Multiple-input soft-switching power converters

A multiple-input power converter transferring energy from multiple input sources to a load comprises a plurality of voltage inputs. The power converter implements soft-switching techniques thereby reducing the converter switching losses and increasing the converter efficiency while using fewer components than presently designed multiple-input power converters. Such a power converter may include multiple input sources, where serially connected switches are coupled to one of the multiple input sources in an input leg. A voltage blocking capacitor is inserted between these input legs. Furthermore, the power converter includes a transformer for isolating the load from the multiple input sources, where the voltage blocking capacitor is connected to the primary winding of the transformer.Board of Regents, University of Texas Syste

Online Brand Community User Segments: A Text Mining Approach

There is a trend that customers increasingly join the online brand community. However, evidence shows that there are nuances between different user segments, and only a small group of users are active. Thus, one key concern marketers face is identifying and targeting specific segments and decreasing user churn rates in an online environment. To this end, this study aims to propose a UGC-based segmentation of online brand community users, identify the characteristics of each segment, and consequently reduce online brand community users' churn rate. We used python to obtain users' post data from a well-known online brand community in China between July 2012 and December 2019, resulting in 912,452 posts and 20,493 users. We then use text mining and clustering methods to segment the users and compare the differences between the segments. Three groups—information-oriented users, entertainment-oriented users, and multi-motivation users—were emerged. Our results imply that entertainment-oriented users were the most active, yet, multi-directional users have the lowest probability of churn, with a churn rate of only 0.607 times than that of users who focus either on information or entertainment. Implications for marketing and future research opportunities are discussed

Power regeneration in the primary suspension of a railway vehicle

This paper presents an assessment of the potential for the use of power regenerating dampers (PRDs) in railway vehicle primary suspension systems equipped with the ‘Hybrid Mode’ and ‘Replace Mode’, and the evaluation of the potential/recoverable power that can be obtained. The power regenerating damper is configured as a hydraulic-electromagnetic based damper. Implications for ride comfort and running safety are also commented for investigating the performance of the suspension system. Several case studies of generic railway vehicle primary suspension systems are modelled and configured to include a power regenerating damper with two different configuration modes. Simulations are then carried out on track with typical irregularities for a generic UK passenger vehicle. The performance of the modified vehicle including regenerated power, ride comfort and running safety is evaluated. Analysis of key influencing factors are also carried out to examine their effects on power capability, ride comfort and running safety to guide the primary suspension design/specification

GlyphDraw: Learning to Draw Chinese Characters in Image Synthesis Models Coherently

Recent breakthroughs in the field of language-guided image generation have yielded impressive achievements, enabling the creation of high-quality and diverse images based on user instructions. Although the synthesis performance is fascinating, one significant limitation of current image generation models is their insufficient ability to generate coherent text within images, particularly for complex glyph structures like Chinese characters. To address this problem, we introduce GlyphDraw, a general learning framework aiming at endowing image generation models with the capacity to generate images embedded with coherent text. To the best of our knowledge, this is the first work in the field of image synthesis to address the generation of Chinese characters. % we first adopt the OCR technique to collect images with Chinese characters as training samples, and extract the text and locations as auxiliary information. We first sophisticatedly design the image-text dataset's construction strategy, then build our model specifically on a diffusion-based image generator and carefully modify the network structure to allow the model to learn drawing Chinese characters with the help of glyph and position information. Furthermore, we maintain the model's open-domain image synthesis capability by preventing catastrophic forgetting by using a variety of training techniques. Extensive qualitative and quantitative experiments demonstrate that our method not only produces accurate Chinese characters as in prompts, but also naturally blends the generated text into the background. Please refer to https://1073521013.github.io/glyph-draw.github.ioComment: 24 pages, 5 figure

Gate-based spin readout of hole quantum dots with site-dependent g−g-factors

The rapid progress of hole spin qubits in group IV semiconductors has been driven by their potential for scalability. This is owed to the compatibility with industrial manufacturing standards, as well as the ease of operation and addressability via all-electric drives. However, owing to a strong spin-orbit interaction, these systems present variability and anisotropy in key qubit control parameters such as the Land\'e $g-$factor, requiring careful characterisation for reliable qubit operation. Here, we experimentally investigate a hole double quantum dot in silicon by carrying out spin readout with gate-based reflectometry. We show that characteristic features in the reflected phase signal arising from magneto-spectroscopy convey information on site-dependent $g-$factors in the two dots. Using analytical modeling, we extract the physical parameters of our system and, through numerical calculations, we extend the results to point out the prospect of conveniently extracting information about the local $g-$factors from reflectometry measurements.Comment: Main manuscript: 12 pages, 8 figures. Supplementary Information: 3 pages, 2 figure

Recent advances and prospect in immune microenvironment and its mechanisms of function in head and neck squamous cell carcinoma

Head and neck cancer (HNC) remains a significant cause of morbidity and mortality. The most prevalent pathology among HNC is head and neck squamous cell carcinoma (HNSCC). The tumor microenvironment (TME) encompasses the components surrounding tumor cells, including immune cells, stromal cells, extracellular matrix (ECM), blood and lymph vessels. Strategies targeting the TME have yielded significant outcomes. Thus, further exploration of the interactions between TME components is crucial. This review discussed recent advances in cytotoxic T lymphocytes (CTL), CD4+ T lymphocytes, regulatory T cells (Treg), myeloid-derived suppressor cells (MDSC), natural killer (NK) cells and tumor-associated macrophages (TAM) in HNSCC TME. The article summarized herein primarily focused on restoring the activity of anti-tumor cells and eliminating the immunosuppressive effects of Treg and so on, to provide new insights for more effective HNSCC therapy