Using thermochromism to simulate blood oxygenation in extracorporeal membrane oxygenation

Introduction: Extracorporeal membrane oxygenation (ECMO) training programs employ real ECMO components, causing them to be extremely expensive while offering little realism in terms of blood oxygenation and pressure. To overcome those limitations, we are developing a standalone modular ECMO simulator that reproduces ECMO’s visual, audio and haptic cues using affordable mechanisms. We present a central component of this simulator, capable of visually reproducing blood oxygenation color change using thermochromism. Methods: Our simulated ECMO circuit consists of two physically distant modules, responsible for adding and withdrawing heat from a thermochromic fluid. This manipulation of heat creates a temperature difference between the fluid in the drainage line and the fluid in the return line of the circuit and, hence, a color difference. Results: Thermochromic ink mixed with concentrated dyes was used to create a recipe for a realistic and affordable blood-colored fluid. The implemented “ECMO circuit” reproduced blood’s oxygenation and deoxygenation color difference or lack thereof. The heat control circuit costs 300 USD to build and the thermochromic fluid costs 40 USD/L. During a ten-hour in situ demonstration, nineteen ECMO specialists rated the fidelity of the oxygenated and deoxygenated “blood” and the color contrast between them as highly realistic. Conclusions: Using low-cost yet high-fidelity simulation mechanisms, we implemented the central subsystem of our modular ECMO simulator, which creates the look and feel of an ECMO circuit without using an actual one.Peer reviewedFinal Accepted Versio

Extracorporeal membrane oxygenation simulation-based training: methods, drawbacks and a novel solution

Introduction: Patients under the error-prone and complication-burdened extracorporeal membrane oxygenation (ECMO) are looked after by a highly trained, multidisciplinary team. Simulation-based training (SBT) affords ECMO centers the opportunity to equip practitioners with the technical dexterity required to manage emergencies. The aim of this article is to review ECMO SBT activities and technology followed by a novel solution to current challenges. ECMO simulation: The commonly-used simulation approach is easy-to-build as it requires a functioning ECMO machine and an altered circuit. Complications are simulated through manual circuit manipulations. However, scenario diversity is limited and often lacks physiological and/or mechanical authenticity. It is also expensive to continuously operate due to the consumption of highly specialized equipment. Technological aid: Commercial extensions can be added to enable remote control and to automate circuit manipulation, but do not improve on the realism or cost-effectiveness. A modular ECMO simulator: To address those drawbacks, we are developing a standalone modular ECMO simulator that employs affordable technology for high-fidelity simulation.Peer reviewe

Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree

open access articleProviding the user with appliance-level consumption data is the core of each energy efficiency system. To that end, non-intrusive load monitoring is employed for extracting appliance specific consumption data at a low cost without the need of installing separate submeters for each electrical device. In this context, we propose in this paper a novel non-intrusive appliance recognition system based on (i) detecting events in the aggregated power signal using a novel and powerful scheme, (ii) applying multiscale wavelet packet tree to collect comprehensive energy consumption features, and (iii) adopting an ensemble bagging tree classifier along with comparing its performance with various machine learning schemes. Moreover, to validate the proposed model, an empirical investigation is conducted on two real and public energy consumption datasets, namely, the GREEND and REDD, in which consumption readings are collected at low-frequencies. In addition, a comprehensive review of recent non-intrusive load monitoring approaches has been conducted and presented, in which their characteristics, performances and limitations are described. The proposed non-intrusive load monitoring system shows a high appliance recognition performance in terms of the accuracy, F1 score and low time complexity when it has been applied to different households from the GREEND and REDD repositories, in which every house includes various domestic appliances. Obtained results have described, e.g., that average accuracies of 97.01% and 96.36% have been reached on the GREEND and REDD datasets, respectively, which outperformed almost existing solutions considered in this framework

A review of human circulatory system simulation: Bridging the gap between engineering and medicine

(1) Background: Simulation-based training (SBT) is the practice of using hands-on training to immerse learners in a risk-free and high-fidelity environment. SBT is used in various fields due to its risk-free benefits from a safety and an economic perspective. In addition, SBT provides immersive training unmatched by traditional teaching the interactive visualization needed in particular scenarios. Medical SBT is a prevalent practice as it allows for a platform for learners to learn in a risk-free and cost-effective environment, especially in critical care, as mistakes could easily cause fatalities. An essential category of care is human circulatory system care (HCSC), which includes essential-to-simulate complications such as cardiac arrest. (2) Methods: In this paper, a deeper look onto existing human circulatory system medical SBT is presented to assess and highlight the important features that should be present with a focus on extracorporeal membrane oxygenation cannulation (ECMO) simulators and cardiac catheterization. (3) Results: A list of features is also suggested for an ideal simulator to bridge the gap between medical studies and simulator engineering, followed by a case study of an ECMO SBT system design. (4) Conclusions: A collection and discussion of existing work for HCSC SBT are portrayed as a guide for researchers and practitioners to compare existing SBT and recreating them effectively. 2021 by the authors.Acknowledgments: This publication was made possible by an Award (GSRA6-2-0418-19015) from the Qatar National Research Fund (a member of the Qatar Foundation). The contents herein are solely the responsibility of the authors. This publication was also supported by Qatar University Internal Grant No. M-CTP-CENG-2020-1. The findings achieved herein are solely the responsibility of the authors.Scopu

Novel domestic building energy consumption dataset: 1D timeseries and 2D Gramian Angular Fields representation

