Antiphase domains are three-dimensional crystal defects commonly arising at the interface of III–V semiconductors and Si. While control over their formation has been achieved, the geometry of the antiphase domain itself that is separated from the mainphase of the crystal by the so-called antiphase boundary, has not yet been fully understood. In this work, we first investigate the interface between GaP and Si itself by cross-sectional scanning tunneling microscopy (XSTM) to reveal possible intermixing within an 8 monolayers wide region. Furthermore, we present an extensive analysis combining transmission electron microscopy and XSTM to elucidate the shape of antiphase domains in GaP. To create a true-to-scale, three-dimensional model of an antiphase domain, firstly, plan-view transmission electron microscopy images are drawn on. Subsequently, the progression of many antiphase boundaries through the GaP crystal as viewed from the (1 1 0) and (1  0) cleavage planes is analyzed all the way down to the atomic level by means of XSTM. This enables a detailed analysis of the shape and physical dimensions of the antiphase domains. A typical measured extension in growth directions is found to be a maximum of 60 nm and the maximum measured extension of the base plane in [ 1 0] and [1 1 0] directions is about 160 nm and 50 nm, respectively. They appear as pyramids with anisotropic base planes whose side facets kink many times.

Abstract:The reduction of cognitive tasks brought about by new developments in service-robots' collaboration with humans in working environments has given rise to new challenges as to how to address safety issues. This paper presents insights from biology, cognitive/neural sciences and sociology that can conquer these new challenges. The main focus lies in sociological variables that ensure safe human-robot interaction in working environments rather than addressing biological ones (avoiding bodily harm) or purely cognitive ones (avoiding any signals that are outside the human's sensory comfort zones). We will present an approach on how to integrate behavioral patterns into the robotic system in order to prevent the problem of reduced cognition in relation to essential features, which are necessary for carrying out this pattern in the context of a human-robot interaction with non-humanoid robots (which is the most typical design of robots used in work environments).

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Background Quality assurance programmes measure and compare certain health outcomes to ensure high quality care in the health care sector. The outcome health related quality of life (HRQOL) is typically measured by patient-reported outcome measures (PROMs). However, certain patient groups are less likely to respond to PROMs than others. This non-response bias can potentially distort results in quality assurance programmes. Our study aims to identify relevant predictors for non-response during assessment using the PROM MacNew Heart Disease questionnaire in cardiac rehabilitation. Methods This is a cross-sectional study based on data from the Swiss external quality assurance programme. All patients aged 18 years or older who underwent inpatient cardiac rehabilitation in 16 Swiss rehabilitation clinics between 2016 and 2019 were included. Patients’ sociodemographic and basic medical data were analysed descriptively by comparing two groups: non-responders and responders. We used a random intercept logistic regression model to estimate associations of patient characteristics and clinic differences with non-response. Results Of 24 572 patients, there were 33.3% non-responders and 66.7% responders. The mean age was 70; 31.0% were women. The regression model showed that being female was associated with non-response (odds ratio (OR) 1.22; 95% confidence interval (95% CI) 1.14–1.30), as well as having no supplementary health insurance (OR 1.49; 95% CI 1.39–1.59). Each additional year of age increased the chance of non-response by an OR of 1.02 (95% CI 1.02–1.02). Not being a first language speaker of German, French, or Italian increased the chance of non-response by an OR of 6.94 (95% CI 6.03–7.99). Patients admitted directly from acute care had a higher chance of non-response (OR 1.23; 95% CI 1.10–1.38), as well as patients being discharged back into acute care after rehabilitation (OR 3.89; 95% CI 3.00–5.04). Each point on the cumulative illness rating scale (CIRS) total score increased the chance of non-response by an OR of 1.05 (95% CI 1.04–1.05). Certain diagnoses also influenced the chance of non-response. Even after adjustment for known confounders, response rates differed substantially between the 16 clinics. Conclusion We have found significant non-response bias among certain patient groups, as well as across different treatment facilities. Measures to improve response rates among patients with known barriers to participation, as well as among different treatment facilities need to be considered, particularly when PROMs are being used for comparison of providers in quality assurance programmes or outcome evaluation.