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Yayın Weakly nonlinear waves in a prestressed thin elastic tube containing a viscous fluid(Pergamon-Elsevier Science Ltd, 1999-11) Antar, Nalan; Demiray, HilmiIn this work, we studied the propagation of weakly nonlinear waves in a prestressed thin elastic tube filled with an incompressible viscous fluid. In order to include the geometrical and structural dispersion into analysis, the wall's inertial and sheer deformation are taken into account in determining the inner pressure-inner cross sectional area relation. Using the reductive perturbation technique, the propagation of weakly nonlinear waves, in the long-wave approximation, is shown to be governed by the Korteweg-de Vries-Burgers (KdVB) equation. Due to dependence of coefficients of the governing equation on the initial deformation, the material and viscosity parameters, the profile of the travelling wave solution to the KdVB equation changes with these parameters. These variations are calculated numerically for some elastic materials and the effects of initial deformation and the viscosity parameter on the propagation characteristics are discussed.Yayın Adaptive incident escalation in SOCs via AI-driven skill-aware assignment and tier optimization(Institute of Electrical and Electronics Engineers Inc., 2026-04-15) Abuaziz, Ahmed; Çeliktaş, BarışModern Security Operations Centers (SOCs) face significant operational bottlenecks driven by escalating alert volumes, increasingly sophisticated cyberattack vectors, and chronic imbalances in analyst workloads. Conventional rule-based escalation models frequently fail to account for the multi-dimensional nature of incident characteristics, the nuances of analyst expertise, and fluctuating operational demands. This study proposes a comprehensive AI-driven framework for intelligent incident assignment and workload optimization. The framework introduces five primary contributions: 1) a multi-factor scoring model that integrates severity and complexity metrics with dynamic workload balancing; 2) two novel optimization algorithms, Quantile-Targeted Normality-Regularized Optimization (QT-NRO) and Joint Optimization of Weights and Thresholds (JOWT), to calibrate scoring coefficients against target analyst utilization; 3) a Large Language Model (LLM) engine leveraging Retrieval-Augmented Generation (RAG) for semantic alignment between incident requirements and analyst expertise; 4) an Adaptive Capacity Zoning mechanism for dynamic workload management; and 5) a novel RAG Relevance Score metric—a pre-resolution, semantic alignment indicator that quantifies analyst-incident assignment quality independently of resolution time, addressing a fundamental limitation of traditional temporal metrics such as Mean Time to Resolution (MTTR) and providing a reusable benchmark applicable to any skill-aware assignment system. In addition, the framework incorporates a feedback-based continuous learning mechanism that utilizes historical resolution data to inform future assignments. An experimental evaluation using 10,021 real-world incidents from Microsoft Defender demonstrates that the JOWT algorithm achieves a tier distribution alignment within 0.8% of targets. LLM-enhanced semantic matching yields improvements between 26.7% and 126.8% in skill alignment across both normal-load and high-load evaluations, while simulations indicate a 31.8% reduction in MTTR. These results substantiate the efficacy of AI-driven methodologies in enhancing SOC operational efficiency and response precision.
