Computational and Applied Science (CAS) is a peer-reviewed academic journal dedicated to publishing high-quality research in computational methods, applied mathematics, data science, artificial intelligence, and information technologies. The journal provides an international platform for researchers, scientists, and practitioners to share innovative theoretical developments, computational techniques, and real-world applications across a wide range of scientific and engineering disciplines. CAS welcomes original research articles, review papers, short communications, and technical reports that contribute to the advancement of computational science and its practical implementations. Areas of interest include, but are not limited to: numerical analysis, mathematical modeling, machine learning, deep learning, algorithms, programming, scientific computing, high-performance computing, data analytics, and ICT applications. The journal aims to support the scientific community by promoting rigorous peer review, open access dissemination, and high visibility for published research. CAS is committed to fostering collaboration between academia and industry and to advancing modern computational and applied science worldwide.
Conceptual Model of a Hybrid Automated AI-Based Code Review System
Ensuring code quality has become one of the important challenges in modern software development processes. As software systems grow, code complexity, architectural complexity, security risks, and the likelihood of technical debt accumulation also increase. Traditional code review processes, which mainly rely on human expertise and manual analysis, do not always ensure fast and consistent evaluation. This paper presents the concept of a Hybrid Automated AI-Based Code Review System, which combines static analysis, rule-based validation, project context processing, and the capabilities of Large Language Models (LLMs). The proposed approach considers code review not merely as a process of generating comments, but as a context-aware multi-stage process that includes code change assessment, risk identification, problem localization, and generation of explainable recommendations. The research methodology is based on the analysis of existing AI-based code review approaches and the development of a hybrid architectural model. The proposed model utilizes multi-source context, including code changes, project structure, code dependencies, static analysis results, testing information, and Pull Request descriptions. The main contribution of the research lies in the development of a conceptual model of a hybrid AI-based code review system that integrates traditional software analysis methods with AI-based contextual reasoning. The proposed approach aims to improve the accuracy, explainability, and practical applicability of code review systems while supporting, rather than replacing, human reviewers.
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Artificial Intelligence and its Role in Crisis Management: Opportunities and Challenges of Technological Intervention
Global crises of the twenty-first century, including pandemics, climate change, natural disasters, and technological emergencies, highlight the need for more effective and systematic approaches to crisis management. Traditional management models often fail to adequately address the complexity and rapid dynamics of modern crises, which significantly increases the importance of technological interventions. In this context, Artificial Intelligence (AI) is increasingly regarded as one of the most promising tools for enhancing crisis prediction, response, and recovery processes. The aim of this paper is to examine the role of artificial intelligence in crisis management by identifying its key applications, opportunities, and challenges. The study is based on a systematic literature review, quantitative research methods, and comparative analysis, providing both theoretical and practical insights into the use of AI in crisis-related decision-making. The findings indicate that AI significantly improves data processing speed, forecasting accuracy, and decision-making efficiency, particularly in the management of health crises, pandemics, climate-related disasters, and economic disruptions. At the same time, the research highlights critical risks associated with the use of artificial intelligence, including issues related to data quality, lack of algorithmic transparency, ethical and legal concerns, and the potential reduction of human oversight. The paper concludes that artificial intelligence should be viewed primarily as a decision-support tool rather than a fully autonomous decision-maker. Its effective and safe integration into crisis management requires a clear ethical framework, high-quality and reliable data, and continuous human supervision.
