两宋时期自然灾害的文学记述与地理分布规律

两宋320年间共发生灾害1739次,灾害种类14种,如此深重的灾害是导致两宋"积贫积弱,国力不强"的一大主要因素.由于宋朝时期的地理气候特点,水灾、旱灾接连不断发生,并且成为两宋最主要的自然灾害.古代自然灾害研究主要依据史书记载,中国是世界上少数灾害多发的国家之一,文学作品中反映出来的灾难与救世观念可以给人们很多启发.以王禹偁为代表的宋代文学家给后世留下了许多记述灾害的文学作品,从文学作品的记述中可以提取古代灾害的准确信息.北宋都城位于开封,南宋京都在杭州,由于史料对当时中央政府所在地省份的记述颇为翔实,两宋自然灾害的省区分布,以河南、浙江灾害次数最多.     

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.     

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.     

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.     

Learning about Environments: The Significance of Primal Landscapes

The way we learn about our environments—be they farms, forests, or tribal lands—has implications for the formulation of environmental policy. This article presents the findings of how residents learned about their environments in two rural case studies conducted in northern Queensland and relates these to the concept of “primal landscapes,” which is concerned with the interaction that occurs between children and the environments in which they mature. Rather than focusing specifically on built environments or natural environments, the article draws on an approach that conceptualizes environment as meaning-laden places in which we live and work, which integrate social, cultural, biological, physical, and economic dimensions. In drawing insights for environmental policy, the article draws attention to the timing of policy interventions, the significance of experiential environmental education, the potential to learn from place-based festivals, and the importance of learning from extreme events such as fires and floods.     

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.     

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。     

“多流程、多技能、多任务”式教学模式的研究与实践

基于"工作过程"教育教学理念,提出"多流程、多技能、多任务"的教育教学模式,将学生的学习课程进行分解、重组,按照"典型任务分析—学习领域设计—具体教学任务实施"的原则,研究了典型任务抽取、学习范围划分以及项目任务的情境教学与实施,并在实际教学过程中取得了良好的学习效果。