Extreme Situation Experienced by Dental Students of the Medical University of Silesia Due to the SARS-CoV-2 Epidemic during the First Lockdown

The pandemic declared in many countries in 2020 due to COVID-19 led to the freezing of economies and the introduction of distance learning in both schools and universities. This unusual situation has affected the mental state of citizens, which has the potential to lead to the development of post-traumatic stress and depression. This study aimed to assess the level of stress in dental students in the context of the outbreak of the SARS-CoV-2 virus pandemic. A survey on the PSS-10 scale was prepared to measure the level of perceived stress. The study included 164 dental students at the Faculty of Medical Sciences of the Medical University of Silesia in Katowice, Poland. The results showed the impact of COVID-19 on the stress of students, with 67.7% reporting high levels of stress. The study also revealed that stress was higher among older female students. This paper recommends that the university provide more intensive psychological care as psychological first aid strategies in epidemics or natural disasters and to consider telemedicine in order to deliver services due to the limitations of the pandemic

Transport as a factor in the location selection process of timber firms

The article Transport as a Factor in the Location Selection Process of Timber Firms examines the important role of transport in the location process of timber firms. The authors emphasise that the efficiency of transport infrastructure and sustainable transport practices are crucial to the success of companies in this industry. The article also raises the issue of access to both domestic and international markets as an important factor influencing the location process of wood businesses. The conclusions presented highlight the importance of including transport aspects in the location analysis and point to the need to support the development of transport infrastructure in order to further develop the industry.W artykule Transport jako czynnik w procesie doboru lokalizacji przedsiębiorstw branży drzewnej analizowana jest istotna rola transportu w procesie lokalizacji przedsiębiorstw branży drzewnej. Autorzy podkreślają, że efektywność infrastruktury transportowej oraz zrównoważone praktyki transportowe mają kluczowe znaczenie dla sukcesu firm w tej branży. Artykuł podnosi również kwestię dostępu do rynków zarówno krajowych, jak i międzynarodowych jako istotny czynnik wpływający na proces lokalizacji przedsiębiorstw drzewnych. Przedstawione wnioski podkreślają znaczenie uwzględnienia aspektów transportowych w analizie lokalizacyjnej i wskazują na potrzebę wsparcia rozwoju infrastruktury transportowej w celu dalszego rozwoju tej branży

Semantic Segmentation of 12-Lead ECG Using 1D Residual U-Net with Squeeze-Excitation Blocks

Analyzing biomedical data is a complex task that requires specialized knowledge. The development of knowledge and technology in the field of deep machine learning creates an opportunity to try and transfer human knowledge to the computer. In turn, this fact influences the development of systems for the automatic evaluation of the patient’s health based on data acquired from sensors. Electrocardiography (ECG) is a technique that enables visualizing the electrical activity of the heart in a noninvasive way, using electrodes placed on the surface of the skin. This signal carries a lot of information about the condition of heart muscle. The aim of this work is to create a system for semantic segmentation of the ECG signal. For this purpose, we used a database from Lobachevsky University available on Physionet, containing 200, 10-second, and 12-lead ECG signals with annotations, and applied one-dimensional U-Net with the addition of squeeze-excitation blocks. The created model achieved a set of parameters indicating high performance (for the test set: accuracy—0.95, AUC—0.99, specificity—0.95, sensitivity—0.99) in extracting characteristic parts of ECG signal such as P and T-waves and QRS complex, regardless of the lead

Semantic Segmentation (U-Net) of Archaeological Features in Airborne Laser Scanning—Example of the Białowieża Forest

