Fluctuation and Noise Letters最新文献

This study examines the effects of foreign direct investment (FDI), corruption mitigation, stock market, e-commerce and energy consumption on environmental quality in 54 countries along the Belt and Road Initiative (BRI) from 1996 to 2016. Using various econometric techniques, the study finds that anti-corruption efforts and financial development have positive impacts on environmental quality, while economic growth, FDI and urbanization have negative impacts. This study also reveals that FDI and corruption control have a significant interaction effect on CO₂ emissions. This study provides important insights and policy implications for achieving sustainable development in BRI countries.

Inertial effects should be considered for micro and nanoswimmers moving in a low-density medium confined by irregular structures that create entropic barriers, where viscous effects are no longer paramount. Here, we present a separation mechanism of self-propelled particles in a two-dimensional asymmetric channel, which leads to the drift of particles of different masses in opposite directions. In particular, this mechanism is based on the combined action of the spatial asymmetry of the channel structure, the temporal asymmetry inherent in particles dynamics and an external static force. This work is relevant for potential applications that can be found in the development of lab-on-a-chip devices and artificial channels for separating particles of different masses.

The interaction between new energy vehicle (NEV) stock prices and the crude oil market is crucial for resource allocation and risk management. This study employs Multifractal detrended cross-correlation analysis (MF-DCCA) to investigate the multifractal characteristics of the cross-correlation between Tesla stock price (TSLA) and crude oil price (Brent), as well as between TSLA and other NEV stocks (excluding TSLA). The experimental results reveal long-term persistence and multiple fractal characteristics in the cross-correlations. Additionally, multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) demonstrates the asymmetry of the cross-correlation during upward or downward trends between TSLA and Brent, as well as between TSLA and other NEV stocks (excluding TSLA). Furthermore, utilizing the transfer entropy (TE) method, we assess the strength and direction of information flows between TSLA and Brent, and between TSLA and other NEV stocks (excluding TSLA). Interestingly, we observe bidirectional information transmission between TSLA and other NEV stocks, while only unidirectional information transmission from NIO to TSLA is evident. These findings provide valuable insights for resource allocation, supply chain management and sustainable development strategies for decision-makers in the NEV market.

Stefan–Boltzmann law, stating the fourth power temperature dependence of the radiation emission by a black-body, was empirically formulated by Stefan in 1874 by fitting existing experiments and theoretically validated by Boltzmann in 1884 on the basis of a classical physical model involving thermodynamics principles and the radiation pressure predicted by Maxwell equations. At first sight the electromagnetic (EM) gas assumed by Boltzmann and following Rayleigh (1900) identifiable as an ensemble of N classical normal-modes, looks like an extension of the classical model of the massive ideal-gas. Accordingly, for this EM gas the internal total energy, U, was assumed to be function of volume V and temperature T as $U=U(V,T)$, and the equation of state was given by $U=3PV$, with P the radiation pressure. In addition, Boltzmann implicitly assumed that, for given values of V and T, U and the number of modes N would take finite values. However, from one hand these assumptions are not justified by Maxwell equations and classical statistics since, in vacuum (i.e., far from the EM sources), the values of N and U diverge, the so-called ultraviolet catastrophe introduced by Ehrenfest in 1911. From another hand, Boltzmann derivation of Stefan law is found to be macroscopically compatible with its derivation from quantum statistics announced by Planck in 1901. In this paper, we present a justification of this puzzling classical/quantum compatibility by noticing that the implicit assumptions made by Boltzmann is fully justified by Planck quantum statistics. Furthermore, we shed new light on the interpretation of recent classical simulations of a black body carried out by Wang, Casati, and Benenti in 2022 who found an analogous puzzling consistency between Stefan–Boltzmann law and their simulations to induce speculations on classical physics and black body radiation that are claimed to require a critical reconsideration of the role of classical physics for the understanding of quantum mechanics.

It is around a century that sample autocorrelation function has been introduced and used as a standard tool in time series analysis. A vast literature can be found on the statistical properties of the sample autocorrelation function. However, it has been highlighted recently that the sum of the sample autocorrelation function over the lags 1 to $T-1$ is −0.5 for all time series of length T. This property produces a big concern for the cases in which all available sample autocorrelations are used in the inference.

This paper provides two new alternative for estimating the autocorrelation function. These estimators come from the idea of singular spectrum analysis which is a non-parametric technique for time series analysis. The paper utilizes a simulation study to illustrate the performance of the new approach. The results suggest that further improvement to the sample autocorrelation is possible and the new methods provide an attractive alternative to the classical approach.

