The social context of childcare practices and child malnutrition in Niger's recent food crisis

In 2004–05, Niger suffered a food crisis during which global attention focused on high levels of acute malnutrition among children. In response, decentralised emergency nutrition programmes were introduced into much of southern Niger. Child malnutrition, however, is a chronic problem and its links with food production and household food security are complex. This qualitative, anthropological study investigates pathways by which children are rendered vulnerable in the context of a nutritional 'emergency'. It focuses on household-level decisions that determine resource allocation and childcare practices in order to explain why practices apparently detrimental to children's health persist. Risk aversion, the need to maintain self-identity and status, and constrained decision making result in a failure to invest extra necessary resources ingrowth-faltering children. Understanding and responding to the social context of child malnutrition will help humanitarian workers to integrate their efforts more effectively with longer-term development programmes aimed at improving livelihood security.     

基于灰色BP网络的火灾事故预测

将BP网络模型与灰色系统预测方法相结合,利用人工神经网络对GM(1,1)模型进行残差修正,建立了火灾事故预测模型.该模型兼具灰色系统与BP网络的优点,不但能体现火灾复杂的灰色系统行为,又能根据每次学习误差的不同调整学习速率,加快收敛速度.与单一的GM(1,1)模型相比,该模型预测结果具有更高的精度.     

When Has an Abandoned Field Become a Semi-Natural Grassland or Heathland?

This study presents a meta-analysis of a collective dataset describing the succession from abandoned fields to semi-natural grassland and heathland vegetation over the past century. The study objectives were to develop a method for statistical discrimination between abandoned fields and semi-natural habitats and to analyze the probability that an abandoned field had developed into a semi-natural habitat. A statistical classification model was developed, based on lists of vascular plants from 2059 plots from Danish semi-natural grasslands and heathlands, and abandoned fields of varying age. This model was shown to discriminate effectively between abandoned fields and semi-natural habitats, and it was found to be potentially useful for the detection of abandoned fields approaching semi-natural vegetation. We suggest that the model may help clarify restoration targets and assess biological condition in formerly cultivated areas. Statistical modeling revealed that succession age, period of abandonment and succession trajectory had significant effects on the probability that abandoned fields reached the semi-natural phase. Our study indicates that restoration projects targeting grassland and heathland should take local species pools and soil fertility into account. Nomenclature: Species names follow Flora Europaea (Tutin and others 1964–1993).     

基于贝叶斯网络的人因可靠性评价

提出一种贝叶斯网络的人因可靠性评价(HRABN)方法,其中的每个因子对应于贝叶斯网络中的节点,该方法可对人因可靠性作定量分析和定性分析。在定性分析上,节点的因果关系(HRA中的因子关系)及需要改进的薄弱节点都直观地显示在层次图中;在定量分析方面,对节点因子后验概率的推断通过HRA中的先验信息(包含仿真数据、现场操作及专家知识等)和最新信息得到。如果人因可靠性贝叶斯网络中的每个节点的先验概率分布和后验概率分布都已知,模型的可信性就可通过贝叶斯因子进行定量验证。贝叶斯网络扩展性好,当有新的节点因子需要考虑时,只需要补充对应的节点;笔者的方法也能很好地应用在不同行业的HRA。     

Citizens of local jurisdictions enhance plant community preservation through ballot initiatives and voter-driven conservation efforts

Open space areas protected by local communities may augment larger scale preservation efforts and may offer overlooked benefits to biodiversity conservation provided they are in suitable ecological condition. We examine protected areas established by local communities through ballot initiatives, a form of direct democracy, in California, USA. We compare ecological conditions of wooded habitats on local ballot protected sites and on sites protected by a state-level conservation agency. Collectively, we found few differences in ecological conditions on each protected area type. Ballot sites had greater invasive understory cover and larger trees. Community dissimilarity patterns suggested ballot sites protect a complementary set of tree species to those on state lands. Overall, geographic characteristics influenced onsite conditions more than details of how sites were protected. Thus, community-driven conservation efforts contribute to protected area networks by augmenting protection of some species while providing at least some protection to others that might otherwise be missed.Electronic supplementary materialThe online version of this article (10.1007/s13280-020-01469-8) contains supplementary material, which is available to authorized users.     

