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.

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 examines how artificial-intelligence technologies can improve the efficiency of distributed computing systems that face challenges of scalability, overload and limited flexibility in responding to external changes. The aim of the study is to explore AI-based methods designed to increase performance in distributed environments. The research draws on a theoretical analysis of publications in the field of distributed computing. Machine-learning algorithms allow forthcoming load changes to be detected in advance and computing tasks to be reassigned automatically, thereby reducing response time and boosting overall performance. Employing neural networks to analyse utilisation and redistribute resources improves the operation of distributed systems and smooths peak-load periods. The findings will be of interest to professionals working with distributed computing systems, cloud technologies and to other researchers investigating methods for enhancing the reliability and performance of computing platforms. The study concludes that integrating artificial intelligence into distributed systems increases their efficiency and resilience, opening new opportunities for optimising modern computing infrastructures.

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 paper examines an architectural model for integrating mining installations into existing building HVAC systems and urban district heating networks using IoT control. The relevance of the study is justified by the rapid growth in the share of low‑grade heat from server farms and mining centers in the overall energy consumption balance. The objectives are to develop a comprehensive five‑level architecture for connecting computational modules to low‑temperature loops; to perform a comparative analysis of three basic schemes (by‑pass, series‑loop, and hybrid‑grid) in terms of PUE and heat utilization factor; and to formulate IoT algorithms for dynamic balancing between computational load and the needs of heat receivers. The novelty of the paper lies in unifying technical, economic, regulatory, and cybernetic aspects into a single model: for the first time, a five‑layer integration structure is proposed—from retrofit of heat‑exchange loops to an edge + cloud platform and interfaces with BMS/SCADA; the advantages of immersion cooling for direct connection to heating systems at temperatures up to 60 °C are demonstrated; predictive algorithms based on LightGBM for forecasting thermal load and dynamically controlling the hash rate are described; and recommendations are given for minimizing financial, technological, and informational risks at all levels of the architecture. The main findings show that, for mining power up to 30 % of the building’s heat demand, the optimal scheme is the by‑pass with minimal intervention in existing engineering networks; when heat power is comparable to the object’s load, it is more advantageous to apply the series‑loop with immersion cooling, yielding up to 98 % savings on mechanical chillers. For district networks, the hybrid‑grid topology with buffer accumulators and complex flow distribution is preferable. OPC UA and MQTT are brought together for assured telemetry. Digital twins and demand-response programs bring energy efficiency and equipment reliability. Multi-level OT security, combined with support for financial hedging instruments, ensures assured resilience against both cyberattacks and crypto-market volatility. Such a paper would be of interest to engineers designing building heating, cooling, and ventilation systems; data center energy efficiency specialists; as well as IoT solution developers for thermal process management.

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.

Additive Manufacturing of advanced Ceramics can prove to be a potential breakthrough for high precision applications like nozzle for glass quenching process. This paper presents a conceptual Life Cycle Analysis (LCA) framework to assess the environmental sustainability of 3D printed ceramic nozzles, focusing on lithographic manufacturing of alumina-based ceramic components. From extensive literature research adjoining ceramic Advanced Manufacturing process, material properties, energy consumption, and end of life analysis, this study explores key indicators that drive environmental impact and design considerations. This study further estimates the impact of sustainable manufacturing on industry 4.0 and strategizes the process considering material efficiency, energy inputs, and circularity potential. The presented facts indicate that advanced manufacturing of ceramic nozzles could substantially minimize waste, improve thermal performance and ensure greater lifecycle sustainability in comparison to traditionally manufactured nozzles. This paper aims to address the gaps by defining key concepts for sustainability-driven design and assessment of AM ceramic components in thermally intensive industrial Application.

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.

The evolution of cyber threats has necessitated the development of sophisticated approaches to cyber threat intelligence (CTI), emphasizing proactive detection, attribution, and mitigation strategies. This study presents a comprehensive exploration of contemporary CTI frameworks, integrating theoretical perspectives with applied methodologies to enhance organizational resilience against cybercrime. The research synthesizes diverse literature spanning threat intelligence collection, evaluation, and operational deployment across cloud computing, IoT, and critical infrastructure environments. Methodologically, the study examines multi-layered intelligence gathering, including the fusion of Indicators of Compromise (IoCs), attack patterns, and Tactics, Techniques, and Procedures (TTPs), highlighting the role of machine learning, federated learning, and predictive blacklisting in enhancing threat attribution. Findings indicate that real-time data integration, coupled with collaborative intelligence sharing, significantly improves detection accuracy and the anticipation of sophisticated attack vectors. Moreover, the research identifies critical gaps in cross-organizational intelligence interoperability, legal constraints, and the ethical implications of automated threat assessment systems. This paper contributes to the field by proposing a holistic framework for CTI that aligns technical, operational, and strategic perspectives, providing actionable insights for academia, industry, and policy-makers.

