学科服务 更多>>

关键词： 工业固体废物   固体废物污染   生活垃圾分类   固废   环境防治   过度包装   监督管理责任   新修订  

摘要： 日前,十三届全国人大常委会第十七次会议通过了修订后的《中华人民共和国固体废物污染环境防治法》(以下简称"固废法"),自2020年9月1日起施行。新修订的固废法明确固体废物污染环境防治强化政府及其有关部门监督管理责任,完善了工业固体废物、生活垃圾。

关键词： 企业家精神   创新精神   冒险精神   企业成长  

摘要： 本文对企业家精神、企业成长及二者相互关系的研究文献进行了回顾,归纳提出了3个基本假设和16个从属假设。借助剔除了缺失项后的2321个中国上市公司面板数据,利用结构方程模型分析工具,透过研究所构建的模型,对本文提出的各个研究假设进行了检验。本文基于样本公司数据,测算了企业家精神水平,并对不同类型企业家精神水平进行了简单分析。研究检验了样本公司数据与所构建模型的拟合性,发现样本公司数据能够与模型较好拟合。隐变量模型检验发现,关于冒险精神与创新精神之间存在互为因果关系的假设,以及冒险精神对企业成长具有显著负向影响的假设,得到了检验的严格的支持;但创新精神与企业成长之间存在显著正向影响的假设没能得到严格支持。在观察变量之间关系方面,只有4个从属假设得到了检验的严格支持。此外,研究发现,子样本与总体样本之间的检验结果存在显著差异,子样本之间在模型匹配性与输出结果上都存在显著差异;只有民营企业与制造业子样本能够与模型很好拟合,并输出有效检验结果。

关键词： 糖尿病患者   血脂检验   继发性并发症  

摘要： 目的:研究血脂检验在糖尿病患者中的应用效果及对继发性并发症发生的作用。方法:2018年4月-2019年5月收治糖尿病患者40例作为试验组,同时选取同期体检的健康志愿者40例作为对照组。比较两组高密度脂蛋白胆固醇(HDL-C)、低密度脂蛋白胆固醇(LDL-C)、甘油三酯(TG)、总胆固醇(TC)及载脂蛋白(ApoA1、ApoB)水平。结果:试验组HDL-C、ApoA1水平显著低于对照组,LDL-C、TG、TC、ApoB水平显著高于对照组,差异均有统计学意义(P<0.05)。结论:对糖尿病患者给予血脂检验能够对其继发性并发症的发生进行有效判断,有利于制定相应的治疗预防措施,具有良好的检验价值。

关键词： 尿液常规检验   生化常规检验   糖尿病   诊断  

摘要： 目的探究尿液常规检验与生化常规检验在糖尿病诊断中的作用效果。方法纳入此次研究中的65例疑似糖尿病患者均选自我院2018年6月-2019年6月,对其分别选择尿液常规检验和生化常规检验,比较两种方法的诊断效果。结果统计对比尿液常规检验和生化常规检验灵敏度以及正确度,组间数据差异显著,具有统计学意义(P<0.05);统计对比尿液常规检验和生化常规检验特异度,组间数据并无统计学意义(P>0.05)。结论生化常规检验对糖尿病进行诊断具有较高的灵敏度、特异度以及正确度,可将糖尿病检出率提升。

关键词： 幼儿园教师   科研参与   专业发展水平   教育科研素养   实证研究  

摘要： 幼儿园教师是"办好学前教育、实现幼有所育"的第一人力资源,他们的专业发展直接关乎教师队伍建设和学前教育质量。本文基于苏州市微观调查数据,使用分层回归及Bootstrap检验法实证分析了科研参与对幼儿园教师专业发展水平的影响,以及教育科研素养的中介作用。研究发现:(1)有、无科研参与经历的幼儿园教师在专业发展水平及其各维度上均存在显著差异;(2)科研参与次数对幼儿园教师专业发展水平(包括专业知识、专业能力)具有显著的直接正向影响;(3)教育科研素养在科研参与次数和专业发展水平的正向关系中发挥部分中介作用,且中介效应大于直接效应;(4)教育科研素养的中介作用存在异质性,它分别在科研参与次数对专业知识、专业能力的影响中起部分中介作用,在科研参与次数对专业理念与师德的影响中起完全中介作用。对此,应该改变"没必要搞科研"抑或"科研增加负担"等刻板印象,鼓励并大力支持幼儿园教师充分更高质量地参与科研,为持续推动幼儿园教师专业发展助力。

关键词： 中医药   中医教育   质量控制   传承   检验医学  

摘要： 谢小兵:中医药是中国五千年历史文明的象征,是我国特有的、与人文充分融合的医学科学。中医药的传承与发展是实现民族文化伟大复兴的重要载体,对我国医药行业的发展有着不可替代的深远意义。随着现代医学的发展和普及,中医药传承与发展受到了极大的影响和冲击,中医药理论、中医医疗模式、中医药秘方及相关成果、中医药人才等面临严重挑战,特别是目前的中医药教育模式、科研模式、医疗模式、宏观管理等关键因素存在诸多矛盾和问题。

