基于大数据挖掘技术的地震舆情感知研究

对地震舆情信息的深入感知和有效管理,能够保障社会和谐发展。提出一个基于大数据技术和深度学习的地震舆情感知平台,基于Hadoop和MongoDB大数据技术实现对海量实时地震舆情数据的处理和存储。基于Word2vec和LSTM的融合模型能够有效实现震后网民的情感识别,为舆情预警提供支持。以台湾5.8级地震舆情数据为例,对该平台进行了验证。实验结果表明,该平台能够有效对海量地震舆情信息进行监测和分析,对地震舆情情感识别精确率达到93.76%;在准确率和收敛速度性能上均高于传统CNN神经网络,能够为后续谣言的准确识别提供有力的支持。本研究可为相关灾害类舆情研究提供一些思路。     

PPARγ/UCP2在甲醛致学习记忆障碍中的作用

为探究过氧化物酶体增殖物激活受体γ/解偶联蛋白2（PPARγ/UCP2）在甲醛（FA）诱导的学习记忆障碍中的作用,本文将C57BL/6小鼠随机分为：对照组、T0070907组（抑制剂组）、3mg/m³ FA组、3mg/m³ FA+T0070907组,进行连续21d的实验暴露,在第22d取脑组织测定脑组织脏体比并匀浆,检测活性氧（ROS）、谷胱甘肽（GSH）、丙二醛（MDA）、核因子κB （NF-κB）、白细胞介素6（IL-6）、PPARγ、UCP2等生化指标,通过Nissl染色观察脑组织的病理学变化.结果发现,与对照组相比,T0070907组和3mg/m³ FA组小鼠大脑皮层神经元受损,GSH含量下降,ROS、MDA、NF-κB、IL-6含量有所上升,而3mg/m³ FA+T0070907组小鼠上述现象更严重.此外,与对照组相比,T0070907组小鼠脑组织中的PPARγ和UCP2含量下降,但3mg/m³ FA组小鼠脑组织中的PPARγ和UCP2含量上升;与3mg/m³ FA组相比,加入抑制剂的3mg/m³ FA+T0070907组小鼠脑组织中的PPARγ和UCP2含量下降.研究结果表明,在加入PPARγ抑制剂后,PPARγ/UCP2含量下降,加重了FA所致小鼠的学习记忆障碍,故PPARγ/UCP2在FA致学习记忆障碍中可能起保护作用.     

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

• A spectral machine learning approach is proposed for predicting mixed antibiotic. • Pretreatment is far simpler than traditional detection methods. • Performance of the model is compared in different influencing factors. • Spectral machine learning is promising in the detection of complex substances. Antibiotics are widely used in medicine and animal husbandry. However, due to the resistance of antibiotics to degradation, large amounts of antibiotics enter the environment, posing a potential risk to the ecosystem and public health. Therefore, the detection of antibiotics in the environment is necessary. Nevertheless, conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation, which impose considerable time and economic costs. In this paper, we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning. With the help of a modified spectrometer, a large number of characteristic images were generated to map antibiotic information. The relationship between characteristic images and antibiotic concentrations was established by machine learning model. The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model. The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment. The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.     

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0.9949,相比于其他模型,该模型具有更优的预测性能。     

基于卷积神经网络的冲击地压预警方法研究*

为探究深度学习在冲击地压预警方面的应用前景,以新疆某冲击地压矿井为研究背景,将深度学习和专家评判运用到微震数据分析中,基于卷积神经网络构建冲击地压预警模型。充分利用一维卷积神经网络对时序数据有较强特征提取能力的优势,以微震数据及其特征参数作为输入,以专家评判值作为标签,借助Python-Keras框架实现冲击地压预警模型的构建和训练。研究结果表明:模型预警效果并不随着训练迭代次数的增加而逐渐最优,存在最优迭代次数,对于所建模型当迭代次数为30时测试集的冲击危险预测结果与专家评判结果基本吻合,同时说明模型可以较好地学习专家评判经验实现冲击地压预警。研究表明所建模型对研究时段内发生的5次大能量矿震事件均进行预警,其准确度较高,具有现场实际应用价值。     

