Numerical modelling of mutual effect among nearby needles in a multi-needle configuration of an atmospheric air dielectric barrier discharge

A numerical study has been conducted to understand the mutual effect among nearby needles in a multi-needle electrode dielectric barrier discharge. In the present paper, a fluid-hydrodynamic model is adopted. In this model, the mutual effect among nearby needles in a multi-needle configuration of an atmospheric air dielectric barrier discharge are investigated using a fluid-hydrodynamic model including the continuity equations for electrons and positive and negative ions coupled with Poisson's equation. The electric fields at the streamer head of the middle needle (MN) and the side needles (SNs) in a three-needle model decreased under the influence of the mutual effects of nearby needles compared with that in the single-needle model. In addition, from the same comparison, the average propagation velocities of the streamers from MN and SNs, the electron average energy profile of MN and SNs (including those in the streamer channel, at the streamer head, and in the unbridged gap), and the electron densities at the streamer head of the MN and SNs also decreased. The results obtained in the current paper agreed well with the experimental and simulation results in the literature. © 2012 by the authors

Pix2HDR -- A pixel-wise acquisition and deep learning-based synthesis approach for high-speed HDR videos

Accurately capturing dynamic scenes with wide-ranging motion and light intensity is crucial for many vision applications. However, acquiring high-speed high dynamic range (HDR) video is challenging because the camera's frame rate restricts its dynamic range. Existing methods sacrifice speed to acquire multi-exposure frames. Yet, misaligned motion in these frames can still pose complications for HDR fusion algorithms, resulting in artifacts. Instead of frame-based exposures, we sample the videos using individual pixels at varying exposures and phase offsets. Implemented on a pixel-wise programmable image sensor, our sampling pattern simultaneously captures fast motion at a high dynamic range. We then transform pixel-wise outputs into an HDR video using end-to-end learned weights from deep neural networks, achieving high spatiotemporal resolution with minimized motion blurring. We demonstrate aliasing-free HDR video acquisition at 1000 FPS, resolving fast motion under low-light conditions and against bright backgrounds - both challenging conditions for conventional cameras. By combining the versatility of pixel-wise sampling patterns with the strength of deep neural networks at decoding complex scenes, our method greatly enhances the vision system's adaptability and performance in dynamic conditions.Comment: 14 pages, 14 figure

Emm type distribution of group A Streptococcus in China during 1990 and 2020: a systematic review and implications for vaccine coverage

BackgroundThe recent increase of group A Streptococcus (GAS) infections in Europe has aroused global concern. We aim to provide molecular biological data for GAS prevention and control in China by analyzing the temporal shift of emm type.MethodsWe collected studies reporting GAS emm types in China from 1990 to 2020 by PRISMA statement and established a summary database including emm types and literature quality assessment. Based on the database we analyzed the geographic distribution of emm types from 1990 to 2020 and assessed the coverage of the known GAS 30-valent vaccine. Outbreak-associated emm types that had been reported over the past 30 years were also included.Results47 high quality studies were included for a systematic analysis of emm type distribution. This generated a database including totally 12,347 GAS isolates and 85 emm types. Shift of dominant emm type was witnessed during the past 30 years in China. In mainland China, dominant types changed from emm3, emm1, emm4, emm12 in 1990s to emm12 and emm1 in 2000s and 2010s. Hong Kong and Taiwan were dominated by emm12, emm4 and emm1, of which emm4 reduced but emm12 increased in 2010s significantly. From 1990 to 2020, newly found emm types were increasingly reported in various regions of China. The reported 30-valent M protein vaccine covered 26 M types prevalent in China, including all dominant types

Case report: Squamous cell carcinoma of the prostate-a clinicopathological and genomic sequencing-based investigation

Squamous differentiation of prostate cancer, which accounts for less than 1% of all cases, is typically associated with androgen deprivation treatment (ADT) or radiotherapy. This entity is aggressive and exhibits poor prognosis due to limited response to traditional treatment. However, the underlying molecular mechanisms and etiology are not fully understood. Previous findings suggest that squamous cell differentiation may potentially arise from prostate adenocarcinoma (AC), but further validation is required to confirm this hypothesis. This paper presents a case of advanced prostate cancer with a combined histologic pattern, including keratinizing SCC and AC. The study utilized whole-exome sequencing (WES) data to analyze both subtypes and identified a significant overlap in driver gene mutations between them. This suggests that the two components shared a common origin of clones. These findings emphasize the importance of personalized clinical management for prostate SCC, and specific molecular findings can help optimize treatment strategies

Investigation of Electrode Electrochemical Reactions in CH3 NH3 PbBr3 Perovskite Single-Crystal Field-Effect Transistors.

