest proportion of total biomass in these intermediate‐diameter classes relative to other continents. Main conclusions: Our approach provides new information on tropical forest structure and can be used to generate accurate field estimates of tropical forest carbon stocks to support the calibration and validation of current and forthcoming space missions. It will reduce the cost of field inventories and contribute to scientific understanding of tropical forest ecosystems and response to climate change

Pan-tropical prediction of forest structure from the largest trees

Aim: Large tropical trees form the interface between ground and airborne observations, offering a unique opportunity to capture forest properties remotely and to investigate their variations on broad scales. However, despite rapid development of metrics to characterize the forest canopy from remotely sensed data, a gap remains between aerial and field inventories. To close this gap, we propose a new pan-tropical model to predict plot-level forest structure properties and biomass from only the largest trees. Location: Pan-tropical. Time period: Early 21st century. Major taxa studied: Woody plants. Methods: Using a dataset of 867 plots distributed among 118 sites across the tropics, we tested the prediction of the quadratic mean diameter, basal area, Lorey's height, community wood density and aboveground biomass (AGB) from the ith largest trees. Results: Measuring the largest trees in tropical forests enables unbiased predictions of plot- and site-level forest structure. The 20 largest trees per hectare predicted quadratic mean diameter, basal area, Lorey's height, community wood density and AGB with 12, 16, 4, 4 and 17.7% of relative error, respectively. Most of the remaining error in biomass prediction is driven by differences in the proportion of total biomass held in medium-sized trees (50–70 cm diameter at breast height), which shows some continental dependency, with American tropical forests presenting the highest proportion of total biomass in these intermediate-diameter classes relative to other continents. Main conclusions: Our approach provides new information on tropical forest structure and can be used to generate accurate field estimates of tropical forest carbon stocks to support the calibration and validation of current and forthcoming space missions. It will reduce the cost of field inventories and contribute to scientific understanding of tropical forest ecosystems and response to climate change