CDIO工程教育模式在高职创业教育中的应用研究——以中国环境管理干部学院为例

创业教育现在已成为我国高等职业教育中的重要一环。高职院校往往开设一门创业教育课程,试图通过该课程达到创业教育的目的,课程过分迷信＂说教、讲授＂的力量,而毕业生创业能力并没有得到预期的培养。通过借鉴CDIO工程教育模式,提出了创业教育应该以创业能力为培养目标,参照CDIO模式的12项标准进行模块化课程开发,通过CDIO循环提高学生的创业综合能力。     

Learning sustainable water practices through participatory irrigation management in Thailand

Participatory irrigation management (PIM) was adopted in Thailand in 2004 to encourage the sustainable use of water in the agricultural sector. The research presented in this paper sought to understand the relationships between public participation, learning, and the implementation of more sustainable water practices through PIM in Thailand. Data was collected through document reviews, observation, informal meetings, and a total of 55 semi‐structured face‐to‐face interviews of local irrigators from two case study regions around the Krasiew Reservoir. Results showed that participating in PIM activities facilitated both instrumental (e.g., water supply and demand data, benefits of on‐time water delivery) and communicative (e.g., reasons for past PIM failure, expectations of fellow farmers) learning among PIM participants. Findings also revealed that social action is fostered through the recognition of human dignity and compassionate communication that instils a sense of ownership and solidarity among irrigators. Sustainable water practices among local farmers were spurred further through learning that the reservoir is a finite water source.     

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.     

Adaptive management for a turbulent future

The challenges that face humanity today differ from the past because as the scale of human influence has increased, our biggest challenges have become global in nature, and formerly local problems that could be addressed by shifting populations or switching resources, now aggregate (i.e., "scale up") limiting potential management options. Adaptive management is an approach to natural resource management that emphasizes learning through management based on the philosophy that knowledge is incomplete and much of what we think we know is actually wrong. Adaptive management has explicit structure, including careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. It is evident that adaptive management has matured, but it has also reached a crossroads. Practitioners and scientists have developed adaptive management and structured decision making techniques, and mathematicians have developed methods to reduce the uncertainties encountered in resource management, yet there continues to be misapplication of the method and misunderstanding of its purpose. Ironically, the confusion over the term "adaptive management" may stem from the flexibility inherent in the approach, which has resulted in multiple interpretations of "adaptive management" that fall along a continuum of complexity and a priori design. Adaptive management is not a panacea for the navigation of 'wicked problems' as it does not produce easy answers, and is only appropriate in a subset of natural resource management problems where both uncertainty and controllability are high. Nonetheless, the conceptual underpinnings of adaptive management are simple; there will always be inherent uncertainty and unpredictability in the dynamics and behavior of complex social-ecological systems, but management decisions must still be made, and whenever possible, we should incorporate learning into management.     

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。     

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.