No. 06 (2025) Volume 07 , Issue 06

Most mentorship and coaching programs today operate on a broken model - they bundle everything together and assume everyone needs the same help. Whether you're studying for competitive exams or navigating career changes, you're forced to pay for comprehensive packages even when you only need guidance in specific areas.

We experienced this problem firsthand. While preparing for civil services, one of us needed help only with modern history and parts of geography, yet had to enroll in expensive full-scale programs covering subjects she'd already mastered. Our other co-founder struggled to find relevant mentors through existing professional networks when facing crucial career decisions.

These frustrating experiences aren't unique. A survey of our 200,000-member community revealed that over 85% prefer personalized mentorship over generic coaching programs, highlighting a massive gap in the market.

Mentr addresses this problem directly. It's an on-demand mentorship platform where users can book live video calls with experts, join immediate sessions with available mentors, or access targeted video content - all focused on their specific needs. Rather than paying for unnecessary content, users get precise help when they need it.

Our platform uses AI-powered matching to connect mentees with relevant mentors based on their queries, not just keyword searches. We've built a modular system that scales individual components independently while maintaining quality through strict mentor vetting and continuous feedback analysis.

With plans to onboard 100+ mentors in year one, Mentr represents a fundamental shift toward accessible, personalized guidance that eliminates the inefficiencies of traditional bundled coaching models.

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.

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.

This study proposes a mechatronic system concept for real-time control of the accelerator’s speed within the gin’s working chamber. The system uses an ultrasonic sensor to measure the density of cotton fractions and adjusts the motor’s rotational speed via a frequency converter, employing PID control based on motor current analysis. This approach reduces jamming and mechanical damage during cotton transport and enhances seed separation efficiency.

This article aims to theoretically generalize and empirically verify the mechanisms that enable service companies not only to withstand external shocks but also to turn them into a source of expansion. The relevance of the research is determined by the fact that the service sector generates more than 67 percent of global GDP, yet faces increasing volatility in costs, demand, and supply chains. The objective of the work is to identify and systematically describe the internal pillars and architectures of value creation capable of ensuring sustainable growth under such conditions. The novelty of the article is manifested in the combination of case study of the E.D.E. with a multicohort analysis of industry, behavioural and macroeconomic statistics; this multiple triangulation allowed for a detailed tracing of the evolution of the inside-out strategy and for the quantitative assessment of the contribution of each of the five pillars (value framework, hybrid revenue structure, digital backbone, inclusive leadership, social capital) to smoothing market turbulence. As a result, three complementary resilience architectures are formulated — platform 24/7, cyclical Revive & Reuse, and expert Embedded Partner — which together turn response speed, cost of ownership, and predictive analytical support into mutually reinforcing competitive advantages. Key findings are as follows: (1) the resilience of a service enterprise is determined by a priori built-in synergy of values, people and technology rather than by reactive anti-crisis measures; (2) a personal culture of responsibility and transparent digital processes are the foundation of long-term staff retention and client trust; (3) a financial cushion, diversified logistics and an internal personnel academy create operational independence, allowing investment even at the moment of external shocks; (4) resilience, as a dynamic ability to convert risks into growth, is scalable through a cloud franchise, IoT analytics, subscription service models and the externalization of educational practices. The article will be useful for managers of service companies, researchers of sustainable development, and consultants on business model transformation.

