Aragie, BerhanuDaba, TesemaPellicane, Giuseppe
9页查看更多>>摘要：We study a stochastic model for the dynamics of charge carriers hopping from a lattice site to a neighboring one, in a one-dimensional (1D) semiconductor layer. Charge carriers are forced to migrate toward the central region by an external, harmonic potential. We also apply a non-uniform temperature, which is a linear combination of the temperature profiles generated by two heat sources. The first one is hot at the two ends of the semiconductor layer and pushes the charge carriers to stay around the center. The second one is hot around the center and produces the opposite effect. The composition of the two temperature profiles across the semiconductor layer generates two symmetric minima with respect to the central region. We show that this model is a bistable system, and by using both analytical and numerical methods we analyze the effect of different controlling parameters on the diffusion of charge carriers. We also study the crossing rate of charge carriers through the thermally activated barrier, and the stochastic resonance (SR) arising in the presence of a time-varying signal. Our results show that the application of an external potential provides a strong spectral amplification peak eta, which occurs at a even lower temperature than the one we reported recently in Aragie (2020). (c) 2022 Elsevier B.V. All rights reserved.
原文链接:
NSTL
Elsevier
Kopsidas, AthanasiosKepaptsoglou, Konstantinos
13页查看更多>>摘要：Metro systems are critical public transport elements in several metropolitan areas around the world. Unexpected disruptions may undermine service provision of metro systems, and thus addressing their negative impacts is of primary importance. A first step towards developing mitigation measures involves the identification of those critical metro stations, whose operation must be preserved. Complex Network Theory (CNT) provides valuable methodological tools for this purpose, as a topological analysis based on centrality measures combined with real-world spatiotemporal data can be used for critical station identification. The objective of this paper is to develop a measure for evaluating metro station criticality based on CNT, considering substitute services during a disruption. A substitute network is defined as the network consisting of the metro stations as nodes and all alternative public transport routes potentially serving those stations outside the metro system, as edges. The form of the substitute network depends on a pre-selected service level. Two graphs are constructed, the metro and the substitute, using an L-space and a P-space representation, respectively. A combination of centrality measures of both networks is utilized for evaluating the stations' criticality. The methodology proposed is applied to a real-world metro system, that of Athens, Greece. A sensitivity analysis is conducted suggesting that the proposed measures manage to capture the tradeoff between centrality and availability of alternatives, considering a station's topological criticality. On top of that, the criticality measure seems to be robust against changes at service levels, but sensitive enough, so that it can be adaptable to each operator's needs. The methodology proposed can be utilized for identifying critical metro stations a priori and thus achieving a more efficient planning, considering metro disruptions.(c) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Mallio, Daniel O.Aarao Reis, Fabio D. A.
13页查看更多>>摘要：Fluctuations of interfaces produced by lattice growth models scale as those of stochastic equations at distances r much larger than the lattice constant a. However, those equations may be derived from the short range interactions through renormalization, which suggests that universal properties may also be observed in short scale fluctuations of the lattice models. We first investigate this question in interfaces with preset exact power law structure factors by expanding the autocovariance function, which is shown to scale as r(2 alpha) + constant at distances as small as r -5a (alpha is the roughness exponent). Next we perform numerical simulations of lattice models in five universality classes, in one and two dimensions, and calculate the autocovariance function and the fluctuation of a local average height in their growth regimes, where finite-size effects are negligible. In cases of normal roughening with alpha > 0, those quantities also scale as affine functions of r(2 alpha) in distances from a to -10a, in contrast with the usual expectation that such relation is applicable only in the hydrodynamic limit. In a model with super-roughening in the Mullins-Herring class, similar relation is applicable with the local roughness exponent in one and two dimensions. In cases with alpha < 0, the fits of those functions diverge in the zero distance limit, in the same form as the one-point fluctuations of the corresponding stochastic equations. Finally, we study competitive models with crossovers in the roughness evolution and show that, at any given time, short and long range fluctuations scale with the exponent alpha of the dominant universality class at that time. Thus, short range correlations at long times do not keep a memory on the short time kinetics. These results reinforce the connection between discrete and continuous growth models by showing that their short range fluctuations have related properties. (c) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Zhou, WenjiaHu, YuanfaCao, JindeLiu, Xiaoyang...
18页查看更多>>摘要：This paper considers the finite-time synchronization (FTS) of coupled neural networks (CNNs) with parameter uncertainties. Based on the adaptive periodically intermittent control method and the finite-time stability theory, some sufficient conditions are derived to achieve synchronization within a finite time. Both the models of CNNs with/without delays are considered and the corresponding upper-bounds of synchronization time are estimated as well. Finally, two illustrative examples are presented to demonstrate the effectiveness and applicability of the theoretical results. (c) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Khlifi, Y.Seddik, S.El Allati, A.
