Aerodynamic optimization via multi-objective micro-genetic algorithm with range adaptation,knowledge-based reinitialization,crowding and ε-dominance

In this study, an optimization of the airfoil of a sailplane is carried out by a recently developed multi-objective genetic algorithm based on microevolution, containing crowding, range adaptation, knowledge-based reinitialization and ε-dominance. Its efficiency was tested on a set of test problems. The results are encouraging, suggesting that very small populations can be used effectively to solve real-world multi-objective optimization problems in many cases of interest.     

The use of data-derived label hierarchies in multi-label classification

Instead of traditional (multi-class) learning approaches that assume label independency, multi-label learning approaches must deal with the existing label dependencies and relations. Many approaches try to model these dependencies in the process of learning and integrate them in the final predictive model, without making a clear difference between the learning process and the process of modeling the label dependencies. Also, the label relations incorporated in the learned model are not directly visible and can not be (re)used in conjunction with other learning approaches. In this paper, we investigate the use of label hierarchies in multi-label classification, constructed in a data-driven manner. We first consider flat label sets and construct label hierarchies from the label sets that appear in the annotations of the training data by using a hierarchical clustering approach. The obtained hierarchies are then used in conjunction with hierarchical multi-label classification (HMC) approaches (two local model approaches for HMC, based on SVMs and PCTs, and two global model approaches, based on PCTs for HMC and ensembles thereof). The experimental results reveal that the use of the data-derived label hierarchy can significantly improve the performance of single predictive models in multi-label classification as compared to the use of a flat label set, while this is not preserved for the ensemble models.     

Shakedown analysis of circular plates using a yield criterion of the mean

Structural optimization based on the shakedown theory is a powerful and promising technique. However, due to the nonlinearities of physical materials and the number of variable loads in real structures, it is computationally complex and time-consuming. To simplify the occurring non-linear, non-convex optimization problems, the paper suggests reducing the number of yield conditions. The so-called a yield criterion of the mean (integral yield condition) is analysed and explained in detail, which allows taking into account one yield condition for the entire finite element instead of multiple point-wise conditions. This approach shows promising results in numerical application to the optimization of a circular plate, considering a possibility of employing the yield criteria of the mean or pointwise yield conditions in different areas of the plate in particular. The methods applied are based on the assumptions of perfect plasticity and small deformations.     

Convergence with a fixed regulator in lattice ordered groups and applications to MV-algebras

A convergence with a fixed regulator v in lattice ordered groups is applied to MV-algebras. For each Archimedean MV-algebra A there exists a v-Cauchy completion A ^* and it is uniquely determined up to isomorphisms over A. The relation between the Dedekind completion A ^∧ of A and A ^* is established. There is solved a question of the existence of the greatest v-Cauchy complete ideal of A.     

Estimation and setting starting values in ARMA algorithms

For the given observations set of the ARMA (autoregressive moving average) process, the likelihood function depends, not only on model parameters, but on the starting values of the input and output. Therefore, it is called theconditional likelihood function. Theunconditional likelihood function can be obtained in two ways. The first is to set the starting values to zero, as is often done, and the second is to set them to the properly estimated values. The difference between these two types of likelihood functions is significant when the given data sequence is short, and any of the zeros of the moving average part is close to the boundary of the unit circle.In this paper the direct method of starting value estimation and its application to two off-line ARMA estimation algorithms, the maximum likelihood (ML) algorithm and the iterative inverse filtering (ITIF) algorithm, is proposed. Experimental results prove both increased efficiency and stability of these algorithms.The importance of setting the starting values properly is also significant when the recursive algorithm, with previously estimated parameters, has to be restarted. The advantage of the proposed reinitialization method is shown on the recursive lattice algorithm working in the block mode.     

Conversion electron Mössbauer spectroscopy of the surface crystallization of the Fe75Co9B16 amorphous alloy

Using a new design of helium-methane gas-flow detector of conversion electrons for Mössbauer spectroscopy, non-uniform nucleation of the primary -Fe-Co phase on both contact and free surfaces of the Ar(+H₂) annealed amorphous Fe₇₅Co₉B₁₆ alloy was observed in its early crystallization stage. In this state the amount of crystalline phase on the contact ribbon side surpasses that on the free one by a factor of three, whereas no traces of volume crystallization were observed in the transmission spectra. By applying ion implantation to both ribbon surfaces, a slight reduction of the crystalline phase contribution was found. Magnetic domain structure observations were performed in order to evaluate the influence of surface crystallization on magnetic properties.     

Influence of Particle Size Distribution,Moisture Content,and Particle Shape on the Flow Properties of Bulk Solids

Abstract

There has been a great deal of research on characterising bulk solids. However, some of the characteristics, particularly the time‐dependent and random changes of properties, still remain insufficiently understood. The focus of this article is to study the influence of particle size, size distribution, moisture content, and particle shape on the unconfined yield strength of bulk solids. We present some interesting results about the influence of the particle size distribution on the unconfined yield strength of a bulk solid and a new method for determining the influence of particle shape on the unconfined yield strength of a bulk solid, which was made possible by the design and use of a novel attritor.     

Impact of computer training courses on reduction of loneliness of older people in Finland and Slovenia

Previous research has shown loneliness as a threat for the quality of life of older people; therefore the goal of the intervention in a quasi-experimental study was to evaluate and discuss the self-reported level of loneliness of older people in Finland and Slovenia before and after a computer intervention. Intervention in a quasi-experimental study was conducted among 58 older participants at the baseline (M = 72.4 years) and 45 older participants at the follow-up research study (M = 72.9 years). Inferential statistics showed a significant difference in the reduction of loneliness between the countries, and a decreased level of loneliness of older people after completing the computer training course. Although older people experience many age-related problems which may reduce their interest in learning information and communication technology (ICT) skills, it is important that they are computer-proficient, because computer engagement can reduce the level of loneliness of older people and in this way has a positive effect on their quality of life.