Philosophical Management of Stress based on Science and Epicurean Pragmatism: A Pilot Study

In the first months of the COVID-19 pandemic, we created and implemented from November 2020 to February 2021 a monthly educational pilot program of philosophical management of stress based on Science, Humanism and Epicurean Pragmatism, which was offered to employees of 26 municipalities in the Prefecture of Attica, Greece. The program named "Philosophical Distress Management Operation System” (Philo.Di.M.O.S.) is novel and unique in its kind, as it combines a certain Greek philosophical tradition (Epicurean) that concurs with modern scientific knowledge. The program was designed to be implemented in a period of crisis;therefore, it used a fast-paced, easy to learn and practice philosophical approach to stress management, based on cognitive psychotherapy. The philosophical approach to stress management has the advantage that it can be offered to most people, regardless of age and educational level. The pilot program was effective in achieving its objectives, shown by statistical comparisons of the trainees' responses to anonymous questionnaires before and after the month-long training. The successful Philo.Di.M.O.S. program, thus, based on a solid scientific and philosophical basis, offers a paradigm of stress management during crises and could be useful in Greece and internationally. © 2022, Christos Yapijakis, Evangelos Protopapadakis, George P. Chrousos.

Transferability Limitations for Covid 3D Localization Using SARS-CoV-2 Segmentation Models in 4D CT Images

In this paper, we investigate the transferability limitations when using deep learning models, for semantic segmentation of pneumonia-infected areas in CT images. The proposed approach adopts a 4 channel input;3 channels based on Hounsfield scale, plus one channel (binary) denoting the lung area. We used 3 different, publicly available, CT datasets. If the lung area mask was not available, a deep learning model generates a proxy image. Experimental results suggest that transferability should be used carefully, when creating Covid segmentation models;retraining the model more than one times in large sets of data results in a decrease in segmentation accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models

Recent studies indicate that detecting radiographic patterns on CT scans can yield high sensitivity and specificity for Covid-19 localization. In this paper, we investigate the appropriateness of deep learning models transferability, for semantic segmentation of pneumonia-infected areas in CT images. Transfer learning allows for the fast initialization/reutilization of detection models, given that large volumes of training data are not available. Our work explores the efficacy of using pre-trained U-Net architectures, on a specific CT data set, for identifying Covid-19 side-effects over images from different datasets. Experimental results indicate improvement in the segmentation accuracy of identifying Covid-19 infected regions. © 2022 ACM.

Assessing the lockdown effects on air quality during COVID-19 era

In this work we investigate the short-term variations in air quality emissions, attributed to the prevention measures, applied in different cities, to mitigate the COVID-19 spread. In particular, we emphasize on the concentration effects regarding specific pollutant gases, such as carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2) and sulphur dioxide (SO2). The assessment of the impact of lockdown on air quality focused on four European Cities (Athens, Gladsaxe, Lodz and Rome). Available data on pollutant factors were obtained using global satellite observations. The level of the employed prevention measures is employed using the Oxford COVID-19 Government Response Tracker. The second part of the analysis employed a variety of machine learning tools, utilized for estimating the concentration of each pollutant, two days ahead. The results showed that a weak to moderate correlation exists between the corresponding measures and the pollutant factors and that it is possible to create models which can predict the behaviour of the pollutant gases under daily human activities. © 2021 The authors and IOS Press.

Machine Learning Tools to Assess the Impact of COVID-19 Civil Measures in Atmospheric Pollution

In January, 2020, a new virus, named SARS-COV-2, was identified and announced to the public;in March the World Health Organization (WHO) declared a worldwide pandemic. To reduce the transmissibility of the new virus, the local authorities, worldwide, introduced a series of measures to flatten the curve. Many of the measures included some form of lockdown and movement restrictions. This unique coordinated worldwide reaction, created an opportunity for researching the effects of low traffic in air quality. In this work we research the relation between the COVID-19 measures and the Air Quality Index (AQI), using four pollutant gases (CO, O3, NO2, SO2). Also, we used a variety of machine learning tools (DNN, DTR, K-NN, Lasso, LReg, MAdam, MGBR, RFR, Ridge) to estimate the accuracy of each method in the prediction of the concentration for each gas one week later. The results showed that after the strict COVID-19 restriction measures the concentration of each pollutant gas reduced rapidly and increased again after the relaxation of lockdown measures. Finally in cases like Australia, where the measures weren't as strict as other countries, no improvement observed. © 2021 ACM.

Deep learning models for COVID-19 infected area segmentation in CT images

Recent studies indicated that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 detection. In this work, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia infected area segmentation in CT images for the detection of COVID-19. We explore the efficacy of U-Nets and Fully Convolutional Neural Networks in this task using real-world CT data from COVID-19 patients. The results indicate that Fully Convolutional Neural Networks are capable of accurate segmentation despite the class imbalance on the dataset and the man-made annotation errors on the boundaries of symptom manifestation areas, and can be a promising method for further analysis of COVID-19 induced pneumonia symptoms in CT images. © 2021 ACM.

Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images

We introduce a deep learning framework that can detect COVID-19 pneumonia in thoracic radiographs, as well as differentiate it from bacterial pneumonia infection. Deep classification models, such as convolutional neural networks (CNNs), require large-scale datasets in order to be trained and perform properly. Since the number of X-ray samples related to COVID-19 is limited, transfer learning (TL) appears as the go-to method to alleviate the demand for training data and develop accurate automated diagnosis models. In this context, networks are able to gain knowledge from pretrained networks on large-scale image datasets or alternative data-rich sources (i.e. bacterial and viral pneumonia radiographs). The experimental results indicate that the TL approach outperforms the performance obtained without TL, for the COVID-19 classification task in chest X-ray images. © 2020 ACM.