Association of TaD14-4D,a Gene Involved in Strigolactone Signaling,with Yield Contributing Traits in Wheat

Tillering is a crucial agronomic trait of wheat; it determines yield and plant architecture. Strigolactones (SLs) have been reported to inhibit plant branching. D14, a receptor of SLs, has been described to affect tillering in rice, yet it has seldomly been studied in wheat. In this study, three TaD14 homoeologous genes, TaD14-4A, TaD14-4B, and TaD14-4D, were identified. TaD14-4A, TaD14-4B, and TaD14-4D were constitutively expressed, and TaD14-4D had a higher expression level in most tissues. TaD14 proteins were localized in both cytoplasm and nucleus. An SNP and a 22 bp insertion/deletion (Indel) at the exon regions of TaD14-4D were detected, forming three haplotypes, namely 4D-HapI, 4D-HapII, and 4D-HapIII. Due to the frameshift mutation in the coding region of 4D-HapII, the interaction of 4D-HapII with TaMAX2 and TaD53 was blocked, which led to the blocking of SL signal transduction. Based on the two variation sites, two molecular markers, namely dCAPS-250 and Indel-747, were developed. Association analysis suggested that haplotypes of TaD14-4D were associated with effective tillering number (ETN) and thousand kernel weight (TKW) simultaneously in four environments. The favorable haplotype 4D-HapIII underwent positive selection in global wheat breeding. This study provides insights into understanding the function of natural variations of TaD14-4D and develops two useful molecular markers for wheat breeding.     

Enhanced wide-activated residual network for efficient and accurate image deblocking

To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity.     

硼中子俘获治疗加速器ECR质子源电磁场仿真设计

电子回旋共振(ECR)质子源具有可给流强高、亮度高、可靠性高、使用频率高、易维护、小型化等优点，因而被硼中子俘获治疗(BNCT)装置的直线加速器所采用。本文利用CST软件对2.45 GHz ECR质子源进行优化设计。优化后ECR质子源的等离子体发生器腔体的尺寸为101.12 mm×45.00 mm，给出了脊波导耦合器的最优尺寸参数，使等离子体发生器腔体内电场强度提高为普通波导的4.5倍。通过Opera-3D对ECR质子源的引出电极结构进行了仿真计算，并给出了优化参数。另外，初步设计了质子源的线圈磁铁系统，优化了磁场分布。本文结果为质子源的研制提供了数据。     

Extraction equilibrium conditions of beryllium and aluminium from a beryl ore for optimal industrial beryllium compound production

This study examines the extraction of beryllium and aluminium from a Nigerian beryl ore using Cyanex®272 in kerosene from an aqueous sulphate pregnant solution. Parameters such as extractant concentration and equilibrium pH that dictates the extraction yield were studied. Under the following conditions: temperature 27?±?2°C, phase ratio 1?1, about 45.50 and 46.76% of beryllium and aluminium were extracted by 0.15?mol?L^?1 Cyanex®272 concentration within 30?min. However, the extraction yield of beryllium and aluminium was increased to 91.68% and 97.89% at equilibrium pH of 3 and 4, respectively, for beryllium and aluminium at 27?±?2°C. A 0.05?mol?L^?1 H₂SO₄ solution was found to be adequate for the stripping of about 99.00% Be and 95.40% Al from the loaded organic phase. The pure solutions containing metal ions were accordingly beneficiated to obtain beryllium and aluminium compounds of industrial values.     

Specific emitter identification for satellite communication using probabilistic neural networks

Electromagnetic signal emitted by satellite communication (satcom) transmitters are used to identify specific individual uplink satcom terminals sharing the common transponder in real environment, which is known as specific emitter identification (SEI) that allows for early indications and warning (I&W) of the targets carrying satcom furnishment and furthermore the real time electromagnetic situation awareness in military operations. In this paper, the authors are the first to propose the identification of specific transmitters of satcom by using probabilistic neural networks (PNN) to reach the goal of target recognition. We have been devoted to the examination by exploring the feasibility of utilizing the Hilbert transform to signal preprocessing, applying the discrete wavelet transform to feature extraction, and employing the PNN to perform the classification of stationary signals. There are a total of 1000 sampling time series with binary phase shift keying (BPSK) modulation originated by five types of satcom transmitters in the test. The established PNNs classifier implements the data testing and finally yields satisfactory accuracy at 8 dB(±1 dB) carrier to noise ratio, which indicates the feasibility of our method, and even the keen insight of its application in military.     

应用膨胀算法提取目标的直接定位算法

为了解决被动雷达系统中的多发射源定位问题，提出了一种基于多重信号分类(MUSIC)算法和图像膨胀(IE)算法的直接定位方法。该方法结合了谱分析中的MUSIC思想，通过对接收量测协方差矩阵进行特征分析求解目标的位置。首先，在目标个数未知的前提下，利用Akaike信息准则(AIC)来确定模型阶数；然后，推导了基于MUSIC的定位代价函数；之后，利用图像膨胀算法处理得到的代价函数平面；最后，膨胀处理后的输出为目标个数及目标位置的估计值。提出的算法有效地解决了目标检测及提取的问题，能够确定多个目标的位置坐标，为后续的定位性能分析提供可能性，也保证了算法的完整性。进一步地分析了多个临近目标情况下影响目标提取性能的主要因素。     

A Review on the Separation of Niobium and Tantalum by Solvent Extraction

Niobium and tantalum are found together in natural minerals. Leaching with concentrated acid solution or alkaline roasting followed by water/dilute acid leaching results in a solution containing these two metals. In this work, we reviewed the extraction and separation of Nb(V) and Ta(V) from leach liquors in the absence and presence of hydrofluoric acid by acidic, neutral extractants and amines. Several solvent extraction systems were compared in the light of the requirement of hazardous chemicals, extraction/separation efficiency, and stability/solubility of extractants. Since the cationic species of Nb(V) and Ta(V) are unstable in the aqueous solution, the application of acidic extractants for extraction and separation of these metals is limited. Amines can extract Nb(V) as well as Ta(V) irrespective of the presence of fluoride ion but the separation of these metals from the loaded amines should be carried out in stripping process, resulting in process complexity. Another disadvantage encountered for amine is related to high viscosity in extraction process. Neutral extractants are considered as promising extractants which are effective to separate Ta(V) and Nb(V) from either non-fluoride or to moderate fluoride ion concentration in terms of extraction, separation and stripping efficiency. Compared to the traditional methyl iso-butyl ketone (MIBK) based system, methyl isoamyl ketone (MIAK) is found to be a promising extractant to separate Nb(V) and Ta(V).     

Distant Supervised Relation Extraction with Cost-Sensitive Loss

Recently, many researchers have concentrated on distant supervision relation extraction (DSRE). DSRE has solved the problem of the lack of data for supervised learning, however, the data automatically labeled by DSRE has a serious problem, which is class imbalance. The data from the majority class obviously dominates the dataset, in this case, most neural network classifiers will have a strong bias towards the majority class, so they cannot correctly classify the minority class. Studies have shown that the degree of separability between classes greatly determines the performance of imbalanced data. Therefore, in this paper we propose a novel model, which combines class-to-class separability and cost-sensitive learning to adjust the maximum reachable cost of misclassification, thus improving the performance of imbalanced data sets under distant supervision. Experiments have shown that our method is more effective for DSRE than baseline methods.