BioTuCSoN: biochemical extension of TuCSoN to support self-organising coordination

Starting from pervasive computing paradigm, we want to face the new system's requirements, concerning, mainly, self-organisation, situatedness and adaptivity, through the definition and execution of nature-inspired patterns. They are extracted by the study of dynamics in biological systems and we consider for their implementation the biochemical tuple spaces model. In particular, the aim of the thesis is to design and realize a first biochemical extension of TuCSoN (technology based on tuple spaces model) and, then, to verify its capabilities by means of a proper case study, that deals with local self-organisation and competition of services in an open and highly-dynamic environment.

Development and characterization of self-assembling nanoparticles using a bio-inspired amphipathic peptide for gene delivery

The design of a non-viral gene delivery vehicle capable of delivering and releasing a functional nucleic acid cargo intracellularly remains a formidable challenge. For systemic gene therapy to be successful a delivery vehicle is required that protects the nucleic acid cargo from enzymatic degradation, extravasates from the vasculature, traverses the cell membrane, disrupts the endosomal vesicles and unloads the cargo at its destination site, namely the nucleus for the purposes of gene delivery. This manuscript reports the extensive investigation of a novel amphipathic peptide composed of repeating RALA units capable of overcoming the biological barriers to gene delivery both in vitro and in vivo. Our data demonstrates the spontaneous self-assembly of cationic DNA-loaded nanoparticles when the peptide is complexed with pDNA. Nanoparticles were < 100 nm, were stable in the presence of serum and were fusogenic in nature, with increased peptide α-helicity at a lower pH. Nanoparticles proved to be non-cytotoxic, readily traversed the plasma membrane of both cancer and fibroblast cell lines and elicited reporter-gene expression following intravenous delivery in vivo. The results of this study indicate that RALA presents an exciting delivery platform for the systemic delivery of nucleic acid therapeutics.

Mathematical model and rule extraction for tool wear monitoring problem using nature inspired techniques

In this paper, pattern classification problem in tool wear monitoring is solved using nature inspired techniques such as Genetic Programming(GP) and Ant-Miner (AM). The main advantage of GP and AM is their ability to learn the underlying data relationships and express them in the form of mathematical equation or simple rules. The extraction of knowledge from the training data set using GP and AM are in the form of Genetic Programming Classifier Expression (GPCE) and rules respectively. The GPCE and AM extracted rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in GP evolved GPCE and AM based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The performance of the data classification using GP and AM is as good as the classification accuracy obtained in the earlier study.

Nature-Inspired Framework for Hyperspectral Band Selection

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Attitudes, Ideologies and Self-Organisation : Information Load Minimisation in Multi-Agent Decision Making

Sophisticated models of human social behaviour are fast becoming highly desirable in an increasingly complex and interrelated world. Here, we propose that rather than taking established theories from the physical sciences and naively mapping them into the social world, the advanced concepts and theories of social psychology should be taken as a starting point, and used to develop a new modelling methodology. In order to illustrate how such an approach might be carried out, we attempt to model the low elaboration attitude changes of a society of agents in an evolving social context. We propose a geometric model of an agent in context, where individual agent attitudes are seen to self-organise to form ideologies, which then serve to guide further agent-based attitude changes. A computational implementation of the model is shown to exhibit a number of interesting phenomena, including a tendency for a measure of the entropy in the system to decrease, and a potential for externally guiding a population of agents towards a new desired ideology.

A study of the nature of coordination in metal monothiocarbamates based on infrared spectroscopy and the HSAB concept

The nature of coordination in metal monothiocarbamates is shown to depend on the hardness or softness of the metal ton. Thus, the monothiocarbamate ion acts as a monodentate ligand with metal-sulphur bending when the metal ion is a soft acid while it acts as a bidentate ligand when the metal ion is a hard acid; it can exhibit either behaviour when the metal ion is a borderline acid. In dialkyltin and dialkylmonocholorotin complexes, the monothiocarbamate ion acts as a bidentate ligand with strong Sn-S bonding while in trialkyl-or triaryl-tin complexes it acts essentially as a monodentate ligand. Thus, R3Sn(I) seems to be a soft or borderline acid while R2Sn(II) is a hard acid.

Locant: A nature inspired location service for ad hoc networks

An efficient location service is a prerequisite to any robust, effective and precise location information aided Mobile Ad Hoc Network (MANET) routing protocol. Locant, presented in this paper is a nature inspired location service which derives inspiration from the insect colony framework, and it is designed to work with a host of location information aided MANET routing protocols. Using an extensive set of simulation experiments, we have compared the performance of Locant with RLS, SLS and DLS, and found that it has comparable or better performance compared to the above three location services on most metrics and has the least overhead in terms of number of bytes transmitted per location query answered.

Nature inspired optimization techniques for the design optimization of laminated composite structures using failure criteria

The design optimization of laminated composites using naturally inspired optimization techniques such as vector evaluated particle swarm optimization (VEPSO) and genetic algorithms (GA) are used in this paper. The design optimization of minimum weight of the laminated composite is evaluated using different failure criteria. The failure criteria considered are maximum stress (MS), Tsai-Wu (TW) and failure mechanism based (FMB) failure criteria. Minimum weight of the laminates are obtained for different failure criteria using VEPSO and GA for different combinations of loading. From the study it is evident that VEPSO and GA predict almost the same minimum weight of the laminate for the given loading. Comparison of minimum weight of the laminates by different failure criteria differ for some loading combinations. The comparison shows that FMBFC provide better results for all combinations of loading. (C) 2010 Elsevier Ltd. All rights reserved.

Cooperative Communication of UAV to Perform Multi-task Using Nature Inspired Techniques

In this paper, the approach for assigning cooperative communication of Uninhabited Aerial Vehicles (UAV) to perform multiple tasks on multiple targets is posed as a combinatorial optimization problem. The multiple task such as classification, attack and verification of target using UAV is employed using nature inspired techniques such as Artificial Immune System (AIS), Particle Swarm Optimization (PSO) and Virtual Bee Algorithm (VBA). The nature inspired techniques have an advantage over classical combinatorial optimization methods like prohibitive computational complexity to solve this NP-hard problem. Using the algorithms we find the best sequence in which to attack and destroy the targets while minimizing the total distance traveled or the maximum distance traveled by an UAV. The performance analysis of the UAV to classify, attack and verify the target is evaluated using AIS, PSO and VBA.

Solving Dynamic Constraint Single Objective Functions using a Nature Inspired Technique

We present in this paper a new algorithm based on Particle Swarm Optimization (PSO) for solving Dynamic Single Objective Constrained Optimization (DCOP) problems. We have modified several different parameters of the original particle swarm optimization algorithm by introducing new types of particles for local search and to detect changes in the search space. The algorithm is tested with a known benchmark set and compare with the results with other contemporary works. We demonstrate the convergence properties by using convergence graphs and also the illustrate the changes in the current benchmark problems for more realistic correspondence to practical real world problems.

Nature-inspired microfluidic propulsion using magnetic actuation.

In this work we mimic the efficient propulsion mechanism of natural cilia by magnetically actuating thin films in a cyclic but non-reciprocating manner. By simultaneously solving the elastodynamic, magnetostatic, and fluid mechanics equations, we show that the amount of fluid propelled is proportional to the area swept by the cilia. By using the intricate interplay between film magnetization and applied field we are able to generate a pronounced asymmetry and associated flow. We delineate the functional response of the system in terms of three dimensionless parameters that capture the relative contribution of elastic, inertial, viscous, and magnetic forces.