Zonguldak Bülent Ecevit University

This study examines how managerial perceptions shape asymmetric cost behaviour across distinct institutional environments, focusing on the cognitive processes underlying strategic cost decisions. Based on survey data from 227 C-level executives in the automotive industry in the United States, United Kingdom, Germany, and France, the study validates the structure of cost antecedents through exploratory and confirmatory factor analyses and establishes measurement invariance to enable cross-country comparisons. The findings show that adjustment costs, regulatory constraints, and profit targets are perceived as the most influential antecedents. German and French managers emphasise institutional constraints such as labour protections and compliance obligations, while U.S. and U.K. managers highlight investor pressure and short-term performance expectations. By integrating behavioural decision-making theories with institutional perspectives, this research moves beyond archival proxies and offers a perception-based, cross-national validation of the antecedents of asymmetric cost behaviour. Although focused on the automotive sector, the findings provide a foundation for future research in industries with different levels of institutional rigidity and innovation intensity. Practically, the results underscore the need for managers to align cost strategies with labour market structures and regulatory frameworks, and for policymakers to balance employee protections with adaptive flexibility mechanisms. The study contributes to a deeper, context-sensitive understanding of asymmetric cost behaviour and offers actionable insights for strategic cost management in a complex global environment.

A hydrophobic deep eutectic solvent composed of di(2-ethylhexyl)phosphoric acid and menthol was synthesized and employed as the carrier liquid for the development of a new ferrofluid for liquid-phase microextraction of bisphenol A (BPA) from water samples. The methodology comprised stirring the water sample with a specified volume of ferrofluid, magnetic separation of the phases, and subsequent desorption of BPA using ethanol. No centrifugation was required, and the microextraction process took approximately 3.5 min. The obtained extract was subjected to analysis by high-performance liquid chromatography equipped with ultraviolet detection. The method demonstrated linearity across the concentration range of 2.5–200 μg L−1 with an r2 value of 0.9985. A limit of detection of 0.8 μg L−1 was achieved, with intra-day and inter-day relative standard deviations not exceeding 6.9

In the welding of dissimilar aluminum alloys, the success of the process primarily depends on the chemical composition and mechanical properties of the base materials. In addition, welding of aluminum alloys is highly complex due to factors such as differences in thermal conductivity, the possibility of hydrogen gas porosity during solidification, and the tendency for hot cracking. Therefore, in order to improve the weld performance of aluminum material pairs with different compositions (dissimilar alloys), it is necessary to evaluate innovative welding techniques such as friction stir welding (FSW) as well as the associated welding parameters. In this research, aluminum materials (Al5083 and Al6061) were joined using friction stir welding at two different feed rates (50 and 100 mm/min), three different spindle speeds (1000, 1500, and 2000 rpm), and three different pin geometries (triangular, square, and cylindrical). Tensile tests and microhardness measurements were conducted to evaluate the weld quality. Temperature measurements were carried out to examine the effect of temperature variations during friction stir welding. Additionally, images from the weld zone and surface roughness measurements were obtained to interpret the relationship between welding parameters and weld quality. As a result, the highest weld strengths were determined as 126.5 MPa for the cylindrical pin geometry (1000 rpm and 50 mm/min), 197 MPa for the square pin geometry (2000 rpm and 100 mm/min), and 212.5 MPa for the triangular pin geometry (2000 rpm and 50 mm/min). Surface roughness values ranged between 1.7 mu m and 5.5 mu m. Moreover, the sample with the highest weld strength (212.5 MPa) exhibited a surface roughness of 3.72 mu m. This indicates that processing parameters directly influence surface roughness. Overall, it was concluded that selecting the appropriate processing parameters (feed rate and rotational speed) and tool geometry (shoulder and pin) is of great importance for achieving adequate weld quality in friction stir welding.

With the advancement of industry, researchers increasingly aim to join different materials to benefit from the superior properties of each component. In this study, we investigate the friction stir spot welding (FSSW) behavior of 3D-printed PLA-based materials, specifically PLA Plus (PLA + ) and short carbon fiber–reinforced PLA (PLA-CF). We welded single-material (PLA + /PLA + , PLA-CF/PLA-CF) and multi-material (50

Background/Objectives: Brain tumors are among the most severe neurological disorders, and their variability in size, morphology, and anatomical location complicates early and accurate diagnosis. Although magnetic resonance imaging (MRI) is the most reliable non-invasive modality for tumor detection, manual interpretation remains time-consuming, subjective, and susceptible to human error. This study aims to develop an optimization-driven hybrid machine learning framework for accurate and computationally efficient automatic brain tumor classification. Methods: The dataset includes 834 MRI images (583-training, 123-validation, 128-independent test). Because YOLOv11 detects tumor and non-tumor regions separately, the sample size doubled during region-based analysis, and all subsequent stages were conducted at the regions of interest (ROI) level. On the independent test set, YOLOv11 achieved 98.87% mAP@50, 98.54% precision, and 98.21% recall. The proposed framework combines automated tumor localization with image standardization using Gaussian noise reduction and bilinear interpolation. From the processed MR images, 39 entropy-based features were extracted. To enhance diagnostic performance and eliminate redundant information, the superb fairy-wren optimization algorithm (SFOA) was applied for feature selection and compared with particle swarm optimization (PSO), Harris hawk optimization (HHO), and puma optimization (PO). Final classification was primarily performed using k-nearest neighbors (kNN), while support vector machines (SVM) were used for comparative evaluation. Results: SFOA reduced the feature dimensionality from 39 to 5 features while achieving 99.20% classification accuracy on the independent test set. In comparison, PSO selected 10 features, HHO selected 6 features and PO selected 10 features, all achieving 98.45% accuracy. The best performance obtained with SVM was 98.45% accuracy (HHO-SVM), which remained lower than the 99.20% achieved by the proposed SFOA-kNN model. Conclusions: The results indicate that combining entropy-based feature extraction with SFOA-driven feature selection and kNN classification significantly enhances diagnostic accuracy while reducing computational complexity, highlighting the strong potential of the proposed framework for integration into computer-aided diagnosis systems to support clinical decision-making.