Using culturomics and social media data to characterize wildlife consumption

Wildlife provides food, medicine, clothing, and other necessities for humans, but overexploitation can disrupt the sustainability of wildlife resources and severely threaten global biodiversity. Understanding the characteristics of consumer behavior is helpful for wildlife managers and policy makers, but the traditional survey methods are laborious and time-consuming. In contrast, culturomics may more efficiently identify the features of wildlife consumption. As a case study of the culturomics approach, we examined tiger bone wine consumption in China based on social media and Baidu search engine data. Tiger bone wine is one of the most purchased tiger products; its consumption is closely related to tiger poaching, which greatly threatens wild tiger survival. We searched a popular social media website for the term “tiger bone wine” and focused on posts that were originally created from 1 January 2012 to 31 December 2018. We filtered and classified posts related to the purchase, sale, or consumption of tiger bone wine and extracted information on providers, consumption motivations, year of production, and place of origin of the tiger bone wines based on the texts and photos of these posts. We found 756 posts related to tiger bone wine consumption, 113 of which mentioned providers of tiger bone wine, including friends (53%), elder relatives (37%), peer relatives (7%), and others (3%). Out of the 756 posts, 266 indicated the motivations of tiger bone wine consumption. Tiger bone wines were consumed as a tonic (34%), medicine (23%), game product (30%), and a symbol of wealth (28%). Some posts indicated ≥2 consumption motivations. These findings were consistent with the search queries from Baidu index. Such information could help develop targeted strategies for tiger conservation. The culturomics approach illustrated by our study is a rapid and cost-efficient way to characterize wildlife consumption.     

基于环境因子和邻近信息的土壤属性空间分布预测

为探索乡镇尺度上土壤属性空间分布预测的最佳方法,以江西省万年县齐埠镇为例,借助四方位搜索法、地统计学和遥感影像分析技术提取环境因子（地形因子和植被覆盖指数）和邻近信息[w（有机质）与w（速效钾）],构建OK法（普通克里金法）、RK1法（仅基于环境因子的回归克里金法）以及RK2法（基于环境因子和邻近信息的回归克里金法）对齐埠镇耕地表层（0~20 cm）土壤w（有机质）、w（速效钾）空间分布进行预测.结果表明:齐埠镇土壤w（有机质）平均值为35.03 g/kg,w（速效钾）平均值为96.73 mg/kg,均为中等空间变异性.对62个样点进行建模,16个测试样点进行独立验证的误差分析表明,RK2法对土壤w（有机质）、w（速效钾）预测结果的均方根误差、平均绝对误差和平均相对误差较OK法分别降低了18.05%、18.01%、21.77%和7.25%、9.49%、9.84%;较RK1法分别降低了22.48%、20.91%、22.02%和9.27%、12.61%、13.52%.研究显示,RK2法明显提高了土壤w（有机质）、w（速效钾）空间分布模拟精度,并且存在改进和提高的空间.      

论水处理工程中的节能

人们在污水处理厂的建设中对污水处理工艺的先进性、可靠性以及工程投资大小考虑较多,而对污水处理厂建成后的运营管理考虑较少,因而造成许多污水处理厂建成后由于运营成本过高而不能正常运行。从能耗角度出发,分析造成运行成本高的主要原因以及相应采取的措施。     

基于FA-BAS-ELM的海洋油气管道外腐蚀速率预测

为提高海洋油气管道外腐蚀速率预测的精度和效率,建立基于因子分析(FA)和天牛须搜索算法(BAS)的极限学习机(ELM)腐蚀速率预测模型。利用FA对影响因素数据集进行降维处理,确定预测模型的输入变量;建立ELM预测模型,并采用BAS对ELM模型的参数进行优化,避免参数取值随机性对模型预测性能的影响;以实海挂片试验为例,通过建模仿真评价模型的预测性能,并与其他模型进行对比分析。结果表明：FA-BAS-ELM预测模型的平均绝对误差(MAPE)仅为1.92%,决定系数R²高达0.994 9,相比于其他模型,该模型具有更优的预测性能。     

浅谈大学生社会实践活动的项目化管理

结合中国环境管理干部学院"千名学生绿色行"活动的项目化管理的具体组织和实践情况,在实际工作中探索大学生社会实践活动项目化管理的科学性和有效性,将项目化管理模式应用于大学生实践活动的管理中,最大限度地利用现有的学生活动资源、规范管理行为、降低管理成本、提高工作效率,实现培养大学生综合素质和创新能力的目的。     

