Eng

The increasing adoption of electric vehicles has driven the development of charging technologies that meet growing demands for power, efficiency, and grid compatibility. This review presents a comprehensive analysis of the EV charging ecosystem, covering Level 3 DC charging stations, power converter topologies, and the role of energy storage systems in supporting grid integration. Commercial solutions and academic prototypes are compared across key parameters such as voltage, current, power, efficiency, and communication protocols. The study highlights trends in charger architectures—including buck, boost, buck–boost, LLC resonant, and full-bridge configurations—while also addressing the integration of stationary storage as a buffer for fast charging stations. Special attention is given to wide-bandgap semiconductors like SiC and GaN, which enhance efficiency and thermal performance. A significant gap persists between the technical transparency of commercial systems and the ambiguity often observed in prototypes, highlighting the urgent need for standardized research reporting. Although converter efficiency is no longer a primary constraint, substantial challenges remain regarding infrastructure availability and the integration of storage with charging stations. This paper seeks to offer a comprehensive perspective on the design and deployment of smart, scalable, and energy-efficient charging systems, with particular emphasis on cascaded and bidirectional topologies, as well as hybrid storage solutions, which represent promising pathways for the advancement of future EV charging infrastructure.

Titanium and, in particular, its alloys are widely used in biomedical applications due to their favorable combination of mechanical properties, such as high strength, low density, low elastic modulus, and excellent biocompatibility. In this study, novel titanium-based alloys were developed using powder metallurgy techniques. The chemical composition of the studied alloys was 93%Ti-7%Fe, 90%Ti-7%Fe-3%Al, and 88%Ti-7%Fe-5%Cr. The metallic powders were processed in a planetary mill, uniaxially compacted, and subsequently sintered at 1300 °C during 2 h under an inert atmosphere. The primary objective was to evaluate the corrosion behavior of these alloys in simulated body fluid solutions, as well as to determine some of the properties, such as the relative density, microhardness, and elastic modulus. The resulting microstructures were homogeneous, with micrometer-scale grain sizes and the formation of intermetallic precipitates generated during sintering. Mechanical tests revealed that the Ti-Fe-Cr alloy exhibited the highest microhardness and Young’s modulus values, followed by Ti-Fe and Ti-Fe-Al. These results confirm a strong correlation between hardness and stiffness, showing that Cr enhances mechanical and elastic properties, while Al reduces them. Corrosion tests demonstrated that the alloys possess high resistance and stability in physiological environments, with a low current density, minimal mass loss, and strong performance even under prolonged exposure to acidic conditions.

Deepfake technology, which utilizes advanced AI models such as Generative Adversarial Networks (GANs), has led to the proliferation of highly convincing manipulated media, posing significant challenges for detection. Existing detection methods often struggle with the low-quality or compressed press, which is prevalent on social media platforms. This paper proposes a novel Deepfake detection framework that leverages No-Reference Image Quality Assessment (NRIQA) techniques, specifically, BRISQUE, NIQE, and PIQUE, to extract quality-related features from facial images. These features are then classified using a Support Vector Machine (SVM) with various kernel functions. We evaluate our method under both intra-dataset and cross-dataset settings. For intra-dataset evaluation, we conduct K-fold cross-validation on two benchmark datasets, DFDC and Celeb-DF (v2), including downsampled versions to simulate real-world degradation. The results show that our method maintains high accuracy even under significant quality loss, achieving up to 98% accuracy on the Celeb-DF (v2) dataset and outperforming several state-of-the-art methods. To improve the transferability of the detection models, we introduce an integrated filtering strategy based on NR-IQA thresholding, which enhances performance in cross-dataset transfer scenarios. This approach yields up to 7% improvement in detection accuracy under challenging cross-domain conditions.