Welcome to IKU Academic Digital Archive System
OpenAccess@IKU is Istanbul Kultur University's Academic Digital Archive System, established in June 2014 to digitally store and provide open access to academic and artistic outputs in line with international standards and intellectual property rights. The system includes various outputs such as articles, presentations, theses, books, book chapters, reports, encyclopedias, and works of art produced by the university's faculty members and students.
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Recent Submissions
Development of Agriculture in Turkey and Changing Dynamics: Factor Analysis Approach
(Springer Science and Business Media Deutschland GmbH, 2025) ÇELEBİ, HASAN HÜSEYİN; Paksoy, Ayşe
This study investigates the factors affecting agricultural productivity in Turkey from 1990 to 2023 using factor analysis. As the global population increases, understanding the variables that influence crop yields becomes crucial for sustaining the food supply. The research examines data such as average temperature, rainfall, inflation rates, and fuel prices, sourced primarily from the Turkish Statistical Institute. By applying factor analysis, the study groups these variables into meaningful clusters that reveal the underlying correlations between them and agricultural outputs like wheat, barley, rice, and corn. The results indicate that certain economic and environmental factors, such as consumer inflation and annual average temperature, consistently influence multiple crops. This research contributes a new perspective to the literature by identifying the key factors that drive agricultural productivity in Turkey, providing insights that can inform future agricultural policies. The study concludes with suggestions for further analysis involving a broader range of variables to enhance understanding of the complex dynamics in Turkish agriculture.
Experimental and Finite Element Analysis Evaluation of the Effect of Variable Parameters on Tensıle Capacity in Knife Plate Connections
(Springer Science and Business Media Deutschland GmbH, 2025) AYDIN, ELİF; Alemdar, Fatih
Welded knife plate connections are widely used in steel bridge construction and play a critical role in structural elements subjected to variable and repeated loading, particularly due to traffic-induced dynamic effects. These connections are vulnerable to fatigue-related damage over time, potentially compromising structural integrity. Therefore, understanding the influence of key parameters on the tensile capacity of such connections is essential for ensuring safe design, extending service life and achieving cost-effective structural solutions.This study systematically investigates the effects of weld length, the gap between the knife plate and the hollow structural section (HSS) and various profile geometries on the tensile performance of knife plate welded connections. Unlike most previous studies that focus on a single parameter, this research adopts a comprehensive parametric approach and contributes to a deeper understanding of the interactions between multiple design variables. The methodology integrates experimental testing with numerical simulations conducted using the ABAQUS finite element software. A total of 15 full-scale tensile experimental tests were carried out and the results were compared with numerical predictions. The findings indicate a strong correlation between the experimental and numerical results with the finite element model achieving over 90% accuracy. These results validate the reliability of the numerical approach and demonstrate its potential to reduce dependence on experimental testing in future optimization and design studies offering significant savings in both time and cost.
Data-Driven Machine Learning Approaches to Demand Forecasting in Supply Chain Management
(IEEE, 2025) ALREFAAI, SAFA; ERMİŞ, MURAT
This study explores pharmaceutical demand forecasting by integrating statistical analysis and machine learning within the context of supply chain management. The dataset, from a pharmaceutical manufacturer, includes six drug categories and around 240 products. Initial data exploration was conducted using Power BI to understand the structure and trends. Statistical models, namely Moving Averages, Single Exponential Smoothing, Trend Analysis, and ARIMA, were applied, focusing specifically on Acantaine (Antibiotics class) for detailed analysis. Machine learning models were trained on the full dataset to improve generalization, but testing was focused on Acantaine, with accuracy metrics computed. A comparative analysis showed that machine learning models, especially Random Forest, significantly outperformed traditional statistical methods in forecasting accuracy. The study underscores the value of machine learning in enhancing prediction and supporting strategic decisions in pharmaceutical supply chains.
Determining Debris Collection Areas for the European Side of İstanbul
(Springer Science and Business Media Deutschland GmbH, 2025) DEMİREL, DUYGUN FATİH; Gönül Sezer, Eylül Damla
It is crucial to rapidly remove the debris generated after an earthquake, dispose of it in designated areas, and manage it using various methods. Especially in cities like Istanbul, which face a constant earthquake threat, it is essential to develop disaster management plans in advance. One key aspect of these plans is identifying suitable locations for debris collection after an earthquake. Studies in the literature show that different methods are used to consider multiple economic and environmental criteria for debris site selection. However, the lack of a similar scientific study for Istanbul is a significant gap. This project proposes a hybrid method that incorporates sustainable approaches to identify debris collection areas on the European Side of İstanbul. The study aims to identify potential debris sites. The selection model will be based on the multi-attribute utility theory (MAUT), considering factors like cost, environmental impact, proximity to debris areas and recycling centres, and effects on water resources and natural habitats.
Crank-Nicolson Method for the Chiral nonlinear Schrödinger Equation
(Institute of Physics, 2025) Wahyuningsih, Ester T.; Magdalena I.; AKKOYUNLU, CANAN
In this paper, we develop a finite difference scheme based on the Crank-Nicolson method for solving the chiral nonlinear Schrödinger (CNLS) equation, which describes the dynamics of nonlinear wave propagation with chirality effects. The CNLS equation supports two types of progressive wave solutions: bright solitons and dark solitons. The proposed Crank-Nicolson scheme is implicit, unconditionally stable, and achieves second-order accuracy in both space and time. To evaluate the accuracy of the method, numerical results are compared with exact analytical soliton solutions. Numerical simulations are presented for the propagation of single bright and dark solitons. The results demonstrate that the Crank-Nicolson method accurately preserves soliton structures, making it an effective tool for studying the dynamics governed by the chiral nonlinear Schrödinger equation. The study demonstrates the effectiveness of the Crank-Nicolson method in capturing the dynamics of chiral nonlinear wave propagation and lays the foundation for further exploration of chiral effects in quantum and optical systems.