Işık Üniversitesi Kurumsal Akademik Bellek
Işık Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
LaSIPDE: Latent-Space Identification of Partial Differential Equations from indirect, high-dimensional measurements
(Frontiers Media SA, 2026-04-14) Koulali, Imane; Turan, Erhan; Eskil, Mustafa Taner
Discovering governing equations from data is a central challenge in scientific machine learning, particularly when observations are high-dimensional and the underlying state variables are not directly accessible. In this work, we introduce a framework for data-driven discovery of partial differential equations (PDEs) from indirect high-dimensional observations. The proposed approach combines nonlinear representation learning through an autoencoder with sparse identification of governing equations in the latent space, enabling simultaneous model reduction and PDE discovery while preserving spatial structure. Unlike existing methods that either operate on observable variables or discover latent ordinary differential equations, our framework identifies PDEs directly in the learned latent coordinates. We validate the approach on high-dimensional observations generated from Burgers and Korteweg-de Vries (KdV) systems, where the true state variables are intentionally hidden. In both cases, the method successfully recovers the correct dynamical operators, including diffusion, nonlinear advection, and dispersive terms. Although the recovered coefficients differ due to latent coordinate transformations, we show both theoretically and empirically that the discovered equations are dynamically equivalent to the ground-truth systems up to an affine transformation. These results demonstrate that governing PDEs can be recovered from indirect, high-dimensional data without access to the physical state variables, providing a foundation for interpretable model discovery in realistic measurement settings.
From solidarity to selective engagement: boundaries of feminist praxis and refugee women in Turkey
(Cambridge University Press, 2026-04-26) Bal, Sinem
This article investigates how feminist praxis in Turkey incorporates refugee women into their advocacy practices, and uncovers the extent to which these interactions expose the boundaries of solidarity. Anchored in Gramscian political theory, it asks whether feminist activism continues to operate as an inclusive counter-hegemonic political sphere, and the degree to which refugee women are incorporated within it. Drawing on interviews with feminist- and migrant-led non-governmental organizations in Turkey, the analysis demonstrates that interactions with refugee women frequently unfold through short-term, humanitarian-oriented, project-funded initiatives rather than collective practices of solidarity. These dynamics highlight tensions between the emancipatory claims of feminist politics and the selective solidarities that take shape under conditions of intersecting inequalities and governance frameworks. Rather than offering a definitive critique of feminist politics, the article treats the question of refugee women as an analytical lens through which the constraints of solidarity within contemporary feminist politics in Turkey become visible.
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, Barış
Modern 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.
Optimizing peak age under intermittent satellite connectivity and store-and-forward
(IEEE Computer Society, 2025-10-29) Arı, Çağrı; Kartal, Özkan Tuğberk; Munari, Andrea; Badia, Leonardo; Uysal, Elif; Kaya, Onur
We consider a real-time task-oriented application operating over an intermittently available satellite-based communication network, aiming to collect status updates generated by a remote sensing device. The system is modeled as a scheduling problem over a finite horizon, corresponding to the duration of the task, to minimize the peak Age of Information at the destination. The number of updates that can be transmitted is constrained by a transmission budget. Moreover, the status updates are subject to delays caused by the store-and-forward operation of the satellites, which may vastly vary depending on the network conditions. We investigate three levels of awareness regarding the connectivity conditions of the satellite network: (i) scheduling without any information about connectivity conditions, (ii) scheduling based solely on the current conditions, and (iii) scheduling based on full connectivity knowledge. The first case admits a relatively simple structure, for which a periodic transmission strategy is adopted. The latter two cases are formulated as semi-Markov decision processes and solved to obtain the optimal transmission scheduling policy. Simulation results demonstrate the impact of connectivity awareness on the application performance at the destination. Through a simple modeling approach, we provide first insights into the practically relevant setting of store-and-forward satellite architectures.
Deprem sonrası geçici barınma mekânlarında adaptif mobilya tasarım kriterleri
(Kocaeli University, 2026-04-30) Yolcu, Fatma Zişan
Depremler, bireyler üzerinde yalnızca fiziksel değil, aynı zamanda bilişsel ve sosyolojik açıdan da kalıcı etkiler bırakmaktadır. Bu etkilerin hafifletilmesinde, kentsel çevreden iç mekân donatılarına uzanan çok katmanlı tasarım kararları belirleyici rol oynamaktadır. Deprem sonrası geçici barınma mekânlarında kullanılan mobilyalar, yalnızca temel işlevleri karşılayan donatılar değil aynı zamanda sarsıntı kaynaklı riskleri azaltan, mekânsal uyumu destekleyen ve kullanıcıların psikososyal iyileşme süreçlerine katkı sağlayan önemli iç mekân bileşenleri olarak değerlendirilmektedir. Geçici barınma mekânlarının zorlu ve belirsiz koşullarında mobilyanın söz konusu nitelikleri sürdürülebilir biçimde karşılayabilmesi, adaptif tasarım yaklaşımıyla bütüncül olarak ele alınmasını gerekli kılmaktadır. Bu çalışma, deprem ve mobilya ilişkisini ele alan bilimsel araştırmaları tematik olarak sınıflandırarak, deprem sonrası geçici barınma mekânlarında kullanılabilecek adaptif mobilya çözümlerinin geliştirilmesi için gerekli tasarım ilkelerini bilimsel temelde ortaya koymayı amaçlamaktadır. Bu amaç doğrultusunda çalışma, deprem sonrası geçici barınma mekânlarında mobilyanın literatürde ele alınış biçimlerini teknik, tasarımsal ve terapötik etki özellikleri bağlamında inceleyen, 23 akademik yayını kapsayan literatür temelli bir derleme olarak kurgulanmıştır. Çalışmada terapötik etki kavramı; mobilyanın kullanıcıda güven duygusu oluşturma, kaygıyı azaltma ve sosyal etkileşimi destekleme potansiyeli üzerinden ele alınmıştır. İçerik analizi bulguları, devrilme riski, ağırlık merkezi, sabitleme ve dayanım gibi teknik; hafiflik, modülerlik ve yerel malzeme kullanımı gibi tasarımsal; güven hissi oluşturma, kaygı azaltma ve sosyal uyumu destekleme gibi terapötik özelliklerin, geçici barınma mekânları için adaptif mobilya tasarımının temel bileşenlerini oluşturduğunu göstermektedir. Sonuç olarak elde edilen bulgular, deprem sonrası geçici barınma mekânlarında kullanılacak mobilyaların adaptif olarak değerlendirilebilmesi için teknik güvenlik, tasarımsal esneklik ve terapötik etki boyutlarının birlikte ele alınmasının zorunlu olduğunu ve bu bütüncül uyarlanabilirliğin afet sonrası belirsiz ve kısıtlı yaşam koşullarında kullanıcı odaklı mekânsal sürekliliğin sağlanmasında belirleyici bir rol oynadığını ortaya koymaktadır.
