选贤任能视角下党政干部选拔的素质观模型

“德才兼备、以德为先”是中国共产党选贤任能的基本原则与内在标准,其外观体现为党政干部个人的选拔晋升。目前,关于党政干部选拔的研究主要集中在对政绩、履历、社会资本等非贤能因素的影响分析,缺乏对党政干部自身素质特别是德才素质影响、作用的实验研究与因果推断。根据人力资源的素质观解释,论文构建了党政干部选拔的素质观模型,认为选贤任能实质上是基于干部的个人素质对其进行选拔任用的过程,其中政治素质和品德是关键,工作才能是核心,教育和职业经历是基础,工作业绩是标志。1288个样本共38640个观察值的离散选择实验结果表明,基层党政干部选拔的影响因素按照重要性程度由高到低依次为政治素质和品德、工作才能、工作业绩、教育和职业经历,不同性别、学历、职务的被调查者对于选拔因素的偏好存在一定差异。基于此,研究论证了选贤任能制度的科学性以及素质观模型的合理性,并对进一步完善选贤任能实践提出了相关建议。

An Intelligent Diagnosis Method of the Working Conditions in Sucker-Rod Pump Wells Based on Convolutional Neural Networks and Transfer Learning

In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump working conditions,due to the lack of a large-scale dynamometer card data set,the advantages of a deep convolutional neural network are not well reflected,and its application is limited.Therefore,this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning,which is used to solve the problem of too few samples in a dynamometer card data set.Based on the dynamometer cards measured in oilfields,image classification and preprocessing are conducted,and a dynamometer card data set including 10 typical working conditions is created.On this basis,using a trained deep convolutional neural network learning model,model training and parameter optimization are conducted,and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards.The experimental results show that transfer learning is feasible,and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network.The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.

Enhancing the powering ability of triboelectric nanogenerator through output signal’s management strategies

As a new branch of efficient and low-cost mechanical energy conversion technology,triboelectric nanogenerator(TENG)is a potential solution to provide a long-term power supply for the Internet of Things(IoT)sensors and portable electronic devices.However,due to inherent working properties of TENG itself such as extremely high internal impedance,pulse,and alternating current(AC)output,TENG can not directly supply power to loads such as batteries efficiently.Based on these,we describe TENG’s performance from a new perspective of powering ability.It consists of two aspects:the ability to transport charge effectively and the ability to output high power quality current steadily.In order to push forward the developments and applications of TENG,it is necessary to improve its power supply capacity from different perspectives.Fortunately,in recent years,a variety of output signal’s management strategies aiming at effectively managing the generated electricity and significantly improving powering ability of TENG have obtained significantly progress.Herein,this paper discusses the working mechanisms and different load characteristics of TENG at first to clarify the electric performance of TENG.Then,on basis of theoretical analysis,the output signal’s management strategies are elaborated from four aspects:improving the cycle output electricity of TENG,increasing the surface charge density of TENG,improving the power quality of TENG-based energy harvesting system,promoting the application of TENG through integrated circuit(IC)technology and TENG network,and the relevant principles and applications are discussed systematically.Finally,the advantages and disadvantages of the above output signal’s management strategies are summarized and discussed,and the future development of the output signal’s management strategies for TENG is prospected.