HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.

Graduating 4th year radiology residents' perception of optimal imaging modalities for neoplasm and trauma: a pilot study from four U.S. universities

OBJECTIVE: Our purpose was to assess 4th year radiology residents' perception of the optimal imaging modality to investigate neoplasm and trauma. MATERIALS AND METHODS: Twenty-seven 4th year radiology residents from four residency programs were surveyed. They were asked about the best imaging modality to evaluate the brain and spine, lungs, abdomen, and the musculoskeletal system. Imaging modalities available were MRI, CT, ultrasound, PET, and X-ray. All findings were compared to the ACR appropriateness criteria. RESULTS: MRI was chosen as the best imaging modality to evaluate brain, spine, abdominal, and musculoskeletal neoplasm in 96.3%, 100%, 70.4%, and 63% of residents, respectively. CT was chosen by 88.9% to evaluate neoplasm of the lung. Optimal imaging modality to evaluate trauma was CT for brain injuries (100%), spine (92.6%), lung (96.3%), abdomen (92.6%), and major musculoskeletal trauma (74.1%); MRI was chosen for sports injury (96.3%). There was agreement with ACR appropriateness criteria. CONCLUSION: Residents' perception of the best imaging modalities for neoplasm and trauma concurred with the appropriateness criteria by the ACR.

A new concept in prophylaxis and therapy: paramunization by poxvirus inducers

The so-called primitive, innate or paraspecific immune system is the phylogenetically older part of the complex immune system. It enables the organism to immediately attack various foreign substances, infectious pathogens, toxins and transformed cells of the organism itself. ,,Paramunity" is defined as an optimal regulated and activated, antigen-nonspecific defence, acquired through continuous active and succesful confrontation with endogenous and exogenous noxes or by means of ,,paramunization" with so called ,,paramunity inducers". Paramunity inducers based on different pox virus species (e.g. Baypamun®, Duphapind®, Conpind) have turned out to be effective and safe when applied with human beings as well as with animals. Pox virus inducers activate phagocytosis and NK-cells in addition to regulation of various cytokines, notably interferon a and g, IL 1, 2, CSF and TNF which comprise the network of the complex paraspecific immune system. The results of experimental work as well as practical use in veterinary medicine have shown that paramunization by pox inducers goes far beyond the common understanding of so-called ,,immuno-therapy". They are ,,bioregulators", because they have 1. a regulatory effect on a disturbed immune system in the sense of an optimal homoeostasis, and 2. simultaneously a regulatory effect between the immune, nervous, circulatory and hormone system. Therefore, the use of paramunization by pox inducers opens a new way of prophylaxis and therapy, not only with regard to infections, but also with regard to different other indications.

Dynamic optimization for the trajectory planning of robot manipulators in the presence of obstacles

This paper presents an approach to the solution of moving a robot manipulator with minimum cost along a specified geometric path in the presence of obstacles. The main idea is to express obstacle avoidance in terms of the distances between potentially colliding parts. The optimal traveling time and the minimum mechanical energy of the actuators are considered together to build a multiobjective function. A simple numerical example involving a Cartesian manipulator arm with two-degree-of-freedom is described.

Linear-quadratic optimal control of descriptor systems

In recent years the analysis and synthesis of (mechanical) control systems in descriptor form has been established. This general description of dynamical systems is important for many applications in mechanics and mechatronics, in electrical and electronic engineering, and in chemical engineering as well. This contribution deals with linear mechanical descriptor systems and its control design with respect to a quadratic performance criterion. Here, the notion of properness plays an important role whether the standard Riccati approach can be applied as usual or not. Properness and non-properness distinguish between the cases if the descriptor system is exclusively governed by the control input or by its higher-order time-derivatives additionally. In the unusual case of non-proper systems a quite different problem of optimal control design has to be considered. Both cases will be solved completely.

Optimal design of 3R manipulators by using classical techniques and simulated annealin

In this paper, the optimum design of 3R manipulators is formulated and solved by using an algebraic formulation of workspace boundary. A manipulator design can be approached as a problem of optimization, in which the objective functions are the size of the manipulator and workspace volume; and the constrains can be given as a prescribed workspace volume. The numerical solution of the optimization problem is investigated by using two different numerical techniques, namely, sequential quadratic programming and simulated annealing. Numerical examples illustrate a design procedure and show the efficiency of the proposed algorithms.

An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.