Analysis of the implementation of blockchain technologies in management to ensure transparency, efficiency and sustainability

This paper tackles the critical problem of how blockchain technology can boost management practices\u27 transparency, efficiency and sustainability in a transitional economy like Ukraine. The focus is to systematically evaluate blockchain\u27s impact on this outcome by analysing the roles of blockchain implementation, digital security, decentralisation, and innovation in Ukraine from 2008q1 to 2023q4. All the variables are stationary at the first difference revealed from the unit root test of Augmented Dickey-Fuller. The series cointegration is confirmed using the bound test of Augmented Dickey-Fuller. The study uses time series data and an Autoregressive Distributed Lag (ARDL) model. The long-run results indicate that decentralization and digital security are incredibly influential in enhancing efficiency and sustainability. Furthermore, ECMt−1 is negative and statistically significant, suggesting a long-term equilibrium adjustment. Such policy recommendations include a supportive regulatory framework, incentives toward sector-specific blockchain applications, secure, transparent digital ecosystems, and using blockchain to its full potential in the sectors

Machine learning algorithms for predicting air quality index: A case study in urban and industrial zones

In urban and industrial areas, the prediction of the air quality index (AQI) is important in order to control the air pollution and protect public health. The goal of this work is to enhance the AQI prediction by making use of the advanced machine learning (ML) and deep learning (DL) models capable of learning spatial and temporal dependencies. The main goal of this research is to examine the performance of the different ML and DL models such as Random Forest (RF), XGBoost, LSTM, Transformer and Temporal Graph Neural Networks (TGNN) for AQI prediction in urban and industrial zones. To capture the variability of data, a multi-source data collection approach is taken by using air quality data (PM2.5, PM10, SO2, NO2, CO, O3), weather data, satellite imagery, and IoT sensor data. The data were pre-processed and engineered in terms of temporal and spatial features and advanced models were used to predict AQI. And key metrics of RMSE, MAE and R2 were used to evaluate model performance. Results indicate that Transformer models achieved the best performance in urban areas, with an RMSE of 14.1 and R² of 0.89, because they can capture long-term temporal patterns. In industrial zones, TGNN models achieved an RMSE of 17.9 and an R² of 0.87 because they could capture spatial correlations and pollution dispersion. Both models exhibited high resilience to extreme pollution events and minimal performance degradation under missing data scenarios in robustness testing. We show that Transformer and TGNN models outperform traditional ML models by a large margin in AQI prediction, especially during high pollution episodes. The results are consistent with real-time air quality monitoring and dynamic policy making in urban and industrial environments. Future work should implement the models in other regions and improve data quality to increase applicability

Innovative approaches to the use of interactive technologies in higher military education to enhance the cultural competence of students (cadets)

Considering the trends in the introduction of various innovative technologies, determining the role of modern integrative technologies in the system of higher military education to develop cadets becomes an urgent task. Therefore, this work aims to determine the main innovative approaches and technologies that will contribute to the development of cultural competence among cadets. The main research methods consisted of content analysis and comparison. Data collection was done using the Prisma approach. Total number of sources: 42. Date range: 2019-2024. The results emphasize the importance of developing cultural competence with the help of modern innovative technologies. Professional cultural training should be aimed at developing 4 main components: knowledge, experience, creativity, and motivation. These tasks are completely solved by the implementation of modern innovative approaches, in particular, the combination of offline and online courses in cultural studies and language training, the organization of special language clubs with a stable international discussion, and various international projects on the study of various cultural aspects on the examples of individual military missions. The conclusions summarize that implementing these approaches requires teachers to use various technologies: multimedia, virtual reality, simulation technologies, etc. They can contribute to forming interactive virtual classrooms, organizing virtual exchanges between students (cadets), and improving their professional skills in general

Formation of Students\u27 Research Skills in Digital Transformation of Physics Teaching

Rapid changes in the composition of the student audience and the need to intensify practical classes force us to reconsider the usual approaches to teaching physics. This study examines the use of digital technologies, including the creation of a virtual laboratory, to enhance students\u27 research skills without sacrificing face-to-face learning. The focus is on how digital tools can replicate hands-on learning and facilitate a more engaging and effective learning process. A mixed-method study was conducted at the Kazakh National Women\u27s Pedagogical University and Kazan Federal University. It included experimental research and a cross-sectional survey to assess students\u27 academic achievement using pre- and post-testing. The data collected were analyzed using ANOVA and other statistical methods. The introduction of virtual laboratories significantly improved student motivation and engagement in the learning process. This platform promoted the development of research skills, allowing students to actively study the material and perform simulation experiments. Teachers used accessible learning paths and tools to create individualized educational programs. At the same time, certain difficulties were identified related to technical aspects and ensuring proper user support. The use of digital technologies, in particular virtual laboratories, contributes to the growth of students\u27 interest in physical culture and the improvement of their research skills. To achieve the best results, it is important to overcome technical difficulties and ensure proper training for both students and teachers. Important conditions for the successful implementation of digital tools in the educational process are the provision of comprehensive support and the organization of high-quality training

