浅析乡镇煤矿企业的财务管理

随着乡镇煤矿企业的发展,改革的不断深化,乡镇煤矿的财务管理问题将显得更加突出,笔者提出了解决办法:一是建立一支高素质的队伍;二是建立健全管理监督机制;三是建立健全财务管理制度;四是加强企业管理。     

对流层风廓线仪探测资料比对分析

本文利用对流层风廓线仪和二次测风雷达的测风实验数据,对其数据处理、统计、比对和分析。结果表明,在低空和高空,二者探测存在一定差值,中间高度具有较好的一致性。     

科技评估标准:国外的经验与启示

科技评估作为一种有效的管理手段,可以优化资源配置,调整研究机构设置及学科布局,提高管理水平与效率,其本身的质量和科学性成为各方关注的焦点。许多拥有悠久科技评估历史的国家,在其科技评估实践中,普遍建立了适合本国的科技评估标准,确保本国科技评估活动的质量。中国科技评估尚处于初始阶段,仍有许多不完善之处。本文在分析国外科技评估标准和特点,总结中国科技评估标准存在问题的基础上,指出科技评估标准在中国运用的可能性,并结合发达国家科技评估标准特点,尝试建立适合中国的评估标准,为中国科技评估工作提供有益的参考。     

运筹学发展的回顾与展望

运筹学是20世纪三四十年代发展起来的一门新兴交叉学科,它主要研究如何应用数学和计算的理论与方法对社会系统和工程系统做出最优或满意的决策。本文概述了运筹学的主要特征和方法,简述了运筹学的发展历程,综述了运筹学几个主要分支的发展状况,介绍了运筹学中十几个有代表性的难题,展望了运筹学未来发展的方向。     

话题讨论与高职学生英语写作能力的培养

高职学生英语写作的最大困难是词汇量少,逻辑思维混乱,语篇结构不合理。基于话题讨论对学生口语能力提高的促进作用,本文探讨如何将话题讨论与高职学生英语写作能力的培养相结合,发现其中的正态相关性。     

某型飞机激励器系统的研究

本文是以激励器系统的研制为基础,研究一种飞机无线电通信导航系统自动化测试技术,同时该激励器系统能够通过429总线板卡模拟通讯导航机载设备的429信号输出来参加航电系统的综合试验,完成对航电系统BCMP控制指令的应答及数据的仿真。     

企业产品创新平台的构建及其转换

创新是当前的焦点，企业围绕创新的关键和落脚点是产品创新的问题，企业产品创新在于企业产品创新平台的构建及转换。因此本文在有关产品创新平台研究成果的基础上，提出了企业产品创新平台内容体系的架构，并对产品创新平台的转换及其识别进行探索性研究，为企业创新及决策提供服务。     

网络舆情管控系统优化研究

当前,我国地方政府在竭力建设数字化政府,但现有的网络舆情管控系统还不完善。通过文献调研、专家访谈,设计问卷,应用系统工程相关理论绘制网络舆情管控系统的因果回路图,找出网络舆情管控的重点,并据此提出优化网络舆情管控系统的方法。研究成果可为相关职能部门科学管控网络舆情提供思路。     

Ecological principle meets cancer treatment: treating children with acute myeloid leukemia with low-dose chemotherapy

Standard chemotherapy regimens for remission induction of pediatric acute myeloid leukemia (AML) are associated with significant morbidity and mortality. We performed a cohort study to determine the impact of reducing the intensity of remission induction chemotherapy on the outcomes of selected children with AML treated with a low-dose induction regimen plus granulocyte colony stimulating factor (G-CSF) (low-dose chemotherapy (LDC)/G-CSF). Complete response (CR) after two induction courses was attained in 87.0% (40/46) of patients receiving LDC/G-CSF. Post-remission therapy was offered to all patients, and included standard consolidation and/or stem cell transplantation. During the study period, an additional 94 consecutive children with AML treated with standard chemotherapy (SDC) for induction (80/94 (85.1%) of the patients attained CR after induction II, P = 0.953) and post-remission. In this non-randomized study, there were no significant differences in 4-year event-free (67.4 vs. 70.7%; P = 0.99) and overall (70.3 vs. 74.6%, P = 0.69) survival in the LDC/G-CSF and SDC cohorts, respectively. After the first course of induction, recovery of white blood cell (WBC) and platelet counts were significantly faster in patients receiving LDC/G-CSF than in those receiving SDC (11.5 vs. 18.5 d for WBCs (P < 0.001); 15.5 vs. 22.0 d for platelets (P < 0.001)). To examine the quality of molecular response, targeted deep sequencing was performed. Of 137 mutations detected at diagnosis in 20 children who attained hematological CR after two courses of LDC/G-CSF (n = 9) or SDC (n = 11), all of the mutations were below the reference value (variant allelic frequency <2.5%) after two courses, irrespective of the treatment group. In conclusion, children with AML receiving LDC/G-CSF appear to have similar outcomes and mutation clearance levels, but significantly lower toxicity than those receiving SDC. Thus, LDC/G-CSF should be further evaluated as an effective alternative to remission induction in pediatric AML.     

Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis

Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.