Event-driven architecture constitutes a software design paradigm through which systems establish communication channels and generate responses to modifications via event mechanisms, rather than depending upon direct synchronous request protocols such as Application Programming Interfaces. This architectural pattern possesses the capability to manage customer interaction activities at real-time scales and substantial operational volumes through the delivery of personalized, temporally appropriate, and contextually relevant engagements spanning multiple communication channels, including electronic mail, short message services, push notification systems, conversational interfaces, and social media platforms. Through decoupling of operational processes and enabling instantaneous communication pathways connecting Marketing Cloud, Sales Cloud, Service Cloud and other external cloud platforms like MuleSoft, Tableau, AWS, Azure, and Google Cloud Platform, organizations acquire the capacity to construct scalable workflow systems that generate automatic responses to customer behavioral actions, such as product viewing activities or shopping cart abandonment scenarios. The architectural framework produces measurable improvements in customer responsiveness metrics, diminishes requirements for manual follow-up procedures, and guarantees seamless coordination among organizational teams while avoiding the introduction of rigid coupling relationships between system components. When multiple systems, particularly cloud-based infrastructures, exchange information through Application Programming Interface channels involving sensitive customer information, payment transaction details, or critical business process data, securing these interfaces becomes paramount for protecting information assets, guaranteeing that exclusively authorized users and systems maintain access privileges, and preventing unauthorized exploitation. Secure multi-cloud exchange architectures can be designed employing OAuth 2.0 authentication frameworks, encryption protocol implementations, and event-based notification mechanisms while maintaining compliance with privacy regulatory frameworks such as the General Data Protection Regulation or the Health Insurance Portability and Accountability Act.

In many large enterprises, scaling agile has become a central part of broader digital transformation efforts. While the benefits of agile at the team level are well documented, the role of managers, particularly those in middle management, remains conceptually underdeveloped and practically ambiguous. This paper presents a structured review of 15 recent studies on large scale agile transformations published between 2019 and 2025. The synthesis identifies four recurring domains of managerial tension. These domains are leadership and authority, decision making and governance, coordination and integration, and performance and sustainability. The analysis interprets these tensions as paradoxes that require managers to reconcile autonomy with control, short term delivery with long term capability building, and local experimentation with enterprise stability. Drawing on paradox theory and dynamic capabilities, the paper develops a conceptual framework that positions middle managers as brokers of paradox across organizational levels. The framework contributes to scholarship by integrating fragmented evidence on managerial work in agile transformations and by extending paradox theory into the context of large scale agile. For practitioners, it offers a lens for redesigning leadership development, governance arrangements, and performance systems so that managers are better equipped to sustain agility over time.

This paper addresses the basic issues with using classical Price Elasticity of Demand (PED) models for commercial applications, particularly due to their inability to apply effective constraints under dynamic market conditions. Classical models usually lead to theoretically optimal, but impractical price recommendations (e.g. unlimited price increases for inelastic goods). This study proposes the Holistic Pricing Approach (HPA), a multi-variable method that unifies data inputs from multiple sources into a single recommendation engine that helps overcome the classical model shortcomings.

The HPA method employs a data fusion system linking three unique data sources: internal economics (e.g., product cost, target gross margins), competitive intelligence (competitor’s prices) and macroeconomic factors (e.g., inflation). These inputs are standardized with an interoperability layer to drive a four-step algorithmic heuristic. This includes a margin anchor price that is subject to adjustments by “competitive boundary checks” and “volume guardrails” to avoid excessive demand erosion.

The effectiveness of the HPA was validated through a theoretical simulation with truncation that was compared to a classic elasticity model. The results showed that the traditional approach maximized margin at the expense of significant volume (20% lost), while HPA successfully balanced preserving margins and market share (5% volume loss). Furthermore, the total profit dollar amount was greater for the HPA strategy, which confirms that the HPA methodology drives increasing economic value.

This study demonstrates that to protect revenue integrity, pricing must be approached as an interoperable ecosystem of constraints rather than a single dimensional elasticity calculation. This approach offers a roadmap for business leaders who, in the face of inflation, need to strike the right balance between increasing prices and preserving market share.

