Ensuring safe food supplies has recently become a serious problem all over the world. Controlling the quality, spoilage, and standing time for products with a short shelf life is a quite difficult problem. However, electronic noses can make all these controls possible. In this study, which aims to develop a different approach to the solution of this problem, electronic nose data obtained from 12 different beef cuts were classified. In the dataset, there are four classes (1: excellent, 2: good, 3: acceptable, and 4: spoiled) indicating beef quality. The classifications were performed separately for each cut and all cut shapes. The ANOVA method was used to determine the active features in the dataset with data for 12 features. The same classification processes were carried out by using the three active features selected by the ANOVA method. Three different machine learning methods, Artificial Neural Network, K Nearest Neighbor, and Logistic Regression, which are frequently used in the literature, were used in classifications. In the experimental studies, a classification accuracy of 100% was obtained as a result of the classification performed with ANN using the data obtained by combining all the tables in the dataset.

Iron metal is the most widely used metal type. This metal, which is used in countless sectors, is processed in different ways and turned into steel. Since steel has a brittle structure compared to iron, defects may occur in the plates during the rolling process. Detection of these defects at the production stage is of great importance in terms of commercial and safety. Machine learning methods can be used in such problems for fast and high accuracy detection. For this purpose, using a dataset obtained from stainless steel surface defects in this study, classification processes were carried out to detect defects with four different machine learning methods. Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms were used for classification processes. The highest classification accuracy was obtained from the 79.44% RF model. Correlation analysis was performed in order to analyze the effects of the features in the dataset on the classification results. It is thought that the classification accuracy of the proposed models is satisfactory for this challenging problem, but needs to be upgraded.

Energy storage systems enable the storage of energy and provide access to carbon-neutral, environmentally friendly energy whenever or wherever it is needed. Lithium-ion batteries are currently the most preferred type among various battery technologies and are widely used in energy storage systems. Some of the features that make lithium-ion batteries advantageous include high energy density, long life, low maintenance requirements, and high operating voltage. The growing demand for energy throughout the day increases the need for batteries with high storage capacity. However, the increased capacity also leads to heating issues in lithium-ion batteries. The heating problem in lithium-ion batteries can result in nonhomogeneous temperature distribution, shortened lifespan, thermal runaway, increased internal resistance, and performance loss. Therefore, an effective thermal management system is essential for cooling lithium-ion batteries. This study aims to provide insight into the forced air cooling of prismatic 280 Ah LiFePo4 batteries, which have limited information in the literature and are more prone to overheating compared to lower-capacity batteries. In this study, five different battery pack case designs, each with different sizes and numbers of air intake holes, were determined and modelled using the SolidWorks program. Within the battery pack cases, 16 280 Ah lithium-ion batteries are placed, and an axial fan is used to cool these batteries. Initially, computational fluid dynamics analyses of the five different designs were performed in the SolidWorks Flow Simulation program. An experiment was then conducted on the design that provided the most efficient thermal management to validate the numerical results. The selected design, fulfilling the purpose of homogeneous temperature distribution and having the minimum temperature difference between batteries, was designated as Design 5. It exhibited a 62 % improvement in cooling performance with a 0.25 °C temperature difference, indicating successful temperature homogeneity between batteries. During a two-hour experiment with a 140 A discharge current, temperature measurements were taken from the surfaces of the batteries using thermocouples. Finally, the maximum error rate between experimental and numerical studies was determined to be 1.47 %, indicating successful validation of the numerical study. The air intake hole optimization, a novel design approach, prevents temperature distribution inhomogeneity caused by the distance of the batteries to the fan and offers an effective way to cool down high-capacity 280 Ah batteries.

Aircraft are used in many fields such as engineering, logistics, transportation and disaster management. With the development of drones, aerial vehicles have become more widely used for entertainment purposes. However, in addition to its useful applications, its malicious use is also becoming widespread. It has become a necessity to eliminate this problem, especially since it poses a significant danger to other aircraft. In order to identify the aircraft and solve this problem quickly, in this study, five different aircraft were classified based on images. In the study, a five-class dataset containing aeroplane, bird, drone, helicopter and malicious UAV (Unnamed Aerial Vehicle) images was used. Three different CNN (Convolutional Neural Network) models were employed to extract the images of features. Image features extracted with SqueezeNet, VGG16, VGG19 models were classified with Artificial Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression (LR) machine learning methods. As a result of the experiments, the most accuracyful result, 92%, was obtained from the classification of the features extracted with the SqueezeNet model with ANN. The models proposed in the study will be integrated into various systems and used in the field of aviation to detect malicious UAVs and take necessary precautions.

This study addresses the growing importance of integrating innovation into corporate sustainability strategies by examining the financial and environmental performance of ten firms listed on the Borsa Istanbul Sustainability Index over a five-year period. The main objective is to develop and test a novel, data-driven analytical framework that reduces reliance on subjective expert judgments while providing actionable insights for sustainability-oriented decision-making. Within this framework, the entropy method from the Multi-Criteria Decision Making (MCDM) approach is first applied to calculate the objective weights of sustainability criteria, ensuring that the analysis is grounded in real performance data. Building on these weights, an innovative reverse Decision-Making Trial and Evaluation Laboratory (DEMATEL) model, implemented through a custom artificial neural network-based software, is introduced to estimate direct influence matrices and reveal the causal relationships among criteria. This methodological advance makes it possible to explore how environmental and financial factors interact with R&D expenditures and to simulate their systemic interdependencies. The findings demonstrate that R&D serves as a central driver of both environmental and financial sustainability, highlighting its dual role in fostering corporate innovation and long-term resilience. By positioning R&D as both an enabler and outcome of sustainability dynamics, the proposed framework contributes a novel tool for aligning innovation with strategic sustainability goals, offering broader implications for corporate managers, policymakers, and researchers.