Theoretical and Experimental Study of Water Transport in a Hollow Cylinder Applied to the Drying of Round Squid Mantle

Until now, attention has been focused solely in the drying of squid mantle from an experimental point of view, neglecting the transport phenomenon studies of water in squid muscle. This work studies the drying of squid mantle (Loligo brasiliensis), previously salted and smoked by liquid smoking (hickory extract), using a tubular dryer in closed cycle with a silicagel fixed bed in series. The mass transfer phenomenon during drying was studied, based on the Fick's second law, with the effective diffusivity supposed constant, which enabled an analytical solution to the problem. The drying curve calculated with the resulting equation was compared with experimental data. The model was applied to a hollow cylinder geometry (round squid mantle), with its internal surface isolated from the drying environment by a plastic film. The latter was given a tubular form and introduced inside the mantle cavity (impervious wall boundary condition).     

Opportunity cost algorithms for reduction of I/O and interprocess communication overhead in a computing cluster

Computing clusters (CC) consisting of several connected machines, could provide a high-performance, multiuser, timesharing environment for executing parallel and sequential jobs. In order to achieve good performance in such an environment, it is necessary to assign processes to machines in a manner that ensures efficient allocation of resources among the jobs. The paper presents opportunity cost algorithms for online assignment of jobs to machines in a CC. These algorithms are designed to improve the overall CPU utilization of the cluster and to reduce the I/O and the interprocess communication (IPC) overhead. Our approach is based on known theoretical results on competitive algorithms. The main contribution of the paper is how to adapt this theory into working algorithms that can assign jobs to machines in a manner that guarantees near-optimal utilization of the CPU resource for jobs that perform I/O and IPC operations. The developed algorithms are easy to implement. We tested the algorithms by means of simulations and executions in a real system and show that they outperform existing methods for process allocation that are based on ad hoc heuristics.     

Sheet molding compound characterization using spiral flow

Sheet molding compound (SMC) is a fiber‐reinforced polymeric composite. It is often used in automotive, marine, and industrial applications over other materials because of its high strength to density ratio, resistance to corrosion, and low cost. There is a demand in the SMC industry to be able to characterize SMC processability. This is particularly true for heavy truck body panels, one of the fastest growing applications of SMC. Because of their large size and high strength requirement, the molding forces have a major influence in the molding cycle. Also because of the long flow paths involved, the ability of the paste to carry glass needs to be properly characterized when developing new SMC materials. In this article, we demonstrate the benefits of using spiral flow as a processability tester. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Characterization of CuInS2 thin films for solar cells prepared by spray pyrolysis

We have made a study of the chemical composition, the electrical, the optical and the structural properties of polycrystalline CuInS₂ thin films prepared by spray pyrolysis to be used for thin film solar cells. These films were deposited starting from aqueous solutions with different chemical compositions ([Cu]/[In] and [S]/[Cu] ratios) and at different substrate temperatures. In all cases, the material is p-type with grains preferentially oriented in the (112) direction of the sphalerite structure. The electro-optical properties show a very strong dependence on the [Cu]/[In] ratio in the solution. Films with copper excess have smaller resistivity and better crystallinity than those which are stoichiometric or have indium excess. The results obtained in this work show the possibility of having CuInS₂ thin films with a wide range of resistivity, a fact that could be important for making solar cells based on this material.     

Development of an RVE using a DEM–FEM scheme under modified approximate periodic boundary condition to estimate the elastic mechanical properties of open foams

Engineering with Computers - In this article, a methodology based on Discrete Element Method (DEM) and Finite Elements Method (FEM) combined with modified Approximate Periodic Boundary Condition...     

An optimization algorithm for multimodal functions inspired by collective animal behavior

Interest in multimodal function optimization is expanding rapidly as real-world optimization problems often demand locating multiple optima within a search space. This article presents a new multimodal optimization algorithm named as the collective animal behavior. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, searcher agents are a group of animals which interact with each other based on the biologic laws of collective motion. Experimental results demonstrate that the proposed algorithm is capable of finding global and local optima of benchmark multimodal optimization problems with a higher efficiency in comparison with other methods reported in the literature.     

Determination of the Secondary Bjerknes Force in Acoustic Resonators on Ground and in Microgravity Conditions

We propose an experimental methodology to determine the secondary Bjerknes force between rigid particles. Measurements done for different particles sizes showed acoustical inter particles interactions. We use and extend the methodology presented in a previous work. The determination of this force will lead us a better understanding of the aggregation process in acoustic resonators. We report in this work, the results of two parabolic flights campaigns performed at the Airbus A300 ZERO-G (Novespace, France).     

Light-weight salient foreground detection for embedded smart cameras

An embedded smart camera is a stand-alone unit that not only captures images, but also includes a processor, memory and communication interface. Battery-powered, embedded smart cameras introduce many additional challenges since they have very limited resources, such as energy, processing power and memory. Computer vision algorithms running on these camera boards should be light-weight and efficient. Considering the memory requirements of an algorithm and its portability to an embedded processor should be an integral part of the algorithm design in addition to the accuracy requirements. This paper presents a light-weight and efficient background modeling and foreground detection algorithm that is highly robust against lighting variations and non-static backgrounds including scenes with swaying trees, water fountains and rain. Compared to many traditional methods, the memory requirement for the data saved for each pixel is very small in the proposed algorithm. Moreover, the number of memory accesses and instructions are adaptive, and are decreased depending on the amount of activity in the scene. Each pixel is treated differently based on its history, and instead of requiring the same number of memory accesses and instructions for every pixel, we require less instructions for stable background pixels. The plot of the number of unstable pixels at each frame also serves as a tool to find the video portions with high activity. The proposed method selectively updates the background model with an automatically adaptive rate, thus can adapt to rapid changes. As opposed to traditional methods, pixels are not always treated individually, and information about neighbors is incorporated into decision making. The results obtained with nine challenging outdoor and indoor sequences are presented, and compared with the results of different state-of-the-art background subtraction methods. The ROC curves and memory comparison of different background subtraction methods are also provided. The experimental results demonstrate the success of the proposed light-weight salient foreground detection method.     

A biased random‐key genetic algorithm for OSPF and DEFT routing to minimize network congestion

Interior gateway routing protocols like Open Shortest Path First (OSPF) and Distributed Exponentially Weighted Flow Splitting (DEFT) send flow through forward links toward the destination node. OSPF routes only on shortest‐weight paths, whereas DEFT sends flow on all forward links, but with an exponential penalty on longer paths. Finding suitable weights for these protocols is known as the weight setting problem (WSP). In this paper, we present a biased random‐key genetic algorithm for WSP using both protocols. The algorithm uses dynamic flow and dynamic shortest path computations. We report computational experiments that show that DEFT achieves less network congestion when compared with OSPF, while, however, yielding larger delays.     

Correspondence of projected 3‐D points and lines using a continuous GRASP

The field of computer vision has experienced rapid growth over the past 50 years. Many computer vision problems have been solved using theory and ideas from algebraic projective geometry. In this paper, we look at a previously unsolved problem from object recognition, namely object recognition when the correspondences between the object and image data are not known a priori. We formulate this problem as a mixed‐integer non‐linear optimization problem in terms of the unknown projection relating the object and image, as well as the unknown assignments of object points and lines to those in the image. The global optimum of this problem recovers the relationship between the object points and lines with those in the image. When certain assumptions are enforced on the allowable projections mapping the object into the image, a proof is provided which permits one to solve the optimization problem via a simple decomposition. We illustrate this decomposition approach on some example scenarios.