Structural Characterization of Different Asphaltenes of Kuwaiti Origin

Asphaltenes obtained by precipitation from crude Kuwaiti oils have been analyzed by proton magnetic resonance (¹H-NMR), carbon-13 nuclear magnetic resonance (¹³C-NMR) and Infrared (IR) spectral techniques. The molecular weight and elemental analysis were also determined. These combined analytical data were used for the characterization of these Kuwaiti oils. The asphaltenes molecular weights range from approximately 4200-6500 with an H/C ratio of 0.91-1.1 with an average 45-71% aromatic carbons. The average side chain length was of 4-6 carbons. It can also be concluded that the asphaltenes under investigation contain 5-9 sets of condensed aromatic rings joined together by bridges of alkyl chains or other hetero atoms and the average number of each of these sets of condensed aromatic rings is nearly 7. There are a number of alicyclic rings and condensed alicyclic rings in asphaltene. The IR spectra showed main molecular groups including OH, NH, SH, C=O and aliphatic and aromatic C-H's.     

Harmonic and intermodulation performance in an R-LED series network

Analytical expressions are obtained for predicting the harmonic and intermodulation performance of R-LED series networks. These expressions are in terms of the ordinary Bessel functions with arguments depenedent on the modulation index.     

Performance analysis of support vector machines classifiers in breast cancer mammography recognition

Support vector machine (SVM) is a supervised machine learning approach that was recognized as a statistical learning apotheosis for the small-sample database. SVM has shown its excellent learning and generalization ability and has been extensively employed in many areas. This paper presents a performance analysis of six types of SVMs for the diagnosis of the classical Wisconsin breast cancer problem from a statistical point of view. The classification performance of standard SVM (St-SVM) is analyzed and compared with those of the other modified classifiers such as proximal support vector machine (PSVM) classifiers, Lagrangian support vector machines (LSVM), finite Newton method for Lagrangian support vector machine (NSVM), Linear programming support vector machines (LPSVM), and smooth support vector machine (SSVM). The experimental results reveal that these SVM classifiers achieve very fast, simple, and efficient breast cancer diagnosis. The training results indicated that LSVM has the lowest accuracy of 95.6107 %, while St-SVM performed better than other methods for all performance indices (accuracy = 97.71 %) and is closely followed by LPSVM (accuracy = 97.3282). However, in the validation phase, the overall accuracies of LPSVM achieved 97.1429 %, which was superior to LSVM (95.4286 %), SSVM (96.5714 %), PSVM (96 %), NSVM (96.5714 %), and St-SVM (94.86 %). Value of ROC and MCC for LPSVM achieved 0.9938 and 0.9369, respectively, which outperformed other classifiers. The results strongly suggest that LPSVM can aid in the diagnosis of breast cancer.     

On ℤ2ℤ2[u]-additive codes

In this paper, a new class of additive codes which is referred to as ?₂ ?₂[u]-additive codes is introduced. This is a generalization towards another direction of recently introduced ?₂ ?₄-additive codes [J. Borges, C. Fernández-Córdoba, J. Pujol, J. Rif´a, and M. Villanueva, ?₂ ?₄-linear codes: Generator matrices and duality, Designs Codes Cryptogr. 54(2) (2010), pp. 167–179]. ?₂ ?₄-additive codes have shown to provide a promising class of codes with their algebraic structure and applications such as steganography. The standard generator matrices are established and by introducing orthogonality the parity-check matrices are also obtained. A MacWilliams-type identity that relates the weight enumerator of a code with its dual is proved. Furthermore, a Gray map that maps these codes to binary codes is defined and some examples of optimal codes which are the binary Gray images of ?₂ ?₂[u]-additive codes are presented.     

Analysing legal status of electronic agents in contracting through interactive websites: comparative study of American,English and EU laws developing Iranian legal system

In the modern world, electronic communications play a significant role in national and international electronic transactions. This issue has forced all legal systems to face up to many emerging legal problems in the context of electronic communications, such as the legal status of electronic agents in contracting electronically. Regarding the legal validity of e-contracts made through interactive websites, the legal status of electronic agents which play an important role in this process is questionable to see whether they are akin to real agents in the physical world or they are only a mere tool of communication. The current article tries to analyse this question by criticising a number of theories and some others based on the technical nature of the Internet, the traditional legal rules and principles and the UNCITRAL regulations on e-commerce and also the statues of American, English and EU laws on e-commerce to develop Iranian legal system.     

Cross level semantic similarity: an evaluation framework for universal measures of similarity

Semantic similarity has typically been measured across items of approximately similar sizes. As a result, similarity measures have largely ignored the fact that different types of linguistic item can potentially have similar or even identical meanings, and therefore are designed to compare only one type of linguistic item. Furthermore, nearly all current similarity benchmarks within NLP contain pairs of approximately the same size, such as word or sentence pairs, preventing the evaluation of methods that are capable of comparing different sized items. To address this, we introduce a new semantic evaluation called cross-level semantic similarity (CLSS), which measures the degree to which the meaning of a larger linguistic item, such as a paragraph, is captured by a smaller item, such as a sentence. Our pilot CLSS task was presented as part of SemEval-2014, which attracted 19 teams who submitted 38 systems. CLSS data contains a rich mixture of pairs, spanning from paragraphs to word senses to fully evaluate similarity measures that are capable of comparing items of any type. Furthermore, data sources were drawn from diverse corpora beyond just newswire, including domain-specific texts and social media. We describe the annotation process and its challenges, including a comparison with crowdsourcing, and identify the factors that make the dataset a rigorous assessment of a method’s quality. Furthermore, we examine in detail the systems participating in the SemEval task to identify the common factors associated with high performance and which aspects proved difficult to all systems. Our findings demonstrate that CLSS poses a significant challenge for similarity methods and provides clear directions for future work on universal similarity methods that can compare any pair of items.     

Random resampling algorithms for addressing the imbalanced dataset classes in insider threat detection

International Journal of Information Security - Cybersecurity threats can be perpetrated by insiders or outsiders. The threats that could be carried out by insiders are far more serious due to...     

Hybrid image steganography method using Lempel Ziv Welch and genetic algorithms for hiding confidential data

Multidimensional Systems and Signal Processing - Digital images are commonly used in steganography due to the popularity of digital image transfer and exchange through the Internet. However, the...     

Estimation of magnitude and time duration of temporary overvoltages using ANN in transmission lines during power system restoration

In most countries, the main step in the process of power system restoration, following a complete/partial blackout, is energization of primary restorative transmission lines. Artificial neural network (ANN) is employed for performing a nonlinear input–output mapping in this work, in order to estimate the temporary overvoltages (TOVs) due to transmission lines energization. In the proposed methodology, Levenberg–Marquardt second order method is used to train the multilayer perceptron. Proposed ANN is trained with equivalent circuit parameters of the network as input parameters, trained ANN has therefore satisfactory generalization capability. Both single and three-phase line energizations are analyzed. The simulated results for 39-bus New England test system, indicate that the proposed technique can estimate the peak values and duration of switching overvoltages with acceptable accuracy.     

An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis

Clustering is a popular data analysis and data mining technique. A popular technique for clustering is based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on the initial state and converges to local optimum solution. This paper presents a new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem. The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO–K, which can find better cluster partition. The performance of the proposed algorithm is evaluated through several benchmark data sets. The simulation results show that the performance of the proposed algorithm is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO–SA), combination of ACO and SA (ACO–SA), combination of PSO and ACO (PSO–ACO), genetic algorithm (GA), Tabu search (TS), honey bee mating optimization (HBMO) and k-means for partitional clustering problem.