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Automatic license plate recognition (ALPR) systems can be found in many different applications. If a person has a driving license, the person probably had already seen smart road camera after a speed limit sign or on a crossroad. Every year number of cars on roads growth very fast. It is also obvious that such systems can be used out-of-road situations. For instance, this type of systems can be used for automatic access control on private property or smart parking, or even log system that are being used literally everywhere. Because of popularity of ALPR systems, there are two main goals, which are being pursued by researches: speed and accuracy of recognition. Speed of the detection is important for real-time systems. Accuracy is important for every system. The more accurate a system is, the more reliable it is. For example, car accident detection systems should be as accurate as possible in order to be used, because no one wants to get billed with the wrongdoing, that wasn’t committed by them. The purpose of the study is to develop high precision automatic license plate detection system with number extraction possibilities. In order to achieve the goal many different modern solutions and technologies were studied and solution is presented. The main technology of the project is artificial intelligence system and, more specifically, convolutional neural network. As the main algorithm Mask R-CNN is used for license plate detection. To present reasonable research, the system was tested on different hardware (CPU, GPU, Raspberry PI 4) and on different datasets.

In this work, the results of radon concentration measurements in samples of multi-storey apartment buildings in eight large cities of Russia are analyzed. Two samples, depending on the year of construction of the building – before and after 2000, were formed in each city on the basis of a quasi-random approach. Radon concentration measurements were performed using radon radiometers equipped with the CR-39 solid-state nuclear track detector. In the flats of multi-storey buildings, two radiometers were installed and flat’s average radon concentration was calculated. The exposure period for the radon radiometers was three months. To estimate the average annual radon concentration, seasonal coefficients calculated for the primary radon entry from building materials were used. In total, radon concentration measurements were carried out in 1032 flats. It was assumed that the combined sample for the cities of Ekaterinburg, Krasnodar, Nizhny Novgorod, Novosibirsk, Tyumen, and Chelyabinsk is representative for the multi-storey buildings in non-capital cities of Russia. The following arithmetic mean annual radon concentrations were obtained in samples of houses built up before and after 2000, respectively: Moscow – 17 and 21 Bq/m3; St. Petersburg – 15 and 25 Bq/ m3, the group of non-capital cities – 25 and 31 Bq/m3. Taking into account the contribution of each group of buildings to the general urban housing stock, the parameters characterizing the radon concentration in multi-storey multi-apartment urban buildings in Russia were calculated as follows: arithmetic mean 25 Bq/ m3; geometric mean 21 Bq/m3; the geometric standard deviation is 1.81. In general, exposure to indoor radon in multi-storey urban buildings in Russia is low both in terms of average value and range of concentrations. There is a tendency towards an increase in indoor radon exposure of the population in new buildings, which is associated with the introduction of construction technologies that increase the energy efficiency of buildings.

In multi-storey buildings, Ra-226 in building materials is the main source of radon entry. Under low air exchange rate (e.g. in energy efficient buildings), radon concentration may exceed reference levels used for radiation protection purposes. Prevention and remediation measures of protection against indoor radon in situation when building materials are the main source of radon entry requires further development. The aim of this work is to evaluate the effectiveness of various radon prevention and mitigation measures in multi-storey buildings under different climatic conditions. On the basis of a dynamic model of indoor radon entry and removing, the following measures were simulated: control over of Ra-226 content in building materials, increasing uncontrolled air infiltration, extending the duration of controlled ventilation, equipping the building with mechanical ventilation, increasing the air exchange rate in non-living premises of the building, and reducing air exchange between living rooms and non-livings premises. The costs of energy for heating associated with different radon prevention and remediation measures were estimated. The conclusion about the potential effectiveness of various measures was drawn by achievement of optimized level of radiation protection, degree of reduction in the radon exposure risk, and cost of heating.

