Cross-calibration of brightness temperature obtained by FY-3B/MWRI using Aqua/AMSR-E data for snow depth retrieval in the Arctic

This study cross-calibrated the brightness temperatures observed in the Arctic by using the FY-3B/MWRI L1 and the Aqua/AMSR-E L2A.The monthly parameters of the cross-calibration were determined and evaluated using robust linear regression.The snow depth in case of seasonal ice was calculated by using parameters of the crosscalibration of data from the MWRI Tb.The correlation coefficients of the H/V polarization among all channels Tb of the two sensors were higher than 0.97.The parameters of the monthly cross-calibration were useful for the snow depth retrieval using the MWRI.Data from the MWRI Tb were cross-calibrated to the AMSR-E baseline.Biases in the data of the two sensors were optimized to approximately 0 K through the cross-calibration,the standard deviations decreased significantly in the range of 1.32 K to 2.57 K,and the correlation coefficients were as high as 99%.An analysis of the statistical distributions of the histograms before and after cross-calibration indicated that the FY-3B/MWRI Tb data had been well calibrated.Furthermore,the results of the cross-calibration were evaluated by data on the daily average Tb at 18.7 GHz,23.8 GHz,and 36.5 GHz(V polarization),and at 89 GHz(H/V polarization),and were applied to the snow depths retrieval in the Arctic.The parameters of monthly cross-calibration were found to be effective in terms of correcting the daily average Tb.The results of the snow depths were compared with those of the calibrated MWRI and AMSR-E products.Biases of 0.18 cm to 0.38 cm were observed in the monthly snow depths,with the standard deviations ranging from 4.19 cm to 4.80 cm.

A New Citation Recommendation Strategy Based on Term Functions in Related Studies Section

Purpose:Researchers frequently encounter the following problems when writing scientific articles:(1)Selecting appropriate citations to support the research idea is challenging.(2)The literature review is not conducted extensively,which leads to working on a research problem that others have well addressed.The study focuses on citation recommendation in the related studies section by applying the term function of a citation context,potentially improving the efficiency of writing a literature review.Design/methodology/approach:We present nine term functions with three newly created and six identified from existing literature.Using these term functions as labels,we annotate 531 research papers in three topics to evaluate our proposed recommendation strategy.BM25 and Word2vec with VSM are implemented as the baseline models for the recommendation.Then the term function information is applied to enhance the performance.Findings:The experiments show that the term function-based methods outperform the baseline methods regarding the recall,precision,and F1-score measurement,demonstrating that term functions are useful in identifying valuable citations.Research limitations:The dataset is insufficient due to the complexity of annotating citation functions for paragraphs in the related studies section.More recent deep learning models should be performed to future validate the proposed approach.Practical implications:The citation recommendation strategy can be helpful for valuable citation discovery,semantic scientific retrieval,and automatic literature review generation.Originality/value:The proposed citation function-based citation recommendation can generate intuitive explanations of the results for users,improving the transparency,persuasiveness,and effectiveness of recommender systems.

Multi-Source Underwater DOA Estimation Using PSO-BP Neural Network Based on High-Order Cumulant Optimization

Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be made because of the mismatch between algorithm model and actual environment model.In addition,the neural network has the ability of generalization and mapping,it can consider the noise,transmission channel inconsistency and other factors of the objective environment.Therefore,this paper utilizes Back Propagation(BP)neural network as the basic framework of underwater DOA estimation.Furthermore,in order to improve the performance of DOA estimation of BP neural network,the following three improvements are proposed.(1)Aiming at the problem that the weight and threshold of traditional BP neural network converge slowly and easily fall into the local optimal value in the iterative process,PSO-BP-NN based on optimized particle swarm optimization(PSO)algorithm is proposed.(2)The Higher-order cumulant of the received signal is utilized to establish the training model.(3)A BP neural network training method for arbitrary number of sources is proposed.Finally,the effectiveness of the proposed algorithm is proved by comparing with the state-of-the-art algorithms and MUSIC algorithm.

Intestinal Microbiota in Early Life and Its Implications on Childhood Health

Trillions of microbes reside in the human body and participate in multiple physiological and pathophysiological processes that affect host health throughout the life cycle. The microbiome is hallmarked by distinctive compositional and functional features across different life periods.Accumulating evidence has shown that microbes residing in the human body may play fundamental roles in infant development and the maturation of the immune system. Gut microbes are thought to be essential for the facilitation of infantile and childhood development and immunity by assisting in breaking down food substances to liberate nutrients, protecting against pathogens, stimulating or modulating the immune system, and exerting control over the hypothalamic–pituitary–adrenal axis.This review aims to summarize the current understanding of the colonization and development of the gut microbiota in early life, highlighting the recent findings regarding the role of intestinal microbes in pediatric diseases. Furthermore, we also discuss the microbiota-mediated therapeutics that can reconfigure bacterial communities to treat dysbiosis.

Theoretical analysis for self-sharpening penetration of tungsten high-entropy alloy into steel target with elevated impact velocities

The“self-sharpening”effect has been observed experimentally in the penetration of tungsten high-entropy alloy(WHEA)into steel targets in previous study.From the microscopic observation of the residual WHEA long-rod projectile(LRP),the multiphase structure at micro-scale of WHEA is the key effects on self-sharpening penetration process.In order to describe the distinctive penetration behavior,the interaction between micro phases is introduced to modify the hydrodynamic penetration model.The yield strengths of WHEA phases are determined based on the solid solution strengthening methods.Combined with the elbow-streamline model,the self-sharpening mechanism is revealed in view of the multi-phase flow dynamics and the flow field in the deformation area of the LRP nose is characterized to depict the shear layer evolution and the shape of the LRP’s nose as well as the determination of the penetration channel.The self-sharpening coefficient considering the reduction of nose radius is proposed and introduced into the penetration model to calculate the depth of penetration and the penetration channel.Results show that the multi-phase interaction at the microscopic level contributes to the inhomogeneous distribution of the WHEA phases.The shear layer evolution separates part of the LRP material from the nose and makes the nose radius decrease more quickly.It is also the reason that WHEA LRPs have a pointed nose compared with the mushroom nose of WHA heavy alloy(WHA)LRPs.The calculated results agree well with the corresponding experimental data of WHA and WHEA LRPs penetrating into semi-infinite medium carbon steel targets with elevated impact velocities.