实物地质资料展览展示研究——以国土资源实物地质资料中心为例

为了让国家馆藏实物地质资料资源“看得见、可获取、用得起、盘得活”,服务于拥有不同需求的专业人员和社会公众用户群体,在展览展示内容、展示方法和结构上有所创新,经过深入、系统地科学研究,理清了实物地质资料展览展示的特色,准确把握和定位了展览大纲,使实物展品、辅助展品、展示方式与展厅所在建筑物融为一体,全面科学地落实和确定了实物地质资料展品和布展方案,创新了实物地质资料展览展示技术方法,提出了以国家馆藏实物地质资料为基础的典型实物展示与多媒体全面展示相结合、模块化展览设计的技术思路,为实物地质资料展览展示的进一步研究提供了技术储备。     

数字花卉植物的全息可视化与交互实现

花卉植物形态、结构与生长过程的数字化和可视化是现代林业科研的重要研究内 容，数字花卉植物在科普、教育、展示等方面都有着广泛的应用与需求。全息影像技术是一种 新兴的前沿技术，其原理源自佩珀尔幻象，通常用于生成立体感强的影像，实现数字幻象与真 实世界的融合，拥有良好的观赏性，用户接受度较高。将全息影像技术与数字花卉植物相关研 究进行结合，可以加强数字花卉植物的可视化效果，从而实现更好的科普、教育、展示效果。 因此在现有的研究基础之上，考虑实现数字花卉植物的全息可视化，并在可视基础上增加了用 户交互实现，设计出一套兼具展示性和交互性的流程，力求总结出一种具有完整可行性的方案， 从而对数字花卉植物的新型表现形式进行深入研究，以期对数字植物研究与应用的进一步发展 提供参考。     

An Advanced Analysis of Cloud Computing Concepts Based on the Computer Science Ontology

Our primary research hypothesis stands on a simple idea: The evolution of top-rated publications on a particular theme depends heavily on the progress and maturity of related topics. And this even when there are no clear relations or some concepts appear to cease to exist and leave place for newer ones starting many years ago. We implemented our model based on Computer Science Ontology (CSO) and analyzed 44 years of publications. Then we derived the most important concepts related to Cloud Computing (CC) from the scientific collection offered by Clarivate Analytics. Our methodology includes data extraction using advanced web crawling techniques, data preparation, statistical data analysis, and graphical representations. We obtained related concepts after aggregating the scores using the Jaccard coefficient and CSO Ontology. Our article reveals the contribution of Cloud Computing topics in research papers in leading scientific journals and the relationships between the field of Cloud Computing and the interdependent subdivisions identified in the broader framework of Computer Science.     

Identification of potential technologies for 1,4-Butanediol production using prospecting methodology

1,4-Butanediol (1,4-BDO) is a four-carbon diol used for industrial applications such as organic solvents, and the production of adhesives, fibers and polyurethanes. 1,4-BDO currently is produced through several petrochemical routes: hydrogenation of maleic anhydride, isomerization of propylene oxide, acetoxylation of butadiene, and the reaction between formaldehyde and acetylene. The current trends in 1,4-BDO production involve the utilization of renewable sources such as biomass. In this context, the present study aimed to identify promising technologies of 1,4-BDO production through prospecting methodology based on the analyses of patents and scientific article, describing the most relevant aspects of those emerging technologies. An increasing amount of 1,4-BDO production focused on biotechnological routes has been reported, with the US heavily involved in the development of new technologies. This study tracked three promising technologies which have potential for application in a biorefinery context because those processes involve (i) production of 1,4-BDO from sugars, classified herein as the biotechnological route; (ii) production of intermediates from sugar fermentation followed by catalytic conversion into 1,4-BDO, classified herein as the hybrid route, and (iii) furan/furfural conversion into 1,4-BDO. © 2020 Society of Chemical Industry     

树立科学的发展观,大力推进皮革工业环境建设

文章将对中国皮革工业的发展历程与现在基本的环境状况作出详尽的介绍与分析,提出必须以科学发展观为指导,加大力度深入探讨皮革工业的环境建设,并且积极的树立健康良好的皮革工业环境建设观念,以此来促进皮革工业健康可持续的发展。     