An application of geometric mixed additive-multiplicative models for lung cancer risk modeling under combined action of radon and smoking is justified in this paper. The geometric mixed model allows: 1) to reduce the discrepancies between estimates of the lung cancer risk for males and females, 2) to predict the population risk under condition of the varying smoking prevalence and changing average indoor radon concentration level. Using the geometric mixed model, the calculation of the lung cancer risks for the Russian Federation population was carried out for different percentages of smokers among the population, an increase in life expectancy, and a change in the average radon concentration level in residential buildings. Assuming that currently rounded average indoor radon concentration in Russia is 50 Bq/m3, the contribution of radon to total mortality in 2009 was 0.46% and 0.20% for male and female, respectively. Modeling has shown that the effect of lung cancer mortality reducing due to the predicted decrease in the proportion of smoking population will partly be offset by an increase in the realization of lung cancer risk with the life expectancy increase. For a hypothetical situation, when the entire population maintain a healthy lifestyle and mortality from cardiovascular, oncological, infectious diseases, diseases of the respiratory tract and from external causes has significantly decreased, the contribution of radon exposure to total mortality will increase to about 0.8%. If the average level of radon in buildings will increase due to energy-efficient technologies widespread implementation in building construction, the contribution of radon to total mortality will be even more noticeable.

A comparative analysis of the radon concentrations in modern multi-storey residential buildings of high energy efficiency class and buildings typical for urban areas of the twentieth century was carried out. The study was conducted in Russian cities located in various climatic zones – Ekaterinburg, Krasnodar, St. Petersburg, Salekhard, Chelyabinsk. The radon concentration in samples of buildings was measured using integrated radon radiometers based on nuclear track detectors according to a single method. The surveyed sample included 498 apartments in multi-apartment buildings. Among all the examined building types, the highest average radon concentration is observed in modern energy-efficient houses – 43 Bq/m3. In other types of buildings, the following average radon concentrations were obtained: brick 2–5 floors – 35 Bq/m3; panel 5 floors – 32 Bq/m3; panel 7–12 floors 1970-1990 years of construction – 22 Bq/m3; brick> 5 floors 1970–1980 years of construction – 20 Bq m3; panel, built since 1990 – 24 Bq/m3. The results of the study confirm the assumption that radon concentration in modern multi-storey energy-efficient houses is on average higher than in typical residential buildings of the Soviet period. The increased accumulation of radon in energy-efficient buildings is associated with a decrease in the building envelope permeability and the contribution of fresh air to the general air exchange. Despite the fact that there were no cases of exceeding hygienic standards for the indoor radon concentration in the framework of this study, the higher radon concentration in buildings of increased energy efficiency requires attention from the point of view of implementing the principle of optimization of radiation protection. In the future, extensive construction of energy-efficient buildings may increase the average and collective doses to the urban population in the Russian Federation.

The paper presents an overview of the international scientific and technical publications on a problem of radon accumulation in new energy efficient buildings and in houses reconstructed according to requirements of energy saving. Energy efficiency is an important requirement of the environmentally sustainable development. Housing and communal services have significant potential for energy saving. In Russia, the construction of highest energy efficiency classes buildings occurs at an accelerated rate and reached 75% of all multiapartment houses built in 2017. The applying of modern technologies that reduce heat loss is accompanied by a decrease in the air exchange rate, which leads to deterioration of indoor air quality, in particular, the accumulation of radon. In the international literature, there are examples of the several times growth of radon concentration after the reconstruction of the building, the average radon concentration in retrofitted buildings increased by 22–120%. In new houses built to meet energy saving requirements, there can also be a significant increase in radon concentration compared to low-energy efficiency classes houses. Excess of sanitary and hygienic norms was found in some countries, including Russia. Radon exposure of dwellers of energy-efficient buildings is largely determined by the living habits. Based on the review data, it can be assumed that the average level of Russian population exposure to radon can increase under conditions of intensive construction of energy-efficient buildings.

