Deformation of a surface layer and coating application using a flexible tool

The microstructure and properties of the functional coatings formed by impact friction processing of product surfaces with a flexible tool are studied. It is established that the processing produces a hard nanostructured surface layer with fragments up to 0.13 µm in size, as it is achieved by severe plastic deformation.     

Changes in the physical properties of honey powder during storage

Honey powders produced by spray drying with the addition of Arabic gum and sodium caseinate were characterised in terms of the influence of storage time on the following physical properties: particle size, moisture content, water activity, bulk density, flowability and hygroscopicity. The storage affected those properties; after 12 weeks of storage, particle size decreased (except Arabic gum powder treated as a control sample), moisture content and water activity increased, hygroscopicity decreased. Changes in bulk density, particle size and moisture content caused the rise of Hausner ratio value; however, the powders were still characterised by a very good flow properties and low cohesiveness. The colour of reconstituted powders was also affected by storage; in most of samples, the darkening, reduction in redness and yellowness were observed. Colour parameters were the most stable in powders obtained with the addition of 2% w/w of sodium caseinate.     

Simulation of characteristics of double-junction solar cells based on ZnSiP2 heterostructures on silicon substrate

Design and operation modes of double-junction monolithic lattice-matched solar cells based on the ZnSiP₂/Si system of materials have been calculated. The effect of the photoactive region thickness and minority carrier lifetime in ZnSiP₂ layers on the efficiency of conversion of the incident solar light energy into electrical power was determined. It is shown that solar cells based on ZnSiP₂/Si heterostructures can provide efficiencies of 28.8% at AM1.5D, 100 mW/cm², and 33.3% at AM1.5D, 200 W/cm².     

The organization of biological sequences into constrained and unconstrained parts determines fundamental properties of genotype–phenotype maps

Biological information is stored in DNA, RNA and protein sequences, which can be understood as genotypes that are translated into phenotypes. The properties of genotype–phenotype (GP) maps have been studied in great detail for RNA secondary structure. These include a highly biased distribution of genotypes per phenotype, negative correlation of genotypic robustness and evolvability, positive correlation of phenotypic robustness and evolvability, shape-space covering, and a roughly logarithmic scaling of phenotypic robustness with phenotypic frequency. More recently similar properties have been discovered in other GP maps, suggesting that they may be fundamental to biological GP maps, in general, rather than specific to the RNA secondary structure map. Here we propose that the above properties arise from the fundamental organization of biological information into ‘constrained'' and ‘unconstrained'' sequences, in the broadest possible sense. As ‘constrained'' we describe sequences that affect the phenotype more immediately, and are therefore more sensitive to mutations, such as, e.g. protein-coding DNA or the stems in RNA secondary structure. ‘Unconstrained'' sequences, on the other hand, can mutate more freely without affecting the phenotype, such as, e.g. intronic or intergenic DNA or the loops in RNA secondary structure. To test our hypothesis we consider a highly simplified GP map that has genotypes with ‘coding'' and ‘non-coding'' parts. We term this the Fibonacci GP map, as it is equivalent to the Fibonacci code in information theory. Despite its simplicity the Fibonacci GP map exhibits all the above properties of much more complex and biologically realistic GP maps. These properties are therefore likely to be fundamental to many biological GP maps.     

Conveyer-belt accidents in mining and metallurgy

The dynamic interaction between the mechanical and electrical drive parameters when damage suddenly appears in the mechanical part of a conveyer belt is investigated. The possibility of using the change in the motor’s electrical parameters as a diagnostic signal is assessed. A model for investigating the dynamic processes in the system consisting of the grid, the induction motor, and the conveyer belt is proposed. The grid and induction motor are described by a model of fourth-order state space, whose output is the torque at the motor shaft. The moments of inertia and pliability of the links between the elements in the mechanical part of the conveyer belt are determined. By gradually identifying the elements with the minimum moment of inertia and distributing their moments of inertia and pliabilities among the adjacent elements, we obtain a three-mass system simulating the mechanical part of the conveyer belt. This dynamic model is solved by means of Matlab Simulink software. The energy characteristics of the drive are determined in dynamic processes following a mechanical accident.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.