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Air pollution is a primary global concern due to its adverse effects on human health and the environment. Accurate air pollution measurement is crucial for developing effective control strategies and evaluating their impact. In Astana, vehicular emissions are recognized as a significant contributor to air pollution. This study aims to determine the reliability of air pollution measurement data in Astana by examining the impact of vehicular emissions on air quality to establish the accuracy of air pollution measurement methods in the city. The study will use a combination of monitoring and modeling techniques to quantify the contribution of vehicular emissions to air pollution in Astana. The monitoring component will involve the deployment of air quality monitoring stations throughout the city to measure levels of pollutants such as particulate matter (PM). The modeling component will use air dispersion models to simulate the dispersal of pollutants from vehicular emissions and predict their concentration levels in different parts of the city. The results of this research will provide insights into the effectiveness of existing air pollution control strategies and may inform future efforts to improve air quality in Astana. Based on the collected data for a certain period, a comparative table is built describing the difference between the actual data from the collection points of air emission indicators. The article will likely interest researchers and policymakers concerned with air pollution and its effects on human health and the environment. The critical point of the article is that air quality is affected not only by transport but also by other emissions.

The study considers building a dynamic model of profit maximization for the car-sharing system and its verification on the example of implementing such a system in Astana (Republic of Kazakhstan). The region, bounded by the administrative boundaries of Astana, was divided into subregions by covering the region with regular hexagons placed side by side. A dataset was built with information on 1168 trips to Astana from January to March 2023. The Kepler visualization service constructed maps of the beginning and end of trips in the region and a map of trips binding to the hexagonal grid cells. Each cell of the grid corresponds to a specific sub-region, for which the quantitative parameters necessary for solving the profit maximization problem in the car-sharing system are calculated. Stations with cars will be placed in the cells of the grid, which will be available to car-sharing service customers. Based on the collected data, a dynamic (four periods per day) and static profit maximization model in the car-sharing system was built. Modeling was carried out based on the built models in the example of Astana. It was established that using a dynamic model of profit maximization in the car-sharing system increases the profit by 3.7%.

The study describes sensors with an inclined grid de-signed to monitor the condition of structures installed on the bridge. The aim of the study is to study the possi-bility of using the developed sensors with an inclined grid to monitor the condition of structures by checking the deformation of various parts of bridges under highway loads, as well as comparing the characteristics of sensors with an inclined grid with conventional Bragg sensors. In this paper, experimental observations show that the results of using sensors with an inclined grid are in excellent agreement with the results obtained using conventional Bragg sensors. Also, a change in the wavelength of the VBR was detected and a comparison of the deformation characteristics for measuring tem-perature without deformation and the VBR for measur-ing deformation characteristics on a sensitive Bragg sensor for 700 seconds.

This article analyzes the most famous sign languages, the correlation of sign languages, and also considers the development of a verbal robot hand gesture recognition system in relation to the Kazakh language. The proposed system contains a touch sensor, in which the contact of the electrical property of the user's skin is measured, which provides more accurate information for simulating and indicating the gestures of the robot hand. Within the framework of the system, the speed and accuracy of recognition of each gesture of the verbal robot are calculated. The average recognition accuracy was over 98%. The detection time was 3ms on a 1.9 GHz Jetson Nano processor, which is enough to create a robot showing natural language gestures. A complete fingerprint of the Kazakh sign language for a verbal robot is also proposed. To improve the quality of gesture recognition, a machine learning method was used. The operability of the developed technique for recognizing gestures by a verbal robot was tested, and on the basis of computational experiments, the effectiveness of algorithms and software for responding to a verbal robot to a voice command was evaluated based on automatic recognition of a multilingual human voice. Thus, we can assume that the authors have proposed an intelligent verbal complex implemented in Python with the CMUSphinx communication module and the PyOpenGL graphical command execution simulator. Robot manipulation module based on 3D modeling from ABB.

This paper formalizes the model of choosing a strategy for reducing air pollution in an urban environment. The model involves determining the optimal location of biotechnological systems – biotechnological filter systems or smart air purification devices based on solving the problem of discrete optimization, taking into consideration the forecast of the air quality index. Two subtasks have been formalized, which make it possible to form a strategy for reducing air pollution. To solve one of the subtasks, a combined selective model for predicting the time series of the Air Quality Index (CSM) was built. The combined model software suite consists of the EMD-ESM hybrid model (Empirical Mode Decomposition-Exponential Smoothing Model), the HWM additive model (Holt-Winters Model), and the adaptive TLM (Trigg-Lich Model). To verify the proposed combined selective model, the time series of air quality indices (AQI) for the city of Nur-Sultan (data from 2010‒2021, period 6 hours) were selected. As a result of verification, it was established that in the case of short-term forecasting of the air quality index time series, the EMD-ESM model has an advantage according to the criterion of a minimum root mean square error (RMSE), δ=0.11. For the case of medium-term forecasting of 3

In the article herein, we consider the theoretical prerequisites of the solar collector accumulatorenergy saving sources One of the solar collector-accumulator main peculiarities is in the fact that it works under coldtemperatures or in the dull days due to design features. There was proposed a new method of the flat solar collectorusing modern materials, thanks to which the collector’s performance upgrades and its cost reduces. Collector’sefficiency is reached owing to the proposed construction, which consists of transparent thermally insulated body,semitransparent color, absorber’s capacity, thermoelectrical heater. Thermoelectrical heater is a main feature, whichis used for water heating. Proposed new method of thermoelectric heating is theoretically substantiated and consistsin adjusting the thermoelectric power to changing the stress in appropriate range, as well, to changing the resistanceof thermoelectric heating elements through switching from sequential circuit to parallel one. The new electric schemehas been offered as a controlled thermoelectric heater inside the collector. Solar collector-accumulator researches andworkings in the mode of electric heating show, that the computation of water heater average value from 5 до 9%confirms the suggestion about the theoretical outcomes of the solar collector accumulator work. There have beenselected thermoelectric capacities values as 1,5, 3,0 and 4,5 kW, liquid optimal temperature (60 oС) has been reachedfor 3 hours at capacities 3,0 and 4,5 kW, it is a good result for a cold season or dull days. Economic analysis of usingthe proposed thermoelectric capacity inside the solar collector is not considered in the work herein and remained forfurther studying.

For people with disabilities, sign language is the most important means of communication. Therefore, more and more authors of various papers and scientists around the world are proposing solutions to use intelligent hand gesture recognition systems. Such a system is aimed not only for those who wish to understand a sign language, but also speak using gesture recognition software. In this paper, a new benchmark dataset for Kazakh fingerspelling, able to train deep neural networks, is introduced. The dataset contains more than 10122 gesture samples for 42 alphabets. The alphabet has its own peculiarities as some characters are shown in motion, which may influence sign recognition. Research and analysis of convolutional neural networks, comparison, testing, results and analysis of LeNet, AlexNet, ResNet and EffectiveNet – EfficientNetB7 methods are described in the paper. EffectiveNet architecture is state-of-the-art (SOTA) and is supposed to be a new one compared to other architectures under consideration. On this dataset, we showed that the LeNet and EffectiveNet networks outperform other competing algorithms. Moreover, EffectiveNet can achieve state-of-the-art performance on nother hand gesture datasets. The architecture and operation principle of these algorithms reflect the effectiveness of their application in sign language recognition. The evaluation of the CNN model score is conducted by using the accuracy and penalty matrix. During training epochs, LeNet and EffectiveNet showed better results: accuracy and loss function had similar and close trends. The results of EffectiveNet were explained by the tools of the SHapley Additive exPlanations (SHAP) framework. SHAP explored the model to detect complex relationships between features in the images. Focusing on the SHAP tool may help to further improve the accuracy of the model, Для людей с ограниченными возможностями жестовый язык является важнейшим средством общения. Поэтому все больше авторов различных работ и ученых по всему миру предлагают решения для использования интеллектуальных систем распознавания жестов рук. Такая система предназначена не только для тех, кто хочет понимать жестовый язык, но и говорить с помощью программного обеспечения для распознавания жестов. В данной работе представлен новый эталонный набор данных для дактильного алфавита казахского языка, способный обучать глубокие нейронные сети. Набор данных содержит более 10122 образцов жестов для 42 алфавитов. Алфавит имеет свои особенности, так как некоторые символы показаны в движении, что может влиять на распознавание жестов. В статье описаны исследования и анализ сверточных нейронных сетей, сравнение, тестирование, результаты и анализ методов LeNet, AlexNet, ResNet и Effectivenet – EfficientNetB7. Архитектура EffectiveNet является самой современной и новой по сравнению с другими рассматриваемыми архитектурами. На этом наборе данных мы показали, что сети LeNet и EffectiveNet превосходят другие конкурирующие алгоритмы. Кроме того, EffectiveNet обеспечивает высочайшую производительность на других наборах данных жестов. Архитектура и принцип работы этих алгоритмов отражают эффективность их применения при распознавании жестового языка. Оценка модели СНС проводится с использованием матрицы точности и штрафов. В периоды обучения LeNet и EffectiveNet показали лучшие результаты: функции точности и потерь имели схожие и близкие тенденции. Результаты EffectiveNet были объяснены с помощью инструментов структуры Аддитивных объяснений Шепли (SHAP). С использованием SHAP исследовалась модель для обнаружения сложных взаимосвязей между элементами изображений. Сосредоточение внимания на инструменте SHAP может помочь еще больше повысить точность модели, Для людей з обмеженими можливостями жестова мова є найважливішим засобом спілкування. Тому все більше авторів різних робіт і вчених по всьому світу пропонують рішення для використання інтелектуальних систем розпізнавання жестів рук. Така система призначена не тільки для тих, хто хоче розуміти жестову мову, а й говорити за допомогою програмного забезпечення для розпізнавання жестів. У даній роботі представлений новий еталонний набір даних для дактильного алфавіту казахської мови, здатний навчати глибокі нейронні мережі. Набір даних містить більше 10122 зразків жестів для 42 алфавітів. Алфавіт має свої особливості, так як деякі символи показані в русі, що може впливати на розпізнавання жестів. У статті описано дослідження та аналіз згорткових нейронних мереж, порівняння, тестування, результати та аналіз методів LeNet, AlexNet, ResNet та Effectivenet – EfficientNetB7. Архітектура EffectiveNet є найсучаснішою і новою в порівнянні з іншими розглянутими архітектурами. На цьому наборі даних ми показали, що мережі LeNet та EffectiveNet перевершують інші конкуруючі алгоритми. Крім того, EffectiveNet забезпечує найвищу продуктивність на інших наборах даних жестів. Архітектура і принцип роботи цих алгоритмів відображають ефективність їх застосування при розпізнаванні жестової мови. Оцінка моделі ЗНМ проводиться з використанням матриці точності і штрафів. У періоди навчання LeNet і EffectiveNet показали кращі результати: функції точності і втрат мали схожі і близькі тенденції. Результати EffectiveNet були пояснені за допомогою інструментів структури адитивних пояснень Шеплі (SHAP). З використанням SHAP досліджувалася модель для виявлення складних взаємозв'язків між елементами зображень. Зосередження уваги на інструменті SHAP може допомогти ще більше підвищити точність моделі

In the Republic of Kazakhstan, diabetes mellitus is one of the socially significant diseases, which causes a high level of prevalence of this disease among the population, as well as high costs for the diagnosis and treatment of diabetes. Statistical models for predicting the number of patients with diabetes mellitus based on regression analysis using technical and economic indicators in the field of medical services for diabetes have been developed. There was a direct correlation between economic factors and the growth of patients with diabetes in the Republic of Kazakhstan. The analysis of the current state of research on predicting patient growth has shown insufficient elaboration in this subject area. The prevalence of diabetes in the country has the character of an epidemic and leads to large financial costs, thereby increasing the costs of regional budgets for the purchase of insulin and other medicines in the regions.

This study is part of a large study in the field of diabetes diagnosis and treatment support. The system implements functions for storing and processing structured and unstructured data. It is also possible to control the system in automatic and manual modes. In manual management mode, unstructured data is loaded into the developed information system platform using the NoSQLBooster tool. Creating and loading unstructured databases in the MongoDB utility in manual management mode is necessary for data loading. This information system uses the Apache Ambari software product, which allows you to manage the Hadoop cluster.

In the work herein we have conducted researches of F-diagram method for the solar system with thermosyphon circulation-diagram method is based on correlation of a lot of simulations, computed non-dimensional variables. Modeling conditions varied in corresponding ranges of thermosyphon circulation double circuit solar system practical constructions' parameters. By means of F-diagram method there have been computed the environmental monthly temperature values with correction index, which shows that the monthly average daily heating degree and direct solar radiation decrease according to weather conditions. It may be noted that monthly temperature load fraction increases along with the collector square growth. It demonstrates that the monthly temperature load fraction is higher in summer in Almaty city (Kazakhstan) (July--the highest value) and lower in winter months (January, February--the lowest value).

The work herein considers the control algorithm and software for creating the digital platform on the Mojo v3 base to monitor operating of the double-circuit flat solar collector with the thermosiphon circulation. We have developed and researched the control algorithm and created the software to control the temperature data of double-circuit flat solar collector with the thermosiphon circulation. In the offered system the metering is performed from 6 digital sensors (DS18B20 Dallas), using 16 wires. Applying Dallas sensors and corresponding software it is possible to control the temperature level and heat quantum. Usage of 6 digital sensors sufficiently upgrades the efficiency of control over the system and increases data processing rate. We have contemplated the possibilities of the sensors configurating for the Mojo v3 platform, as well, the scheme of the solar collectors’ control.

Medicine is one of the rich sources of data, generating and storing massive data, begin from description of clinical symptoms and end by different types of biochemical data and images from devices. Manual search and detecting biomedical patterns is complicated task from massive data. Data mining can improve the process of detecting patterns. Stomach disorders are the most common disorders that affect over 60% of the human population. In this work, the classification performance of four non-linear supervised learning algorithms i.e. Logit, K-Nearest Neighbour, XGBoost and LightGBM for five types of stomach disorders are compared and discussed. The objectives of this research are to find trends of using or improvements of machine learning algorithms for detecting symptoms of stomach disorders, to research problems of using machine learning algorithms for detecting stomach disorders. Bayesian optimization is considered to find optimal hyperparameters in the algorithms, which is faster than the grid search method. Results of the research show algorithms that base on gradient boosting technique (XGBoost and LightGBM) gets better accuracy more 95% on the test dataset. For diagnostic and confirmation of diseases need to improve accuracy, in the article, we propose to use optimization methods for accuracy improvement with using machine learning algorithms.