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Yayın Design trade-offs and considerations for improving the PCB current carrying capacity in high power density power electronics applications(IEEE, 2022-03-24) Büyükdeğirmenci, Veysel Tutku; Kozarva, Ömer F.; Milletsever, Özgür C.; Hava, Ahmet MasumThis paper investigates printed circuit board (PCB) design trade-offs and considerations to maximize the current carrying capacity of traces in PCB-based power electronics applications. Many existing designs rely on methodologies through empirical data presented by the outdated IPC-2152 standard. A design methodology to maximize the utilized PCB area and improve thermal performance is introduced. To assess this methodology, lumped parameter (LP) and finite element (FE) models are developed and computational fluid dynamics (CFD) simulations are carried out. Thermal via placement strategies are investigated and maximum allowable power dissipation on the PCB traces is calculated. Simulations and analyses are experimentally validated on a PCB-based 100kW three-phase three-level inverter. The that results show that the thermal and electrical models discussed in this paper have superior accuracy compared to traditional formulations.Yayın Grammar or crammer? the role of morphology in distinguishing orthographically similar but semantically unrelated words(Institute of Electrical and Electronics Engineers Inc., 2025) Ercan, Gökhan; Yıldız, Olcay TanerWe show that n-gram-based distributional models fail to distinguish unrelated words due to the noise in semantic spaces. This issue remains hidden in conventional benchmarks but becomes more pronounced when orthographic similarity is high. To highlight this problem, we introduce OSimUnr, a dataset of nearly one million English and Turkish word-pairs that are orthographically similar but semantically unrelated (e.g., grammar - crammer). These pairs are generated through a graph-based WordNet approach and morphological resources. We define two evaluation tasks - unrelatedness identification and relatedness classification - to test semantic models. Our experiments reveal that FastText, with default n-gram segmentation, performs poorly (below 5% accuracy) in identifying unrelated words. However, morphological segmentation overcomes this issue, boosting accuracy to 68% (English) and 71% (Turkish) without compromising performance on standard benchmarks (RareWords, MTurk771, MEN, AnlamVer). Furthermore, our results suggest that even state-of-the-art LLMs, including Llama 3.3 and GPT-4o-mini, may exhibit noise in their semantic spaces, particularly in highly synthetic languages such as Turkish. To ensure dataset quality, we leverage WordNet, MorphoLex, and NLTK, covering fully derivational morphology supporting atomic roots (e.g., '-co_here+ance+y' for 'coherency'), with 405 affixes in Turkish and 467 in English.Yayın Economic dynamics of air pollution in Türkiye and Pakistan: an empirical assessment of the Environmental Kuznets Curve and pollution-led growth(IGI Global, 2026) Taşbaşı, Aslı; Akhtar, MahamTürkiye and Pakistan, despite differing levels of economic development, face similar macroeconomic challenges such as income inequality, inflation and debt. Both countries also experience environmental pressures from industrialization and rapid urbanization, with air pollution emerging as a critical concern affecting economic productivity and sustainable development. This study conducts a comparative analysis of air pollution in Türkiye and Pakistan from 1980 to 2023, using the Autoregressive Distributed Lag (ARDL) bounds testing approach to examine the short and long run relationships between air pollution, urbanization, industrialization, energy consumption and macroeconomic policies. The analysis tests the Environmental Kuznets Curve (EKC) for Türkiye and the pollution-led growth hypothesis for Pakistan. Findings reject the EKC for Türkiye but confirm pollution-led growth in Pakistan, offering insights for effective environmental regulation and sustainable development strategies.Yayın Privacy-preserving cyber threat intelligence: a framework combining private information retrieval, federated learning, and differential privacy(Institute of Electrical and Electronics Engineers Inc., 2025-09-21) Çamalan, Emre; Çeliktaş, BarışThreat Intelligence Platforms (TIPs) are essential for sharing indicators of compromise (IoCs), but querying them can leak sensitive organizational data. We propose a privacy-preserving framework that combines Private Information Retrieval (PIR), Federated Learning (FL), and Differential Privacy (DP) to mitigate this risk. Our approach addresses both content-level and metadata-level privacy concerns while supporting collaborative learning across organizations. It ensures that sensitive query patterns remain hidden, local threat data never leaves organizational boundaries, and model updates are protected against inference attacks. The framework integrates with existing TIPs such as MISP and OpenCTI, requiring minimal operational changes. We implement a prototype using a simulated Abuse IP dataset and evaluate it on latency, accuracy, and communication overhead. The system supports private queries in under 300 ms and maintains over 95% model accuracy under DP noise. These results indicate that strong privacy can be achieved with minimal performance trade-offs, making the approach viable for real-world CTI environments.Yayın A deployment-oriented privacy-preserving CTI framework: integrating PIR, federated learning, differential privacy, and practical hardenings(Institute of Electrical and Electronics Engineers Inc., 2026) Çamalan, Emre; Çeliktaş, BarışThreat Intelligence Platforms (TIPs) enable organizations to share indicators of compromise (IoCs), yet the operational CTI lifecycle exposes multiple, largely independent privacy surfaces: query content and access-pattern leakage during IoC lookup, gradient and membership inference risks during collaborative model training, and residual metadata side-channels in network traffic. Existing work addresses these surfaces in isolation; no prior framework orchestrates their joint mitigation within a single, deploymentoriented CTI pipeline under explicit guarantee boundaries. We present a prototype workflow-level privacy orchestration for cyber threat intelligence that coordinates four mechanisms across the query-learn-update lifecycle: (i) Private Information Retrieval (PIR) to hide queried IoC indices, (ii) cross-silo federated learning (FL) to keep raw CTI data local, (iii) a formal client-level Differential Privacy (DP) mechanism for federated model training to protect against inversion and membership inference attacks, and (iv) practical privacy hardenings, namely fixed-shape PIR batching (a traffic-shaping mechanism, not a cryptographic PIR guarantee) and secure aggregation simulated under an honest-but-curious coordinator assumption, to mitigate residual side-channel leakage. The contribution is therefore one of CTI-specific workflow orchestration and systematic evaluation, not of new cryptographic primitives: formal (ε, δ) guarantees apply exclusively to the differentially private federated learning component, while the remaining mechanisms serve as deployment-oriented hardenings under stated assumptions. We implement a working prototype over a two-million-row AbuseIPDB-style IoC dataset. Under a two-server non-colluding assumption, PIR queries complete in approximately 40 seconds with 16MB transfer per fixed batch. Local Random Forest and Logistic Regression baselines reach 89.0% and 77.00% accuracy, respectively, while federated variants with DP-FedAvg (gradient clipping and RDP-based privacy accounting) demonstrate a quantified privacy–utility trade-off across multiple noise levels. A corrected canonical single-round (T=1) baseline establishes the reconciled reference operating point; reviewer-driven multi-round experiments (T ∈ {1, 10, 20}) and an auxiliary clip-norm sensitivity analysis (C ∈ {0.5, 1.0, 2.0}) further characterize how privacy budgets, model utility, and training stability evolve beyond the single-round setting, with all (ε, δ) values computed via RDP composition for the corresponding configuration. The framework aligns with recent advances in secure aggregation and privacy-preserving CTI analytics, and is designed to be compatible with GDPR, CCPA, ISO/IEC 27701, and NIST 800-53 privacy principles, demonstrating prototype-level feasibility for regulation-aware CTI collaboration across organizations.