Objective: To study the levels of radon accumulation in kindergartens in Sverdlovsk Region and to deter­mine the relationship between radon concentration, and building characteristics. To perform a detailed anal­ysis of radon entry and accumulation in the kindergarten, in which radon concentration exceeds the safety standards.Materials and methods: Track detectors REI-4 on the base of LR-115 film were used. Detectors were installed in playrooms and bedrooms for day nap. In each kindergarten, at least 3 rooms were examined. The measurements were carried out twice a year, during the warm and cold seasons. The duration of exposure of the detectors was 2–3 months in each season. The AlphaGUARD radon monitor was installed in the kinder­garten with a high level of radon concentration.Results: It is shown that the distribution of radon concentration in the kindergartens of the Sverdlovsk region can be described by a lognormal distribution with an average value of 60 Bq/m3, a geometric mean of 44 Bq/m3 and a standard geometric deviation of 2.23. A statistically significant differ­ence was found between the mean values of radon concentration in subgroups of buildings constructed at different periods. For the kindergartens constructed in 1950–1969, the average radon concentration was 85 Bq/m3, for buildings built in 1970–1989 – 54 Bq/m3. When comparing subgroups of rooms with different types of windows (all buildings, ground floors) it was found that in rooms with PVC windows, radon concentration is almost twice as high then in rooms with wooden windows. For the kindergarten, in which the annual radon concentration in the playroom was 620 Bq/m3, it was shown, that if the proper ventilation regime of this room is observed, the average radon concentration during the presence of children does not exceed 120 Bq/m3.Conclusions. Factors affecting the indoor radon accumulation in the kindergartens are the type of building materials associated with different periods of building construction and the type of windows. When radon concentration measured in kindergartens reach sanitary standards, it is advisable to conduct an additional study of the daily radon variations, since during the active operation of the premises (working hours) radon levels may be significantly lower

Objectives: Studying the influence of the method of selecting a control group in the radon case-control study on assessment of dose-effect dependence. Study the effect of additional additive or multiplicative risk factors on the results of the study. Materials and Methods: Simulation of a large-scale radon epidemiological case-control study. Analyzing of the influence of the representative value of the comparison subgroup on the results of calculating the odds ratio of the incidence of radiation-induced lung cancer. Analysis of different variants of standardization of the control group on the correctness of the dose-effect dependence. Development of methods for identifying risk factors that affect the incidence of lung cancer and determining the mechanism of their influence (additive or multiplicative). Results: It is shown that the insufficient representativeness of the comparison subgroup used in calculating the odds ratio can significantly distort the estimates of dose-effect dependence. In the presence of factors (sex, smoking, etc.), correlating both with the incidence of lung cancer and of radon concentration, complete standardization of such factors is necessary. Multiplicative risk factors without such correlation do not require additional standardization in the selection of a control group. Calculation of the odds ratio for various risk factors in subgroups differing in the levels of spontaneous cancer incidence allows to determine the significance of this factor for the incidence of lung cancer, and to establish the preferential mechanism of the influence of this factor (multiplicative or additive). Conclusions. When carrying out the radon case-control study, it is necessary to identify risk factors that correlate both with the incidence of lung cancer, and with the radon concentration, to assess the mechanism of action of these factors and to made the complete standardization of the control group for them.

Objective: A simulation of a large-scale epidemiological case-control study to identify the relationship between exposure to radon and lung cancer in the presence of factors that distort the results of the assessment of exposure to radon in homes. Materials and Methods: Analysis of sources of uncertainties arising during radon epidemiologic case-control studies. Evaluation of the uncertainties caused by the errors of the measurements of the long-term variations in the radon concentration, exposure to radon in other places of the human habitat, except dwellings, etc. Simulation by Monte Carlo technique of radon epidemiologic study, comparable to the combined European radon study, and assessment of uncertainties, which affect the evaluation of dose-effect dependence. Results: The multiplicative error in the assessment of individual exposure based on the radon concentration is shown generally caused by the combined effect of long-term variations of the radon concentration and the differences in the levels of the radon concentration in living houses and other places of the human habitat. The logarithmic standard deviation of this errors σerr is from 0,70 to 0,90. The estimated value of this error is 2,0 times higher than the value used for correction of the results of the combined European radon study. It is shown that for the σerr

Implementation of reference level conception for optimization of protection against radon in dwellings requires thorough consideration of a number of associated problems. With regard to both experience with regulation of indoor radon exposure in Russia and ICRP recommendations, Russian national reference level for radon should be expressed using annual radon concentration and set at level of 200 Bq/m3.

