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This study explores the relationship between the adoption of industrial robots and workplace injuries using data from the United States (US) and Germany. Our empirical analyses, based on establishment-level data for the US, suggest that a one standard deviation increase in robot exposure reduces work-related injuries by approximately 16%. These results are driven by manufacturing firms (−28%), while we detect no impact on sectors that were less exposed to industrial robots. We also show that the US counties that are more exposed to robot penetration experience a significant increase in drug-or alcohol-related deaths and mental health problems, consistent with the extant evidence of negative effects on labor market outcomes in the US. Employing individual longitudinal data from Germany, we exploit within-individual changes in robot exposure and document similar effects on job physical intensity (−4%) and disability (−5%), but no evidence of significant effects on mental health and work and life satisfaction, consistent with the lack of significant impacts of robot penetration on labor market outcomes in Germany.

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Saúl Morales RodriguézAuthor

The success of deep learning depends heavily on the availability of large datasets, but in robotic manipulation there are many learning problems for which such datasets do not exist. Collecting these datasets is time-consuming and expensive, and therefore learning from small datasets is an important open problem. Within computer vision, a common approach to a lack of data is data augmentation. Data augmentation is the process of creating additional training examples by modifying existing ones. However, because the types of tasks and data differ, the methods used in computer vision cannot be easily adapted to manipulation. Therefore, we propose a data augmentation method for robotic manipulation. We argue that augmentations should be valid, relevant, and diverse.

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Summary: Stem cells in human urine have the potential to regenerate tissue.

Source: wake forest baptist medical center.

The Wake Forest Institute for Regenerative Medicine (WFIRM) researchers who were the first to identify that stem cells in human urine have potential for tissue regenerative effects, continue their investigation into the power of these cells.

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LeviPrint is a system that uses acoustic manipulation for assembling objects without physical contact. It generates acoustic fields that trap small particles, glue droplets and elongated stick-like elements that can be manipulated and reoriented as they are levitated. It is a fully functional system for manufacturing 3D structures using contactless manipulation.

It was developed by researchers from the UPNA/NUP-Public University of Navarre Asier Marzo and Iñigo Ezcurdia, who together with Rafael Morales (Ultraleap Ltd, UK) and Marco Andrade (University of São Paulo, Brazil) are authors of the paper “LeviPrint: Contactless Fabrication using Full Acoustic Trapping of Elongated Parts.”

This research is due to be presented in August in Vancouver (Canada) at SIGGRAPH, a conference on and where companies such as Nvidia, Disney Research and Facebook Reality Labs present their work.

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In recent years, engineers worldwide have been trying to devise new battery and energy storage technologies that are more sustainable and cost-effective. One of the solutions attracting particular interest is sodium-based battery technology.

Sodium-ion batteries could have numerous advantages over conventional and widely used lithium-based batteries. Most notably, as is abundant on our planet and can be easily sourced, they could be affordable and easy to produce on a large-scale.

Despite their possible advantages, most developed so far exhibited low energy densities, due to the relatively large atomic size of sodium and its considerable weight. Typically, these batteries exhibit energy densities below 160 Wh kg-1, which is significantly lower than that of .

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Extrusion-based 3D printing/bioprinting is a promising approach to generating patient-specific, tissue-engineered grafts. However, a major challenge in extrusion-based 3D printing and bioprinting is that most currently used materials lack the versatility to be used in a wide range of applications.

New nanotechnology has been developed by a team of researchers from Texas A&M University that leverages colloidal interactions of nanoparticles to print complex geometries that can mimic tissue and organ structure. The team, led by Dr. Akhilesh Gaharwar, associate professor and Presidential Impact Fellow in the Department of Biomedical Engineering, has introduced colloidal solutions of 2D nanosilicates as a platform technology to print complex structures.

2D nanosilicates are disc-shaped inorganic nanoparticles 20 to 50 nanometers in diameter and 1 to 2 nanometers in thickness. These nanosilicates form a “house-of-cards” structure above a certain concentration in water, known as a colloidal solution.

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