Education for sustainable development – an inter‐disciplinary pilot module for undergraduate engineers and scientists

The coming together of a number of initiatives provided a springboard for an innovative pilot programme in education for sustainable development. For some time problem‐based learning has gathered momentum as an approach to educating professionals. More recently, there has been a burgeoning interest in inter‐disciplinary approaches to the complex societal and environmental issues that face the world as a whole. At the same time, professional engineering institutions have been reflecting on appropriate approaches to the education and development of future engineers. Building on work already undertaken by some of the authors, the Royal Academy of Engineering sponsored an inter‐disciplinary pilot programme in sustainable development for undergraduate engineers and scientists in the University of Manchester. The pilot was innovative not only in its inter‐disciplinary approach to sustainable development but also in its approach to the development of the curriculum. Inter‐disciplinary exercises were designed that enabled a contextual, active, collaborative and cumulative approach to learning. The assessment was also designed to align to the learning approach. Evaluation of the pilot programme suggests that it was well received by the students, and the post‐doctoral researchers who acted as facilitators, and also that there were gains in both understanding of the issues and also in approaches to learning.     

Learning about knowledge management for improving environmental impact assessment in a government agency: the Western Australian experience

How does knowledge management (KM) by a government agency responsible for environmental impact assessment (EIA) potentially contribute to better environmental assessment and management practice? Staff members at government agencies in charge of the EIA process are knowledge workers who perform judgement-oriented tasks highly reliant on individual expertise, but also grounded on the agency’s knowledge accumulated over the years. Part of an agency’s knowledge can be codified and stored in an organizational memory, but is subject to decay or loss if not properly managed. The EIA agency operating in Western Australia was used as a case study. Its KM initiatives were reviewed, knowledge repositories were identified and staff surveyed to gauge the utilisation and effectiveness of such repositories in enabling them to perform EIA tasks.     

公共部门危机学习动态过程及其知识管理研究

当前国内外对企业组织学习和危机管理的研究都已经较为成熟,在经历了几次大的危机之后,公共危机管理引起了我国许多学者的重视,但是关于高校危机学习的研究开展并不多,而国外的学者已经先一步提出了危机学习的概念并取得一些进展。随着危机种类越来越多,发生频率越来越高,高校必须要学习掌握应对危机的方法,并以此来教育学生。然而,无论是在危机管理中还是在组织学习中,知识管理都是不可或缺的一部分。因此本文以理论研究的方式,借鉴国内外学者对危机学习中知识管理的研究,希望能对高校危机学习的更深一步探索提供思路。     

环境类高等职业教育产学研用合作机制研究

环境类高等职业教育具有鲜明的非行业背景和人才需求领域广泛而应用岗位相对集中的特殊性。在对国内部分环境类高职院校和环境类高职专业就产学研用合作机制及运行模式实地调研的基础上,对现存的环境类高职院校产学研用合作模式进行了深入分析,提出了"依托政府,实施‘双轨并行’工学结合"的产学研用合作机制与运行模式。     

北宋后期黄河上游地区耕地格局重建——以河湟谷地为例

黄河上游的河湟谷地是人类活动历史悠久的地区之一,是黄河沿线耕地开垦的最西端,聚落和耕地重建对黄河流域人类活动和全球变化研究具有重要的现实意义。通过梳理大量历史文献资料,建立了北宋后期（1117年）河湟谷地聚落和耕地数据库,以聚落空间分布为基础,运用网格化模型,重建了北宋后期河湟谷地耕地格局。结果表明：（1）北宋后期河湟谷地聚落集中分布在黄河、湟水河干流沿岸海拔相对较低、坡度较缓的河流冲积和洪积平原。（2）河湟谷地的耕地总面积为304.67 km²,分配有耕地的网格仅占全区的16.16%,垦殖率最高的网格仅为24.16%,其中,湟水河谷耕地分布网格约占全部的74.06%,黄河干流约占25.94%,说明北宋后期耕地开垦主要集中在湟水河流域。（3）从聚落分布的海拔高度来看,大部分耕地网格分布在海拔2600 m以下的川水地区,说明当时耕地分布范围不大,海拔高度极大地限制了耕地的分布,耕地主要分布在灌溉条件相对优越的地区。（4）基于聚落重建耕地空间格局,其结果符合耕地随聚落分布的事实,更具有可靠性。     

