Istanbul Technical University
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A new equivalent circuit design for a multi-gate MOSFET device: investigation of electrical behavior and application in current mirror
No full texthttps://doi.org/10.1007/s10470-025-02530-
Medical Image Segmentation via 3D Rubik Convolutions
No full texthttps://doi.org/10.1109/siu66497.2025.1111245
Correction: Design of the monolithic planar isotropic auxetic piezo-resistive sensor glove to detect human hand motion
No full texthttps://doi.org/10.1007/s10853-025-11312-
Microbial fuel cells: A potent and sustainable solution for heavy metal removal
No full textThe global water pollution problem is becoming increasingly crucial. One of the major contributors to water pollution is the presence of heavy metals. Heavy metals pose significant threat to both humans and all ecosystems. Various factors influence the removal of heavy metals from wastewater, including pH, temperature, natural organic matter (NOM), and ionic strength, which vary based on the chemical properties of the pollutants. More effective and modern approaches receive attention and extensively researched to substitute traditional methods such as adsorption, membrane filtration, and chemical-based separation. Among these methods, Microbial fuel cells (MFCs) are particularly intriguing. This review article focuses on MFCs and their potential applications in various fields, including clean water production. MFCs represent an innovative technology that not only generates electricity, but also demonstrates significant potential for heavy metal removal from wastewater. Cathodic chamber of MFCs effectively reduces heavy metals, while organic substrates act as carbon and electron donors in the anodic chamber. Through various mechanisms, including direct and indirect metal reduction, biofilm formation (metal sequestering), electron shuttling, and synergistic interactions among microbial communities, microorganisms exhibit remarkable efficiency in removing metals. Studies showed that dual- and single-chamber MFCs could efficiently remove a range of heavy metals, including chromium, cobalt, copper, vanadium, mercury, gold, selenium, lead, magnesium, manganese, zinc, and sodium, while simultaneously generating electricity, achieving high removal efficiencies ranging from 25% to 99.95%. This range of efficiency varies depending on the specific contaminant being targeted, the concentration of the contaminant, as well as the operating conditions such as pH and temperature. Moreover, MFCs demonstrated a wide range of power outputs, typically ranging from 0.15 W/m² to 6.58 W/m², depending on the specific configuration and conditions. These findings underscore the potential of MFCs as a sustainable and efficient approach for both wastewater treatment and energy generation.https://doi.org/10.62063/rev-
Accurate AI-Driven Emergency Vehicle Location Tracking in Healthcare ITS’s Digital Twin
No full textCreating a Digital Twin (DT) for Healthcare Intelligent Transportation Systems (HITS) is a hot research trend focusing on enhancing HITS management, particularly in emergencies where ambulance vehicles must arrive at the crash scene on time and track their real-time location is crucial to the medical authorities. Despite the claim of real-time representation, a temporal misalignment persists between the physical and virtual domains, leading to discrepancies in the ambulance's location representation. This study proposes integrating AI predictive models, specifically Support Vector Regression (SVR) and Deep Neural Networks (DNN), within a constructed mock DT data pipeline framework to anticipate the medical vehicle's next location in the virtual world. These models align virtual representations with their physical counterparts, i.e., metaphorically offsetting the synchronization delay between the two worlds. Trained meticulously on a historical geospatial dataset, SVR and DNN exhibit exceptional prediction accuracy in MATLAB and Python environments. Through various testing scenarios, we visually demonstrate the efficacy of our methodology, showcasing SVR and DNN's key role in significantly reducing the witnessed gap within the HITS's DT. This transformative approach enhances real-time synchronization in emergency HITS by approximately 88% to 93%.8 pages, 8 figures, 5th IEEE Middle East & North Africa COMMunications Conference (MENACOMM'25), Lebanon Feb 20-23, 2025https://doi.org/10.1109/menacomm62946.2025.10910975https://dx.doi.org/10.48550/arxiv.2502.03396http://arxiv.org/abs/2502.03396https://doi.org/10.1109/MENACOMM62946.2025.10910975https://doi.org/10.48550/arXiv.2502.0339
Psychological Inflexibility, Mindfulness, Emotion Regulation, Self-compassion, and Anxiety in Adults: A Serial Mediation Model
No full textThis study examines the mediating roles of mindfulness, emotion regulation, and self-compassion in the relationship between psychological inflexibility and anxiety in adults. The research was conducted within the scope of a quantitative study using a cross-sectional design, and data were collected from 443 university students. Participants completed validated measurement instruments assessing psychological inflexibility, mindfulness, emotion regulation, self-compassion, and anxiety. Mediation analyses revealed that psychological inflexibility significantly predicted higher levels of anxiety and that this relationship was partially mediated by mindfulness, emotion regulation, and self-compassion. Specifically, psychological inflexibility was found to increase difficulties in emotion regulation and negatively affect mindfulness and self-compassion, which in turn contributed to higher anxiety levels. These findings highlight the importance of targeting psychological flexibility, mindfulness, emotion regulation, and self-compassion in clinical interventions aimed at reducing anxiety. The results provide empirical support for acceptance-based interventions, such as Acceptance and Commitment Therapy, in promoting psychological well-being.https://doi.org/10.21666/muefd.1643883https://dergipark.org.tr/tr/pub/muefd/issue/90668/164388
From global production networks to global knowledge networks a case study on Ford-Otosan automotive company in Türkiye
No full texthttps://doi.org/10.1080/13511610.2025.257969
Dynamic Ransomware Analysis using CAPEv2 and Retrieval-Augmented Generation
No full texthttps://doi.org/10.1109/ubmk67458.2025.1120679
Development of PEEK Matrix Polymer Composite and Additive Manufacturing by Pellet Extrusion Method
No full texthttps://doi.org/10.1007/978-3-031-50470-9_
Enhancing crop classification with growing Degree days: bridging classical models and regional generalization
No full texthttps://doi.org/10.1080/2150704x.2025.256812