Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651

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Now showing 1 - 10 of 2125
  • Article
    Queue-Responsive Adaptive Signal Control vs. Webster Optimization: A Multi-Criteria Simulation Assessment at a Signalized Intersection
    (MDPI, 2026) Albdairi, Mustafa; Almusawi, Ali
    Traffic signal control at signalized intersections plays a key role in mitigating urban congestion, reducing vehicle emissions, and improving road safety. This study examines three signal control strategies at a four-approach isolated intersection simulated using the Simulation of Urban Mobility (SUMO) microscopic traffic simulator: a baseline fixed-time plan, a Webster-optimized fixed-time plan, and a queue-responsive adaptive controller implemented through the Traffic Control Interface (TraCI). The strategies were evaluated under balanced traffic demand of 600 vehicles per hour per approach over a 3600 s simulation period. Performance was assessed using eight indicators related to mobility, environmental impact, and safety, including average delay, travel time, queue length, network speed, throughput, CO2 emissions, fuel consumption, and time-to-collision events. The results indicate that the adaptive controller produced the greatest improvements, reducing delay by 14.3%, travel time by 13.6%, CO2 emissions by 9.3%, fuel consumption by 9.4%, and TTC conflicts by 11.2%, while increasing network speed by 47.9%. The Webster-optimized plan achieved moderate improvements, lowering delay by 4.8% and fuel consumption by 5.0% without additional infrastructure requirements. Overall, the findings suggest that both signal re-timing and queue-responsive adaptive control can enhance intersection performance, with the preferred approach depending on available infrastructure and implementation costs.
  • Article
    A Multi-Scenario Evaluation of Adaptive Fuzzy Logic Algorithms for Intelligent Traffic Signal Management in Urban Intersections
    (Nature Portfolio, 2026) Dvorsky, Jiri; Martinovic, Jan; Shaheen, Sumaira; Riaz, Muhammad Bilal; Qadri, Syed Shah Sultan Mohiuddin; Slaninova, Katerina
    The article presents a performance analysis of the advanced adaptive control systems of traffic lights that are based on the advanced fuzzy logic. They include Modified Intuitionistic Fuzzy Logic Algorithm (MIFLA) and the Modified Interval Type-2 fuzzy logic (MIT2FL) at a four-leg intersection. In this article, there is an integration of these fuzzy models with the SUMO platform with respect to the weaknesses of the traditional fixed-time traffic lights, particularly in rapidly urbanizing areas. This will be to achieve a real-time dynamic control system. The simulation matrix was a grid of the nine scenarios in which the performance of the controllers was assessed to some extent, depending on the traffic and directional imbalances. The results reveal that the MIT2FL is more effective than the MIFLA and the Modified Webster benchmark. MIT2FL is less divergent, has shorter queuing times, and is more flexible. This occurs when the demand is high, and the traffic conditions are not proportional. This work is significant because it provides fuzzy logic controllers that can deal with uncertainty. It also creates a benchmarking model of a typical multi-scenario. Moreover, it gives the opportunity for reproducibility of the findings in real traffic implementation. The innovations will assist in making the city smarter and easier to move around. They manage congestion, delays, and improve the sustainability of smart traffic control.
  • Article
    A Covering Tour-Based Inventory Routing Framework for Humanitarian Logistics
    (Springer, 2026) Kanik, Zehra B.; Uzgören Kazanç, H. Cansın; Soysal, Mehmet; Coelho, Leandro C.; Kazanc, H. Cansin Uzgoren
    In post-disaster situations, swiftly delivering humanitarian assistance to victims amid chaos and uncertainty poses a significant challenge in practice. Furthermore, efficient distribution of restricted resources, effective inventory control, and optimal resource allocation remain imperative priorities for humanitarian organizations that strive to meet urgent needs under adverse conditions. This study proposes a two-echelon Covering Inventory Routing Problem (CIRP) that integrates the Inventory Routing Problem (IRP) and the Covering Tour Problem (CTP) to support decision-making in the distribution of medical kits in post-disaster humanitarian logistics. A scenario-based probabilistic Mixed-Integer Linear Programming (MILP) model is introduced to decrease costs while adequately addressing unpredictable demand. The applicability of the model was assessed through scenario analysis and a case study. In addition, a three-phase matheuristic algorithm is proposed to solve the CIRP. The results demonstrate that integrating IRP and CTP in a two-echelon structure improves both cost efficiency and the reach of aid delivery under uncertainty. The use of a static-dynamic inventory approach, together with coordinated routing, effectively minimizes emergency shipments and adapts to fluctuating demand, providing valuable support for decision-making in real-time humanitarian contexts. The three-phase matheuristic achieved cost reductions of over 70% relative to the model's incumbent solution within the first hour on large-scale instances, highlighting its practical use in accelerating decision-making amid post-disaster uncertainty.