Development of a Monitoring Dataset for Logic-Based Electromagnetic Field Risk Assessment
Cities today are full of technologies that create electromagnetic fields: mobile base stations, power lines, Wi-Fi networks and many other devices. Because of this, it is important to measure EMF levels and to understand what they mean for people. This paper works on three connected tasks for the city of Tbilisi. The “Avlabari” district is used as the pilot study area. The tasks are: to collect and combine the existing EMF data from base stations, power lines, Wi-Fi zones and similar sources, to find the geographic zones where the EMF radiation density is high, and to check how much EMF monitoring data is available for Tbilisi, how good this data is, and where the gaps are. Our measurements in our study area show these electric field levels: 1.2–4 V/m near the Georgian National University SEU, 2.8–6,1 V/m near mobile base stations, 1.5–4 V/m near the metro station, 1.5–3 V/m near clinics, and 0.3–2 V/m in open, less populated areas. All these values are far below the international reference levels for the general public, which correspond to roughly 41–61 V/m in the mobile-network frequency bands. However, the values change a lot from place to place, and they are higher than the average levels reported for some Western European cities. The available data for Tbilisi is still incomplete, so we also propose a fuzzy logic model. The model uses EMF strength, distance to the source and exposure time as inputs, and it gives a risk level as output: Low, Medium, High or Very High. Finally, the paper describes the main gaps in the EMF data for Tbilisi and gives recommendations for a better monitoring system. The results show that fuzzy logic is a useful and effective bridge between incomplete urban EMF measurements and clear risk communication.
Urban Transport Systems Optimization Through Real-Time Data Analytics
Urban traffic congestion continues to increase travel time, fuel consumption, and environmental impact in modern cities, and Tbilisi is no exception: traffic intensity at signalized intersections varies sharply depending on time of day, direction, and the workday–weekend cycle. This study proposes an integrated, AI-assisted framework that connects real-time route-level traffic observation, short-term forecasting, and adaptive signal control within a single decision-support pipeline. Traffic data were collected at five-minute intervals for twelve origin–destination route pairs surrounding one critical intersection in Tbilisi using the Google Routes API, with automated acquisition and storage implemented through AWS Lambda, EventBridge, and Amazon S3, producing a structured dataset of 23,556 observations and 75 variables. A LightGBM regression model forecasts short-term delay at two horizons (t+15 and t+30 minutes), while a two-stage multi-class classifier separates normal from congested traffic states and, within the congested subset, distinguishes slow traffic from jam conditions. Forecasts are translated into operational signal-control actions through a rule-based decision layer and evaluated in a SUMO/TraCI microscopic simulation environment against a fixed-time baseline, a max-pressure-style pressure-adaptive controller, a bridge-model hybrid controller, and an experimental Deep Q-Network (DQN) reinforcement-learning controller. Results show that LightGBM achieved R² = 0.82 and R² = 0.77 at the two forecasting horizons, while the two-stage classifier reached 90.6% overall accuracy. In simulation, the pressure-adaptive controller reduced mean waiting time by approximately 33.6% relative to the fixed-time baseline, while the DQN controller achieved an average reduction of approximately 23.3% across multiple random seeds. The proposed framework demonstrates a reproducible, data-driven pathway from real-time observation to adaptive traffic-signal decision-making, without removing engineering judgment from the control loop.
An AI-Driven Multimethod Framework for Inclusive Education: A Cross-Regional Survey Analysis
The integration of Artificial Intelligence (AI) into educational science offers transformative opportunities to advance inclusive education, particularly for students with visual impairments. This study presents an AI-driven analysis of surveys conducted across European and Georgian higher education institutions, drawing on responses from students, faculty, medical professionals, and psychologists. Using a mixed-methods design, the research combines traditional statistical analysis with advanced AI methodologies, including machine learning algorithms, natural language processing (NLP), and predictive modeling. The findings reveal nuanced patterns of needs, challenges, and institutional practices, and the paper provides evidence-based recommendations for policymakers and educators. This work demonstrates the capacity of AI methodologies to substantially enhance the rigor, accuracy, and practical applicability of educational research on disability inclusion. This study not only applies AI techniques but also conceptualizes a unified multimethod analytical framework for inclusive education research.
