Magnetization relaxation of uniaxial anisotropic ferromagnetic particles with linear reaction dynamics driven by DC/AC magnetic field

The response of uniaxial anisotropic ferromagnetic particles with linear reaction dynamics subjected to alternating current(AC) or direct current(DC) bias magnetic field is evaluated by the reaction–diffusion equation for the probability distribution function of the molecular concentration in the spherical coordinate system. The magnetization function and the probability distribution function of the magnetic particles in the reaction system are derived by using the Legendre polynomials and Laplace transform. We discuss the characteristics of magnetization and probability distribution of the magnetic particles with different anisotropic parameters driven by a DC and AC magnetic fields, respectively. It is shown that both the magnetization and the probability distribution decrease with time increasing due to the reaction process. The uniformity of the probability distribution and the amplitude of the magnetization are both affected by the anisotropic parameters.Meanwhile, the difference between the case with linear reaction dynamics and the non-reaction case is discussed.     

Optimal Randomness for Stochastic Configuration Network (SCN) with Heavy-Tailed Distributions

Stochastic Configuration Network (SCN) has a powerful capability for regression and classification analysis. Traditionally, it is quite challenging to correctly determine an appropriate architecture for a neural network so that the trained model can achieve excellent performance for both learning and generalization. Compared with the known randomized learning algorithms for single hidden layer feed-forward neural networks, such as Randomized Radial Basis Function (RBF) Networks and Random Vector Functional-link (RVFL), the SCN randomly assigns the input weights and biases of the hidden nodes in a supervisory mechanism. Since the parameters in the hidden layers are randomly generated in uniform distribution, hypothetically, there is optimal randomness. Heavy-tailed distribution has shown optimal randomness in an unknown environment for finding some targets. Therefore, in this research, the authors used heavy-tailed distributions to randomly initialize weights and biases to see if the new SCN models can achieve better performance than the original SCN. Heavy-tailed distributions, such as Lévy distribution, Cauchy distribution, and Weibull distribution, have been used. Since some mixed distributions show heavy-tailed properties, the mixed Gaussian and Laplace distributions were also studied in this research work. Experimental results showed improved performance for SCN with heavy-tailed distributions. For the regression model, SCN-Lévy, SCN-Mixture, SCN-Cauchy, and SCN-Weibull used less hidden nodes to achieve similar performance with SCN. For the classification model, SCN-Mixture, SCN-Lévy, and SCN-Cauchy have higher test accuracy of 91.5%, 91.7% and 92.4%, respectively. Both are higher than the test accuracy of the original SCN.     

Calibration Invariance of the MaxEnt Distribution in the Maximum Entropy Principle

The maximum entropy principle consists of two steps: The first step is to find the distribution which maximizes entropy under given constraints. The second step is to calculate the corresponding thermodynamic quantities. The second part is determined by Lagrange multipliers’ relation to the measurable physical quantities as temperature or Helmholtz free energy/free entropy. We show that for a given MaxEnt distribution, the whole class of entropies and constraints leads to the same distribution but generally different thermodynamics. Two simple classes of transformations that preserve the MaxEnt distributions are studied: The first case is a transform of the entropy to an arbitrary increasing function of that entropy. The second case is the transform of the energetic constraint to a combination of the normalization and energetic constraints. We derive group transformations of the Lagrange multipliers corresponding to these transformations and determine their connections to thermodynamic quantities. For each case, we provide a simple example of this transformation.     

Transient Dynamics in the Random Growth and Reset Model

A mean-field type model with random growth and reset terms is considered. The stationary distributions resulting from the corresponding master equation are relatively easy to obtain; however, for practical applications one also needs to know the convergence to stationarity. The present work contributes to this direction, studying the transient dynamics in the discrete version of the model by two different approaches. The first method is based on mathematical induction by the recursive integration of the coupled differential equations for the discrete states. The second method transforms the coupled ordinary differential equation system into a partial differential equation for the generating function. We derive analytical results for some important, practically interesting cases and discuss the obtained results for the transient dynamics.     

Quantum Hacking on an Integrated Continuous-Variable Quantum Key Distribution System via Power Analysis

In quantum key distribution (QKD), there are some security loopholes opened by the gaps between the theoretical model and the practical system, and they may be exploited by eavesdroppers (Eve) to obtain secret key information without being detected. This is an effective quantum hacking strategy that seriously threatens the security of practical QKD systems. In this paper, we propose a new quantum hacking attack on an integrated silicon photonic continuous-variable quantum key distribution (CVQKD) system, which is known as a power analysis attack. This attack can be implemented by analyzing the power originating from the integrated electrical control circuit in state preparation with the help of machine learning, where the state preparation is assumed to be perfect in initial security proofs. Specifically, we describe a possible power model and show a complete attack based on a support vector regression (SVR) algorithm. The simulation results show that the secret key information decreases with the increase of the accuracy of the attack, especially in a situation with less excess noise. In particular, Eve does not have to intrude into the transmitter chip (Alice), and may perform a similar attack in practical chip-based discrete-variable quantum key distribution (DVQKD) systems. To resist this attack, the electrical control circuit should be improved to randomize the corresponding power. In addition, the power can be reduced by utilizing the dynamic voltage and frequency scaling (DVFS) technology.     