基于智能优化算法的河流水质模型参数的优化简

为探索河流水质模型参数新的求解方法,根据有限的实测数据,分别应用免疫进化优化算法和免疫进化优选的捕食搜索算法,对河流水质模型计算公式中的多参数进行优化。将优化得到的计算公式用于国内外若干河流的河段中DO浓度值的拟合,并与实测结果进行了比较。结果表明,将免疫进化优化算法或免疫进化优选的捕食搜索算法优化得到的水质模型参数精度不仅较高,而且相对稳定,从而为河流水质模型参数的优化提供了一种新方法。     

地震救援行动的影响因素分析

从探讨救援行动的概念和响应过程出发,在分析国内外相关资料的基础上,结合我国的实际情况,分析了影响地震救援行动的方面和因素,以及它们对救援行动可能产生的影响,以期为地震救援的理论和实践提供有益的补充。     

大型公共场所动态引导人移动路径设计方法

针对方向式动态引导在大型公共场所安全疏散中的重要作用,以及对动态引导人移动路径研究不足的现状,提出利用比值函数设计动态引导人移动路径的方法。并借鉴TSP(旅行商问题)方法的求解思路,提出可用于动态引导人移动路径优化的禁忌搜索算法。以一个内部布局较为复杂的大型公共场所模拟场景为例,利用MA-ESM疏散模型,分别模拟了引导人最优移动路径、最短移动路径和影响面积最大路径3种不同情景下疏散效果。结果表明,利用影响面积和路径长度比值最大的优化算法计算得出的引导人移动路径,既可满足在相同时间内引导更多的人员进行疏散,又可节省总体的疏散时间,充分保证疏散效果,验证了笔者所提出的动态引导人移动路径优化算法的合理性。     

Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm

ABSTRACT

Wind speed forecasting plays an important role in power grid dispatching management. This article proposes a short-term wind speed forecasting method based on random forest model combining ensemble empirical modal decomposition and improved harmony search algorithm. First, the initial wind speed data set is decomposed into several ensemble empirical mode functions by EEMD, then feature extraction of each sub-modal IMF is performed using fast Fourier transform to solve the cycle of each sub-modal IMF. Next, combining the high-performance parameter optimization ability of the improved harmony search algorithm, two optimal parameters of random forest model, number of decision trees, and number of split features are determined. Finally, the random forest model is used to forecast the processing results of each submodal IMF. The proposed model is applied to the simulation analysis of historical wind data of Chaoyang District, Liaoning Province from April 27, 2015 to May 22, 2015. To illustrate the suitability and superiority of the EEMD-RF-IHS model, three types of models are used for comparison: single models including ANN, SVM, RF; EMD combination models including EMD-ANN, EMD-SVM, EMD-RF; EEMD combination models including EEMD-ANN, EEMD-SVM, EEMD-RF. The analysis results of evaluation indicators show that the proposed model can effectively forecast short-term wind data with high stability and precision, providing a reference for forecasting application in other industry fields.     

基于CYCBD和麻雀搜索算法的滚动轴承故障特征提取方法

目的解决在较强的噪声环境下最大二阶循环平稳盲解卷积(MaximumSecond OrderCyclostationary Blind Deconvolution,CYCBD)算法在滚动轴承故障特征提取时效果欠佳的问题,为滚转尾翼导弹的尾翼滚动轴承故障诊断提供方法参考。方法提出一种利用麻雀搜索算法(SparrowSearchAlgorithm,SSA)优化CYCBD算法的方法,将CYCBD算法解卷积的包络谱熵作为SSA寻优的适应度函数,利用SSA高效地寻找出合适的循环频率以及滤波器长度,选择自适应参数后,再使用CYCBD算法有效解卷得到周期脉冲特征。结果同时对比SSA优化CYCBD前后进行故障特征提取的包络谱图,CYCBD的噪声幅值不超过0.13 m/s^(2),峰值不超过0.29 m/s^(2),用SSA优化CYCBD的噪声幅值不超过0.08 m/s^(2),峰值不超过0.32 m/s^(2),故障频率成分更加突显,无论是噪声幅度,还是峰值幅度特性,均较CYCBD有了较大改善。结论仿真实验验证了SSA优化CYCBD方法能够更加清晰地辨识到故障特征频率及其倍频成分,其具有良好的工程应用前景。