Applying Feature-Weighted Gradient Decent K-Nearest Neighbor to Select Promising Projects for Scientific Funding

Due to its outstanding ability in processing large quantity and high-dimensional data, machine learning models have been used in many cases, such as pattern recognition, classification, spam filtering, data mining and forecasting. As an outstanding machine learning algorithm, K-Nearest Neighbor (KNN) has been widely used in different situations, yet in selecting qualified applicants for winning a funding is almost new. The major problem lies in how to accurately determine the importance of attributes. In this paper, we propose a Feature-weighted Gradient Decent K-Nearest Neighbor (FGDKNN) method to classify funding applicants in to two types: approved ones or not approved ones. The FGDKNN is based on a gradient decent learning algorithm to update weight. It updatesthe weight of labels by minimizing error ratio iteratively, so that the importance of attributes can be described better. We investigate the performance of FGDKNN with Beijing Innofund. The results show that FGDKNN performs about 23%, 20%, 18%, 15% better than KNN, SVM, DT and ANN, respectively. Moreover, the FGDKNN has fast convergence time under different training scales, and has good performance under different settings.     

基于VSE的大南湖一矿通风系统现状仿真与评估

为了掌握大南湖一矿通风系统现状,科学评估其安全性、稳定可靠性及合理性,对全矿进行了通风参数精准普查,基于VSE软件对通风系统运行状况进行了仿真与评估。结果表明大南湖一矿通风系统存在部分巷道微风、回风大巷风速偏高、井下通风设施较多、部分角联分支易发生风流转向、矿井通风阻力分布不合理等问题,为系统的优化改造指明了方向。     

办好《铀矿冶》,为铀矿冶事业发展作出新贡献

回顾了《铀矿冶》不平凡的20年历程，结合核工业北京化工冶金研究院取得的科研成果，评价了《铀矿冶》在我国铀矿科技和生产中所起的重要作用。     

关于矿业城市的几个问题——在第六届中国矿业城市发展论坛发言

矿业城市在发展的道路上面临着困难,同时也面临新的发展机遇。矿业城市的兴衰关系区域经济协调发展和国家建设全局的大事。所以,要用科学发展观来指导矿业城市振兴,促进矿业城市转型,提高矿业城市持续发展的能力。为此,建议国家把振兴矿业城市与开发大西北、振兴东北老工业基地一样作为一项重大战略性任务来做。     

基于保水采煤理念的地质环境承载力研究

我国西部生态脆弱区采煤引起的水资源破坏和地质环境变化,已经严重制约了矿区经济、社会、环境的可持续发展。通过梳理保水采煤、煤矿区地质环境承载力的研究进展,结合目前西部富煤缺水地区的地质环境现状,提出了基于保水采煤理念的地质环境承载力内涵及基本研究思路。分析认为:基于保水采煤理念的地质环境承载力研究,应将保水采煤理念与地质环境承载力理论有机融合,系统分析"采煤活动—水资源破坏—地质环境效应"作用机理,从寻求煤炭开采、水资源保护、地表生态环境保护3者之间的最优解中阐述内涵。地质环境承载力评价方法研究,可采用属性分析、生态需求分析、数值分析、突变理论等方法,重点研究评价指标的量化方法、综合评价方法、承载能力及承载状态等级划分方法。对于承载能力的计算,可采用模拟实验,取得典型煤矿区承载能力下地质环境与开采强度临界值的关系数据,进而提取出控制和减轻煤矿区地质环境问题的保水采煤技术方法,确定合理的开采强度和规模。在承载力评价和计算的基础上,提出地质环境承载力影响因素的协同监测技术方法,并给出了承载力预警等级判定标准。所提出的基于保水采煤理念的地质环境承载力内涵及基本研究思路,以期丰富和发展保水采煤及地质环境承载力的科学内涵,为西部生态环境脆弱区煤炭资源开发与水资源保护协调发展提供理论与技术支持。