The paper is devoted to the authors’ tools for automated debugging of fragmented programs in the LuNA (Language for Numerical Algorithms) system. The LuNA system uses approach of fragmented programming, which allows the researcher to design parallel programs in an automated mode.

The main problem discussed in the paper is the detection of errors specific to LuNA programs. The operation algorithms of four developed tools for automated debugging of LuNA programs are described. Three of them are based on the static analysis approach and differ in the intermediate representations used. The first uses an abstract syntax tree (AST). The second uses a data dependency graph (DDG). The third tool generates a Prolog program containing a set of facts about the original LuNA program and rules corresponding to erroneous situations. Due to the use of various intermediate representations, each of the static analysis tools is able to detect its own types of errors. The last tool uses the “post-mortem analysis” approach. Each process of a parallel program collects a trace file during its operation and at the end of the work these files are analyzed by a special utility.

The paper also describes the developed utility for testing automated debugging tools. The set of test programs with various errors, as well as error-free programs, has been developed  for this utility.

The last part describes the designed mechanism for integrating the developed automated debugging tools into a single software package.

The article presents the development and implementation of an information-computational system (ICS) specifically designed for assessing greenhouse gas fluxes from the Earth’s surface using data assimilation methods. This system effectively interfaces with the MOZART-4 chemical transport model and utilizes satellite data from AIRS, ensuring accurate and reliable estimations of gas fluxes. At the core of this system lies the application of the ensemble Kalman filter algorithm, LETKF, for methane flux estimation, enabling the consideration of uncertainties in both data and model representations. The paper extensively discusses the architectural and technical solutions employed, emphasizing the necessity of adapting the system to the intricacies of the utilized model, thereby enhancing result accuracy. Furthermore, it elaborates on the mathematical foundations of the methodology and includes a practical demonstration of the algorithm’s application within the information-computational system framework. The findings of this research hold significant value as they pave the way for further advancements in utilizing such systems for environmental modeling and data assimilation endeavors, contributing to our understanding and management of the Earth’s environment.

The objective of the study is to elaborate and develop auxiliary analytical approaches for the implementation of expert activities. The problem of effective support of expert safety assessment is relevant at all stages of the life cycle of the organization’s production equipment. Traditional assessment methods may face a lack of data. In the course of research, new methods have been developed that should be both convenient for digitalization and automation of evaluation algorithms, and generate new information (conduct DATA MINING) for experts during data processing. The paper offers new possibilities for the use of cluster analysis, cluster analysis methods for the field of safety assessment, safety culture. As a result of the research, approaches to the formation of data clusters for methods of assessing the safety of production and equipment are proposed, and emphasis is placed on the analysis of these clusters using graph theory. The article highlights the features of the use of cluster analysis for security assessment. Recommendations are given for the development of additional methods of safety assessment with the possibility of their subsequent automation (by computers) in the context of digital transformation of expertise and support of control systems of technological processes.

The article contains detailed information on the application of the theory of queuing systems (QMS) in the Internet of Things (IoT) networks. The article discusses in detail the mathematical models used to analyze and optimize the provision of services in various systems, including IoT. Various aspects of the use of queue theory in IoT networks are highlighted, such as traffic modeling, data transfer optimization, and the use of stochastic models for more accurate analysis. The study of the characteristics of the communication channel in IoT networks is a key and urgent problem in the context of the rapid development of IoT technologies. With the growing number of connected devices, it becomes critically important to ensure the efficiency and reliability of the communication channel, as well as optimize the use of IoT device resources. This study is aimed at studying and theoretical analysis of the characteristics of the communication channel in IoT using queuing systems. The paper analyzes the features of the communication channel in IoT, examines channel modeling methods, analyzes data transmission delays, evaluates and increases throughput, applies queuing system methods and explores the applications of the results obtained, predicts the development of IoT and makes a final review of scientific work. The Internet of Things (IoT) is a network of interacting devices that use sensors and unique identifiers to exchange information. The widespread use of IoT in smart homes, energy, medicine, logistics and other sectors is accelerating thanks to modern artificial intelligence and machine learning technologies.

The article presents the results of numerical experiments using model data to estimate ground-level methane concentrations using the MOZART-4 model. Various approaches to integrating observational data and their application to various scientific and practical applications are discussed, including monitoring and analysis of methane sources, both anthropogenic and natural. These results illustrates the practical use of data assimilation to collect statistical data on the dynamics of emissions activity in specific subregions, which can be useful for estimating activity levels and processing large data sets to identify the most interesting and potentially promising areas for obtaining more detailed data analysis.