Improved charge transport and trap-state distribution in donor–acceptor-type semiconducting copolymer with a fluoropolymer dielectric film

This study presents a p-type doping method for donor–acceptor-type conjugated semiconducting copolymer-based field-effect transistors (FETs) with a fluoropolymer dielectric film. The polymeric FET, which initially comprises a non-polar polymer dielectric layer (poly (methyl methacrylate), PMMA), shows ambipolar behavior owing to the well-balanced electron-accepting and -donating properties of the cyclopentadithiophene (CDT) and pyridyl-2,1,3-thiadiazole (PTz)-based conjugated polymer backbone system. However, when combined with an amorphous fluoropolymer (CYTOP) dielectric layer, the FET device exhibits that their ambipolar behavior remarkably changes to a high-performance p-type FET; the hole mobility enhanced by a factor of ~3 and the threshold voltage significantly shifted from −29 V to −12 V. The density of trap states in the CDT-PTz-based polymeric FETs with a CYTOP dielectric layer, which was estimated from the temperature-dependent transfer characteristics, was narrower and shallower than that of polymeric FETs with a PMMA dielectric layer. As such, it can be inferred that the deep-trap states are filled with additional doped charges from the surface polarization induced by the fluorinated dielectrics at the semiconductor-dielectric interface.     

A Mixture Model of Truncated Zeta Distributions with Applications to Scientific Collaboration Networks

The degree distribution has attracted considerable attention from network scientists in the last few decades to have knowledge of the topological structure of networks. It is widely acknowledged that many real networks have power-law degree distributions. However, the deviation from such a behavior often appears when the range of degrees is small. Even worse, the conventional employment of the continuous power-law distribution usually causes an inaccurate inference as the degree should be discrete-valued. To remedy these obstacles, we propose a finite mixture model of truncated zeta distributions for a broad range of degrees that disobeys a power-law behavior in the range of small degrees while maintaining the scale-free behavior. The maximum likelihood algorithm alongside the model selection method is presented to estimate model parameters and the number of mixture components. The validity of the suggested algorithm is evidenced by Monte Carlo simulations. We apply our method to five disciplines of scientific collaboration networks with remarkable interpretations. The proposed model outperforms the other alternatives in terms of the goodness-of-fit.     

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.