Airborne Laser Scanning (ALS) technology can be used to identify features of terrain relief in forested areas, possibly leading to the discovery of previously unknown archaeological monuments. Spatial interpretation of numerous objects with various shapes and sizes is a difficult challenge for archaeologists. Mapping structures with multiple elements whose area can exceed dozens of hectares, such as ancient agricultural field systems, is very time-consuming. These archaeological sites are composed of a large number of embanked fields, which together form a recognizable spatial pattern. Image classification and segmentation, as well as object recognition, are the most important tasks for deep learning neural networks (DLNN) and therefore they can be used for automatic recognition of archaeological monuments. In this study, a U-Net neural network was implemented to perform semantic segmentation of the ALS-derived data including (1) archaeological, (2) natural and (3) modern features in the Polish part of the Białowieża Forest. The performance of the U-Net segmentation model was evaluated by measuring the pixel-wise similarity between ground truth and predicted segmentation masks. After 83 epochs, The Dice-Sorensen coefficient (F1 score) and the Intersect Over Union (IoU) metrics were 0.58 and 0.5, respectively. The IoU metric reached a value of 0.41, 0.62 and 0.62 for the ancient field system banks, ancient field system plots and burial mounds, respectively. The results of the U-Net deep learning model proved very useful in semantic segmentation of images derived from ALS data

Recognition of Drivers’ Activity Based on 1D Convolutional Neural Network

Background and objective: Driving a car is a complex activity which involves movements of the whole body. Many studies on drivers’ behavior are conducted to improve road traffic safety. Such studies involve the registration and processing of multiple signals, such as electroencephalography (EEG), electrooculography (EOG) and the images of the driver’s face. In our research, we attempt to develop a classifier of scenarios related to learning to drive based on the data obtained in real road traffic conditions via smart glasses. In our approach, we try to minimize the number of signals which can be used to recognize the activities performed while driving a car. Material and methods: We attempt to evaluate the drivers’ activities using both electrooculography (EOG) and a deep learning approach. To acquire data we used JINS MEME smart glasses furnished with 3-point EOG electrodes, 3-axial accelerometer and 3-axial gyroscope. Sensor data were acquired on 20 drivers (ten experienced and ten learner drivers) on the same 28.7 km route under real road conditions in southern Poland. The drivers performed several tasks while wearing the smart glasses and the tasks were linked to the signal during the drive. For the recognition of four activities (parking, driving through a roundabout, city traffic and driving through an intersection), we used one-dimensional convolutional neural network (1D CNN). Results: The maximum accuracy was 95.6% on validation set and 99.8% on training set. The results prove that the model based on 1D CNN can classify the actions performed by drivers accurately. Conclusions: We have proved the feasibility of recognizing drivers’ activity based solely on EOG data, regardless of the driving experience and style. Our findings may be useful in the objective assessment of driving skills and thus, improving driving safety

Classification of Roads and Types of Public Roads Using EOG Smart Glasses and an Algorithm Based on Machine Learning While Driving a Car

Driving a car is an activity that became necessary for exploration, even when living in the present world. Research exploring the topic of safety on the roads has therefore become increasingly relevant. In this paper, we propose a recognition algorithm based on physiological signals acquired from JINS MEME ES_R smart glasses (electrooculography, acceleration and angular velocity) to classify four commonly encountered road types: city road, highway, housing estate and undeveloped area. Data from 30 drivers were acquired in real driving conditions. Hand-crafted statistical features were extracted from the physiological signals to train and evaluate a random forest classifier. We achieved an overall accuracy, precision, recall and F1 score of 87.64%, 86.30%, 88.12% and 87.08% on the test dataset, respectively

Classification of Roads and Types of Public Roads Using EOG Smart Glasses and an Algorithm Based on Machine Learning While Driving a Car

Driving a car is an activity that became necessary for exploration, even when living in the present world. Research exploring the topic of safety on the roads has therefore become increasingly relevant. In this paper, we propose a recognition algorithm based on physiological signals acquired from JINS MEME ES_R smart glasses (electrooculography, acceleration and angular velocity) to classify four commonly encountered road types: city road, highway, housing estate and undeveloped area. Data from 30 drivers were acquired in real driving conditions. Hand-crafted statistical features were extracted from the physiological signals to train and evaluate a random forest classifier. We achieved an overall accuracy, precision, recall and F1 score of 87.64%, 86.30%, 88.12% and 87.08% on the test dataset, respectively

Experiences of the Telemedicine and eHealth Conferences in Poland—A Cross-National Overview of Progress in Telemedicine

