Real-Time Simulation of Efficient Energy Management Algorithms for Electric Vehicle Chargers

Transportation electrification is happening at a rapid pace around the globe in response to the climate change mitigation measures taken by the regulatory agencies to curb tailpipe emissions. As the electric vehicle technology evolved, the size of on-board storage units has increased, which require charging from an external energy source. Renewable charging of electric vehicles is an attractive option to reduce the carbon footprint of an electric vehicle. The intermittent nature of the renewables necessitates a storage unit to provide continuous power. With a battery complementing solar generation, a power converter is deployed to interface these sources and storage units with the electric vehicle for charging. The converter shall now have to operate to quench the charging requirements by sourcing power from solar generation and storage elements. The converter also has to capture the generated solar power during the non-charging period and store it in the battery. All these functional requirements demand a robust energy management strategy to utilize all available sources and storage units efficiently without compromising load requirements. A Stateflow-based energy management algorithm for a three-port converter is proposed in this work. The proposed algorithm is implemented using OPAL-RT, and the real-time simulation results are presented

Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

Funder: DOE, Office of Science, DE-AC02-06CH11357Abstract: We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine learning (ML) scheme coupled with a Bayesian optimization technique that evaluates the GAP model. We apply this scheme to a Hafnium dioxide (HfO2) dataset generated from a “melt-quench” ab initio molecular dynamics (AIMD) protocol. Our results show that the active learning scheme, with no prior knowledge of the dataset, is able to extract a configuration that reaches the required energy fit tolerance. Further, molecular dynamics (MD) simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO2 with near ab initio precision and quench rates (i.e., 1.0 K/ps) not accessible via AIMD. The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment. In addition, the calculated diffusion constants are in good agreement with previous ab initio studies

Identification of cardiac organ damage in arterial hypertension: insights by echocardiography for a comprehensive assessment

: Arterial hypertension, a widespread disease, whose prevalence increases with age, represents a major risk factor for cardiovascular events, causing damage in several organs, including the heart. In this context, echocardiography has a clear and pivotal role, being able to assess cardiac morphology and detect haemodynamic changes induced by this disease. 2018 European Society of Cardiology/European Society of Hypertension guidelines on AH identified main echo parameters such as left ventricular mass, relative wall thickness and left atrial volume, for detecting cardiac organ damage. The present review highlights the advantage of additional echocardiographic parameters such as diastolic measurement and both thoracic and abdominal aortic dimensions. An overlook on aortic valve should also be suggested to detect aortic regurgitation and stenosis, both frequent complications in hypertensive patients. In this kind of comprehensive assessment, the combination of standard and advanced echocardiography (speckle tracking echocardiography and, with a lesser extent, three-dimensional echocardiography) could be considered to improve the diagnostic accuracy, stratify prognosis and address management in arterial hypertension

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Power of 280: Measuring the Impact of Elon Musk's Tweets on the Stock Market

This research aims to measure the extent up to which Elon Musk’s Twitter posts may influence stock prices of companies tagged, specifically the essential market metrics such as price, volume, and returns obtained from listed stocks. Intra-day data from well-recognized data providers have been utilized, for a period of 900 minutes, ranging from -600 minutes to +360 minutes, with the 0th minute being the time of his tweet (t=0). The study incorporates an event study and has adopted sentiment analysis and cumulative abnormal returns and found a significant impact of his tweets on five of the eight events

Queues with retrial/self-generation of priorities /postponement of work and some related reliability problems

Application of Queueing theory in areas like Computer networking, ATM facilities, Telecommunications and to many other numerous situation made people study Queueing models extensively and it has become an ever expanding branch of applied probability. The thesis discusses Reliability of a ‘k-out-of-n system’ where the server also attends external customers when there are no failed components (main customers), under a retrial policy, which can be explained in detail. It explains the reliability of a ‘K-out-of-n-system’ where the server also attends external customers and studies a multi-server infinite capacity Queueing system where each customer arrives as ordinary but can generate into priority customer which waiting in the queue. The study gives details on a finite capacity multi-server queueing system with self-generation of priority customers and also on a single server infinite capacity retrial Queue where the customer in the orbit can generate into a priority customer and leaves the system if the server is already busy with a priority generated customer; else he is taken for service immediately. Arrival process is according to a MAP and service times follow MSP