Cosmological analysis of reconstructed $${\mathcal {F}}(T,T_{\mathcal {G}})$$ models

In this paper, we analyze cosmological consequences of the reconstructed generalized ghost pilgrim dark energy \({\mathcal {F}}(T,T_{\mathcal {G}})\) models in terms of redshift parameter z. For this purpose, we consider power-law scale factor, scale factor for two unified phases and intermediate scale factor. We discuss graphical behavior of the reconstructed models and examine their stability analysis. Also, we explore the behavior of equation of state as well as deceleration parameters and \(\omega _{\Lambda }-\omega _{\Lambda }^{'}\) as well as \(r-s\) planes. It is found that all models are stable for pilgrim dark energy parameter 2. The equation of state parameter satisfies the necessary condition for pilgrim dark energy phenomenon for all scale factors. All other cosmological parameters show great consistency with the current behavior of the universe.     

用纳米压痕法分析溶剂对聚醚醚酮(PEEK)力学性能的影响

Poly (ether ether ketone)(PEEK) is a high-performance semi-crystalline thermoplastic polymer.Exposure of the polymeric surface to solvents can have a strong effect like softening/swelling of polymeric network or dissolution.In this study,nano-indentation analysis was performed to study the effect of acetone on the surface mechanical properties of PEEK using different exposure time.The experiments were performed with a constant loading rate (10 nm/s) to a maximum indentation displacement (1000 nm).A 30-second hold segment was included at the maximum load to account for any creep effects followed by an unloading segment to 80% unloading.The indentation hardness and the elastic modulus were computed as a continuous function of the penetration displacement in the continuous stiffness mode (CSM) indentation.The experimental data showed that the peak load decreased from ~5.2 mN to ~1.7 mN as exposure time in solvent environment increased from 0 to 18 days.The elastic modulus and the hardness of PEEK samples also displayed a decreasing trend as a function of exposure time in the solvent environment.Two empirical models were used to fit the experimental data of hardness as a function of exposure time which showed a good agreement with the experimental values.     

Four-dimensional parameter estimation of plane waves using swarming intelligence

This paper proposes an efficient approach for four-dimensional(4D) parameter estimation of plane waves impinging on a 2-L shape array. The 4D parameters include amplitude, frequency and the two-dimensional(2D) direction of arrival,namely, azimuth and elevation angles. The proposed approach is based on memetic computation, in which the global optimizer, particle swarm optimization is hybridized with a rapid local search technique, pattern search. For this purpose,a new multi-objective fitness function is used. This fitness function is the combination of mean square error and the correlation between the normalized desired and estimated vectors. The proposed hybrid scheme is not only compared with individual performances of particle swarm optimization and pattern search, but also with the performance of the hybrid genetic algorithm and that of the traditional approach. A large number of Monte–Carlo simulations are carried out to validate the performance of the proposed scheme. It gives promising results in terms of estimation accuracy, convergence rate, proximity effect and robustness against noise.     

Performance enhanced down-stream signalling for next generation long-reach 10 Gbit/s passive optical networks

In this paper, we have analyzed the signal processing methods both in digital and optical domain to enhance the transmission performance of downstream signalling in long reach passive optical networks (LR-PONs). The impact of non-linear (NL) equalization through signal processing, i.e. Volterra Equalization (VE), Digital Backpropagation (BP) and Optical Phase Conjugation with Non-linearity Module (OPC-NM) is investigated, in 10 Gbit/s (XG) DP-QPSK long-reach wavelength division multiplexed (WDM) PONs without midspan repeaters over 120 km standard single mode fibre (SMF) link for down-stream signals. Due to the compensation of optical Kerr effects, the sensitivity penalty is reduced to 2 dB by BP algorithm, 1.5 dB by VE algorithm and 2.69 dB by OPC-NM. Moreover, with the implementation of NL equalization technique we are able to get the transmission distance of 126.6 km SMF for the 1:1024 split-ratio at 5 GHz channel spacing in the non-linear region. Furthermore, the concept of super passive optical network (S-PON) is also evaluated, which involves a repeater stage consisting of optical amplifiers, to study the feasibility for receiver side signal processing and simplification.     