基于链式多重插补的WOA-ELM煤与瓦斯突出预测模型*

为了提高缺失数据下煤与瓦斯突出预测准确率,提出1种基于链式支持向量机多重插补（MICE_SVM）的鲸鱼优化算法（WOA）-极限学习机（ELM）预测模型,以淮南朱集矿区为例,选取5个煤与瓦斯突出影响指标作为模型特征,采用提出的MICE_SVM算法插补突出事故数据中缺失值,利用WOA优选ELM输入层权值及隐含层阈值,构建煤与瓦斯突出预测模型,将插补后数据用于WOA-ELM模型的训练与测试,并与其他模型的预测效果对比。研究结果表明:MICE_SVM插补前、后的有突出数据预测准确率分别为83.02%,90.41%,MICE_SVM显著提高了有突出预测准确率,对无突出和整体的预测准确率提高不明显;数据插补后WOA优化ELM对无突出、有突出和整体的预测准确率分别为97.94%,96.25%,96.48%,较优化前分别提高了5.79%,5.84%,5.55%,数据插补后WOA-ELM为最佳预测模型。     

基于深度学习的工程结构损伤识别研究进展

为避免或减轻工程结构在建造和运营期间因结构振动产生不同程度损伤,造成安全隐患危及人们生命财产安全,针对结构振动损伤识别技术展开研究,探讨不同深度学习方法发展情况及其利弊,寻找更具可行性的损伤识别方法,并对其最新研究及应用现状进行全面综述。研究结果表明:应用深度学习开发新的结构损伤识别技术,无需冗余的数据预处理以及手工提取损伤特征,实现以较高精度实现损伤识别任务;一维卷积神经网络（1D-CNN）以其独特的应用优势,在数据样本有限条件下较二维卷积神经网络（2D-CNN）表现更为出色。研究结果可为数据驱动的结构损伤识别问题提供新思路,进一步完善土木结构健康监测研究体系。     

新沂地震台监测环境保护区的建立与管理

新沂地震台依据《中华人民共和国防震减灾法》和中国地震局《地震及前兆数字化技术规范》,向新沂市政府提出"建立地震监测保护区"的建议,经过积极努力,征得地方政府的批准,最终成功地划定了台站监测保护区范围,并制定了保护区管理方案。     

Uncertainty propagation in vegetation distribution models based on ensemble classifiers

Ensemble learning techniques are increasingly applied for species and vegetation distribution modelling, often resulting in more accurate predictions. At the same time, uncertainty assessment of distribution models is gaining attention. In this study, Random Forests, an ensemble learning technique, is selected for vegetation distribution modelling based on environmental variables. The impact of two important sources of uncertainty, that is the uncertainty on spatial interpolation of environmental variables and the uncertainty on species clustering into vegetation types, is quantified based on sequential Gaussian simulation and pseudo-randomization tests, respectively. An empirical assessment of the uncertainty propagation to the distribution modelling results indicated a gradual decrease in performance with increasing input uncertainty. The test set error ranged from 30.83% to 52.63% and from 30.83% to 83.62%, when the uncertainty ranges on spatial interpolation and on vegetation clustering, respectively, were fully covered. Shannon’s entropy, which is proposed as a measure for uncertainty of ensemble predictions, revealed a similar increasing trend in prediction uncertainty. The implications of these results in an empirical distribution modelling framework are further discussed with respect to monitoring setup, spatial interpolation and species clustering.     

Monitoring and adaptive management: resolving social and organisational issues to improve information sharing in natural resource management

Adaptive, or 'learning by doing', approaches are often advocated as a means of providing increased understanding within natural resource management. However, a number of organisational and social issues need to be resolved if these approaches are to be used successfully. A case study in the South Island high country of New Zealand is used to review what is needed to support an ongoing community-based monitoring and adaptive management programme. First, the case study is described, paying attention to the social context of the resource management problem. The results of a workshop that explored this problem are then outlined, along with a proposed information flow suggested by participants. Requirements for future steps to resolve these problems (such as information protocols and a multi-stakeholder information system) are discussed. Finally, some broad lessons are drawn from this exercise that could help others developing similar approaches.