There is a need for legislation, guidelines, and organizational structures in the management of Information Systems (IS) in South Africa’s public sector to ensure that technological solutions support strategic direction. This study synthesizes feedback received from 55 GITOs (Government IT Officers) to understand tools that shape IS operations and corporate governance. The findings provide a complex picture of governance and can be regarded as follows: There are acts such as the Financial Management Acts and ICT laws, which set standards dealing in accountability and security other frameworks include the DPSA’s: Corporate Governance of ICT Policy Framework (CGICTPF), the King IV, COBIT, and ITIL. These principles are given effect through internal governance instruments such as the ICT steering committees and the audit committees. The findings are presented using bar graphs, which indicate that more than half the identified GITOs consider essential tools such as the CGICTPF, PFMA, and audit committees. These findings of the study are connected to the previous literature with considerations of comparability with international standards and areas of concern, including resource limitation as well as the trade-off between conformity and creativity. Finally, it outlines recommendations for improving the IS governance in the context of the South African public sector, including the need for constant improvement of frameworks and the improvement of IS leadership competencies for public sector IT professionals.

 This article analyzes the potential of the existing Bluetooth detector network for constructing traffic models and analyzing traffic flow redistribution. It also attempts to validate readings from stationary radar detectors by comparing them with reference data obtained through video analysis. This article monitors traffic detector readings and assesses the compliance of traffic detector data received by the automated traffic management system and the VCC platform with actual data. The technology links traffic detectors to control actions based on the developed traffic management technology. Suggestions are made regarding the need for calibration and adjustment of traffic detector positions. A roadmap is proposed for addressing the identified deficiencies and improving the quality of data, which is the foundation for safe and effective traffic management within the ITS framework.

This article examines the general principles for assessing the effectiveness of unmanned aerial complexes (UAC), including groups of unmanned aerial vehicles (UAVs). It analyzes current methods for assessing effectiveness in performing various UAC tasks. Key performance indicators are substantiated as probabilistic characteristics. General probabilistic performance indicators and specific indicators characterizing individual stages of UAC use are formulated: the probability of object detection by a UAV group and the probability of timely resolution of the task of interfering with the object (providing assistance to victims), taking into account time delay characteristics. Particular attention is paid to solving problems of searching for objects in a given area of the Earth's surface due to the relevance and prevalence of such problems. Mathematical relationships are presented for determining the probability of finding an object in a given area, determining the probability of detecting an object by a given deadline, and calculating the mathematical expectation of the number of objects detected by a given deadline.

The purpose of the research is to develop additional capabilities of the cybernetics methodology for conducting numerical assessments and analyzing the state of management, control and safety of technical systems. The purpose of the study is to formulate and present elements of a methodology for analyzing quantitative patterns for ergatic systems, characteristics of projects of technical systems, equipment, devices, technologies for various fields of industry, nuclear energy, which can be used to describe their conditions, compare projected, planned or applied objects of study based on data from their technical specifications, passports and procedures., without using long-term experimental or operational data. The quantitative pattern analysis approach proposed on the basis of the ideal technical system model makes it possible to evaluate and analyze the advantages of innovative technologies, to make a comparative assessment of the state of management and safety of technical complexes, to form justifications for the preparation of optimal measures for programs and plans for the implementation of experimental and commercial technical projects, their control, modifications and upgrades, to develop recommendations for a comprehensive analysis in during the examinations. The features of the application of the equation of state hypothesis for the analysis of real technical systems are presented. The use of auxiliary approaches to evaluate the characteristics of technical systems allows for the development of reasoned management decisions based on a comprehensive analysis of a larger number of properties and quantities under consideration.

We benchmark practical ways to accelerate Stable Video Diffusion (SVD) inference for image-to-video. All methods use a fixed setup (1024×576 input, 25 frames) with an FP16 baseline at 25 denoising steps. We compare eight techniques: UNet INT8 weight-only quantization, torch.compile+TF32, step reduction, distilled weights (AnimateLCM), semi-structured 2:4 sparsity, LCM mode/scheduler, and keyframe generation with RIFE interpolation as post-processing. We measure latency and peak VRAM, and track quality/motion via CLIP similarity, tSSIM, and tLPIPS. Keyframes+RIFE achieves the highest speedup while preserving strong conditioning to the input. LCM provides a balanced ~2× speedup, whereas aggressive step cuts (and untuned 2:4) can degrade motion.

