基于国际化产科教融合实训平台的教学模式改革与实践

在分析当前省属工科院校教育以能力为导向培养目标完成实施过程中遇到国际化困难问题的基础上，以青岛大学材料学科为例，提出了基于国际化产科教融合实训平台的教学模式改革举措，并经过实践验证证实其切实可行。包括：充分整合校内外材料学科整体资源，建立了国际化的产科教深度融合实训平台，构建了高效的国际化产科教深度融合协同育人实训平台动态管理运行机制；以实训平台为依托实现师资队伍、教学内容、教学模式、科研等全方位的国际化改革；基于国际化产教研融合实训平台的教学模式改革的实践收到显著成效，对全国省属高等院校理工学科专业建设国际化改革的推广有较好的示范作用。     

工科学校本科生科研训练与实践——以节能减排大赛为例

Scientific research training is an essential part of undergraduate learning, which plays an important role in improving students' knowledge utilization and scientific literacy. Taking the participation process of "Energy conservation and emission reduction competition" as an example, this paper briefly introduces the undergraduate scientific research training of students majoring in polymer materials and engineering from their own perspective, and the way to combine the discipline and school characteristics to reflect the thinking of engineering students in scientific research and practical application.     

FBMC系统改进序列算法的定时同步技术

为提高滤波器组多载波（Filter Bank Multicarrier,FBMC）系统在低信噪比下的定时同步准确率和抗频偏性能,基于FBMC系统与正交频分复用（Orthogonal Frequency Division Multiple,OFDM）系统的相似性,对OFDM系统定时同步中的Park测度函数进行改进并引入到FBMC系统中。利用共轭对称结构能够提高定时精度和加权处理能够更好地抵抗频偏影响的特点设计同步序列,进行共轭对称和加权处理。仿真结果表明,基于改进序列的Park算法相较共轭对称序列的TR1算法,其信噪比提前3 dB达到了100%定时准确率,实现了低信噪比下的高精度定时同步,且具有更强的抗频偏性能;相较原Park算法,相同信噪比下,改进序列的Park算法的频偏均方差（Mean Square Error,MSE)也小于原Park算法的频偏MSE,抗频偏性能表现更优。     

电气类专业群双创人才培养的探索与实践

针对科技产业发展人才需求和专创融合教育改革中存在的问题,本文从构建双创型人才培养模式、完善实践教学体系、深化校企合作和建立特色创新创业工作室四个方面,对厦门理工学院电气类专业群创新创业人才培养进行探索与实践。实施效果表明,已取得积极成效,具有一定推广价值。     

面向应用型本科的“计算机控制系统”课程教学改革与探索

应用型本科院校以培养高素质应用型人才作为培养目标。为达到预期的培养目标,需要在专业培养方案设置、课程知识体系、课程教学模式与方法等各个环节进行有针对性的优化设计。《计算机控制系统》课程兼有深入的理论基础和面向实际应用的特点,因此在教学改革时需要把握好理论与实践的关系。针对理论教学,在优化调整讲授内容的基础上,通过引入工程案例法将核心知识点串联起来,并制作“慕课”形式的教学视频作为线下教学的补充;针对实验教学,设置更多的设计性和综合性实验并引入三级训练项目作为补充。改革后的教学内容与工程实践的结合更加紧密,教学素材与方法也更加丰富,极大地提升了学生的学习自主性,更有利于学生分析和解决实际问题能力的培养。     

研究型大学本硕博贯穿式培养探索与实践

为顺应高等教育对高素质人才培养的迫切需求,从学生的核心诉求出发,探索和实践了本科生-硕士生-博士生贯穿式培养模式,提出了卓有成效的具体措施,包括改进现有培养方案、将人才培养与高水平科学研究相融合、建设人才梯队,有效提高了本科阶段与研究生阶段培养的连贯性,初步形成了本硕博贯穿式人才培养体系,为高等学校人才培养提供借鉴和参考。     

一种运用SVM分类的多源态势数据关联方法

阐述如何利用多手段获取的战场多源目标态势数据生成战场态势一张图,进一步提高海上方向的监控能力。针对多源目标态势数据存在的时空误差、属性模糊等问题,提出一种运用支持向量机(Support Vector Machine,SVM)分类的多源态势数据关联方法。运用机器训练学习方式,获取不同误差下的多源态势关联,识别非线性特征参数,构建多源态势数据关联算法模型,有效形成目标批号和时空位置的唯一性。通过模拟真实数据进行验证,结果表明该算法实现效果较好,在多源态势融合方面具有较好的实用价值。     

基于霍尼韦尔WEBs的智能照明监控实训系统设计与实现

从建筑智能化专业楼宇自控系统工程技术课程的实训教学实际需要出发,以模拟实际智能建筑现场为背景,开发了智能照明监控实训系统。该系统使用霍尼韦尔WEBs为系统软件平台,应用霍尼韦尔Spyder DDC控制器PUB6438S作为系统的控制核心,利用人体红外感应、定时时间和光照度感应等方法进行实时控制。学生利用该实训教学系统可以更加深刻理解楼控系统软硬件开发的整个生命周期,对楼控项目设计和编程调试等应用技术进行系统学习和实训,从而提升职业院校学生的工程实践能力。     

新工科视阈下石油化工类高校应用化学专业课程体系重构

Taking a typical local and industry-orientated applied chemistry major as an example with focusing on the major changes in the form and content of the petrochemical industry, the way to reform the core curriculum system and teaching contents is presented. Based on the changing tendency of refinement of the petrochemical industry, a complete curriculum system with distinctive industry characteristics is set up according to the principles of the emerging engineering education (3E) with several reasonable suggestions. A new model for the construction of a 3E major in chemistry-related majors is suggested. It is a good reference for the construction of curriculum systems for other local and industry-orientated colleges who want to make 3E reform.     

Improving (Q)SAR predictions by examining bias in the selection of compounds for experimental testing

ABSTRACT

Existing data on structures and biological activities are limited and distributed unevenly across distinct molecular targets and chemical compounds. The question arises if these data represent an unbiased sample of the general population of chemical-biological interactions. To answer this question, we analyzed ChEMBL data for 87,583 molecules tested against 919 protein targets using supervised and unsupervised approaches. Hierarchical clustering of the Murcko frameworks generated using Chemistry Development Toolkit showed that the available data form a big diffuse cloud without apparent structure. In contrast hereto, PASS-based classifiers allowed prediction whether the compound had been tested against the particular molecular target, despite whether it was active or not. Thus, one may conclude that the selection of chemical compounds for testing against specific targets is biased, probably due to the influence of prior knowledge. We assessed the possibility to improve (Q)SAR predictions using this fact: PASS prediction of the interaction with the particular target for compounds predicted as tested against the target has significantly higher accuracy than for those predicted as untested (average ROC AUC are about 0.87 and 0.75, respectively). Thus, considering the existing bias in the data of the training set may increase the performance of virtual screening.