This study investigates stagnant atmospheric conditions in Almaty, Kazakhstan, a city nestled within a complex terrain. These conditions, characterized by weak local winds and inversion layers, trap pollutants within the city, particularly during winter. The Weather Research & Forecasting (WRF) model was employed to simulate atmospheric conditions using Local Climate Zone data. Verification of the model’s accuracy was achieved through comparisons with data from weather stations and the Landsat-9 satellite. The model successfully reproduced the observed daily temperature variations and weak winds during the testing period (13–23 January 2023). Comparisons with radiosonde data revealed good agreement for morning temperature profiles, while underestimating the complexity of the evening atmospheric structure. The analysis focused on key air quality factors, revealing cyclical patterns of ground-level and elevated inversions linked to mountain-valley circulation. The model effectively captured anabatic and katabatic flows. The study further examined the urban heat island (UHI) using a virtual rural method. The UHI exhibited daily variations in size and temperature, with heat transported by prevailing winds and katabatic flows. Statistical analysis of temperature and wind patterns under unfavorable synoptic situations revealed poor ventilation in Almaty. Data from three Januaries (2022/2023/2024) were used to create maps showing average daytime and nighttime air temperatures, wind speed, and frequency of calm winds.

The digitized text of handwriting would conduce to automate the business processes of many companies, simplifying the work of human being. For example, our state postal service does not have an automated mail processing system that recognizes handwritten addresses on an envelope. Each incoming correspondence is registered in the system by the operator. Automation of this business process on registering post mailing will significantly reduce expenses of postal service on mail delivery.There are two main approaches to handwriting recognition, namely hidden Markov models (HMM) and artificial neural networks (ANN). The methods proposed in this article are based on ANN. The first model is based on deep convolutional neural networks (DCNN) [1] for feature extraction and a fully connected multilayer perceptron (fully connected MLP) for word classification. The next model under consideration, called SimpleHTR, proposed by Harald Scheidl [2], has layers of a convolutional neural network (CNN) and layers of a recurrent neural network (RNN) for disseminating information through an image. Finally, the Connectionist Temporal Classification (CTC) decoding algorithm is executed, which adduces the text to the final version.Models were learned on the dataset of handwritten city names from Cyrillic words. 21,000 images were collected (42 classes of 500 handwriting samples). To increase the data set for training, 207,438 images from available samples were generated.As a result, two approaches for handwriting recognition were analyzed and the SimpleHTR model showed the best results over all.

Population growth and expansion of urban areas increase the need for the introduction of intelligent passenger traffic monitoring systems. Accurate estimation of the number of passengers is an important condition for improving the efficiency, safety and quality of transport services. This paper proposes an approach to the automatic detection and counting of people using computer vision and deep learning methods. While YOLOv8 and DeepSORT have been widely explored individually, our contribution lies in a task-specific modification of the DeepSORT tracking pipeline, optimized for dense passenger environments, strong occlusions, and dynamic lighting, as well as in a unified architecture that integrates detection, tracking, and automatic event-log generation. Our new proprietary dataset of 4047 images and 8918 labeled objects has achieved 92% detection accuracy and 85% counting accuracy, which confirms the effectiveness of the solution. Compared to Mask R-CNN and DETR, the YOLOv8 model demonstrates an optimal balance between speed, accuracy, and computational efficiency. The results confirm that computer vision can become an efficient and scalable replacement for traditional sensory passenger counting systems. The developed architecture (YOLO + Tracking) combines recognition, tracking and counting of people into a single system that automatically generates annotated video streams and event logs. In the future, it is planned to expand the dataset, introduce support for multicamera integration, and adapt the model for embedded devices to improve the accuracy and energy efficiency of the solution in real-world conditions.

This study evaluates the effectiveness of the Weather Research and Forecasting (WRF) model in simulating high-resolution atmospheric conditions for Almaty, Kazakhstan, a city prone to stagnant winter air. While the previously used Bougeault and Lacarrere scheme for parameterizing the planetary boundary layer was applied in high-resolution modeling, the number of vertical levels was increased, and a detailed local climate zones (LCZs) map was included. Ground-based observations from meteorological stations and monitoring stations, remote sensing data, and radiosonde measurements are used to verify the model. Comparison results with ground-based observations show that the WRF model with the LCZ map provides a better representation of the wind and thermal regimes of Almaty compared to the three-class land use map, including in high resolution. A good correspondence of wind direction is demonstrated by comparing the modeling results with pollutant transport plumes recorded by remote sensing data. In addition, a good correlation was found between land surface temperature from satellite data and air temperature simulated by WRF with a resolution of 333 m. A comparison of simulated data and aerological measurements confirmed that downscaling did not have a significant impact on boundary layer calculations. Analysis of turbulent processes showed that the adopted model effectively describes the attenuation and dissipation of turbulent kinetic energy and reflects the typical diurnal variations of meteorological processes in the atmosphere of Almaty in the anticyclonic winter period. The results of high-resolution WRF modeling can form the basis for the development of a hybrid system capable of modeling atmospheric processes at the building level.

This research paper investigates methodologies for forecasting the demand for railway tickets, a crucial aspect of optimizing resource allocation, enhancing service quality, and increasing revenue in railway transportation systems. The study explores various time series analysis techniques, including ARIMA, SARIMAX, and neural networks such as LSTM and CNN, to predict ticket demand. Railway ticket sales data spanning several years was utilized to train and validate the models. The performance of each model was evaluated based on key metrics like Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Demand is usually defined in Origin-Destination (OD) matrices, which represent movements from the origin station to the destination station for all OD pairs. The proposed CNN model predicts the OD matrix, whereas other models predict demand for each OD pair separately. Findings indicate that the CNN model gives more accurate predictions compared to ARIMA, SARIMAX, and LSTM models. Hence, interdependencies between various OD pairs allow for a more holistic and accurate prediction model.

Control of the status of the country's resources allows making reasonable decisions both in the field of state regulation, and for the benefit of sustainable and safe development of the territory. Construction of such system requires complex application of a number of modern concepts and information technologies (Cloud computing, Big Data, Data Mining, machine learning, etc.). The problem of constructing the resource monitoring system as parts of the following generation of e-government system is formulated.