Hematopoietic progenitor constituents and adherent cell progenitor morphology isolated from black-rumped agouti (Dasyprocta prymnolopha, Wagler 1831) bone marrow

Stem cells are present in the adult tissues of most diverse species. Bone marrow is recognized to be the most exploited site to obtain stem cells and cell progenitors. The objective of the present study was to characterize hematopoietic progenitor (HP) morphology and analyze the performance of adherent cell progenitors (ACPs) cultivated in vitro from black‐rumped agouti bone marrow (Dasyprocta prymnolopha). Bone marrow aspirates were obtained from tibia crest and used to prepare histological slides and identify cell morphology. Cells were also scattered on culture plates for later isolation, expansion, and quantification. Smears obtained from bone marrow demonstrated HPs at different stages of maturity. In culture, these cells showed fibroblastoid morphology and a strong tendency to form colonies, demonstrated by the presence of cell aggregates, cytoplasmic elongations lying side by side. An 80% cell confluence was observed at 18 days in culture and progressive reduction in the percentage of nonadherent mononuclear cells. After eight passes, a mean cell viability of 96.07% was observed, from a pool of 1.6 × 10⁷ cells (ACP). Thirteen 25‐cm² culture bottles were trypsinized, resuspended in freezing medium, stored in 14 criotubes at a concentration of 1 × 10⁶ cells per milliliter, and placed in liquid nitrogen at ?196°C. Agouti bone marrow demonstrated high plasticity, moreover different HP lines, and a population of adherent cells demonstrated morphology similar to mesenchymal stem cells in culture. Microsc. Res. Tech. 2012. © 2012 Wiley Periodicals, Inc.     

Volatilization of hydrogen sulfide from a quiescent surface

Air-water mass transfer of hydrogen sulfide from a shallow tank with a quiescent surface under the influence of weak wind stress on the water surface was studied numerically using a two-dimensional model. The flow field in the tank was investigated using a computational code based on a finite volume, which is used to numerically solve momentum, mass and continuity conservation equations. The results show that water phase flow field is strongly dependent on the wind-induced surface velocity and the aspect ratio of the tank. Based on the numerical study, the liquid-side mass transfer coefficient is correlated with Reynolds number (R(e)), tank aspect ratio (AR) and Schmidt number (S(c)). Overall mass transfer coefficient (K(L)) values extend further downstream as the R(e) number increases.     

Development of an Autonomous Robot for Gas Storage Spheres Inspection

Inspection for corrosion of gas storage spheres at the welding seam lines must be done periodically. Until now this inspection is being done manually and has a high cost associated to it and a high risk of inspection personel injuries. The Brazilian Petroleum Company, Petrobras, is seeking cost reduction and personel safety by the use of autonomous robot technology. This paper presents the development of a robot capable of autonomously follow a welding line and transporting corrosion measurement sensors. The robot uses a pair of sensors each composed of a laser source and a video camera that allows the estimation of the center of the welding line. The mechanical robot uses four magnetic wheels to adhere to the sphere’s surface and was constructed in a way that always three wheels are in contact with the sphere’s metallic surface which guarantees enough magnetic atraction to hold the robot in the sphere’s surface all the time. Additionally, an independently actuated table for attaching the corrosion inspection sensors was included for small position corrections. Tests were conducted at the laboratory and in a real sphere showing the validity of the proposed approach and implementation.     

BENCHMARKING RIVER HABITAT IMPROVEMENT

River ecosystems have witnessed a long history of human pressure, particularly the disruption of freshwater fish populations. The awareness of this situation has led to many habitat improvement projects, with a variable degree of success. In natural situations, fish populations co‐inhabit throughout the hydrological cycle with different degrees of adequacy, and the sequence of favourable and unfavourable conditions dictates abiotic constraints and biotic interactions that shape the final biological assemblages. We postulate that a part of unsuccessful restoration results is related to insufficient closeness to the natural habitat conditions of the river type that is to be restored, including the naturally adverse periods. We used the river2d model to predict habitat availability as weighted usable area (WUA) at a degraded site that is to be restored, for two native Mediterranean species and their life stages—the Southwestern nase Iberochondrostoma almacai and the Arade chub Squalius aradensis. We then analysed the yearly evolution of the natural WUA at a nearby reference site. Overall, the reference site exhibited the longest periods during which the WUA was continuously lower than the chosen WUA thresholds for each of the four bioperiods. Considerable divergences from natural habitat availability values can be seen for the spawning, rearing and growth bioperiods. Restoration outcomes can result in appreciable deviations—favourable or unfavourable to fish populations—from the WUA occurring under natural conditions over the course of the year. Restoration should therefore take account of local hydraulic and habitat patterns that govern population dynamics and result in the final fish assemblage. Copyright © 2011 John Wiley & Sons, Ltd.     

