Mobilisation capacity for agri-environmental management

The integration of environmental concerns into agricultural policies - through agri-environment measures (AEM) - has seen a fast development across Europe. This paper conceives AEM as an evolving instrument, a product that takes shape, gets diffused and taken up in, by and through networks of relations. Success then depends on the mobilisation or active participation of all those who may support and develop it. Using the examples of the Flanders' and Walloon regions of Belgium, the paper sets out to examine the mechanisms by which mobilisation for agri-environmental management develops, and by doing so, to gain a better understanding of mobilisation capacity as a concept to be used for evaluating policy implementation in this area. The study follows AEM along the various trajectories of implementation (design, distribution, application). The findings reveal how mobilisation capacity is gradually built-up by the interplay between AEM and the networks it connects to. The case illustrates well how such interactions occur all the way from administration offices to farmers' fields, and that their nature can be very different (e.g., formal and informal, durable and short-lived, expected and unexpected). It is concluded that in evaluating actor-networks, one should look at them in an open and fluid manner, that is, not to privilege any particular configuration or form of attachment over the other, not take intentions and objectives as a starting point but instead address the opportunities for synergies, and be aware that any network built around the instrument may change its content and the way it functions.     

Estimating an homogeneous series of a population abundance indicator despite changes in data collection procedure: A hierarchical Bayesian modelling approach

Abundance indicators are required both to assess and to manage wild populations. As new techniques are developed and teams in charge of gathering the data change, data collection procedures (DCPs) can evolve in space and time. How to estimate an homogeneous series of abundance indicator despite changes in DCP? To tackle this question a hierarchical Bayesian modelling (HBM) approach is proposed. It integrates multiple DCPs in order to derive a single abundance indicator that can be compared over space and time irrespective of the DCP used. Compared to single DCP models, it takes further advantage for abundance estimation of the joint treatment of a larger set of spatio-temporal units. After presenting the general formulation of our HBM approach, it is applied to the juvenile Atlantic salmon (Salmo salar L.) population of the River Nivelle (France). Posterior model checking, using χ² discrepancy measure, do not reveal any inadequacy between the model and the data. Despite a change in the DCP used (successive removals to catch-per-unit of effort), a unique abundance indicator for the 425 spatio-temporal units (site × year) sampled over twenty-four years (1985-2008) is estimated. The HBM approach allows the assessment of precision of the abundance estimates and shows variation between DCPs: a reduction in precision is observed during the most recent years (2005-2008) when only the catch-per-unit of effort DCP was used. The merits and generality of our HBM approach are discussed. We contend it extends previous single DCP models or inter-calibration of two DCPs, and it could be applied to a wide range of specific situations (taxon and DCPs).     

考虑组织因素的液化天然气泄漏安全评价方法

为考虑组织因素对于人因失误发生的根本性影响,提出一种以贝叶斯网络(BN)为工具的人因风险分析方法,并用于液化天然气泄漏的安全评价。先将认知可靠性与失误分析方法(CREAM)的共同绩效条件(CPC)分成3层,即组织层、技术系统层和个人层。以BN为工具,利用其因果推理和诊断推理的功能,进行人因失误预测和追溯的双向分析。将考虑组织因素的人员操作失误概率引入浮式液化天然气船(FLNGV)装卸过程LNG泄漏的BN中,进行LNG泄漏的安全评价以及事故原因的重要度分析。结果表明,"执行"失误是LNG泄漏最主要的事故原因,而个人层的CPC会对执行失误产生较大的影响。     