The collision frequency of electron-neutral-particle in weakly ionized plasmas with non-Maxwellian velocity distributions

The collision frequencies of electron-neutral-particle in weakly ionized complex plasmas with the non-Maxwellian velocity distributions are studied. The average collision frequencies of electron-neutral-particle in plasmas are accurately derived. We find that these collision frequencies are significantly dependent on the power-law spectral indices of non-Maxwellian distribution functions and so they are generally different from the collision frequencies in plasmas with a Maxwellian velocity distribution, which will affect the transport properties of the charged particles in plasmas. Numerically analyses are made to show the roles of the spectral indices in the average collision frequencies respectively.     

Inverse Bremsstrahlung absorption coefficient in inhomogeneous plasma with nonextensive electron velocity distribution and Tsallis exponential electron density profile

Inverse bremsstrahlung (collisional) absorption of the laser beam is studied in plasma with a generalized (q-nonextensive) electron velocity distribution and some kind of generalized electron density profile. It is shown that for some values of parameters designating the q-nonextensive electron velocity distribution function and its generalized density profile, the calculated absorption coefficient reduces to the already known cases with Maxwellian velocity distribution with linear and exponential density profiles.     

Large impact in electrical properties of polypropylene by improving the filler-matrix interface effect in PP/PS blends

Inorganic nanoparticles are widely used to improve space charge behavior, DC breakdown strength and other electrical properties of polymer insulating materials, but the uniform distribution of inorganic nanofillers in matrix is difficult due to their agglomeration and bad compatibility with the polymeric matrix. In this paper, polypropylene (PP)/polystyrene (PS) blends were prepared to suppress space charge accumulation and improve DC breakdown strength. Polypropylene-g-polystyrene (PP-g-PS) graft copolymer was used as compatibilizer to improve the compatibility of PP matrix and PS filler. The evolution of microstructure of PP/PS blends were investigated by scanning electron microscope (SEM), the space charge distributions were measured by a pulsed electro-acoustic (PEA) system, and DC breakdown strength was also tested. The morphologies show that the size of PS particles reduced to 310 nm when the content of PP-g-PS graft copolymer increased to 24 wt%, and the interaction between PP matrix and PS particles enhanced. The presence of PS particles in all PP/PS blends suppressed the space charge accumulation compared to neat PP, but the DC breakdown strength in uncompatibilized blend was lower than neat PP. The increasing of content of PP-g-PS improved the DC breakdown strength with the maximum value of 408.9 kV/mm was obtained. This may attribute to excellent interface structure formed between PP matrix and PS particles.     

Effect of preparation methods and carbon black distribution on mechanical properties of short pineapple leaf fiber-carbon black reinforced natural rubber hybrid composites

The aim of this work is to improve the performance of natural rubber reinforced with a hybrid of pineapple leaf fiber with carbon black. When there are multiple components to be mixed into a rubber matrix, mixing can be carried out in more than one way. Thus, in this study, the effects of preparation method and the resulting carbon black distribution on the mechanical properties of the hybrid composite were evaluated. Pineapple leaf fiber (PALF) and carbon black contents were fixed at 10 parts (by weight) and 30 parts (by weight) per hundred parts of rubber (phr), respectively. In order to improve the dispersion, PALF with rubber was prepared as a masterbatch. Carbon black was added to the compound either as a single portion or as two separate portions, one in the PALF masterbatch and the other in the main mixing step. It was found that, despite using the same final compound formulation, the mixing scheme significantly affected the medium strain region of the vulcanizate stress-strain curve. No stress drop in this strain region was observed for the two-step mixing scheme. Models for composites with different preparation methods are proposed and discussed.     

Absorption,transportation and distribution of imidacloprid in maize

The neonicotinoid imidacloprid is a very important insecticide in maize cultivation in China. A laboratory experiment was conducted to investigate its absorption, transportation and distribution in maize plants. Plants were exposed to an aqueous solution of imidacloprid at five concentrations (10, 20, 50, 100 and 200 μg/mL). The residues of imidacloprid in different plant parts were determined by using a quick and effective method of high-performance liquid chromatography. Results showed that the average recoveries ranged from 85.16% to 102.23%, with relative standard deviations of 1.82–4.40% at three different spiking levels in each different matrix. The half-lives of imidacloprid in hydroponic maize water were from 5.33 to 11.55 days. The concentrations in roots, stems and leaves were from 5.61 to 7.48, 1.03 to 4.03 and 0 to 30.57 μg/mL after 6 h–7 days exposed in 10 μg/mL imidacloprid aqueous solutions, respectively. Our study showed that imidacloprid was strongly absorbed by roots and mainly accumulated in leaves. Quantity ratio and bioconcentration factors (BCFs) were also used to estimate the distribution and accumulation in maize. The values of quantity ratios were the highest in the leaves while lowest in the root after 7 days treatments, with the ranking of leaves > stem > root. The BCFs were 0.63–1.66, 0.52–0.92 and 3.20–6.78 in root, stem and leaves, respectively. These results demonstrated that the exposed time and imidacloprid application concentrations were also the main factors influencing the absorption. This study enhances our understandings of the uptake and distribution of imidacloprid in maize plants.