This study analyzes existing strategies for automated recovery within self-healing cloud infrastructures. The research is grounded in a review of findings from previous scientific publications. The analysis demonstrates that intelligent remediation methods can not only reduce downtime but also enhance the economic resilience of cloud infrastructure, paving the way toward fully autonomous, self-healing digital platforms. The scientific contribution of this work lies in the first comparative evaluation of the effectiveness of rule-based approaches, ML-prioritized methods, genetic algorithms, and DQN agents in multi-cloud Kubernetes environments. Its practical significance is reflected in the proposed modern approach of implementing a hybrid pipeline with a DQN-based scheduler, which achieves more than a 70% reduction in downtime and establishes a balance between recovery speed and cost-efficiency in real-world cloud platforms. The insights presented in this study will be particularly valuable to researchers in the field of autonomous distributed systems and cloud infrastructure reliability, especially those engaged in the development and formal verification of self-healing and automated failure correction mechanisms. Furthermore, the analysis of the effectiveness of these techniques holds practical relevance for leading DevOps/PlatformOps architects and SRE specialists seeking to enhance the availability and resilience of critical services through the integration of advanced automated recovery algorithms.

The behavior of ion interactions during low saline water flooding in sandstone reservoirs plays a crucial role in enhanced oil recovery (EOR) processes. This study investigates the impact of ion interactions on the displacement efficiency and fluid dynamics during low saline water flooding (LSWF) using a numerical modeling approach. A comprehensive model was developed to simulate ion exchange, electrostatic forces, and permeability alterations in sandstone formations. Results indicate that LSWF significantly alters the pore structure and wettability, leading to improved oil recovery. This work provides insights into the potential for low saline water flooding as a viable EOR method, emphasizing the role of ion interactions in optimizing recovery processes.

This article presents an analysis of frameworks designed for AI-driven orchestration of cloud resources, focusing on contemporary methods and architectural models aimed at improving the efficiency, adaptability, and energy performance of cloud computing environments. The study includes a comprehensive review of applied machine learning techniques, deep learning, reinforcement learning algorithms, evolutionary algorithms, and hybrid approaches used for workload prediction, resource allocation optimization, and autonomous decision-making. The paper identifies key integration challenges, computational overhead, issues of interpretability and security, and outlines development prospects through the implementation of Explainable AI and standardized modular architectures. The findings demonstrate the potential of the proposed approaches for practical implementation in dynamic cloud infrastructures. The insights provided in this article will be of interest to researchers and professionals working in the fields of distributed computing, cloud technologies, and artificial intelligence, as it analyzes modern frameworks designed to build efficient coordination systems within hybrid computing environments. Moreover, the material will be useful for specialists and academics seeking to integrate cutting-edge technological solutions into corporate and research projects, enabling optimized data processing and enhanced adaptability of information systems in an era of continuous digital transformation.

This article presents a methodology for customizing third-party packages in PHP projects using Composer. Drawing on established extension patterns (Decorator, Adapter, Bridge), principles of API-centric architecture (PSR-4, Service Providers, Semantic Versioning), and event-driven mechanisms (Composer Hooks, PSR-14 Event Dispatcher, task queues), the paper outlines an integrated framework that enables safe and scalable modifications without directly forking dependencies. The proposed methodology is informed by a comparative analysis of prior research, allowing for a comprehensive examination of Composer-based third-party package configuration. The results demonstrate a reduction in technical debt and improved maintainability of projects while preserving the ability to apply automated updates. The conceptual strategies outlined here will be of particular interest to senior PHP architects and lead developers responsible for ensuring the scalability and reliability of enterprise web applications. Moreover, the analysis of dependency customization practices offers practical value to researchers and graduate students in software engineering, especially those focused on the evolution of package management tools and the optimization of CI/CD processes within DevOps ecosystems.

Quantum calculations, based on the principles of quantum mechanics, can significantly change the information technology industry. Unlike classical computers, quantum computers use qubits that can be superposed, which allows them to significantly accelerate the solution of complex tasks such as optimization, big data processing, and cryptography. The article examines the impact of quantum computing on information processing, data security, and artificial intelligence, as well as the challenges facing the industry, including the problems of qubit stability and the need for retraining specialists. Quantum technologies have great potential, but for their mass application, it is necessary to overcome a number of technical and theoretical obstacles.

The integration of renewable energy sources into modern power systems presents significant challenges due to inherent uncertainties in resource availability, demand fluctuations, and technical performance. Monte Carlo simulation has emerged as a powerful tool for addressing these uncertainties in renewable energy planning and optimization. This paper presents a comprehensive review of Monte Carlo applications across solar, wind, and hybrid renewable energy systems over the past two decades. Through systematic analysis of 75+ peer-reviewed publications, we identify key methodological trends, implementation challenges, and emerging opportunities. The review reveals that while Monte Carlo methods have been extensively applied to single-source renewable systems, significant gaps exist in addressing correlated uncertainties across hybrid configurations and real-time operational scenarios. We propose a novel unified framework that integrates machine learning-enhanced sampling techniques with traditional Monte Carlo approaches to improve computational efficiency while maintaining accuracy. The framework addresses five critical uncertainty dimensions: resource variability, demand stochasticity, equipment degradation, market price fluctuations, and grid integration constraints. Case studies demonstrate that the proposed framework reduces computational time by 40-60% compared to traditional methods while improving prediction accuracy by 15-25%. This review provides researchers and practitioners with a structured approach to implementing Monte Carlo simulations for renewable energy planning under uncertainty, contributing to more robust and economically viable renewable energy deployment strategies.

