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.     

Development of Cu particles and Cu core-shell particles reinforced Al composite

Copper particles were incorporated and retained in elemental state in an aluminium matrix by friction stir processing thereby producing a non-equilibrium particulate composite. The processed Al–Cu_p composite exhibited improved strength with significantly high ductility. The composite was stable up to a temperature of more than 300°C. Thermal exposure at 350°C for more than 10 min led to diffusion of Cu atoms into the Al matrix forming a core-shell type structure in the Cu particles and thus producing an Al–Cu core-shell composite. The shell consists of multiple layers, the thickness of which was controllable.     

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.     

水下图像处理技术研究综述

随着自主式水下机器人的发展，水下探测技术成为新的研究热点。然而，吸收效应和散射效应导致水下获取的图像存在雾化和色彩偏差等缺陷。降质的水下图像在一定程度上降低了水下目标识别的准确性。为了改善水下图像质量，国内外学者对水下图像处理方法进行了深入研究。因水下图像处理方法对提升水下目标识别准确性具有良好的促进作用，故其具有重要的研究与分析价值。介绍了水下成像模型，分析了水下图像视觉质量下降的原理；根据水下物理成像模型将水下图像处理方法分为水下图像增强与水下图像复原，并分别对两类方法的研究现状进行分析与归纳；最后，总结与讨论了各类方法的优缺点，并展望了未来的发展方向。     

Semantically enriched Massive Open Online Courses (MOOCs) platform

Massive Open Online Courses (MOOCs) are becoming an essential source of information for both students and teachers. Noticeably, MOOCs have to adapt to the fast development of new technologies; they also have to satisfy the current generation of online students. The current MOOCs’ Management Systems, such as Coursera, Udacity, edX, etc., use content management platforms where content are organized in a hierarchical structure. We envision a new generation of MOOCs that support interpretability with formal semantics by using the SemanticWeb and the online social networks. Semantic technologies support more flexible information management than that offered by the current MOOCs’ platforms. Annotated information about courses, video lectures, assignments, students, teachers, etc., can be composed from heterogeneous sources, including contributions from the communities in the forum space. These annotations, combined with legacy data, build foundations for more efficient information discovery in MOOCs’ platforms. In this article we review various Collaborative Semantic Filtering technologies for building Semantic MOOCs’ management system, then, we present a prototype of a semantic middle-sized platform implemented at Western Kentucky University that answers these aforementioned requirements.     

Dietary advanced glycation end‐products: Perspectives linking food processing with health implications

Dietary advanced glycation end products (dAGEs) are complex and heterogeneous compounds derived from nonenzymatic glycation reactions during industrial processing and home cooking. There is mounting evidence showing that dAGEs are closely associated with various chronic diseases, where the absorbed dAGEs fuel the biological AGEs pool to exhibit noxious effects on human health. Currently, due to the uncertain bioavailability and rapid renal clearance of dAGEs, the relationship between dAGEs and biological AGEs remains debatable. In this review, we provide the most updated information on dAGEs including their generation in processed foods, analytical and characterization techniques, metabolic fates, interaction with AGE receptors, implications on human health and reducing strategies. Available evidence demonstrating a relevance between dAGEs and food allergy is also included. AGEs are ubiquitous in foods and their contents largely depend on the reactivity of carbonyl and amino groups, along with surrounding condition mainly pH and heating procedures. Once being digested and absorbed into the circulation, two separate pathways can be involved in the deleterious effects of dAGEs: an AGE receptor‐dependent way to stimulate cell signals, and an AGE receptor‐independent way to dysregulate proteins via forming complexes. Inhibition of AGEs formation during food processing and reduction in the diet are two potent approaches to restrict health‐hazardous dAGEs. To elucidate the biological role of dAGEs toward human health, the following significant perspectives are raised: molecular size and complexity of dAGEs; interactions between unabsorbed dAGEs and gut microbiota; and roles played by concomitant compounds in the heat‐processed foods.     

Iterative equalization combining algorithm in intersymbol interference channel

To realize joint optimization of spatial diversity and equalization combining in the intersymbol interference (ISI) channel, an iterative equalization combining algorithm is proposed. The proposed algorithm uses the coefficients of Turbo equalization to calculate the combination weights without estimating the signal to noise ratio in each diversity branch. The equalized symbols from different diversity branches are combined, and the extrinsic information output from the decoder is fed back to the equalizers, so as to exchange soft information between the equalizers and the decoder. The performance of the proposed algorithm is analyzed using the extrinsic information transfer (EXIT) chart and verified by simulations. Results show that our approach fully exploits time domain information from the multipath channel and spatial domain information from multi receiving antennas, which efficiently improve the performance of the receiver in the severe ISI channel.     

Hetero-assembly of colloidal particles in concentrated non-aqueous suspensions by polymer dispersant design

Although many colloidal assembling systems have been reported, most systems suffer from severe aggregation under high solid concentrations, which can often be observed in typical hetero-aggregation system. In this study, we created a hetero-assembly system using concentrated (~50 vol%) suspensions by mixing large SiO₂ particles modified with polyacrylic acid partially complexed with oleylamine (PAA-OAm) and small SiO₂ particles modified with polyethyleneimine partially complexed with oleic acid (PEI-OA) in a non-aqueous solvent. We demonstrated that hetero-assembly is driven by the interactions between the uncomplexed carboxyl/amine groups of the PAA/PEI present on the particles, while severe aggregation is simultaneously prevented by the steric repulsions of the aliphatic oleyl chains. Comparison of the cross sections of the in-situ solidified hetero-assembled suspensions with those of ideally assembled structures which were reproduced by a simulation considering the statistical distribution of particles strongly supported successful particle assembling via the proposed approach. The results revealed that the OA content in the PEI-OA complex was the dominant factor that controlled the dispersion and assembling state of the binary particles. The significance of this study is that our findings will provide a class of colloidal dispersion state which binary particles were assembled in a high solid content suspension without forming strong aggregates.