Accurate forecasting and effective risk assessment are critical components of modern business decision-making. With the rapid growth of data availability and computational power, machine learning (ML) has emerged as a powerful tool for improving predictive accuracy across diverse business domains. This study presents a cross-domain analysis of commonly used machine learning models for business forecasting and risk assessment, focusing on their applicability, performance, and limitations in different contexts. The research examines supervised learning models—including linear regression, decision trees, random forests, support vector machines, and neural networks—across financial forecasting, credit risk assessment, demand prediction, and operational risk management. Using secondary datasets and prior empirical findings, the study compares model performance based on prediction accuracy, interpretability, scalability, and robustness. The analysis highlights that while complex models such as neural networks and ensemble methods often achieve higher predictive accuracy, simpler models retain importance due to their transparency and ease of implementation. Furthermore, the study emphasizes that no single machine learning model is universally optimal; rather, model effectiveness depends on domain characteristics, data quality, and business objectives. The findings contribute to the growing literature on applied machine learning by offering a structured framework for selecting appropriate models across business domains. This research provides practical insights for managers, analysts, and policymakers seeking to integrate machine learning into forecasting and risk assessment processes while balancing performance and interpretability.

Chaos Engineering has conventionally been seen as a technical field dedicated to introducing controlled errors into distributed systems to identify vulnerabilities and enhance system resilience. This system-centric perspective has yielded considerable progress in cloud-native reliability; however, insufficient focus has been directed towards the human aspect of resilience engineering—particularly, how chaos experimentation can enhance learning, bolster cognitive preparedness, and fortify the competencies of engineering teams functioning amidst uncertainty. This study presents a Human-Centered chaotic Engineering (HCCE) Model, an innovative framework that reconceptualizes chaotic experiments as organized learning interventions instead of merely system stressors. Utilizing concepts from resilience engineering, DevOps culture, Site Reliability Engineering (SRE), and experiential learning theory, the proposed model identifies chaos experiments as tools to improve mental frameworks regarding failure, decrease incident response time, cultivate an antifragile team culture, and strengthen rapid decision-making. This paper illustrates, via case studies from enterprise DevOps and SRE Dojo programs, how chaos-driven learning settings promote psychological safety, facilitate collaborative problem-solving, and cultivate engineers who are not merely system operators but practitioners of resilience. The research posits that the forthcoming advancement in Chaos Engineering is not solely in automating fault injection or enhancing observability, but in fostering high-reliability teams adept at anticipating, adapting to, and learning from disruptions. The findings present a distinct viewpoint that integrates sociotechnical systems theory with practical enterprise engineering, establishing Chaos Engineering as a transformative educational framework for contemporary software organizations.

This article analyzes land resource use in Yangiyol District based on the NDVI, NDWI, and NDBI spectral indices. The study utilized index maps derived from satellite imagery. The results enabled the identification and spatial assessment of vegetation cover, water bodies, and built-up areas. The findings are of practical importance for land-use planning and monitoring.

Modern enterprises face critical challenges in managing cloud infrastructure at scale, where traditional manual approaches create bottlenecks that impede innovation and introduce operational risks. This article presents a comprehensive examination of an intelligent operations framework designed to address these challenges through serverless orchestration, automated discovery, and integrated governance mechanisms. The framework leverages event-driven architectures and infrastructure as code principles to transform infrastructure management from error-prone manual processes into automated, self-managing systems that deliver consistent outcomes across hybrid and multi-cloud environments. By embedding security and compliance capabilities throughout the automation platform rather than treating them as afterthoughts, the framework establishes comprehensive governance while maintaining operational agility. The article explores architectural foundations built on serverless technologies that minimize infrastructure overhead while maximizing scalability, dynamic asset discovery engines that maintain real-time inventory across distributed cloud environments, and modular workflow implementations spanning provisioning, maintenance, and access management. Integration with enterprise authentication systems ensures all automated actions remain properly authorized and auditable, while continuous compliance monitoring enables proactive remediation of policy violations. The framework incorporates sophisticated observability capabilities encompassing metrics, logs, and distributed traces to provide comprehensive visibility into both infrastructure health and automation performance. Through rigorous testing practices and careful orchestration of deployment phases, the framework balances security responsiveness with operational stability while addressing persistent challenges in identity management, configuration drift detection, and policy enforcement across heterogeneous cloud platforms.