Chaos is prevalent in both nature and science, appearing in data, time series and complex systems. Chaotic systems exhibit numerous uncertainties, akin to noise, which challenge researchers to distinguish or analyze potential underlying patterns or even identify the type of system involved. However, determining the kind of chaotic system is essential, as it enables prediction, synchronization, control, treatment and application. This study employs machine learning to classify chaotic data through a simulation involving three types of research data: Lorenz data, Lorenz combined with Gaussian white noise, Gaussian white noise and pink noise, utilizing six distinct algorithms. The most effective testing results are achieved using Mobilenet, with a classification accuracy of 97.38% and a loss of 0.2680 across these six data types.

Known key exchange schemes offering information-theoretic (unconditional) security are complex and costly to implement. Nonetheless, they remain the only known methods for achieving unconditional security in key exchange. Therefore, the explorations for simpler solutions for information-theoretic security are highly justified. Lin et al. [1] proposed an interesting hardware key distribution scheme that utilizes thermal-noise-free resistances and DC voltages. A crypto analysis of this system is presented. It is shown that, if Eve gains access to the initial shared secret at any time in the past or future, she can successfully crack all the generated keys in the past and future, even retroactively, using passively obtained and recorded voltages and currents. Therefore, the scheme is not a secure key exchanger, but it is rather a key expander with no more information entropy than the originally shared secret at the beginning. We also point out that the proposed defense methods against active attacks do not function when the original shared secret is compromised because then the communication cannot be efficiently authenticated. However, they do work when an unconditionally secure key exchanger is applied to enable the authenticated communication protocol.

This paper explores the variations in cross-correlations among the carbon trading market, geopolitical risk (GPR) and economic policy uncertainty (EPU), focusing on their multifractality and asymmetric properties. Therefore, the study employs the multifractal detrended cross-correlation analysis (MF-DCCA) and multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) approaches to examine these relationships from a three-dimensional perspective. Also, the study looks into cross-correlations at different phases of carbon trading. Our findings reveal that the multifractal cross-correlations between the carbon trading market and GPR or EPU exhibit anti-persistence and asymmetry. The anti-persistent cross-correlations are notably stronger when GPR experiences a downtrend or when EPU undergoes an uptrend. Furthermore, distinct fluctuation patterns emerge across various trends and scales on Hurst surfaces. In the short term, the carbon trading market exhibits heightened sensitivity to changes in GPR or EPU when they rise or fall. The cross-correlations remain multifractal, anti-persistent and asymmetric across different stages of carbon trading. Notably, the multifractality of cross-correlations is most pronounced for the series pair EUA/GPR in Phase II and for the series pair EUA/UK EPU in Phase III and Phase IV. Except for the series pair EUA/UK EPU in Phase II, the anti-persistent cross-correlations are more pronounced during uptrend than downtrend for the other series pairs. Moreover, the series pair EUA/UK EPU exhibits the highest degree of asymmetry at all stages.

Since 2019, novel coronavirus pneumonia has been rampant around the world, and when outbreaks occur, Meetings, Incentives, Conferences and Exhibitions (MICE) events are often affected to varying degrees. In addition, in the context of the epidemic, consumers have increasingly taken the participation of MICE in charitable activities as a measure of their social responsibility and judged MICE events as good or bad accordingly. Therefore, the impact of deep learning-based good cause marketing and the Internet on MICE events in the context of the epidemic has attracted much attention. Based on the CiteSpace analysis, this study systematically reviewed the impact system of cause-related marketing on exhibition activities and fitted the neural network model with a single-factor inter-group experiment. The results show that when the complete data set is divided into 70% training set and 30% test set, the model with the training function of Train lm and seven hidden layers performs best in all models. This shows that in the process of charity marketing, the fit between consumers and charity activities determines the attitude and willingness of consumers to participate in charity marketing.

A tree-valued instantaneous noise-based logic with exponential Hilbert space is proposed. The third value is an uncertain bit value, which can be useful in artificial intelligence applications. The signal carrying the ternary universe has a significant advantage over the signal of the standard binary universe: its amplitude is never zero during any clock period. All known binary logic gates for exponential Hilbert space work for the binary sub-space in the ternary logic in the same way as they do in binary logic. This is useful when zeros cause problems in the exponential Hilbert space version of binary logic. Ternary logic is advantageous in various applications, including AI and natural language processing.