Abstract: Phytoremediation under greenhouse condition was investigated as an alternative to clean up an industrially-multi-contaminated soil with both petroleum hydrocarbons and heavy metals. The aim of this work was to study the ability of three sunflower (Helianthus annuus) cultivars (M, H2, and P) to absorb/degrade V, Ni, Cu, Pb, benzo(a)pyrene, and TPH (total petroleum hydrocarbons) from multi-contaminated soil. Assays were performed with three sunflower cultivars, suitable for commercial biodiesel production, in pots containing 5 kg of soil for a period of 40 days. All three varieties were able to reduce the concentration of heavy metals, benzo(a)pyrene, and TPH in the soil, however, the contaminants removal varied according to the assay conditions employed. The highest removal percentage of Ni, Pb, and Cu were obtained for H2 cultivar. On the other hand, the highest removal percentage of V was obtained when the soil was treated with M cultivar. TPH removal did not vary according to the use of different sunflower cultivars. Nevertheless, the highest removal percentage of benzo(a)pyrene was obtained with the M cultivar. The results demonstrated the potential of the phytoremediation technique using sunflower for the treatment of soil multi-contaminated by heavy metals and hydrocarbon’s petroleum.

Abstract: The microbial inactivation/sterilization is an important part of our daily healthy life. This paper presents the overview on the sterilization process. Comparing the effectiveness of the sterilization agents it has been found that the short High Electric Field [HEF] pulse is the most efficient inactivation agent. This HEF pulse creates pores on the cell surface and kills the cell. Electroporation plays an important role in the inactivation of the biological cell or killing of the cell and spore. The active chemical species produced in the propagation phase of the plasma jet are considered to be the basic inactivation agent for plasma based sterilization. Along with the emission intensity of the active chemical species in the plasma jet, maximum electric field of the plasma jet has been estimated in this paper and is around 84 kV/cm. A new inactivation agent, the electric field strength of the plasma jet is proposed for the first time in this paper. It has been shown that the electric field produced in the plasma bullet is sufficient enough to have an adverse effect on the cell exposed to plasma bullet and is the main inactivation agent of plasma based inactivation. Stepwise inactivation process is described.

Abstract: Though there are some limitations with the use of natural dyes, the use of the dyes is increasing day by day due to the eco-friendly approach of the people. The extraction process of natural dyes from their sources and methods of application of the dyes on different fabrics are very important factor. This work concerns with the extraction and purification of natural dyestuff from a plant Curcuma Longla L. and dyeing of cotton and polyester fabric in exhaust dyeing method. The main coloring component of turmeric is curcumin, which produces yellow color on the textile materials. The purified curcumin produces various shades on cotton and polyester fabric with different dyeing parameters and use of mordants. The color fastness properties of the dyed fabrics are also good.

Abstract: Activity recognition from computer vision plays an important role in research towards applications like human computer interfaces, intelligent environments, surveillance or medical systems. In this paper, we propose a gesture recognition system based on a deep learning architecture and show how it performs when trained with changing multimodal input data on an Italian sign language dataset. The results show the importance of choosing the right data representation for activity recognition tasks.

Abstract: Poultry production, facing modernization and increasing competitiveness, shows itself to be enterprising in the adoption of new technologies which enable increased productivity. Knowing that poultry productivity and rectal temperature (Tr) are affected by environmental conditions, this research was done with the objective of developing and evaluating artificial neural networks (ANNs) for the prediction of Tr in function of thermal conditions (air temperature, Tair; relative humidity, RH; and air velocity, V). The architecture chosen for this purpose was a single hidden layer Multilayer Perceptron (MLP), which was developed and trained under Scilab 4.1.1 aimed with ANN toolbox 0.4.2. The total data available, 139 data points obtained from literature, was divided into two sets, training (94) and validation (45). The selected MLP presented excellent results, providing estimates with an average error of 0.78% for the training set and 1.02% for the validation set. Thus, artificial neural networks constitute an appropriate and promising methodology to solve problems related to poultry production.

Abstract: In the development of a HMM model-based exon controller, data sets addition on training phase causes decrease on the model's accuracy if the training parameters are not reconfigured. The decline in accuracy is caused by the high insertion at intron and deletion in the exon region. It shows the influence of the state definition, transition matrix and data sets on the performance of the resulting model. In this research, the analyses of the performance of the two HMM-based models with 20 states structure that are developed base on the previous HMM-based exon controller model, are conducted. In the first model, the nucleotides are distributed to the entire state. In the second model, the sum of nucleotides in the certain state is determined. The data sets for training and testing model are DNA Plasmodium falciparum data with two exons. They are taken from GenBank database. At the first step, characteristics of data are identified based on the length distribution of the first exon, the second exon, and introns. Testing of the model is conducted to identify the performance of the model based on the Correlation Coefficient (CC) and the distribution of insertion and a deletion in the exon and intron position. It aims to identify high incompatibility of the model to the data. The last step, the relationship between state composition, the determination of the transition and decreasing of the model accuracy is analyzed. The result analysis of the model shows the dependence of accuracy of model to the invisible pattern of the DNA data. State composition by determining the nucleotide population in each state in the first model gives better performance compared to just distribute the number of nucleotide among the state in the second model. The analysis result can be used as the foundation of the importance of the data sets clustering in the model development process so that the model contains sub-models, each for specific data set.