Abstract  Carbon modifications (anode matrices) were produced from renewable vegetable raw materials, agricultural crop waste, by pyrolysis in a quartz reactor without access of oxygen at 900°C, as well as by mechanical synthesis, and their characteristics were examined. [ABSTRACT FROM AUTHOR]

Idiopathic pulmonary fibrosis (IPF) is a lethal fibrosing interstitial lung disease with a mean survival time of less than 5 years. Nonspecific presentation, a lack of effective early screening tools, unclear pathobiology of early-stage IPF and the need for invasive and expensive procedures for diagnostic confirmation hinder early diagnosis. In this study, we introduce a new screening tool for IPF in primary care settings that requires no new laboratory tests and does not require recognition of early symptoms. Using subtle comorbidity signatures identified from the history of medical encounters of individuals, we developed an algorithm, called the zero-burden comorbidity risk score for IPF (ZCoR-IPF), to predict the future risk of an IPF diagnosis. ZCoR-IPF was trained on a national insurance claims database and validated on three independent databases, comprising a total of 2,983,215 participants, with 54,247 positive cases. The algorithm achieved positive likelihood ratios greater than 30 at a specificity of 0.99 across different cohorts, for both sexes, and for participants with different risk states and history of confounding diseases. The area under the receiver-operating characteristic curve for ZCoR-IPF in predicting IPF exceeded 0.88 and was approximately 0.84 at 1 and 4 years before a conventional diagnosis, respectively. Thus, if adopted, ZCoR-IPF can potentially enable earlier diagnosis of IPF and improve outcomes of disease-modifying therapies and other interventions., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background In this retrospective, observational study we introduce the Cardiac Comorbidity Risk Score, predicting perioperative major adverse cardiac events (MACE) after elective hip and knee arthroplasty. MACE is a rare but important driver of mortality, and existing tools, eg, the Revised Cardiac Risk Index demonstrate only modest accuracy. We demonstrate an artificial intelligence-based approach to identify patients at high risk of MACE within 4 weeks (primary outcome) of arthroplasty, that imposes zero additional burden of cost/resources. Methods and Results Cardiac Comorbidity Risk Score calculation uses novel machine learning to estimate MACE risk from patient electronic health records, without requiring blood work or access to any demographic data beyond that of sex and age, and accounts for variable/missing/incomplete information across patient records. Validated on a deidentified cohort (age >45 years, n=445 391), performance was evaluated using the area under the receiver operator characteristics curve (AUROC), sensitivity/specificity, positive predictive value, and positive/negative likelihood ratios. In our cohort (age 63.5±10.5 years, 58.2% women, 34.2%/65.8% hip/knee procedures), 0.19% (882) experienced the primary outcome. Cardiac Comorbidity Risk Score achieved area under the receiver operator characteristics curve=80.0±0.4% (95% CI) for women and 80.1±0.5% (95% CI) for males, with 36.4% and 35.1% sensitivities, respectively, at 95% specificity, significantly outperforming Revised Cardiac Risk Index across all studied age-, sex-, risk-, and comorbidity-based subgroups. Conclusions Cardiac Comorbidity Risk Score, a novel artificial intelligence-based screening tool using known and unknown comorbidity patterns, outperforms state-of-the-art in predicting MACE within 4 weeks postarthroplasty, and can identify patients at high risk that do not demonstrate traditional risk factors.

Here, we develop digital biomarkers for autism spectrum disorder (ASD), computed from patterns of past medical encounters, identifying children at high risk with an area under the receiver operating characteristic exceeding 80% from shortly after 2 years of age for either sex, and across two independent patient databases. We leverage uncharted ASD comorbidities, with no requirement of additional blood work, or procedures, to estimate the autism comorbid risk score (ACoR), during the earliest years when interventions are the most effective. ACoR has superior predictive performance to common questionnaire-based screenings and can reduce their current socioeconomic, ethnic, and demographic biases. In addition, we can condition on current screening scores to either halve the state-of-the-art false-positive rate or boost sensitivity to over 60%, while maintaining specificity above 95%. Thus, ACoR can significantly reduce the median diagnostic age, reducing diagnostic delays and accelerating access to evidence-based interventions.