Effects of temperature and impurities on the DC conductivity of snow

Several major parameters affecting the electrical behavior of natural snow, namely volume conductivity and density, liquid water content and conductivity of water melted from snow, are crucial for the characterization of the electrical performance of snow-covered HV insulators. However, little study has been devoted to this subject, despite of its importance. These parameters are found to vary significantly with snow composition and purity as well as with other parameters, such as temperature, airborne pollutants, electric field strength and polarity. From laboratory experiments carried out on a large number of snow samples, it was found that DC conductivity of snow shows a peak at about -2degC. This apparently curious behavior near the melting temperature is attributed to important changes to the microstructure of snow. A correlation between DC conductivity and snow temperature was established     

Modeling of the AC Arc Discharge on Snow-covered Insulators

A mathematical model for predicting the ac flashover voltage of snow-covered insulators is presented. The arc constant parameters in air gaps and inside snow, as well as the arc reignition condition are determined using a cylindrical model. The effects of the arc length on the arc constants parameters are also investigated. The model is then applied to an EPDM insulator artificially covered with natural snow. There is good concordance between the flashover results determined from the mathematical model and those obtained experimentally.     

Determination of 4-nonylphenol and 4-tert-octylphenol compounds in various types of wastewater and their removal rates in different treatment processes in nine wastewater treatment plants of Iran

Alkylphenols(APs), considered as xenoestrogenic compounds, mainly exist as 4-nonylphenol(4-NP) and 4-tertoctylphenol(4-t-OP) in environments. The high stability and accumulation of APs in aquatic systems have caused endocrine disruption. In this study we measured APs in the wastewater influent and effluent samples, from the urban, rural, livestock, commercial and hospital wastewater treatment plants(WWTPs) in Iran. Dispersive liquid–liquid microextraction(DLLME) combined with gas chromatography–mass spectrometry(GC–MS) was used for the extraction and determination of 4-NP and 4-t-OP. In these treatment plants, various processes such as activated sludge, aerated lagoon, moving bed biofilm reactor and activated sludge along with wetland were applied. The highest concentration of 4-NP and 4-t-OP was observed in commercial and livestock sewages. The activated sludge along with wetland and then the MBBR process showed the highest removal rates of pollutants. The rates of biodegradability and accumulation in sludge were determined and also the specific adsorption coefficient Kdand the organic carbon–water partition coefficient kOCof the sludge for APs were calculated.     

A generalised optimal linear quadratic tracker with universal applications – part 1: continuous-time systems

This paper presents a generalised optimal linear quadratic analog tracker (LQAT) with universal applications for the continuous-time (CT) systems. This includes: (1) a generalised optimal LQAT design for the system with the pre-specified trajectories of the output and the control input and additionally with both the input-to-output direct-feedthrough term and known/estimated system disturbances or extra input/output signals; (2) a new optimal filter-shaped proportional plus integral state-feedback LQAT design for non-square non-minimum phase CT systems to achieve a minimum phase-like tracking performance; (3) a new approach for computing the control zeros of the given non-square CT system; and (4) a one-learning-epoch input-constrained iterative learning LQAT design for the repetitive CT system.     

TAMER: an adaptive task allocation method for aging reduction in multi-core embedded real-time systems

The Journal of Supercomputing - Technology scaling has exacerbated the aging impact on the performance and reliability of integrated circuits. By entering into nanotechnology era in recent years,...     

Model-based human gait recognition using leg and arm movements

We have presented a model-based approach for human gait recognition, which is based on analyzing the leg and arm movements. An initial model is created based on anatomical proportions, and a posterior model is constructed upon the movements of the articulated parts of the body, using active contour models and the Hough transform. Fourier analysis is used to describe the motion patterns of the moving parts. The k-nearest neighbor rule applied to the phase-weighted Fourier magnitude of each segment’s spectrum is used for classification. In contrast to the existing approaches, the main focus of this paper is on increasing the discrimination capability of the model through extra features produced from the motion of the arms. Experimental results indicate good performance of the proposed method. The technique has also proved to be able to reduce the adverse effects of self-occlusion, which is a common incident in human walking.     

Multi-objective discrete urban road network design

This paper addresses the problem of designing urban road networks in a multi-objective decision making framework. Given a base network with only two-way links, and the candidate lane addition and link construction projects, the problem is to find the optimal combination of one-way and two-way links, the optimal selection of network capacity expansion projects, and the optimal lane allocations on two-way links to optimize the reserve capacity of the network, and two new travel time related performance measures. The problem is considered in two variations; in the first scenario, two-way links may have different numbers of lanes in each direction and in the second scenario, two-way links must have equal number of lanes in each direction. The proposed variations are formulated as mixed-integer programming problems with equilibrium constraints. A hybrid genetic algorithm, an evolutionary simulated annealing, and a hybrid artificial bee colony algorithm are proposed to solve these two new problems. A new measure is also proposed to evaluate the effectiveness of the three algorithms. Computational results for both problems are presented.     

An enhanced beam search algorithm for the Shortest Common Supersequence Problem

The Shortest Common Supersequence Problem asks to obtain a shortest string that is a supersequence of every member of a given set of strings. It has applications, among others, in data compression and oligonucleotide microarray production. The problem is NP-hard, and the existing exact solutions are impractical for large instances. In this paper, a new beam search algorithm is proposed for the problem, which employs a probabilistic heuristic and uses the dominance property to further prune the search space. The proposed algorithm is compared with three recent algorithms proposed for the problem on both random and biological sequences, outperforming them all by quickly providing solutions of higher average quality in all the experimental cases. The Java source and binary files of the proposed IBS_SCS algorithm and our implementation of the DR algorithm and all the random and real datasets used in this paper are freely available upon request.     

CQARPL: Congestion and QoS-aware RPL for IoT applications under heavy traffic

The Journal of Supercomputing - The Internet of Things (IoT) provides a common platform to connect the heterogeneous devices over the internet. Hence, the number of devices connected via the...