Multilayered Distributed Routing for Power Efficient MANET Performance

In this paper a new power efficient routing algorithm for MANETs with self-organizing and self-routing features is described and its performance analyzed in different simulation scenarios. The algorithm has the logic of a non-cooperative routing algorithm based on the evaluation of a weight parameter, the latter being a function of properties of the MANET nodes related to the nominal available power and the transmission range. A self-estimation of this weight parameter for each node is introduced in the routing process based on the status and functional history of the node. The routing is based on network layering, formation of service areas in each layer and choice of nodes from these areas to have the functionality of default gateways. The proposed algorithm, named service zone gateway prediction (SZGP), is a hybrid type of routing mechanism, incorporating pre-computed multipath hop-by-hop distributed routing, with a periodically updated hierarchical multilayered structure. The results from the simulation experiments show that the performance of the proposed SZGP algorithm in relation to the basic performance parameters such as packet delivery ratio, delay and throughput are similar to those of the well-known AODV algorithm, but in relation to power efficiency the proposed algorithm outperforms AODV significantly. This is due to the fact that such an approach reduces the overall number of broadcasts in the network and ensures a reliable and energy efficient connection by balancing the load among the nodes.     

Modeling metal binding to soils: the role of natural organic matter

The use of mechanistically based models to simulate the solution concentrations of heavy metals in soils is complicated by the presence of different sorbents that may bind metals. In this study, the binding of Zn, Pb, Cu, and Cd by 14 different Swedish soil samples was investigated. For 10 of the soils, it was found that the Stockholm Humic Model (SHM) was able to describe the acid-base characteristics, when using the concentrations of "active" humic substances and Al as fitting parameters. Two additional soils could be modeled when ion exchange to clay was also considered, using a component additivity approach. For dissolved Zn, Cd, Ca, and Mg reasonable model fits were produced when the metal-humic complexation parameters were identical for the 12 soils modeled. However, poor fits were obtained for Pb and Cu in Aquept B horizons. In two of the soil suspensions, the Lund A and Romfartuna Bhs, the calculated speciation agreed well with results obtained by using cation-exchange membranes. The results suggest that organic matter is an important sorbent for metals in many surface horizons of soils in temperate and boreal climates, and the necessity of properly accounting for the competition from Al in simulations of dissolved metal concentrations is stressed.     

Gelatin as a promising printable feedstock for microbial fuel cells (MFC)

The microbial fuel cell (MFC) is an energy transducer that can directly produce electricity from bacterial oxidation of organic matter. MFCs consist of two reaction chambers (anode and cathode) separated by a semipermeable membrane. This study describes the work carried out towards the optimization of critical MFC components, with 3D fabricated materials. The response of the optimized fuel cells, which were fed with soft materials such as gelatin, alginate and Nafion?, is also reported. The optimised components were the membrane and the cathode electrode. A conventional Nafion membrane was substituted with a custom made terracotta sheet and the electrode used was a single sheet of carbon veil coated with an activated carbon paste. The results showed that among the soft materials tested within the anodic chamber, gelatin performed the best; it also revealed that even after a 10-day starvation period gelatin demonstrated better longevity. These results show that MFCs have the potential to be 3D-printed monolithically using the EVOBOT platform.     

Parenteral Lipid Emulsions in Guinea Pigs Differentially Influence Plasma and Tissue Levels of Fatty Acids,Squalene, Cholesterol,and Phytosterols

Lipid emulsions are made by mixing vegetable and/or fish oils with egg yolk and contain different types and amounts of fatty acids and sterols. This study assessed the effects of oral diet, soybean oil (SO)-, fish oil (FO)-, a mixture of olive and soybean oil (OOSO)-, and a mixture of fish, olive, coconut, and soybean oil (FOCS)-based emulsions on plasma triacylglycerols and plasma and tissue fatty acid and sterol content following acute and chronic intravenous administration in the guinea pig. Upon acute administration, peak triacylglycerols were highest with SO and lowest with OOSO. Upon chronic administration, the plasma triglyceride levels did not increase in any group over that of the controls. Fatty acid levels varied greatly between organs of animals on the control diets and organs of animals following acute or chronic lipid administration. Squalene levels increased in plasma following acute administration of OOSO, but plasma squalene levels were similar to control in all emulsion groups following chronic administration. Total plasma phytosterol levels were increased in the SO, OOSO, and FOCS groups following both acute and chronic infusions, whereas phytosterols were not increased following FO infusion. Total phytosterol levels were higher in liver, lung, kidney and adipose tissue following SO and OOSO. Levels were not increased in tissues after FO and FOCS infusion. These results indicate that fatty acid and sterol contents vary greatly among organs and that no one tissue reflects the fatty acid or sterol composition of other tissues, suggesting that different organs regulate these compounds differently.     

A Comparison of Word- and Sense-Based Text Categorization Using Several Classification Algorithms

Most of the text categorization algorithms in the literature represent documents as collections of words. An alternative which has not been sufficiently explored is the use of word meanings, also known as senses. In this paper, using several algorithms, we compare the categorization accuracy of classifiers based on words to that of classifiers based on senses. The document collection on which this comparison takes place is a subset of the annotated Brown Corpus semantic concordance. A series of experiments indicates that the use of senses does not result in any significant categorization improvement.