Misleading symptoms after a minor blunt chest trauma. Thoracoscopic treatment of diaphragmatic rupture

We present a case of missed diaphragmatic rupture which was treated thoracoscopically. Rupture of the diaphragm is a serious complication of blunt trauma. The diaphragmatic injury can easily be overlooked. This report illustrates the diagnostic dilemma in a patient where the injury was missed at the time of initial presentation. The role of thoracoscopy both for diagnosis and therapy is discussed.     

A modified expectation maximization algorithm for penalized likelihood estimation in emission tomography

The maximum likelihood (ML) expectation maximization (EM) approach in emission tomography has been very popular in medical imaging for several years. In spite of this, no satisfactory convergent modifications have been proposed for the regularized approach. Here, a modification of the EM algorithm is presented. The new method is a natural extension of the EM for maximizing likelihood with concave priors. Convergence proofs are given.     

Preface

Preface

Preface     

Distributed boundary coverage with a team of networked miniature robots using a robust market-based algorithm

We study distributed boundary coverage of known environments using a team of miniature robots. Distributed boundary coverage is an instance of the multi-robot task-allocation problem and has applications in inspection, cleaning, and painting among others. The proposed algorithm is robust to sensor and actuator noise, failure of individual robots, and communication loss. We use a market-based algorithm with known lower bounds on the performance to allocate the environmental objects of interest among the team of robots. The coverage time for systems subject to sensor and actuator noise is significantly shortended by on-line task re-allocation. The complexity and convergence properties of the algorithm are formally analyzed. The system performance is systematically analyzed at two different microscopic modeling levels, using agent-based, discrete-event and module-based, realistic simulators. Finally, results obtained in simulation are validated using a team of Alice miniature robots involved in a distributed inspection case study.     

MODELING AND SIMULATION OF THE HUMANOID ROBOT HOAP-3 IN THE OPENHRP3 PLATFORM

The aim of this work is to model and simulate the humanoid robot HOAP-3 in the OpenHRP3 platform. Our purpose is to create a virtual model of the robot so that different motions and tasks can be tested in different environments. This will be the first step before testing the motion patterns in the real HOAP-3. We use the OpenHRP3 platform for the creation and validation of the robot model and tasks. The procedure followed to reach this goal is detailed in this article. In order to validate our experience, different walking motions are tested and the simulation results are compared with the experimental ones.     

A model-based parameter estimation approach for numerical analysisof single-mode optical fibers

An efficient numerical method is proposed and implemented for the analysis of propagation characteristics of single-mode optical fibers with arbitrary refractive index profile. The method follows the concept of the so-called model-based parameter estimation, and the Pade algorithm is used to construct a low-order rational approximant of the spectral domain Green's function, obtained by solving a hierarchy of static problems. The sought eigenfrequency and field distribution are then estimated by computing, respectively, the dominant pole and the related residual of the rational approximant. A number of profiles are analyzed and experiments show that very accurate results can be cheaply obtained through this technique     

The statistical analysis of data from small groups.

The authors elaborate the complications and the opportunities inherent in the statistical analysis of small-group data. They begin by discussing nonindependence of group members' scores and then consider standard methods for the analysis of small-group data and determine that these methods do not take into account this nonindependence. A new method is proposed that uses multilevel modeling and allows for negative nonindependence and mutual influence. Finally, the complications of interactions, different group sizes, and differential effects are considered. The authors strongly urge that the analysis model of data from small-group studies should mirror the psychological processes that generate those data. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

When similarity breeds content: Need for closure and the allure of homogeneous and self-resembling groups.

Four studies explored the relation between members' need for cognitive closure and their feelings toward groups. It was found that high (vs. low) need for closure individuals liked in-groups and out-groups more as function of the degree to which their membership was perceived as homogeneous (Studies 1-4), provided it was also self-similar (Studies 3 and 4). These results are discussed in terms of the relation between need for closure and homogeneous (vs. heterogeneous) groups' apparent potential as "closure providers." (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Automatic calibration of urban drainage model using a novel multi-objective genetic algorithm.

In order to successfully calibrate an urban drainage model, multiple calibration criteria should be considered. This raises the issue of adopting a method for comparing different solutions (parameter sets) according to a set of objectives. Amongst the global optimization techniques that have blossomed in recent years, Multi Objective Genetic Algorithms (MOGA) have proved effective in numerous engineering applications, including sewer network modelling. Most of the techniques rely on the condition of Pareto efficiency to compare different solutions. However, as the number of criteria increases, the ratio of Pareto optimal to feasible solutions increases as well. The pitfalls are twofold: the efficiency of the genetic algorithm search worsens and decision makers are presented with an overwhelming number of equally optimal solutions. This paper proposes a new MOGA, the Preference Ordering Genetic Algorithm, which alleviates the drawbacks of conventional Pareto-based methods. The efficacy of the algorithm is demonstrated on the calibration of a physically-based, distributed sewer network model and the results are compared with those obtained by NSGA-II, a widely used MOGA.