9页查看更多>>摘要：An entangled steady-state behavior between two quantum thermal refrigeration machines is evaluated. Each machine is made up of three qubits of two-level, where each qubit interacts with its proper reservoir. The coupling between the qubits and the reservoirs is investigated with weak interaction. In addition, thermodynamic quantities such as heat flux are examined as a function of entanglement. The effect of temperature on entanglement is also studied in the cases of resonance and non-resonance. The obtained results show that the steady-state entanglement is more robust in the case of resonance than in the case of non-resonance. However, the maintenance in this case corresponds to the higher cooling power of the machine. (c) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Wang, YiSun, QiZhang, ZiluChen, Liqing...
13页查看更多>>摘要：By studying the parameters of the multifractal spectrum and their economic significance, a new multifractal measure Rf is constructed, which extracts price fluctuation informa-tion from different various levels. To evaluate the performance of the new multifractal measure, using 1-min high-frequency data from the US S & P 500 index and China's CSI 300 index as the research samples, we empirically compare Rf with the mainstream risk measure model - conditional value at risk (CVaR). We apply the Spearman rank correlation test to the two measures, formulate investment strategies under the two measures according to a uniform investment standard, and simulate investments. The results show that Rf has risk identification capability and that its average prediction accuracy, investment benefit and Sharpe ratio are higher than those of the CVaR model.(c) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Iftikhar, M. TahirUsman, M.Khan, Khalid
13页查看更多>>摘要：We investigate the multipartite entanglement and the trace distance for the two-dimensional anisotropic spin -1/2 XXZ lattice with Dzyaloshinskii-Moriya interaction. It is found that for a many-body quantum system the multipartite entanglement is more advantageous than the bipartite entanglement due to the monogamy property. Both the quantifiers, the multipartite entanglement and the trace distance, decreases with an increase in the anisotropy and the Dzyaloshinskii-Moriya interaction tends to restore the spoiled entanglement. The quantum reormalization group method is used to compute the stable and the unstable fixed points. We observe that the quantum phase transition point is independent of the chosen quantifier as the thermodynamic limit is reached. After sufficient iterations of the quantum renormalization group, we observe two different saturated values of both the quantifiers that represent two separate phases, the spin-fluid phase and the Neel phase. The first derivative and the scaling behavior of the renormalized entanglement quantifiers are calculated. At quantum phase transition point, the non-analytic behavior of the first derivative of the two quantifiers as a function of lattice size is examined and it is found that the universal finite-size scaling law is obeyed. Furthermore, we observe that at the critical point the scaling exponent for the multipartite entanglement and the trace distance can describe the correlation length of the model.(C) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
D'Amato, ValeriaLevantesi, SusannaPiscopo, Gabriella
14页查看更多>>摘要：This paper focuses on the prediction of cryptocurrency volatility. The stock market volatility represents a very influential aspect that affects a wide range of decisions in business and finance. Recently, the volatility spillovers between the cryptocurrency market and other financial markets are detecting. Nevertheless, the cryptocurrency volatility forecasts underperform the market dynamics. This paper develops a suitable model to capture the cryptocurrency volatility dynamics. We base on deep learning techniques, which produce more reliable results than standard methods in finance by capturing complex data interactions. Specifically, we refer to a Jordan Neural Network, which is a parsimonious recurrent neural network showing more predictability power compared to other models designed for time series, the Self Exciting Threshold Autoregressive model models and the Non-Linear Autoregressive Neural Networks. Empirical evidence is provided using data from three different cryptocurrencies, Bitcoin, Ripple, and Ethereum. (c) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Manna, S. S.Chakrabarti, Bikas K.Biswas, Soumyajyoti
9页查看更多>>摘要：We have studied few social inequality measures associated with the sub-critical dynamical features (measured in terms of the avalanche size distributions) of four self-organized critical models while the corresponding systems approach their respective stationary critical states. It has been observed that these inequality measures (specifically the Gini and Kolkata indices) exhibit nearly universal values though the models studied here are widely different, namely the Bak-Tang-Wiesenfeld sandpile, the Manna sandpile and the quenched Edwards-Wilkinson interface, and the fiber bundle interface. These observations suggest that the self-organized critical systems have broad similarity in terms of these inequality measures. A comparison with similar earlier observations in the data of socio-economic systems with unrestricted competitions suggest the emergent inequality as a result of the possible proximity to the self-organized critical states. (c) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
