Tissue engineering is a very promising field of regenerative medicine. Life expectancy has been increasing, and tissue replacement is increasingly needed in patients suffering from various degenerative disorders of the organs. The use of adult mesenchymal stem cells (e.g. from adipose tissue or from bone marrow) in tissue engineering seems to be a promising approach for tissue replacements. Clinical applications can make direct use of the large secretome of these cells, which can have a positive influence on other cells around. Another advantage of adult mesenchymal stem cells is the possibility to differentiate them into various mature cells via appropriate culture conditions (i.e. medium composition, biomaterial properties, and dynamic conditions). This review is focused on current and future ways to carry out tissue replacement of damaged bones and blood vessels, especially with the use of suitable adult mesenchymal stem cells as a potential source of differentiated mature cells that can later be used for tissue replacement. The advantages and disadvantages of different stem cell sources are discussed, with a main focus on adipose-derived stem cells. Patient factors that can influence later clinical applications are taken into account.
The objective of this study was to investigate the relative salt tolerance of four eggplant cultivars (Solanum melongena L.) by studying chlorophyll (Chl) fluorescence parameters during the vegetative growth stage under increasing salinity levels. The plants were grown in pots filled with peat under controlled conditions and were subjected to the salt stress ranging from 0 (control), 20, 40, 80, and 160 mM NaCl for 25 days. The results showed that the increasing NaCl concentration affected hardly the maximum quantum yield of photosystem (PS) II. The quantum yield of PSII (ΦPSII) decreased significantly in ‘Adriatica’ and ‘Black Beauty’ under the salt stress. The photochemical quenching decreased in ‘Black Beauty’ and nonphotochemical quenching increased in ‘Adriatica’ under the salt stress. The Chl fluorescence parameters did not change significantly under the salt stress in ‘Bonica’ and ‘Galine’, revealing their tolerance to salinity. After 25 days of the salt stress, the plant growth was reduced in all cultivars, however, this decline was more pronounced in ‘Adriatica’ and ‘Black Beauty’. Additionally, a significant correlation between the biomass and ΦPSII was observed in ‘Adriatica’ and ‘Black Beauty’. Our results suggest that ΦPSII can be used as a diagnostic tool to identify salt-tolerant egg-plant cultivars., S. Hanachi, M. C. Van Labeke, T. Mehouachi., and Obsahuje bibliografii
Weathering profiles in tropical regions usually present great heterogeneity and anisotropy of geological materials. High structural complexity and great bedrock irregularity are added when these profiles are composed of metamorphic rocks. Therefore, geological-geotechnical research initiatives in these regions imply indirect methods associated with direct methods. In this context, we studied the San Juan dam foundation in the Dominican Republic, geologically composed of young residual schist soil cover (up to 20 m), in which occurs schist layers of low resistance to SPT (2 SPT blows/30 cm) consistent with a massive and stratified marble rock, which tends to concentrate karst cavities. This geological condition, associated with the vast extent of the dam influence area, motivated the adoption of an indirect method by electrical resistivity intending to identify sites with the possibility of occurrence of cavities filled or not under the reservoir foundation and in the dam axis itself. Subsequently, a more rational initiative of mixed drillings was carried out in sites with such possibility, resulting in discarding these hypotheses and demonstrating that these cavities correspond to graphite schists and non-karst marbles, competent materials as dam foundation.
Distribution of the goods from a producer to a customer is one of the most important tasks of transportation. This paper focuses on the usage of genetic algorithms (GA) for optimizing problems in transportation, namely vehicle routing problem (VRP). VRP falls in the field of NP-hard problems, which cannot be solved in polynomial time. The problem was solved using genetic algorithm with two types of crossover, both including and leaving-out elitism, setting variable parameters of crossover and mutation probability, as well as prevention of creating invalid individuals. The algorithm was programmed in Matlab, tested on real world problem of spare parts distribution for garages, while the results were compared with another heuristic method (Clarke-Wright method). Genetic algorithm provided a better solution than the heuristic Clarke-Wright method.