PPARγ/UCP2在甲醛致学习记忆障碍中的作用

为探究过氧化物酶体增殖物激活受体γ/解偶联蛋白2（PPARγ/UCP2）在甲醛（FA）诱导的学习记忆障碍中的作用,本文将C57BL/6小鼠随机分为：对照组、T0070907组（抑制剂组）、3mg/m³ FA组、3mg/m³ FA+T0070907组,进行连续21d的实验暴露,在第22d取脑组织测定脑组织脏体比并匀浆,检测活性氧（ROS）、谷胱甘肽（GSH）、丙二醛（MDA）、核因子κB （NF-κB）、白细胞介素6（IL-6）、PPARγ、UCP2等生化指标,通过Nissl染色观察脑组织的病理学变化.结果发现,与对照组相比,T0070907组和3mg/m³ FA组小鼠大脑皮层神经元受损,GSH含量下降,ROS、MDA、NF-κB、IL-6含量有所上升,而3mg/m³ FA+T0070907组小鼠上述现象更严重.此外,与对照组相比,T0070907组小鼠脑组织中的PPARγ和UCP2含量下降,但3mg/m³ FA组小鼠脑组织中的PPARγ和UCP2含量上升;与3mg/m³ FA组相比,加入抑制剂的3mg/m³ FA+T0070907组小鼠脑组织中的PPARγ和UCP2含量下降.研究结果表明,在加入PPARγ抑制剂后,PPARγ/UCP2含量下降,加重了FA所致小鼠的学习记忆障碍,故PPARγ/UCP2在FA致学习记忆障碍中可能起保护作用.     

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

• A spectral machine learning approach is proposed for predicting mixed antibiotic. • Pretreatment is far simpler than traditional detection methods. • Performance of the model is compared in different influencing factors. • Spectral machine learning is promising in the detection of complex substances. Antibiotics are widely used in medicine and animal husbandry. However, due to the resistance of antibiotics to degradation, large amounts of antibiotics enter the environment, posing a potential risk to the ecosystem and public health. Therefore, the detection of antibiotics in the environment is necessary. Nevertheless, conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation, which impose considerable time and economic costs. In this paper, we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning. With the help of a modified spectrometer, a large number of characteristic images were generated to map antibiotic information. The relationship between characteristic images and antibiotic concentrations was established by machine learning model. The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model. The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment. The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.     

Uncertainty propagation in vegetation distribution models based on ensemble classifiers

Ensemble learning techniques are increasingly applied for species and vegetation distribution modelling, often resulting in more accurate predictions. At the same time, uncertainty assessment of distribution models is gaining attention. In this study, Random Forests, an ensemble learning technique, is selected for vegetation distribution modelling based on environmental variables. The impact of two important sources of uncertainty, that is the uncertainty on spatial interpolation of environmental variables and the uncertainty on species clustering into vegetation types, is quantified based on sequential Gaussian simulation and pseudo-randomization tests, respectively. An empirical assessment of the uncertainty propagation to the distribution modelling results indicated a gradual decrease in performance with increasing input uncertainty. The test set error ranged from 30.83% to 52.63% and from 30.83% to 83.62%, when the uncertainty ranges on spatial interpolation and on vegetation clustering, respectively, were fully covered. Shannon’s entropy, which is proposed as a measure for uncertainty of ensemble predictions, revealed a similar increasing trend in prediction uncertainty. The implications of these results in an empirical distribution modelling framework are further discussed with respect to monitoring setup, spatial interpolation and species clustering.     

Monitoring and adaptive management: resolving social and organisational issues to improve information sharing in natural resource management

Adaptive, or 'learning by doing', approaches are often advocated as a means of providing increased understanding within natural resource management. However, a number of organisational and social issues need to be resolved if these approaches are to be used successfully. A case study in the South Island high country of New Zealand is used to review what is needed to support an ongoing community-based monitoring and adaptive management programme. First, the case study is described, paying attention to the social context of the resource management problem. The results of a workshop that explored this problem are then outlined, along with a proposed information flow suggested by participants. Requirements for future steps to resolve these problems (such as information protocols and a multi-stakeholder information system) are discussed. Finally, some broad lessons are drawn from this exercise that could help others developing similar approaches.