The dynamic and transient performance analysis of a three-phase interior rotor concentrated winding permanent magnet synchronous generator (CW-IPMSG) with was presented.  In this paper. The study was done in direct-phase variables concentering only the fundamental magneto-motive force (MMF). The machine’s inductance was determined using winding function theory (WFT). The derived inductance was used to determine performance characteristics of the machine’s variables such as phase current, load current and electromagnetic torque. The study was validated in MATLAB/Simulink to observe the performance of the characteristics of the generator. The study was carried out at no-load condition, under load perturb, as well as increase and decrease of capacitor. It was observed that the permanent magnet synchronous generator had slightly better output performance with capacitor assistance.     

This study examines the conceptual models for optimizing infrastructure solutions for ISPs based on cloud technologies. The relevance of this research is justified by the rapid technological advancements that serve as the foundation for infrastructure solutions in internet service providers (ISPs). Their optimization requires a systematic approach that considers load balancing, distributed data storage, security issues, and regulatory aspects. However, there are contradictions in the scientific literature regarding optimization methods. The goal of this article is to systematize the understanding of conceptual models for optimizing ISP solutions, taking into account modern cloud technologies and their evolution. The conducted analysis identified key research directions and existing gaps in studying the interrelationship between technical, economic, and legal factors. As a result, an author's perspective was formulated on the prospects of integrating cloud solutions into ISP infrastructure, considering scalability efficiency, fault tolerance, and information security. This includes deep integration of analytical tools, synergy with quantum computing technologies, and standardization unification. The presented materials will be useful for researchers in the field of digitalization, network technology specialists, internet service providers, and developers of relevant platforms.

The article is devoted to the development and experimental validation of scalable ETL pipelines for HR data, aimed at bridging the gap between the volume of heterogeneous workforce events and the capabilities of traditional nightly processes. The relevance of the study is determined by the exponential growth of the HR technology market to USD 40.45 billion in 2024 and its forecasted doubling by 2032 at a 9.2% CAGR, as well as by the fragmentation of corporate systems, which leads to data incompleteness, inconsistency, and latency in turnover metrics and talent-development program effectiveness analysis. The work is aimed at formalizing requirements for Extraction, Transformation, Loading, Scalability, and Observability; at designing a containerized architecture based on Kubernetes, Apache Airflow, Spark, and Flink-CDC; and to ensure low latency, exactly-once semantics as well as linear scaling up to 32 worker pods with an efficiency η of 0.78 or greater. The novelty of the work lies in the first formal model that integrates adaptive API-request throttling with idempotent SCD-attribute transformations for a hybrid Iceberg/Snowflake storage layer and a complete observability system using Prometheus and OpenTelemetry with real-time alerts. An experimental evaluation on a private Kubernetes cluster under load up to 10⁸ records per day demonstrated end-to-end latency ≤ 15 min in batch mode and p95 latency reduction to 48s in near-real-time mode, throughput up to 18.7k records/min with linear worker scaling (η = 0.82), and full lineage-graph traceability in compliance with GDPR. The main conclusions confirm that the proposed architecture provides reliable and reproducible HR-data integration with minimal latency and predictable cost, paving the way for practical deployment in large enterprises. This article will be helpful to data engineers, cloud-architecture designers, and project managers in HR analytics automation.

A novel lubricant containing 0.10 wt % of Renox-modified Buckminster-fullerene nanoparticles (C₆₀-NP) was applied to steel components and evaluated after multiscale sliding that alternated dry and boundary-lubricated regimes. Post-mortem scanning electron microscopy (1 µm–500 µm) revealed complete suppression of under-surface cracks and a pronounced autonomous flattening of micro-asperities. Tapping-mode atomic-force microscopy (5 µm–200 nm windows) showed that the treated surface is blanketed by a continuous 1–3 nm tribofilm composed of 1.08–1.10 nm nanoparticles that concentrate on asperity crests. Residual-stress analysis with the sin²ψ method on the {311} ferrite reflection produced a slope of 0.00105, corresponding to an in-plane tensile stress of 115 MPa—far below the threshold associated with delamination wear in untreated steel reported in the project appendix. These convergent observations demonstrate that friction-induced welding of C₆₀-NP forms a self-regenerating nano-bearing film that simultaneously lowers shear stress, blocks dislocation emission and restores surface topography. These findings demonstrate a friction-driven, self-assembled carbon–metal nanofilm that simultaneously delivers anti-wear and restorative functionality, offering a compelling technological basis for industrial deployment.

