Abstract : Future 6G physical layers need to work together to improve peak power, spectrum confinement, reliability, energy, and latency when channels are not stationary and service slices are not the same. Traditional approaches for reducing PAPR and shaping spectra, such as PTS/SLM, ?-law companding, and fixed orthogonal precoding, work to some extent but are not very reliable across different types of waveforms (UFMC, OTFS) and power-domain multiplexing (NOMA). To suggest a single, AI-driven signal-shap
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Abstract : Drilling super duplex stainless steel (SDSS 2507) is challenging because of its high strength and poor thermal conductivity, which lead to excessive heat generation during machining. In conventional flood cooling, the generated heat is not sufficiently dissipated, and prolonged exposure to cutting fluids can pose health risks to operators. Cryogenic cooling, particularly using liquid nitrogen (LN?), offers an effective alternative for machining hard-to-cut materials. This study evaluates the dri
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Abstract : We construct the triple difference sequence spaces ?^3 (p?^3 q)=(?^3 )_(p?^3 q), where p=(?_uvw^mnk ) is an infinite three-dimensional matrix of Padovan numbers ?_mnk defined by (?_uvw^mnk )={?(?_mnk/(?_(u+5,v+5,w+5)-2),&m?u,n?v,k?w,@0,&m>u,n>v,k>w)? and ?_q^3 is a q-difference operator of third order. We obtain some inclusion relations, topological properties, Schauder basis and ?-,?- and ?- duals of the newly defined space. We examine some geometric properties. We introduce and study some basi
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Abstract : The fast growth in the scale of large-scale data systems in sectors like cybersecurity, finance, healthcare, and industrial surveillance has enhanced the necessity of powerful anomaly detecting methods that can operate without indicated data. This paper explores the use of unsupervised machine learning to detect anomalies in high-dimensional and large-scale unhomogeneous data. Four exemplary algorithms, namely: Isolation Forest, One-Class Support Vector Machine (OC-SVM), Local Outlier Factor (LO
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Abstract : As enterprise storage deployments at cloud scale reveal persistent cost overruns, a growing cohort of organizations is pursuing cloud repatriation: the structured migration of workloads from the public cloud back to on-premises infrastructure. Cloud Volumes ONTAP (CVO), deployed on Microsoft Azure, delivers the full ONTAP feature set over Azure Premium SSD managed disks under a capacity-based licensing model. While operationally familiar, its all-in annual expenditure for highcapacity, high-availability workloads significantly exceeds the equivalent on-premises total cost of ownership (TCO) over a multi-year horizon. This article presents a technically rigorous examination of the repatriation pathway using SnapMirror as the native migration engine. The analysis covers the economic case for repatriation with a quantitative cost comparison; the SnapMirror Extended Data Protection (XDP) replication architecture and its block-level transfer mechanics; a phase-structured migration procedure from pre-assessment through cutover; the reverse-resync strategy that converts the vacated CVO instance into a warm disaster recovery standby; and post-migration governance. The article draws on foundational distributed storage design principles, scalable system architecture theory, and recent empirical studies on cloud data placement optimization, migration downtime reduction, and distributed file system performance to contextualize the SnapMirror methodology within the broader enterprise storage literature. The article demonstrates that SnapMirror-based repatriation eliminates third-party tooling dependency, preserves the full snapshot history on the destination, and constrains user-visible downtime to a window of fifteen to thirty minutes
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Abstract : Enterprise analytics environments face critical scalability challenges with manual and reactive data quality programs that cannot accommodate cloud-native, sub-hourly data integration pipelines ingesting data from hundreds of sources. This paper presents a self-healing data pipeline architecture comprising declarative quality rules, observability-driven telemetry, lineage-aware diagnosis, and orchestration-integrated remediation. The approach implements declarative rules for uniform handling of data quality across datasets, columns, keys, and relationships. A formal taxonomy of failure modes, including freshness, completeness, schema, semantic, constraint, duplicative, and relationship failures, provides a shared lexicon applicable to both automated repairs and manual remediation. Remediation strategies, including quarantine, selective replay, targeted backfill, and contractual rollback employ guardrail policies to ensure reversibility, auditability, and manageable cold start constraints. The framework emphasizes structural properties such as idempotency, determinism, explicit data contracts, incremental checkpointing, and promotion workflows that enable safe automated remediation without proprietary platform lock-in. Trust in automation is built incrementally through detection pilots, controlled remediation progression, and scale, supported by standardized platform-level structures that reduce organizational effort and maintain auditability for large engineering teams
