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Prispevek predstavlja izhodišča za razpravo o jezikovnih smernicah in učnih ciljih v vrtcih in šolah s slovenskim učnim jezikom v Tržaški in Goriški pokrajini ter v večstopenjski šoli s slovensko-italijanskim dvojezičnim poukom v Špetru. Izhaja iz rezultatov kvantitativne raziskave o poteku pouka na daljavo med epidemijo covid-19, izvedene med pedagoškim kadrom, dijaki in starši otrok, ki obiskujejo vrtce in šole s slovenskim učnim jezikom in dvojezičnim slovensko-italijanskim poukom v Italiji. Izsledke dopolnjuje analiza petnajstih polstrukturiranih globinskih intevjujev z učitelji o didaktično-metodičnih izzivih pri poučevanju učencev, katerih prvi ali primarni jezik ni slovenski, slovenščina pa tudi ni jezik okolja, v katerem živijo.

Epidemiological modeling is an important problem around the world. This research presents COVID-19 analysis to understand which model works better for different regions. A comparative analysis of three growth curve fitting models (Gompertz, Logistic, and Exponential), two mathematical models (SEIR and IDEA), two forecasting models (Holt’s exponential and ARIMA), and four machine/deep learning models (Neural Network, LSTM Networks, GANs, and Random Forest) using three evaluation criteria on ten prominent regions around the world from North America, South America, Europe, and Asia has been presented. The minimum and median values for RMSE were 1.8 and 5372.9 the values for the mean absolute percentage error were 0.005 and 6.63 and the values for AIC were 87.07 and 613.3, respectively, from a total of 125 experiments across 10 regions. The growth curve fitting models worked well where flattening of the cases has started. Based on region’s growth curve, a relevant model from the list can be used for predicting the number of infected cases for COVID-19. Some other models used in forecasting the number of cases have been added in the future work section, which can help researchers to forecast the number of cases in different regions of the world.

The paper proposes a graph-theoretical approach to auscultation, bringing out the potential of graph features in classifying the bioacoustics signals. The complex network analysis of the bioacoustics signals - vesicular (VE) and bronchial (BR) breath sound - of 48 healthy persons are carried out for understanding the airflow dynamics during respiration. The VE and BR are classified by the machine learning techniques extracting the graph features – the number of edges (E), graph density (D), transitivity (T), degree centrality (Dcg) and eigenvector centrality (Ecg). The higher value of E, D, and T in BR indicates the temporally correlated airflow through the wider tracheobronchial tract resulting in sustained high-intense low-frequencies. The frequency spread and high-frequencies in VE, arising due to the less correlated airflow through the narrow segmental bronchi and lobar, appears as a lower value for E, D, and T. The lower values of Dcg and Ecg justify the inferences from the spectral and other graph parameters. The study proposes a methodology in remote auscultation that can be employed in the current scenario of COVID-19.

Fakultete bodo predavanja izvajale deloma v živo, deloma prek spleta, v skupnih prostorih študentskih domov pa bodo obvezne maske. Študij na daljavo je po mnenju študentov manj kakovosten in napornejši, univerzam pa prinaša visoke stroške.

A lot of new technological applications are emerging to combat the deadly novel coronavirus. This research study gives an overview of different applications that are developed by Government Institutions, Private Firms, and Individual Citizens across the world. The applications are reviewed based on their widespread use, effectiveness, availability to the broader audience, cost to implement it, concerns regarding the privacy and information collected by these apps and systems. The major eight areas of technology applications covered in this study are Contact Tracing, Social Distancing & Mask Detection, Live-feeds based Dashboards, Information Searching, Big Data and Robotics, Web-based Disease Surveillance Tools, Patient-level Information, Doctor-Patient interaction, and Informatory Chatbots. More than 100 apps were collected for this research survey to conclude the different categories in which technology is being used for decision making. This study will be useful for various health administrators, professionals, researchers, and academicians.

The development of novel digital auscultation techniques has become highly significant in the context of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series, fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried out with 37 breath sound signals. The spectral analysis brings out the signatures of VB and BB through the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal component analysis helps in classifying VB and BB sound signals through the feature extraction from the power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through lung auscultation.

The COVID-19 pandemic has become a major threat to the whole world. Analysis of this disease requires major attention by the government in all countries to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed and trained for Indian region till 10th May, 2020, and testing has been done for the number of cases for the next three weeks. Machine learning models such as SEIR model and Regression model have been used for predictions based on the data collected from the official portal of the Government of India in the time period of 30th January, 2020, to 10th May, 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0, which is the spread of the disease, was calculated to be 2.84. Expected cases are predicted around 175K--200K in the three-week time period of test data, which is very close to the actual numbers. This study will help the government and doctors in preparing their plans for the future.