This article examines the effectiveness of combining Terraform and Kubernetes within DevOps workflows. Against the backdrop of microservices architectures and cloud-native environments, the synergy between Infrastructure as Code (IaC) and container orchestration has become increasingly important. Our contribution lies in systematically exploring how Terraform and Kubernetes can be used together during provisioning, CI/CD pipelines, and autoscaling. We compare their feature sets, review real-world cluster-deployment case studies, and discuss state-management strategies and self-healing mechanisms. Key recommendations cover modular infrastructure design, clear separation of responsibilities, and adoption of GitOps principles. Drawing on official documentation, English-language vendor publications, and industry reports, our analysis identifies the integration’s benefits for faster application delivery, higher system stability, and repeatable processes. We employ comparative documentation review, content analysis of DevOps community resources, and case-study methodology. Practical guidance for optimizing Terraform–Kubernetes collaboration concludes the paper. These insights will be valuable to DevOps engineers, architects, and deployment-automation specialists, reflecting current industry trends and laying groundwork for future research.

This article examines synchronization methods for multi-detector phased systems that integrate spatially distributed transmit–receive nodes into a single coherent structure. The study's primary aim is to determine the technical requirements for temporal, frequency, and phase alignment of the elements, and to analyze the hardware and algorithmic means for achieving them. The relevance of this work is driven by the rapid development of phased arrays and distributed radar and astronomical systems, where even tens of picoseconds of desynchronization lead to significant loss of coherent gain and degradation of spatial resolution. Contemporary network protocols such as IEEE 1588 provide only microsecond-level accuracy, which is insufficient for the often-required budgets on the order of tens of picoseconds; therefore, a multi-level architecture is necessary, combining highly stable reference oscillators, zero-delay hardware buffers, deterministic data-transfer interfaces, and digital correction algorithms. The novelty of this research lies in the comprehensive comparison and integration of four classes of solutions: a distributed clock tree with LVDS and fiber-optic lines and zero-delay PLL buffers; deterministic SYSREF frame distribution according to JESD204B/C; bidirectional microwave wireless exchange with pilot-tone synchronization; and digital corrections via cross-correlation and Kalman-consensus algorithms to compensate residual drifts. A methodology for budgeting phase slip—accounting for source jitter, port trace dispersion, and network delays—is presented, enabling early identification and elimination of design bottlenecks. The key conclusion demonstrates the effectiveness of the multi-level scheme: an external hardware-network loop provides coarse phase alignment and frequency stability at the level of single to tens of picoseconds. In contrast, the internal digital loop maintains instantaneous coherence with phase errors of only a few degrees, even when nodes are separated by hundreds of meters or during GNSS outages. Systematic summation of contributions from jitter, trace skew, and network delays guarantees ≥ 90% coherent gain and the specified dynamic range. This article will be helpful to engineers developing phased antenna arrays, distributed radar, and interferometric systems, as well as researchers in precise frequency–time distribution.

This article presents a structural-comparative analysis of the applicability of various materials for pressure brake discs in racing cars under extreme thermal and mechanical loads. The study is conducted within an interdisciplinary framework combining materials science, thermal modeling, and engineering mechanics. Special attention is given to microstructural and fractographic analysis of steels (AISI 1020, AISI 4140, SS420), carbon-ceramic composites (C/C, SiC), and their combinations in hybrid layered configurations. Differences in material behavior are identified based on key criteria such as thermal expansion, oxidation resistance, microcrack formation, and residual deformation. Based on numerical modeling in ANSYS and analysis of a real track profile (“Michigan 2019” circuit), a correlation between thermocyclic degradation and track configuration, rotor geometry, and ventilation features is established. Comparative analysis shows that C/C and SiC discs provide more uniform wear and stable friction coefficients at temperatures above 1000 °C, while steels exhibit limited suitability under intensive braking conditions. The potential of biomimetic textures and fluoropolymer (PTFE) coatings to enhance heat dissipation efficiency is substantiated. The article will be of interest to specialists in motorsport engineering, materials science, thermomechanics, and brake system design, as well as developers of composite structures operating under high thermal loads.

