模具设计与制造专业群虚拟仿真实训基地建设路径研究

针对模具设计与制造专业群实训教学开展过程中存在的问题,开展专业群虚拟仿真实训基地的建设,通过精密模具智能制造生产线仿真实训工厂、模具数字化设计及智能成型单元综合应用平台、开放共享课程资源等3个方面内容的建设,引入行业新技术、新工艺、新标准,确保虚拟仿真教学资源与学生职业能力培养相一致。基地建设依托智能制造新技术,以培养高层次复合型技术技能人才为主线,满足实训教学为核心,多方共享为目标,助力区域经济发展,服务"鲁班工坊"国际化建设,最终实现虚拟仿真实训基地区域示范性效果。     

Enhancement of a global lake and reservoir database to aid climate studies and resource monitoring utilizing satellite radar altimetry

Here, we describe the expansion and enhancement of a large (surface area ≥100 km²) lake and reservoir database (1.GREALD). These efforts have also resulted in the spin-off of two additional databases, one containing lakes and reservoirs (2.GREALD, area 10–99 km²), and the other containing ephemeral lakes (3.GELD, area ≥100 km²). These databases are unique sources for projects that utilize satellite radar altimeter data to monitor surface water levels. While 1.GREALD aims to be a complete catalog, 2.GREALD focuses on reservoirs in response to applied sciences programs that monitor water and energy resources. The creation of 3.GELD has climate change objectives as well as water resources and ecosystem conservation applications. The recording of information pertaining to the potential overpasses (waterbody crossings) of the current and archive satellite altimeters is a primary objective as is the need to highlight any form of controlled water level variation. The permanent water databases now contain 6282 entries, half experience some form of anthropogenic influence and ～430 have been identified as potential climatically sensitive terminal lakes. The revised integral surface area distribution is a power law with exponent ?1.016. Statistics reveal that with altimetric repeat visit times of 10-day to monthly, at least 80% of the permanent water bodies (≥10 km²) have been overflown at some period since the 1990s. Current information on water use and reservoir formation date show that the primary use of the reservoir class is hydroelectric power, and that China, Brazil, India, Turkey, and Vietnam dominate the dam building in recent decades.     

Cognitive and affective processes for learning science in immersive virtual reality

As immersive virtual reality (IVR) systems proliferate in classrooms, it is important to understand how they affect learning outcomes and the underlying affective and cognitive processes that may cause these outcomes. Proponents argue that IVR could improve learning by increasing positive affective and cognitive processing, thereby supporting improved performance on tests of learning outcome, whereas opponents of IVR contend that it could hurt learning by increasing distraction, thereby disrupting cognitive learning processes and leading to poorer learning outcomes. In a media comparison study, students viewed a biology lesson either as an interactive animated journey in IVR or as a slideshow on a desktop monitor. Those who viewed the IVR lesson performed significantly worse on transfer tests, reported higher emotional arousal, reported more extraneous cognitive load and showed less engagement based on EEG measures than those who viewed the slideshow lesson, with or without practice questions added to the lessons. Mediational analyses showed that the lower retention scores for the IVR lesson were related to an increase in self-reported extraneous cognitive load and emotional arousal. These results support the notion that immersive environments create high affective and cognitive distraction, which leads to poorer learning outcomes than desktop environments.     

公民水素养评价表征因素模型构建与分析

针对我国水资源紧缺、水生态损害、水环境污染等相关水问题,以提升全民爱水惜水节水护水的意识和能力为目标,从水素养概念出发,通过扎根理论质性研究方法对公民水素养评价的表征因素进行筛选和提炼,最终得出包含水知识、水态度、水行为为主范畴的10个表征因素并运用解释结构模型(ISM)深入剖析各因素之间的内在联系。在此基础上建立了3级多层次递阶结构模型,进一步精确描述系统各要素两两之间的直接关系。结果表明:公民水素养评价的表征因素自上而下形成了表象层、中间层和根源层等3个层级,可直观、清晰地反映表征因素之间的递阶、层级关系;水态度在各表征因素中具有调节作用以及各表征因素之间存在内在逻辑关系。研究成果为提高公民水素养水平提供一定的科学依据和政策参考。     

蒙古国稀土矿地质特征及资源潜力

蒙古国蕴含丰富的稀土矿资源。本文通过对蒙古国成矿地质背景及典型稀土矿床进行研究,将蒙古国稀土矿床划分为碱性花岗岩型、碳酸岩型、伟晶岩型、离子吸附型、稀土砂矿型等5种成因类型,其中碱性花岗岩型和碳酸岩型是蒙古国稀土矿的主要成因类型;将蒙古国划分为3个稀土成矿省和7个稀土成矿带,并结合蒙古国成矿地质背景及矿产开发情况,初步划分出图瓦-库苏古尔稀土矿潜力区、肯特稀土矿潜力区和达兰扎达嘎德稀土矿潜力区共3处稀土矿勘查开发潜力区。     

岩溶城市(贵阳市)水资源可持续利用评价研究

为研究贵阳市水资源可持续利用评价问题,基于DPSIR模型构建贵阳市水资源可持续利用评价指标体系,运用熵权法与综合指数模型测算2001~2015年贵阳市水资源可持续利用综合指数,并以障碍度模型对影响水资源可持续利用的障碍因子进行诊断。结果表明,2001~2015年,除压力指数外,驱动力、状态、影响、响应和综合指数均呈波动上升的趋势;可持续利用类型可划分为失调(2001~2006年)和协调(2007~2013年)两个阶段;人均GDP、万元GDP耗水量、森林覆盖率、三产增加值占GDP比重、生活污水处理率为5大影响贵阳市水资源可持续利用的重要因素。研究结果为贵阳市水资源可持续利用提供指导。     

基于联系数的安徽省水资源承载力评价及空间差异诊断分析

为定量评价安徽省水资源承载力的整体状况,诊断分析导致水资源承载力空间差异的主要因素,采用集对分析理论中的联系数方法诊断评价安徽省水资源承载现状,并利用减法集对势识别导致水资源承载力空间差异的主要因素。结果表明,安徽省江南地区水资源承载力最好,皖中地区次之,淮北地区的水资源承载力最差;从子系统层面分析,江南地区水资源承载支撑力子系统最好,皖中地区次之,淮北地区最差,但淮北地区水资源承载压力最小;从指标层面诊断分析,人均水资源量是导致安徽省各市水资源承载状况存在空间差异的最主要因素。研究成果可为安徽省水资源合理配置与调控提供技术支持。     

氨浸-草酸盐沉淀法回收废弃线路板中金属铜的工艺研究

采用氨浸-草酸盐沉淀法回收废弃线路板中的金属铜,考察了氨水浓度、NH₄Cl溶液浓度、液固比、反应温度和时间对铜浸出率的影响。结果表明,最佳工艺条件为:氨水浓度10%、NH₄Cl溶液浓度1.5 mol/L、液固比10∶1、反应温度60 ℃、反应时间3 h,在此工艺条件下,铜浸出率高达99.25%。在铜的富集过程中,调节溶液pH值至1.5,60 ℃下反应30 min,铜沉淀率达到了98.15%。     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.