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.     

Effect of Boronizing on Operating Stability of Steel AISI 304L under Erosion Impact of Hard Particles

Metal Science and Heat Treatment - The effect of solid boronizing at 950°C for 2 and 4 h on the phase composition, microstructure, hardness and abrasive wear of steel AISI 304L is studied...     

A new boundary finite element method for fluid–structure interaction problems

In order to study problems on fluid–structure interaction, we have used a mixed formulation which couples the classical functional of the structure with a new variational formulation by integral equations for the fluid. This formulation has the advantage over the finite element methods of avoiding the discretization of the fluid domain. Furthermore, unlike collocation methods, the explicit calculation of the Hadamard finite part of the singular integrals is avoided. This leads after discretization by boundary finite elements to a small and symmetrical algebraic system. Typical examples are presented that demonstrate the efficiency of this variational formulation by studying the sound transmission through a baffled plane structure and through a flexible panel backed by a rigid cavity. These include the calculation of the transmission loss factor and the determination of which modes dominate the noise transmission. Good agreement is obtained between numerical results and analytical results found in the literature.     

Interference characterization in the unlicensed band

We consider performance of a wireless communication receiver in the presence of a field of continuous wave (CW) interferers that are randomly distributed according to a Poisson process in space and frequency domain in the unlicensed band. From our theoretical model, we derive an accurate analytical expression for average bit error rate (BER). Judging from our results for a strong desired signal, when the user density grows up to ten times its value for a given signal to noise ratio (SNR), BER performance falls by 9.09% and 8.51 % for BPSK and DPSK respectively.     

A novel energy-efficient encryption algorithm for secure data in WSNs

The Journal of Supercomputing - Wireless sensor networks (WSNs) are typically deployed environments, often very hostile and without assistance. A certain level of security must be provided....     

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.     

Robust control of T‐S fuzzy systems with time‐varying delay using new approach

This paper aims to design a controller to robustly stabilize uncertain nonlinear systems with time‐varying delay and norm bounded uncertainties via Takagi–Sugeno (T‐S) fuzzy model. The stabilization conditions are given in the form of linear matrix inequalities using a single Lyapunov–Krasovskii functional (LKF) combining the introduction of some relaxation matrices and only one tuning parameter. In comparison with the existing techniques in the literature, the proposed approach has two major advantages. The first is the reduction of computational complexity when the number of IF‐THEN rules, r, is big. The second concerns the conservatism reduction. Several examples are given to show the effectiveness and the merits of the design procedure. Copyright © 2009 John Wiley & Sons, Ltd.     

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.     

Evidence for internal phosphorus loading,hypoxia and effects on phytoplankton in partially polymictic Lake Simcoe,Ontario

Hypoxia and cyanobacterial blooms were extensive in Lake Simcoe during the 1980s and are still a problem to a lesser degree despite extensive nutrient load reduction from the catchment basin. The continuing signs of productivity indicate a potential internal phosphorus (P) source. Internal P load, as a redox-dependent release from bottom sediments, is hard to determine in a large, relatively shallow and partially unstratified lake such as Lake Simcoe. Of the lake's three major basins, only Kempenfelt Bay stratifies long enough to develop hypoxia in the stagnant summer hypolimnion. The following indications of sediment P release are available from historic data: 1) hypolimnetic hypoxia still occurs in Kempenfelt Bay although the hypoxic factor (number of days that an area equal to the bay's surface area is overlain by water of ≤ 2 mg/L dissolved oxygen, DO) has decreased substantially and significantly from 15.8 d/yr (1980–1994) to 4.0 d/yr (1995–2011); 2) hypoxic factors for other lake sections and at different DO levels also indicate widespread hypoxia; 3) concentrations of redox dependent metals, Fe and Mn, increase with depth; and 4) euphotic zone P and chlorophyll concentrations increase and water clarity decreases during fall turnover. Cyanobacterial blooms appear to occur in response to internal load as supported by occasional cyanobacteria counts. These indicators provide evidence that internal loading is likely occurring and affecting the water quality in Lake Simcoe. We expect that further monitoring, specific for internal load, will corroborate these results.