基于工作过程的高职“教学做一体化”课程探究

“教学做一体化”课程有利于理论教学与实践教学相结合,有利于高职学校长期健康的发展。“教学做一体化”教学模式是基于实际工作过程导向,以工作中典型项目任务引领,激发学生兴趣,以满足培养高等技能型人才的迫切需要。一体化课程的构建应通过项目设立、建立小组、实施、反馈等环节完成。     

大气腐蚀数据降维最优维度研究

目的确定金属大气腐蚀数据降维的最优维度。方法分别采用PCA、MDS、Isomap和LLE四种方法对大气腐蚀数据进行降维处理,并采用集成学习算法建立预测模型。针对不同的降维方法和近邻点个数计算,使用MAPE(Mean Absolute Percentage Error,相对百分误差绝对值的平均值)对预测结果进行评价,将最佳预测速率所对应的维度作为最优维度。结果不同的降维方法和近邻参数作用下,最优维度从2维到7维不等。流形学习方法对大气腐蚀数据进行降维的MAPE均小于线性降维方法。结论适合每种降维方法的最优维度可能是不同的,通过对MAPE进行对比,得到大气腐蚀数据在各种降维方法的最优维度。     

Accident data for the Semantic Web

This paper describes concerns about the documentation, dissemination and use of lessons learned from mishap investigations, impediments posed by current practices, and opportunities for improvement. Lessons are presently developed, documented and stored primarily in narrative form and relational databases, and disseminated in many forms and media, including the Internet. Current practices pose many impediments to maximized development, dissemination and use. Investigation process research and new data concepts behind the Semantic Web, exploited elsewhere, offer potential opportunities to overcome these impediments. To exploit these opportunities, formation of a working group to develop an improved Semantic Web-friendly mishap investigation lessons learning system is proposed. An example illustrating an alternative approach is described to support a reasonable expectation that an alternative lessons learning system could be developed.     

Modelling the relationship of driver license and offense history with fatal and serious injury (FSI) crash involvement

Introduction: Previous research has indicated that increases in traffic offenses are linked to increased crash involvement rates, making reductions in offending an appropriate measure for evaluating road safety interventions in the short-term. However, the extent to which traffic offending predicts fatal and serious injury (FSI) crash involvement risk is not well established, prompting this new Victorian (Australia) study. Method: A preliminary cluster analysis was performed to describe the offense data and assess FSI crash involvement risk for each cluster. While controlling demographic and licensing variables, the key traffic offenses that predict future FSI crash involvement were then identified. The large sample size allowed the use of machine learning methods such as random forests, gradient boosting, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. This was done for the ‘all driver’ sample and five sometimes overlapping groups of drivers; the young, the elderly, and those with a motorcycle license, a heavy vehicle license endorsement and/or a history of license bans. Results: With the exception of the group of drivers who had a history of bans, offense history significantly improved the accuracy of models predicting future FSI crash involvement using demographic and licensing data, suggesting that traffic offenses may be an important factor to consider when analyzing FSI crash involvement risk and the effects of road safety countermeasures. Conclusions: The results are helpful for identifying driver groups to target with further road safety countermeasures, and for showing that machine learning methods have an important role to play in research of this nature. Practical Application: This research indicates with whom road safety interventions should particularly be applied. Changes to driver demerit policies to better target offenses related to FSI crash involvement and repeat traffic offenders, who are at greater risk of FSI crash involvement, are recommended.     

PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS1

ABSTRACT: The cascade correlation neural network was used to predict the two-year peak discharge (Q₂) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q₂ by regression. Our research shows Q₂ was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds.     

Learning for sustainability through CIDA's “Community-based pest management in Central American agriculture” project: a deliberative,experiential and iterative process

Community-based approaches to environmental management provide opportunities for public engagement in local decision-making processes. This qualitative case study examines learning as resulted from participation in CIDA's “Community-based pest management in Central American agriculture” project. Outcomes include learning about alternative farming practices, human and natural environments, and safer pesticide use. Many participants learnt how to work more effectively with rural communities. For some, this changed their perspective about life and their role in society. Activities that fostered learning outcomes, including sustainability-related outcomes, were: planning and implementing project activities, experimenting on farm demonstration plots, participating in outreach workshops, and students doing rural practica. The learning process, involving international university collaborators and rural participants, is analyzed particularly as it relates to cultural context, collective action, and sustainability. Findings affirm that how the public participates in environmental management decisions influences the breadth and depth of learning outcomes; practical and policy implications are discussed.     