The effect of harvest periods on the chemical compositions of essential oils of sage (Salvia aucheri L.) leaves

The essential oils of sage leaves (Salvia aucheri Bentham var. canescens Boiss. & Heldr.), growing wild in South Anatolia, were extracted by hydrodistillation and analysed by GC and GC-MS. The percentage yields of the essential oils from sage leaves harvested at different years were 1.0%, 1.3%, 1.3%, 1.0%, 1.4%, 1.5% and 1.2%, respectively. In this study, 1,8-cineole, camphre, camphene α-pinene and β-pinene were identified as the major components of sage leaves collected at different periods. The main constituents of sage oil collected over the years were 1,8-cineole (35.01-48.06%), camphre (13.58-23.92%), camphene (6.77-8.82%), α-pinene (5.79-8.54%) and β-pinene (4.32-6.28%).     

Two new antimicrobial metabolites from the endophytic fungus, Seimatosporium sp

Two new acaranoic acids, named seimatoporic acid A and B (1, and 2), together with six known compounds, R-(-)-mellein (3), cis-4-hydroxymellein (4), trans-4-hydroxymellein (5), 4R-hydroxy-5-methylmellein (6), (-)-5-hydroxymethylmellein (7), and ergosterol (8) were isolated from an endophytic fungus, Seimatosporium sp, by a bioassay-guided procedure. The structures of the new compounds have been assigned from analysis of the 1H and 13C NMR spectra, DEPT, and by 2D COSY, HMQC, HMBC and NOESY experiments. A mixture of compounds 1 and 2 showed strong antifungal activity against Botrytis cinerea, Septoria tritici, and Pyricularia oryzae.     

Mathematical Analysis of Reaction–Diffusion Equations Modeling the Michaelis–Menten Kinetics in a Micro-Disk Biosensor

In this study, we have investigated the mathematical model of an immobilized enzyme system that follows the Michaelis–Menten (MM) kinetics for a micro-disk biosensor. The film reaction model under steady state conditions is transformed into a couple differential equations which are based on dimensionless concentration of hydrogen peroxide with enzyme reaction (H) and substrate (S) within the biosensor. The model is based on a reaction–diffusion equation which contains highly non-linear terms related to MM kinetics of the enzymatic reaction. Further, to calculate the effect of variations in parameters on the dimensionless concentration of substrate and hydrogen peroxide, we have strengthened the computational ability of neural network (NN) architecture by using a backpropagated Levenberg–Marquardt training (LMT) algorithm. NNs–LMT algorithm is a supervised machine learning for which the initial data set is generated by using MATLAB built in function known as “pdex4”. Furthermore, the data set is validated by the processing of the NNs–LMT algorithm to find the approximate solutions for different scenarios and cases of mathematical model of micro-disk biosensors. Absolute errors, curve fitting, error histograms, regression and complexity analysis further validate the accuracy and robustness of the technique.     

Rheological properties and the interface in polycarbonate/impact modifier blends: Effect of modifier shell molecular weight

Core-shell impact modifiers are used to enhance the impact strength of thermoplastics such as polycarbonate. The shell of the modifier is designed specifically to interact with the matrix polymer because interfacial adhesion between the modifier and matrix is important in improving the impact strength. Several methods have been proposed to study the interactions at the modifier/matrix interface. One measure of this interaction is the strength of lap joints. The degree of interactions at the interface can be characterized as the thickness of the interfacial region where the chains of the two polymers mix. Yet another aspect is related to the effect of interfacial interactions on the dynamic mechanical properties of the blend. Previous studies have shown that the viscoelastic properties of these blends deviate from the emulsion models that have been proposed for such blends. The deviation of the measured viscoelastic behavior of these blends compared to that predicted by the models has been attributed to the formation of network structure of particles in the blend. The formation of the network structure is a consequence of larger effective volumes of the particles due to interactions at the interface with the matrix. This study provides a means of using rheological properties and the emulsion models to estimate the extent of interaction at the modifier/matrix interface. In blends used in this study it can be shown that the interactions between the modifier and matrix extend far beyond the boundary between the two and the estimated effective volume fraction of modifier is much larger than the actual modifier content in the blend. The effective volume fraction is frequency dependent and decreases with increasing frequency. The data suggest that beyond certain frequencies the modifier no longer interacts with the matrix and the system has properties similar to the matrix with holes. The data are presented which indicate that, within the range studied, lower modifier shell molecular weight results in a higher level of interaction with polycarbonate. © 1998 John Wiley & Sons, Inc. J Polym Sci B: Polym Phys 36: 1095–1105, 1998     

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.