AI-Assisted Editorial Decision Making in Open Journal Systems (OJS): A Framework for Manuscript Scope Analysis and Reviewer Recommendation
The increasing volume of manuscript submissions has created significant challenges for editorial boards in maintaining efficient and consistent editorial workflows. Initial manuscript screening, assessment of manuscript relevance to the journal scope, and identification of appropriate reviewers are among the most time-consuming tasks in academic publishing. Although Open Journal Systems (OJS) provides a comprehensive platform for managing editorial processes, these tasks are still performed primarily through manual evaluation. This study presents an AI-assisted editorial support framework designed for Open Journal Systems (OJS). The proposed approach applies natural language processing techniques to analyze manuscript titles, abstracts, keywords, and reviewer expertise profiles. Textual information is transformed into numerical representations using the Term Frequency–Inverse Document Frequency (TF-IDF) vectorization method, while cosine similarity is employed to measure semantic relevance between submitted manuscripts, journal scope descriptions, and reviewer expertise. Based on these similarity scores, the system recommends the most relevant journal scope categories and suitable reviewers for each submitted manuscript. The proposed framework is implemented as a Python-based prototype and demonstrates the feasibility of integrating artificial intelligence into editorial workflows without replacing the role of the editor. Instead, the system functions as an intelligent decision-support tool that provides transparent and consistent recommendations during the initial manuscript screening process. The proposed approach has the potential to reduce editorial workload, improve reviewer assignment, and enhance the overall efficiency of manuscript management within Open Journal Systems.
Design and Evaluation of a Hybrid Georgian Voice Assistant for Public Services
Voice assistants have become an essential component of modern human-computer interaction, allowing users to access information through natural speech. However, most commercial voice assistants provide limited support for low-resource languages. One such language is Georgian, for which limited support for voice technologies makes it difficult for citizens to access local public services through voice interaction. This limitation is especially important for elderly users and people with visual impairments, who often use voice interfaces instead of traditional graphical user interfaces. This paper presents the design, implementation, and evaluation of a hybrid Georgian language voice assistant that provides voice access to information about public transport and cultural events across Tbilisi. The proposed system combines cloud-based speech recognition, natural language understanding, and speech synthesis technologies within a web application architecture. Speech recognition is performed using Google Cloud Speech-to-Text Chirp 3 [3], while speech synthesis uses Microsoft Edge Neural Text-to-Speech. Intent recognition combines rule-based processing with Google Gemini 2.5 Flash, creating a hybrid architecture that improves robustness while maintaining low latency. Real-time information is obtained from TKT.ge and Google Maps services. The developed prototype was evaluated through technical performance measurements and user-centered usability testing. Experimental results show that the proposed hybrid architecture provides reliable Georgian speech processing with acceptable latency and high usability.
AI Framework for Academic Progress Monitoring and Personalized Assignment Recommendation in Higher Education
The rapid development of Artificial Intelligence (AI) has significantly expanded its applications in higher education, particularly in the fields of learning analytics and personalized learning. Continuous monitoring of students' academic progress and providing personalized learning recommendations remain major challenges in higher education. This paper proposes an AI-assisted framework for academic progress monitoring and personalized assignment recommendation based on a Decision Tree classification model for predicting students' expected academic performance. The proposed approach integrates students' learning activities, formal assessment results, and historical instructor decisions to predict the expected grade category and automatically generate personalized assignments with an appropriate level of difficulty. A prototype of the framework was implemented in Python using the scikit-learn library and evaluated on a dataset of 50 students. Experimental results demonstrate that the proposed framework provides transparent and interpretable predictions, reduces the effort required for continuous monitoring of student progress, and supports the effective implementation of personalized learning. Rather than replacing instructor judgment, the proposed approach serves as an intelligent decision-support tool that can be integrated into existing learning management systems and adapted to a wide range of higher education courses.
