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.     

Analysis of e-business models in real estate

We use Canvas Application Framework to analyze e-business models of Realestate.yahoo.com, Yahoo.com, Redfin.com, Realtor.com, Trulia.com, Zillow.com, and Craigslist.com. We investigate the similarities and differences among e-business models and how e-business model differentiation affects company success. We looked at which business model components, such as distribution channels, business partnerships, attracting customers, revenue streams, etc., are important. We further investigate whether the web-advertising, brokerage, virtual value-chain, or the diversified internet business model is used by each company, and which ones are more successful. We discover similarities and differences among e-business models. Differences include the use of revenue streams for generating revenue, the extent of partnerships, and the role they play in a company’s success.     

Polyvinylidene fluoride nanofibers obtained by electrospinning and blowspinning: Electrospinning enhances the piezoelectric β-phase – myth or reality?

Considerable effort has been devoted to improving the properties of PVDF (polyvinylidene fluoride), arguably the most technologically important piezoelectric polymer. Electrospinning has been found to be a particularly effective method of producing PVDF nanofibers with superior piezoelectric properties due to the resulting exceptionally high fraction of the piezoelectrically active crystalline β-phase. It is typically assumed that the high external electric fields applied during electrospinning enhance the formation of this β-phase, with the confused literature offering various unsatisfactory mechanistic explanations. However, by comparing PVDF nanofibers produced by two different processes (electrospinning and blowspinning), we show that the electric field is entirely unnecessary; indeed, the crystallization dynamics are principally driven by the applied mechanical stress, as evidenced by structurally identical 200 nm diameter PVDF fibers produced with and without external electric fields.     

Large-scale linked data integration using probabilistic reasoning and crowdsourcing

We tackle the problems of semiautomatically matching linked data sets and of linking large collections of Web pages to linked data. Our system, ZenCrowd, (1) uses a three-stage blocking technique in order to obtain the best possible instance matches while minimizing both computational complexity and latency, and (2) identifies entities from natural language text using state-of-the-art techniques and automatically connects them to the linked open data cloud. First, we use structured inverted indices to quickly find potential candidate results from entities that have been indexed in our system. Our system then analyzes the candidate matches and refines them whenever deemed necessary using computationally more expensive queries on a graph database. Finally, we resort to human computation by dynamically generating crowdsourcing tasks in case the algorithmic components fail to come up with convincing results. We integrate all results from the inverted indices, from the graph database and from the crowd using a probabilistic framework in order to make sensible decisions about candidate matches and to identify unreliable human workers. In the following, we give an overview of the architecture of our system and describe in detail our novel three-stage blocking technique and our probabilistic decision framework. We also report on a series of experimental results on a standard data set, showing that our system can achieve a 95 % average accuracy on instance matching (as compared to the initial 88 % average accuracy of the purely automatic baseline) while drastically limiting the amount of work performed by the crowd. The experimental evaluation of our system on the entity linking task shows an average relative improvement of 14 % over our best automatic approach.     

Global stabilization of a class of delay discrete-time nonlinear systems via state and output feedback

This paper deals with stabilization of a class of delay discrete-time nonlinear systems through state and output feedback. We provide an explicit bounded state feedback law as an extension of the Jurdjevic-Quinn method, from nonlinear theory, to this class of systems. Next, we present a useful and systematic approach to design an observer for the same class of systems. Then, we show how the global stabilization problem via dynamic output feedback can be solved by using the two previous results. Finally, numerical examples are given to illustrate the effectiveness of the proposed design method.     

Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment

In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.     

Simulation-based investigations on the drape behavior of 3D woven fabrics made of commingled yarns

Composites based on 3D woven fabrics offer excellent mechanical properties due to the nearly non-crimp reinforcement fibers. Additionally, a reduced number of process steps results from the 3D nature of the reinforcement. Their in-plane and out-of-plane behaviors are purposefully adjustable to the expected loading conditions. A previously introduced modelling approach is extended to the simulation of 3D woven fabrics. The tensile, shear and bending behaviors of the fabric are considered in a material formulation for large deformations using shell elements. The model is successfully validated and parameter studies show the significant influence of shear and bending parameters on forming results. Furthermore, it is shown that also the forming process parameters have a significant influence on the draping results. It is concluded that the shearing and wrinkling can be reduced to a minimum with adapted material and process parameters.     

Detection of pathogenic Escherichia coli strains in groundwater in the Yaoundé region (Cameroon,Central Africa)

This study was aimed at assessing the prevalence of pathogenic Escherichia coli strains, relative to the total count of E. coli, faecal coliforms and other heterotrophic mesophilic aerobic bacteria (HMAB) isolated in groundwater in the equatorial region of Cameroon (Central Africa). Bacteria were isolated using standard methods. Pathogenic E. coli strains were then identified using haemagglutination and antisera tests. The maximum abundance of HMAB, faecal coliforms and E. coli strains were 4.9 × 10⁶, 5.6 × 10³ and 1 × 10³ colony‐forming units (CFU)/100 mL, respectively. The count of pathogenic E. coli strains reached 3 CFU/100 mL. The counts of commensal and pathogenic E. coli strains underwent temporal and spatial fluctuations. In 21% of sampling sites, the abundance of faecal coliforms was significantly correlated to that of E. coli (P < 0.05). However, the isolated bacterial count was not significantly correlated to that of the pathogenic E. coli strains (P > 0.05). The bacteria abundance dynamics may be impacted by many interacting factors.     

Characteristics of wet‐spun and thermally treated poly acrylonitrile fibers

The performance of carbon fibers depends on the quality of the precursor and the conditions of the thermal treatment. In detail, for a PAN precursor fiber the viscosity of a spinning dope and the draw ratio during the spinning process needs to be considered. Through wet spinning, different types of PAN precursor fibers with defined spinning parameters, including solid content, solvent content in a bath, and especially draw ratio resulting in defined cross section diameters, were fabricated and analyzed with tensile tests, density investigations, SEM, TGA‐MS, FTIR, and XRD. The results show that the mechanical properties of the fibers correlate to crystallinity. The cross section diameter is strongly related to the morphology of the fibers after thermal treatment. By extending the postdrawing of PAN fibers high tenacities were obtained at the cost of the cross section shape. In addition, TGA measurements reveal trapped residues of the wet spinning process as well as show several chemical reactions takes place at the same time at different temperatures. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43698.