基于人力资源价值的企业“技能树”评价模型构建研究——以株洲市烟草公司为例

本文运用文献回顾、半结构化访谈和问卷调查的方法,构建了株洲市烟草公司人力资源价值评价模型。评价模型包含个人成长、活动参与、荣誉表彰、勤勉敬业、科技创新、安全工作、负面清单直接否决制、开展调研情况、项目或专项工作开展情况和培训授课情况等10个维度。模型的建立为株洲市烟草公司有效激发人才活力、挖掘人才潜力与释放人才红利提供了有力依据。

Investigation on short-term burst pressure of plastic pipes reinforced by cross helically wound steel wires

Plastic pipes reinforced by cross helically wound steel wires (PSP), which have exhibited excellent mechanical performance, consist of inner polyethylene (PE) layer, winding layer and outer PE layer. The winding layer is composed of two monolayers where steel wires are cross helically wound. An analytical procedure is developed to predict the short-term burst pressure of PSP as the monolayer is assumed to be elastic and orthotropic. The 3D anisotropic elasticity and Maximum Stress Failure Criterion are employed in the formulation of the elasticity problem. Good agreement between the theoretical results and the experimental data shows that the proposed approach can well predict the short-term burst pressure of PSP.

A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques

Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.

Implications of geoengineering under the 1.5 ℃ target： Analysis and policy suggestions

The Paris Agreement introduced a 1.5 ℃ target to control the rise in global temperature, but clear arrangements for feasible implementation pathways were not made. Achieving the 1.5 ℃ target imposes high requirements on global emission reduction. Nationally Determined Con- tributions of all Parties are far from the 1.5 ℃ target, and conventional emission reduction technologies and policies will also have difficulty in fulfilling this task. In this context, geoengineering is gaining interest in the international arena. The Paris Agreement includes afforestation, carbon capture, utilization and storage, and negative emission technologies such as bio-energy with carbon capture and store. All of these techniques are CO2 removal technologies that belong to geoengineering. Solar radiation management, which is highly controversial, has also attracted increased attention in recent years. Although the outline of the IPCC Special Report on 1.5 ℃ does not include a specific section on geoengineering issues yet, geoengineering is an unconventional technical option that cannot be avoided in research and discussions on impact assessment, technical options, ethics, and international governance under the 1.5 ℃ target. On the basis of analyzing and discussing abovementioned issues, this paper proposes several policy suggestions for China to strengthen research on and response to geoengineering.