Morphological Classification of Tobacco Ash Using a Deep Convolutional Neural Network: From Sherlock Holmes's "monograph" to modern computer vision
Arthur Conan Doyle's literary character Sherlock Holmes claimed the ability to visually distinguish the ashes of up to 140 varieties of tobacco. This paper examines how far that idea is realisable with modern artificial intelligence. We propose a methodological framework in which a convolutional neural network (CNN) is used to classify microscopic images of tobacco ash into five categories. The paper describes the data-collection protocol, model architecture, training procedure, and illustrative results. We show that the mineral and morphological features of ash: colour, texture, fibrous structure, carry sufficient discriminative information for machine classification, although forensic application requires combination with spectroscopic methods
Self-Inflicted Denial of Service (Self-DDoS) in Distributed Systems, Mechanisms and Mitigating Architectural Strategies: When a System "Attacks" Itself, An Analysis of Retry Storms, Thundering Herds, and Cascading Failure
"Self-DDoS" (self-inflicted denial of service) describes the phenomenon in which a distributed system, absent any external attacker, achieves the same outcome as a targeted DDoS attack, full or partial unavailability of service purely as a consequence of its own architectural flaws. This paper examines five core mechanisms: the retry storm, the thundering herd, cascading failure, the autoscaling feedback loop, and the cache stampede. For each mechanism a schematic diagram is provided, together with the corresponding mitigation pattern exponential backoff with jitter, the circuit breaker, bulkhead isolation, rate limiting, and load shedding. The mechanisms are further illustrated through a controlled fault-injection experiment on a Kubernetes test cluster, comparing fixed-interval and jittered-backoff retry policies under simulated downstream latency.
Review and Analysis of Cyberattack Detection Methods in Corporate Networks
This paper presents and analyzes existing methods for detecting network attacks. The purpose of reviewing the principal existing attack-detection technologies is to examine the effectiveness of the attack-detection technologies currently available, along with their main advantages and shortcomings. On the basis of the methods discussed, it can be concluded that, in terms of software implementation and the quality of attack detection, the signature-based detection method remains one of the most effective approaches. The vast majority of contemporary systems rely solely on the signature-based method to identify the impact of an attack, or combine it with mechanisms for detecting anomalies in monitored network behavior.
Mathematical Modeling of the Rheological Behavior of Asphalt Concrete for Smart City Road Infrastructure
The paper addresses the mathematical modeling of the rheological behavior of asphalt concrete within the context of Smart City road infrastructure. A modified version of the Burgers model is proposed, integrating components of elasticity, viscous flow, and delayed elasticity. The model is calibrated for conditions of high temperature and cyclic loading, characteristic of the South Caucasus climatic zone. A scheme for real-time analysis of road pavement data, equipped with a sensor network, is proposed, enabling the prediction of the development of deformation mechanisms and the optimization of infrastructure repair work schedules. The results showed that the modified Burgers model describes real deformations 12–15% more accurately compared to classical Maxwell-Kelvin models.
AI at Half the Price: Harnessing Off-Grid Hydropower and Direct Cooling for Scalable Intelligence
The rapid growth of artificial intelligence has significantly increased semiconductor investment, exposing energy and cooling infrastructure as the primary bottleneck for computational progress [1]. Using the Republic of Georgia as an illustrative case study, this paper proposes a framework for developing a decentralized network of AI data centers co-located with small and medium-sized hydroelectric power plants [11]. This model, which is equally applicable to other hydro-rich geographies such as Canada, Alaska, the Pacific Northwest, Kazakhstan, Nepal, Tibet, Scandinavia, the Alps and the Carpathians in Europe, targets the primary operational costs in AI: electricity consumption and thermal management. By eliminating grid-related transmission losses (10%-20%) and utilizing river water for direct cooling (further 25% to 50% electricity consumption reduction), our approach can reduce total electricity usage by up to 60% in optimal scenarios. This strategy offers a practical and economically sustainable method to support high-performance AI workloads globally.
Real-Time Presentation of Courier Movement in Web and Mobile Applications Using Socket.IO and Message Broker Architectures
Real-time tracking has become an integral part of modern courier services. Customers expect continuous monitoring of the status and location of their shipments, from the moment they leave the warehouse until delivery. While this functionality appears simple from a user perspective, developing such a system presents significant technical challenges. It must handle a continuous stream of location updates from hundreds of couriers simultaneously, providing this information to customers with minimal latency. This article presents a real-time courier tracking system based on Socket.IO, a message broker, and a backend service responsible for data storage and synchronization. The proposed architecture decouples the system's core functional components, including data collection, event processing, data persistence, and customer interaction. This modular design enhances scalability, improves system performance, and increases fault tolerance, preventing failures in one component from affecting the entire system. The proposed solution demonstrates how modern event-driven technologies can be combined to provide reliable and efficient real-time tracking for web and mobile applications.
