Overcoming COVID-19 pandemic: emerging challenges of human factors and the role of cognitive ergonomics

The present study, an expert review, aimed to discuss the emerging challenges of overcoming COVID-19 from the perspective of human factors and the importance of cognitive ergonomics in helping to cope with the epidemic. Identifying these challenges and the use of cognitive ergonomics to optimize human well-being and system performance can be effective in managing COVID-19. Generally, two main preventive approaches such as social distancing and patient care or treatment approaches are being utilized in response to COVID-19. In this paper, human factors challenges that could emerge from covid-19 preventive approaches were discussed. Social distancing forces presence and increases automated systems that lead to increases in cognitive needs, mental workload, stress, etc. Challenges of treatment and health care include the increased workload of healthcare personnel, stress, changing work systems and task allocation that led to fatigue and stress, threats to patient safety, and disruption of interpersonal interactions from a cognitive ergonomic perspective. It is concluded that the challenges of coping with COVID-19 were numerous and important from the perspective of human factors and the role of cognitive ergonomics is important in controlling the disease;hence, it should be taken into consideration.

Enhancing the student experience in pre‐ and mid‐COVID‐19 pandemic period by upgrading the power electronics laboratory practice curriculum

Laboratory practices, which represent a vital part of electrical engineering education, have especially in the last few years been subjected to numerous challenges. The paper presents a concept of upgrading the laboratory practice curriculum in power electronics by introducing computer simulations. Due to the recognized shortcomings of the previous approach, the curriculum was closely reviewed, compared to the concepts from existing literature, and intensively upgraded by the introduction of the Ansys Simplorer computer program. The intensity of the process upgrade was enhanced by the COVID‐19 pandemic and related lockdowns. The introduced curriculum changes enabled the students to approach individual topics more gradually, reducing the gaps between the behavior of ideal and real power electronics circuits. The results of student feedback, obtained by a web‐based survey and a pre‐exam quiz, demonstrate that students recognize the new approach as being more gradual and beneficial, enabling them to improve their understanding of specific phenomena and to master the topics of power electronics with ease and satisfaction.

From offline learning to online learning: Moderating effect of relationship inertia and satisfaction

The progress of educational technology and the demand of home learning under COVID‐19 have promoted the rapid development of online learning all over the world. The existing research focuses on offline learning or online learning but seldom discusses their switching process. This paper uses the questionnaire method and structural equation model to analyze the influencing factors of switching intention and behavior from offline learning to online learning. The results show that the switching intention is significantly affected by perceived usefulness, perceived ease of use, and computer self‐efficacy, and negatively affected by the perceived risk. The switching intention makes a significant positive effect on switching behavior. When the learner's offline learning relationship inertia and satisfaction are higher, the relationship between switching intention and switching behavior will be weakened. At the end of the paper, some suggestions are put forward for reference for the related parties of online learning.

Virtual labs for the study of enzymatic stirred tank bioreactors

Virtual laboratories have successfully proven to be very versatile and intuitive when simulating experiments in science, biotechnology, and engineering. These tools must complement the experiments carried out in real labs or pilot plants. This study describes the creation of a virtual laboratory through the Easy JavaScript Simulation platform. A web‐based simulation of an enzymatic stirred‐tank bioreactor has been built using a dynamic model. This simulation reproduces the behavior of a continuous bioreactor, including the deviations of ideal mixing conditions as by the use of an in tanks‐in‐series model for nonideal flow. This article describes the continuous dynamic model in a stirred tank bioreactor, as well as the operation of a tool capable of carrying out virtual practice with students. Practice scripts have been developed that should be used by students during the practical classes. This interactive tool is powerful and useful to develop many experiments by varying the different input parameters, saving time and resources. In addition, the tool allows following teaching sessions in specific situations such as the health situation derived from the pandemic caused by COVID‐19.

Gamifying structural analysis assessments for first‐year architecture engineering students

The structural analysis module is a challenge for both teachers and students. The module content is usually presented to students in the form of a set of equations when solved, the structure of internal forces is obtained. Usually, the assignments adopted in such modules are paper‐based exams. Such a strategy may assess the capacity of the students to employ different sets of equations to solve a problem. However, this is not enough for a vivid educational atmosphere. Transforming these equations into a digital simulation is the best solution for the education process. Digitalization is more appealing to nowadays students and it gives the teacher a wide spectrum of discussions without the hindrance of calculations time. It is also a mitigation for the online teaching process during the Covid‐19 pandemic. This paper presents a digital simulation of different structures using a simple tool that enables students to visualize the simultaneous interaction between geometry, loading, boundary conditions, and internal forces. Furthermore, transforming this tool into an offline mobile app helps both the teacher and the student to gamify the investigation of any structure.

Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems

Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify patterns in big data, and make decision around them. Neural networks are now consistently used as universal function approximators for data with underlying mechanisms that are incompletely understood or exceedingly complex. However, neural networks alone ignore the fundamental laws of physics and often fail to make plausible predictions. Here we integrate data, physics, and uncertainties by combining neural networks, physics informed modeling, and Bayesian inference to improve the predictive potential of traditional neural network models. We embed the physical model of a damped harmonic oscillator into a fully-connected feed-forward neural network to explore a simple and illustrative model system, the outbreak dynamics of COVID-19. Our Physics Informed Neural Networks seamlessly integrate data and physics, robustly solve forward and inverse problems, and perform well for both interpolation and extrapolation, even for a small amount of noisy and incomplete data. At only minor additional cost, they self-adaptively learn the weighting between data and physics. They can serve as priors in a Bayesian Inference, and provide credible intervals for uncertainty quantification. Our study reveals the inherent advantages and disadvantages of Neural Networks, Bayesian Inference, and a combination of both and provides valuable guidelines for model selection. While we have only demonstrated these different approaches for the simple model problem of a seasonal endemic infectious disease, we anticipate that the underlying concepts and trends generalize to more complex disease conditions and, more broadly, to a wide variety of nonlinear dynamical systems.

Wie geht es dem akademischen Mittelbau?

ZusammenfassungZum sogenannten Mittelbau zählen Doktoranden und Doktorandinnen, Postdocs, Nachwuchsgruppenleiter und -leiterinnen, Junior- und Tenure-Track-Professoren und -Professorinnen. Insbesondere Promovierende sowie Postdoktoranden und Postdoktorandinnen sind, noch mehr als früher, von den komplexen strukturellen und finanziellen Problematiken des Wissenschafts- und Lehrbetriebs betroffen, und das in vielerlei Hinsicht über Fächer hinweg. Der Flaschenhals auf dem Weg zur Professur oder einer anderweitig verstetigten Forschungs- oder Lehrstelle führt in der akademischen Karriere zu prekären Beschäftigungsverhältnissen. Die schwierige Vereinbarkeit von Familie und akademischer Karriere erzeugt eine zusätzliche Benachteiligung, insbesondere von Wissenschaftlerinnen. Mangelnde Qualitätssicherung sowie fehlende zuverlässige und vertrauenswürdige Prozesse erschweren die Aufdeckung und Aufarbeitung von Konflikten während der Promotionsphase und der Zeit des Postdoktorats. Der Beirat des wissenschaftlichen Nachwuchses (GI-WiN) der Gesellschaft für Informatik e. V. (GI) fordert und empfiehlt in einem Positionspapier (https://doi.org/10.1007/s00287-020-01250-x) mehrere Maßnahmen zur Stabilisierung von Karriere- und Beschäftigungssituation des Mittelbaus. Der vorliegende Artikel fasst die Ergebnisse einer Umfrage zur Ergänzung und empirischen Untermauerung dieser Empfehlungen zusammen. Die vorliegende Umfrage wurde mit dem Anspruch erhoben, Daten aus allen Fachbereichen – nicht nur der Informatik – zu sammeln. In den Ergebnissen zeigte sich, dass diesen vielfältigen Herausforderungen begegnet werden muss, die in Nicht-MINT-Fächern und MINT-Fächern ähnlich empfunden werden. Die Schaffung von unbefristeten Stellen für die Phase nach der Promotion wurde im Rahmen der Umfrage als besonders wünschenswert angesehen. Vorgeschlagen wurde auch die Abschaffung von Lehrstühlen und die Einführung einer Departmentstruktur. Des Weiteren wurde die Trennung von Begutachtung und Betreuung vor allem bei der Promotion angeregt. Die letzten 2 Jahre im Zeichen der COVID-19-Pandemie waren für einen großen Teil der Betroffenen von fehlendem fachlichen Austausch und sowohl beruflichen als auch privaten Zusatzbelastungen geprägt. Diese Ergebnisse können über die genannten Fachbereiche hinaus eine Entscheidungsgrundlage für eine gerechtere Wissenschaftspolitik liefern und bessere Arbeits- und Karrierebedingungen für den wissenschaftlichen Nachwuchs erwirken.

A Survey on Machine Learning in COVID-19 Diagnosis

Since Corona Virus Disease 2019 outbreak, many expert groups worldwide have studied the problem and proposed many diagnostic methods. This paper focuses on the research of Corona Virus Disease 2019 diagnosis. First, the procedure of the diagnosis based on machine learning is introduced in detail, which includes medical data collection, image preprocessing, feature extraction, and image classification. Then, we review seven methods in detail: transfer learning, ensemble learning, unsupervised learning and semi-supervised learning, convolutional neural networks, graph neural networks, explainable deep neural networks, and so on. What’s more, the advantages and limitations of different diagnosis methods are compared. Although the great achievements in medical images classification in recent years, Corona Virus Disease 2019 images classification based on machine learning still encountered many problems. For example, the highly unbalanced dataset, the difficulty of collecting labeled data, and the poor quality of the data. Aiming at these problems, we propose some solutions and provide a comprehensive presentation for future research.