In finite-size thermal systems that exhibit second-order phase transition, the fluctuations of the order parameter ϕn obey type I intermittent dynamics at their pseudocritical temperature Tpc. Moreover, as recently demonstrated, spontaneous symmetry breaking (SSB) is gradually completed as temperature is reduced until reaching an SSB completion temperature, TSSB. Within this temperature zone, ϕn obey the dynamics of critical intermittency. This behavior has also been observed in pre-seismic fracture-induced electromagnetic emissions (FEME) of the MHz band—a real-world finite-size system undergoing a second-order phase transition. Interestingly, MHz FEME has recently been found to consistently present indications of tricritical dynamics after the SSB. We examine here whether this could also be true for a finite-size thermal system. We conduct a numerical experiment for the 3D Ising model at different temperatures by gradually reducing temperature beyond SSB and analyze order parameter fluctuations using the method of critical fluctuations (MCF) and a recently introduced wavelet-based method for detecting scaling behavior in noisy experimental data. Our results reveal that power-laws still exist within a very narrow zone of temperatures right after SSB completion for the 3D Ising model. These power-laws are shown to be compatible with another form of intermittency that determines the dynamics of the order parameter fluctuations close to the Griffiths tricritical point. As a possible interpretation of this finding, we suggest that our results imply that 3D Ising presents, just below TSSB, an imprint approaching the Griffiths tricritical point from the second-order phase transition line.

In this paper, the effect of temperature on Single-Electron Transistor (SET) electrical behavior is investigated. In particular, a study of the current-voltage (I-V) curves according to parameter (temperature and gate voltage) variation is presented. Among others, the interesting phenomenon of the N-type negative differential resistance is reported as the temperature increases from absolute zero (0 K) to room temperature. