It is well known that the quality of biogas directly affects its energy value and efficiency of use in power plants, as well as determines the profitability of production. However, it can be difficult to identify deviations in the quality of biogas at early stages, especially in industrial conditions, as this requires in-depth knowledge of the physico-chemical characteristics of the gas. As part of this project, an intelligent biogas quality management system was developed using Internet of Things technologies designed to monitor key parameters such as temperature, methane levels and gas humidity. The ESP32 microcontroller, programmed in C, was used as the control module, which collected data from sensors and transferred them to cloud storage. A web application has also been created that provides the user with the ability to remotely monitor and control the system in real time from any device connected to the Internet. Notifications and alarms could be transmitted via WhatsApp messenger. The system has demonstrated high efficiency in increasing the stability and productivity of biogas production.

This research presents a comprehensive investigation into the application of machine learning techniques for addressing the pervasive security challenges within Internet of Things (IoT) networks. With the exponential growth of interconnected devices, ensuring the integrity and confidentiality of data transmissions has become increasingly critical. In this study, we deploy and evaluate seven distinct machine learning methods tailored to the IoT network intrusion detection problem. Leveraging the rich and diverse UNSW-NB15 dataset, encompassing real-world network traffic scenarios, our analysis encompasses a thorough examination of both traditional and state-of-the-art algorithms. Through rigorous experimentation and performance evaluation, we assess the efficacy of these methods in accurately detecting and classifying various forms of network intrusions. Our findings provide valuable insights into the strengths and limitations of different machine learning approaches for enhancing the security posture of IoT environments, thereby facilitating informed decision-making for network administrators and cybersecurity practitioners.

Urban air pollution driven by road traffic poses a significant public health challenge in cities with high vehicle density and frequent congestion, particularly in topographically constrained environments such as Almaty, Kazakhstan. This study presents a web-based digital twin prototype for the integrated monitoring and analysis of traffic flow and air quality in Almaty, Kazakhstan. The system autonomously collects data from the TomTom Traffic, OpenWeather Air Pollution, and WAQI APIs and official population statistics for five fixed monitoring stations, computing traffic density, vehicles per hour, road congestion, estimated CO₂ emissions, an air pollution index, and a population exposure index, and providing real-time dashboard visualization alongside longitudinal data accumulation. Over a 50-day deployment (26 May–16 July 2026), 4961 real co-located observations across 18 active days were analyzed; records generated by the prototype’s fallback mechanism during API outages were excluded from the scientific analysis. During this summer period, PM2.5 was low (mean ≈ 6 µg/m³) and spatially uniform, and showed no statistically significant association with traffic intensity (r ≈ −0.03). Traffic indicators were instead weakly but significantly correlated with the vehicle-emitted gases NO₂ (r ≈ 0.16) and CO (r ≈ 0.10), which they preceded by up to about one hour. A short-horizon PM2.5 nowcasting task, evaluated across temporal resolutions with time-series cross-validation, was dominated by temporal persistence, with traffic-derived features contributing negligibly. The absence of a summer traffic–PM2.5 association does not preclude such a relationship during the heating season, when particulate levels are higher. The results indicate that the traffic–air-quality relationship in Almaty is season- and pollutant-dependent, and demonstrate a lightweight, reproducible platform suitable for longitudinal monitoring and future heating-season assessment.

