Noise discrimination method based on charge distribution of CMOS detectors for soft X-ray

Complementary metal-oxide semiconductor (CMOS) sensors have been widely used as soft X-ray detectors in several fields owing to their recent developments and unique advantages. The parameters of CMOS detectors have been extensively studied and evaluated. However, the key parameter signal-to-noise ratio in certain fields has not been sufficiently studied. In this study, we analysed the charge distribution of the CMOS detector GSENSE2020BSI and proposed a two-dimensional segmentation method to discriminate signals according to the charge distribution. The effect of the two-dimensional segmentation method on the GSENSE2020BSI dectector was qualitatively evaluated. The optimal feature parameters used in the two-dimensional segmentation method was studied for G2020BSI. However, the two-dimensional segmentation method is insensitive to feature parameters.Comment: 19 pages, 13 figures, submitted to NIM-

Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient

Diffusion models (DMs) are a type of generative model that has a huge impact on image synthesis and beyond. They achieve state-of-the-art generation results in various generative tasks. A great diversity of conditioning inputs, such as text or bounding boxes, are accessible to control the generation. In this work, we propose a conditioning mechanism utilizing Gaussian mixture models (GMMs) as feature conditioning to guide the denoising process. Based on set theory, we provide a comprehensive theoretical analysis that shows that conditional latent distribution based on features and classes is significantly different, so that conditional latent distribution on features produces fewer defect generations than conditioning on classes. Two diffusion models conditioned on the Gaussian mixture model are trained separately for comparison. Experiments support our findings. A novel gradient function called the negative Gaussian mixture gradient (NGMG) is proposed and applied in diffusion model training with an additional classifier. Training stability has improved. We also theoretically prove that NGMG shares the same benefit as the Earth Mover distance (Wasserstein) as a more sensible cost function when learning distributions supported by low-dimensional manifolds

Integrating Wildfires Propagation Prediction Into Early Warning of Electrical Transmission Line Outages

Wildfires could pose a significant danger to electrical transmission lines and cause considerable losses to the power grids and residents nearby. Previous studies of preventing wildfire damages to electrical transmission lines mostly analyze wildfire and power system security independently due to their differences in disciplines and cannot satisfy the requirement of the power grid for active and timely responses. In this paper, we have designed an integrated wildfire early warning system framework for power grids, taking prediction of wildfires and early warning of line outage probability together. First, the proposed model simulates the spatiotemporal process of wildfires via a geography cellular automata model and predicts when and where wildfires initially get into the security buffer of an electrical transmission line. It is developed in the context of electrical transmission line operating with various situations of topography, vegetation, wind and, especially, multiple ignition points. Second, we have proposed a line outage model (LOM), based on wildfire prediction and breakdown mechanisms of the air gap, to predict the breakdown probability varying with time and the most vulnerable poles at the holistic line scale. Finally, to illustrate the validation and rationality of our proposed system, a case study for a 500-kV transmission line near Miyi county, China, is presented, and the results under various wildfire situations are studied and compared. By integrating wildfire prediction into the LOM and alarming the holistic line breakdown probability along time, this paper makes a significant contribution in the early warning system to prevent transmission lines to be damaged by wildfires, illustrating the related breakdown mechanisms at the line operation level rather than laboratory experiments only. Meanwhile, the implementation of cellular automata model under comprehensive environmental conditions and simulation of the breakdown probability for the 500-kV transmission line could serve as references for other studies in the community

Prevotella copri alleviates sarcopenia via attenuating muscle mass loss and function decline

Background: The gut microbiome and fecal metabolites have been found to influence sarcopenia, but whether there are potential bacteria that can alleviate sarcopenia has been under-investigated, and the molecular mechanism remains unclear.Methods: To investigate the relationships between the gut microbiome, fecal metabolites and sarcopenia, subjects were selected from observational multi-ethnic study conducted in Western China. Sarcopenia was diagnosed according to the criteria of the Asian Working Group for Sarcopenia 2014. The gut microbiome was profiled by shotgun metagenomic sequencing. Untargeted metabolomic analysis was performed to analyse the differences in fecal metabolites. We investigated bacterium with the greatest relative abundance difference between healthy individuals and sarcopenia patients, and the differences in metabolites associated with the bacteria, to verify its effects on muscle mass and function in a mouse model.Results: The study included 283 participants (68.90% females, mean age: 66.66 years old) with and without sarcopenia (141 and 142 participants, respectively) and from the Han (98 participants), Zang (88 participants) and Qiang (97 participants) ethnic groups. This showed an overall reduction (15.03% vs. 20.77%, P = 0.01) of Prevotella copri between the sarcopenia and non-sarcopenia subjects across the three ethnic groups. Functional characterization of the differential bacteria showed enrichment (odds ratio = 15.97, P = 0.0068) in branched chain amino acid (BCAA) metabolism in non-sarcopenia group. A total of 13 BCAA and their derivatives have relatively low levels in sarcopenia. In the in vivo experiment, we found that the blood BCAA level was higher in the mice gavaged with live P. copri (LPC) (P &lt; 0.001). The LPC mice had significantly longer wire and grid hanging time (P &lt; 0.02), longer time on rotor (P = 0.0001) and larger grip strength (P &lt; 0.0001), indicating better muscle function. The weight of gastrocnemius mass and rectus femoris mass (P &lt; 0.05) was higher in LPC mice. The micro-computed tomography showed a larger leg area (P = 0.0031), and a small animal analyser showed a higher lean mass ratio in LPC mice (P = 0.0157), indicating higher muscle mass.Conclusions: The results indicated that there were lower levels of both P. copri and BCAA in sarcopenia individuals. In vivo experiments, gavage with LPC could attenuate muscle mass and function decline, indicating alleviating sarcopenia. This suggested that P. copri may play a therapeutic potential role in the management of sarcopenia.</p