How to trust a few among many

The presence of numerous and disparate information sources available to support decision-making calls for efficient methods of harnessing their potential. Information sources may be unreliable, and misleading reports can affect decisions. Existing trust and reputation mechanisms typically rely on reports from as many sources as possible to mitigate the influence of misleading reports on decisions. In the real world, however, it is often the case that querying information sources can be costly in terms of energy, bandwidth, delay overheads, and other constraints. We present a model of source selection and fusion in resource-constrained environments, where there is uncertainty regarding the trustworthiness of sources. We exploit diversity among sources to stratify them into homogeneous subgroups to both minimise redundant sampling and mitigate the effect of certain biases. Through controlled experiments, we demonstrate that a diversity-based approach is robust to biases introduced due to dependencies among source reports, performs significantly better than existing approaches when sampling budget is limited and equally as good with an unlimited budget.     

Encoding Cryptographic Primitives in a Calculus with Polyadic Synchronisation

We thoroughly study the behavioural theory of epi, a ??-calculus extended with polyadic synchronisation. We show that the natural contextual equivalence, barbed congruence, coincides with early bisimilarity, which is thus its co-inductive characterisation. Moreover, we relate early bisimilarity with the other usual notions, ground, late and open, obtaining a lattice of equivalence relations that clarifies the relationship among the ??standard?? bisimilarities. Furthermore, we apply the theory developed to obtain an expressiveness result: epi extended with key encryption primitives may be fully abstractly encoded in the original epi calculus. The proposed encoding is sound and complete with respect to barbed congruence; hence, cryptographic epi (crypto-epi) gets behavioural theory for free, which contrasts with other process languages with cryptographic constructs that usually require a big effort to develop such theory. Therefore, it is possible to use crypto-epi to analyse and to verify properties of security protocols using equational reasoning. To illustrate this claim, we prove compliance with symmetric and asymmetric cryptographic system laws, and the correctness of a protocol of secure message exchange.     

Visualization of fibrous and thread-like data

Thread-like structures are becoming more common in modern volumetric data sets as our ability to image vascular and neural tissue at higher resolutions improves. The thread-like structures of neurons and micro-vessels pose a unique problem in visualization since they tend to be densely packed in small volumes of tissue. This makes it difficult for an observer to interpret useful patterns from the data or trace individual fibers. In this paper we describe several methods for dealing with large amounts of thread-like data, such as data sets collected using Knife-Edge Scanning Microscopy (KESM) and Serial Block-Face Scanning Electron Microscopy (SBF-SEM). These methods allow us to collect volumetric data from embedded samples of whole-brain tissue. The neuronal and microvascular data that we acquire consists of thin, branching structures extending over very large regions. Traditional visualization schemes are not sufficient to make sense of the large, dense, complex structures encountered. In this paper, we address three methods to allow a user to explore a fiber network effectively. We describe interactive techniques for rendering large sets of neurons using self-orienting surfaces implemented on the GPU. We also present techniques for rendering fiber networks in a way that provides useful information about flow and orientation. Third, a global illumination framework is used to create high-quality visualizations that emphasize the underlying fiber structure. Implementation details, performance, and advantages and disadvantages of each approach are discussed.     

Use of sesame stalk biomass for the removal of Ni(II) and Zn(II) from aqueous solutions

In this study an agricultural residue, sesame stalk, was evaluated for the removal of Ni(II) and Zn(II) metal ions from aqueous solutions. Biosorption studies were carried out at different pH, biosorbent dosage, initial metal ion concentrations, contact time, and solution temperature to determine the optimum conditions. The experimental data were modeled by Langmuir, Freundlich, Dubinin-Radushkevich (D-R) and Temkin isotherm models. Langmuir model resulted in the best fit of the biosorption data. The pseudo-first-order and pseudo-second-order kinetic models were used to describe the kinetic data and to evaluate rate constants. The best correlation was provided by the second-order kinetic model. The thermodynamic parameters such as ΔG°, ΔH° and ΔS° were calculated for predicting the nature of adsorption. The experimental results showed that sesame stalk can be used as an effective and low-cost biosorbent precursor for the removal of heavy metal ions from aqueous solutions.     

Biodegradation of lipids from olive oil mill wastewaters in a stationary basket bioreactor with immobilized Bacillus spp. cells - influence of internal diffusion

The study is focused on the external and internal mass transfers of lipids during their biodegradation process in a bioreactor with stationary basket bed of immobilized Bacillus spp. cells. By means of the lipid mass balance for a single particle of biocatalyst, considering the kinetic model adapted for the immobilized bacterial cells, specific mathematical models have been developed to estimate their mass flows in the liquid boundary layer surrounding the particle and inside the particle. The values of mass flows are significantly influenced by the internal diffusion velocity of lipids and the rate of their consumption, but also by the position inside the basket bed. These influences accumulated led to the appearance of a biological inactive region near the particle centre, its magnitude varying from 1.3 to 49.4% of the overall volume of particles.     

Neural computing models for prediction of permeability coefficient of coarse-grained soils

Correlations are very significant from the earliest days; in some cases, it is essential as it is difficult to measure the amount directly, and in other cases it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternate statistical tool, and new techniques such as artificial neural networks, fuzzy inference systems, genetic algorithms, and their hybrids were employed for developing the predictive models to estimate the needed parameters, in the recent years. Determination of permeability coefficient (k) of soils is very important for the definition of hydraulic conductivity and is difficult, expensive, time-consuming, and involves destructive tests. In this paper, use of some soft computing techniques such as ANNs (MLP, RBF, etc.) and ANFIS (adaptive neuro-fuzzy inference system) for prediction of permeability of coarse-grained soils was described and compared. As a result of this paper, it was obtained that the all constructed soft computing models exhibited high performance for predicting k. In order to predict the permeability coefficient, ANN models having three inputs, one output were applied successfully and exhibited reliable predictions. However, all four different algorithms of ANN have almost the same prediction capability, and accuracy of MLP was relatively higher than RBF models. The ANFIS model for prediction of permeability coefficient revealed the most reliable prediction when compared with the ANN models, and the use of soft computing techniques will provide new approaches and methodologies in prediction of some parameters in soil mechanics.     

Sink placement on a 3D terrain for border surveillance in wireless sensor networks

With their distributed nature and redundant operation capability, wireless sensor networks are very suitable for border surveillance scenarios that track intruders trying to breach to a safe side. In such scenarios, keeping the operation going on for as long as possible is the most important aspect of the network. We propose that by placing sink at a carefully selected coordinate will results in a longer living network. We also place restrictions on the candidate locations so that the sensing quality of the network is above a useful predetermined value and the sink is placed in a relatively safe location to avoid destruction. In order to find the suitable coordinates we propose a modified lifetime metric which takes quality and safety measures into account. We also propose a genetic algorithm which uses a discrete event simulator-in-the-loop over a three dimensional terrain to find locations for the sink that fits the given quality and safety restrictions. Using a three dimensional underlying terrain makes the proposed approach more realistic. The results obtained for various sensor network scenarios indicate that the proposed algorithm can find locations that increase the lifetime by also considering the sensing quality and safety.