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Yayın Circuit model for given reflectance data constructed with mixed lumped and distributed elements for high speed/high frequency communication systems(IEEE, 2005) Yarman, Bekir Sıddık Binboğa; Şengül, Metin; Kılınç, Ali; Aksen, AhmetIn this paper, a reflectance-based "non linear interpolation method" is presented to model the measured or computed data, obtained from a "passive one-port physical device" using mixed lumped and distributed elements. Mixed element model is constructed with cascade connection of series inductors [L], commensurate transmission lines or so called Unit Elements [UE] and shunt capacitors[C]. Basis of the new model rests on the numerical generation of the scattering parameters of the lossless two-port constructed with cascade connection of simple [L]-[UE]-[C] elements which describes a lossless 2-port in Darlington sense. The new modeling technique does not require direct optimization of the circuit elements of the selected topology. Rather, two-variable reflection coefficient is directly determined by means of a non linear but "convergence guaranteed" interpolation process to best fit the given data. A low-pass filter input reflection coefficient modeling example is included to exhibit the utilization of the proposed modeling method.Yayın Two-port network parameters(CRC Press Taylor & Francis Group, 2016) Grebennikov, Andrei; Kumar, Narendra; Yarman, Bekir Sıddık Binboğa[No abstract available]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.
