泸县节水农业发展潜力分析

水在农业发展中起重要作用,发展节水农业是缓解水资源供需矛盾、提高农业综合生产能力的重要途径。建设节水型农业,提高水的有效利用率,坚持节水灌溉、坚持节水农业的发展,不断改善和完善农业生产条件,将促进水资源可持续利用和社会经济可持续发展。本文获取泸县耕地面积、主要农作物种植面积、水资源利用及水利工程变化数据,主要农作物平均灌溉定额和节水灌溉定额数据,运用灰色预测法对泸县未来几年的主要农作物灌溉用水量进行预测,结果显示:2014-2017年水稻、玉米、小麦在采用节水技术后共计用水量分别为:14.2亿m3、11.08亿m3、0.46亿m3,共计节水量分别为:14.04亿m3、11.41亿m3、0.59亿m3。研究得出:应该大力发展节水农业.改进节水技术.提高水的利用率.节约水资源。     

日本九州大学图书馆馆藏《朱子语类》版本辨正--与《〈朝鲜古写徽州本朱子语类〉的传播过程考订》商补

日本九州大学图书馆馆藏黄士毅所编徽州刊《朱子语类》之古朝鲜写本,上承朱子语录之最早编本《池录》,下启今传黎靖德所编《语类大全》,在《朱子语类》版本发展史上具有重要价值。因其卷帙浩繁、情况复杂,学界对该版本的认识存在较多误差。通过古写本与成化本的全面比勘,结合其版本情况,对古写本成书的大致年代、原本卷目数量及其与今传通行本间存在的卷目次序差异进行介绍,并对古写本与《蜀类》《徽类》的关系进行辨析。     

Differences between English and Chinese Sales Promotion Letters

This paper aims to figure out the differences in English and Chinese sales promotion letters by analyzing ten selected letters from business writing books and websites, which might provide more valuabl...     

Characterization of chaotic multiscale features on the time series of melt index in industrial propylene polymerization system

The chaos characteristics of melt index have been first explored, and the Hilbert–Huang transform method and time delay embedding method are applied to multiscale dynamic analysis on the time series of the melt index (MI) in the propylene polymerization industry. The research results show that the embedding delay is 2, the embedding dimension is 5, the correlation dimension D₂ is 1.57, and the maximum Lyapunov exponent is 0.143 for the melt index series, which provide clear evidence of chaotic multiscale features in the propylene polymerization process. Three intrinsic mode functions (IMFs) are decomposed from the melt index time series; the presence of non-integer fractal correlation dimension and positive finite maximum Lyapunov exponent are found in some IMF components. The PP melt index series are divided into two chaotic signals, a determined signal and a random signal respectively, and its complexity is therefore reduced. Furthermore, the coupling of subscale structures of the propylene polymerization is explored with the dimension of interaction dynamics and a robust algorithm for detecting interdependence. It is found that IMF(2) is the main driver in the coupling system of IMF(1)and IMF(2). All these provide a guideline for studying propylene polymerization process with chaotic multiscale theory and may offer more candidate tools to model and control propylene polymerization system in the future.     

基于智能视觉的假肢虚拟再生仿真技术的算法研究

研究假肢虚拟再生仿真方法。腿部结构较为复杂,残缺再生过程需要根据不同腿部残缺根源部位进行方向和角度的预测,但是由于腿部角度和生长方向推断过程信息过少,造成传统的虚拟腿部残缺再生仿真无法取得足够信息,只能根据简单的向上,造成模拟效果不好。为此,提出了一种基于智能视觉的假肢虚拟再生仿真方法。根据误差四元数状态变换相关理论,能够对假肢虚拟再生的方向和旋转角度进行有效的预测,从而为假肢虚拟再生仿真提供准确的依据。根据双目立测三维模拟方法,建立假肢虚拟再生仿真三维模型。实验结果表明,利用该算法进行假肢虚拟再生仿真,可以真实的反映残肢不同位置特征点的相关性,从而为假肢虚拟再生手术提供依据。     

卫星遥感技术在森林资源动态变化预测的应用研究

利用遥感技术提出一种面向卫星遥感技术的森林资源动态变化预测方法,该方法可以根据拍摄到的遥感图像信息进行分析和识别,准确的分析出当前森林资源的健康状态,从而预测出森林资源在未来一段时间内病虫害的变化趋势。     

基于专利分析概念模型的中国机器人技术发展预测

基于专利分析的概念模型,从技术发展方向、技术发展水平和技术发展潜力三个方面对中国机器人技术发展进行预测,以验证模型的应用性。预测结果表明：中国机器人整体技术处于成长期,但主要的子技术还存在明显差异：其中机械手子技术已位于成长期的中后期阶段,转向机构子技术则位于成长期的前期阶段,非电变量的控制或调节系统技术以及一般的控制或调节系统则刚进入成长期。因此,企业、高校以及科研院所应积极抓住机器人技术发展的大好时机,开展联合攻关,实现机器人关键技术的重大突破,促进产业转型与升级,提升先进制造业的产业技术竞争力。     

The Future Development of Energy-Environment-Economy Integrated Assessment Modelling

<正>One of the greatest challenges faced by the energy and economic system is the risk of environmental and climate change.The models that have been developed to study the principle and dynamics of the energy,environment,and economic system are called the     

青海省东乘公麻金矿床Ⅵ号矿体原生晕特征及深部找矿预测

东乘公麻金矿床是青海省巴颜喀拉造山带北缘Au-Sb成矿带上新发现的中型金矿床,由于受“生态保护第一,尊重群众意愿”政策影响,勘查工作主要以深部勘查为主,本文选择该矿床有代表性的VI号矿体,研究其构造原生晕轴向分带序列。研究结果表明：VI号矿体原生晕分带序列为：（Au、Sb、As）→（Bi、Pb）→（Ag、Cu、Zn）;分带序列呈现“反分带”趋势,地球化学参数“反转”,前、尾晕共存,预测VI号矿体深部延伸较好或有隐伏矿体存在的可能性,圈定了成矿靶区,以期为矿山下一步探矿工作提供科学依据。     

MTGCN: A multi-task approach for node classification and link prediction in graph data

Both node classification and link prediction are popular topics of supervised learning on the graph data, but previous works seldom integrate them together to capture their complementary information. In this paper, we propose a Multi-Task and Multi-Graph Convolutional Network (MTGCN) to jointly conduct node classification and link prediction in a unified framework. Specifically, MTGCN consists of multiple multi-task learning so that each multi-task learning learns the complementary information between node classification and link prediction. In particular, each multi-task learning uses different inputs to output representations of the graph data. Moreover, the parameters of one multi-task learning initialize the parameters of the other multi-task learning, so that the useful information in the former multi-task learning can be propagated to the other multi-task learning. As a result, the information is augmented to guarantee the quality of representations by exploring the complex constructure inherent in the graph data. Experimental results on six datasets show that our MTGCN outperforms the comparison methods in terms of both node classification and link prediction.