专业人才可雇佣性的探讨

企业外在环境的变化使传统的雇佣关系发生根本变化,基于可雇佣性的新契约正是适应这种变革而出现的,这种新契约不但有利于企业发展,还有利于专业人员自我价值的实现.     

辽中南城市群产业状况调查及结构优化研究

产业结构作为以往经济增长的结果和未来经济增长的基础，已经成为经济发展的基本要素。以辽中南城市群作为研究对象，调查其产业结构现状，探讨影响和决定产业之间比例关系的因素。分析产业结构的变动趋势，提出优化该地区产业结构的方案。优化辽中南地区的产业结构，主要从深化产业分工，提升产业功能入手，以期形成既具有强大的综合竞争力，又具备活力，符合产业结构发展趋势，符合辽中南地区要素禀赋的辽中南产业群。     

我国城镇职工收入分配差距的现状研究——基于泰尔指数的视角

党的“十八大”提出“到2020年要实现城乡居民人均收入比2010年翻一番”,为了实现这个目标,进一步深化收入分配改革就成为关键手段,而收入分配的重点领域就是城镇职工的收入分配。文章采用泰尔指数作为比较城镇职工收入分配差异性现状的指标,分析发现：总体上看,不管是在企业内部,还是在企业之间,在不同性质企业就业的城镇职工收入分配差距在逐步缩小,但是在不同性质企业就业的职工收入分配缩小比例并不一致,部分企业甚至有扩大收入分配差距的趋势。因此,在深化收入分配改革时,要综合考虑不同性质企业城镇职工收入分配的现状,在坚持既有的收入分配改革政策之外,还应当采取一些措施向某类企业倾斜。     

Exploring the effect of COVID-19 pandemic lockdowns on urban cooling: A tale of three cities

COVID-19 pandemic has had a major impact on our society, environment and public health, in both positive and negative ways. The main aim of this study is to monitor the effect of COVID-19 pandemic lockdowns on urban cooling. To do so, satellite images of Landsat 8 for Milan and Rome in Italy, and Wuhan in China were used to look at pre-lockdown and during the lockdown. First, the surface biophysical characteristics for the pre-lockdown and within-lockdown dates of COVID-19 were calculated. Then, the land surface temperature (LST) retrieved from Landsat thermal data was normalized based on cold pixels LST and statistical parameters of normalized LST (NLST) were calculated. Thereafter, the correlation coefficient (r) between the NLST and index-based built-up index (IBI) was estimated. Finally, the surface urban heat island intensity (SUHII) of different cities on the lockdown and pre-lockdown periods was compared with each other. The mean NLST of built-up lands in Milan (from 7.71 °C to 2.32 °C), Rome (from 5.05 °C to 3.54 °C) and Wuhan (from 3.57 °C to 1.77 °C) decreased during the lockdown dates compared to pre-lockdown dates. The r (absolute value) between NLST and IBI for Milan, Rome and Wuhan decreased from 0.43, 0.41 and 0.16 in the pre-lockdown dates to 0.25, 0.24, and 0.12 during lockdown dates respectively, which shows a large decrease for all cities. Analysis of SUHI for these cities showed that SUHII during the lockdown dates compared to pre-lockdown dates decreased by 0.89 °C, 1.78 °C, and 1.07 °C respectively. The results indicated a high and substantial impact of anthropogenic activities and anthropogenic heat flux (AHF) on the SUHI due to the substantial reduction of huge anthropogenic pressure in cities. Our conclusions draw attention to the contribution of COVID-19 lockdowns (reducing the anthropogenic activities) to creating cooler cities.