Dispersal networks for enhancing bacterial degradation in heterogeneous environments

Successful biodegradation of organic soil pollutants depends on their bioavailability to catabolically active microorganisms. In particular, environmental heterogeneities often limit bacterial access to pollutants. Experimental and modelling studies revealed that fungal networks can facilitate bacterial dispersal and may thereby improve pollutant bioavailability. Here, we investigate the influence of such bacterial dispersal networks on biodegradation performance under spatially heterogeneous abiotic conditions using a process-based simulation model. To match typical situations in polluted soils, two types of abiotic conditions are studied: heterogeneous bacterial dispersal conditions and heterogeneous initial resource distributions. The model predicts that networks facilitating bacterial dispersal can enhance biodegradation performance for a wide range of these conditions. Additionally, the time horizon over which this performance is assessed and the network’s spatial configuration are key factors determining the degree of biodegradation improvement. Our results support the idea of stimulating the establishment of fungal mycelia for enhanced bioremediation of polluted soils.     

A methodology to model causal relationships on offshore safety assessment focusing on human and organizational factors

INTRODUCTION: Focusing on people and organizations, this paper aims to contribute to offshore safety assessment by proposing a methodology to model causal relationships. METHOD: The methodology is proposed in a general sense that it will be capable of accommodating modeling of multiple risk factors considered in offshore operations and will have the ability to deal with different types of data that may come from different resources. Reason's "Swiss cheese" model is used to form a generic offshore safety assessment framework, and Bayesian Network (BN) is tailored to fit into the framework to construct a causal relationship model. The proposed framework uses a five-level-structure model to address latent failures within the causal sequence of events. The five levels include Root causes level, Trigger events level, Incidents level, Accidents level, and Consequences level. To analyze and model a specified offshore installation safety, a BN model was established following the guideline of the proposed five-level framework. A range of events was specified, and the related prior and conditional probabilities regarding the BN model were assigned based on the inherent characteristics of each event. RESULTS: This paper shows that Reason's "Swiss cheese" model and BN can be jointly used in offshore safety assessment. On the one hand, the five-level conceptual model is enhanced by BNs that are capable of providing graphical demonstration of inter-relationships as well as calculating numerical values of occurrence likelihood for each failure event. Bayesian inference mechanism also makes it possible to monitor how a safety situation changes when information flow travel forwards and backwards within the networks. On the other hand, BN modeling relies heavily on experts' personal experiences and is therefore highly domain specific. IMPACT ON INDUSTRY: "Swiss cheese" model is such a theoretic framework that it is based on solid behavioral theory and therefore can be used to provide industry with a roadmap for BN modeling and implications. A case study of the collision risk between a Floating Production, Storage and Offloading (FPSO) unit and authorized vessels caused by human and organizational factors (HOFs) during operations is used to illustrate an industrial application of the proposed methodology.     

Integration of Wireless Sensor Networks into Cyberinfrastructure for Monitoring Hawaiian “Mountain-to-Sea” Environments

Monitoring the complex environmental relationships and feedbacks of ecosystems on catchment (or mountain)-to-sea scales is essential for social systems to effectively deal with the escalating impacts of expanding human populations globally on watersheds. However, synthesis of emerging technologies into a robust observing platform for the monitoring of coupled human-natural environments on extended spatial scales has been slow to develop. For this purpose, the authors produced a new cyberinfrastructure for environmental monitoring which successfully merged the use of wireless sensor technologies, grid computing with three-dimensional (3D) geospatial data visualization/exploration, and a secured internet portal user interface, into a working prototype for monitoring mountain-to-sea environments in the high Hawaiian Islands. A use-case example is described in which native Hawaiian residents of Waipa Valley (Kauai) utilized the technology to monitor the effects of regional weather variation on surface water quality/quantity response, to better understand their local hydrologic cycle, monitor agricultural water use, and mitigate the effects of lowland flooding.     

基于遥感和GIS的华东区域洪涝灾害动态监测系统的研究和建立

以NOAA卫星资料为主要信息源,以地面高程、上地利用等地形地貌资料为辅助信息源,研制了华东区域多重因子空间分析模式,在GIS平台上建立洪涝灾害动态监测和分析系统,从而提高了洪涝灾害判识的准确性和客观性。     

基于长短时记忆网络的水质预测模型研究

在当前水质数据急剧增加的背景下,为了挖掘水质时间序列中的更多信息,提升水质预测的精度,构建了基于缺失值填补算法和长短时记忆网络(LSTM)相结合的水质预测模型。通过缺失值填补算法进行水质数据的缺失值处理,利用LSTM网络分别构建不同水质参数的预测模型,以太湖水质监测数据为样本,对模型进行检验。结果表明,基于缺失值填补算法-LSTM的水质预测模型适应性强,相较传统SVM、BP神经网络、RNN、LSTM模型预测精度更高,对水环境保护具有重要意义。     