  • Article
    A Machine-Learning-Based Multi-Hazard GIS-AHP Framework for Wind Turbine Siting under Earthquake-Landslide Coupling
    (IOP Publishing Ltd, 2026) Dincer, Ali Ersin; Demir, Abdullah; Ozturk, Sevki; Kalpakci, Volkan; Dilmen, Omer
    This study presents a machine-learning-based multi-hazard geographical information system (GIS)-analytical hierarchy process (AHP) framework for wind turbine siting that explicitly accounts for the coupled effects of earthquake and landslide hazards. The primary innovation lies in the development of a conditional weighting algorithm that integrates machine-learning-derived hazard assessments with structural engineering logic. Landslide susceptibility is first modeled using a random forest classifier trained on a comprehensive inventory of historical landslide data and 12 geo-environmental conditioning factors, producing a high-resolution susceptibility map with excellent predictive performance (AUC = 0.86). Feature importance analysis indicates that slope, hydrological indices, and geological conditions are the dominant controls on landslide occurrence. This data-driven map is then integrated with earthquake hazard zones and additional environmental and technical constraints within a GIS-AHP framework to generate a comprehensive wind turbine suitability assessment. Results show that explicitly accounting for earthquake-landslide coupling leads to a nearly 20% reduction in high and very high suitability areas, accompanied by an expansion of low and moderate suitability zones, highlighting the limitations of single-hazard planning approaches. The main contribution of this study lies in advancing renewable energy planning through the explicit integration of interdependent natural hazards, demonstrating how earthquake-resistant foundation strategies can simultaneously mitigate landslide risks.
  • Article
    Finite Biorthogonal M Matrix Polynomials
    (Pleiades Publishing, 2026) Güldoğan Lekesiz, E.
  • Article
    An Uncertainty-Gated Neuro-Symbolic Framework for High-Coverage Topic Modeling and Trend Analysis in Scholarly Corpora with LLM Assistance
    (IEEE-Inst Electrical Electronics Engineers Inc, 2026) Demir, Onur; Saran, Murat
    The rapid growth of scientific literature demands scalable methods that can track research evolution, yet density-based topic models such as BERTopic systematically exclude low-density documents as outliers, obscuring emerging and niche research areas. We propose a Neuro-Symbolic, Uncertainty-Gated Framework that recovers these outliers through geometric centroid reassignment and an ontological entropy gate derived from the Computer Science Ontology (CSO), routing only genuinely ambiguous cases to a local Large Language Model (Qwen2.5-14B via Ollama). A controlled ablation study demonstrates that centroid reassignment provides the largest coverage gain (+ 22.9 percentage points (pp)), the CSO entropy gate preserves niche-topic integrity, and selective LLM routing adds an additional + 5.9 pp. On 12,535 Turkish computer engineering theses (TR-CS; 2001-2025), the full pipeline raises coverage from 75.5% +/- 1.2 % (Bare BERTopic) to 95.7% +/- 0.4% (five-seed means) while maintaining competitive coherence (NPMI = 0.112 +/- 0.006) and cross-seed stability (AMI = 0.832 +/- 0.015), at similar to 15x fewer LLM calls than a fully generative Pure-LLM baseline. Mann-Kendall trend tests on the high-coverage series identify 69 statistically significant trends (FDR q < 0.05), and cross-corpus validation on similar to 200K arXiv CS abstracts confirms that the architecture generalizes beyond the primary dataset. The framework offers a reproducible, cost-effective solution for monitoring scientific developments in rapidly evolving fields.
  • Article
  • Conference Object
    Data-Driven Identification of Lamb Wave Dynamics for Fatigue Monitoring of AL5754 Plates
    (IOS Press bv, 2026) Altay, Özkan; Ünver, Hakkı Özgür; Lotfi, Bahram; Özbayoğlu, Ahmet Murat; Balyemez, Ozan Berkay; Sait, Ferit
  • Article
    A User-Centric Domain-Adaptive Quality Model for Benchmarking Generative AI Systems
    (Institute of Electrical and Electronics Engineers Inc., 2026) Esirik, Buse Erol; Gokalp, Ebru
    Generative AI (GenAI) systems operate across diverse application domains where quality priorities shift dynamically in response to user expectations and contextual requirements. This variability calls for a comprehensive quality model that enables stakeholder-driven weight recalibration to support product evaluation and selection. However, existing approaches do not simultaneously account for GenAIspecific attributes, user-centric quality priorities, and domain-adaptive evaluation mechanisms. To bridge this gap, this study proposes the User-Centric Generative AI Quality Model (UC-GAIQM), a domainadaptive framework in which Analytic Hierarchy Process (AHP) weights can be recalibrated to reflect quality priorities across different application scenarios and user profiles. The proposed model was developed through a mixed-methods, three-phase research design. In the first phase, a Systematic Literature Review (SLR) and Multivocal Literature Review (MLR) established the theoretical foundation. In the second phase, a quantitative survey of active GenAI users (n = 111) validated eight quality dimensions through exploratory and confirmatory factor analysis (alpha = 0.94, KMO = 0.88, CFI = 0.943). In the third phase, a three-round expert-driven Delphi study confirmed the structural validity of the model (Kendall's W = 0.84), and an AHP study demonstrated the weight recalibration mechanism. UC-GAIQM comprises eight quality dimensions and thirty sub-dimensions aligned with key ISO/IEC standards, the NIST AI Risk Management Framework, and the EU AI Act. The results demonstrate that the proposed model facilitates dynamic, context-sensitive evaluation of GenAI products by enabling quality priority adaptation across application domains.
  • Article
    Mathematical Analysis and Numerical Simulations of Ternary Hybrid Nanoparticles Using Eyring Prandtl Model
    (Engineered Science Publisher, 2026) Abdalla, Bahaaeldin; Zeb, Hussan; Jarad, Fahd; Oweidi, Khalid Fanoukh Al; Ali, Zeeshan; Abdeljawad, Thabet; Shah, Kamal