浅析城市生态系统特征

系统阐述了城市生态系统的特征,城市是一个复合的生态系统,具有人工化倾向,是一个不完整的,不能完全实现自我稳定的生态系统,并受社会、经济等多种因素制约的生态系统,容易导致严重的城市生态环境危机。     

五大连池火山区土壤和植被分布与特征

根据五大连池火山区独特的火山地貌及其生态条件,研究了新期火山与石龙熔岩台地、老期火山与盾状和坡状熔岩台地、河溪低地、湖泡河溪水域区的土壤和植被类别分布与特征;提出了土壤和植被的发生演化生态系列。     

The Food (In)Security Impact of Land Redistribution in South Africa: Microeconometric Evidence from National Data

The South African land-reform program has been widely criticized for its slow pace as well as its apparent lack of contribution to poverty reduction. No econometric evidence of the impact of land transfers has been provided to date and this paper attempts to fill this gap by considering the impact of receiving a land grant on households’ food insecurity. Propensity score matching and univariate probit estimates using two national household surveys indicate that, on average, land grant recipients are more food insecure than comparable non-participants. Recursive bivariate probit estimates suggest that selection bias is not driving this result.     

墙体参数模糊下穿墙雷达运动目标成像

针对墙参数模糊条件下运动目标成像速度慢、墙参数重建精度差等问题,提出了一种改进拟牛顿-粒子群优化(Limited Broyden Fletcher Goldfarb Shanno-Particle Swarm Optimization,LBFGS-PSO)算法,建立了LBFGS-PSO算法模型,解决了传统拟牛顿算法和粒子群算法计算速度慢、误差较大等问题。该算法与块正交匹配追踪（Block Orthogonal Matching Pursuit,BOMP）算法相结合不仅可以精确重建边墙位置,还能够准确地重建多径效应环境中的运动目标和静止目标,算法的计算速度和精度得到了一定程度的提高。仿真结果和数据分析验证了所提方法的性能。     

基于XGBoost和LSTM模型的金融时间序列预测

随着人工智能快速发展,深度学习模型预测金融时间序列成为热点问题.数据及特征选取是决定模型效果的重要环节,用XGBoost模型进行特征优化并预测黄金价格涨跌趋势,再与LSTM模型比较预测效果.用XGBoost分析动量因子特征重要性并选取有效指标;形态因子做历史回测并选取胜率较高的K线指标,预测准确率提升1.5％.以相同因子为LSTM模型特征值预测准确率提升6.5％,达到80％.以欧元和浦发银行股价数据为样本均证实K线指标有效且LSTM模型预测效果优于XGBoost.     

“倒挂型”土地利用总体规划的耕地保护效果评价

研究目的:揭示"倒挂型"土地利用总体规划与耕地保护效果之间的关系,以期为国土空间规划的编制提供参考。研究方法:倾向值匹配法。研究结果:(1)耕地"倒挂型"规划对耕地面积净变化的影响具有统计显著性,2006—2008年,耕保指标"倒挂型"规划使耕地面积净变化量平均减少2.270万hm2;(2)2010—2016年,耕保指标"倒挂型"规划使耕地面积净变化量平均减少0.496万hm2;(3)建设用地"倒挂型"规划对耕地面积净变化量并无显著影响。研究结论:在省级政府耕地保护责任目标考核的制度环境下,耕地"倒挂型"规划对地方政府形成一定压力,促使各级政府采取措施加以应对,从而使得"倒挂型"规划在一定程度上降低了耕地净减少量。     

“一带一路”倡议能否提升对外直接投资企业盈利能力？

基于2011-2015年A股上市公司与商务部《境外投资企业（机构）名录》数据，利用倾向得分匹配方法（PSM）和双重差分法（DID）考察“一带一路”倡议对中国对外直接投资企业盈利能力的影响及作用机制。研究发现：“一带一路”倡议显著提升了中国对沿线国家投资企业的盈利能力。机制检验表明，“一带一路”倡议能够通过推进中国与沿线国家间的经济融合和文化包容来提升对外投资企业的盈利能力，而政治互信在样本期内不显著。进一步研究发现，“一带一路”倡议对企业盈利能力的提升作用主要体现在国有企业、新兴优势产业以及投资目的为非邻近中国的“一带一路”国家上。     

采用航迹特征的智能机型识别

针对空战目标识别中机型自动识别比较困难的问题，提出了采用航迹特征的智能目标识别方法。利用卷积神经网络（Convolutional Neural Network，CNN）分层学习特征的能力，训练CNN算法模型自动地从航迹数据中学习有用的特征并分类。利用沿海实地采集的15个类别的飞机航迹数据，经一系列数据预处理后作为智能识别算法的训练和测试数据，在验证实验中描述了算法网络的相关配置,对比了CNN与其他分类器的识别结果。实验结果表明，CNN具有很好的识别性能。     

Estimating matching functions when recruiting intensity matters

The article estimates matching functions – the relationships between new hires, job vacancies and unemployment – using monthly time series data for Sweden. The data contain rich information on types of job vacancies which provides information on firms’ recruiting efforts. The analysis shows that the aggregate flow of new hires is well explained by an augmented version of a standard matching function that incorporates data on firms’ recruiting intensity.     

The German labor market during the Great Recession: Shocks and institutions

This paper analyzes Germany's unusual labor market experience during the Great Recession. We estimate a general equilibrium model with a detailed labor market block for post-unification Germany. This allows us to disentangle the role of institutions (short-time work, government spending rules) and shocks (aggregate, labor market, and policy shocks) and to perform counterfactual exercises. We identify positive labor market performance shocks (likely caused by labor market reforms) as the key driver for the “German labor market miracle” during the Great Recession.