Abstract
Aim To analyse the impact of COVID-19 on professional nursing practice environments and patient safety culture. Background The relationship between work environments and patient safety has been internationally recognized. In 2020, the pandemic imposed enormous challenges, yet the impact on these variables remains unknown. Method This is a quantitative observational study, conducted in a Portuguese hospital, with 403 registered nurses. A self-completion questionnaire was used. Results The impact on the Structure and Outcome components of nursing professional practice environments was positive. Although the Process component remained favourable to quality of care, a negative trend was confirmed in almost all dimensions. The results regarding safety culture showed weaknesses; 'teamwork within units' was the only dimension that maintained a positive culture. Conclusion Positive responses regarding patient safety were significantly associated with the quality of the nursing professional practice environment. The need to invest in all dimensions of safety culture emerges to promote positive professional environments. Implications for nursing management Improving professional nursing practice environments can be achieved through managers' investment in the participation and involvement of nurses in the policies and functioning of institutions, as well as promoting an open, fair and participatory safety culture that encourages reporting events and provides adequate support for professionals.

Abstract
The advance of technology in the automotive industry brought several new functionalities providing more efficiency and safety. This, however, has one important concern: the development has become more complex. AMALTHEA is a framework for automotive system design and development in a model-based development fashion. It includes several features, including testing, software design, simulation and traceability. This paper presents ongoing work to integrate GPU tracing in the AMALTHEA standard format for tracing execution events, thus enabling platform heterogeneity to be supported in the tracing model. © 2022, Ada-Europe. All rights reserved.

Abstract
Switched reluctance machines (SRM) are simple, robust and fault tolerant machines, usually operating under strong nonlinear characteristics. Hence, SRM modeling is a most demanding task, in particular core losses. Non-sinusoidal flux density waveforms in different stator and rotor core sections, in addition to lamination non-uniform distribution are challenging phenomena to be addressed. This is still an ongoing research field. The purpose of this paper is to develop a comparative analysis between a linear and non-linear simulation model for core loss distribution in a three-phase 6/4 SRM. Five different steady-state operation modes will be addressed.

Abstract
Fiber optic-based refractometers is a thoroughly researched field, with many different configurations being used. However, most designs require external calibration using substances of known refractive index (RI) and their fabrication process might be impractical and time consuming, creating the need for a quick and accurate method of measuring RI of different substances. A simple method for simultaneous measurement in real-time of RI and thickness of polymer thin films is presented, allowing dynamic measurements in the presence of changing environmental parameters, such as temperature or humidity. This method, which does not require previous calibration, is based on an inline Fabry-Perot (FP) cavity, created by dipping the tip of a cleaved optical fiber (OF) in a polymer solution. The procedure consists of using the equations of the low finesse FP interferometers to directly extract information from the structure created, such as RI and cavity length, by working in the spectral window from 1500 to 1600nm. The method was validated by creating FP cavities with liquids of known RI, for which a typical precision of 3 x 10(-3) was achieved, along with errors lower than 0.6% and 1% for RI and cavity length determination, respectively, The procedure was then used to monitor three different curing processes, namely the temperature curing of Sylgard (TM) 184, the UV curing of Norland Optical Adhesives (TM) 65 and the mixing and curing of Ceys (TM) Araldite epoxy glue. Both RI and cavity length were compared to reference values, showing excellent agreement with the experimental results for a method that does not require external calibration.

Abstract
The current technological advances have pushed the quantification of exercise intensity to new era of physical exercise sciences. Monitoring physical exercise is essential in the process of planning, applying, and controlling loads for performance optimization and health. A lot of research studies applied various statistical approaches to estimate various physiological indices, to our knowledge, no studies found to investigate the relationship of facial color changes and increased exercise intensity. The aim of this study was to develop a non-contact method based on computer vision to determine the heart rate and, ultimately, the exercise intensity. The method was based on analyzing facial color changes during exercise by using RGB, HSV, YCbCr, Lab, and YUV color models. Nine university students participated in the study (mean age = 26.88 +/- 6.01 years, mean weight = 72.56 +/- 14.27 kg, mean height = 172.88 +/- 12.04 cm, six males and three females, and all white Caucasian). The data analyses were carried out separately for each participant (personalized model) as well as all the participants at a time (universal model). The multiple auto regressions, and a multiple polynomial regression model were designed to predict maximum heart rate percentage (maxHR%) from each color models. The results were analyzed and evaluated using Root Mean Square Error (RMSE), F-values, and R-square. The multiple polynomial regression using all participants exhibits the best accuracy with RMSE of 6.75 (R-square = 0.78). Exercise prescription and monitoring can benefit from the use of these methods, for example, to optimize the process of online monitoring, without having the need to use any other instrumentation.

Abstract
Advances in data-driven deep learning for chest X-ray image analysis underscore the need for explainability, privacy, large datasets and significant computational resources. We frame privacy and explainability as a lossy single-image compression problem to reduce both computational and data requirements without training. For Cardiomegaly detection in chest X-ray images, we propose HeartSpot and four spatial bias priors. HeartSpot priors define how to sample pixels based on domain knowledge from medical literature and from machines. HeartSpot privatizes chest X-ray images by discarding up to 97% of pixels, such as those that reveal the shape of the thoracic cage, bones, small lesions and other sensitive features. HeartSpot priors are ante-hoc explainable and give a human-interpretable image of the preserved spatial features that clearly outlines the heart. HeartSpot offers strong compression, with up to 32x fewer pixels and llx smaller filesize. Cardiomegaly detectors using HeartSpot are up to 9x faster to train or at least as accurate (up to +.01 AUC ROC) when compared to a baseline DenseNet121. HeartSpot is post-hoc explainable by re-using existing attribution methods without requiring access to the original non-privatized image. In summary, HeartSpot improves speed and accuracy, reduces image size, improves privacy and ensures explainability.