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Our study addresses the need for universal monitoring solutions given the diverse environmental impacts of surface mining operations. We present a solution combining remote sensing and machine learning techniques, utilizing a dataset of over 2000 satellite images annotated with ten distinct labels indicating mining area components. We tested various approaches to develop comprehensive yet universal machine learning models for mining area segmentation. This involved considering different types of mines, raw materials, and geographical locations. We evaluated multiple satellite data set combinations to determine optimal outcomes. The results suggest that radar and multispectral data fusion did not significantly improve the models' performance, and the addition of further channels led to the degradation of the metrics. Despite variations in mine type or extracted material, the models' effectiveness remained within an Intersection over Union value range of 0.65–0.75. Further, in this research, we conducted a detailed visual analysis of the models' outcomes to identify areas requiring additional attention, contributing to the discourse on effective mining area monitoring and management methodologies. The visual examination of models' outputs provides insights for future model enhancement and highlights unique segmentation challenges within mining areas. [ABSTRACT FROM AUTHOR]

Background: Studies have shown the risk factors for COVID-19 severity in children, including comorbidities, but information on the infection course in children with life-limiting conditions is sparse. Aim: To describe the effect of COVID-19 on pediatric patients receiving palliative care due to life-limiting conditions. Design: We conducted retrospective cohort study. The WHO Clinical Progression Scale was used to measure COVID-19 severity. Setting/participants: Seven of the 24 invited pediatric palliative care centers participated in this study. We analyzed the medical records of children under palliative care with confirmed COVID-19 (January 2020–April 2022) Results: Records of 60 patients with COVID-19 aged 0.24 to 21.6 years (mean (SD); 9.8 (6.6)) were collected. The largest group of patients with COVID-19 was children with congenital malformations and chromosomal abnormalities (42%); the most common manifestation was fever (85%). Bacterial coinfection was confirmed in 17 (28%) children. Fifteen (25%) children required hospitalization, including four admitted to the Intensive Care Unit. Mild COVID-19 was identified in 44 (73%) children, moderate in 11 (18%), severe in 3 (5%), and death in 2 (3%). Six of the 20 eligible children were vaccinated against SARS-CoV-2, followed by 16 mothers and fathers. Conclusion: In the study population initial presentation of COVID-19 was predominantly a mild; however, the small sample size precluded definitive conclusions. For children under palliative care, we should identify if they have an advance care plan for COVID-19, such as desires for intensive care support. Further studies are needed to define the short and long-term effects of COVID-19 in children with life-limiting conditions. [ABSTRACT FROM AUTHOR]

This study aims at investigating associations between COVID-19 mortality and SARS-COV-2 variants spread during the second wave of COVID-19 pandemic in Europe. For 38 European countries, data on numbers of COVID-19 deaths, SARS-COV-2 variants spread through time using Nextstrain classification, demographic and health characteristics were collected. Cumulative number of COVID-19 deaths and height of COVID-19 daily deaths peak during the second wave of the pandemic were considered as outcomes. Pearson correlations and multivariate generalized linear models with selection algorithms were used. The average proportion of B.1.1.7 variant was found to be a significant predictor of cumulative COVID-19 deaths within two months before the peak and between 1 January–25 February 2021, as well as of the deaths peak height considering proportions during the second wave and the pre-peak period. The average proportion of EU2 variant (S:477 N) was a significant predictor of cumulative COVID-19 deaths in the pre-peak period. Our findings suggest that spread of a new variant of concern B.1.1.7 had a significant impact on mortality during the second wave of COVID-19 pandemic in Europe and that proportions of EU2 and B.1.1.7 variants were associated with increased mortality in the initial phase of that wave. [ABSTRACT FROM AUTHOR]

Introduction. Granulosa cell tumor (GCT) is a kind of neoplasma which rise up from granulosa cells, a part of ovarian's stroma. Because of possibility of late relapse this tumor should be regarded as potential malignant. GCT is a hormonally active neoplasma. Tumor's cells secrete estradiol the most frequently, androgen, progesterone and prolactin rarely. Prognosis is very good, 5-years survival rates in stage I range 75-95%. Aim. We're presenting a case of 15 years old girl with juvenile granulosa cell tumor (JGCT) of a right ovary as an example of atypical medical history and difficulty in establishing optimal way of treatment. Leading symptoms was timbre changing which has been observed from 6 months. Ascertained high level of androgen in blood sample, in MRI revealed a tumor of a right ovary. Because of suspicion that a left ovary could be hypoplastic a surgeon decided to shell the tumor and left rest of the healthy right ovary. Because of attendance of neoplasma cells in the place of coagulation and a character of the tumor decided about a next operation (remove rest of the right ovary and a right fallopian tube) and chemotherapy. Conclusion. We need to determine an optimal treatment way of JGCT due to protect girl and women from unnecessary procedures and to assure the best quality of life. [ABSTRACT FROM AUTHOR]

This research shows the methodological assumptions related to estimating solar energy potential in Warsaw. All calculations were performed with the use of an open-source "r.sun" model available within the GRASS-GIS 6.4. software. Introductory simulations were validated against airborne images confirming the high quality of model calibration against astronomical and simplified 3D model of buildings in Warsaw. Subsequent automation procedures were created for the GNU/Linux supercomputing clusters in order to reduce computation time. To limit the final size of the database, results for the area of 517,000,000 square meters were clipped to cover the surface of rooftops only, using a binary data type (i.e. 0 - shadow, 1 - sunny areas) and synthetic atmospheric conditions (i.e. taking into account annual changes of sun declination, cloudiness, Linke atmospheric turbidity coefficient, etc.). Such a solution enables the following calculations: direct, diffuse and total solar radiation for every square meter of a building's surface in Warsaw. The obtained GIS layers were then transformed as input information for other modules of a fully interactive IT-toolbox for estimating solar potential, which allows citizens and decision makers to estimate how much energy they can derive in a specific point from solar panels, when they can expect a return on their investment and how it will affect the natural environment. [ABSTRACT FROM AUTHOR]

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In Poland only 50-60% of persons who have experienced myocardial infarction return to work. Bearing in mind that psychophysical condition changes after such an event, this group of people has to be readapted to work. Factors that determine good work performance among post-infarction workers have been not yet investigated. The aim of our study is to identify those factors and to define their role in the readaptation process. The first stage of our project involved the development of a theoretical model of readaptation to work after myocardial infarction. This model is described in this paper. It comprises the following components: medical evaluation of the workers' health status, his or her subjective assessment of work ability, expectations (optimistic vs. pessimistic attitude), motivation to work, social support, and job characteristics.