Stochastic free vibration analyses of composite shallow doubly curved shells – A Kriging model approach

This paper presents the Kriging model approach for stochastic free vibration analysis of composite shallow doubly curved shells. The finite element formulation is carried out considering rotary inertia and transverse shear deformation based on Mindlin’s theory. The stochastic natural frequencies are expressed in terms of Kriging surrogate models. The influence of random variation of different input parameters on the output natural frequencies is addressed. The sampling size and computational cost is reduced by employing the present method compared to direct Monte Carlo simulation. The convergence studies and error analysis are carried out to ensure the accuracy of present approach. The stochastic mode shapes and frequency response function are also depicted for a typical laminate configuration. Statistical analysis is presented to illustrate the results using Kriging model and its performance.     

Digital Microfluidics for Manipulation and Analysis of a Single Cell

The basic structural and functional unit of a living organism is a single cell. To understand the variability and to improve the biomedical requirement of a single cell, its analysis has become a key technique in biological and biomedical research. With a physical boundary of microchannels and microstructures, single cells are efficiently captured and analyzed, whereas electric forces sort and position single cells. Various microfluidic techniques have been exploited to manipulate single cells through hydrodynamic and electric forces. Digital microfluidics (DMF), the manipulation of individual droplets holding minute reagents and cells of interest by electric forces, has received more attention recently. Because of ease of fabrication, compactness and prospective automation, DMF has become a powerful approach for biological application. We review recent developments of various microfluidic chips for analysis of a single cell and for efficient genetic screening. In addition, perspectives to develop analysis of single cells based on DMF and emerging functionality with high throughput are discussed.     

A new numerical algorithm to check the observability and bad-data processing ability of the metering scheme

This paper provides a unified approach for the optimization of measurements placements employed for power system online monitoring through state estimation. The proposed methodology, which can be suitable for the mixed measure system, preserves state estimation observability and bad-data processing capability by employing numerical algorithms for observability checking, critical measurements and critical couple identification. First, node injection radix measurements and measurement categories are defined. According to the above definitions, the coefficient matrix can be solved. The analysis on the column vectors of the coefficient matrix can determine each measurement classification. Furthermore, the numbers of each measurement class contains can determine bad-data processing capability. The observability can be checked by the type number of measurements. The proposed method is illustrated with the IEEE39-bus system and the IEEE118-bus system. Results from the case studies are presented to demonstrate that the approach adequately fulfills the desired properties related to observability, bad-data processing, cost, and robustness.     

A GIS plug-in for Bayesian belief networks: Towards a transparent software framework to assess and visualise uncertainties in ecosystem service mapping

The complexity and spatial heterogeneity of ecosystem processes driving ecosystem service delivery require spatially explicit models that take into account the different parameters affecting those processes. Current attempts to model ecosystem service delivery on a broad, regional scale often depend on indicator-based approaches that are generally not able to fully capture the complexity of ecosystem processes. Moreover, they do not allow quantification of uncertainty on their predictions. In this paper, we discuss a QGIS plug-in which promotes the use of Bayesian belief networks for regional modelling and mapping of ecosystem service delivery and associated uncertainties. Different types of specific Bayesian belief network output maps, delivered by the plug-in, are discussed and their decision support capacities are evaluated. This plug-in, used in combination with firmly developed Bayesian belief networks, has the potential to add value to current spatial ecosystem service accounting methods. The plug-in can also be used in other research domains dealing with spatial data and uncertainty.     

Quality enhancement of the abundant under‐valued crustacean,lobster krill (Munida spp.), during its chilled storage

Lobster krill (Munida genus) represents an under‐valued crustacean frequently caught on European fishing banks. In this work, its sensory, microbiological and biochemical qualities were evaluated during chilled storage. Additionally, the effects of a prestorage antimelanosic treatment consisting of soaking in sodium metabisulphite (SMB) solutions at two different concentrations (0.25% and 0.75%) were also studied. SMB prestorage treatment provided lobster specimens that still exhibited acceptable sensory quality after 10 days of storage, while control specimens were unacceptable at that time. SMB treatment also resulted in a significant (P < 0.05) inhibition of microbial growth, mainly of Enterobacteriaceae, psychrotrophes and proteolytic bacteria. Low lipid oxidation levels were observed for all batches; however, a significantly higher (P < 0.05) retention of polyunsaturated fatty acids was found in SMB‐treated lobster, especially in the 0.75% SMB batch. The results presented here open the way to the potential commercialisation of currently under‐utilised lobster krill as a chilled product.     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

无线电综合测试仪校准装置的研究及其不确定度分析

This calibration device of radio comprehensive tester is studied and established in this paper. It solves the verification and calibration of a large number of general-purpose radio comprehensive testers. The calibration device consists of four calibration sub- systems and calibration software system, including RF generator, audio generator, audio analyzer and RF analyzer. It also researches on the configuration of standard equipment and analyzes the uncertainty of the calibration devices, ensuring accurate and reliable val- ue transfer.     

Economic Indicators

Currency exchanges rates – updated monthly. Economic indicators of industrial production. Countries included: France, Germany, Italy, the United Kingdom, the United States, Canada, Japan (Total G-7), Eurozone and Total EU-28 (Total OECD). Updated monthly. Economic indicators of car registration in the United States, Canada, France, Germany, Italy, Poland, Spain, Turkey, the United Kingdom, Japan, Republic of Korea and Other OECD (Total OECD-30). Updated monthly. Monthly averages of crude steel production in thousand metric tons for the United States, Canada, Brazil, Mexico, Austria, Belgium, Czech Republic, France, Germany, Italy, Netherlands, Poland, Spain, Sweden, Turkey, the United Kingdom, Russia, Ukraine, South Africa, Iran, China, India, Japan, Republic of Korea, Taiwan and Australia.     

飞机撞击下高温气冷堆乏燃料厂房损伤研究

本文针对典型高温气冷堆乏燃料厂房在双发商用飞机撞击载荷下的响应及结构完整性开展研究，并探讨结构特性对撞击损伤的影响。对乏燃料厂房及飞机分别建立有限元模型，通过弹体-目标相互作用分析模拟了飞机撞击过程，综合IAEA与NRC的评价准则对乏燃料厂房在飞机撞击下的损伤程度进行评估。数值结果表明：厂房上对应于机身及发动机的撞击位置发生可接受的局部损伤；乏燃料贮存井墙体对于提高构筑物抗飞机撞击能力有重要作用。此外，构筑物外形对损伤有很大影响，圆柱形壳体的抗飞机撞击能力显著强于方形厂房，是核电厂厂房设计的优化方向之一。     

Trajectories of knowledge economy in SSA and MENA countries

In the first critical assessment of knowledge economy dynamic paths in Africa and the Middle East, but for a few exceptions, we find overwhelming support for diminishing cross-country disparities in knowledge-based economy dimensions. The paper employs all the four components of the World Bank's Knowledge Economy Index (KEI): economic incentives, innovation, education, and information infrastructure. The main finding suggests that sub-Saharan African (SSA) and the Middle East and North African (MENA) countries with low levels of KE dynamics and catching-up their counterparts of higher KE levels. We provide the speeds of integration and time necessary to achieve full (100%) integration. Policy implications are also discussed.