Least squares support vector machines with tuning based on chaotic differential evolution approach applied to the identification of a thermal process

In the past decade, support vector machines (SVMs) have gained the attention of many researchers. SVMs are non-parametric supervised learning schemes that rely on statistical learning theory which enables learning machines to generalize well to unseen data. SVMs refer to kernel-based methods that have been introduced as a robust approach to classification and regression problems, lately has handled nonlinear identification problems, the so called support vector regression. In SVMs designs for nonlinear identification, a nonlinear model is represented by an expansion in terms of nonlinear mappings of the model input. The nonlinear mappings define a feature space, which may have infinite dimension. In this context, a relevant identification approach is the least squares support vector machines (LS-SVMs). Compared to the other identification method, LS-SVMs possess prominent advantages: its generalization performance (i.e. error rates on test sets) either matches or is significantly better than that of the competing methods, and more importantly, the performance does not depend on the dimensionality of the input data. Consider a constrained optimization problem of quadratic programing with a regularized cost function, the training process of LS-SVM involves the selection of kernel parameters and the regularization parameter of the objective function. A good choice of these parameters is crucial for the performance of the estimator. In this paper, the LS-SVMs design proposed is the combination of LS-SVM and a new chaotic differential evolution optimization approach based on Ikeda map (CDEK). The CDEK is adopted in tuning of regularization parameter and the radial basis function bandwith. Simulations using LS-SVMs on NARX (Nonlinear AutoRegressive with eXogenous inputs) for the identification of a thermal process show the effectiveness and practicality of the proposed CDEK algorithm when compared with the classical DE approach.     

Topological Active Models optimization with Differential Evolution

The Topological Active Model is an active model focused on segmentation tasks. It provides information about the surfaces and the inside of the detected objects in the scene. The segmentation process turns into a minimization task of the energy functions which control the model deformation. In this work we propose a new optimization method of the segmentation model that uses Differential Evolution as an alternative evolutionary method that minimizes the decisions of the designer with respect to others such as genetic algorithms. Moreover, we hybridized Differential Evolution with a greedy search to integrate the advantages of global and local searches at the same time that the segmentation speed is improved. We also included in the local search the possibility of topological changes to perform a better adjustment in complex surfaces, topological changes that introduce the necessary mechanism to divide the mesh in the case of the presence of several objects in the scene.     

Temporal Bayesian Knowledge Bases – Reasoning about uncertainty with temporal constraints

Time is ubiquitous. Accounting for time and its interaction with change is crucial to modeling the dynamic world, especially in domains whose study of data is sensitive to time such as in medical diagnosis, financial investment, and natural language processing, to name a few. We present a framework that incorporates both uncertainty and time in its reasoning scheme. It is based on an existing knowledge representation called Bayesian Knowledge Bases. It provides a graphical representation of knowledge, time and uncertainty, and enables probabilistic and temporal inferencing. The reasoning scheme is probabilistically sound and the fusion of temporal fragments is well defined. We will discuss some properties of this framework and introduce algorithms to ensure groundedness during the construction of the model. The framework has been applied to both artificial and real world scenarios.     

Comments on “A novel dead time compensator for stable processes with long dead times” [Journal of Process Control 22 (2012) 612–625]

This short communication analyzes the results recently presented in the paper “On a novel dead time compensator for stable processes with long dead times” published in the Journal of Process Control. In the mentioned paper it is argued that the proposed strategy, called modified Smith predictor (MSP), gives better performance than the filtered Smith predictor (FSP) dead-time compensator for stable processes with dead time. In fact MSP has the same structure as FSP and only some specific tuning rules of the filters are proposed. Therefore, in this work some aspects of the comparative analysis and tuning rules presented in the referred paper are discussed to show that MSP is a particular case of FSP and that for some particular cases its tuning rule does not allow for a good closed-loop response. Some simulation case studies are used to illustrate these aspects.     

A modular Petri net to modeling and scenario analysis of a network of road traffic signals

The dynamic behavior of a group of traffic signals controlling a network of intersections is a complex discrete event system that can be modeled by Petri nets. The approach used in this paper proposes a components-based design, which increases modularity, reduces complexity and is a good practice according to modern Systems Engineering. The main system elements are specified based on the proposed Petri net component with time intervals associated to places. The specified models are simulated through the common token player algorithm, and formal analysis using invariants and theorem proving are applied to verify models' soundness and to reason on specific scenarios.