基于GAN的小样本腐蚀失厚率数据增强方法

目的 对小样本腐蚀失厚率数据进行数据增强,实现数据扩充,以提升后续分析模型的预测精度,减轻过拟合程度,并提升模型的泛化能力。方法 利用生成对抗网络(Generative Adversarial Networks,GAN)扩充腐蚀失厚率数据,使数据分布更加全面。对生成数据进行降维可视化分析,探究生成数据与原始数据样本的分布规律,分析数据增强合理性,并从多个算法模型、多个评价指标角度对分析预测能力、泛化能力进行评估。结果 生成数据填补了原始数据在样本空间分布的薄弱环节,加入生成数据后,各机器学习算法模型得出的MSE均值为未加入生成数据的61.72%~91.74%,皮尔逊均值为99.01%~113.64%,预测准确度提升,结果关联性更强,模型泛化能力增强。结论 GAN能有效对小样本腐蚀失厚率数据进行增强,数据扩充对分析预测提供正向支持,生成数据不宜多于原始数据,防止扰乱训练样本分布,同时存在生成数据多样性受限的问题。     

A new approach of integrating industry prior knowledge for HAZOP interaction

Accidents often occur in the petrochemical industry, which have a negative impact on society and the environment. Learning Process Safety Knowledge (PSK) from accident cases is essential to prevent accidents and improve safety level. Hazard and Operability Analysis (HAZOP) is a popular hazard risk analysis method. Its report contains large-scale PSK, which can provide safety analysis and decision support for the industry. Subject to the characteristics of PSK, existing researches mine them in the form of sequence labeling. However, there are two intractable problems that cause the PSK mined by the model to be inaccurate. (1) PSK in HAZOP is domain specific, which is rare or even absent in general-domain texts. (2) The entity boundaries are ambiguous. Most domain-specific entities for HAZOP lack boundary characters. Inaccurate security knowledge is not acceptable from the perspective of process safety engineering. To solve the problems, we present a PSK mining architecture with External Lexicon Prior knowledge called EDPMA, EDPMA is prior knowledge-based multi-task HAZOP knowledge mining model. Specifically, EDPMA consists of prior knowledge constructor and sequence labeling model. The prior knowledge constructor expresses prior knowledge in the form of word embedding by three steps. For the sequence annotation model, we improve its embedding and decoding layers. The former incorporated the word vectors generated by the prior knowledge constructor, and the latter added the task of entity boundary prediction. We conduct multiple evaluation experiments on HAZOP datasets. The experimental results show that the accuracy, recall and F1-score of the EDPMA model are 92.92%, 91.85% and 92.38% respectively, which is better than the existing research. Our study represents a meaningful attempt to introduce prior knowledge in HAZOP knowledge mining and makes an important contribution to intelligence the field of process safety.     

基于生态系统服务供需的厦漳泉地区生态网络空间优化

耦合生态系统服务供需和景观生态安全格局,解构城市群地区生态空间结构,是优化农业—城镇—生态网络空间布局的基础。基于生态系统服务供需理论,以福建东南沿海厦漳泉城市群为例,综合生态系统服务最重要区域和生态红线边界确定供给源地,结合自然、经济多要素构建消费格局,应用最小累积阻力模型建立景观生态安全格局,识别需求分区、生态流网络、关键节点等组分,采用分层叠加分析重构厦漳泉地区生态网络空间。结果显示：（1）供给源地占研究区总面积的41.64%,生态缓冲区、生态敏感区和生产生活区分别占22.67%、31.58%和4.11%;（2）供给源地集中于中西部丘陵山区,耗费低值区与供给源地分布一致,而高值区呈“井”字形分布特征,集中于沿海一带;（3）生态流通道网状化程度较高,中西部主要由森林构成,东部沿海区域主要由城镇水系和绿地系统、周边农田构成,驱动因素类型和强度均呈现显著的空间异质性;（4）提出构建厦漳泉地区“九区四组团一带九廊道多中心”的生态网络空间优化布局,协调区域间生产生活生态功能组合,促进经济—生态空间协同发展。     

