不同套袋方式对石榴果实品质及安全性的影响

以陕西临潼石榴为研究对象,研究套袋方式（纸袋、膜袋）对石榴生长规律、石榴品质及安全性的影响.结果表明,套袋栽培技术能明显改善石榴果实的色泽及光滑度,减少裂果率,增加单果重量;套袋石榴果实中还原糖含量明显低于对照组（未套袋）石榴果实,而可滴定酸含量略高于对照组果实;套袋石榴果实中重金属（Pb、As、Hg）含量、农药残留量（氯氰聚酯）明显低于对照组（未套袋）石榴果实.石榴套袋栽培技术是生产优质石榴,提高石榴生产效益的重要措施.     

Ensemble of naïve Bayesian approaches for the study of biofilm development in drinking water distribution systems

Various studies have been performed in relation to the influence that a number of characteristics of drinking water distribution systems (DWDSs) have on biofilm development. Nevertheless, their joint influence, apart from a few exceptions, has scarcely been studied due to the complexity of the community and the environment. In this paper, we apply various machine learning algorithms based on naïve Bayesian networks. Alternatives for the base naïve Bayesian model to outperform individual performances while maintaining simplicity are suggested. These alternatives include augmentation of the arcs in the graph, and initial bagging approaches. Finally, a combination of different naïve approaches in a bagging process that produces explanatory hybrid decision trees is proposed. As a result, it is possible to achieve a deeper understanding of the consequences that the interaction of the relevant hydraulic and physical factors of DWDSs has on biofilm development.     

基于神经网络集成学习股票预测模型的研究

基于深度学习的原理构建出六层长短记忆神经网络，通过集成学习中Bagging方法组合8个长短记忆神经网络。使用基于神经网络集成学习模型预测中国人民币普通股市场。实验测试了从2012年1月4日到2017年12月29日这期间的上海证券综合指数、深圳证券综合指数、上证50指数、沪深300指数、中小企业板指数和创业企业板指数。实验结果为模型的准确率达到58.5%，精确率为58.33%，召回率为73.5%，[F1]值为64.5%，AUC值为57.67%，取得了较好的预测效果。     

采前套袋结合低浓度乙烯利替代蜜橘果实乙烯褪绿处理的可行性研究

本文探讨采前套袋结合低浓度乙烯处理对蜜橘果实采后乙烯褪绿行为的影响。用黄色单层果袋对重庆北碚早熟蜜橘果实进行采前套袋处理，采收后分别设置对照组（CK）、1000 mg/kg乙烯利处理组（ETH）、仅套袋处理组（套袋+0 mg/kg ETH）、套袋+250 mg/kg乙烯利处理组（套袋+250 mg/kg ETH）、套袋+500 mg/kg乙烯利处理组（套袋+500 mg/kg ETH）和套袋+1000 mg/kg乙烯利处理组（套袋+1000 mg/kg ETH），并将所有果实置于20℃的环境中贮藏。结果表明:随着贮藏期的延长，套袋结合乙烯利处理能更好改善早熟蜜橘果实的呈色问题，贮藏第6 d后，套袋+250 mg/kg ETH处理、套袋+500 mg/kg ETH处理和套袋+1000 mg/kg ETH处理均能显著提高果实的色差参数（a*和a*/b*）（P<0.05）。在整个贮藏期，套袋+250 mg/kg ETH处理能够延缓蜜橘果实腐烂率和褐变指数的上升，并能维持果实硬度，对果实可溶性固形物和V_C含量无不良影响。此外，采收时，采前套袋处理的蜜橘果实的游离氨基酸总含量与对照组相比未发生显著性变化（P>0.05），采前套袋处理对蜜橘果实风味无不良影响，但能提高蜜橘果实中与衰老抗病有关的精氨酸的含量。综合分析，采前套袋结合低浓度乙烯利（套袋+250 mg/kg ETH）在改善果实褪绿效果的同时，能降低常规乙烯褪绿引起的腐烂、果蒂褐变等问题，且该处理操作简单，在柑橘产业中具有一定的应用前景。     

水泥包装自动套袋装置控制系统的设计

目的 为了提升水泥袋装过程的自动化水平，减少长期以来对人工套袋的过度依赖。方法 在现有的回转式水泥包装机技术基础上，设计一款与之相适应的自动套袋控制系统。对套袋装置的结构原理进行简要阐述，同时明确控制系统的设计要点，决定采取以“HMI+PLC”的分层控制结构，在硬件选型的基础上，又对控制程序和组态界面进行了开发。结果 经过现场实验验证，文中所设计的控制系统操作简便、可靠稳定、易于维护，在粉尘浓度较大的环境中运行良好，有效实现了供袋、取袋、开袋以及套袋等工序流程的自动化控制，套袋速度接近1 500袋/h，且套袋成功率达到98%。结论 系统通过触摸屏界面完成远程操控，将套袋工人从恶劣的粉尘环境中解放出来，为自动套袋技术难题攻关提供了可参考方案，提升了水泥生产行业的自动化水平，具有广阔的应用前景。     

