Regulation of the NADH supply by nuoE deletion and pncB overexpression to enhance hydrogen production in Enterobacter aerogenes

In this study, seven mutants from E. aerogenes IAM1183 wildtype were constructed via different strategies including deletion of lactate dehydrogenase, disruption of NADH dehydrogenase gene nuoE, overexpression of pncB and a combination of both to regulate of the NADH supply to enhance hydrogen production. Compared with the parental strain, the hydrogen yields of the strains IAM1183-E, IAM1183-L and IAM1183-EL increased by 23.3, 81.7 and 97.9%, respectively. When the pncB gene was overexpressed, the hydrogen yield of IAM1183/P, IAM1183-E/P, IAM1183-L/P and IAM1183-EL/P increased by 39.0, 6.5, 5.9, and 5.1% compared with the respective original knockout strains. Among them, the total hydrogen yield of strain IAM1183-EL/P with highest production efficiency was 58% higher than IAM1183. Further metabolite analysis indicated that the knockout of nuoE and ldhA, combined with the overexpression of pncB, resulted in a redistribution of the metabolic fluxes in E. aerogenes, which led to an improvement of the hydrogen yield.     

独立学院《环境监测实验》课程教学改革

从独立学院"应用型专业人才"的培养目标出发,结合我院近年来开展《环境监测实验》课程积累的经验,对本实验课程的教学内容、教学方法和课程评价体系等进行了改革。强调理论与实验内容有机结合,通过激发学生的学习兴趣,提高学生的实践应用能力,以满足社会对环境科学与工程专业应用型专业人才的需求。     

Model-based detection of critical driving situations with fuzzy logic decision making

A precise knowledge about the current driving condition is getting increasingly important for future driver assistance systems like global chassis control or collision avoidance systems for avoiding any critical driving situation. Moreover a precise knowledge about the driving situation can be used in testing, in evaluation, and for comparison of new passenger cars. A two degree of freedom model of vehicle lateral dynamics is used to derive a characteristic velocity stability indicator (CVSI). The CVSI is used to distinguish between different driving and stability conditions (i.e. understeering, oversteering, and neutralsteering). This forms the basis for a driving condition detection system with fixed thresholds. It is then extended to a detection system with fuzzy logic thresholds. The CVSI and the fuzzy systems are compared experimentally using (i) a slalom test drive on an icy road and (ii) a stationary circular test drive on a dry asphalt road.     

A unified approach to some inequalities of Ostrowski-Grüss type

Based on the Euler-Maclaurin formula in the spirit of [1], we provide a unified approach to some inequalities of Ostrowski-Grüss type, which include some existing results as special cases. Some illustrative examples are also included.     

弱导光纤的标量衍射光束特性分析

仿照罗兰圆光学系统构造罗兰球光学系统 ,在罗兰球中简化电磁波的瑞利 索末菲标量衍射积分公式 ,推导出折射率阶跃分布的弱导光纤LP模衍射场分布和空间频谱的计算公式。提出LP0 1模远场衍射分布存在类似于平面波圆孔夫琅禾费衍射艾里斑的中央亮斑概念 ,并给出中央亮斑角半宽度的计算公式。     

TCP self-clocking and bandwidth sharing

We propose a simple queueing model for TCP transfers sharing a bottleneck link and examine its behavior when the buffer at the bottleneck is large compared to the bandwidth-delay product. This model explains some behaviors of TCP that have already been observed, and predicts other behaviors that are new. We present measurements that demonstrate these behaviors in the current Internet.     

表演驱动的矢量化二维表情动画

为了方便快捷生成生动的卡通表情动画,提出将表情数据重定向到卡通形象上的算法.该算法由用户在卡通形象上标记出与表情数据相对应拓扑结构的控制点,以此控制点集合与表情数据首帧控制点集合做比对;使用得到的比例关系将后续表情数据重定向到卡通形象上,从而形成针对卡通形象的表情数据.实验结果表明,运用文中算法可以制作出较为生动形象的卡通表情动画.     

基于AROP程序包的类Landsat遥感影像配准与正射纠正试验和精度分析

遥感影像的精确配准和正射纠正是进行图像融合、变化检测、图像镶嵌、定量遥感建模、多时相和多传感器影像协同应用的基础和前提。以美国国家航空和航天管理局下设LEDAPS(Landsat Ecosystem Disturbance Adaptive Processing System)课题组开发的配准与正射纠正程序包AROP(Automated Registration and Orthorectification Package)为例,详细阐述了其配准的原理与程序设计流程,并对其配准的精度进行了分析和评价。试验表明:AROP程序包算法能够找出足够的控制点,且控制点分布较为均匀,配准误差小于0.5个像元。误差特征表现为:扫描误差明显大于航向误差,误差的结果与影像漂移、DEM、坡度存在一定的相关性,高程和坡度是影响配准精度的主要因素之一。该程序包目前能够用于对我国CBERS影像的正射校正以及波段不匹配处理,但是对HJ卫星CCD影像数据配准还有待于进一步研究。     

Application of iot medical image detection and prenatal genetic testing in obstetric clinic

The use of new techniques and methods can be significantly improved during prenatal examination and screening of fetal genetic abnormalities. Expanding the number of vectors in screening and screening for single-cell gene diseases in fetal DNA will increase the probability of success of the whole screening test.Recently, sub chromosomal abnormalities have been introduced into prenatal care. The lack of suitable tools makes it very difficult to obtain information about collection, management, and prenatal genetic testing. The purpose of this study is to improve the accessibility of nutritional information by using the advantages of advanced medical image detection and integrating the IoT (Internet of Things) and prenatal genetic testing platforms in obstetric outpatient clinics. Records are kept that the platform will allow IoT to interact with sensor practitioners and provide immediate access to medical images prior to delivery. This proposed system provides an IoT application for managing obstetric outpatient information and prenatal genetic testing requirements. The requirement for establishment is the realization of the development of an IoT platform for complete medical image detection information management with IoT input required for integration with existing medical information systems. In response, the purpose of the study is to use integrated IoT applications to help nutrition professionals, researchers, patients, and especially mothers, to enhance their collection of medical imaging and prenatal genetic testing IoT sensor based information transfer.     

Financial distress prediction using support vector machines: Ensemble vs. individual

Financial distress prediction (FDP) is of great importance to both inner and outside parts of companies. Though lots of literatures have given comprehensive analysis on single classifier FDP method, ensemble method for FDP just emerged in recent years and needs to be further studied. Support vector machine (SVM) shows promising performance in FDP when compared with other single classifier methods. The contribution of this paper is to propose a new FDP method based on SVM ensemble, whose candidate single classifiers are trained by SVM algorithms with different kernel functions on different feature subsets of one initial dataset. SVM kernels such as linear, polynomial, RBF and sigmoid, and the filter feature selection/extraction methods of stepwise multi discriminant analysis (MDA), stepwise logistic regression (logit), and principal component analysis (PCA) are applied. The algorithm for selecting SVM ensemble's base classifiers from candidate ones is designed by considering both individual performance and diversity analysis. Weighted majority voting based on base classifiers’ cross validation accuracy on training dataset is used as the combination mechanism. Experimental results indicate that SVM ensemble is significantly superior to individual SVM classifier when the number of base classifiers in SVM ensemble is properly set. Besides, it also shows that RBF SVM based on features selected by stepwise MDA is a good choice for FDP when individual SVM classifier is applied.