The article examines the problem of ensuring end-to-end data consistency in distributed multi-stage event processing pipelines, which are actively used in modern real-time systems. The relevance of the study is determined by the rapid growth of streaming analytics needs and the widespread use of Apache Kafka, making message latency, duplication, and disorder critical factors for industries ranging from fintech to IoT. The goal of this work is to propose a formal model that unifies an extended event representation and a set of invariants that guarantee correct processing even in the presence of component failures. The novelty of the approach lies in the formalization of an event as a tuple ⟨id, tsₛᵣ????, p, v, σ⟩, where id is responsible for deduplication, tsₛᵣ???? records the time of occurrence, p specifies the partition, v is the payload, and σ is the schema version, which enables ordering recovery and supports format evolution. The pipeline is modeled as a directed acyclic graph (DAG) of operators having the properties of determinism, idempotence, and monotonicity. CRDT aggregates are used for convergence in duplication; SLA alerts from watermark mechanisms are used to minimize data loss. The main findings indicate that, under specified conditions, the system can tolerate delays, failures, and redeliveries without compromising consistency. Extended events and formal operators enable state recovery; stream semantics are ensured by four invariants. This research is particularly relevant for professionals designing and operating real-time event-driven systems, stream processing applications, microservices architectures, and high-integrity data integration pipelines.

With an emphasis on Oracle HCM Cloud, this study investigates methods for lowering organizational costs through the use of ERP cloud solutions.  It lists five interconnected routes to efficiency: intelligent automation, process standardization, increased transparency, centralization of HR activities, and the removal of manual operations.  The study illustrates how these tactics result in quantifiable results using a combination of literature analysis and a technical case study.  The main feature is the Setup Extractor tool from Deloitte, an automation solution based on BI Publisher and XML that was created to simplify configuration moving between environments.  This tool illustrates how focused automation can increase the strategic advantages of ERP systems by decreasing human labor, setup mistakes, and deployment time. Real-world implementations' empirical findings demonstrate increased employee engagement, workforce productivity, and cost savings in a variety of HR disciplines.  By bridging the gap between theoretical models and practical results, this study highlights how crucial it is to add intelligent tools to ERP cloud platforms in order to achieve long-term, significant cost reduction. HR directors, consultants for digital transformation, ERP implementation teams, and decision-makers in large corporations looking to update personnel management and cut expenses may find useful information in this article. Additionally, it adds to professional and scholarly discussions about the importance of focused automation in cloud ERP ecosystems.

This article examines the application of dynamic difficulty algorithms to optimize player retention and monetization metrics in free-to-play projects through an empirical study conducted within a gaming environment. The fact that key indicators of a project’s viability in the F2P industry, such as D1/D7/D30 retention, directly correlate with LTV and operating profit, makes the research relevant. Traditional static difficulty curves give rise to the “difficulty paradox” — boredom or frustration that accelerates churn. In contrast, DDA promises to keep the player in Csíkszentmihályi’s “flow” zone by balancing challenge and skill. This study aims to demonstrate, on causal data, the effect of algorithmically adaptive difficulty on user retention and revenue. The novelty of the work lies in a large-scale randomized controlled experiment that combines the segmentation of “at-risk” and “core-spender” cohorts, as well as an A/B-testing and RCT methodology, to evaluate DDA as a scalable product parameter rather than merely a UX enhancement. The main findings show that night-by-night decreasing difficulty for the “at-risk” subgroup increases D30 retention by 3 percentage points, yields, on average, one additional day of play and ten more rounds per user per month, and an LTV uplift of $ 0.08 per user, where IAP and 21% by advertising generate 79% of the increase. The effect is heterogeneous: the “core-spender” segment primarily exhibits a financial response, whereas “frustrated” players increase their play activity without significant growth in spending. A comparative analysis revealed that simple heuristics offer a baseline uplift, while classical ML models can ensure up to a 20% retention growth. Additionally, RL agents and hybrid fuzzy-RL solutions can retain players longer at comparable computational costs. At the same time, generative LLM-based controllers open up prospects for unifying DDA approaches. This article will be helpful to game-product analysts, personalization-system developers, and monetization managers in the video-game industry.

As microservices proliferate in enterprise architectures, ensuring reliable interactions between independently developed services is paramount. Traditional end-to-end and integration testing techniques often fail to scale in dynamic, decentralized environments. Consumer-driven contract testing, as enabled by the open-source tool PACT, offers a structured methodology to verify service interactions against predefined contracts. This paper introduces the principles of contract testing, examines PACT in depth, compares it with other frameworks such as Spring Cloud Contract and Dredd, and presents a reproducible case study from a real-world e-commerce application. We demonstrate how PACT can significantly reduce production defects, improve developer autonomy, and enhance CI/CD integration, establishing it as a valuable approach for modern service validation.