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Rodzina Pruszkowskich, jedna z najmożniejszych rodzin wywodzących się z Prószkowa na Opolszczyźnie, jest dobrze znana badaczom. Pierwszych jej przedstawicieli spotykamy na przełomie XIII i XIV w., ale największą karierę zrobili dopiero po 1526 r. w służbie Habsburgów. W 1560 r. Pruszkowscy zostali włączeni przez cesarza Ferdynanda I do grona czeskich baronów, uzyskując status wolnych panów stanowych (Freiherren). Otrzymali przy tej okazji nowy herb. Jego wizerunek nie budzi w historiografii wątpliwości. Od lat trwa natomiast spór o wygląd herbu używanego przez rodzinę w okresie średniowiecza. Sięgając po wszelkie dostępne dziś źródła o charakterze heraldycznym (pieczęcie, herbarze, zabytki sepulkralne, list herbowy), autor podejmuje trud ustalenia jego najbardziej prawdopodobnego kształtu.

In 2022, the National Program for Influenza Prevention coalition will have its 10th anniversary; it is one of Poland’s oldest educational initiatives. The National Program for Influenza Prevention was initiated to prevent a further decline and promote influenza prevention in the A(H1N1) post-pandemic years. In this review, we summarize the structure and operational model of the coalition and identify core functional elements that make it a key non-governmental organization involved in the prophylactics of communicable diseases. The coalition-based organization can operate in a complex environment, such as vaccinations requiring scientific, economic, social, and psychological involvement, and communications with different groups. Anchored to the history of the National Program for Influenza Prevention, we review Poland’s vaccination landscape changes from the last ten years.

Introduction: The numerous evidence showing spectrum of vitamin D effects on human health resulted in both updates of vitamin D supplementation guidelines for general population and concerns on potential risk of hypercalcaemia. The aim of this study was to analyse trends in serum 25-hydroxyvitamin D concentration (25(OH)D) change over the 30 years of operation of a single pediatric diagnostic unit.Materials and methods: Calcium-phosphate metabolism markers and 25(OH)D concentrations were analyzed in a group that consisted of newborns and infants commissioned for diagnostics due to suspected calcium-phosphate metabolic disturbances (n = 3,163; mean age 8.0 ± 3.0 months).Results: 25(OH)D < 10 ng/ml was noted in 4.5% of patients (n = 163), 10–20 ng/ml in 14.7% (n = 465), 20–30 ng/ml in 23.9% (n = 756) and 30–50 ng/ml in 35.9% (n = 1,136). The mean 25(OH)D concentration in analyzed group was 37.5 ± 24.5 ng/ml. In patients with 25(OH)D concentration < 10 ng/ml a normal calcaemia (2.25–2.65 mmol/l) was noted in 83.4% cases (n = 136). Eighty one patients had 25(OH)D concentrations above 100 ng/ml with co-existing calcaemia in range of 2.6–4.38 mmol/l (mean Ca = 2.69 mmol/l). Hypocalcaemia (Ca < 2.25 mmol/l) was observed in 0.54%, (n = 17). 13.8% patients revealed calcium levels >2.65 mmol/l (n = 435). In general, the mean calcium-phosphate markers values were within the reference range for age. The highest mean 25(OH)D concentration of 51.8 ng/ml ± 38.8 was noted in years 1981–1999 (n = 305). The lowest mean 25(OH)D value was observed in years 2010–2011 (29.0 ng/ml ± 13.6; n = 412). The trend of decreasing 25(OH)D concentration during analyzed time period was significant (r = −0.29, p < 0.0001).Conclusions: Eighty percentage of children aged 0–36 months had 25(OH)D concentration >20 ng/ml, however, during 3 decades a mean 25(OH)D concentrations trended significantly to decrease. A direct relationship between low 25(OH)D concentration and hypocalcaemia was not observed nor between high 25(OH)D concentration and hypercalcemia.

Abstract Accurate tracking of harmful gas concentrations is essential to swiftly and effectively execute measures that mitigate the risks linked to air pollution, specifically in reducing its impact on living conditions, the environment, and the economy. One such prevalent pollutant in urban settings is nitrogen dioxide (NO2), generated from the combustion of fossil fuels in car engines, commercial manufacturing, and food processing. Its elevated levels have adverse effects on the human respiratory system, exacerbating asthma and potentially causing various lung diseases. However, precise monitoring of NO2 requires intricate and costly equipment, prompting the need for more affordable yet dependable alternatives. This paper introduces a new method for reliably calibrating cost-effective NO2 sensors by integrating machine learning with neural network surrogates, global data scaling, and an expanded set of correction model inputs. These inputs encompass differentials of environmental parameters (such as temperature, humidity, atmospheric pressure), as well as readings from both primary and supplementary low-cost NO2 detectors. The methodology was showcased using a purpose-built platform housing NO2 and environmental sensors, electronic control units, drivers, and a wireless communication module for data transmission. Comparative experiments utilized NO2 data acquired during a five-month measurement campaign in Gdansk, Poland, from three independent high-precision reference stations, and low-cost sensor data gathered by the portable measurement platforms at the same locations. The numerical experiments have been carried out using several calibration scenarios using various sets of calibration input, as well as enabling/disabling the use of differentials, global data scaling, and NO2 readings from the primary sensor. The results validate the remarkable correction quality, exhibiting a correlation coefficient exceeding 0.9 concerning reference data, with a root mean squared error below 3.2 µg/m3. This level of performance positions the calibrated sensor as a dependable and cost-effective alternative to expensive stationary equipment for NO2 monitoring.

Abstract Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO2 levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO2 poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO2 sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO2 sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m3). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches.

