A GPU-based simulation system for infrared images of deep space targets

Abstract In study of deep space targets recognition, infrared images of deep space targets are needed for repeat testing and evaluating. Since the limitation of deep space flight experiments, it is difficult to obtain sufficient infrared images under different conditions. Infrared image simulation technology is brought up to solve this problem efficiently. The principle of deep space targets infrared imaging was studied. Based on the infrared sensor's optical properties, a hierarchical imaging model was built. The infrared camera and all the effects were simulated respectively, including motion trail of target and space objects, blurring, dispersion, blind elements, and noise. A mixed noise model was introduced by combining the random noise and Perling noise model. In the image simulating process, Graphic Processing Unit was used to produce noise image in real time. According to the reference photo of infrared sensors, infrared simulated images were evaluated using histogram distribution, the trend of intensity, and Signal to Noise Ratio, and the results show these images satisfied targets recognition algorithm

A surface-enhanced Raman scattering (SERS)-active optical fiber sensor based on a three-dimensional sensing layer

AbstractTo fabricate a new surface-enhanced Raman scattering (SERS)-active optical fiber sensor, the design and preparation of SERS-active sensing layer is one of important topics. In this study, we fabricated a highly sensitive three-dimensional (3D) SERS-active sensing layer on the optical fiber terminal via in situ polymerizing a porous polymer material on a flat optical fiber terminal through thermal-induced process, following with the photochemical silver nanoparticles growth. The polymerized polymer formed a 3D porous structure with the pore size of 0.29–0.81μm, which were afterward decorated with abundant silver nanoparticles with the size of about 100nm, allowing for higher SERS enhancement. This SERS-active optical fiber sensor was applied for the determination of 4-mercaptopyridine, crystal violet and maleic acid The enhancement factor of this SERS sensing layer can be reached as about 108. The optical fiber sensor with high sensitive SERS-active porous polymer is expected for online analysis and environment detection

On the positive effect of UVC light during the removal of benzothiazoles by photoelectro-Fenton with UVA light

Benzothiazole (BTH) and 2-hydroxybenzothiazole (2-OH-BTH) are ubiquitous pollutants in aquatic ecosystems. This article reports their photoelectro-Fenton (PEF) treatment, either alone or mixed, in sulfate medium at pH 3.0 using an IrO2-based/air diffusion cell that generates H2O2 under UVA and/or UVC irradiation. UVC-PEF was more effective than UVA-PEF to remove the target pollutants, which suggests a positive impact of OH formed via Fenton's reaction and photo-induced homolysis of H2O2 in the former method. In addition, BTH disappeared more quickly than 2-OH BTH. Full-time UVA-/UVC-PEF outperformed UVC-PEF and UVA-PEF to mineralize the mixtures, although requiring a much higher energy consumption. The evolution of generated H2O2 and homogeneous OH confirmed the positive contribution of UVC photolysis in UVA-PEF. Part-time use of UVC radiation in UVA-PEF yielded a similar total organic carbon removal, with much lower energy consumption. BTH was oxidized to 2-OH-BTH, which was subsequently transformed into other five heteroaromatics

High-throughput sequencing of RNAs isolated by cross-linking immunoprecipitation (HITS-CLIP) reveals Argonaute-associated microRNAs and targets in Schistosoma japonicum

Sequences of SjAgo-associated novel miRNAs by the HITS-CLIP assay. (XLSX 16 kb

Microstructure-Empowered Stock Factor Extraction and Utilization

High-frequency quantitative investment is a crucial aspect of stock investment. Notably, order flow data plays a critical role as it provides the most detailed level of information among high-frequency trading data, including comprehensive data from the order book and transaction records at the tick level. The order flow data is extremely valuable for market analysis as it equips traders with essential insights for making informed decisions. However, extracting and effectively utilizing order flow data present challenges due to the large volume of data involved and the limitations of traditional factor mining techniques, which are primarily designed for coarser-level stock data. To address these challenges, we propose a novel framework that aims to effectively extract essential factors from order flow data for diverse downstream tasks across different granularities and scenarios. Our method consists of a Context Encoder and an Factor Extractor. The Context Encoder learns an embedding for the current order flow data segment's context by considering both the expected and actual market state. In addition, the Factor Extractor uses unsupervised learning methods to select such important signals that are most distinct from the majority within the given context. The extracted factors are then utilized for downstream tasks. In empirical studies, our proposed framework efficiently handles an entire year of stock order flow data across diverse scenarios, offering a broader range of applications compared to existing tick-level approaches that are limited to only a few days of stock data. We demonstrate that our method extracts superior factors from order flow data, enabling significant improvement for stock trend prediction and order execution tasks at the second and minute level

Impact of honey on radiotherapy-induced oral mucositis in patients with head and neck cancer: A systematic review and meta-analysis

© Annals of Palliative Medicine. Background: Oral mucositis is one of the most frequent, irreversible and distressing complications faced by head and neck cancer (HNC) patients undergoing radiotherapy. Several studies have investigated the role of honey in the prevention and alleviation of radiation-induced oral mucositis in HNC patients, however, a definitive conclusion has not yet been generated. We performed this updated systematic review and metaanalysis to determine whether honey can prevent and alleviate radiation-induced oral mucositis in HNC patients. Methods: We searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL) and China National Knowledge Infrastructure (CNKI) through October 2019. We searched and selected literature, extracted data and assessed risk of bias accordingly, and then conducted statistical analyses with RevMan software version 5.3. Results: Seven trials involving 412 patients were included in the final analysis. Meta-analyses showed that honey did not decrease the incidence of radiation-induced oral mucositis [(relative risk (RR), 0.69; 95% confidence interval (CI), 0.40-1.18; P=0.18]; however, relieved the severity of oral mucositis (RR, 0.22; 95% CI, 0.13-0.38; P \u3c 0.001), maintained or increased weight (RR, 1.92; 95% CI, 1.33-2.77; P \u3c 0.001) and reduced the treatment interruption related to oral mucositis (RR, 0.13; 95% CI, 0.02-0.97; P=0.05). Qualitative analysis also revealed a decreased incidence of oral mucositis in the honey group. Conclusions: Based on limited evidence, honey may have a clinical benefit against radiation-induced oral mucositis in HNC patients. However, future trials with large-scale and rigorous methods are warranted to further establish the role of honey in the management of radiation-induced oral mucositis

A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition

Recent studies on pedestrian attribute recognition progress with either explicit or implicit modeling of the co-occurrence among attributes. Considering that this known a prior is highly variable and unforeseeable regarding the specific scenarios, we show that current methods can actually suffer in generalizing such fitted attributes interdependencies onto scenes or identities off the dataset distribution, resulting in the underlined bias of attributes co-occurrence. To render models robust in realistic scenes, we propose the attributes-disentangled feature learning to ensure the recognition of an attribute not inferring on the existence of others, and which is sequentially formulated as a problem of mutual information minimization. Rooting from it, practical strategies are devised to efficiently decouple attributes, which substantially improve the baseline and establish state-of-the-art performance on realistic datasets like PETAzs and RAPzs. Code is released on https://github.com/SDret/A-Solution-to-Co-occurence-Bias-in-Pedestrian-Attribute-Recognition.Comment: Accepted in IJCAI2