Çankaya University (Turkish: Çankaya Üniversitesi) is a private university in Ankara, Turkey. It was established on July 9, 1997 by the Sıtkı Alp Education Foundation. The university began its teaching in the Fall 1997 semester. Sıtkı Alp is the chairman of the board of trustees. English language is predominant medium of teaching, learning and research at the Çankaya University.The 2020 Times Higher Education World University Rankings ranked the Çankaya University in the category of the top 401–500 internationally and as 87th among the Emerging Economies Universities. It was the first year that the university was the highest ranked university among both public and private universities in Turkey.
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.
In this paper we consider a non-integer (fractional)-order nonselfadjoint boundary-value problem so that the fractional-order equation is a kind of left-definite equation. We construct a dissipative operator in a Sobolev space H^1(a,b) and we introduce several results on the spectral properties of the related operators. In particular, we construct an inverse operator with the aid of the Dirac-delta function and we apply Krein’s theorem to the inverse operator which is compact having a nuclear imaginary component.
Over time, catastrophes have increasingly caused significant material and human losses. Effective logistics management in humanitarian aid is crucial to minimizing these impacts. Infrastructure damage from disasters introduces uncertainties that must be considered when routing trucks for relief item delivery. This study proposes a Mixed Integer Programming model for the Two-Echelon Vehicle Routing Problem in Humanitarian Aid Logistics (2E-VRP-HAL) to minimize total travel time. An earthquake scenario in Kartal, Istanbul is used to demonstrate the model's accuracy and applicability while accounting for road closures. A diverse fleet, including trucks and pedestrians, addresses delivery challenges, with handover stations enabling access to unreachable areas. To address larger problem instances, a set partitioning approach is used to cluster demand points, followed by a MIP-based local search heuristic to refine the results. Numerical analysis shows up to 15.83
In this study, the separation performance of cyclone separators with different geometric configurations was investigated using a hybrid approach that combines Computational Fluid Dynamics, the Discrete Element Method, and Artificial Neural Networks. In the first stage, the flow field was solved using the Reynolds-Averaged Navier–Stokes equations together with the Reynolds Stress Model turbulence closure, and particle motion was evaluated in detail through DEM. To examine the effect of geometric parameters, the inlet aspect ratio, vortex finder diameter, and cylinder height were systematically assessed. The results revealed the formation of a pronounced Rankine-type vortex structure inside the cyclone and showed that secondary flow regions intensified as the vortex finder diameter and cylinder height increased, thereby reducing the separation efficiency. In the inlet section, an optimal aspect ratio was identified. In the second stage, an ANN model was developed to expand the limited dataset obtained from the CFD–DEM analyses. By optimizing the activation function and the number of neurons, the best performance was achieved with a ReLU-based neural network containing a single hidden neuron, reaching a test-set accuracy of approximately R2≈0.991 and an overall fit of R2≈0.895. The ANN model also captured interaction trends between flow velocity and geometry that could not be observed with the limited CFD dataset. This hybrid approach provides an effective and low-cost method for performance prediction and optimization in cyclone separator design.
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.