A new way of identification of minerals was suggested. The identification was based on chemometric analysis of measured IR spectra of selected minerals. IR spectra were collected using diffuse reflectance technique. The discriminant analysis and principal component analysis were used as chemometric methods. Five statistical models were created for separation and identification of clay minerals. Up to 60 samples of various mineral standards (clay minerals, feldspars, carbonates, sulphates and quartz) from different localities were selected for the creation of statistical models. The results of this study confirm that the discriminant analysis of IR spectra of minerals could provide a powerful tool for mineral identification. Even differentiation of muscovite from illite and identification of mixed structures of illite-smectite were achieved., Michal Ritz, Lenka Vaculíková and Eva Plevová., and Obsahuje bibliografii
Leaf area estimation is an important measurement for comparing plant growth in field and pot experiments. In this study, determination of the leaf area (LA, cm2) in soybean [Glycine max (L.) Merr] involves measurements of leaf parameters such as maximum terminal leaflet length (L, cm), width (W, cm), product of length and width (LW), green leaf dry matter (GLDM) and the total number of green leaflets per plant (TNLP) as independent variables. A two-year study was carried out during 2009 (three cultivars) and 2010 (four cultivars) under field conditions to build a model for estimation of LA across soybean cultivars. Regression analysis of LA vs. L and W revealed several functions that could be used to estimate the area of individual leaflet (LE), trifoliate (T) and total leaf area (TLA). Results showed that the LW-based models were better (highest R 2 and smallest RMSE) than models based on L or W and models that used GLDM and TNLP as independent variables. The proposed linear models are: LE = 0.754 + 0.655 LW, (R2 = 0.98), T = -4.869 + 1.923 LW, (R2 = 0.97), and TLA = 6.876 + 1.813 ΣLW (summed product of L and W terminal leaflets per plant), (R2 = 0.99). The validation of the models based on LW and developed on cv. DPX showed that the correlation between calculated and measured LA was strong. Therefore, the proposed models can estimate accurately and massively the LA in soybeans without the use of expensive instrumentation. and E. Bakhshandeh, B. Kamkar. J. T. Tsialtas
A method for identification of parameters of a non-linear dynamic system, such as an induction motor with saturation effect taken into account, is presented in this paper. Adaptive identifier with structure similar to model of the system performs identification. This identifier can be regarded as a special neural network, therefore its adaptation is based on the gradient descent method and Back-Propagation well known in the neural networks theory. Parameters of electromagnetic subsystems were derived from the values of synaptic weights of the estimator after its adaptation. Testing was performed with simulations taking into account noise in measured quantities. Deviations of identified parameters in case of electrical parameters of the system were up to 1% of real values. Parameters of non-linear magnetizing curve were identified with deviations up to 6% of real values. Identifier was able to follow sudden changes of rotor resistance, load torque and moment of inertia.
This paper evaluates the feasibility of using an Artificial Neural Network (ANN) model for estimating the nominal shear capacity of Reinforced Concrete (RC) beams against diagonal shear failure subjected to shear and flexure. A feedforward back-propagation ANN model was developed utilizing 622 experimental data points of RC beams, which include 111 deep beams data and 20 beams tested for low longitudinal steel ratios. The ANN model was trained on 70% of the data and then it was validated using the remaining 30% data (new data were not used for training). The trained ANN model was compared with three existing approaches, including the American Concrete Institute (ACI) code. The ANN model predictions when compared to the experimental data were very favorable, regarding also the other approaches. The prediction of ANN model was also checked for size effect and deep beams separately. The ANN model was found to be very robust in all situations. The safe form of ANN model was also derived and compared with the design equations of the three methods.
The optimization problem of two or more special-purpose functions of the energy system is subjected to an analysis. Based on experience of our research and general knowledge of partial solutions of energy system optimization at the level of control of production and power energy supply by energy companies in the Czech Republic, a special-purpose (cost) function has been defined. By analysing the special-purpose function, penalty and limitations have been defined. Using the fuzzy logic, a set of suitable solutions for the special-purpose function is accepted. An optimum of the special-purpose function is looked for using the simulated annealing method. The history of electricity consumption is sorted by day and by hour, representing the multidimensional data. When using the cluster analysis, type daytime diagrams of consumption are defined. Type daytime diagrams form prototypes of identified clusters. The so-called self-organizing neural network with Kohonen map attached is used to perform the cluster analysis. The result of our research is presented by an experiment.
A new method to detect damages on crates of beverages is investigated. It is based on a pattern-recognition-system by an artificial neural network (ANN) with a feedforward multilayer-perceptron topology. The sorting criterion is obtained by mechanical vibration analysis which provides characteristic frequency spectra for all possible damage cases and crate models. To support the network training, a large number of numerical data-sets is calculated by the finite-elementmethod (FEM). The combination of artificial neural networks with methods of numerical simulation is a powerful instrument to cover the broad range of possible damages. First results are discussed with respect to the influence of modelling inaccuracies of the finite-element-model and the support of the ANN by training-data obtained from numerical simulation. Also the feasibility of neuro-numerical ANN training will be dwelled on.