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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.

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.