Enhancing CI/CD Security with Artificial Intelligence: State of the Art, Challenges, and Integrated Approaches
Continuous Integration and Continuous Delivery (CI/CD) pipelines have become a core component of modern software engineering, enabling rapid and automated deployment of applications. While these practices significantly improve development efficiency, they also introduce new and complex security risks, including supply chain attacks, configuration drift, and large-scale propagation of vulnerabilities through automated pipelines [6, 7, 8]. Traditional security mechanisms, which rely primarily on static rules and signature-based detection, are increasingly inadequate in highly dynamic DevSecOps environments [3, 12]. This paper investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance the security of CI/CD systems. The proposed approach integrates anomaly detection, supervised classification, Explainable AI (XAI), federated learning, and adversarial robustness mechanisms into a unified analytical architecture [2, 4, 5]. The study argues that explainability and privacy-preserving learning are critical for adoption in regulated and mission-critical environments [2, 4, 7]. A conceptual and architectural framework is presented, together with an experimental evaluation strategy, demonstrating how AI-driven security analytics can provide proactive, transparent, and scalable protection for modern CI/CD pipelines.
Application of Shortest Path Algorithms in Telecommunication Channel Coding: A Comparative Study
Reliable communication over noisy channels requires efficient error-correcting mechanisms. Convolutional channel codes enable error correction by introducing redundancy, while decoding algorithms reconstruct the most probable transmitted sequence. The decoding process can be formulated as a shortest path problem in a weighted directed graph known as a trellis. The Viterbi algorithm performs maximum likelihood decoding by minimizing cumulative path metrics. This paper presents a mathematical formulation of convolutional decoding as a shortest path problem and compares the Viterbi algorithm with classical shortest path algorithms, including the Bellman–Ford algorithm and Dijkstra's algorithm. Structural similarities, complexity differences, and practical implications are analyzed.
PM Methodology Implementation in IT Management
The implementation of project management methods in IT management is one of the most important factors in the effective planning and resolution of contemporary managerial tasks. It contributes to the development and adoption of appropriate approaches and methodologies for addressing governance challenges within organizations, supports the formation of a new work culture, and ultimately creates a new value chain that provides competitive advantage in the market. Accordingly, this article discusses the importance of having a well-defined strategy for addressing managerial tasks, including the critical role of a properly structured IT governance framework and the existence of IT departments and a Project Management Office (PMO) based on that framework.
Use of Artificial Intelligence in IT Project Management
With the rapid development of technologies, project management is becoming increasingly complex, and new management approaches and organizational cultures are emerging. Companies seek to adopt as many innovations as possible in order to gain a competitive advantage in the market. The constantly changing environment presents continuous challenges for IT project managers, who must find new and innovative ways to manage and execute their activities more effectively. This article examines the benefits of using artificial intelligence (AI) in project management and explores how AI can support IT project managers in their daily activities and help prevent project-related issues.
When Digital Attacks Turn Physical: National Security in the Age of Cyber Threats
Cyber attacks are usually aimed at compromising personal, business or state sensitive information and IT systems, either for gaining access to data, finance or performing online sabotage. The world has seen various examples of cyber attacks affecting everyday life, from delaying or cancelling multiple flights to the destruction of physical infrastructure. The paper explores the growing interconnection between cyberspace and the physical world, emphasizing how cyber incidents targeting critical infrastructure, such as healthcare facilities, energy systems or transportation networks can directly endanger citizens’ safety and disrupt national resilience. The reader will have an overview of the key challenges in the cyber space, which is an alarm for the national security. The text analyses various cyber attacks which managed to affect citizens’ physical security and pose serious threats to the overall security of the countries. Emphasis will be made on the nature of the modern cyber space and how malicious actors adapt to changes in the world, demanding both private and state sector to be cyber aware and alert to avoid becoming victims. The research highlights the current vulnerabilities and makes prognosis for the future, how and why cyber attacks can cause even more danger to everyday life. Moreover, information will be provided about the possible solutions, based on the foreign experience, research data and statistics. The study concludes that safeguarding national and citizen security in the 21st century requires not only technological adaptation, but also strategic foresight and coordinated governance at all levels, in all sectors.