Reliable urban air-quality assessment increasingly depends on integrating heterogeneous data sources, yet doing so in data-sparse cities, where reference monitoring is limited and low-cost sensors dominate, remains an open challenge. This study presents a reproducible framework for integrating and analyzing heterogeneous urban sensor data, demonstrated on Almaty, Kazakhstan. Two complementary sources were linked at the station level through a shared identifier: a historical low-cost network of 146 stations (2020–2026; 544,943 records) and a real-time collector providing traffic and source-context features. A three-stage quality-control pipeline retained 442,322 records across 39 stations. Spatial inequality was quantified with complementary indices, attributed to emission sources through geometric descriptors, and used to compare predictive models. Exposure was substantially unequal (winter, seasonally standardized Gini 0.197; full-period Gini 0.210; population-weighted Gini 0.09), with the population-weighted annual mean PM2.5 (19.8 µg/m³) nearly four times the WHO annual guideline (5 µg/m³); virtually the entire population lives in districts whose annual mean exceeds this guideline. The inequality showed a spatial association with position downwind (p = 0.056), reported as exploratory given Almaty’s lack of a consistent prevailing winter wind. On the sparse network, regression failed to predict continuous concentrations (R² ≈ 0), and once evaluation leakage was removed, classification of high-exposure locations did not exceed a prevalence-aware baseline either (best model 72%, 95% CI overlapping the baseline), indicating that neither task is reliably validated at this network size—a cautionary finding for data-sparse settings generally. The framework provides a transferable template for air-quality assessment in data-scarce urban environments.

Бұл жұмыста маятникті-инерциялық конус тәрізді роторды импульсті басқарумен тұрақты және айнымалы магниттермен бірге қолданатын генератордың жұмысын зерттеу және талдау қарастырылған. Ұсынылған конструкция тербелмелі маятниктің қасиеттерін және инерциялық ротордың сақтау сипаттамаларын біріктіреді, бұл механикалық энергияны айналмалы қозғалысқа, одан әрі электрлік энергияға тиімді түрлендіруге мүмкіндік береді. Модельде магниттік қозудың әртүрлі режимдері ескеріледі: айналуды тұрақтандыруға арналған тұрақты магнит өрісі және энергия импульстарын адаптивті басқаруға арналған айнымалы. Сандық эксперименттер жүйенің параметрлерін (инерция моменті, кедергі, үйкеліс коэффициенті, импульстердің амплитудасы мен ұзақтығы) өзгерте отырып жүргізілді, сонымен қатар шығыс сипаттамаларын визуализациялау жүзеге асырылды: бұрыштық жылдамдық, ЭҚК, шығыс тогы және жинақталған энергия. Генератордың конструкциясы инерциялық тербелістерге байланысты ұзақ айналуды қамтамасыз етуге мүмкіндік беретін маятник тұрақтандырғышы ретінде де жұмыс істейтін магнитпен жабдықталған массивті конустық роторды қамтиды. Ротордың айналмалы қозғалысы қозғалмайтын катушкаларда электр қозғаушы күшті индукциялайды, ал генерациядан алынған энергия сыртқы жүктемені қоректендіруге бөлінеді және өздігінен зарядтау жүйелері бұрыштық жылдамдықтың төмендеуімен токтың қысқа мерзімді импульстары арқылы жүзеге асырылады, бұл энергия шығынын азайтуға және құрылғының жалпы тиімділігін арттыруға мүмкіндік береді. Модель Python ортасында кітапхана арқылы жүзеге асырылады NumPy, сонымен қатар сандық модельдеу 10 секундқа дейінгі уақыт аралығында орындалып, құрылымдық параметрлердің (ротордың массасы мен радиусы, жүктеме кедергісі, үйкеліс коэффициенті, бұрылыстар саны және магнит ағыны) әсеріне салыстырмалы талдау жүргізілді.

This paper explores the potential use of artificial intelligence (AI) and machine learning (ML) in the automated design, testing, and optimization of parachute suits. The purpose of the study-improving flight safety and maneuverability using advanced technology. Research methods include development of MO algorithms to analyze flight data and optimize suit design, integration of sensors for real-time monitoring of flight parameters, and modeling and simulation of various configurations to improve aerodynamic performance of suits. Integrated monitoring systems enable the collection of data on suit status and flight parameters, which helps to identify potential hazards in a timely manner and improve jump reliability. The expected results include the development of recommendations for the development of high-performance parachute suits with improved aerodynamic properties and the integration of AI and CS in the design and operation of parachute systems. The research demonstrates the great potential of using modern technologies to transform parachute jumps, providing a high level of safety and efficiency.