Air pollution remains a considerable contemporary challenge affecting life quality, the environment, and economic well-being. It encompasses an array of pollutants—gases, particulate matter, biological molecules—emanating from sources such as vehicle emissions, industrial activities, agriculture, and natural occurrences. Nitrogen dioxide (NO2), a harmful gas, is particularly abundant in densely populated urban areas. Given its detrimental impact on health and the environment, precise monitoring of NO2 levels is crucial for devising effective strategies to mitigate risks. However, precise measurement of NO2 presents challenges as it traditionally relies on expensive and heavy (therefore, stationary) equipment. This has led to the pursuit of more affordable alternatives, though their dependability is frequently questionable. This study introduces an innovative technique for precise calibration of low-cost NO2 sensors. Our methodology involves statistical preprocessing of sensor measurements to align their distributions with reference data. The core of the calibration model is an artificial neural network (ANN), trained to synchronize sensor and reference time series measurements. It incorporates environmental variables such as temperature, humidity, and atmospheric pressure, along with readings from redundant NO2 sensors for cross-referencing, and short time series of primary sensor NO2 measurements. This enables efficient learning of typical sensor changes over time in relation to these factors. Additionally, an interpolative kriging model serves as an auxiliary surrogate to enhance the correction process’s reliability. Validation using an autonomous monitoring platform from Gdansk University of Technology, Poland, and public reference station data gathered over five months shows remarkable calibration accuracy, with a correlation coefficient close to 0.95 and RMSE of 2.4 µg/m3. These results position the corrected sensor as an attractive and cost-effective alternative to conventional NO2 measurement methods.

In environmental science, where information from sensor devices are sparse, data fusion for mapping purposes is often based on geostatistical approaches. We propose a methodology called adaptive distance attention that enables us to fuse sparse, heterogeneous, and mobile sensor devices and predict values at locations with no previous measurement. The approach allows for automatically weighting the measurements according to a priori quality information about the sensor device without using complex and resource-demanding data assimilation techniques. Both ordinary kriging and the general regression neural network (GRNN) are integrated into this attention with their learnable parameters based on deep learning architectures. We evaluate this method using three static phenomena with different complexities: a case related to a simplistic phenomenon, topography over an area of 196 $ {km}^2 $ and to the annual hourly $ {NO}_2 $ concentration in 2019 over the Oslo metropolitan region (1026 $ {km}^2 $ ). We simulate networks of 100 synthetic sensor devices with six characteristics related to measurement quality and measurement spatial resolution. Generally, outcomes are promising: we significantly improve the metrics from baseline geostatistical models. Besides, distance attention using the Nadaraya–Watson kernel provides as good metrics as the attention based on the kriging system enabling the possibility to alleviate the processing cost for fusion of sparse data. The encouraging results motivate us in keeping adapting distance attention to space-time phenomena evolving in complex and isolated areas.

In this article, a stress monitoring system tailored for individuals with Autism Spectrum Disorders (ASD) and developed for the educational institution is presented. People with ASD face problems with effective stress management due to their high self-perceived levels of stress, poor ability to cope with it, and difficulties with the accurate detection of the source of stress. Consistently, being able to measure stress appears to be highly vital for this specific group. The design and construction of the measuring system are introduced. The stress monitoring system including autonomic wearable device (wristband), both the electronics and mechanical part, as well as the software application for data analysis are shown. The wearable device includes sensors for measuring heart rate, skin resistance, temperature and movement. A dedicated software application allows for generating reports to evaluate therapeutic effects. The complex stress monitoring system has been tested in an educational institution. It can provide significant support in the stress management of people with ASD and become a meaningful assistance in the therapy of neurodiverse individuals.

This paper presents an algorithm for real-time detection of the heart rate measured on a person’s wrist using a wearable device with a photoplethysmographic (PPG) sensor and accelerometer. The proposed algorithm consists of an appropriately trained LSTM network and the Time-Domain Heart Rate (TDHR) algorithm for peak detection in the PPG waveform. The Long Short-Term Memory (LSTM) network uses the signals from the accelerometer to improve the shape of the PPG input signal in a time domain that is distorted by body movements. Multiple variants of the LSTM network have been evaluated, including taking their complexity and computational cost into consideration. Adding the LSTM network caused additional computational effort, but the performance results of the whole algorithm are much better, outperforming the other algorithms from the literature.

This paper presents an algorithm for the measurement of the human heart rate, using photoplethysmography (PPG), i.e., the detection of the light at the skin surface. The signal from the PPG sensor is processed in time-domain; the peaks in the preprocessed and conditioned PPG waveform are detected by using a peak detection algorithm to find the heart rate in real time. Apart from the PPG sensor, the accelerometer is also used to detect body movement and to indicate the moments in time, for which the PPG waveform can be unreliable. This paper describes in detail the signal conditioning path and the modified algorithm, and it also gives an example of implementation in a resource-constrained wrist-wearable device. The algorithm was evaluated by using the publicly available PPG-DaLia dataset containing samples collected during real-life activities with a PPG sensor and accelerometer and with an ECG signal as ground truth. The quality of the results is comparable to the other algorithms from the literature, while the required hardware resources are lower, which can be significant for wearable applications.

The application of a Bluetooth skin resistance sensor in assisting people with Autism Spectrum Disorders (ASD), in their day-to-day work, is presented in this paper. The design and construction of the device are discussed. The authors have considered the best placement of the sensor, on the body, to gain the most accurate readings of user stress levels, under various conditions. Trial tests were performed on a group of sixteen people to verify the correct functioning of the device. Resistance levels were compared to those from the reference system. The placement of the sensor has also been determined, based on wearer convenience. With the Bluetooth Low Energy block, users can be notified immediately about their abnormal stress levels via a smartphone application. This can help people with ASD, and those who work with them, to facilitate stress control and make necessary adjustments to their work environment.