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.     

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.     

A clonal selection algorithm for the generalized cell formation problem considering machine reliability and alternative routings

The cell formation is the first step in the design of Cellular Manufacturing systems. It consists of grouping parts with similar processing needs into cells and identifying the set of machines needed to process these parts. The aim is to minimize the material handling costs and maximize the use of the machines. In this paper, the machine reliability and the alternative process routings are taken into account to form the production cells. The presence of these factors in addition to the production volume, operation sequence and production time makes the problem more realistic but also more complex. Most authors solve this kind of problems by mathematical programming approaches that require large amounts of computational efforts. Therefore, a modified version of the Clonal Selection Algorithm is introduced and a local search mechanism is adopted in this paper. The obtained results are compared with those of the Branch and Bound (B&B) method using LINGO software. The comparison reveals the effectiveness and the efficiency of the proposed method in terms of both solution quality and computation time required.     

A four variable refined plate theory for nonlinear cylindrical bending analysis of functionally graded plates under thermomechanical loadings

In this paper we present a new application for a four variable refined plate theory to analyse the nonlinear cylindrical bending behavior of functionally graded plates subjected to thermomechanical loadings. This recent theory is based on the assumption that the transverse displacements consist of bending and shear components in which the bending components do not contribute toward shear forces and, likewise, the shear components do not contribute toward bending moments. The theory accounts for a quadratic variation of the transverse shear strains across the thickness, and satisfies the zero traction boundary conditions on the top and bottom surfaces of the plate without using shear correction factors. The material properties are assumed to vary continuously through the thickness of the plate according to a power-law distribution of the volume fraction of the constituents. The non-linear strain-displacement relations in the von Karman sense are used to study the effect of geometric non-linearity. The solutions are achieved by minimizing the total potential energy and the results are compared to the classical and the first-order theories reported in the literature. It can be concluded that the proposed theory is accurate and simple in solving the nonlinear cylindrical bending behavior of functionally graded plates.     

Combining Digital and Traditional Visualisation Techniques in Community-Based Planning and Design

Public participation is a key component in environmental planning and design. Yet too often, architects and planners are not equipped with effective tools and visualisation techniques to generate meaningful public input. Architects and planners are increasingly turning to computer technology; computer imaging can be used to support exploration of alternatives by enabling community members to visualise opportunities and scenarios before committing to a course of action (McClure, 1997). This paper explains how a Geographic Information System (GIS) and an artist played critical roles in a participatory planning process in Chicago's Pilsen neighbourhood. The GIS provided community leaders, planners, architects and designers with an interactive visualisation of the neighbourhood context. It also provided examples of design prototypes in relation to their geographic context. The artist, on the other hand, translated neighbourhood residents' ideas into quick sketches, merging their ideas and thoughts into a shared neighbourhood vision. Both of these elements – the GIS and the human artist – provided a means for residents to visualise past, present, and future neighbourhood conditions, enabling them to have a greater voice in the design of their neighbourhood. Our findings suggest that effective visualisation may be achieved through combining traditional and computerised visualisation tools, and that effective visualisation could assist in bridging the gap between professional designers and non-designers.     

Evaluating leakages through GMB/GCL composite liners considering random hole distributions in wrinkle networks

This paper presents a study on the development of a hydraulic connectivity analysis-based approach for evaluating leakage rates through geomembrane (GMB)-geosynthetic clay liner (GCL) composite liners considering random hole distributions in a GMB wrinkle network. An algorithm for hydraulic connectivity analysis was developed to find the hydraulically connected wrinkles from a wrinkle network, and an explicitly expressed criterion is proposed to define the hydraulic connection between wrinkles under the assumption that only one of the two adjacent wrinkles is possible to be damaged. A Monte Carlo simulation was used to evaluate the probability weighted average of the total leakage through multiple randomly distributed holes considering numerous possible combinations of locations of holes. The proposed approach was applied to typical examples reported in the literature and shows that it can objectively quantify the effect of the hydraulic properties of the liner and overburden pressure on the hydraulic connectivity between wrinkles in a wrinkle network. The proposed approach also allowed assessing the effect of different probabilities of various hole distributions on the calculated leakage, which was demonstrated to be non-negligible, especially when the hole frequency is small.     

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.     

Electrodeposition of lead dioxide on Fe electrode: application to the degradation of Indigo Carmine dye

Journal of Applied Electrochemistry - The modification of an iron electrode was carried out according to the following two steps. In a first step, a cathodic reduction is performed to form a film...