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

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

Enhancing Clinical Learning Through an Innovative Instructor Application for ECMO Patient Simulators

© 2018 The Authors. Reprinted by permission of SAGE PublicationsBackground. Simulation-based learning (SBL) employs the synergy between technology and people to immerse learners in highly-realistic situations in order to achieve quality clinical education. Due to the ever-increasing popularity of extracorporeal membrane oxygenation (ECMO) SBL, there is a pressing need for a proper technological infrastructure that enables high-fidelity simulation to better train ECMO specialists to deal with related emergencies. In this article, we tackle the control aspect of the infrastructure by presenting and evaluating an innovative cloud-based instructor, simulator controller, and simulation operations specialist application that enables real-time remote control of fullscale immersive ECMO simulation experiences for ECMO specialists as well as creating custom simulation scenarios for standardized training of individual healthcare professionals or clinical teams. Aim. This article evaluates the intuitiveness, responsiveness, and convenience of the ECMO instructor application as a viable ECMO simulator control interface. Method. A questionnaire-based usability study was conducted following institutional ethical approval. Nineteen ECMO practitioners were given a live demonstration of the instructor application in the context of an ECMO simulator demonstration during which they also had the opportunity to interact with it. Participants then filled in a questionnaire to evaluate the ECMO instructor application as per intuitiveness, responsiveness, and convenience. Results. The collected feedback data confirmed that the presented application has an intuitive, responsive, and convenient ECMO simulator control interface. Conclusion. The present study provided evidence signifying that the ECMO instructor application is a viable ECMO simulator control interface. Next steps will comprise a pilot study evaluating the educational efficacy of the instructor application in the clinical context with further technical enhancements as per participants’ feedback.Peer reviewedFinal Accepted Versio

Appliance identification using a histogram post-processing of 2D local binary patterns for smart grid applications

Identifying domestic appliances in the smart grid leads to a better power usage management and further helps in detecting appliance-level abnormalities. An efficient identification can be achieved only if a robust feature extraction scheme is developed with a high ability to discriminate between different appliances on the smart grid. Accordingly, we propose in this paper a novel method to extract electrical power signatures after transforming the power signal to 2D space, which has more encoding possibilities. Following, an improved local binary patterns (LBP) is proposed that relies on improving the discriminative ability of conventional LBP using a post-processing stage. A binarized eigenvalue map (BEVM) is extracted from the 2D power matrix and then used to post-process the generated LBP representation. Next, two histograms are constructed, namely up and down histograms, and are then concatenated to form the global histogram. A comprehensive performance evaluation is performed on two different datasets, namely the GREEND and WITHED, in which power data were collected at 1 Hz and 44000 Hz sampling rates, respectively. The obtained results revealed the superiority of the proposed LBP-BEVM based system in terms of the identification performance versus other 2D descriptors and existing identification frameworks.Comment: 8 pages, 10 figures and 5 table

Developing an IT tool for improving workforce motivation and capabilities: An empirical case study with reference to Qatar

Human, Organisation and Technology (HOT) are all important components of IS. However, organisations look to technology as the main tool of change that can help them achieve their goals. This change usually concerns the needs of the organisation, and not the needs of its human resources, despite it being the latter that is the principal actor that any organisation depends upon to achieve its goals. The aim of this research is to develop an IT tool that itself can satisfy the workforce humanistic needs. In order to develop this IT tool, a theoretical investigation and practical experimentations were conducted in a series of case studies involving government organisations in Qatar. Based on the theoretical investigation, an approach was proposed based on Socio-Technical Theory (STT), supported by learning from the ETHICS application of STT concepts and Client-Led Information systems Creation (CLIC) application of Soft System Methodology (SSM) principles. This approach was used to guide the development of the IT tool which was then used in actual organisational work environments to assess its impact on the Qatari workforce's motivation and capabilities. Empirical results from this research show that an IT itself cannot be used to improve workforce motivation and capabilities in the case of Qatar. However, IT can do this by supporting a work environment enabled by necessary managerial practises and work environment requirements. Based on the proposed approach, this requires firstly, an understanding of the needs of the social subsystem of the organisation to improve workforce motivation and capability; then these needs should be developed into functions that are enabled by the work environment and supported by the IT tool. Lastly, the new IT tool needs to be integrated into the existing technical subsystem of the organisation. These findings have both theoretical and practical implications. They contribute to a better understanding of the role of IT in improving the workforce's motivation and capabilities. They extend the application of STT principles in the area of developing human-focused IS by finding an alternative to the participatory approach via learning from SSM principles. They also provide specific understanding of how to develop an IT tool as well as what the work environment needs to provide to enable the application of the IT tool, to improve workforce motivation and capabilities. This PhD research also has social implications for the way IT is utilised in organisations. It can affect areas of IS utilisation and workforce well-being, as well as the role of leadership in maximising the value of IS from a human-focused perspective and the area of utilising IT in a virtual team to consider their humanistic needs