AI-Assisted Programming in Practice Conceptual Foundations and Data-Informed Observations
The increasing use of large language models in programming practice has transformed how software is designed, implemented, and validated. While existing studies largely focus on performance, productivity, or tool ecosystems, less attention has been paid to the role of requirement specification and interpretation in AI-assisted programming. This study examines how differences in task specification influence the behavior of humans and artificial intelligence during program construction. The analysis is based on a comparative, practice-informed observation of programming tasks executed under two contrasting conditions: an incompletely specified task and a fully, explicitly specified task. Using a controlled programming context and the same execution environment, the study investigates how algorithmic assumptions, interpretative choices, and code structure emerge in each scenario for both human developers and AI-generated solutions. The focus is placed not on execution speed or optimality, but on interpretative variability, stability across executions, and qualitative properties of the resulting program code, including structural complexity and readability. The observations show that incompletely specified tasks create a wide interpretative space in which AI-generated solutions exhibit substantial variability and, in some cases, exceed the intended scope of the task through functionally abundant or overly complex implementations. In contrast, explicit specification significantly reduces interpretative divergence and leads to conceptual convergence between human-written and AI-generated code. However, even under fully specified conditions, differences remain in qualitative aspects of code structure, with AI-generated solutions tending toward higher structural complexity. These findings highlight specification as a critical boundary condition in AI-assisted programming and emphasize the evolving role of humans from code production toward interpretation, validation, and refinement in modern programming workflows.
Leadership Dynamics as Determinants of Project Success
The research examines the impact of leadership on project success given the increasingly complex and collaborative frameworks of projects today. While it is imperative to have technical competence and a detailed plan, this study underlines the importance of leadership as one fundamental element of successful outcomes. This research looks at how specific leadership behaviours influence the different aspects of project success such as achievement of goals, delivery on time, budget compliance, stakeholder satisfaction, and most importantly team cohesion. A meaningful investigation of the research issue was conducted using a quantitative survey of more than 200 project practitioners involved in a variety of international projects using collaborative environments. The survey captured perceptions of leadership behaviours and their associated dimensions of project performance. In addition to descriptive data, the study employed regression analysis in order to identify support for strong relationships between transformational leadership characteristics and high levels of team cohesion and overall project success. The findings from this article also describe the value of adaptive, motivator, and communicator leadership, in the context of dynamic project environments. This research contributes to the wider understanding of leadership as a strategic resource in project management and offers organizations practical implications to improve project deliverables through excellent leadership.
Performance and Efficiency Evaluation of GitOps-Based Deployment Workflows
GitOps represents a modern approach to infrastructure and software deployment that leverages Git to enable continuous, automated, and controlled operational processes. This study examines the core functional mechanisms of GitOps, including declarative configuration, the reconciliation loop, and the pull-based deployment model. Special attention is given to Argo CD and Flux as leading GitOps tools, with an emphasis on their integration with Kubernetes. The paper analyzes real-world operational workflows, architectural components, and security mechanisms associated with GitOps-based systems. The efficiency of GitOps is investigated across diverse application domains, ranging from technology startups to highly regulated industries, including banking and healthcare. A comparative analysis is conducted between GitOps-based approaches and traditional CI/CD models. The concluding section outlines future perspectives for the evolution and expansion of GitOps, including standardization efforts, scalability challenges, and the integration of emerging technologies such as artificial intelligence, serverless architectures, and edge computing. This work serves as a practical guideline for organizations seeking to adopt GitOps practices and improve the efficiency and reliability of their operational processes [1].
