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1.
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. 相似文献
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Insider trading is a kind of criminal behavior in stock market by using nonpublic information. In recent years, it has become the major illegal activity in China’s stock market. In this study, a combination approach of GBDT (Gradient Boosting Decision Tree) and DE (Differential Evolution) is proposed to identify insider trading activities by using data of relevant indicators. First, insider trading samples occurred from year 2007 to 2017 and corresponding non-insider trading samples are collected. Next, the proposed method is trained by the GBDT, and initial parameters of the GBDT are optimized by the DE. Finally, out-of-samples are classified by the trained GBDT–DE model and its performances are evaluated. The experiment results show that our proposed method performed the best for insider trading identification under time window length of ninety days, indicating the relevant indicators under 90-days time window length are relatively more useful. Additionally, under all three time window lengths, relative importance result shows that several indicators are consistently crucial for insider trading identification. Furthermore, the proposed approach significantly outperforms other benchmark methods, demonstrating that it could be applied as an intelligent system to improve identification accuracy and efficiency for insider trading regulation in China stock market. 相似文献
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The perpetual energy production of a wind farm could be accomplished (under proper weather conditions) if no failures occurred. But even the best possible design, manufacturing, and maintenance of a system cannot eliminate the failure possibility. In order to understand and minimize the system failures, the most crucial components of the wind turbines, which are prone to failures, should be identified. Moreover, it is essential to determine and classify the criticality of the system failures according to the impact of these failure events on wind turbine safety. The present study is processing the failure data from a wind farm and uses the Fault Tree Analysis as a baseline for applying the Design Structure Matrix technique to reveal the failure and risk interactions between wind turbine subsystems. Based on the analysis performed and by introducing new importance measures, the “readiness to fail” of a subsystem in conjunction with the “failure riskiness” can determine the “failure criticality.” The value of the failure criticality can define the frame within which interventions could be done. The arising interventions could be applied either to the whole system or could be focused in specified pairs of wind turbine subsystems. In conclusion, the method analyzed in the present research can be effectively applied by the wind turbine manufacturers and the wind farm operators as an operation framework, which can lead to a limited (as possible) design‐out maintenance cost, failures' minimization, and safety maximization for the whole wind turbine system. 相似文献
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为了提高采油树平板闸阀密封圈的密封性能,在泛塞封的基础上,设计一种密封圈本体唇边开有锯齿状凸起的新型柔性密封结构;运用有限元分析方法模拟密封圈的工作状况,分析柔性密封结构特性参数对密封圈密封性能的影响,获得不同柔性密封圈结构参数下密封面间接触应力分布规律,并对新型密封结构进行优化。结果表明:密封面间最大接触应力随唇边锯齿数量、唇边夹角度数的增大而增大,随唇谷夹角度数的增大而减小;新型密封结构选择锯齿数量为3、唇边夹角为20°、唇谷夹角为30°的特性参数时,其最大接触应力比常规Y形密封圈提高了15倍;新型柔性密封结构的密封圈与阀杆、阀盖壁面间接触应力比常规Y形密封圈有显著提高,提高了密封圈的密封性能。 相似文献
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Ratnabali Biswas Kaushik Chowdhury Dharma P. Agrawal 《International Journal of Wireless Information Networks》2006,13(4):303-315
Wireless sensor network is an emerging technology that enables remote monitoring of large geographical regions. In this paper, we address the problem of distributing attributes over such a large-scale sensor network so that the cost of data retrieval is minimized. The proposed scheme is a data-centric storage scheme where the attributes are distributed over the network depending on the correlations between them. The problem addressed here is similar to the Allocation Problem of distributed databases. In this paper, we have defined the Allocation Problem in the context of sensor networks and have proposed a scheme for finding a good distribution of attributes to the sensor network. We also propose an architecture for query processing given such a distribution of attributes. We analytically determine the conditions under which the proposed architecture is beneficial and present simulation results to demonstrate the same. To the best of our knowledge, this is the first attempt to determine an allocation of attributes over a sensor network based on the correlations between attributes. 相似文献
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探讨分类挖掘技术在高校实际工作中的应用方式与应用领域。以高校人事管理为模型,应用数据挖掘中决策树算法,对高校人力资源数据源中的信息进行分析,发现其中有价值的数据模式,寻找其中存在的关系和规则,对高校人才规划提供比较客观的决策支持。 相似文献
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