Predicting Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) populations and associated grain damage in smallholder farmers’ maize stores: A machine learning approach

Prostephanus truncatus is a notorious pest of stored-maize grain and its spread throughout sub-Saharan Africa has led to increased levels of grain storage losses. The current study developed models to predict the level of P. truncatus infestation and associated damage of maize grain in smallholder farmer stores. Data were gathered from grain storage trials conducted in Hwedza and Mbire districts of Zimbabwe and correlated with weather data for each site. Insect counts of P. truncatus and other common stored grain insect pests had a strong correlation with time of year with highest recorded numbers from January to May. Correlation analysis showed insect-generated grain dust from boring and feeding activity to be the best indicator of P. truncatus presence in stores (r = 0.70), while a moderate correlation (r = 0.48) was found between P. truncatus numbers and storage insect parasitic wasps, and grain damage levels significantly correlated with the presence of Tribolium castaneum (r = 0.60). Models were developed for predicting P. truncatus infestation and grain damage using parameter selection algorithms and decision-tree machine learning algorithms with 10-fold cross-validation. The P. truncatus population size prediction model performance was weak (r = 0.43) due to the complicated sampling and detection of the pest and eight-week long period between sampling events. The grain damage prediction model had a stronger correlation coefficient (r = 0.93) and is a good estimator for in situ stored grain insect damage. The models were developed for use under southern African climatic conditions and can be improved with more input data to create more precise models for building decision-support tools for smallholder maize-based production systems.     

Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach

Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.     

某铜铁矿尾矿库废水神经网络预测研究

金属矿山尾矿库废水中含有H^ 、CN^-和多种金属离子，是矿区及其流域酸性污染和金属污染的主要污染源。在收集某铜铁矿尾矿库废水14年水质监测资料基础上，运用人工神经网络的改进BP算法对其8项水质指标进行了分析、分类和仿真预测，揭示了该矿尾矿库废水的污染特性，查清了主要污染物。     

灰色理论在煤层瓦斯含量预测中的应用

应用灰色系统理论，建立了预测煤层瓦斯含量的灰色系统GM（1，1）模型，并用残差模型对预测模型进行了修正。在测定煤层瓦斯含量的基础上进行了实际应用。结果表明，该模型的计算精度符合工程精度要求，可用于矿井煤层瓦斯含量的预测。研究结果为采取针对性的瓦斯防治措施提供了技术依据，同时也为煤层瓦斯含量预测探讨了新的方法。     

济三煤矿3下煤层开采沉陷预测模型

以综放面支承压力分布规律研究成果为基础,建立了济三煤矿3下煤层开采沉陷预测模型。利用不同深度、不同工作面长度的两个工作面的开采沉陷实测结果耦合,解决了济三煤矿3下煤层开采沉陷预测模型中有关“常数”的确定问题。现场实测证明,所建立的开采沉陷预测模型的预测精度能满足客观实际的要求。     

无底柱分段崩落法开采沉陷预测研究

针对无底柱分段崩落法的采矿特点，把概率积分法引入金属矿山开采沉陷预测中，并通过模型修正提高预测精度。利用GIS强大的图形表现力和空间分析能力实现开采沉陷预测的可视化，能够直观掌握开采沉陷对周围环境的影响程度及范围。通过将预测结果与地表观测数据及实际情况的对比分析，验证了此预测方法的适用性。     

基于GM(1,1)模型的矿井瓦斯涌出量预测研究

应用灰色系统理论，建立了预测矿井瓦斯涌出量的灰色系统GM(1，1)模型，并用残差序列对预测模型进行了修正。实例表明，该模型的计算精度符合工程实际，可用于矿井瓦斯涌出量的预测。     

三峡库区巫山新县城库岸再造模式及塌岸预测

简要介绍了三峡库区巫山新县城地质环境，分析了库岸再造模式，对最终库坡塌岸宽度进行了预测。     

Prediction and Analysis of O3 Based on the ARIMA Model

Despite of the small amount in the atmosphere, ozone is one of the most critical atmospheric component as it protects human beings and any other life on the earth from the sun''s high frequency ultraviolet radiation. In recent decades, the global ozone depletion caused by human activities is well known and produces an "ozone hole", the most direct consequence of which is the increase in ultraviolet radiation, which will affect human survival, climatic environment, ecological environment and other important adverse impacts. Due to the implementation of the Montreal protocol and other agreement, the total amount of ozone depleting substance in the atmosphere has been prominent reduced, which will lead to a new round of regional climate change. Therefore, predicting the changes of the total ozone in the future will have an important guiding significance for predicting the future climate change and making reasonable measures to deal with the climate change. In this paper, based on the ozone data of 1979 to 2016 in the southern hemisphere and ARIMA model algorithm, using time series analysis, we obtain prediction effect of ARIMA model is good by Ljung-Box Q-test and , and the model can be used to predict the future ozone change. With the help of SPSS software, the future trend of the total ozone can be predicted in the future 50 years. Based on the above experiment results, the global ozone change in the future 50 years can be forecasted, namely the atmospheric ozone layer will return to its 1980''s standard by the middle of this century at the global scale.