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Abstract : The development of cross-platform UI at scale requires not only style guides, design patterns, and design language systems but also a design system architecture that enforces principled contracts between visual decisions, behavioral expectations, and accessibility semantics. Design tokens encode visual intention in a portable, platform-agnostic format, enabling design decisions to be semantically mapped to platform representations without re-encoding at each surface. Component contracts abstract stable interaction semantics, accessibility expectations, state transitions, and error handling behaviors that must remain consistent across device classes and rendering backends. Together, these architectural mechanisms address the fundamental sources of interface drift—behavioral divergence, semantic discontinuity, and undetected accessibility regressions—all of which compound across platform-driven release cycles. Interaction correctness and cross-platform consistency are treated as first-class reliability properties, not post-hoc quality concerns. Token standardization and tool-agnostic interchange formats motivate treating tokens as versioned public interfaces subject to the same governance disciplines applied to APIs. Breaking change detection, managed escape hatches, and continuous drift detection pipelines maintain interface integrity as teams scale and surface proliferation increases. Quality is measured through metrics including token coverage, component reuse rates, contract coverage, and assertion parity across platform targets.
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Abstract : Modernizing the enterprise payments infrastructure requires a consistent architecture across heterogeneous ERPs, CRMs, and commerce and retail stacks? Converging regulatory compliance‚ fraud‚ and omnichannel use cases compound the technical challenge that legacy point-to-point integrations cannot sustainably solve? A central Payment Orchestration Layer (POL) based on security-first tokenization principles can eliminate cardholder data (CHD) at all layers within the enterprise application stack: hosted payment fields, iFrame-based card-not-present flows, and PCIvalidated Point-to-Point Encryption (P2PE) in retail card-present environments. Apache Kafka's exactly-once semantics can enforce financial integrity within automated distributed settlement and posting processes by atomically pairing payment lifecycle events with relevant financial accounting commands? Network tokenization per EMVCo Payment Tokenisation Technical Framework maximizes approvals while reducing exposure to fraud with domain-restricted, cryptographically bound surrogates. Mutual TLS with certificate-bound access tokens per IETF RFC 8705 limits the attack surface across use cases for all integration boundaries. SCA orchestration as per EMV 3-D Secure 2.2.0 allows for optimized, frictionless transactions to remain within the PSD2 regulatory envelope. SAP Cloud Integration is the connectivity mechanism through which FI-AR and GL postings are then automatically triggered by payment events, without CHD traversing enterprise systems. This eliminates paper invoice costs, increases transaction realization rates, and improves receivables visibility across all channels
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Abstract : The growing deployment of industrial robotics and artificial intelligence–driven automation across logistics, manufacturing, and large-scale industrial environments has fundamentally altered the requirements placed on the underlying compute and network infrastructure that supports these systems. Traditional on-premises deployments, while providing acceptable proximity to field devices, fail to deliver the elastic scalability and operational automation that modern robotics platforms demand; at the same time, purely centralized cloud architectures introduce latency characteristics that are structurally incompatible with the deterministic timing requirements of real-time control loops. This review examines the architectural principles governing multitier cloud network infrastructures designed to reconcile these competing demands, with particular focus on metro-proximate Local Zone deployments, hyperscale backbone connectivity between infrastructure tiers, failure domain isolation strategies, and deterministic network monitoring frameworks. The review further analyzes how workload separation across well-defined infrastructure tiers allows robotic command and control functions to operate with low-latency determinism while resource-intensive application services scale freely within centralized cloud regions. A simulation-based quantitative evaluation conducted across four architectural configurations, the proposed four-tier architecture, a region-centric cloud baseline, a generic three-tier edge-cloud baseline, and an on-premises reference, demonstrates, under simulation conditions representative of public-internet-routed region-centric deployments, a 17.6-fold reduction in P99 control loop latency, a 411-fold improvement in backbone failure recovery time, and a fleet-aggregate availability of 99.94% relative to 97.83% for the region-centric baseline. The architectural patterns discussed here offer a durable engineering foundation for organizations building production-grade robotics systems at scale
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