Using deep-belief networks to understand propensity for livelihood change in a rural coastal community to further conservation

Overharvesting of terrestrial and marine resources may be alleviated by encouraging an alternative configuration of livelihoods, particularly in rural communities in developing countries. Typical occupations in such areas include fishing and farming, and rural households often switch livelihood activities to suit climate and economic conditions. We used a machine-learning tool, deep-belief networks (DBN), and data from surveys of a rural Philippine coastal community to examine household desire to change livelihood. This desire is affected by a variety of factors, such as income, family needs, and feelings of work satisfaction, that are interrelated in complex ways. In farming households, livelihood changes often occur to diversify resources, increase income, and lessen economic risk. The DBN, given its multilayer perceptron structure, has a capacity to model nonlinear relationships among factors while providing an acceptable degree of accuracy. Relative to a set of 34 features (e.g., education, boat ownership, and work satisfaction), we examined the binary response variables desire to change work or not to change work. The best network had a test set accuracy of 97.5%. Among the features, 7 significantly affected desire to shift work: ethnicity, work satisfaction, number of persons in a household in ill health, number of fighting cocks owned, fishing engagement, buy-and-sell revenue, and educational level. A cross-correlation matrix of these 7 features indicated households less inclined to change work were those engaged in fishing and retail buying and selling. For fishing, provision of economic and other incentives should be considered to encourage changing from this occupation to allow recovery of fishery resources.     

Sexual selection and the evolutionary effects of copying mate choice

We examine the evolutionary consequences of copying mate choice using models in which the preferences of younger females are affected by the mate choices that they observe older females making. We introduce two models of copying, termed single mate copying and mass copying, corresponding to situations in which immature females imprint on the choices of only one or of a very large number of older females, respectively. Female mating preferences are assumed to evolve only through cultural evolution, while the male trait on which they act is inherited either via a haploid autosomal or a Y-linked locus. Results show that the preference and male trait can rapidly coevolve, with a positive frequencey-dependent advantage to the more common male trait allele. This process can cause a display trait that lowers male viability to increase in a population. Mass copying results in stronger frequency dependence than does single mate copying. Mass copying and, under some conditions, single mate copying lead to two alterative stable equilibria for the male trait. Neither copying model supports variation at the male trait locus, and copying makes it more difficult for a novel male trait phenotype to spread. Correspondence to: M. Kirkpatrick     

Using equation discovery to revise an Earth ecosystem model of the carbon net production

Equation discovery approaches to automated modeling from observed data usually derive equation-based models from scratch rather than from an initial model already established in the domain of use. In this paper, we present an approach that uses new or recent observational data to improve an existing equation-based model. The approach is used to reduce the error of the Earth ecosystem model of the net production of carbon in the atmosphere. We revise the initial ecosystem model in two directions. First, we calibrate the values of the constant parameters in the model on new observational data. Second, we allow the use of alternative equation structures for some of the sub-models of the initial model and use our approach to choose among them. Experiments show that both revision of values of the constant parameters and revision of the structures of sub-models can considerably reduce the error of the initial model.     

Evaluating Children's Conservation Biology Learning at the Zoo

Millions of children visit zoos every year with parents or schools to encounter wildlife firsthand. Public conservation education is a requirement for membership in professional zoo associations. However, in recent years zoos have been criticized for failing to educate the public on conservation issues and related biological concepts, such as animal adaptation to habitats. I used matched pre‐ and postvisit mixed methods questionnaires to investigate the educational value of zoo visits for children aged 7–15 years. The questionnaires gathered qualitative data from these individuals, including zoo‐related thoughts and an annotated drawing of a habitat. A content analysis of these qualitative data produced the quantitative data reported in this article. I evaluated the relative learning outcomes of educator‐guided and unguided zoo visits at London Zoo, both in terms of learning about conservation biology (measured by annotated drawings) and changing attitudes toward wildlife conservation (measured using thought‐listing data). Forty‐one percent of educator‐guided visits and 34% of unguided visits resulted in conservation biology‐related learning. Negative changes in children's understanding of animals and their habitats were more prevalent in unguided zoo visits. Overall, my results show the potential educational value of visiting zoos for children. However, they also suggest that zoos’ standard unguided interpretive materials are insufficient for achieving the best outcomes for visiting children. These results support a theoretical model of conservation biology learning that frames conservation educators as toolmakers who develop conceptual resources to enhance children's understanding of science. Evaluación del Aprendizaje de Biología de la Conservación por Niños en el Zoológico Jensen