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In recent years, deep learning has been widely used to solve the bottleneck problem of synthetic aperture radar (SAR) automatic target recognition (ATR). However, most current methods rely heavily on a large number of training samples and have many parameters which lead to failure under limited training samples. In practical applications, the SAR ATR method needs not only superior performance under limited training data but also real-time performance. Therefore, we try to use a lightweight network for SAR ATR under limited training samples, which has fewer parameters, less computational effort, and shorter inference time than normal networks. At the same time, the lightweight network combines the advantages of existing lightweight networks and uses a combination of MnasNet and NetAdapt algorithms to find the optimal neural network architecture for a given problem. Through experiments and comparisons under the moving and stationary target acquisition and recognition (MSTAR) dataset, the lightweight network is validated to have excellent recognition performance for SAR ATR on limited training samples and be very computationally small, reflecting the great potential of this network structure for practical applications., Comment: 6 pages, 3 figures, published in 2023 IEEE Radar Conference (RadarConf23)

The tail gas with relatively high calorific value, generated from the depleted reduction of copper slag, can be used as fuel for gas turbines. In this study, the flame speed calculation module in PREMIX of CHEMKIN was used to simulate the combustion characteristics of the tail gas after sulfide removal, the effects of equivalence ratio, gas preheating temperature, and the CO2 concentration on the flame propagation speed and the flame combustion temperature were mainly investigated. Results show that the flame propagation speed and the flame combustion temperature increase continuously with the increase in preheating temperature, while it decreases continuously with the increase in CO2 concentration. Additionally, the peak flame combustion temperature appears at an equivalent ratio of 1.1, while the peak flame propagation speed appears at an equivalent ratio of 2.0, which indicates that the flame combustion temperature is not the main factor affecting flame propagation speed for tail gas.

The C and CO reduction proportional fraction was studied in this work to evaluate consumption of the reducer accurately. Based on two-step reaction mechanism (TSRM) theory, a phase equilibrium calc...

A technology system of copper slag thermal energy recovery and direct reduction (TER-DR) was proposed in this paper. The effects of CaO addition and C/O on the reduction characteristics were discus...

Nano-zero-valent iron biochar derived from almond shell (nZVI-ASBC) was used for hexavalent chromium (CR) removal. Experiments showed that pH was the main factor (p &lt, 0.01) that affected the experimental results. At a dosage of 10 mg&middot, L&minus, 1 and pH of 2&ndash, 6, in the first 60 min, nZVI-ASBC exhibited a removal efficiency of 99.8%, which was approximately 20% higher than the removal yield at pH 7&ndash, 11. Fourier transform infrared spectroscopy results indicated N-H was the main functional group that influenced the chemisorption process. The pseudo second-order dynamics and Langmuir isotherm models proved to be the most suitable. Thermodynamic studies showed that the reaction was exothermic and spontaneous at low temperatures (T &lt, 317 K). Various interaction mechanisms, including adsorption and reduction, were adopted for the removal of Cr(VI) using the nZVI-ASBC composite. The findings showed that the BC-modified nZVI prepared with almond shell exerts a good effect and could be used for the removal of Cr(VI).

We develop a distribution regression model under endogenous sample selection. This model is a semi-parametric generalization of the Heckman selection model. It accommodates much richer effects of the covariates on outcome distribution and patterns of heterogeneity in the selection process, and allows for drastic departures from the Gaussian error structure, while maintaining the same level tractability as the classical model. The model applies to continuous, discrete and mixed outcomes. We provide identification, estimation, and inference methods, and apply them to obtain wage decomposition for the UK. Here we decompose the difference between the male and female wage distributions into composition, wage structure, selection structure, and selection sorting effects. After controlling for endogenous employment selection, we still find substantial gender wage gap -- ranging from 21\% to 40\% throughout the (latent) offered wage distribution that is not explained by composition. We also uncover positive sorting for single men and negative sorting for married women that accounts for a substantive fraction of the gender wage gap at the top of the distribution., 85 pages, 4 tables, 40 figures, includes supplement

We develop a distribution regression model under endogenous sample selection. This model is a semiparametric generalization of the Heckman selection model that accommodates much rich patterns of heterogeneity in the selection process and effect of the covariates. The model applies to continuous, discrete and mixed outcomes. We study the identi fication of the model, and develop a computationally attractive two-step method to estimate the model parameters, where the fi rst step is a probit regression for the selection equation and the second step consists of multiple distribution regressions with selection corrections for the outcome equation. We construct estimators of functionals of interest such as actual and counterfactual distributions of latent and observed outcomes via plug-in rule. We derive functional central limit theorems for all the estimators and show the validity of multiplier bootstrap to carry out functional inference. We apply the methods to wage decompositions in the UK using new data. Here we decompose the difference between the male and female wage distributions into four effects: composition, wage structure, selection structure and selection sorting. We uncover positive sorting for single men and negative sorting for married women that accounts for a substantial fraction of the gender wage gap at the top of the distribution. These fi ndings can be interpreted as evidence of assortative matching in the marriage market and glass-ceiling in the labor market.

This study aimed to re-evaluate the concordance between FISH and IHC for HER-2 status, and evaluate its clinicopathologic significance in breast cancer by the new guideline (2014) in China. We determined the HER-2 status in 589 cases of invasive breast cancer, the concordance and correlation between these two results and their relationship to cliniopathological characteristics were evaluated. The rate of HER-2 gene amplification identified by FISH was 31.07% (183/589). Concordance was detected in 93.67% with IHC 0/+, 32.26% in IHC 2+, and 61.16% in IHC 3+. The concordance of 54.67% was observed with a Kappa coefficient of 0.189 (P = 0.000), and a positive correlation was found between IHC and FISH (r = 0.427, P = 0.000). Moreover, the expression of ER was negatively correlated with HER-2 gene amplification and the expression of Her-2/neu protein (r = -0.419, P < 0.001; r = -0.144, P < 0.001; respectively), and the PR expression and ER/PR status was negatively correlated with HER-2 gene amplification (r = -0.226, P < 0.001; r = -0.258, P < 0.001), but not significantly with Her-2/neu expression. Interestingly, the tumor size and histological grade were positive correlation with HER-2/neu protein expression. The coincidently positive rate between FISH and IHC in breast cancer is very high, IHC score 0/+ is strongly consistent with HER-2 gene amplification, IHC score 2+~3+ is a preferred method to detect the HER-2 gene status, FISH still remains a standard method of evaluation of the HER-2 gene amplification.

Based on biomass micron fuel (BMF) with particle size less than 250 μm, a cyclone combustion concept was presented and a lab-scale cyclone furnace was designed to evaluate the feasibility. The influences of equivalence ration (ER) and particle size of BMF on combustion performance were studied, as well as temperature distribution in the combustion chamber. The results show that BMF combustion in the cyclone furnace is reliable, with rational temperature distribution inside furnace hearth, lower CO emission, soot concentration and C content in ashes. As ER being 1.2, the temperature in the chamber is maximized up to 1200 °C. Smaller particles results in better combustion performances.