The progress in telemedicine can be observed globally and locally. Technological changes in telecommunications systems are intertwined with developments in telemedicine. The recent COVID-19 pandemic has expanded the potential of teleconsultations and telediagnosis solutions in all areas of medicine. This article presents: (1) an overview of milestones in the development of telecommunications systems that allow progress in telemedicine and (2) an analysis of the experiences of the last seven conferences of telemedicine and eHealth in Poland. The telemedicine and eHealth conferences have grown steadily in Poland since their inception in the late 1990s. An exemplary conference program content was used to assess the scientific maturity of the conference, measured by the indices of research dissemination and the impact of publications. The overview presents progress in selected areas of telemedicine, looking at local developments and broader changes. The growing interest in telemedicine in the world’s medical sciences is demonstrated by visibility metrics in Google Scholar, Pubmed, Scopus and Web of Science. National scientific events are assumed to raise interest in the population and influence the creation of general policies. As seen in the example of Poland, the activity of the scientific community gathered around the Polish Telemedicine Society led to novel legal acts that allowed the general practice of telemedicine during the SARS-CoV-2 pandemic. Local scientific conferences focusing on telemedicine research can be a catalyst for changes in attitudes and regulations and the preparation of recommendations for the practice of telemedicine and electronic health. On the basis of the results of this study, it can be concluded that the progress in telemedicine cannot be analyzed in isolation from the ubiquitous developments in technology and telecommunications. More research is needed to assess the cumulative impact of long-standing scientific conferences in telemedicine, as exemplified by the telemedicine and eHealth conferences in Poland

The detection of alcohol intoxication using electrooculography signals from smart glasses and machine learning techniques

The operation of a motor vehicle under the influence of alcohol poses a significant risk to the safety of the driver, passengers, and other road users. Electrooculographic (EOG) signal analysis can be used to understand the movements and behavior of the eyes while driving. In our study, we used smart glasses to collect EOG data from nine participants who used a driving simulator. Their level of alcoholic intoxication was simulated by drunk vision goggles at three different levels of inebriation (0, 1, 2, and 3‰ blood alcohol content). We used machine learning algorithms (decision trees, support vector machines, nearest-neighbor classifiers, boosted trees, bagged trees, subspace discriminant classifier, subspace k nearest-neighbor classifier, and RUSBoosted Trees) to analyze the data. The Bagged Trees achieved the highest accuracy of 79%. The most important features to detect simulated alcohol intoxication were the blink rate and the velocity of the saccade, a rapid simultaneous movement of both eyes in the same direction. Our study shows the potential of using smart glasses and machine learning for the automated detection of alcohol intoxication, even when alcohol consumption is simulated

Experiences of the Telemedicine and eHealth Conferences in Poland—A Cross-National Overview of Progress in Telemedicine

The progress in telemedicine can be observed globally and locally. Technological changes in telecommunications systems are intertwined with developments in telemedicine. The recent COVID-19 pandemic has expanded the potential of teleconsultations and telediagnosis solutions in all areas of medicine. This article presents: (1) an overview of milestones in the development of telecommunications systems that allow progress in telemedicine and (2) an analysis of the experiences of the last seven conferences of telemedicine and eHealth in Poland. The telemedicine and eHealth conferences have grown steadily in Poland since their inception in the late 1990s. An exemplary conference program content was used to assess the scientific maturity of the conference, measured by the indices of research dissemination and the impact of publications. The overview presents progress in selected areas of telemedicine, looking at local developments and broader changes. The growing interest in telemedicine in the world’s medical sciences is demonstrated by visibility metrics in Google Scholar, Pubmed, Scopus and Web of Science. National scientific events are assumed to raise interest in the population and influence the creation of general policies. As seen in the example of Poland, the activity of the scientific community gathered around the Polish Telemedicine Society led to novel legal acts that allowed the general practice of telemedicine during the SARS-CoV-2 pandemic. Local scientific conferences focusing on telemedicine research can be a catalyst for changes in attitudes and regulations and the preparation of recommendations for the practice of telemedicine and electronic health. On the basis of the results of this study, it can be concluded that the progress in telemedicine cannot be analyzed in isolation from the ubiquitous developments in technology and telecommunications. More research is needed to assess the cumulative impact of long-standing scientific conferences in telemedicine, as exemplified by the telemedicine and eHealth conferences in Poland