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.

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.

Background: Modern computational infrastructures—spanning cloud platforms, GPU-accelerated data centers, and enterprise data lakes—face interlocking challenges: increasing failure rates under intensive workloads, the necessity for secure continuous integration/continuous deployment (CI/CD) practices, and the demand to convert vast heterogeneous data into actionable intelligence (Zhang, 2022; Liu et al., 2023). A coherent, interdisciplinary framework that links predictive analytics, DevSecOps practices, and high-performance fault diagnosis is essential to raise reliability while maintaining scalability and security (Kumar, 2019; Konneru, 2021).

Methods: This article synthesizes theoretical foundations and applied methodologies from the provided literature to produce an integrative conceptual and operational framework. We perform a detailed cross-domain synthesis of techniques from predictive analytics, data engineering and lakehouse architectures, DevSecOps security integrations (SAST/DAST/SCA), high-performance geospatial and GPU computing approaches, and contemporary fault-prediction studies. The methodology includes comparative evaluation of algorithmic families, pipeline architectures, and failure-detection strategies, mapped onto practical system boundaries and operational constraints for cloud and on-premise GPU deployments (Kukreja & Zburivsky, 2021; Li, 2020; Liu et al., 2023).

Results: The synthesis highlights three convergent design principles: (1) unified telemetry and data curation through lakehouse principles to enable low-latency, high-fidelity feature generation (Kukreja & Zburivsky, 2021); (2) embedding predictive analytics into DevOps cycles to create anticipatory operations—thereby improving decision latency and reducing mean time to repair (Kumar, 2019); and (3) a layered fault-diagnosis approach combining supervised predictive models for imminent hardware faults with unsupervised anomaly detection to capture novel failure modes (Peterson et al., 2022; Xie et al., 2021; Liu et al., 2023). The integrated model demonstrates conceptual pathways to reduce unscheduled downtime, decrease non-revenue-impacting water in infrastructure analogues, and enhance security posture inside CI/CD (Kwikima et al., 2024; Konneru, 2021).

Conclusion: A resilient data-driven infrastructure must combine lakehouse data engineering, DevSecOps-integrated CI/CD, and robust predictive diagnostics tailored for high-performance workloads. The proposed framework provides a guide for engineering organizations to structure telemetry, model development, and deployment while accounting for security, scale, and the unique failure characteristics of GPUs and cloud components. Research and industrial practice will benefit from empirical validation campaigns, standardized telemetry schemas, and community-driven benchmarks for fault prediction and remediation orchestration (Liu et al., 2023; Lin & Gupta, 2021).

This article explores the integration of blockchain technology with traditional database systems to create hybrid data management solutions that leverage the immutability and security of blockchain alongside the query efficiency and flexibility of conventional databases. A framework for implementing blockchain-integrated databases is proposed, examining performance optimization strategies and potential applications across finance and supply chain domains. The work addresses critical challenges in maintaining data integrity while preserving query performance, establishing a foundation for future implementations that can revolutionize secure data management in sensitive environments. Key architectural models, including sidechain, event sourcing, and validation layer approaches, are evaluated against implementation complexity and performance considerations. Additionally, selective blockchain commitment strategies and consensus mechanism selection techniques are presented to help organizations overcome the inherent tensions between security guarantees and computational efficiency, enabling the practical adoption of these hybrid systems in enterprise environments.

During digital transformation, retail companies with legacy IT infrastructures struggle to maintain service dependability, scalability, and agility. Many mainframes, on-premise applications, batch processing processes, and monolithic codebases were not designed for today's dynamic operational contexts. Google-developed Site Reliability Engineering (SRE) approaches including Service Level Objectives (SLOs), automation, and blameless postmortems can bridge the gap between outdated systems and modern operational excellence. This article proposes gradual adoption, cultural change, and measurable service reliability improvements for legacy retail environments adopting SRE. A concentrated SRE rollout helped a national retail chain reduce toil and improve mean time to detect (MTTD), mean time to resolve (MTTR), and MTTR. The model shows that incremental SRE adoption can modernize legacy systems and prepare them for future innovation without comprehensive re-architecture.