Recently, the DevSecOps practice has improved companies’ agile development of secure software, reducing problems and improving return on investment. However, overreliance on security tools and traditional security techniques can facilitate the implementation of vulnerabilities in different stages of the software lifecycle. The evolution, principles, and importance of DevSecOps in contemporary software engineering. DevSecOps arises from the recognition that traditional security measures often lag the rapid pace of DevOps development cycles, leading to vulnerabilities and breaches. By integrating security early and continuously throughout the software development lifecycle, DevSecOps aims to proactively identify and mitigate risks without impeding the agility and speed of DevOps practices. The core principles of DevSecOps, emphasizing automation, collaboration, and cultural transformation. Automation streamlines security processes, enabling the automated testing and validation of code for vulnerabilities. Collaboration fosters communication and shared responsibility among developers, operations, and security teams, breaking down silos and promoting a collective approach to security. Cultural transformation involves cultivating a security-first mindset across the organization, where security is not an inherent part of the development process. The importance of DevSecOps cannot be overstated in today's digital landscape, where cyber threats are omnipresent, and the cost of security breaches is staggering. By integrating security into every stage of the DevOps pipeline, organizations can enhance their resilience to cyber-attacks, comply with regulatory requirements, and build trust with customers. DevSecOps represents a holistic approach to software development that prioritizes security without compromising speed or innovation. Embracing DevSecOps principles is imperative for organizations seeking to stay ahead in complex and hostile digital environment.

Study background: Incidents of accidents at construction sites are higher in developing countries than in developed countries. The construction industry comprises several people with different backgrounds and different tasks performed by them. This study examined the effect of the occupational accidents on job performance of a construction firm.

Materials and methods: Structured questionnaires enclosing questions on demographic characteristics, general knowledge of safety and legalities, accident occurrence, safety management systems were administered to 110 workers of the construction firm. Computed data were subjected to some statistical analyses such as Chi-square and Pearson's Correlation Coefficient (r) and visualised in tables, graph and scatter plot.

Results: The results of the study revealed that males dominated with 84.5 % while females were estimatedly 15 %. Majority (40%) possessed secondary school qualification whereas 6 % had no formal education. It was revealed that, a higher knowledge of safety legalities whereas low records of accidents were recorded among the construction workers of the firm indicating a good safety management system. The result established a weak negative correlation (r= -0.164) between accident and job performance (labor productivity) and a statistically not significant association between the accident occurrence and absenteeism as (χ2 = 0.4291, p > 0.512) and a significant association between nature of accident and absenteeism (χ2 =6.7360, p < 0.009).

Conclusion and recommendation: The study concludes that a good safety management system and a positive employee attitude would reduce absenteeism, and occupational accidents and further increase labor productivity as demonstrated in the conceptual framework.

This paper utilizes AI tools to enhance the ongoing test cycle in a DevOps environment, thereby creating Metabase. This data architecture is robust and scalable, supporting a highly responsive release process. The project is vital since the releases have become more frequent; standard automation has already reached its limit, increasing the costs of maintaining scripts and, consequently, resulting in a significantly higher total cost due to the discovery of many more defects. The novelty of this work is grounded in an approach to choosing and applying AI tools through comparative analysis over available commercial and open-source platforms, supported by content analysis of empirical use cases and quantitative assessments from industry reports. Herein, a methodology is presented that consists of architectural solutions for data lake organization, continuous model training scenarios, and ML endpoints integrated into the CI/CD pipeline, which hosts Predictive Test Selection, as well as self-healing and test-case prioritization mechanisms. It narrates the creation of the prompt-engineer position and the connections between QA/ML experts and organizational facets. This paper discusses the application of clear AI measures for risk assessments. The final calls shown here indicate that intelligent automation enables reducing a regression set to a barely necessary size while maintaining 99.9% bug detection and minimizing false alert failures. This, in turn, leads to improvements in MTTR and TCO quality. TestPilot and FlakeFlagger verify Meta’s practices; furthermore, it is anticipated that forecasts will retain a CAGR of 20.9% in the global AI testing market growth. Solution maturity, encompassing both SaaS and on-premises models, offers a flexible choice to regulated industries. Metabase architecture is shown in which raw and processed data are kept separately to ensure timely model retraining as well as to minimize computational costs. This article will be helpful for software architects, QA managers, DevOps teams, and ML engineering specialists involved in building scalable and resilient testing architectures.

