A scoping review of post-disaster social support investigations conducted after disasters that struck the Australia and Oceania continent

This scoping review provides a summary of research findings on social support dynamics in the wake of disasters that occurred on the continent of Australia and Oceania between 1983 and 2013. Forty-one studies, quantitative and qualitative, were summarised, investigating different facets of post-disaster supportive interactions. All inquiries assessed disasters resulting from natural hazards, with the majority of them conducted following events in Australia and New Zealand. The review revealed similar patterns of post-disaster social support dynamics that routinely unfold after disastrous incidents all over the world. Consistent with the disaster mental health literature, the documentation of social support mobilisation and social support deterioration processes was common. Salutary direct effects of supportive behaviours on post-disaster psychological distress were also highly evident. Most studies, however, posed research questions or hypotheses that lacked empirical or theoretical grounding. In conclusion, the review offers several recommendations on how to advance research on post-disaster social support.     

TUGAI: An Integrated Simulation Tool for Ecological Assessment of Alternative Water Management Strategies in a Degraded River Delta

The development of ecologically sound water allocation strategies that account for the needs of riverine ecosystems is a pressing issue, especially in semiarid river basins. In the Aral Sea Basin, a search for strategies to mitigate ecological and socioeconomic deterioration has been in process since the early 1990s. The Geographic Information System–based simulation tool TUGAI has been developed to support the policy determination process by providing a simple, problem-oriented method to assess ecological effects of alternative water management strategies for the Amudarya River. It combines a multiobjective water allocation model with simple, spatially explicit statistical and rule-based models of landscape dynamics. Changes in environmental conditions are evaluated by a fuzzy habitat suitability index for Populus euphratica, which is the dominant species of the characteristic riverine Tugai forests. Water management scenarios can be developed by altering spatiotemporal water distribution in the delta area or the amount of water inflow into the delta. Outcomes of scenario analysis are qualitative comparisons of the ecological effects of different options for a time period of up to 28 years. The given approach utilizes different types of knowledge, from quantitative hydrological data to qualitative local expert knowledge. The main purpose of the tool is to integrate the knowledge in a comprehensive way to make it available for discussions on alternative policies in moderated workshops with stakeholders. In this article, the modules of the tool, their integration, and three hypothetical scenarios are presented. Based on the experience gained when developing the TUGAI tool, we propose that the general framework can be transferred to other areas where tradeoffs in water allocation between the environment and other water users are of major concern. The potential for a simulation tool to structure and inform a complex resource management situation by involving local experts and stakeholders in the development of possible future scenarios will become increasingly valuable for transparent and participatory resource management.     

Framing of Geoengineering Affects Support for Climate Change Mitigation

The growing recognition that climate change mitigation alone will be inadequate has led scientists and policymakers to discuss climate geoengineering. An experiment with a US sample found, contrary to previous research, that reading about geoengineering did not reduce conservatives’ skepticism about the existence of anthropogenic climate change. Moreover, depending on how it is framed, geoengineering can reduce support for mitigation among both conservatives and non-conservatives. When geoengineering is framed as a major solution, people worry less about climate change, leading to reduced mitigation support. When framed as disastrous, people perceived geoengineering as riskier, also leading to a decrease in mitigation support. A more moderate framing of geoengineering as a partial solution is less susceptible to moral hazard effects. Overall, results suggest that geoengineering will not lessen political polarization over anthropogenic climate change, and could undercut support for mitigation efforts. Instead, framing geoengineering as a small piece to solving a big puzzle seems to be the best strategy to weaken this potential moral hazard.     

纳米晶粒TiO2多孔微球的制备及其光催化性能的研究进展

介绍了纳米晶粒TiO2多孔微球的主要制备方法、主要参量及表征、光催化性能研究进展,指出了纳米晶粒TiO2多孔微球制备过程的技术关键与基本参量的功能。针对目前存在的问题,提出了今后的主要研究方向,并展望了纳米晶粒TiO2多孔微球在水处理领域的应用前景。     

基于模糊层次分析法的应急能力评估方法探讨

对危险行业的安全生产事故应急能力进行安全评估,可以持续改进应急管理工作,提高事故应急能力的总体水平。作为综合性的安全评估方法,层次分析法在安全研究的多个领域中得到了应用。通过介绍安全生产事故应急管理能力及其评估要求,分析国内外应急能力评估的现状,提出了应急能力评估体系建立的总体思路。论述了事故应急能力评估指标集的建立方法和基于模糊层次综合评价法的评估模型构建方案,初步建立起事故应急管理能力评估方法。     

危化品事故应急辅助决策支持系统的研究

该文提出了以一个事故工程文件处置、管理一次事故的思想。根据危险化学品事故发生的一般特点和事故处置的一般程序,建立了危险化学平事故应急辅助决策支持软件平台。该软件平台利用GIS技术实现了事故的基本情况、事故初始处置、事故事态发展、现场监测预警等功能,在软件平台的基础上搭配相应的硬件设备组成一套完整的应急辅助决策系统。     

地震灾害综合应急能力评估研究

汶川大地震在给我国带来巨大的悲痛和损失的同时,也检验了我国政府的突发事件应急能力。此次抗震救灾凸显出我国地震灾害应急能力存在的一些不足。建立“突发事件综合应急能力评估指标体系”的目的是为了评估政府的突发事件应急能力,以评促建。地震灾害由于其不同于其它突发事件的特性,其应急管理也有其特定的内容。现以地震灾害特征为标准,选取13个省市为样本,套用已有评估指标体系,分析和评估我国地震灾害应急能力。研究认为,我国应加强地震灾害应急能力建设,同时认为已有评估指标体系能满足使用要求。     

长江经济带城市对外服务能力时空演变分析

基于城市流强度模型测算2000年以来长江经济带126个地级以上城市在全国范围内的对外服务能力,进而采用改良锡尔系数、马尔可夫链和ESDA方法对城市对外服务能力的时空演变特征进行探讨。结果表明:研究时段内长江经济带各市城市流强度都有显著提升,但区域间的非均衡现象明显,城市对外服务能力呈现出由东部向西部的梯度递减;省内差异对城市对外服务能力的差异贡献率最高,是空间尺度差异的主要来源;对外服务能力存在明显的俱乐部趋同现象和空间自相关性,对外服务能力相似的区域聚集分布,东、中、西部分别形成长三角、武汉-长沙、成渝等对外服务区域中心;时空演变表现出阶段性特征,经历由极化向均衡的转变。最后指出多中心发展和寻找新增长点应成为长江经济带城市对外服务能力提升的方向。     

A spatially explicit backcasting approach for sustainable land-use planning

General backcasting as a decision support and planning method starts from desired future states and simulates developments backwards until reaching the present state. Development pathways that reveal steps to be taken to reach a certain future state, and milestones that serve as interim goals, are created during the process. Backcasting has hitherto only been applied in workshops or as a theoretical framework and no spatially explicit backcasting model has previously been established. This paper presents the development of a spatially explicit backcasting model. The proposed model first creates a future scenario utilizing an agent-based model and then simulates backwards. It is implemented using the programming language Python. The model has been applied to a case study for sustainable land-use planning in Salzburg, Austria. The results of the model run show a successful backcasting of land-use classes from a future state back to the present, in 10 year time steps.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.