A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks

Introduction

This study describes a method for reducing the number of variables frequently considered in modeling the severity of traffic accidents. The method's efficiency is assessed by constructing Bayesian networks (BN).

Method

It is based on a two stage selection process. Several variable selection algorithms, commonly used in data mining, are applied in order to select subsets of variables. BNs are built using the selected subsets and their performance is compared with the original BN (with all the variables) using five indicators. The BNs that improve the indicators’ values are further analyzed for identifying the most significant variables (accident type, age, atmospheric factors, gender, lighting, number of injured, and occupant involved). A new BN is built using these variables, where the results of the indicators indicate, in most of the cases, a statistically significant improvement with respect to the original BN.

Conclusions

It is possible to reduce the number of variables used to model traffic accidents injury severity through BNs without reducing the performance of the model.

Impact on Industry

The study provides the safety analysts a methodology that could be used to minimize the number of variables used in order to determine efficiently the injury severity of traffic accidents without reducing the performance of the model.     

A novel method for mapping reefs and subtidal rocky habitats using artificial neural networks

Reefs and subtidal rocky habitats are sites of high biodiversity and productivity which harbour commercially important species of fish and invertebrates. Although the conservation management of reef associated species has been informed using species distribution models (SDM) and community based approaches, to date their use has been constrained to specific regions where the locality and spatial extent of reefs is well known. Much of the world's subtidal habitats remain either undiscovered or unmapped, including coasts of intense human use. Consequently, to facilitate a stronger understanding of species-environmental relationships there is an urgent need for a cost and time effective standard method to map reefs at fine spatial resolutions across broad geographical extents. We used bathymetric data (∼250 m resolution) to calculate the local slope and curvature of the seabed. We then constructed artificial neural networks (ANNs) to forecast the probability of reef occurrence within grid cells as a function of bathymetric and slope variables. Testing over an independent data set not used in training showed that ANNs were able to accurately predict the location of reefs for 86% of all grid cells (Kappa = 0.63) without over fitting. The ANN with greatest support, combining bathymetric values of the target grid cell with the slope of adjacent grid cells, was used to map inshore reef locations around the Southern Australian coastline (∼250 m resolution). Broadly, our results show that reefs are identifiable from coarse-scale bathymetry data of the seabed. We anticipate that our research technique will strengthen systematic conservation planning tools in many regions of the world, by enabling the identification of rocky substratum and mapping in localities that remain poorly surveyed due to logistics or monetary constraints.     

Community knowledge networks: an action-orientated approach to energy research

The Climate Change Act 2008 commits the UK to reducing carbon emissions by 80% of 1990 levels by 2050. With household emissions constituting more than a quarter of current total energy use in the UK, energy practices in the home have taken on increased policy attention. In this paper, we argue that the UK government's approach is founded upon a variant of methodological individualism that assumes that providing greater energy information to individuals will effect behaviour change in relation to energy use. Such an approach is potentially limited in its effectiveness and does not afford appropriate recognition to all those affected by energy policy. In contrast to this approach, we set out an alternative perspective, a community knowledge networks approach to energy and justice which recognises the contexts and relationships in which people live and use energy. Such an approach emphasises situated knowledge and practices in order to gain a greater understanding of how individuals and communities use energy, but, importantly, offers a means for affording greater recognitional justice to different social groups.     

Moving beyond “mitigation and adaptation”: examining climate change responses in New Zealand

Despite the apparent failure of international negotiations and renewed criticism of the accuracy of climate science, responses to climate change continue in households, cities, fields, and meeting rooms. Notions of “doing something about”, or “taking action on” or “mitigating and adapting” to climate change inform practices of carbon trading, restoring native forests, constructing wind turbines, insulating houses, using energy efficient light bulbs, and lobbying politicians for more or less of these actions. These expressions of agency in relation to climate change provide the focus of our enquiry. We found that relationships or social networks linked through local government are building capabilities to respond to climate change. However, the framework of “mitigation–adaptation” will need to be supplemented by a more diverse suite of mental models for making sense of climate change. Use of appropriate languages, cultural reference points, and metaphors embedded in diverse histories of climates and change will assist actors in their networked climate change responses.