Plant algae method for arsenic removal from arsenic contaminated groundwater

Field studies were carried out in Urumqi River Basin in Northwest China. The study focused on experimentation on a plant algae method that was tested by taking various water chemistries into consideration. The results from a greenhouse experiment evaluated for four doses of P (0, 100, 200, and 300 μmol/L) using two ferns (30 and 60 day old) on 15 L of contaminated groundwater per plant revealed that the biomass of 30-day old ferns gained was higher than 60-day fern. As solution-P increased from 0 to 450 μmol/L, Phosphorus concentration in the fronds increased from 1.9 to 3.9 mg/kg and 1.95 to 4.0 mg/kg for 30-d and 60-d ferns respectively. This study showed that the plant algae method may be a good solution to maximize arsenic uptake in the short term under normal climatic conditions.     

Histology, histochemistry and stereology of the adipose fin of Prochilodus lineatus

The adipose fin is small, nonpared, and usually located medially between the dorsal and caudal fin. Its taxonomic occurrence is very restrict; thus, it represents an important trace for taxon distinction. As it does not play a known vital physiological roll and it is easily removed, it is commonly used in marking and recapture studies. The present study characterizes the adipose fin of Prochilodus lineatus, as it is poorly explored by the literature. The adipose fin consists basically of a loose connective core, covered by a stratified epithelium supported by collagen fibers. At the epithelium, pigmented cells and alarm substance cells are found. Despite the name, adipocytes or lipid droplets are not observed on the structure of the fin.     

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.     

A global search framework for practical three-dimensional packing with variable carton orientations

This article aims to tackle a practical three-dimensional packing problem, where a number of cartons of diverse sizes are to be packed into a bin with fixed length and width but open height. Each carton is allowed to be packed in any one of the six orientations, and the carton edges are parallel to the bin edges. The allowance of variable carton orientations exponentially increases the solution space and makes the problem very challenging to solve. This study first elaborately devises the packing procedure, which converts an arbitrary sequence of cartons into a compact packing solution and subsequently develops an improved genetic algorithm (IGA) to evolve a set of solutions. Moreover, a novel global search framework (GSF), utilizing the concept of evolutionary gradient, is proposed to further improve the solution quality. Numerical experiments indicate that IGA provides faster and better results and GSF demonstrates its superior performance, especially in solving relative large-size and heterogeneous instances. Applying the proposed algorithms to some benchmarking cases of the three-dimensional strip packing problem also indicates that the algorithms are robust and effective compared to existing methods in the literature.     

Underwater Vehicle Localization with Complementary Filter: Performance Analysis in the Shallow Water Environment

Rapid development of underwater technology during the last two decades yielded more affordable sensors and underwater vehicles, and, as a result, expanded their use from exclusively offshore industry towards smaller interdisciplinary research groups. Regardless of application, knowing the location of the vehicle operating underwater is crucial. Relatively inexpensive solution is sensor fusion based on a dynamic model of the vehicle aided by a Doppler Velocity Log and a Ultra-Short Base Line position system. Raw data from the sensors are highly asynchronous and susceptible to outliers, especially in shallow water environment. This paper presents detailed sensor analysis based on experimental data gathered in shallow waters, identifies outliers, presents an intuitive and simple sensor fusion algorithm and finally, discusses outlier rejection. The approach has been experimentally verified on medium size remotely operated vehicle.     

Self-emergence of a common lexicon by evolution in teams of autonomous agents

In multi-agent systems, the study of language and communication is an active field of research. In this paper we present the application of evolutionary strategies to the self-emergence of a common lexicon in a population of agents. By modeling the vocabulary or lexicon of each agent as an association matrix or look-up table that maps the meanings (i.e. the objects encountered by the agents or the states of the environment itself) into symbols or signals we check whether it is possible for the population to converge in an autonomous, decentralized way to a common lexicon, so that the communication efficiency of the entire population is optimal. We have conducted several experiments aimed at testing whether it is possible to converge with evolutionary strategies to an optimal Saussurean communication system. We have organized our experiments alongside two main lines: first, we have investigated the effect of the population size on the convergence results. Second, and foremost, we have also investigated the effect of the lexicon size on the convergence results. To analyze the convergence of the population of agents we have defined the population's consensus when all the agents (i.e. 100% of the population) share the same association matrix or lexicon. As a general conclusion we have shown that evolutionary strategies are powerful enough optimizers to guarantee the convergence to lexicon consensus in a population of autonomous agents.     

Classical and superposed learning for quantum weightless neural networks

A supervised learning algorithm for quantum neural networks (QNN) based on a novel quantum neuron node implemented as a very simple quantum circuit is proposed and investigated. In contrast to the QNN published in the literature, the proposed model can perform both quantum learning and simulate the classical models. This is partly due to the neural model used elsewhere which has weights and non-linear activations functions. Here a quantum weightless neural network model is proposed as a quantisation of the classical weightless neural networks (WNN). The theoretical and practical results on WNN can be inherited by these quantum weightless neural networks (qWNN). In the quantum learning algorithm proposed here patterns of the training set are presented concurrently in superposition. This superposition-based learning algorithm (SLA) has computational cost polynomial on the number of patterns in the training set.     

A new robust algorithmic for multi-camera calibration with a 1D object under general motions without prior knowledge of any camera intrinsic parameter

In computer vision, camera calibration is a necessary process when the retrieval of information such as angles and distances is required. This paper addresses the multi-camera calibration problem with a single dimension calibration pattern under general motions. Currently, the known algorithms for solving this problem are based on the estimation of vanishing points. However, this estimate is very susceptible to noise, making the methods unsuitable for practical applications. Instead, this paper presents a new calibration algorithm, where the cameras are divided into binocular sets. The fundamental matrix of each binocular set is then estimated, allowing to perform a projective calibration of each camera. Then, the calibration is updated for the Euclidean space, ending the process. The calibration is possible without imposing any restrictions on the movement of the pattern and without any prior information about the cameras or motion. Experiments on synthetic and real images validate the new method and show that its accuracy makes it suitable also for practical applications.     

A case study on business process recovery using an e‐government system

Business processes have become one of the key assets of organization, since these processes allow them to discover and control what occurs in their environments, with information systems automating most of an organization's processes. Unfortunately, and as a result of uncontrolled maintenance, information systems age over time until it is necessary to replace them with new and modernized systems. However, while systems are aging, meaningful business knowledge that is not present in any of the organization's other assets gradually becomes embedded in them. The preservation of this knowledge through the recovery of the underlying business processes is, therefore, a critical problem. This paper provides, as a solution to the aforementioned problem, a model‐driven procedure for recovering business processes from legacy information systems. The procedure proposes a set of models at different abstraction levels, along with the model transformations between them. The paper also provides a supporting tool, which facilitates its adoption. Moreover, a real‐life case study concerning an e‐government system applies the proposed recovery procedure to validate its effectiveness and efficiency. The case study was carried out by following a formal protocol to improve its rigor and replicability. Copyright © 2011 John Wiley & Sons, Ltd.