In this study, a numerical simulation was used to evaluate the insulation performance of polyethylene (PE) and polyvinyl chloride (PVC) under varied environmental and electrical conditions. Tests were conducted at temperatures of 22 °C and 55 °C, with current levels of 40 A and 60 A, examining key parameters such as electric field intensity, current density, and Joule heating. The results show that, under lower temperature and current conditions, PE demonstrates greater current capacity but suffers from increased Joule heating and energy loss. Conversely, PVC provides more stable insulation with lower energy dissipation. At higher temperatures and currents, PE experiences significant electrical stress and thermal loading, increasing the risk of overheating, while PVC maintains consistent performance. These findings offer valuable guidance for selecting optimal insulation materials in power distribution systems.

Motor imagery (MI) is a widely used paradigm in brain–computer interface (BCI) systems, with applications in rehabilitation and neuroscience. In this study, magnetoencephalography (MEG) signals were employed to analyze MI and other mental imagery tasks. MEG provides high spatial resolution, facilitating the classification of imagery-related signals. This study aims to enhance the classification of motor and cognitive imagery (CI) tasks using a public MEG dataset including four distinct tasks: imagining the movement of hands (H) or feet (F), performing arithmetic subtraction (S), and forming words (W). MEG signals were decomposed using five signal-decomposition methods: Empirical Wavelet Transform (EWT), Maximal Overlap Discrete Wavelet Transform (MODWT), Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), and Multivariate Variational Mode Decomposition (MVMD). Feature extraction was performed using the Common Spatial Patterns (CSP), with t-test-based feature selection. Subsequently, commonly used machine learning algorithms were employed to classify MI and CI tasks. The results indicate that MVMD and MODWT achieved the highest accuracies when combined with the Artificial Neural Networks. MVMD yielded superior performances in (H and W: 79.2%; F and S: 75.8%; and F and W: 73.8%) tasks. MODWT achieved high accuracies in the H and W (75.9%) and F and W (76.3%) tasks. Overall, motor and non-motor pairs (H and W, F and W) yielded higher accuracy than the cognitive pair (W and S).

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impairments in social interaction and communication, along with atypical behavioral patterns. Affected individuals often seem isolated in their inner world and exhibit particular sensory reactions. The World Health Organization has indicated a persistent increase in the global prevalence of autism, with approximately 1 in 127 persons affected worldwide. This study contributes to the growing research effort by presenting a comprehensive analysis of functional connectivity patterns for ASD prediction using rs-fMRI datasets. A novel approach was used for ASD identification using the ABIDE II dataset, based on functional networks derived from BOLD signals. The sparse functional brain connectome (Lean-NET) model is employed to construct subject-specific connectomes, from which local graph metrics are extracted to quantify regional network properties. Statistically significant features are selected using Welch’s t-test, then subjected to False Discovery Rate (FDR) correction and classified using a Support Vector Machine (SVM). Our experimental results demonstrate that locally derived graph metrics effectively discriminate ASD from typically developing (TD) subjects and achieve accuracy ranging from 70% up to 91%, highlighting the potential of graph learning approaches for functional connectivity analysis and ASD characterization.

Polylactic acid (PLA) is an eco-friendly polymer known for its biodegradability and biocompatibility, yet its properties are sensitive to recycling and sterilization. These processes may cause chain scission and structural irregularities, leading to reduced strength, brittleness, or unpredictable deformation. In this study, PLA and recycled PLA (Re-PLA) specimens were produced by FDM 3D printing with different infill rates (25%, 50%, 75%), layer thicknesses (0.15, 0.20, 0.25 mm), and printing orientations (0°, 45°, 90°). Steam sterilization at 121 °C and 1 bar for 15 min simulated biomedical conditions. Mechanical, surface, degradation, and biocompatibility properties were examined using three-point bending, roughness measurements, SEM, and cell viability tests. Results showed that infill rate was the main parameter affecting flexural strength and surface quality, while orientation increased roughness. Sterilization and recycling made deformation less predictable, particularly in St-Re-PLA. SEM revealed stronger bonding at higher infill, but more brittle fractures in PLA and Re-PLA, while sterilized specimens showed ductile features. No visible degradation occurred at any infill level. Regression analysis confirmed that second-order polynomial models effectively predicted flexural strength, with layer thickness being most influential. These findings provide critical insights into optimizing PLA and Re-PLA processing for biomedical applications, particularly in the production of sterilizable and recyclable implantable devices.

Ti6Al4V alaşımı mükemmel korozyon direnci, düşük yoğunluk, yüksek mekanik özellikler ve biyouyumluluk özellikleri nedeniyle ortodonti, medikal implant, cerrahi aletler, gıda ve havacılık endüstrisi gibi birçok alanda kullanılmaktadır. Özellikle biyomalzeme olarak kullanımı korozif ortamlara karşı mekanizmasının anlaşılmasını zorunlu kılmıştır. α+β alaşım gurubuna sahip Ti6Al4V alaşımının ısıl işlem sonrası birçok özelliği iyileştirilebilir. Bu çalışmada, Ti6Al4V alaşımına 1100°C sıcaklıkta 20 dak ısıl işlem uygulanarak su, hava ve yağ ortamlarında soğutulmuştur. Farklı soğutma ortamlarında işlem görmüş numunelerin SBF (yapay vücut sıvısı) ve Hank solüsyonlarına maruz kalması sonucu mikro yapıdaki faz dönüşümlerinin sertlik, yüzey kalitesi, işlenebilirlik ve korozyon özelliklerine etkileri incelenmiştir. Sonuçlara göre yüksek sıcaklıklarda oluşan ikincil fazların malzemenin mekanik ve korozyon özelliklerini etkilediği tespit edilmiştir. Soğutma ortamının ve korozif ortamın mekanik özellikler ve korozyon direnci üzerinde önemli bir etkiye sahip olduğu belirlenmiştir. Yağ ve havada gerçekleştirilen soğutma işlemi sonrasında her iki çözeltide de parçaların korozyon direncinin arttığı gözlemlenmiştir. Ayrıca işleme sonrası kesici uçlarda uç yüzeyinde kaplama ayrılması ve serbest yüzey aşınması tespit edilmiştir.