从说课看高职高专院校学情

高职高专学生由于特定的生理和心理特点,在选修体育课程过程中出现随群现象,学习积极性不高。通过说课对高职高专院校的学情加以分析,以便科学地、有目地、有针对性地安排教学内容,增强学生的自信心,提高他们的心理素质和身体素质。     

Measurement, Collaborative Learning and Research for Sustainable Use of Ecosystem Services: Landscape Concepts and Europe as Laboratory

Policies at multiple levels pronounce the need to encompass both social and ecological systems in governance and management of natural capital in terms of resources and ecosystems. One approach to knowledge production and learning about landscapes as social–ecological systems is to compare multiple case studies consisting of large spaces and places. We first review the landscape concepts’ biophysical, anthropogenic, and intangible dimensions. Second, we exemplify how the different landscape concepts can be used to derive measurable variables for different sustainability indicators. Third, we review gradients in the three dimensions of the term landscape on the European continent, and propose to use them for the stratification of multiple case studies of social–ecological systems. We stress the benefits of the landscape concepts to measure sustainability, and how this can improve collaborative learning about development toward sustainability in social–ecological systems. Finally, analyses of multiple landscapes improve the understanding of context for governance and management.     

火炸药企业重大事故隐患危险性评估的计算机自学习实现

通过对火炸药工厂重大事故隐患危险性评估方法的分析,以计算机自学习的基本结构为主线,详细探讨了以机械学习策略完成该评估程序中对新危险品源自学习的过程。对此过程中知识表示等几个应注意的问题进行了描述     

基于数据驱动的汽车部件温度预测模型研究

目的 基于某汽车在中国吐鲁番地区自然暴露的部件温度变化试验数据,预测该车在美国凤凰城地区气象环境下的汽车部件温度变化。方法 把汽车部件的温度作为输出变量,提取影响汽车部件温度变化的关键特征(试验时间、大气温度、太阳辐照)作为输入变量,同时运用公式对不同纬度地区部件受到的太阳辐照进行修正,以消除地理位置的影响。利用Python等软件构建机器学习模型,用吐鲁番试验数据训练模型,然后预测该车部件在美国凤凰城地区的温度变化。结果 梯度提升机模型具有良好的泛化能力和预测精度,其预测值与实际值的平均绝对误差均在3.3°以内,拟合优度R²均大于0.90。BP神经网络和随机森林算法模型也具有较好的预测精度。结论 利用汽车在我国试验站点进行的自然暴露试验数据,可以预测该汽车部件在国外相似地区气象条件下的温度变化。该研究对于依据汽车部件在我国的自然暴露试验结果预测其他国家相似地区自然环境下汽车部件的温度变化具有一定的指导意义。     

Catalyzing success in community-based conservation

Efforts to devolve rights and engage Indigenous Peoples and local communities in conservation have increased the demand for evidence of the efficacy of community-based conservation (CBC) and insights into what enables its success. We examined the human well-being and environmental outcomes of a diverse set of 128 CBC projects. Over 80% of CBC projects had some positive human well-being or environmental outcomes, although just 32% achieved positive outcomes for both (i.e., combined success). We coded 57 total national-, community-, and project-level variables and controls from this set, performed random forest classification to identify the variables most important to combined success, and calculated accumulated local effects to describe their individual influence on the probability of achieving it. The best predictors of combined success were 17 variables suggestive of various recommendations and opportunities for conservation practitioners related to national contexts, community characteristics, and the implementation of various strategies and interventions informed by existing CBC frameworks. Specifically, CBC projects had higher probabilities of combined success when they occurred in national contexts supportive of local governance, confronted challenges to collective action, promoted economic diversification, and invested in various capacity-building efforts. Our results provide important insights into how to encourage greater success in CBC.     

Introducing a common taxonomy to support learning from failure in conservation

Although some sectors have made significant progress in learning from failure, there is currently limited consensus on how a similar transition could best be achieved in conservation and what is required to facilitate this. One of the key enabling conditions for other sectors is a widely accepted and standardized classification system for identifying and analyzing root causes of failure. We devised a comprehensive taxonomy of root causes of failure affecting conservation projects. To develop this, we solicited examples of real-life conservation efforts that were deemed to have failed in some way, identified their underlying root causes of failure, and used these to develop a generic, 3-tier taxonomy of the ways in which projects fail, at the top of which are 6 overarching cause categories that are further divided into midlevel cause categories and specific root causes. We tested the taxonomy by asking conservation practitioners to use it to classify the causes of failure for conservation efforts they had been involved in. No significant gaps or redundancies were identified during this testing phase. We then analyzed the frequency that particular root causes were encountered by projects within this test sample, which suggested that some root causes were more likely to be encountered than others and that a small number of root causes were more likely to be encountered by projects implementing particular types of conservation action. Our taxonomy could be used to improve identification, analysis, and subsequent learning from failed conservation efforts, address some of the barriers that currently limit the ability of conservation practitioners to learn from failure, and contribute to establishing an effective culture of learning from failure within conservation.     