This paper presents a structured analysis of common engineering failures in early-stage blockchain startups. Drawing on practical experience within the Ethereum, Solana, Polkadot, and Cosmos SDK ecosystems, it identifies five recurrent categories of technical pitfalls: inadequate system design, fragile or overly coupled architectures, improper use of cross-chain protocols, insufficient build and release automation, and limited observability and runtime diagnostics. The study employs a case-based methodology across five representative projects, covering a wide range of protocol layers and architectural patterns—from decentralized messengers to cross-chain bridge infrastructure.
Findings demonstrate that the adoption of mature engineering practices—such as Command–Query Responsibility Segregation (CQRS), event sourcing, finite state machines, proxy contract standards (EIP-1967, EIP-2535), and type safety in Rust—substantially improves system resilience and extensibility. Particular emphasis is placed on Inter-Blockchain Communication (IBC) as a robust standard for secure interoperability across heterogeneous chains. The paper also highlights how automated CI/CD pipelines, multi-layer telemetry, and centralized alerting frameworks support early fault detection and operational responsiveness. The study concludes that minimizing custom low-level implementations in favor of standardized, modular approaches is critical for building secure, auditable, and scalable decentralized systems.

The theory of object-oriented programming (OOP) has been used as a paradigm in the development of software engineering that has lasted over a few decades. Although software industry is changing rapidly with other languages and paradigm, OOP now is very much present in the design and architecture of present-day systems. An example of the survival of theoretical concepts is the .NET platform which started with early Common Language Runtime (CLR) and more recently modern ASP.NET Core framework and more up-to-date versions of the C# programming language, covering encapsulation, polymorphism, inheritance and abstraction.
This paper therefore seeks to address how the fundamental OOP concepts were used in creating the .NET ecosystem and how it mutated. It aims to understand what these principles are embodied in such aspects as runtime behaviors, language features, framework architecture, and design practice. The paper, using qualitative thematic synthesis on 30 peer-reviewed scholarly articles, theses, technical reports, and case-based assessments fuses theoretical framework with practice forms of implementation at various levels of .NET.
The findings reveal a consistent alignment between .NET's design philosophy and object-oriented theory. These values have been retained by key features like use of generics, dependency injection, interface programming, and popularization of design patterns. Additionally, more recent C# additions such as LINQ, immutable records, pattern matching, and async/ await reveal a practical shift to hybridization: merging the idea of functional programming performance and structure, with OO program modularity. Quantitative measurements indicated multi-threaded queries performed at 25%-35% higher level using PLINQ vs. the traditional LINQ, in multicore scenarios. The boxing overhead was minimized and memory consumption improved by up to 20% through the use of generic collections in .NET. Entity Framework queries with LINQ demonstrated an increase of up to 30% in readability and maintainability with no decrease in the performance at run time.
These findings indicate that OOP still offers sustainable and flexible model of regulating software complexity especially in large-sized enterprise systems. Although it is a subject of discussions regarding its theoretical limitations, the real-life experience of the evolution of .NET platforms clearly points to that the OOP is relevant in the development of scalable, maintainable, and robust applications.
The study comes to the conclusion that the OOP theory is not the one that is only historically important but the one that is actively used in designing the more recent programming platform, such as the .NET. With the current trend toward hybrid and multiparadigm languages, the interface of the OOP theory to such systems as the .NET platform would provide a great point of view in both educational and business spheres. The study confirms both the current relevance of OOP in the current software infrastructure and predisposes the chance to research the paradigm convergence, language design, and architectural resilience in the high-scale environments.

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.

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.

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.

The accelerating complexity, scale, and adversarial nature of modern cyber environments have rendered traditional, human-centric cyber defense strategies increasingly insufficient. Autonomous cyber defense, particularly approaches grounded in reinforcement learning and simulation-based experimentation, has emerged as a promising paradigm capable of adapting to dynamic threats, reasoning under uncertainty, and responding at machine speed. This article presents a comprehensive, theory-driven research investigation into autonomous cyber defense systems, with a particular focus on reinforcement learning agents trained within cyber simulation environments. Drawing exclusively on the provided body of literature, the study synthesizes advances in cyber operations research gyms, autonomous agent design, reward shaping, adversarial robustness, and the emerging threat of poisoned or trojaned learning agents. The article methodologically integrates insights from foundational intrusion detection research, deep reinforcement learning, stochastic games, and graph-based reasoning to articulate a unified conceptual framework for autonomous cyber defense. Results are presented as an extensive descriptive analysis of observed patterns, theoretical behaviors, and empirical findings reported across prior studies, emphasizing both capabilities and vulnerabilities. The discussion critically interrogates the limitations of current approaches, including closed-world assumptions, dataset bias, reward misalignment, and susceptibility to adversarial manipulation, while also exploring counter-arguments and mitigation strategies. Finally, the article outlines future research directions, emphasizing trustworthy autonomy, causal reasoning, and zero-day threat mitigation in complex software ecosystems. By providing a deeply elaborated, publication-ready synthesis, this work aims to serve as a foundational reference for researchers and practitioners seeking to advance the state of autonomous cyber defense.