Simple regge pole model for the reactionπ − p →η’n

The most recently measured differential cross-section data forπ ⁻ p → η’n has been fitted by using a simple Regge pole model with phenomenological residue functions. It has also been observed that this inelastic process has the scaling property.     

A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies

Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive overview of different aspects of anomaly-based network intrusion detection systems (NIDSs). Additionally, contemporary malicious activities in network systems and the important properties of intrusion detection systems are discussed as well. The present survey explains important phases of NADSs, such as pre-processing, feature extraction and malicious behavior detection and recognition. In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively discussed; moreover, some details about currently available benchmark datasets for training and evaluating machine learning techniques are provided by the researchers. In the end, potential challenges together with some future directions for machine learning-based NADSs are specified.     

Application of Euler Neural Networks with Soft Computing Paradigm to Solve Nonlinear Problems Arising in Heat Transfer

In this study, a novel application of neurocomputing technique is presented for solving nonlinear heat transfer and natural convection porous fin problems arising in almost all areas of engineering and technology, especially in mechanical engineering. The mathematical models of the problems are exploited by the intelligent strength of Euler polynomials based Euler neural networks (ENN’s), optimized with a generalized normal distribution optimization (GNDO) algorithm and Interior point algorithm (IPA). In this scheme, ENN’s based differential equation models are constructed in an unsupervised manner, in which the neurons are trained by GNDO as an effective global search technique and IPA, which enhances the local search convergence. Moreover, a temperature distribution of heat transfer and natural convection porous fin are investigated by using an ENN-GNDO-IPA algorithm under the influence of variations in specific heat, thermal conductivity, internal heat generation, and heat transfer rate, respectively. A large number of executions are performed on the proposed technique for different cases to determine the reliability and effectiveness through various performance indicators including Nash–Sutcliffe efficiency (NSE), error in Nash–Sutcliffe efficiency (ENSE), mean absolute error (MAE), and Thiel’s inequality coefficient (TIC). Extensive graphical and statistical analysis shows the dominance of the proposed algorithm with state-of-the-art algorithms and numerical solver RK-4.     

Synthesis,Characterization, and Magnetic Behavior of Cobalt-Ferrite Nanoparticles under Variant Temperature Conditions

Physics of the Solid State - Wet chemical method was applied for the synthesis of cobalt-ferrite nanoparticles. The physicochemical properties were investigated by number of analytical techniques....     

Study of Nonlinear Models of Oscillatory Systems by Applying an Intelligent Computational Technique

In this paper, we have analyzed the mathematical model of various nonlinear oscillators arising in different fields of engineering. Further, approximate solutions for different variations in oscillators are studied by using feedforward neural networks (NNs) based on the backpropagated Levenberg–Marquardt algorithm (BLMA). A data set for different problem scenarios for the supervised learning of BLMA has been generated by the Runge–Kutta method of order 4 (RK-4) with the “NDSolve” package in Mathematica. The worth of the approximate solution by NN-BLMA is attained by employing the processing of testing, training, and validation of the reference data set. For each model, convergence analysis, error histograms, regression analysis, and curve fitting are considered to study the robustness and accuracy of the design scheme.     

A quantile-based Time at Risk: A new approach for assessing risk in financial markets

In this paper, we provide a new measure for evaluation of risk in financial markets. This measure is based on the return interval of critical events in financial markets or other investment situations. Our main goal was to devise a model like Value at Risk (VaR). As VaR, for a given financial asset, probability level and time horizon, gives a critical value such that the likelihood of loss on the asset over the time horizon exceeds this value is equal to the given probability level, our concept of Time at Risk (TaR), using a probability distribution function of return intervals, provides a critical time such that the probability that the return interval of a critical event exceeds this time equals the given probability level. As an empirical application, we applied our model to data from the Tehran Stock Exchange Price Index (TEPIX) as a financial asset (market portfolio) and reported the results.     

Influence of Compatibilizer-Type and Processing Approach on the Dispersion of OMMT in High-Density Polyethylene Matrix

High-density polyethylene/organoclay nanocomposites were prepared via melt intercalation in an internal mixer using both a direct mixing and master batching method. Two types of maleic anhydride grafted polyethylene, high-density polyethylene grafted maleic anhydride, and linear low-density polyethylene grafted maleic anhydride, (HDPE-g-MA, LLDPE-g-MA) were used as compatibilizers to enhance the dispersibility of nanoclay in HDPE. Dispersion of organoclay in the nanocomposites was characterized by using X-ray diffraction (XRD), transmission electron microscopy (TEM), and rheological mechanical spectroscopy (RMS). Effects of clay content and degree of clay dispersion on the rheological and tensile properties were also investigated. Furthermore, the effect of order of mixing on the dispersion and distribution of the clay layers was studied. The obtained results showed that organoclay in the nanocomposites were dispersed homogeneously and exfoliated better when HDPE-g-MA and the direct mixing route were used. Although in the master batching method clay intercalated better, clay layers chiefly remain in compatibilizer rich areas. On the other hand, direct mixing was observed to lead to clay particles being dispersed in the HDPE matrix or at the interface of the matrix and compatibilizer and, consequently, better improvement in the tensile modulus was achieved. It was determined that the compatibilizer with the higher miscibility with the matrix was the key factor for achieving better exfoliation of clay sheets.     

High Pressure High Temperature Dynamic Rheometry of Sulfonated Polyacrylamide Solutions in Electrolyte Media as Used for Oil Recovery Applications

Sulfonated polyacrylamide (SPAA) solutions were prepared and the effects of pressure, polymer concentration, and water temperature, pH and salinity on their rheological behavior were investigated using a concentric cylinder dynamic rheometer equipped with a high pressure cell. According to the rheological flow curves the shear stress of SPAA solutions increased less than in proportion to their shear rates; that is, a shear thinning effect occurred. For polymer solutions containing 15,000 ppm of SPAA, shear viscosity, and stress were nearly insensitive to pressure. However, the shear viscosity and stress of SPAA solutions were affected by temperature and this effect was more evident at lower pressure. The flow curves indicated the shear viscosity and stress of the samples increased with increasing SPAA concentration and pH of the water, but were decreased with increasing water salinity and temperature.