State-of-the-art applications of machine learning in the life cycle of solid waste management

● State-of-the-art applications of machine learning (ML) in solid waste (SW) is presented. ● Changes of research field over time, space, and hot topics were analyzed. ● Detailed application seniors of ML on the life cycle of SW were summarized. ● Perspectives towards future development of ML in the field of SW were discussed. Due to the superiority of machine learning (ML) data processing, it is widely used in research of solid waste (SW). This study analyzed the research and developmental progress of the applications of ML in the life cycle of SW. Statistical analyses were undertaken on the literature published between 1985 and 2021 in the Science Citation Index Expanded and Social Sciences Citation Index to provide an overview of the progress. Based on the articles considered, a rapid upward trend from 1985 to 2021 was found and international cooperatives were found to have strengthened. The three topics of ML, namely, SW categories, ML algorithms, and specific applications, as applied to the life cycle of SW were discussed. ML has been applied during the entire SW process, thereby affecting its life cycle. ML was used to predict the generation and characteristics of SW, optimize its collection and transportation, and model the processing of its energy utilization. Finally, the current challenges of applying ML to SW and future perspectives were discussed. The goal is to achieve high economic and environmental benefits and carbon reduction during the life cycle of SW. ML plays an important role in the modernization and intellectualization of SW management. It is hoped that this work would be helpful to provide a constructive overview towards the state-of-the-art development of SW disposal.     

Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste. ● Feature selection methods were used to improve models’ prediction accuracy. ● The best model predicted C, H, and N contents with accuracy R ² ≥ 0.93, 0.87, 0.97. ● Some suitable models showed insensitivity to spectral noise. ● Under moisture interference, the models still had good prediction performance. Elemental composition is a key parameter in solid waste treatment and disposal. This study has proposed a method based on infrared spectroscopy and machine learning algorithms that can rapidly predict the elemental composition (C, H, N, S) of solid waste. Both noise and moisture spectral interference that may occur in practical application are investigated. By comparing two feature selection methods and five machine learning algorithms, the most suitable models are selected. Moreover, the impacts of noise and moisture on the models are discussed, with paper, plastic, textiles, wood, and leather as examples of recyclable waste components. The results show that the combination of the feature selection and K-nearest neighbor (KNN) approaches exhibits the best prediction performance and generalization ability. Particularly, the coefficient of determination (R²) of the validation set, cross validation and test set are higher than 0.93, 0.89, and 0.97 for predicting the C, H, and N contents, respectively. Further, KNN is less sensitive to noise. Under moisture interference, the combination of feature selection and support vector regression or partial least-squares regression shows satisfactory results. Therefore, the elemental compositions of solid waste are quickly and accurately predicted under noise and moisture disturbances using infrared spectroscopy and machine learning algorithms.     

Spatial prediction of soil contamination based on machine learning: a review

● A review of machine learning (ML) for spatial prediction of soil contamination. ● ML have achieved significant breakthroughs for soil contamination prediction. ● A structured guideline for using ML in soil contamination is proposed. ● The guideline includes variable selection, model evaluation, and interpretation. Soil pollution levels can be quantified via sampling and experimental analysis; however, sampling is performed at discrete points with long distances owing to limited funding and human resources, and is insufficient to characterize the entire study area. Spatial prediction is required to comprehensively investigate potentially contaminated areas. Consequently, machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution. The characteristics, advantages, and applications of machine learning models used to predict soil pollution are reviewed in this study. Satisfactory model performance generally requires the following: 1) selection of the most appropriate model with the required structure; 2) selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability; 3) improvement of model reliability through comprehensive model evaluation; and 4) integration of geostatistics with the machine learning model. With the enrichment of environmental data and development of algorithms, machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.     

Between uncertainty and hope: Young leaders as agents of change in sustainable small-scale fisheries

The path to sustainable small-scale fisheries (SSF) is based on multiple learning processes that must transcend generational changes. To understand young leaders from communities with sustainable SSF management practices in Mexico, we used in-depth interviews to identify their shared motivations and perceptions for accepting their fishing heritage. These possible future decision-makers act as agents of change due to their organizational and technological abilities. However, young people are currently at a crossroads. Many inherited a passion for the sea and want to improve and diversify the fishing sector, yet young leaders do not want to accept a legacy of complicated socioenvironmental conditions that can limit their futures. These future leaders are especially concerned by the uncertainty caused by climate change. If fishing and generational change are not valued in planning processes, the continuity of fisheries, the success of conservation actions, and the lifestyles of young fishers will remain uncertain.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s13280-021-01639-2.