Financial institutions suffer volatility, regulatory scrutiny, cyber risks, and complex technical linkages. System outages and operational failures can influence market stability, customer trust, and regulatory compliance in this setting. For proactive financial system design that can predict, withstand, and recover from interruptions with little service deterioration, resilience engineering is essential.

This article examines financial system resilience engineering strategies in detail. It covers redundancy, observability, adaptive capacity, microservices, multi-region deployments, service meshes, Site Reliability Engineering (SRE), chaotic testing, and real-time monitoring. It also examines worldwide regulatory frameworks like the UK FCA recommendations, EU DORA regulation, and US FFIEC standards, highlighting regulatory alignment in operational resilience.

JPMorgan Chase's resilience architecture is examined in detail, along with AI-driven observability, Zero Trust architectures, edge computing, and blockchain-based settlements. This research integrates technical, operational, and compliance methods to help financial institutions maintain uptime and service continuity in a dynamic digital economy.

This article covers the issue of automating product decisions from A/B tests, trying to knit together what have pretty much been disparate and sometimes even ad hoc stages of experimentation into a single, reproducible, scalable pipeline that includes hypothesis planning, traffic control, streaming analytics, statistical evaluation, and safe rollout. The growth of this inquiry is motivated by the rapid increase in numbers of digital experiments and correspondingly strong demand for A/B testing tools--and the tremendous weakness of traditional manual processes: more than 90% of spreadsheets have errors and one typo in Excel can cost billions undermining the product teams' confidence in the experimental results. The novelty of the work lies in a comprehensive analysis of modern experiment factory architectures that integrate feature flags, Apache Kafka–based streaming analytics, frequentist and Bayesian evaluation methods, multi-armed bandit algorithms, reinforcement learning, and elements of causal ML. A six-layer pipeline concept was proposed in which each stage (from the hypothesis catalog to automatic rollback and result archiving) is implemented by automated means without analyst involvement. Results show that automated A/B processes shrink the experiment cycle from weeks to hours, allow for parallel launch of hundreds of tests, reduce error risk, and speed delivery of winning variants to production. Sequential analysis keeps the false-positive rate under control below 5% along with false discovery rate control; Bayesian modes provide for proper decisions in small samples; and multi-armed bandits plus reinforcement learning virtually eliminate traffic loss during simultaneous exploration and exploitation. The automated system increases the frequency of releases, further improves conversions, and helps improve data-driven culture within organizations. The paper will be helpful to product managers, data analysts, DevOps engineers, and CTOs who are responsible for building and scaling an experimentation platform and establishing a seamless cycle of product decision-making.

Delays in vendor payments within public sector organisations, especially with the procurement of assistive technology for individuals with disabilities, pose significant difficulties to service efficiency, equity, and operational responsibility. Conventional payment systems are impeded by human verification procedures, fragmented data flows, and regulatory obstacles that frequently prolong service delivery timelines. This study introduces a research-based system that utilizes blockchain-enabled smart contracts to enhance vendor payment processes and eradicate service delays in AT procurement.   
This report conducts a comprehensive examination of current payment infrastructures and regulatory frameworks, identifying significant failure points within the systems utilized by Departments of Rehabilitation (DOR) and other agencies. The proposed architecture utilizes smart contracts to automate payment authorization, ensure compliance, and openly and effectively enforce contract requirements. Incorporated within the smart contracts are policy-driven logic rules that reflect state procurement standards and Workforce Innovation and Opportunity Act (WIOA) fiscal guidelines, facilitating real-time verification of service milestones and secure cash distribution.

Research findings suggest that blockchain-enabled payment systems can save processing time by as much as 70%, reduce administrative errors, and create immutable audit trails that enhance oversight and accountability. This system enhances vendor trust and minimizes conflicts by facilitating transparent, condition-based transactions. The report continues by delineating essential factors, including scalability, regulatory compliance, cybersecurity, and integration with older systems like Cal JOBS and AWARE.     

This research adds to the expanding literature on public sector innovation, providing a prospective answer for agencies aiming to improve efficiency and dependability in service delivery to at-risk groups.

