Қазақ ұлттық қыздар педагогикалық университеті / Казахский национальный женский педагогический университет

Постоянный URI для этого сообществаhttps://cris.qyzpu.kz/handle/123456789/1

Просмотр

Результаты поиска

Показано: 1 - 4 из 4

Некоторые метрики заблокированы вашими
настройками согласия
Almaty PM2.5 exposure inequality: data and analysis code (v1.0.4)
(European Organization for Nuclear Research, 2026-09-28)
Saya Sapakova
;
Аскар Сапаков
;
Omirlan Auelbekov
;
Lyailya Tukenova
;
Gulnar Astaubayeva
Data and fully executed analysis code accompanying the article "Integrating Heterogeneous Sensor Data for Source-Aware Air-Quality Analysis in Data-Sparse Urban Networks: A Case Study of Almaty" (Data, MDPI; under review). The package contains: the historical low-cost PM2.5 network for Almaty (146 stations, hourly, 2020–2026; air_quality_data.zip); the real-time source-context collector (25 co-identified stations, traffic and CHP-geometry features; city_twin_data.csv); official district populations as of 1 August 2026 (district_populations.csv); Supplementary Tables S1–S4 (Supplementary_Materials.docx); a data schema with the station aq_id ↔ location_id mapping and processing conventions (SCHEMA.md); and a Jupyter notebook (almaty_pm25_full_pipeline.ipynb) that reproduces every table and figure in the manuscript. The notebook covers quality control, seasonally standardized inequality indices, WHO exceedance with a daily-completeness rule (local time, ≥18 h/day), source attribution relative to the CHP plant, Moran's I, school-level exposure, QC-parameter sensitivity analysis, and a leakage-free predictive-modelling benchmark with a simulated random baseline, Bonferroni correction and McNemar tests. District-level PM2.5 values (manuscript Table 2) require a station-to-district assignment not included in this package. License: CC BY 4.0.
Некоторые метрики заблокированы вашими
настройками согласия
Applying Machine Learning to Detect Depression-Related Texts on Social Networks
(Springer Science+Business Media, 2021-01-01)
Shirinkyz Tleubergenovna Shekerbekova
;
Meruyert Yerekesheva
;
Lyailya Tukenova
;
Kuralay Turganbay
;
Zhazira Kozhamkulova
Цитирования (OpenAlex) 3Цитирования (OpenCitations) 1
Некоторые метрики заблокированы вашими
настройками согласия
Research of the Finite Difference Numerical Method Using Artificial Intelligence for Solving Problems of Incompressible Fluid Flow in a Rectangular Domain with Initial and Boundary Conditions
(2025-01-01)
Lyailya Tukenova
;
Omirlan Auelbekov
;
S. Sapakova
;
Bayan Abduraimova
;
Ж. Р. Уалиев
This study explores the enhancement of the finite difference method using artificial intelligence (AI) to model incompressible fluid flow within a rectangular domain.Accurate and efficient modeling of incompressible flows is crucial in hydrodynamics and aerodynamics, where traditional numerical methods like finite difference often face limitations in accuracy and computational speed, especially for complex problems with varied initial and boundary conditions.By integrating AI techniques such as neural networks and machine learning, this approach enables adaptive parameter selection, optimizing the calculation process, improving accuracy, and reducing computational demands.The combined finite difference and AI approach demonstrated significantly reduced computational complexity and improved accuracy, as verified across multiple test cases with different boundary conditions.Results indicate that AI-enhanced numerical methods improve the efficiency and reliability of solutions for complex hydrodynamic problems, offering faster, more precise modeling of fluid flow behaviors.The study highlights the potential of AI to enhance classical numerical methods, enabling the development of more accurate fluid flow models even with limited computational resources.The novelty of this work lies in the specific integration of machine learning with the finite difference method, presenting an adaptive approach that extends beyond traditional techniques.This integration opens up new possibilities for handling complex fluid dynamics scenarios, expanding the applications of numerical methods in scientific and engineering calculations.
Некоторые метрики заблокированы вашими
настройками согласия
A Web-Based Digital Twin for Traffic and Air Quality Monitoring: A Prototype Study in Almaty, Kazakhstan
(Multidisciplinary Digital Publishing Institute, 2026-08-18)
Saya Sapakova
;
Аскар Сапаков
;
Omirlan Auelbekov
;
Lyailya Tukenova
;
Sakhybay Tynymbayev
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.

Қазақ ұлттық қыздар педагогикалық университеті / Казахский национальный женский педагогический университет

Просмотр

Фильтры

Настройки

Сортировать по

результатов на странице

Результаты поиска

QyzPU CRIS

Университет

Данные