Blood viscosity and triglyceridemia. Findings using a co-axial cylindrical viscosimeter at low "shear rates"

Examination of blood viscosity at low shear rates using a co-axial cylinder viscometer showed a significant difference between the means of values observed in hypertrigliceridemic patients compared with that of control subjects. This result differs from what has been reported by most workers although generally greater shear rates have been used. Calculation of the "r" coefficient and plotting of the regression line for each shear rate showed that there is no linear correlation between blood viscosity and triglyceridaemia, whose variations occur quite independently. It is suggested that the absence of a correlation between the two parameters examined may depend on various factors, of which the most important are those pertaining to the rheological properties of red blood cells and to the structure and chemical and physical characteristics of the triglyceride molecule and of the lipoproteins and chylomicrons which transport them.     

The hyper-cube framework for ant colony optimization.

Ant colony optimization is a metaheuristic approach belonging to the class of model-based search algorithms. In this paper, we propose a new framework for implementing ant colony optimization algorithms called the hyper-cube framework for ant colony optimization. In contrast to the usual way of implementing ant colony optimization algorithms, this framework limits the pheromone values to the interval [0,1]. This is obtained by introducing changes in the pheromone value update rule. These changes can in general be applied to any pheromone value update rule used in ant colony optimization. We discuss the benefits coming with this new framework. The benefits are twofold. On the theoretical side, the new framework allows us to prove that in Ant System, the ancestor of all ant colony optimization algorithms, the average quality of the solutions produced increases in expectation over time when applied to unconstrained problems. On the practical side, the new framework automatically handles the scaling of the objective function values. We experimentally show that this leads on average to a more robust behavior of ant colony optimization algorithms.     

On the Invariance of Ant Colony Optimization

Ant colony optimization (ACO) is a promising metaheuristic and a great amount of research has been devoted to its empirical and theoretical analysis. Recently, with the introduction of the hypercube framework, Blum and Dorigo have explicitly raised the issue of the invariance of ACO algorithms to transformation of units. They state (Blum and Dorigo, 2004) that the performance of ACO depends on the scale of the problem instance under analysis. In this paper, we show that the ACO internal state - commonly referred to as the pheromone - indeed depends on the scale of the problem at hand. Nonetheless, we formally prove that this does not affect the sequence of solutions produced by the three most widely adopted algorithms belonging to the ACO family: ant system, MAX-MIN ant system, and ant colony system. For these algorithms, the sequence of solutions does not depend on the scale of the problem instance under analysis. Moreover, we introduce three new ACO algorithms, the internal state of which is independent of the scale of the problem instance considered. These algorithms are obtained as minor variations of ant system, MAX-MIN ant system, and ant colony system. We formally show that these algorithms are functionally equivalent to their original counterparts. That is, for any given instance, these algorithms produce the same sequence of solutions as the original ones.     

Ant algorithms and stigmergy

Ant colonies, and more generally social insect societies, are distributed systems that, in spite of the simplicity of their individuals, present a highly structured social organization. As a result of this organization, ant colonies can accomplish complex tasks that in some cases far exceed the individual capacities of a single ant. The study of ant colonies behavior and of their self-organizing capacities is interesting for computer scientists because it provides models of distributed organization which are useful to solve difficult optimization and distributed control problems. In this paper we overview some models derived from the observation of real ants, emphasizing the role played by stigmergy as distributed communication paradigm, and we show how these models have inspired a number of novel algorithms for the solution of distributed optimization and distributed control problems.     

Ant colony system: a cooperative learning approach to the travelingsalesman problem

This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using an indirect form of communication mediated by a pheromone they deposit on the edges of the TSP graph while building solutions. We study the ACS by running experiments to understand its operation. The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and we conclude comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs     

Behavior analysis and training-a methodology for behaviorengineering

We propose Behavior Engineering as a new technological area whose aim is to provide methodologies and tools for developing autonomous robots. Building robots is a very complex engineering enterprise that requires the exact definition and scheduling of the activities which a designer, or a team of designers, should follow. Behavior Engineering is, within the autonomous robotics realm, the equivalent of more established disciplines like Software Engineering and Knowledge Engineering. In this article we first give a detailed presentation of a Behavior Engineering methodology, which we call Behavior Analysis and Training (BAT), where we stress the role of learning and training. Then we illustrate the application of the BAT methodology to three cases involving different robots: two mobile robots and a manipulator. Results show the feasibility of the proposed approach.     

Preseasonal intranasal immunotherapy in birch-alder allergic rhinitis. A double-blind study

A double-blind, placebo-controlled study was carried out to test the clinical efficacy and safety of local nasal immunotherapy (LNIT) in powder form. Twenty-two patients suffering from allergic rhinitis strictly associated with early spring symptoms, with positive skin prick tests and RAST for birch-alder, all responders to a specific nasal provocation test (NPT), received randomly active or placebo treatment for 4 months. Immunotherapy consisted of administration of a set of capsules containing progressively increasing amounts of birch (Betula pendula) and speckled alder (Alnus incana) allergens in powder form with controlled granulometry. The active (birch-alder) and placebo (lactose) group completed the treatment according to a similar schedule. During the pollen season (March-April), the patients who took the active treatment reported less sneezing and rhinorrhea than the placebo group, on the basis of a symptoms score, and the differences were statistically significant; the need for drugs (terfenadine) was also significantly reduced. These findings agreed well with the results of specific NPT after the treatment; only patients in the active group had a higher threshold dose of nasal specific reactivity to birch-alder allergens than in tests before the LNIT.