The European Journal of Research and Development

Impact of Phase Inversion and Process Parameters on Alkyd Emulsion Properties

Pelin Aksoy, Selim Çifçi — Fri, 23 May 2025 00:00:00 +0300

In conventional alkyd coatings, the alkyd resin is dissolved in organic solvents, such as mineral spirits. However, due to growing health and environmental concerns, there is a rising interest in waterborne alkyd coatings. Alkyd emulsions, therefore, represent a viable alternative. Waterborne alkyd resins significantly reduce VOC emissions by using water as the dispersion medium, making them highly important for research in this area. The main objective of this study is to synthesize emulsified alkyd resins by replacing solvents with water, using nonionic and anionic surfactants, for the alkyd resins widely used in the paint industry for many years. Soybean oil/sunflower oil alkyd resins were emulsified with the anionic surfactant MAXEMUL 7201 and the nonionic MAXEMUL 7101. This paper investigates the effect of emulsification temperature, mixer model and rotation speed on the formation of alkyd emulsion The synthesized alkyd resins were evaluated based on acid value, viscosity, gloss, Fourier transform infrared spectroscopy (FTIR), gel permeation chromatography (GPC), and particle size analysis.

Machine Learning Models and Explainable Artificial Intelligence Approaches for Intrusion Detection in IoT Networks

Asuman Besi Kütük, Özlem Çoşkun, Hikmet Kütük, İbrahim Kök — Sat, 31 May 2025 00:00:00 +0300

The rapid spread of Internet of Things (IoT) technologies and the rapidly increasing use of IoT devices offer technological transformation and innovative solutions in many areas from daily life to industrial processes. However, the resource constraints, simple operating systems, non-standard protocols and embedded software of IoT devices make them vulnerable to cyber-attacks. This makes IoT networks risky against malicious attacks and increases the size of security threats. Moreover, the complexity and heterogeneity of IoT networks render traditional security approaches inadequate and increase the need for advanced solutions. In this context, the need for methods for detecting and preventing attacks on IoT networks that are not only reliable and effective, but also understandable by users and security experts has become increasingly critical. This need for network security necessitates the development of strategies that will both secure technical infrastructures and increase the trust of human elements interacting with these infrastructures. In this context, the need for more interpretable, explainable and transparent security approaches is increasing. In particular, machine learning (ML) and deep learning (DL) based intrusion detection systems offer effective solutions to security problems such as anomaly detection and attack classification. The comprehensibility of the decision mechanisms of the models used enables both security experts to manage the systems more effectively and users to have more confidence in the security measures taken. Explainable Artificial Intelligence (XAI) techniques make the decision processes of ML and DL models transparent, allowing to understand how and why attacks are detected. Accordingly, it has become a critical requirement for security systems not only to achieve high accuracy rates, but also to make the decisions taken interpretable. In this study, the effectiveness of artificial intelligence (ML and DL) techniques for the detection and classification of security threats in IoT networks is analysed. In addition, the applications of XAI methods such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME) and Explain Like I'm 5 (ELI5) for IoT security are investigated. It is shown how these methods make the decision processes of ML and DL models used in IoT networks more transparent and provide a better analysis. As a result, this study presents an approach that combines both performance and explainability in IoT security. By demonstrating the effectiveness of XAI-supported ML and DL models, it aims to contribute to future research and innovative security solutions for enhancing security in IoT networks.

Two-Factor Authentication System for Protecting Metadata and Connected Vehicles

Huseyin Karacali, Nevzat Donum, Efecan Cebel — Fri, 27 Jun 2025 00:00:00 +0300

Connected vehicles are becoming increasingly common, and they store a significant amount of data about their drivers and their surroundings. This data is attractive to attackers, and there is a need for effective security measures to protect it. This study proposes a two-factor authentication (2FA) system to protect the metadata stored in connected vehicles and the vehicles themselves. The system consists of two main components: the Central Security Unit (CSU) and the AutoGuard (AG) mobile application. The CSU is integrated with the Remote Keyless Entry System (RKES), while the AG is installed on the authorized driver's phone. The 2FA process begins when the remote key is in proximity to the vehicle. This triggers the CSU, which then initiates the second authentication factor. The AG prompts the driver to enter a valid security method, such as a biometric, pattern, or PIN code. If the second authentication is successful, the AG authorizes the CSU and the vehicle doors are opened by the RKES. The driver is notified by CSU through AG if the 2FA process is unsuccessful. As a result, this system aims to protect the metadata stored in authorized users' vehicles and their vehicles from unauthorized invaders. Furthermore, it is possible to enhance the accuracy of the 2FA system by integrating the location of the phone with AG functionality into the authentication system.