A Cloud-Based Virtual Sensor Approach for Intelligent Marine Water Quality Monitoring
Recent studies indicate that the practical implementation of virtual sensors is closely associated with Internet of Things (IoT) technologies, which enable continuous data acquisition and reliable transmission from distributed sensing devices. The rapid scalability of IoT systems and the exponential growth of telemetric data streams have created an increasing demand for cloud computing integration, allowing efficient storage, processing, and real-time analysis of large-scale datasets [1], [2]. The literature emphasizes that cloud platforms provide a favorable environment for the deployment of virtual sensing frameworks, as they support centralized data management, dynamic allocation of computational resources, and advanced analytical capabilities [3], [4]. The integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms with IoT-based cloud infrastructures is widely recognized as a key enabler for the development of intelligent monitoring systems. Numerous studies demonstrate that AI/ML techniques significantly enhance anomaly detection accuracy, predictive analytics performance, and real-time decision support, particularly when virtual sensors are employed to estimate environmental parameters or validate physical measurements [1], [5], [6]. This approach reduces the impact of measurement noise and sensor drift while improving the overall reliability and robustness of monitoring systems. Although peer-reviewed publications specifically focused on Azure IoT Central remain relatively limited, the broader scientific and technical literature confirms the potential of cloud-based IoT platforms to provide secure communication, scalable analytics, real-time data ingestion, and predictive insight generation [2], [3], [7],[8]. Therefore, existing research suggests that the integration of virtual sensors, IoT technologies, and cloud infrastructures represents a promising and still underexplored direction for the development of intelligent marine water quality monitoring systems, reinforcing the scientific relevance and practical significance of this study.
Performance Optimization of CI/CD Pipelines through Redundant Operation Elimination
The proposed approach provides actionable insights for DevOps practitioners and supports data-driven decision making in CI/CD pipeline design. This article presents an experimental analysis of Continuous Integration and Continuous Delivery/Deployment (CI/CD) pipelines with a focus on performance optimization through the identification and elimination of redundant operations. Based on controlled experiments conducted in virtual machine environments and real-world CI/CD pipelines, the study demonstrates that unnecessary duplication of build and restore steps significantly increases execution time and resource consumption. A mixed-methods research approach is employed, combining quantitative performance measurements with a practical case study implemented in Azure DevOps. The results indicate a measurable reduction in average build and integration time after applying targeted optimization strategies, including framework-specific configurations, caching mechanisms, and pipeline restructuring. The findings confirm that systematic CI/CD optimization improves software delivery efficiency and supports faster, more reliable release cycles. The study contributes practical guidelines for DevOps teams seeking to enhance pipeline performance while maintaining code quality and system stability [1,2].
Data Processing Pipeline for Course-Level Outcome Analytics in Higher Education: Time-Based and Clustering-Based Hybrid Approaches
One of the key tasks of quality assurance in higher education is the systematic monitoring of course-level outcomes and the adoption of appropriate decisions based on the analysis of these results. Although quality assurance standards and guidelines place strong emphasis on data-informed monitoring and the periodic review of programmes, practical analytical approaches applied at the course level often remain diverse and unevenly formalized. This situation is largely influenced by the institutional autonomy granted to higher education institutions, which results in diverse practices across institutions. The aim of this paper is to review and comparatively analyze contemporary data-informed and analytics-based approaches to course-level quality assurance. The study is based on a systematic examination of existing approaches, including the analysis of aggregated assessment data, the use of learning analytics and educational data mining, as well as the role of explainable analytics in supporting academic and managerial decision making. The review is complemented by a practice-oriented example based on real data, which examines the longitudinal analysis of assessment outcomes for the same course over multiple academic years. The findings indicate that the analysis of grade distributions and performance dynamics can be effectively used to identify the need for improvements in course content, teaching methods, allocation of instructional time and assessment systems. The paper highlights the specific characteristics of different approaches, their strengths and limitations, and emphasizes the importance of using modern technologies to support course-level quality assurance processes.