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.     

利用黏弹性阻尼器减小结构的风振和地震反应

通过黏弹性阻尼器在工程结构抗震中所起的作用,阐述了黏弹性阻尼器增强结构抗震、抗风、抗振动和安全性的能力,介绍了黏弹性阻尼器性能及在国内外的使用和发展情况.     

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.     

人机界面在大型除尘系统控制中的应用

介绍了人机界面在大型脉冲袋式除尘系统控制中的一个应用实例,从实际使用情况看,人机界面在大型脉冲袋式除尘系统控制中的应用是成功的。     

系统的可靠性--保障人和机器的安全

评价一台机器的优劣,除了看其自动化程度高低外,还应该注重其系统安全是否有保障。对今天的原始设备制造商、机器制造商和最终用户而言,安全已经越来越成为需优先考虑的因素。主要讨论机器设备实现安全保护的方式,以及实现保护和运行之间的最大限度平衡的方案。     

PCA-SVR在煤层瓦斯含量预测中的应用

针对煤层瓦斯含量与其影响因素之间存在着复杂的非线性关系,建立了基于主成分分析和支持向量回归机的煤层瓦斯含量预测模型。该模型有效地解决了小样本、非线性预测的问题,并发挥了主成分分析法消除输入变量间相关性的优点,减少了输入变量个数,提高了预测精度和收敛速度。通过实证分析,该模型的预测精度高,能够直接用于煤矿现场预测煤层瓦斯含量。     

Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in a southern Chinese city

An image-recognition-based diagnosis system of pipe defect types was established. 1043 practical pipe images were gathered by CCTV robot in a southern Chinese city. The overall accuracy of the system is 84% and the highest accuracy is 99.3%. The accuracy shows positive correlation to the number of training samples. Closed circuit television (CCTV) systems are widely used to inspect sewer pipe conditions. During the diagnosis process, the manual diagnosis of defects is time consuming, labor intensive and error prone. To assist inspectors in diagnosing sewer pipe defects on CCTV inspection images, this paper presents an image recognition algorithm that applies features extraction and machine learning approaches. An algorithm of image recognition techniques, including Hu invariant moment, texture features, lateral Fourier transform and Daubechies (DBn) wavelet transform, was used to describe the features of defects, and support vector machines were used to classify sewer pipe defects. According to the inspection results, seven defects were defined; the diagnostic system was applied to a sewer pipe system in a southern city of China, and 28,760 m of sewer pipes were inspected. The results revealed that the classification accuracies of the different defects ranged from 51.6% to 99.3%. The overall accuracy reached 84.1%. The diagnosing accuracy depended on the number of the training samples, and four fitting curves were applied to fit the data. According to this paper, the logarithmic fitting curve presents the highest coefficient of determination of 0.882, and more than 200 images need to be used for training samples to guarantee the accuracy higher than 85%.     

Learning from the rubble: the case of Christchurch,New Zealand,after the 2010 and 2011 earthquakes

Learning after a disaster is crucial in creating more resilient places. However, many societies are repeatedly overwhelmed by disasters. This can be because of missed opportunities to learn in post‐disaster settings or because of actions implemented that seem to be highly relevant to recovery in the short term, but potentially constrain aspirations in the longer term. This paper assesses learning processes among state and non‐state actors and the ways in which these are bridged and scaled up to wider improvements in governance. Aiming to enrich understanding of post‐disaster learning, it explores different actors’ response actions after the earthquakes in Christchurch, New Zealand, in 2010 and 2011. On the one hand, ‘learning by doing’ is occurring, yet, on the other hand, systemic learning is hindered by mismatches between top‐down steering and bottom‐up initiatives. The study concludes that better linking and synergising of learning processes among different levels is vital for enhancing resilience in post‐disaster societies.     

永磁同步曳引机冲顶事故分析

依据现行电梯标准中对安全控制的基本要求,从事故现象入手,对一起永磁同步曳引机冲顶事故进行分析,指出了电梯在电气控制回路设计和部件选型中存在的一些常见问题,模拟分析出了可能的事故过程,最后对改进电梯控制系统设计提出了一些建议。希望在电梯控制回路的绝缘保护等问题上能够引起业内人士对电路控制结构安全设计的足够重视。