This article describes the main design features, technical characteristics and scope of application of the FSM-230 polycrystalline solar photovoltaic module (230 W). The module consists of an anodized aluminum frame and tempered glass, and has high mechanical strength. Silicon photocells are protected from external environmental influences and mechanical damage due to the fact that they are placed between protective layers based on lamination technology. The FSM-230 module is used in autonomous and backup power supply systems, and together with a charge regulator, battery and inverter, it forms an independent power supply complex. The voltage at the open contacts of the module is 36.6 V, the operating current is 7.8 A, the short-circuit current is 8.42 A, and the efficiency is 16–18%. A plastic waterproof contact box (IP65) and bypass diodes ensure reliable operation of the module. The manufacturer guarantees a 25-year service life of the module and long-term maintenance of power at 80–90%.

This paper introduces a practical construction to raise flight protection standards during the critical pre-flight phases. The method shifts the allocation of duties from ground personnel to the cockpit crew in a structured manner and establishes a risk-evaluated checkpoint at the aircraft stand. This includes how information is handled within the plane's electronic flight bag (EFB). The proposed framework comprises a clean airworthiness transfer strategy, an algorithmic gate for go/hold decisions, governance for de/anti-icing traceability, and checklist/EFB human-performance scaffolds. The objective centers on a reproducible instruction set for operators that binds station-season hazards, MEL/CDL constellations, and local runway dependencies into a single auditable decision path. Methods include comparative analysis, structured synthesis, and cross-source triangulation. The work summarizes recent details from open and professional publications to develop safety actions. The result is ways of measuring the success of airlines, training programs, and ground teams.

The article examines an architectural approach to resume analysis based on Retrieval-Augmented Generation (RAG), designed to overcome the systemic limitations of traditional keyword-matching algorithms (like TF-IDF and BM25) and the inherent constraints of large language models (LLMs) used in isolation under conditions of an overloaded and semantically heterogeneous hiring market. The relevance of the work is driven by the growth in the volume and variability of resumes, the need to capture latent semantic correspondences between experience phrasing and vacancy requirements, and the risks of algorithmic biases, as well as the plausible yet unreliable generation of personnel decisions. The study aims to formalize a dual-loop scheme for processing a resume corpus, in which dense semantic retrieval over vector representations of document fragments is coupled with answer generation strictly constrained by the retrieved context and complex refusal rules under insufficient grounds. The scientific novelty lies in interpreting the RAG approach as a mechanism of search-based non-parametric memory for a corporate resume array, where the chunking strategy (determined at the ingestion phase) and the retrieval parameters such as topK and similarity. Threshold, directly governing the scope and quality of information passed to the retrieval act as controllable regulators of the recall–noise–cost trade-off, and where requirements for explainability, traceability, and privacy are derived from HR-specific constraints rather than declared post factum. It is demonstrated that separating retrieval and generation functions, offloading compute-intensive corpus preparation into an asynchronous loop, and locally deploying models jointly reduce LLM load, decrease the incidence of hallucinations, and enable verifiable candidate ranking based on the semantic proximity of the experience to the recruiter’s query. It is concluded that the reliability of systems of this class is determined not by model strength, but by the architecture of source control and the discipline of context management. The article will be helpful for researchers and engineers developing intelligent talent selection systems, as well as for practicing recruiters and HR analysts implementing RAG solutions in corporate processes.

