Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
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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 UzgorenIn 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 Citation - WoS: 51Citation - Scopus: 66Existence and Uniqueness of Solutions to Fractional Differential Equations in the Frame of Generalized Caputo Fractional Derivatives(Springer, 2018) Gambo, Y. Y.; Ameen, R.; Jarad, Fahd; Abdeljawad, T.The generalized Caputo fractional derivative is a name attributed to the Caputo version of the generalized fractional derivative introduced in Jarad et al. (J. Nonlinear Sci. Appl. 10:2607-2619, 2017). Depending on the value of. in the limiting case, the generality of the derivative is that it gives birth to two different fractional derivatives. However, the existence and uniqueness of solutions to fractional differential equations with generalized Caputo fractional derivatives have not been proven. In this paper, Cauchy problems for differential equations with the above derivative in the space of continuously differentiable functions are studied. Nonlinear Volterra type integral equations of the second kind corresponding to the Cauchy problem are presented. Using Banach fixed point theorem, the existence and uniqueness of solution to the considered Cauchy problem is proven based on the results obtained.Conference Object Citation - WoS: 12Citation - Scopus: 11Md Study of Energetics, Melting and Isomerization of Aluminum Microclusters(Springer, 2006) Boyukata, Mustafa; Guvenc, Ziya B.Voter and Chen version of an Embedded Atom Model has been applied to study the locally stable structures, energies, melting, isomerization and growth patterns of small aluminum clusters, Al(n), in the size range of n = 2 - 13. Using molecular dynamics and thermal quenching simulations, the global minima and the other locally stable structures have been distinguished from those stationary structures that correspond to saddle points of the potential energy surface. A large number (10000) of independent initial configurations generated at high temperatures has been used to obtain the stable isomers, and the probabilities of sampling different basins of attractions, for each size of the clusters. Their energy spectra have been determined and melting, and isomerization dynamics are investigated.Conference Object Citation - WoS: 2Citation - Scopus: 4Estimation of Cross Sections for Molecule-Cluster Interactions by Using Artificial Neural Networks(Springer, 2006) Boyukata, Mustafa; Kocyigit, Yucel; Guvenc, Ziya B.The cross sections Of D-2 (v,j) + Ni-n (T), n = 19 and 20, collision systems have been estimated by using Artificial Neural Networks (ANNs). For training, previously determined cross section values via molecular dynamics simulation have been used. The performance of the ANNs for predicting any quantities in molecule-cluster interaction has been investigated. Effects of the temperature of the clusters and the rovibrational states of the molecule are analyzed. The results are in good agreement with previous studies.Article Citation - WoS: 10Citation - Scopus: 18Identifying the Space Source Term Problem for Time-Space Diffusion Equation(Springer, 2020) Karapinar, Erdal; Kumar, Devendra; Sakthivel, Rathinasamy; Nguyen Hoang Luc; Can, N. H.; Luc, Nguyen HoangIn this paper, we consider an inverse source problem for the time-space-fractional diffusion equation. Here, in the sense of Hadamard, we prove that the problem is severely ill-posed. By applying the quasi-reversibility regularization method, we propose by this method to solve the problem (1.1). After that, we give an error estimate between the sought solution and regularized solution under a prior parameter choice rule and a posterior parameter choice rule, respectively. Finally, we present a numerical example to find that the proposed method works well.Article Citation - WoS: 5Citation - Scopus: 5Weighted Dynamic Hardy-Type Inequalities Involving Many Functions on Arbitrary Time Scales(Springer, 2022) El-Deeb, Ahmed A.; Mohamed, Karim A.; Baleanu, Dumitru; Rezk, Haytham M.The objective of this paper is to prove some new dynamic inequalities of Hardy type on time scales which generalize and improve some recent results given in the literature. Further, we derive some new weighted Hardy dynamic inequalities involving many functions on time scales. As special cases, we get continuous and discrete inequalities.Article Citation - WoS: 8Citation - Scopus: 9Bitcoin Price Prediction Using Sentiment Analysis and Empirical Mode Decomposition(Springer, 2025) Arslan, SerdarCryptocurrencies have garnered significant attention recently due to widespread investments. Additionally, researchers have increasingly turned to social media, particularly in the context of financial markets, to harness its predictive capabilities. Investors rely on platforms like Twitter to analyze investments and detect trends, which can directly impact the future price movements of Bitcoin. Understanding and analyzing Twitter sentiments can potentially provide insights into future Bitcoin price movements and can shed light on how investor sentiment affects cryptocurrency markets. In this study, we explore the correlation between Twitter activity and Bitcoin prices by examining tweets related to Bitcoin price sentiments. Our proposed model consists of two distinct networks. The first network exclusively utilizes historical price data, which is further decomposed into various components using the Empirical Mode Decomposition method. This decomposition helps mitigate the impact of irregular fluctuations on Bitcoin price predictions. Each of these