Upravljanje otporno na kvarove modularnim prekidačko-reluktantnim strojem nadahnuto prirodom

Fault tolerance is an obligatory feature in safety critical applications (aeronautical, aerospace, medical and military applications, power plants, etc.), where loss of life, environmental disasters, equipment destructions or unplanned downtimes must be avoided. For such applications, a novel bio-inspired motion control system is proposed. All its three components (the switched reluctance machine, the power converter and the control system) are designed to be as fault tolerant as possible. This paper describes all these three fault tolerant components: the bio-inspired control system having self-healing capabilities, the power converter with an extra leg and the fault tolerant modular machine. The theoretical expectations and simulation results are validated by means of laboratory experiments.Otpornost na kvarove je nužnost u sigurnosno kritičnim aplikacijama (aeronautičke, zrakoplovne, medicinske i vojne aplikacije, elektrane itd.), gdje je potrebno izbjeći smrtne slučajeve, prirodne nepogode, uništenje opreme ili neplanirane prekide u radu. Za takve aplikacije, predložen je novi slijedni sustav nadahnut prirodom. Sve tri komponente (prekidačko-reluktantni stroj, pretvarač i sustav upravljanja) su projektirani da budu što je više moguće otporni na kvarove. Ovaj rad opisuje sve tri komponente: sustav upravljanja nadahnut prirodom sa samoliječećim svojstvima, pretvarač s dodatnom granom i modularni stroj otporan na kvarove. Teoretska očekivanja i simulacijski rezultati su provjereni laboratorijskim eksperimentima

Machine learning on FPGA for event selection

Real-time data processing is a frontier field in experimental particle physics. The application of FPGAs at the trigger level is used by many current and planned experiments (CMS, LHCb, Belle2, PANDA). Usually they use conventional processing algorithms. LHCb has implemented Machine Learning (ML) elements for real-time data processing with a triggered readout system that runs most of the ML algorithms on a computer farm. The work described in this article aims to test the ML-FPGA algorithms for streaming data acquisition. There are many experiments working in this area and they have a lot in common, but there are many specific solutions for detector and accelerator parameters that are worth exploring further. This report describes the purpose of the work and progress in evaluating the ML-FPGA application

Using a computer simulation for teaching communication skills: A blinded multisite mixed methods randomized controlled trial

Objectives: To assess advanced communication skills among second-year medical students exposed either to a computer simulation (MPathic-VR) featuring virtual humans, or to a multimedia computer-based learning module, and to understand each group’s experiences and learning preferences. Methods: A single-blinded, mixed methods, randomized, multisite trial compared MPathic-VR (N = 210) to computer-based learning (N = 211). Primary outcomes: communication scores during repeat interactions with MPathic-VR’s intercultural and interprofessional communication scenarios and scores on a subsequent advanced communication skills objective structured clinical examination (OSCE). Multivariate analysis of variance was used to compare outcomes. Secondary outcomes: student attitude surveys and qualitative assessments of their experiences with MPathic-VR or computer-based learning. Results: MPathic-VR-trained students improved their intercultural and interprofessional communication performance between their first and second interactions with each scenario. They also achieved significantly higher composite scores on the OSCE than computer-based learning-trained students. Attitudes and experiences were more positive among students trained with MPathic-VR, who valued its providing immediate feedback, teaching nonverbal communication skills, and preparing them for emotion-charged patient encounters. Conclusions: MPathic-VR was effective in training advanced communication skills and in enabling knowledge transfer into a more realistic clinical situation. Practice implications: MPathic-VR’s virtual human simulation offers an effective and engaging means of advanced communication training

Cohort profile: the ESC EURObservational Research Programme Non-ST-segment elevation myocardial infraction (NSTEMI) Registry

Aims The European Society of Cardiology (ESC) EURObservational Research Programme (EORP) Non-ST-segment elevation myocardial infarction (NSTEMI) Registry aims to identify international patterns in NSTEMI management in clinical practice and outcomes against the 2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without ST-segment-elevation. Methods and results Consecutively hospitalised adult NSTEMI patients (n = 3620) were enrolled between 11 March 2019 and 6 March 2021, and individual patient data prospectively collected at 287 centres in 59 participating countries during a two-week enrolment period per centre. The registry collected data relating to baseline characteristics, major outcomes (inhospital death, acute heart failure, cardiogenic shock, bleeding, stroke/transient ischaemic attack, and 30-day mortality) and guideline-recommended NSTEMI care interventions: electrocardiogram pre- or in-hospital, prehospitalization receipt of aspirin, echocardiography, coronary angiography, referral to cardiac rehabilitation, smoking cessation advice, dietary advice, and prescription on discharge of aspirin, P2Y12 inhibition, angiotensin converting enzyme inhibitor (ACEi)/angiotensin receptor blocker (ARB), beta-blocker, and statin. Conclusion The EORP NSTEMI Registry is an international, prospective registry of care and outcomes of patients treated for NSTEMI, which will provide unique insights into the contemporary management of hospitalised NSTEMI patients, compliance with ESC 2015 NSTEMI Guidelines, and identify potential barriers to optimal management of this common clinical presentation associated with significant morbidity and mortality