This dataset, collected in 2022 in a domestic household in the UK, provides appliance-level power consumption data and ambient environmental conditions as a timeseries and as a collection of 2D images created using Gramian Angular Fields (GAF). The importance of the dataset lies in (a) providing the research community with a dataset that combines appliance-level data coupled with important contextual information for the surrounding environment; (b) presents energy data summaries as 2D images to help obtain novel insights from the data using data visualization and Machine Learning (ML). The methodology involves installing smart plugs to a number of domestic appliances, environmental and occupancy sensors, and connecting the plus and the sensors to a High-Performance Edge Computing (HPEC) system to privately store, pre-process, and post-process data. The heterogenous data include several parameters, including power consumption (W), voltage (V), current (A), ambient indoor temperature (°C), relative indoor humidity (RH%), and occupancy (binary). The dataset also includes outdoor weather conditions based on data from MET Norway including temperature (°C), outdoor humidity (RH%), barometric pressure (hPA), wind bearing (deg), and windspeed (m/s). This dataset is valuable for energy efficiency researchers, electrical engineers, and computer scientists to develop, validate, and deploy and computer vision and data-driven energy efficiency systems

Improving In-Home Appliance Identification Using Fuzzy-Neighbors-Preserving Analysis Based QR-Decomposition

This paper proposes a new appliance identification scheme by introducing a novel approach for extracting highly discriminative characteristic sets that can considerably distinguish between various appliance footprints. In this context, a precise and powerful characteristic projection technique depending on fuzzy-neighbors-preserving analysis based QR-decomposition (FNPA-QR) is applied on the extracted energy consumption time-domain features. The FNPA-QR aims to diminish the distance among the between class features and increase the gap among features of dissimilar categories. Following, a novel bagging decision tree (BDT) classifier is also designed to further improve the classification accuracy. The proposed technique is then validated on three appliance energy consumption datasets, which are collected at both low and high frequency. The practical results obtained point out the outstanding classification rate of the time-domain based FNPA-QR and BDT. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Acknowledgements. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Improving in-home appliance identification using fuzzy-neighbors-preserving analysis based QR-decomposition

This paper proposes a new appliance identification scheme by introducing a novel approach for extracting highly discriminative characteristic sets that can considerably distinguish between various appliance footprints. In this context, a precise and powerful characteristic projection technique depending on fuzzy-neighbors-preserving analysis based QR-decomposition (FNPA-QR) is applied on the extracted energy consumption time-domain features. The FNPA-QR aims to diminish the distance among the between class features and increase the gap among features of dissimilar categories. Following, a novel bagging decision tree (BDT) classifier is also designed to further improve the classification accuracy. The proposed technique is then validated on three appliance energy consumption datasets, which are collected at both low and high frequency. The practical results obtained point out the outstanding classification rate of the time-domain based FNPA-QR and BDT

Novel domestic building energy consumption dataset: 1D timeseries and 2D Gramian Angular Fields representation

This dataset, collected in 2022 in a domestic household in the UK, provides appliance-level power consumption data and ambient environmental conditions as a timeseries and as a collection of 2D images created using Gramian Angular Fields (GAF). The importance of the dataset lies in (a) providing the research community with a dataset that combines appliance-level data coupled with important contextual information for the surrounding environment; (b) presents energy data summaries as 2D images to help obtain novel insights from the data using data visualization and Machine Learning (ML). The methodology involves installing smart plugs to a number of domestic appliances, environmental and occupancy sensors, and connecting the plus and the sensors to a High-Performance Edge Computing (HPEC) system to privately store, pre-process, and post-process data. The heterogenous data include several parameters, including power consumption (W), voltage (V), current (A), ambient indoor temperature (°C), relative indoor humidity (RH%), and occupancy (binary). The dataset also includes outdoor weather conditions based on data from MET Norway including temperature (°C), outdoor humidity (RH%), barometric pressure (hPA), wind bearing (deg), and windspeed (m/s). This dataset is valuable for energy efficiency researchers, electrical engineers, and computer scientists to develop, validate, and deploy and computer vision and data-driven energy efficiency systems.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Design of ultra-fast electric vehicle battery charger

As the world move towards using Electric Vehicles (EVs) as a sustainable way of commuting, the demand for finding solutions to charge EVs as quickly as filling a fuel tank of an Internal Combustion Engine (ICE) vehicle increases. In this paper, the performance of a 2kW Cuk converter operating in Continuous Conduction Mode (CCM) and Discontinuous Inductor Conduction Mode (DICM) are assessed for Ultra-Fast Charging (UFC) of low voltage EV batteries such as the one used in golf carts. Besides, state-space modeling for the Cuk converter operating in DICM is carried out to design the output current controller. The designs were simulated and verified using MATLAB/Simulink, and the results show that the size and the complexity of the controller can be reduced when the Cuk converter operates in DICM, meeting the requirements of international standards.Qatar Foundation; Qatar National Research FundScopu

Self-Powered IoT-Enabled Water Monitoring System

While usable water on earth is sparse and costly to treat, statistics show excessive use worldwide. Penalties are issued to limit the excessive consumption; however, their impact is marginal as they do not identify the reason for the exorbitant consumption. Therefore, giving users the full awareness about their water consumption will greatly help in minimizing the water waste. This article aims to present a smart self-powered water monitoring system that leverages IoT and cloud computing. The system consists of an IoT device that can be installed at any water source, a cloud application to receive the data from the devices, and a mobile app to visualize the water consumption at every monitored source. This system gives the user the ability to identify the location and time of the excessive usage and leaks on the mobile phone. Experiment results confirm the self-powered premise of the solution. A prototype of mobile application and backend have been developed and tested.This work was partially supported by Microsoft for being a winner project in Microsoft Imagine Cup 2017 Competition - Middle East & Africa