Using machine learning to disentangle homonyms in large text corpora

Systematic reviews are an increasingly popular decision‐making tool that provides an unbiased summary of evidence to support conservation action. These reviews bridge the gap between researchers and managers by presenting a comprehensive overview of all studies relating to a particular topic and identify specifically where and under which conditions an effect is present. However, several technical challenges can severely hinder the feasibility and applicability of systematic reviews, for example, homonyms (terms that share spelling but differ in meaning). Homonyms add noise to search results and cannot be easily identified or removed. We developed a semiautomated approach that can aid in the classification of homonyms among narratives. We used a combination of automated content analysis and artificial neural networks to quickly and accurately sift through large corpora of academic texts and classify them to distinct topics. As an example, we explored the use of the word reintroduction in academic texts. Reintroduction is used within the conservation context to indicate the release of organisms to their former native habitat; however, a Web of Science search for this word returned thousands of publications in which the term has other meanings and contexts. Using our method, we automatically classified a sample of 3000 of these publications with over 99% accuracy, relative to a manual classification. Our approach can be used easily with other homonyms and can greatly facilitate systematic reviews or similar work in which homonyms hinder the harnessing of large text corpora. Beyond homonyms we see great promise in combining automated content analysis and machine‐learning methods to handle and screen big data for relevant information in conservation science.     

大型活动拥挤踩踏事故BP神经网络安全评估方法应用分析

根据大型活动拥挤踩踏事故是一个极其复杂的多因素、多变量、多层次的人-机-环境系统,利用数学建模的思路,引入人工神经网络评估方法;以沈阳世博会开幕式为例,通过前馈型BP网络训练和测试,将开幕式举办过程中的人员拥挤踩踏事故风险系统划分为人的因素、物的因素、环境因素、管理因素4个子系统和拥挤踩踏事故综合评估总系统,分别对其进行拟合分析,在各个系统的拟合残差满足条件的情况下,得出量化的评估预测值及BP神经网络的使用范围。研究实例表明,BP神经网络评估结果直观易懂,可操作性强,是值得推广运用的一种评估方法。     

文化对机组资源管理的影响

针对国内民航机组资源管理(CRM)及训练中忽视文化的作用和影响的现状,在调研和分析国外CRM与文化影响的基础上,笔者结合国内民航的运行特点,总结、分析了民族文化、组织文化,以及职业文化对机组协作与配合,CRM训练等方面的积极与消极影响,提出中国民航应根据自身的文化特点调整CRM训练的内容和实施方式,并应从3个层面采取措施来提高CRM及其训练的效果,即航空公司CRM训练的设计、实施与评估;飞机制造商在进行驾驶舱人-机界面设计,以及政府部门在对航空器进行适航审定时,应充分识别不同文化的差异、在强化各种文化类型积极影响的同时,努力降低或消除其消极影响。     

Social network characteristics and resource access among formerly displaced households in Lira,Uganda

In rural African communities, the support of extended family, friends, and neighbours is essential in borrowing or leveraging land, labour, food, and money, especially at times of social and financial turmoil. Little is known, though, about the nature of the networks and the conditions under which they may generate greater support in post‐conflict communities. This study, conducted in the Lira district of northern Uganda, examined the composition, proximity, and size of the networks that households utilise to gain access to resources during and after resettlement. Network structures with more kin and co‐resident ties were found to offer greater resource access to households in post‐conflict settings. Furthermore, there was a lack of meaningful linkages outside of a person's own village, especially with regard to households whose head has no or a low level of education. These findings enhance understanding of the specific role of relationships in social support for resource access among households in post‐conflict communities.     

Case studies in co-benefits approaches to climate change mitigation and adaptation

Attempts to mitigate greenhouse gas emissions or manage the effects of climate change traditionally focus on management or policy options that promote single outcomes (e.g., either benefiting ecosystems or human health and well-being). In contrast, co-benefits approaches to climate change mitigation and adaptation address climate change impacts on human and ecological health in tandem and on a variety of spatial and temporal scales. The article engages the concept of co-benefits through four case studies. The case studies emphasize co-benefits approaches that are accessible and tractable in countries with human populations that are particularly vulnerable to climate change impacts. They illustrate the potential of co-benefits approaches and provide a platform for further discussion of several interdependent principles relevant to the implementation of co-benefits strategies. These principles include providing incentives across multiple scales and time frames, promoting long-term integrated impact assessment, and fostering multidimensional communication networks.