Optimal scheduling of electrical power in energy-deficient scenarios using artificial neural network and Bootstrap aggregating

In a developing country like Pakistan, where the electrical power demand is more than the generated power, maintaining the power system stability is a big challenge. In such cases it becomes, thus, essential to shed just the right amount of load to keep a power system stable. This paper presents a case study of Pakistan’s power system where the generated power, the load demand, frequency deviation and the load shed during a 24-h duration have been provided. The data have been analyzed using two techniques; the conventional artificial neural network (ANN) by implementing feed forward back propagation model and the Bootstrap aggregating or bagging algorithm. The simulation results reveal the superiority of the Bootstrap aggregating algorithm over a conventional ANN technique using feed forward back propagation model.     

Using Iterated Bagging to Debias Regressions

Breiman (Machine Learning, 26(2), 123–140) showed that bagging could effectively reduce the variance of regression predictors, while leaving the bias relatively unchanged. A new form of bagging we call iterated bagging is effective in reducing both bias and variance. The procedure works in stages—the first stage is bagging. Based on the outcomes of the first stage, the output values are altered; and a second stage of bagging is carried out using the altered output values. This is repeated until a simple rule stops the process. The method is tested using both trees and nearest neighbor regression methods. Accuracy on the Boston Housing data benchmark is comparable to the best of the results gotten using highly tuned and compute- intensive Support Vector Regression Machines. Some heuristic theory is given to clarify what is going on. Application to two-class classification data gives interesting results.     

Tree Induction for Probability-Based Ranking

Tree induction is one of the most effective and widely used methods for building classification models. However, many applications require cases to be ranked by the probability of class membership. Probability estimation trees (PETs) have the same attractive features as classification trees (e.g., comprehensibility, accuracy and efficiency in high dimensions and on large data sets). Unfortunately, decision trees have been found to provide poor probability estimates. Several techniques have been proposed to build more accurate PETs, but, to our knowledge, there has not been a systematic experimental analysis of which techniques actually improve the probability-based rankings, and by how much. In this paper we first discuss why the decision-tree representation is not intrinsically inadequate for probability estimation. Inaccurate probabilities are partially the result of decision-tree induction algorithms that focus on maximizing classification accuracy and minimizing tree size (for example via reduced-error pruning). Larger trees can be better for probability estimation, even if the extra size is superfluous for accuracy maximization. We then present the results of a comprehensive set of experiments, testing some straightforward methods for improving probability-based rankings. We show that using a simple, common smoothing method—the Laplace correction—uniformly improves probability-based rankings. In addition, bagging substantially improves the rankings, and is even more effective for this purpose than for improving accuracy. We conclude that PETs, with these simple modifications, should be considered when rankings based on class-membership probability are required.     

Smoothed Bagging with Kernel Bandwidth Selectors

Recently, a combined approach of bagging (bootstrap aggregating) and noise addition was proposed and shown to result in a significantly improved generalization performance. But, the level of noise introduced, a crucial factor, was determined by trial and error. The procedure is not only ad hoc but also time consuming since bagging involves training a committee of networks. Here we propose a principled procedure of computing the level of noise, which is also computationally less expensive. The idea comes from kernel density estimation (KDE), a non-parametric probability density estimation method where appropriate kernel functions such as Gaussian are imposed on data. The kernel bandwidth selector is a numerical method for finding the width of a kernel function (called bandwidth). The computed bandwidth can be used as the variance of added noise. The proposed approach makes the trial and error procedure unnecessary, and thus provides a much faster way of finding an appropriate level of noise. In addition, experimental results show that the proposed approach results in an improved performance over bagging, particularly for noisy data.     

基于Bagging算法的水库水沙联合智能调度

水库水沙调度是一项非常复杂的综合性问题,存在大量的非线性关系。传统的调度方法往往难以达到满意的效果。该文提出了一种基于Bagging算法的水库调度方法。用神经网络建立水库调度预测函数,在此基础上,利用Bagging算法构造了基于神经网络的预测函数序列进行预测,预测的平均值用于水库调度。实验结果表明,符合实际情况,且具有明显的经济安全效益。