AgenticAIs makes autonomous decisions, optimize, and organize workflows on their own are becoming a disruptive layer in cloud-native software delivery. Karpenter collaborated with Kubernetes to enable intelligent node provisioning, simplify resource allocation, and provide production-grade reliability at scale. As explored in this paper, Agentic AI enhances the following Kubernetes-based DevOps processes: optimizing CI/CD orchestration, forecasting resource demand, accelerating fault recovery, and improving application lifecycle management. Continuous monitoring and control of cluster behavior, advanced diagnostics, and targeted corrective actions are all aspects of the Integrated Agentic AI models that makes great independent operations with limited human control a possibility. The paper presents key technical underpinnings, including multi-agent systems (for example MCP server), reinforcement learning, operator-based AI control loops, and AI-based policy enforcement. Practical examples are examined, such as automated scaling, self-healing clusters, smart canary rollouts, drift detection, and cost-constrained resource allocation. Issues such as model reliability, governance, interpretability, and production-grade security are addressed with mitigating solutions. Combining existing practices and fresh innovations, this article can serve as a comprehensive guide for leaders in the engineering community, DevOps teams, and platform designers who want to use Agentic AI in Kubernetes-driven environments. The insights highlight how automated intelligence can greatly reduce development cycles, minimize operational friction, and enable continuous, dependable delivery in the evolving, dynamic ecosystem of production.

The article is devoted to the analysis and systematization of principles for designing scalable frontend architectures aimed at effective integration with artificial intelligence (AI) systems. The relevance of the study is determined by the exponential growth of the use of AI technologies, including generative models, in user interfaces, which generates new, increased requirements for the flexibility, performance, and fault tolerance of front-end systems. The scientific novelty of the work consists in the formulation of a comprehensive architectural model based on the author’s practical experience in the domain of AdTech/MediaTech platforms. Within the framework of the study, the main challenges of integrating AI into the frontend are identified and structured, including state management, rendering of dynamic content, and ensuring low response latency. Contemporary design approaches are analyzed, including micro-frontends, server-side rendering, and API-first design. Particular emphasis is placed on the principles of system decomposition, performance optimization, and compliance with digital accessibility requirements. The purpose of the work is to develop and theoretically substantiate a set of architectural principles intended for building scalable frontend systems capable of natively interacting with AI services. To achieve this goal, methods of systems analysis of scientific literature, comparative analysis of architectural patterns, as well as the case study method based on the author’s practical experience, are employed. In conclusion, the proposed modular AI-integrated architecture (MAI-FA) is presented, and conclusions are formulated regarding its applicability in the context of high-load and complex web systems. The findings presented in the article will be of interest to frontend architects, lead developers, and technical managers involved in the design of complex web applications with intensive use of AI.

In this article, the inhibition efficiency of the corrosion inhibitor obtained on the basis of monoethanolamine, methyl methacrylate and phosphoric acid was studied by electrochemical methods, Electrochemical impedance spectroscopy (EIS) measurements and potentiodynamic polarization measurements.

The paper surveys numerical practices used to predict and mitigate vibroacoustic loads inside composite payload fairings across liftoff and early ascent. Novelty lies in a unified mapping between exterior-source solvers and interior structure–cavity models, linking unsteady RANS for launch-pad environments with FE–SEA backbones, transfer-matrix screening for multilayer curved shells, and FE–BEM spot checks. The review compares transmission-control options suitable for composite structures, including locally resonant liners, partial porous fills, micro-perforated hierarchical sandwiches, and high-intensity nonlinear stacks, against mass and manufacturability constraints.

Special attention is given to deflector-induced source shaping, coherence-preserving load transfer, and parameter identification for blanket and liner impedances. The objective is to distill a staged workflow that reconciles accuracy with design-cycle cost while sustaining qualification margins for avionics. Methods include comparative synthesis, model-taxonomy analysis, and normalization of reported vibroacoustic metrics to one-third-octave SPL and transmission loss.

Because vibroacoustic qualification margins are mission-critical for launch vehicles, and because composite fairings represent an area of engineering central to the aerospace sector, these modeling strategies support industry reliability in advanced structural–acoustic design.