感潮河网降雨径流污染空间分析与模拟

为了探究茅洲河流域感潮河网面源污染空间分布特征和降雨径流污染规律,基于空间分析、统计分析与流域水动力-水质耦合模拟方法,对典型降雨情景下河网水质情况进行模拟分析,提出基于水质改善目标的生态补水点位空间布局优化策略.研究表明,层次聚类凝聚算法和K-均值法迭代组合可以较好地实现面源污染分级与分类;茅洲河各支流中,石岩渠、松岗河中上游等河道（段）由于面源污染负荷相对较高且缺乏生态补水,雨后水质恢复缓慢;基于补水总量不变原则,对生态补水方案进行局部优化,优化结果可使雨后受污染重点河道（段）水质恢复速度加快一倍以上,提高了流域水质的整体稳定性.研究结论可为进一步认识茅洲河流域水污染特征、实现流域水环境精细化管理提供支撑.     

An inverse approach for piping networks monitoring

Spills and leakages of hazardous fluids from piping networks may pose a significant safety risk to population, industrial plants and the environment. Therefore in fluid distribution the problem of monitoring the network status in order to identify abnormal conditions and locate leakages arises. In the paper an inverse approach resorting to a multi-layer perceptron back-propagation Artificial Neural Network (ANN) is proposed, in order to locate leakages based on pressure and flow rate information. Strategies for generating input data and for correlating by ANN such data to the fluid distribution system status are presented. A two-level architecture is selected, composed by a main ANN at the first level and several branch-specific second-level ANNs in cascade to the main one. The branch in which the leakage occurs is identified, resorting to the ANN operating at the first level, while the specific second-level ANN is activated to estimate accurately the magnitude and location of the leakage in the selected branch.     

The contribution of dry deposited ammonia and sulphur dioxide to the composition of precipitation from continuously open gauges

A series of experiments using bulk precipitation collectors of the type used in the UK precipitation chemistry network measured the amounts of NH₄⁺, SO₄²⁻ and other ions that could be washed from funnels (diameter 15 cm) exposed to a wide range of NH₃ and SO₂ concentrations over periods from hours to days. In dry conditions, the average deposition flux of NH₃ was between 50 and 120 nmol NH₄⁺ funnel⁻¹ d⁻¹ (0.1–0.3 kg N ha⁻¹ yr⁻¹), and was independent of the concentration of NH₃. Dry deposition of NH₃ to wet funnels at small NH₃ concentrations was almost 5 times that to dry funnels under the same conditions (average 240 nmol funnel⁻¹ d⁻¹; 0.7 kg ha⁻¹ yr⁻¹), and increased with increasing NH₃ concentrations. The amount of NH₄⁺ ions remaining on the funnel surface was inversely proportional to the vapour pressure deficit during the experiment. This result was interpreted as a dependence on the duration of surface wetness, with greater deposition of NH₄⁺ when evaporation rates of surface water were small.The amount of SO₂ deposited on funnel surfaces was closely related to the amount of NH₃ deposited, in both wet and dry conditions, but was not strongly correlated with the SO₂ concentration. At low NH₃ and SO₂ concentrations the average deposition to dry funnels was 70 nmol SO₄²⁻ funnel⁻¹ d⁻¹ (0.5 kg ha⁻¹ yr⁻¹), and to wet funnels was approximately 2.5 times larger. The results are interpreted in terms of the balance between the rate of evaporation of surface water, and the rate of oxidation of SO₂, which leads to the ‘fixing’ of NH₄⁺ ions on the surface as involatile salts.It is predicted that dry deposition of NH₃ to funnel surfaces across the UK Secondary Network could account for as much as one-half of the measured bulk wet deposition at sites where wet deposition of NH₄–N is small. The amount of dry deposition depends on how long and how often funnel surfaces are wetted by rain or dew, and on the air concentrations of NH₃. These predictions are based on funnels being wetted only once per day. More frequent wetting would increase the contribution from dry deposition, and the consequent overestimate of wet deposition of NH₄–N across the UK by using data obtained from bulk collectors. To some extent this overestimate may be offset by microbial degradation and loss of NH₄–N in weekly bulk precipitation samples during collection and storage.