A Review of Deep Learning Approaches with CMR Images in the Diagnosis of Cardiovascular Diseases

Gülsüm Kemerli, Tayyip Özcan — Fri, 27 Jun 2025 00:00:00 +0300

Cardiovascular disease (CVD) is one of the leading causes of death worldwide, which has led to the recent intensification of Deep Learning (DL) studies in the field of cardiology. Patients usually experience symptoms such as rapid fatigue, edema below the knee and ankle, chest pain, shortness of breath, and palpitations. The most common types of CVD include coronary artery disease, arrhythmias, congenital heart defects, cardiomyopathy, valvular heart failure, and angina. Electrocardiography (ECG), blood tests, physical examination, and medical imaging are the most effective tools for diagnosing diseases. In recent years, cardiac magnetic resonance imaging (CMRI) has been increasingly used for the diagnosis, follow-up, treatment planning, and prognosis of CVDs. However, the large number of slices and low contrast of CMRI data make diagnosing CVD difficult. Deep learning techniques are being applied to diagnose CVD with CMRI data to overcome these difficulties, and intensive research continues to be conducted in this field. It is important to keep abreast of developments so that these studies can significantly impact clinical applications. This review aims to be a stepping stone for researchers in this process by comprehensively reviewing studies on CVD detection using DL methods on CMRI images.

A Data Fusion Method Combining Image, Sensor, and Survey Data for Efficiency and Usability Analysis of Electric Power Tools in Industrial Environments

Kader Nikbay Oylum, Turgay Tugay Bilgin — Sat, 28 Jun 2025 00:00:00 +0300

The increasing integration of advanced technologies and automation in industrial production has heightened the importance of operational efficiency and safety. Among the critical components influencing workforce performance and product quality is the effective use of electric hand tools. However, the limited availability of comprehensive datasets and the absence of robust labeling methodologies present significant challenges for accurate data analysis and predictive modeling. This study addresses these limitations by incorporating field-collected data and multiple data acquisition techniques to identify relevant features for machine learning applications. An initial dataset comprising 51 attributes was systematically reduced to 16 through feature selection processes, enhancing its suitability for subsequent computational modeling. Several classification algorithms were evaluated for data labeling, with the Decision Tree method demonstrating superior performance in terms of accuracy. Despite these promising results, the dataset’s limited sample size (64 individuals) restricts the generalizability and reliability of machine learning outcomes. To mitigate this constraint, data augmentation techniques will be employed to generate synthetic instances, thereby expanding the dataset. Upon achieving a sufficient sample size, machine learning models will be developed to predict individuals’ proficiency with electric hand tools. This research contributes to the foundational knowledge required for efficient data collection, accurate labeling, and the development of predictive models in industrial settings.

Mechanical and Environmental Comparison of Natural Fibers and Glass Fiber in the L-RTM Method

Nazmi Türkhan, Kübra Tuna, Hüseyin Arıkan, Yusuf Uzun — Sun, 29 Jun 2025 00:00:00 +0300

The applicability of flax fiber-reinforced composites as an environmentally friendly alternative to glass fiber-reinforced plastics (GFRP), commonly used in outdoor structures such as water slides, has been investigated. While glass fiber is associated with high energy consumption and significant environmental impacts, flax fiber offers a sustainable solution due to its renewable nature, low density, and biodegradable properties. The mechanical and environmental performance of flax fiber-reinforced composites manufactured using the L-RTM (Light Resin Transfer Molding) method was evaluated, with a particular focus on sensitivity to water and moisture, and design considerations to mitigate these effects were discussed. In this method, L-RTM is employed as a vacuum-assisted, closed-mold technique particularly suited for medium-scale production of high-quality components with smooth surfaces on both sides. In this context, the essential conditions for natural fiber-reinforced composites to serve as a viable alternative to glass fiber in water slide applications have been identified.

Smart Classroom Scheduling and Resource Optimization for Educational Institutions: Integrating AI and Multi-Objective Decision Support

Osman Çaylı, Atınç Yılmaz — Mon, 07 Jul 2025 00:00:00 +0300

The demand for high-quality education delivery in increasingly dynamic and competitive educational markets has intensified the need for intelligent and adaptive scheduling systems. Manual classroom scheduling methods, which rely on human decision-making, often fail to optimize critical resources such as classrooms, teachers, and student time, leading to inefficiencies and economic losses. This paper proposes a comprehensive Smart Classroom Scheduling and Optimization System (LMSOPT) that leverages Artificial Intelligence (AI), advanced time-series forecasting, constraint-based multi-objective optimization, and real-time data integration. The proposed system employs Long Short-Term Memory (LSTM) neural networks for highly accurate demand forecasting, alongside heuristic and metaheuristic optimization algorithms such as Constraint Programming (CP), Genetic Algorithms (GA), and Tabu Search. The system aims to dynamically balance multiple conflicting objectives: maximizing classroom occupancy rates, minimizing student waiting times, and aligning teacher availability with student preferences. The expected contributions are multifold: significant operational cost savings, measurable improvements in resource utilization, increased student satisfaction, and the creation of an extensible research framework for AI applications in education management. The study aligns with national strategies for digital transformation and supports the vision of data-driven decision-making in educational administration. Empirical results and comparative analyses are presented to validate the system’s effectiveness and demonstrate its replicability for institutions of various scales.