Optoelectronic devices based on metal halide perovskites, including solar cells and light-emitting diodes, have attracted tremendous research attention globally in the last decade. Due to their potential to achieve high carrier mobilities, organic-inorganic hybrid perovskite materials can enable high-performance, solution-processed field-effect transistors (FETs) for next-generation, low-cost, flexible electronic circuits and displays. However, the performance of perovskite FETs is hampered predominantly by device instabilities, whose origin remains poorly understood. Here, perovskite single-crystal FETs based on methylammonium lead bromide are studied and device instabilities due to electrochemical reactions at the interface between the perovskite and gold source-drain top contacts are investigated. Despite forming the contacts by a gentle, soft lamination method, evidence is found that even at such "ideal" interfaces, a defective, intermixed layer is formed at the interface upon biasing of the device. Using a bottom-contact, bottom-gate architecture, it is shown that it is possible to minimize such a reaction through a chemical modification of the electrodes, and this enables fabrication of perovskite single-crystal FETs with high mobility of up to ≈15 cm2 V-1 s-1 at 80 K. This work addresses one of the key challenges toward the realization of high-performance solution-processed perovskite FETs

The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition

In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sensor malfunctions, and environmental unpredictability can severely impact the performance of autonomous systems. The 2024 RoboDrive Challenge was crafted to propel the development of driving perception technologies that can withstand and adapt to these real-world variabilities. Focusing on four pivotal tasks -- BEV detection, map segmentation, semantic occupancy prediction, and multi-view depth estimation -- the competition laid down a gauntlet to innovate and enhance system resilience against typical and atypical disturbances. This year's challenge consisted of five distinct tracks and attracted 140 registered teams from 93 institutes across 11 countries, resulting in nearly one thousand submissions evaluated through our servers. The competition culminated in 15 top-performing solutions, which introduced a range of innovative approaches including advanced data augmentation, multi-sensor fusion, self-supervised learning for error correction, and new algorithmic strategies to enhance sensor robustness. These contributions significantly advanced the state of the art, particularly in handling sensor inconsistencies and environmental variability. Participants, through collaborative efforts, pushed the boundaries of current technologies, showcasing their potential in real-world scenarios. Extensive evaluations and analyses provided insights into the effectiveness of these solutions, highlighting key trends and successful strategies for improving the resilience of driving perception systems. This challenge has set a new benchmark in the field, providing a rich repository of techniques expected to guide future research in this field.Comment: ICRA 2024; 32 pages, 24 figures, 5 tables; Code at https://robodrive-24.github.io

Players’ Value Prediction Based on Machine Learning Method

Abstract It is important to predict football players’ value, especially during transfer period. This paper uses the player information and value data of the game FIFA 18 as data source. It is able to realize the prediction of its players’ best positions and values. After reducing the dimensionality of the value prediction model, a cluster analysis on the player’s position is introduced, and then grid search method is adopted to adjust the Xgboost parameters, Finally, Xgboost method is used to predict the player’s worth. The experimental results show that certain accuracy is achieved, but t there is still room for improvement in the accuracy of prediction. Discussions based on experiment results are made.</jats:p

Metasurface-Enabled Honey Encryption: A New Paradigm for Secure Data Transmission

Honey encryption is a potent data security method that thwarts attackers by generating deceptive yet plausible messages, but its large-scale adoption is hindered by cumbersome and time-intensive encryption processes, and this study aims to address this limitation by proposing a novel encryption framework that, for the first time to our knowledge, integrates honey encryption with metasurfaces to enhance both security and efficiency. The methodology involves employing a Fast Fourier Transform (FFT)-based iterative algorithm to optimize the phase distribution of metasurfaces, ensuring it matches the amplitude of the target image, which enables rapid and effective encryption and decryption processes. Key results demonstrate that the proposed framework outperforms traditional encryption methods in both security and transmission efficiency, with successful realization of secure data transmission through metasurface-based modulation. In conclusion, this integration of honey encryption and metasurface technology not only addresses the inefficiencies of conventional honey encryption but also shows significant promise for wide-ranging applications in data encryption and secure communications, leveraging the advancing metasurface technology