Eco-environmental information system of Tianjin City─Design, implementation and applications

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The difficulties of systematic reviews

The need for robust evidence to support conservation actions has driven the adoption of systematic approaches to research synthesis in ecology. However, applying systematic review to complex or open questions remains challenging, and this task is becoming more difficult as the quantity of scientific literature increases. We drew on the science of linguistics for guidance as to why the process of identifying and sorting information during systematic review remains so labor intensive, and to provide potential solutions. Several linguistic properties of peer‐reviewed corpora—including nonrandom selection of review topics, small‐world properties of semantic networks, and spatiotemporal variation in word meaning—greatly increase the effort needed to complete the systematic review process. Conversely, the resolution of these semantic complexities is a common motivation for narrative reviews, but this process is rarely enacted with the rigor applied during linguistic analysis. Therefore, linguistics provides a unifying framework for understanding some key challenges of systematic review and highlights 2 useful directions for future research. First, in cases where semantic complexity generates barriers to synthesis, ecologists should consider drawing on existing methods—such as natural language processing or the construction of research thesauri and ontologies—that provide tools for mapping and resolving that complexity. These tools could help individual researchers classify research material in a more robust manner and provide valuable guidance for future researchers on that topic. Second, a linguistic perspective highlights that scientific writing is a rich resource worthy of detailed study, an observation that can sometimes be lost during the search for data during systematic review or meta‐analysis. For example, mapping semantic networks can reveal redundancy and complementarity among scientific concepts, leading to new insights and research questions. Consequently, wider adoption of linguistic approaches may facilitate improved rigor and richness in research synthesis.     

Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed.This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables.The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills.Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.     

GRAPHICALLY MODELING STAKEHOLDER VALUES1

ABSTRACT: This study was designed to give insight into the values considered in environmental policy and illustrate the respondents' personal constructions related to creating policy through the development of a graphic tool. Using both qualitative and quantitative methods, this study anonymously captured the intensity of values currently held by decision makers and tracked changes in values over time. Data were acquired through observations of and interviews with stakeholders in a five‐year wetland mitigation bank permitting process at the Galveston District of the U.S. Army Corps of Engineers. The study included public and private sector respondents in both governmental and nongovernmental roles. The resulting representations of professional and personal stakeholders' values can be used as a tool for improving information exchange in policy negotiation and provides insight into the complexity of individual and corporate perceptions of a policy issue.     

不同pH值对磷矿废石磷素浸出特性影响的实验研究

采用EPA 1314pH酸碱预滴定实验和pH浸出实验,研究了pH对磷矿废石磷元素浸出特性的影响,以及磷矿废石的酸碱缓冲容量。实验结果表明:磷矿废石的碱缓冲容量小,pH从8.75上升到13.50消耗了0.1mmol/g的NaOH,磷矿废石酸的缓冲容量较大,pH从5.50到1.00,共消耗2.2mmol/g的H2SO4;浸提液的酸碱性是影响磷矿废石中磷元素浸出的重要参数,酸性条件下很大程度上促进了磷元素的浸出,随着H+离子浓度的增加,总磷浓度随之升高。相反在碱性条件下的反应过程中,OH-阻碍磷元素的浸出,消耗正磷酸根离子和羟基磷酸根离子,使其生成沉淀,有一定的固磷作用,为磷元素的固化提供了更多途径。     

基于BERT-BILSTM-CRF模型的电力行业事故文本智能分析*

为解决电力行业事故报告文本较长、语义复杂,难以进行有效文本识别问题,提出1种以BERT作为底层的预训练模型,并设计1种双重注意力机制编码器,结合BILSTM-CRF深度挖掘事故文本语义特征,从而实现文本智能分析。首先构建电力词典,通过对BERT预训练,进行BIO标注,然后引入BILSTM-CRF模型实现对文本标签智能分类,最后将该模型与现行其他4种深度学习模型进行对比。研究结果表明:该模型智能识别精确率、召回率及F1值(查准率)均达到约97%,较其他4种模型中效果最好的模型分别提高0.02,0.03,0.02。研究结果可为电力行业事故报告文本分析提供1种新思路。     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.     

基于演练决策文本分析的决策者分类及应急决策差异研究

为研究特大地震初期的高层决策行为，通过演练模拟决策者在可得灾情信息下进行决策部署的情形，采集参演决策者独立确认的最终决策文本为样本，对比分析样本对草案修改的方式和程度，将决策者分为独立型、合作型和依赖型。结构化处理提取样本的决策任务，分析与灾情信息及决策建议的相关性。结果表明:在高层决策者中合作型人数比例最高，群组内决策差异较小，质量相对稳定；独立型决策者偏好主观判断，对灾情的关注以及草案漏洞的审查等指标明显低于其他类型；依赖型决策者在使用合理化建议的前提下易做出高质量决策，但在决策支持失误而非漏洞的条件下，能否保证决策质量有待进一步验证。     

Roads and Landscape Pattern in Northern Wisconsin Based on a Comparison of Four Road Data Sources

Abstract:  Roads are important components of landscapes; they fragment habitat, facilitate invasive species spread, alter hydrology, and influence patterns of land use. Previous research on the ecological impacts of roads may have underestimated their effect because currently available sources of road data do not include the full road network. We compared differences in road density and landscape pattern among U.S. Census Bureau TIGER line files, U.S. Geological Survey 1:100,000-scale digital line graphs, and U.S. Geological Survey 1:24,000-scale digital raster graphics in northern Wisconsin to road data derived from 1:40,000-scale digital orthophotos. Road density measured from digital orthophotos (2.82 km/km²) was significantly greater than that of digital raster graphics (1.62 km/km²) and more than double that of digital line graphs (1.21 km/km²) and TIGER (1.27 km/km²) data. The increased road densities in raster graphics and orthophoto data were mainly due to the addition of minor roads. When all roads were used to define patch boundaries, landscape metrics produced with orthophoto data showed significantly greater levels of fragmentation than those based on line or raster graphics. For example, maximum patch size was 1074 ha and total edge was 109 km for line graphs, compared with 686 ha and 211 km for orthophoto data. Roads are missing in commonly used data, primarily because mapping standards systematically exclude minor roads. These standards are not ecologically based and may result in false assumptions about the ecological effects of roads. We recommend that future studies take special consideration of the completeness of road data and consider whether all ecologically relevant roads are included.     

复杂河流系统水污染的计算机图形模拟

讨论在河流系统水污染规划中应用交互式计算机图形技术。通过对复杂河流系统水污染的图形模拟,提出建立图模型的一般理论和方法,并就浑太流域河流水污染案例给出实际模拟结果。