Nov 2026· IEEE transactions on power electronics· Vol 41, pp. 19487-19498· 0 citations· 25 references
Abstract
With the rapid growth of artificial intelligence and data center computing power demands, power supply systems face dual challenges of high power density and high efficiency. These trends drive the bus voltage toward high-voltage direct current. However, existing transformer solutions struggle to simultaneously meet the requirements of extremely high step-down ratios and compact layouts. This article proposes a magnetically integrated fractional-turn matrix transformer with a high step-down ratio for high-performance DC–DC conversion. Based on the flux cancelation principle, the proposed scheme systematically integrates discrete fractional-turn units into a single high-density magnetic component. To address circuit-magnetic compatibility challenges during integration, a vertical excitation loop is innovatively designed. A “flux-free region” via-hole is constructed in the center of the magnetic plate. This structure accurately reconstructs the magnetic coupling characteristics of fractional turns. Meanwhile, it forms a low-impedance current path and effectively eliminates parasitic interference in the drive loop. Based on this scheme, a 1.2-kW, 1-MHz LLC-DCX resonant converter prototype is designed and fabricated. This prototype achieves direct conversion from 400 to 6.25 V (64:1). Experimental results demonstrate that the prototype achieves an ultra-high power density of 2.5 kW/in3 and a peak efficiency of 97.40%. These results verify the superiority of the proposed scheme for next-generation high-performance computing applications.
Investigating how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments finds that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes.
This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning techniques to ASD, highlighting key challenges and opportunities, particularly the need for models that can integrate complex data to improve diagnostic accuracy and treatment outcomes.
Rafael Muñoz-Terol, Jesús Peral, Sandra Amador et al.· Heliyon· 4 citations· ⚡1
This paper identifies two different routes through which models can acquire geometrically separable features: they can learn them from complementary co-occurrence signals in general language data, including text-number co-occurrence and cross-number interaction, or from multi-token addition problems.
This work explores image generation using flow matching using flow matching and proposes an iterative process that can be integrated into virtually any generative modeling technique, thereby enhancing the performance and robustness of image synthesis systems.
Eldad Haber, Shadab Ahamed, Md Shahriar Rahim Siddiqui et al.· SIAM Journal on Scientific C...· 3 citations
Tensor networks are powerful formats for compressing large-scale data. However, their application to general data processing has been limited by the difficulty of performing nonlinear operations. Here, we introduce iterative tensor network transformations (ITNTs), a general algorithmic framework for the element-wise evaluation of elementary and nonlinear filtering functions on data encoded as tensor trains (TTs), a class of tensor networks. Our approach operates entirely in the compressed domain, enabling efficient computation on exponentially large datasets while maintaining a controlled computational cost. We demonstrate its power in two key areas: (I) evaluating highly nonlinear elementary and filtering functions on a 3D reactive flow field, enabling high-fidelity reaction rate computation and region filtering, and (II) finding extrema in complex optimization problems, such as solving Max-SAT instances on spaces up to $2^{70}$ configurations. These results establish ITNT as a foundational tool that provides tensor network methods with the capability for general-purpose data science and large-scale optimization.
Xiao Wang, Tomohiro Hashizume, Pia Siegl et al.· 2 citations
A benchmark built on the Speech Accessibility Project (SAP) dataset is introduced that tests whether diagnosis labels, clinician-derived speech ratings, and progressively richer clinical descriptions improve transcription accuracy for dysarthric speech, finding that current models do not meaningfully use this context.
P. Moure, Niclas Pokel, Bilal Bounajma et al.· arXiv.org· 2 citations