Electric buses use regenerative braking a lot to make them more energy-efficient and lower the amount of particles they release into the air. This means that friction braking parts are used less often. This change makes brake parts last longer, but it also changes the thermal cycles and wear patterns, which can cause corrosion, uneven wear, and problems with braking performance that regular maintenance schedules based on time or mileage can't fix. This paper suggests a way to use high-resolution telematics and machine-learning techniques to predict when friction brakes in electric buses will need maintenance. We process operational data like regenerative and hydraulic braking signals, deceleration behavior, thermal cycles, state-of-charge limits, and passenger load estimates to create a Brake Wear Index and train hybrid models that use both Random Forest and LSTM architectures with Weibull reliability estimation. Results show that wear prediction accuracy has improved and that there are up to 40% fewer unplanned maintenance events than with scheduled maintenance methods. The results show how important it is to use regenerative-aware diagnostic analytics to make sure that electric buses run safely, cheaply, and reliably in urban transport networks.

The rapid digital transformation of financial institutions has increased the demand for secure, scalable, and compliant platforms that can support advanced analytics and artificial intelligence (AI). Salesforce Hyperforce, a cloud-native re-architecture of the Salesforce platform, enables enterprises to leverage public cloud infrastructure while meeting regulatory and performance requirements. This paper examines how Hyperforce facilitates AI and analytics adoption in financial services by enabling elastic compute, real-time data integration, and seamless connections with data warehouses such as Snowflake and BigQuery. It evaluates architectural patterns, compliance considerations, and case studies where Hyperforce drives financial insights, fraud detection, and personalized banking experiences.

The increasing reliance on data-driven decision-making in financial services has intensified the demand for platforms that can seamlessly support advanced analytics and artificial intelligence (AI) while maintaining regulatory compliance and operational resilience. Salesforce Hyperforce, a cloud-native re-architecture of the Salesforce platform, addresses these needs by deploying Salesforce services on hyperscaler infrastructures such as AWS, Azure, and Google Cloud. By enabling data residency controls, elastic compute capabilities, and enhanced integration options, Hyperforce creates a robust foundation for financial institutions to harness AI and analytics at scale.

This paper explores how Hyperforce empowers banks and financial organizations to unlock real-time insights by integrating seamlessly with modern data ecosystems, including Snowflake, BigQuery, and AI/ML frameworks. It highlights use cases such as fraud detection, customer personalization, risk assessment, and regulatory reporting—areas where the convergence of Hyperforce infrastructure and AI-driven analytics generates measurable business value. Furthermore, the study evaluates architectural patterns, data governance models, and compliance strategies critical for adopting Hyperforce in highly regulated financial environments.

Through a combination of technical analysis and real-world case studies, this work demonstrates that Salesforce Hyperforce is not only an enabler of cloud-scale CRM but also a strategic platform for financial analytics innovation. By leveraging cloud-native infrastructure, institutions can achieve faster time-to-insight, enhanced scalability, and improved resilience while maintaining customer trust and adherence to stringent regulatory frameworks. The findings suggest that Hyperforce, when aligned with AI and analytics strategies, represents a pivotal step in shaping the future of intelligent, customer-centric financial ecosystems.

The present online pharmacy market lacks real-time drug-drug interaction detection during the shopping experience. This paper presents a unique system to detect interactions directly in e-commerce pharmacy shopping carts, reducing the risk of adverse drug reactions that could lead to potential hospitalizations. The hybrid system combines the current rule-based checking using commercial databases (DrugBank, First DataBank) with Large Language Models (LLMs) to improve contextual analysis through Retrieval-Augmented Generation (RAG). A three-layer design comprising of interaction detection, LLM enhancement, and user experience layers is proposed, to achieve under 500ms response times through microservices architecture and multi-tier caching, while generating user-friendly natural language explanations. A confidence scoring mechanism flags uncertain outputs for further pharmacy review and intervention to ensure user safety. The system also addresses critical limitations of current similar tools requiring use of separate interaction checkers by providing seamless cart-level integration. The proposed evaluation methodology targets >90% sensitivity for major interactions and >80% specificity to minimize pharmacist fatigue due to false positives.