The use of blockchain technology for managing digital assets in marketing management

This study explores blockchain technologies\u27 contemporary and promising phenomenon, among the most effective and operational digital assets in marketing management. Using data from global industry giants such as Coca-Cola, H&M, and Nike, it demonstrates how blockchain technologies are employed to manage digital assets (e.g., customer data, advertising campaigns, and software) within marketing management. The study compares the effectiveness of solutions before and after blockchain implementation and identifies key success factors, challenges, and drawbacks. Blockchain technology is one of the most reliable data transmission and storage methods. It enables recording time, date, and individual or company details in distinct blocks sequentially linked into a continuous chain. This system employs a highly complex mathematical and technical mechanism, which ensures a high level of security for users while allowing precise tracking and verification of all transactions. Blockchain technologies are crucial in enhancing transparency in advertising campaigns and marketing management. These technologies provide a reliable and immutable digital record of every transaction and data modification within the advertising sector. All actions, such as ad placements, impressions, and clicks, are recorded on the blockchain and remain accessible to all system participants. This transparency allows advertisers, publishers, and marketing agencies to track each stage of their campaigns effortlessly. The transparency blockchain ensures helps combat fraud – one of the most significant challenges in the advertising industry. All participants can review the history of data modifications and verify their authenticity, thereby reducing risks for advertisers and increasing trust in advertising platforms. Furthermore, blockchain improves verification and user identification processes, reducing the number of fraudulent views and unwanted clicks. As a result, advertising campaigns become more effective and transparent, benefiting both advertisers and end consumers

Evaluation of the incidence of optical and physical characteristics on the performance of a Fresnel Linear Collector prototype

This article aims to evaluate the optical and thermal behavior of a small Fresnel linear concentrator prototype developed under the appropriate technology paradigm. The system was developed by the Energy, Automation and Control Systems Research Group of the Technological Units of Santander, Colombia for water heating. The study of the device was developed from a series of simulations that took into account the optical and thermal factors of the real system, and a series of alternative scenarios that seek to improve the performance of the device were evaluated. The simulation process was carried out by applying the "TRNSYS" Software in order to study the dynamic behavior of the concentrator and the "Soltrace" Software applying the Monte Carlo Ray Tracing method. The results obtained showed that the improvement scenarios proposed to evaluate the optical characteristics of the primary reflection system do not significantly increase the performance of the device, while the optical characteristics applied to the secondary reflection system do reflect a significant in-crease. Finally, the variation of flow and the area of the preheater show a direct relationship in performance, reaching values that predict the ideal value of the operating variable

EEG-based image classification using an efficient geometric deep network based on functional connectivity

To ensure that the FC-GDN is properly calibrated for the EEG-ImageNet dataset, we subject it to extensive training and gather all of the relevant weights for its parameters. Making use of the FC-GDN pseudo-code. The dataset is split into a "train" and "test" section in Kfold cross-validation. Ten-fold recommends using ten folds, with one fold being selected as the test split at each iteration. This divides the dataset into 90% training data and 10% test data. In order to train all 10 folds without overfitting, it is necessary to apply this procedure repeatedly throughout the whole dataset. Each training fold is arrived at after several iterations. After training all ten folds, results are analyzed. For each iteration, the FC-GDN weights are optimized by the SGD and ADAM optimizers. The ideal network design parameters are based on the convergence of the trains and the precision of the tests. This study offers a novel geometric deep learning-based network architecture for classifying visual stimulation categories using electroencephalogram (EEG) data from human participants while they watched various sorts of images. The primary goals of this study are to (1) eliminate feature extraction from GDL-based approaches and (2) extract brain states via functional connectivity. Tests with the EEG-ImageNet database validate the suggested method\u27s efficacy. FC-GDN is more efficient than other cutting-edge approaches for boosting classification accuracy, requiring fewer iterations. In computational neuroscience, neural decoding addresses the problem of mind-reading. Because of its simplicity of use and temporal precision, Electroencephalographys (EEG) are commonly employed to monitor brain activity. Deep neural networks provide a variety of ways to detecting brain activity. Using a Function Connectivity (FC) - Geometric Deep Network (GDN) and EEG channel functional connectivity, this work directly recovers hidden states from high-resolution temporal data.  The time samples taken from each channel are utilized to represent graph signals on a topological connection network based on EEG channel functional connectivity. A novel graph neural network architecture evaluates users\u27 visual perception state utilizing extracted EEG patterns associated to various picture categories using graphically rendered EEG recordings as training data. The efficient graph representation of EEG signals serves as the foundation for this design. Proposal for an FC-GDN EEG-ImageNet test. Each category has a maximum of 50 samples. Nine separate EEG recorders were used to obtain these images. The FC-GDN approach yields 99.4% accuracy, which is 0.1% higher than the most sophisticated method presently availabl