Facilitating Deep Learning for Edge Computing: A Case Study on Data Classification

https://attend.ieee.org/dsc-2022/sicsa-event/Deep Learning (DL) is increasingly empowering technology and engineering in a plethora of ways, especially when big data processing is a core requirement. Many challenges, however, arise when solely depending on cloud computing for Artificial Intelligence (AI), such as data privacy, communication latency, and power consumption. Despite the elevating popularity of edge computing, its overarching issue is not the lack of technical specifications in many edge computing platforms but the sparsity of comprehensive documentation on how to correctly utilize hardware to run ML and DL algorithms. Due to its specialized nature, installing the full version of TensorFlow, a common ML library, on an edge device is a complicated procedure that is seldom successful, due to the many dependent software libraries needed to be compatible with varying architectures of edge computing devices. Henceforth, in this paper, we present a novel technical guide on setting up the TensorFlow Lite, a lightweight version of TensorFlow and demonstrate a complete workflow of model training, validation, and testing on the ODROID-XU4. Results are presented for a case study on energy data classification using the outlined model show almost 7 times higher computational performance compared to cloud-based AI

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it will aid in better decision-making to reduce wasted energy and promote sustainable and energy efficient behavior. In this regard, this paper is an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. Specifically, an extensive survey is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. To the best of the authors' knowledge, this is the first review article that discusses anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumption, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, (iv) platforms for reproducibility and (v) privacy-preservation. Following, insights about current research trends are discussed to widen the applications and effectiveness of the anomaly detection technology before deriving future directions attracting significant attention. This article serves as a comprehensive reference to understand the current technological progress in anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-the-art data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored

Edge AI for Internet of Energy: Challenges and Perspectives

The digital landscape of the Internet of Energy (IoE) is on the brink of a revolutionary transformation with the integration of edge Artificial Intelligence (AI). This comprehensive review elucidates the promise and potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a meticulously curated research methodology, the article delves into the myriad of edge AI techniques specifically tailored for IoE. The myriad benefits, spanning from reduced latency and real-time analytics to the pivotal aspects of information security, scalability, and cost-efficiency, underscore the indispensability of edge AI in modern IoE frameworks. As the narrative progresses, readers are acquainted with pragmatic applications and techniques, highlighting on-device computation, secure private inference methods, and the avant-garde paradigms of AI training on the edge. A critical analysis follows, offering a deep dive into the present challenges including security concerns, computational hurdles, and standardization issues. However, as the horizon of technology ever expands, the review culminates in a forward-looking perspective, envisaging the future symbiosis of 5G networks, federated edge AI, deep reinforcement learning, and more, painting a vibrant panorama of what the future beholds. For anyone vested in the domains of IoE and AI, this review offers both a foundation and a visionary lens, bridging the present realities with future possibilities

Cloud Energy Micro-Moment Data Classification: A Platform Study

Energy efficiency is a crucial factor in the well-being of our planet. In parallel, Machine Learning (ML) plays an instrumental role in automating our lives and creating convenient workflows for enhancing behavior. So, analyzing energy behavior can help understand weak points and lay the path towards better interventions. Moving towards higher performance, cloud platforms can assist researchers in conducting classification trials that need high computational power. Under the larger umbrella of the Consumer Engagement Towards Energy Saving Behavior by means of Exploiting Micro Moments and Mobile Recommendation Systems (EM)3 framework, we aim to influence consumers behavioral change via improving their power consumption consciousness. In this paper, common cloud artificial intelligence platforms are benchmarked and compared for micro-moment classification. The Amazon Web Services, Google Cloud Platform, Google Colab, and Microsoft Azure Machine Learning are employed on simulated and real energy consumption datasets. The KNN, DNN, and SVM classifiers have been employed. Superb performance has been observed in the selected cloud platforms, showing relatively close performance. Yet, the nature of some algorithms limits the training performance.Comment: This paper has been accepted in IEEE RTDPCC 2020: International Symposium on Real-time Data Processing for Cloud Computin