In this paper, using fractional differential and integral operators, constructed are two-dimensional mathematical models of viscoelastic deformation, which are characterized by memory effects, spatial non-locality, and self-organization. The fractal rheological models by Maxwell, Kelvin and Voigt, their structural properties and the influence of the fractional integro-differential operator on the process of viscoelasticity are investigated.
Using the Laplace transform method and taking into account the properties of the fractional differential apparatus, analytical relations are obtained in the integral form for describing the stresses of generalized two-dimensional fractional-differential rheological models by Maxwell, Kelvin, and Voigt. Since the fractional-differential parameters of fractal models allow describing deformation-relaxation processes more perfectly than traditional methods, algorithmic aspects of identification of structural and fractal parameters of models are presented in the work.
Explicit expressions have been obtained to describe the deformation process for one-dimensional fractional-differential models by Voigt, Kelvin, and Maxwell. The results of identification of structural and fractal parameters of the Maxwell and Voigt models are presented. The estimates of the accuracy of the obtained identification results were found using the statistical criterion based on the correlation coefficient. The influence of fractional-differential parameters on deformation-relaxation processes is investigated.

Stochastic games are discussed as a priva-te class of a general dynamic games. A certain class of lexicographic noncooperative games is studied - lexi-cographic stochastic matrix games . The problem of the existence of Nash equilibrium is studied with two analyses - standard and nonstandard way. Standard means using the same kind of mixed strategies in case of scalar games. In this case in lexi-cographic stochastic matrix game Nash equilibrium may not be existed. Its existence takes place in relevant stochastic affine matrix game to the existence of Nash equilibrium. In game a set of Nash equi-librium is given by means of relevant stochastic affine matrix game's set of equilibrium. The sufficient condi-tions of the existance such affine game is proved. In nonstandard way of analyses we use such mixed stra-tegies, they use components with lexicog-raphic probabilites. In this case the kinds of subsets of a set of equilibrium in game are described.

Dynamics of Malaria parasite diagnosis is complex and been widely studied. Research is on-going on the effects of climatic variations on symptomatic malaria infection. Malaria diagnosis can be asymptomatically or symptomatically low, mild and high. An analytical program is needed to detect individual malaria parasite counts from complex network of several infection counts. This study adopted the experimental malaria parasite counts collected from selected hospitals in Minna Metropolis, Niger State, Nigeria and Climatic data collected at the time the experiment was conducted from NECOP, Bosso, FUT Minna, Niger State, Nigeria. One thousand and two hundred (1,200) experimental data were collected and two classifiers Support Vector Machine (SVM), Artificial Neural Network (ANN) do the prediction. Experimental results indicated that SVM produced Accuracy 85.60%, Sensitivity 84.06%, Specificity 86.49%, False Positive Rate(FPr) 0.1351% and False Negative Rate(FNr) 0.1594% than Neural Network model of Accuracy 48.33%, Sensitivity 60.61%, Specificity 45.48%, low False Positive Rate (FPr) 0.5442% and False Negative Rate(FNr) 0.3939% as depicted in their respective confusion matrix.

Wireless sensor networks are present almost everywhere because of their extensive variety of utilization. However, sensor nodes are battery constrained. Therefore, proficient utilization of power turns into testing issues. Aggregated data at the base station, by individual nodes cause a flood of information which results in greater power consumption. To avoid or minimize this issue a new technique of data aggregation has been proposed. In this paper, we proposed enhanced novel energy aware hierarchical cluster-based (ENEAHC) routing protocol with the aim to: minimizing as much as total energy consumption and to enhance the performance of the energy efficient protocol by using inter-cluster based data aggregation. LZW based data aggregation likewise connected to the Cluster head (CH) to improve more results. Performance results show ENEAHC scheme reduce the end-to-end energy consumption and prolong the lifetime of the network compared to well known clustering algorithms i.e. LEACH and NEAHC. We design the actual relay node selecting issue like a non-linear programming issue and make use of property of compress sensing to find the optimal solution. The results are evaluated at the end of this paper through simulation.

This paper describe a parallel sorting algorithm which is the combination of counting sort and ternary search. The proposed algorithm is based on split and concurrent selection strategy. First of all the data sequence is distributed among the different processors and are sorted in parallel using counting sort. Then it applies ternary search to find the index position of all elements globally to find the correct position of each elements in data sequence. This paper analyses the computational complexity of proposed parallel sorting algorithm and compares it with some of existing algorithms. The results of proposed algorithms shows that it is better than existing parallel sorting algorithm like parallel merge sort and binary search based sorting algorithm.

In this article a new design of a current mode full-adder is proposed through the field effect transistors based on carbon nanotubes. The outperformance of the current mode full-adder constructed by CNTFET compared to that of constructed by CMOS is observable in the simulation and comparisons. This circuit operates based on triple input majority function. The simulation is run by HSPICE software according to the model proposed in Stanford University for CNTFETs at 0.65 V power supply voltage. The proposed circuit outperforms compared to the previous current mode full-adders in terms of speed, accuracy and PDP.

In software engineering software defect class prediction can help to take decision for proper allocation of resources in software testing phase. Identification of highly defect prone classes will get more attention from tester as well as security experts. In recent years various artificial techniques are used by researchers in different phases of SDLC. Main objective of the study is to compare the performances of Hybrid Search Based Algorithms in prediction of defect proneness of a class in software. Statistical test are used to compare the performances of developed prediction models, Validation of the models is performed with the different releases of datasets.