Immersive learning of the Sustainable Development Goals (SDGs) in literature from 2020 to 2024

This research presents an innovative approach to immersive learning, defining it as an artificial system that simulates key psychological processes, including memory, language, perception, and human intelligence. Within this framework, the Sustainable Development Goals (SDGs) serve as essential reference points for the literature review. A unique aspect of this study is its comprehensive methodology for searching, analyzing, and modeling categories related to immersive learning and the Sustainable Development Goals (SDGs). The research involved a documentary, transversal, exploratory, and retrospective analysis using a sample of abstracts published in journals indexed in international repositories, along with a keyword search conducted from 2020 to 2024. The findings indicate the presence of awareness nodes, policies, and programs that illustrate the central themes, groupings, and structures of the learning network. The study concludes with a strong recommendation to extend the model for empirical validation and to explore immersive learning scenarios connected to the SDGs in uncertain and contingent contexts, such as during the pandemic. This approach aims to provide a practical tool for educators, psychologists, and professionals in education and sustainable development

Improving understanding of the DevOps framework using Essence a visual representation

DevOps aims to achieve effective integration between software development (Dev) and system operations (Ops) through the implementation of agile practices. These practices allow for a quick response to frequent changes that arise in the development cycle for a software product. In the advancements of agile practices in DevOps, it is common to find a lack of conceptual and visual integration in the DevOps development cycle, which complicates the understanding of these practices in the various DevOps phases. In this paper, we propose a visual representation of the practices in the DevOps using Essence, a standard framework for creating, adopting, and improving best practices in software engineering.  Such a visual representation is an addition to the DevOps framework, which integrates processes, practices, principles, values, metrics and main concepts of DevOps. The proposed solution aims to enhance the understanding and impact of      practices in DevOps, providing a comprehensible vision for industry professionals. Essence serves as a structured method to encapsulate and visualize the various elements of practices, ensuring that they are accessible and easily understandable. This visual representation helps bridge the gap between theoretical      concepts and their practical application in a DevOps environment. By doing so, it facilitates better communication and collaboration among team members, leading to more efficient and streamlined operations. The results indicated the understandability and clarity of the representation of practices in the DevOps lifecycle, making it a useful tool for industry professionals seeking to optimize their DevOps workflows. 

Generating user stories and acceptance criteria through extensions to the iStar framework

1. Introduction: Currently, user stories and acceptance criteria are the most used primary artifact for the documentation and specification of requirements in the software development life cycle, mainly due to their concise notation and natural language. However, these artifacts are usually poorly written, generating process debts, and therefore, quality defects in the system that is developed. To minimize this problem, the capabilities of the iStar Framework—which is a framework for objective-oriented requirements modeling—have been extended, defining 9 transformation rules, a process and an application example, which will support the transformation of iStar models in models adapted to the structure of user stories and acceptance criteria according to the Gherkin format. 2. Method: For this, a systematic and detailed process was followed made up of process elements as well as roles, subprocesses, activities and input and output artifacts. Likewise, the evaluation of the proposal was carried out through a focus group made up of professionals who are experts in agile approaches, user stories and acceptance criteria. 3. Results and discussion: The results show that the participants determined that the proposed rules and the process defined for their application are clear, complete, suitable and applicable in agile projects, and that in addition; provides a valuable tool to companies to improve the identification, documentation, specification and development of functional requirements. 4. Conclusions: The proposed transformation rules, process and application example allow professionals in software organizations to have a graphic and general vision of the relationships between actors and activities within a software system, which can be translated into the generation of specifications. of functional requirements through user stories and acceptance criteria minimizing documentation defects