components is then separately processed by Long Short-Term Memory (LSTM) networks. The second network focuses on modeling user sentiments and emotions in conjunction with Bitcoin market data. User opinions are categorized into positive and negative classes and are integrated with historical data to predict the next-day price using LSTM networks. Finally, the outputs of each network are combined to form the ultimate prediction values. Experimental results demonstrate that Twitter sentiment can effectively helps us predict Bitcoin price trends. Furthermore, to validate our proposed model, we compared it with several state-of-the-art methods. The results indicate that our approach outperforms these existing models in terms of accuracy. © 2025 Elsevier B.V., All rights reserved.Article Citation - WoS: 17Citation - Scopus: 20Effect of Urbanization on Surface Runoff and Performance of Green Roofs and Permeable Pavement for Mitigating Urban Floods(Springer, 2024) Ozturk, Sevki; Yilmaz, Kutay; Dincer, A. Ersin; Kalpakci, VolkanFloods are increasingly becoming a significant concern due to climate change, global warming, and excessive urbanization. The Intergovernmental Panel on Climate Change (IPCC) has projected that global warming will continue to contribute to more frequent and severe floods and hydrological extremes. In response to these challenges, nature-based solutions (NBSs) have gained recognition as effective approaches to mitigate the adverse impacts of floods by focusing on ecosystem conservation, restoration, and sustainable utilization of natural resources. This study examines a flood that occurred in the Erkilet District of Kayseri, T & uuml;rkiye on September 22, 2022, as a result of intense rainfall. It involves a thorough on-site investigation to assess the hydraulic, hydrologic, and geotechnical attributes of the study area. The findings from the field study indicate that the primary cause of the flood is attributed to excessive urbanization. To further analyze the impact of urbanization, a hydraulic model is developed considering both the physical and topographical conditions of the study area for both the year 2006 and 2022. The simulation results reveal that the extent of inundation area and water depth has increased significantly due to the excessive urbanization that occurred within a 16-year period. Additionally, the effectiveness of green roofs and permeable pavements as NBSs to mitigate urban flooding is explored. The implementation of green roofs and permeable pavements shows promising results, reducing the adverse effects of urban floods by 3% to 8%, depending on their specific locations and configurations. However, the results suggest that NBSs alone cannot fully prevent floods so they should complement gray infrastructure. The novelty of the study lies in its ability to demonstrate the impact of urbanization and the effectiveness of nature-based solutions in mitigating flood extent based.Article Citation - WoS: 13Citation - Scopus: 11An Expanded Analysis of Local Fractionalintegral Inequalities Via Generalized (s,p)-Convexity(Springer, 2024) Li, Hong; Lakhdari, Abdelghani; Jarad, Fahd; Xu, Hongyan; Meftah, BadreddineThis research aims to scrutinize specific parametrized integral inequalities linked to 1,2, 3, and 4-point Newton-Cotes rules applicable to local fractional differentiable generalized (s,P)-convex functions. To accomplish this objective, we introduce a novel integral identity and deduce multiple integral inequalities tailored to mappings within the aforementioned function class. Furthermore, we present an illustrative example featuring graphical representations and potential practical applications.Article Covid-19 Pandemic Microplastics Environmental Impacts Predicted by Deep Random Forest (Drf) Predictive Model(Springer, 2024) Chen, Liping; Sabonchi, Arkan K. S.; Nanehkaran, Yaser A.BackgroundMicroplastic pollution is a pressing issue with far-reaching environmental and public health consequences. This study delves into the intricacies of predicting microplastic pollution during the COVID-19 pandemic in Tehran, Iran.MethodsThe research introduces a rigorous comparative analysis that evaluates the predictive prowess of the Deep Random Forest algorithm and established benchmarks, such as Random Forest, Decision Trees, Gradient Boosting, AdaBoost, and Support Vector Machine. The evaluation process encompasses a meticulous 70-30 training-testing split of the main data set. Performance is assessed by analysis metrics, including ROC and statistical errors. The primary data set encompasses distinct categories, including household wastes, hospital wastes, clinics wastes, and unknown-originated susceptible waste which is categorized in Infected items, PPEs, SUPs, Test kits, Medical packages, Unknown-originated pandemic mircoplastic waste. Deliberately, this data set was partitioned into training and testing subsets, ensuring the robustness and reliability of subsequent analyses. Approximately 70% of the main database was allocated to the training data set, with the remaining 30% constituting the testing data set.ResultsThe findings underscore the proposed algorithm's supremacy, boasting an impressive AUC = 0.941. This exceptional score reflects the model's precision in categorizing microplastics. These results have profound implications for environmental management and public health during pandemics.ConclusionsThe study positions the proposed model as a potent tool for microplastic pollution prediction, encouraging further research to refine predictive models and tap into new data sources for a more comprehensive understanding of microplastic dynamics in urban settings.