This article presents a comprehensive analysis of the physicochemical and biological mechanisms underlying reverse-osmosis membrane fouling, along with the development and optimization of preventive strategies to enhance the operational stability of membrane systems. The relevance of this research is determined by the growing freshwater scarcity and the rapid expansion of desalination capacities, where over 65% of produced water is obtained through reverse osmosis. The work aims to integrate classical and modern non-invasive fouling diagnosis methods—from SEM-EDS, ATR-FTIR, and XPS to optical coherence tomography and online ATP/BGP sensors—to delineate four primary foulant types and identify key intervention points at the early stages of deposit formation. The novelty of the study lies in the design and optimization of cascade preventive strategies that combine fine physicochemical pretreatment, targeted chemistry, and adaptive control of cleaning and reagent dosing protocols via machine-learning algorithms. The proposed closed-loop control model—from deep diagnostics to automatic adjustment of operating parameters—enables a substantial extension of the maintenance interval and a reduction in the total cost per cubic meter of treated water. Key results demonstrate that: Inorganic fouling can be effectively suppressed by antiscalants and pH regulation, preventing irreversible carbonate and sulfate deposit formation; Organic deposits and colloidal particles are most robustly removed by combining surfactant-enhanced cleaning and membrane-surface modification with hydrophilic coatings; Biofouling is controlled through two-stage biocide protocols triggered by early signals from ATP sensors, which reduces cleaning costs and downtime to a critical minimum. Online monitoring of hydraulic and biological markers is integrated with trainable algorithms that flexibly adapt flow parameters and chemical protection in real-time. This article will appeal to specialists in membrane technology and desalination, as well as researchers and developers of reverse osmosis systems and preventive water quality management strategies.

This article explores the transformative role of AWS deep learning technologies in financial crime detection and prevention. It examines how advanced neural networks and cloud infrastructure enable financial institutions to overcome the limitations of traditional rule-based systems, significantly enhancing both security capabilities and customer experience. The article shows various deep learning frameworks, including CNNs, LSTMs, and GNNs, for detecting different types of financial crimes, analyzes implementation architectures on AWS, and presents a comprehensive case study demonstrating substantial improvements in fraud detection rates and operational efficiency. Additionally, the article addresses emerging trends, implementation recommendations, and regulatory considerations that will shape the future of AI-based financial crime prevention.

This study explores the application of blockchain technology to enhance the integrity and reliability of concrete test logs in civil engineering projects. Traditional methods of recording and managing concrete test data are susceptible to tampering, errors, and loss, which can compromise structural safety and project outcomes. The proposed solution leverages cryptographic hashing and immutable distributed ledgers to securely timestamp each test entry, ensuring tamper-proof records with verifiable audit trails. The system integrates seamlessly with existing concrete testing workflows by capturing test data directly from devices, encrypting it, and submitting hashes to a blockchain network. Smart contracts automate verification processes, improving transparency and accountability. The study further evaluates the solution’s security performance, transaction efficiency, and usability through simulation and prototype testing. Results indicate significant improvements in data immutability, regulatory compliance, and long-term storage capabilities compared to traditional systems. However, challenges such as transaction latency, scalability, industry resistance, and data privacy require careful mitigation through hybrid blockchain models, targeted training, and regulatory engagement. Future directions include integration with Internet of Things (IoT) sensors for real-time monitoring, AI-driven predictive analytics, and interoperability with Building Information Modeling (BIM) systems. This blockchain-enabled approach promises to transform construction quality assurance by embedding security and transparency throughout the data lifecycle, fostering safer, more accountable, and digitally advanced civil engineering practices.

Computer vision (CV) is increasingly embedded in enterprise workflows. This article presents a comprehensive analysis of how CV systems are being used to automate complex visual tasks, replace repetitive labor, and enhance decision-making in different industries at scale. Special attention is given to the key determinants of CV effectiveness and operational challenges companies face when implementing the technology. The author notes that treating computer vision not as a static tool but as an evolving infrastructure, organizations can unlock substantial value while preparing for the next generation of AI-driven optimization.