Our study introduces a real-time, non-destructive strategy for monitoring self-healing in biocement by integrating genetically engineered bioluminescent microorganisms. Microcracking in concrete infrastructure imposes annual repair expenditures exceeding US$18 billion in the United States, underscoring the need for effective in-situ diagnostics. Although microbially induced calcium carbonate precipitation (MICP) offers an attractive self-healing mechanism, existing evaluation techniques are invasive, intermittent, and incapable of capturing healing kinetics. We engineered three bacterial strains— Sporosarcina pasteurii, Bacillus subtilis, and Pseudomonas aeruginosa—to constitutively express luciferase, enabling emission of quantifiable light signals proportional to metabolic activity during mineralisation. Laboratory experiments across diverse environmental conditions and encapsulation schemes revealed a robust correlation (R² = 0.92) between bioluminescence intensity and calcium carbonate precipitation rate, with microcracks as small as 10 µm reliably detected. Field-scale validation under simulated climatic cycles confirmed sustained signal integrity over 24 monitoring events during twelve months, while achieving crack-closure efficiencies between 75 % and 89 %. The proposed biosensing platform furnishes unprecedented insight into temporal healing dynamics, facilitating optimisation of microbial formulations, predictive maintenance scheduling, and deeper elucidation of microbe–mineral interactions in cementitious matrices. Its implementation could significantly extend service life and reduce lifecycle costs of critical infrastructure assets. Beyond concrete, the technology can be adapted to other structural materials where real-time, autonomous health monitoring is imperative.

Background: This article analyzes the optimization of software development processes in distributed teams through the use of agile methodologies and modern digital tools. The study covers the theoretical foundations of agile approaches, including iterative development, continuous feedback, and adaptive planning, while also examining practical methods and tools that enhance communication and knowledge management in distributed work environments. Methods: The methodological approach includes a comparative literature review, which has made it possible to identify both success factors and challenges in implementing agile practices in distributed teams. As a result of the study, a comprehensive optimization model has been proposed, integrating theoretical principles with practical tools such as Jira, Confluence, Slack, Microsoft Teams, and GitLab, along with recommendations for adapting organizational culture and management processes to improve development efficiency. Findings: The findings demonstrate that the combination of integrated digital solutions with agile methodologies contributes to shorter development cycles, improved product quality, and enhanced communication flows. Novelty and applications: The insights presented in this article are relevant to researchers in information technology, professionals involved in business process optimization, and managers of distributed teams seeking to implement advanced software development methodologies in the context of global digital transformation.

A General Rate Case (GRC) is a formal regulatory process where a utility company requests approval from a regulatory body—such as the California Public Utilities Commission (CPUC)—to adjust customer rates based on projected costs and revenues. Utilities typically submit GRC filings every three years, presenting detailed financial, operational, and capital data. The goal is to justify rate changes needed to support infrastructure, operations, and returns for investors. The process includes public hearings, stakeholder input, and regulatory review before rates are approved, modified, or denied. GRC filings are complex and labour-intensive, often requiring manual data extraction, analysis, and documentation. Automation tools such as SAP HANA can significantly streamline this process by consolidating data from multiple sources, performing cost allocations, validating inputs, and generating regulatory reports. Automation reduces errors, saves time, and ensures better compliance. Key components of GRC preparation include capital data (financial, physical, human, and intellectual), budget codes for tracking expenditures, and workpaper groups that organize supporting documents. Witness areas define expert testimony topics, while special remaps update compliance programs. GRC tools facilitate risk assessment, policy management, and reporting, supporting both GRC-specific filings and broader utility compliance efforts. Automating GRC processes enhances transparency, accuracy, and regulatory responsiveness.

