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Collaborative robots can improve ergonomics on factory floors while allowing a higher level of flexibility in production. The evolution of robotics and cyber-physical systems in size and functionality has enabled new applications which were never foreseen in traditional industrial robots. However, the current human-robot collaboration (HRC) technologies are limited in reliability and safety, which are vital in risk-critical scenarios. Certainly, confusion about European safety regulations has led to situations where collaborative robots operate behind security barriers, thus negating their advantages while reducing overall application productivity. Despite recent advances, developing a safe collaborative robotic system for performing complex industrial or daily tasks remains a challenge. Multiple influential factors in HRC make it difficult to define a clear classification to understand the depth of collaboration between humans and robots. In this article, we review the state of the art in reliable collaborative robotic work cells and propose a reference model to combine influential factors such as robot autonomy, collaboration, and safety modes to redefine HRC categorization.

Additional file 1: Fig. S1. Flowchart of participant selection for the UKIVAS cohort (1). Fig. S2. Flowchart of participant selection for the RKD cohort (2). Fig. S3. Participant locations at diagnosis for i). UKIVAS and ii). RKD cohort. Shading represents the absolute number of participants recruited to the study in that region, at the time of their diagnosis. Tendency to cluster around vasculitis referral centres is observed. Fig. S4. Distribution of vitD-UVB (kJ/m2) (a and b) and CW-D-UVB (kJ/m2) (c and d) by month and season: CW-D-UVB peaks in late summer and nadirs in winter. Fig. S5. The relationship between latitude (degrees) and a). annual daily average vitD-UVB (kJ/m2, Pearson correlation coefficient (corr) -0.98, p

A survey was conducted to determine teachers' perceived needs in direct instruction and related professional activities and to determine teachers' familiarity with 14 sets of instructional materials, which were said to represent materials available through Special Education Instructional Materials Centers (SEIMC). Data indicated consistency of findings across states, teaching categories, and teaching levels. It was found that in general, teachers' major problems centered in direct instruction, not pupil characteristics, professional relationships, classroom arrangement and management, or psychological information. Curricular areas of greatest difficulty were said to be social studies, language arts, seatwork, and science. Data indicated that teachers consistently had problems finding instructional materials and student activities. It was also found that special education teachers knew only three of the 14 materials presented: Peabody Language Development Kits, Frostig Program for Development of Visual Perception, and Readers' Digest Skill Builders. Regular teachers knew only of the Readers' Digest Skill Builders. Teacher needs for audio-visual materials seemed to have been met. Implications from the study centered on the SEIMC's need to educate and familiarize teachers with the broad range of instructional materials available in the centers. (CB)

Applying deep learning models to MRI scans of acute stroke patients to extract features that are indicative of short-term outcome could assist a clinician’s treatment decisions. Deep learning models are usually accurate but are not easily interpretable. Here, we trained a convolutional neural network on ADC maps from hyperacute ischaemic stroke patients for prediction of short-term functional outcome and used an interpretability technique to highlight regions in the ADC maps that were most important in the prediction of a bad outcome. Although highly accurate, the model’s predictions were not based on aspects of the ADC maps related to stroke pathophysiology.