ABSTRACT
This paper presents the evolution of a master course taught simultaneously at multiple master specialisations. The analysis of the course structure, content, teaching and evaluation modes has been presented over several years, including both pre-pandemic and during Covid19 pandemic time. The challenges of dealing with a highly heterogeneous student group are explained and solutions that have been implemented over the years are analysed by means of their effect in student satisfaction scores and learning effect percentage. Copyright (C) 2022 The Authors.
ABSTRACT
Digital Twins DT have been considered recently as the leap forging technology for digital and physical world fusion. Our analysis to the triple crisis that the world is experiencing as a result of COVID-19 has enabled us to identify several challenges to overcome for current decision-making systems and industrial environments that are constrained to develop adaptative management systems, flexible networks and smart business continuity plans. The impacts of both geospatial and business intelligence as well as advanced simulation have motivated our proposition of a new generic framework based on digital twins to deal with these challenges. Our proposed framework by combining first digital twins with business intelligence tools aims to develop smart information models, value-driven DT architectures and context aware services that tries to mimic real-environment dynamics, and according to its developed smart engines prevent the occurrence of unpredictable events. Secondly, by integrating to this combination, location intelligence provides multi-perspective modelling and geo-statistic data integration with real-time operational data into digital twins offers a generic system for prognostication, optimization and on-line and offline learning. The framework is integrated across the paper within the efforts to deal with the triple crisis of the current pandemic with a focus on Middle East and North Africa MENA regions and particularly Morocco and concretized through an application use case within the field of protective facial masks production. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
This paper provides a visionary perspective on human-machine collaboration in a medical cyber-physical system (MCPS) during the 2020 pandemic context. For the time being, medical specialists in the operating room (OR) or Intensive Care Units (ICU) face special responsibilities when the procedures involve a patient with an infectious disease (e.g., COVID-19) that can cause several complications. The added workload of the anesthesiologist can be diminished by the context-aware pervasive assistance in the decision- making process for maintaining the optimal anesthesia and hemodynamics of the patient. A self-aware control system, with feedback from patient's monitored parameters and several surgical/ICU contextual data, is able to adapt his action accordingly, increasing treatment accuracy. The three main parts of general anesthesia (neuromuscular blockade, hypnosis and analgesia) and the hemodynamics (cardiac output, blood pressure) are perused from a global objective viewpoint, while intersecting the anesthesiologist's action upon his request. The integrative anesthesiologist-in-the-loop cyber-physical system (CPS) is emerging as an intelligent solution for hybrid control of anesthesia's depth, instead of total autonomous closed-loop controllers. This paper aims to create awareness throughout the anesthesiologists about the usefulness of integrating automation and data exchange in their clinical practice for providing increased attention to alarming situations. Moreover, it proposes an opening horizon for multi-disciplinary research. Hence, the connection between clinical and engineering frameworks envisages significant patient safety. © 2021 IEEE.
ABSTRACT
The development of transportation and communication means and the opening up of the world due to the industrial, economic and social revolutions and the emergence of advanced urbanization have resulted in an acceleration of globalization, worldwide supply chains dependencies and greater openness of the world’s ecosystems. At present, the world is experiencing an unparalleled health crisis due to the SARS 2 or covid-19 pandemic, which has given rise to socio-economic crises across the world. In the absence of a vaccine, countries are being forced to revolutionize their response and preparedness policies to health emergencies and compel themselves to the new global dynamic. Our paper, based on feedback from countries, proposed artificial intelligence solutions, the capitalization of standards-based knowledge in the face of covid-19 impacts and the concept of territorial intelligence, contributes to this global effort by proposing sustainable, smart and generic solutions against the current pandemic. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Over the last few years, the world has seen many social, industrial, and technological revolutions. The latter has enabled a combination of expertise from different fields in order to manage a wide range of multidimensional issues such as integrated societies and industrial ecosystems achievement, urban planning, transport management, sustainable development and environmental protection and currently pandemics management. Super smart society's vision that is driving the 5.0 social revolutions is at the heart of the current situation that requires system resilience, sustainability, proactivity, interoperability and collaborative intelligence between society, economy, and industry. Establishing communication bridges between different entities, of different natures and with different objectives implies solutions that reinforce the development of efficient, dynamic, and communicating business models on a large scale, merging cyber and physical spaces. Through this paper we explored the potential of digital twins for the development of a new vision of world global dynamics under the aegis of a virus whose parameters are still elusive to date. © 2020 International Society for Photogrammetry and Remote Sensing. All rights reserved.