This article analyses the role of cloud computing as a key catalyst for digital transformation within enterprises. The relevance of the topic is supported by up-to-date, verified statistical data demonstrating an unprecedented surge in investments in cloud technologies and their all-encompassing impact on the business environment from 2021 to 2025. A scientific gap has been identified in the lack of a holistic understanding of cloud computing’s multifaceted effects on the strategic, operational, and cultural dimensions of transformation. The aim of the study is to systematise the mechanisms by which cloud computing accelerates digital transformation and to assess their influence. The scientific novelty lies in the description of a comprehensive model that accounts for the synergistic effect of cloud technologies on digital maturity. The author’s hypothesis asserts that cloud computing not only enhances efficiency and scalability but is also essential for fostering a flexible and innovative organisational culture. A wide-ranging review of current, peer-reviewed literature is presented, illustrating researchers’ approaches and the controversies in the existing scientific discourse. Key findings include a detailed analysis of statistical data on the growth of the cloud-services market and its impact on critical business metrics, as well as graphical and tabular representations confirming cloud computing’s catalytic role. The work will interest senior executives, IT directors, academic researchers, and practitioners involved in digital-transformation initiatives, as well as anyone seeking to maximise the potential of cloud technologies for sustainable enterprise development.

As financial institutions increasingly migrate their core platforms to microservices-based architectures, the challenge of managing distributed transactions has gained critical importance. Banking APIs typically require atomicity and consistency across multiple services—such as account management, fraud detection, notifications, and audit trails all of which operate independently with isolated data stores. In such an ecosystem, ensuring consistency, performance, and fault tolerance becomes a balancing act that traditional and modern transaction patterns attempt to resolve differently.  This paper explores and contrasts two dominant approaches to distributed transaction management: the Two-Phase Commit (2PC) protocol and the Saga Pattern, particularly in the context of mission-critical banking applications. 2PC has long been considered the gold standard for ensuring atomicity and strong consistency in distributed systems. However, its blocking nature, reliance on a centralized coordinator, and vulnerability to network partitions make it less suitable for high-throughput, globally distributed systems common in modern fintech platforms.  On the other hand, the Saga Pattern, an eventual consistency model that orchestrates a sequence of local transactions with compensating rollback operations—offers better fault tolerance and non-blocking behavior. Yet, its trade-offs include the complexity of compensating logic, lack of strict ACID guarantees, and potential for data anomalies if not carefully implemented. To ground the discussion in real-world reliability needs, I introduce a chaos engineering-based simulation that demonstrates the behavior of both 2PC and Saga under controlled failure scenarios, such as inter-service latency spikes and partial service outages. We benchmark recovery times, resource locking, system availability, and data reconciliation behavior using a representative banking microservice architecture deployed in a containerized environment.  My findings reveal that Saga outperforms 2PC in terms of availability and fault recovery, making it suitable for user-facing, latency-sensitive operations. However, 2PC remains superior for operations demanding immediate consistency and compliance with strict audit requirements, such as core ledger updates. Based on this analysis, we propose a hybrid transaction strategy that applies 2PC to core financial operations and Saga to surrounding auxiliary services, striking a balance between performance and correctness. This study offers practical design insights for architects building resilient, scalable, and regulation-compliant financial systems. It also highlights the need for adaptive orchestration platforms capable of dynamically selecting transaction models based on context and SLA requirements.

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.

This study aims to identify and systematically compare the main large-scale agile frameworks that companies can adopt to manage the work of large scale and distributed teams. The companies can consciously perform better decision on the choice of the framework that fits the practices and challenges of their organizations. The work employs a qualitative approach supporting an exploratory analysis identifying the processes of migration to large scale agile. First the assessment criteria for scaling agile are discussed. Second these criteria used to perform a comparative analysis fo 3 large scale agile frameworks i.e. SAFe, LeSS and Nexus. The findings reveal there isn’t a dominant large scale agile framework in all dimensions. However, framework like Nexus offer low technical complexity accommodating the changes easily while other frameworks like SAFe offer high level of scalability more demanding and deep efforts changing work processes in organization.

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.

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.

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 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.

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 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.

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.

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.

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.

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.