关键词： 薪酬激励   人力资源管理效率   DEA模型   回归分析  

摘要： 合理的薪酬体系与企业生存发展息息相关,而我国薪酬体系主要存在的问题是制度不健全,薪酬结构过于单一,绩效考核评价体系存在漏洞,薪酬水平不能适应现代企业发展的需求.薪酬激励对企业人力资源管理效率的影响主要通过三大机制,分别是调动员工的积极性,帮助企业留住高素质人才,提升企业员工的凝聚力.本文实证分析表明:员工基本工资水平提升1个单位,能够促进企业人力资本管理效率上升0.166个单位,员工奖金水平提升1个单位能够带动企业人力资源管理效率上升0.415个单位.与国有企业相比私营企业更有利于促进其人力资源管理效率水平提升.同时企业经营时间越长,越有利于促进其人力资源管理效率水平提升.

关键词： Land cover classification   In-situ reference data   LUCAS   Sentinel-2   Remote sensing  

摘要： In this study, we test the use of Land Use and Coverage Area frame Survey (LUCAS) in-situ reference data for classifying high-resolution Sentinel-2 imagery at a large scale. We compare several pre-processing schemes (PS) for LUCAS data and propose a new PS for a fully automated classification of satellite imagery on the national level. The image data utilizes a high-dimensional Sentinel-2-based image feature space. Key elements of LUCAS data pre-processing include two positioning approaches and three semantic selection approaches. The latter approaches differ in the applied quality measures for identifying valid reference points and by the number of LU/LC classes (7-12). In an iterative training process, the impact of the chosen PS on a Random Forest image classifier is evaluated. The results are compared to LUCAS reference points that are not pre-processed, which act as a benchmark, and the classification quality is evaluated by independent sets of validation points. The classification results show that the positional correction of LUCAS points has an especially positive effect on the overall classification accuracy. On average, this improves the accuracy by 3.7%. This improvement is lowest for the most rigid sample selection approach, PS2, and highest for the benchmark data set, PS0. The highest overall accuracy is 93.1% which is achieved by using the newly developed PS3;all PS achieve overall accuracies of 80% and higher on average. While the difference in overall accuracy between the PS is likely to be influenced by the respective number of LU/LC classes, we conclude that, overall, LUCAS in-situ data is a suitable source for reference information for large scale high resolution LC mapping using Sentinel-2 imagery. Existing sample selection approaches developed for Landsat imagery can be transferred to Sentinel-2 imagery, achieving comparable semantic accuracies while increasing the spatial resolution. The resulting LC classification product that uses the newly d

关键词： Stable gene selection   Self-representation   Fuzzy classifier  

摘要： In recent years, microarray technology and gene expression profiles have been widely used to detect, predict, or classify the samples of various diseases. The presence of large genes in these profiles and the small number of samples are known challenges in this field and are widely considered in previous papers. In previous studies, other topics such as the noise of microarray data or the dependence of selected genes on samples have been less considered. Therefore, we have tried to address these two issues by using a fuzzy classifier and stability index of selected genes, respectively. The proposed method is based on the regression function between the genes and class labels which is determined by the self-representing method. This regression function is determined individually for each class of the database. To minimize the effect of noise in microarray data, a fuzzy classifier is applied in the proposed model. Four databases of gene expression profiles are examined in this article, and the results indicate that the proposed model has a relative advantage over the previous methods.

关键词： Autism spectrum disorder   fNIRS   Time series   Classification   Machine learning   Sample entropy  

摘要： Objectives To assess the possibility of distinguishing autism spectrum disorder (ASD) based on the characteristic of spontaneous hemodynamic fluctuations and to explore the location of abnormality in the brain. Methods Using the sample entropy (SampEn) of functional near-infrared spectroscopy (fNIRS) from bilateral inferior frontal gyrus (IFG) and temporal cortex (TC) on 25 children with ASD and 22 typical development (TD) children, the pattern of mind-wandering was assessed. With the SampEn as feature variables, a machine learning classifier was applied to mark ASD and locate the abnormal area in the brain. Results The SampEn was generally lower for ASD than TD, indicating the fNIRS series from ASD was unstable, had low fluctuation, and high self-similarity. The classification between ASD and TD could reach 97.6% in accuracy. Conclusions The SampEn of fNIRS could accurately distinguish ASD. The abnormality in terms of the SampEn occurs more frequently in IFG than TC, and more frequently in the left than in the right hemisphere. Significance The results of this study may help to understand the cortical mechanism of ASD and provide a fNIRS-based diagnosis for ASD.