The exponential growth in the size of Large Language Models (LLMs) creates significant barriers to their local deployment, primarily due to Video RAM (VRAM) shortages on single devices. The aim of this work is to identify and substantiate the most effective parallelism strategy for LLM inference on consumer Graphics Processing Unit (GPU) clusters connected via a slow PCIe bus. Research methods included a series of experiments comparing a monolithic architecture (NVIDIA RTX A6000) and a distributed system (2x NVIDIA RTX 3090) using the vLLM framework. The impact of Tensor Parallelism (TP) and Pipeline Parallelism (PP) on key metrics – throughput, latency (TTFT, TPOT), and power consumption stability – was analyzed while running the DeepSeek-R1-Distill-Llama-14B model. The results unequivocally indicate the unsuitability of Tensor Parallelism for systems without NVLink due to critical synchronization delays. It is proven that Pipeline Parallelism is the only viable strategy for PCIe clusters, ensuring high throughput despite the presence of idle periods («bubbles») and a less stable power consumption profile compared to the monolithic solution. In conclusion, recommendations for using multi-GPU configurations are formulated: they represent the optimal economic choice for memory-critical tasks, such as Retrieval-Augmented Generation (RAG), allowing VRAM scaling at a significantly lower cost than professional analogs.

In the paper we present a comparative study of binarization methods for color images of static signatures made with ballpoint pens of different types and colors. Signature image binarization is the first step before calculating its features and verification. Because of the uneven flow of ink from ballpoint pens, images of signatures made with such pens present particular challenges. A comparison of digital signature image preprocessing methods aimed at preserving the shape of the signature lines in the binary representations is conducted. A comparative analysis of binarization methods for color signature images is performed based on four methods from different classes: global thresholding (Otsu, Kapura), locally adaptive thresholding (Sauvola), and a method of direct indexing the RGB color space into two classes: white and black pixels. For the first time, empirical objective criteria for the quality of a binary signature representation in the absence of a reference are proposed, based on the analysis of connected components and the skeleton of the binary signature representation. Experiments were performed on images from the publicly available CEDAR database and a database of signatures collected during the research. It has been shown that Kapur's method provides the best preservation of signature form in its binary representation, outperforming other methods, including the popular Otsu method. We propose a four-step procedure for generating a binary signature representation. This procedure consists of scanning a color signature at 300 or 600 DPI in the RGB model, converting the color image to grayscale using principal component analysis (PCA), binarization by the Kapoor method, and post-processing the binary image. This method is intended for developing static signature verification systems.

Described is a possible approach to solving the problem of obtaining a forecast of output parameters of cellular structures with those combinations of values of parameters of the object which are in previously given ranges based on studies already carried out, based on use of previously obtained results of variant calculations. Provides portions of a suite of programs that use MATHCAD to implement this approach. Comparisons of methods for eliminating heteroskedasticity with different selection of weighting factors based on the use of the weighted least squares method were made.

The article presents an algorithm for automatic extraction of metadata from executable software files at the disassembly stage. Methods for reading binary content, converting byte sequences into string representation, and filtering the obtained data using PowerShell and strings.exe utility are considered. An experiment was conducted confirming the possibility of extracting structural and textual elements of executable modules without their complete decompilation. The aim of this work is to improve the efficiency of analyzing binary applications by highlighting internal structural data at an early research stage. Methods used include static analysis, reverse engineering, text analytics, PowerShell scripting, and command-line tools. Results achieved include a working algorithm capable of extracting basic metadata characterizing the structure and functional dependencies of executable code. The scientific novelty of the research consists in the following points: A new algorithm has been developed for extracting metadata from executable files without decompiling or loading them into a disassembler; A method of binary analysis with sequential transformation of byte arrays into text form with data filtration has been proposed; Integration of PowerShell and strings.exe utilities into a unified analysis scheme ensuring compatibility and automation has been implemented; Classification of extracted data into textual, system, and structural elements has been defined; Textual analytics technique has been applied for the first time in the context of binary analysis, thus expanding reverse engineering methods.

The subject of research is the analysis of the use of calculation automation tools for the use of factorial, regression, and correlation analyses. The purpose of the article is to present the well‒known and author's experience of using the Python language for applying factorial, regression and correlation analyses and the experience of the authors in their application. The main aspects of the theoretical foundations of these methods of analysis are considered. The classification of software tools for automation of statistical calculations is given. The advantages and limitations of programming languages (Python, R), commercial platforms (SPSS, SAS, Stata), BI platforms (Tableau, Power BI, Zoho Analytics), open platforms (KNIME, RapidMiner), specialized statistical packages (JASP, Jamovi) are considered. The methodology of the application of automated statistical analysis is detailed, including the preparation of initial data, the choice of method, tincture of parameters, performing calculations and obtaining primary results. The author's methodology for automating complex analysis for evaluating the use of neural networks, the Internet of Things and blockchain in the university's educational process is presented.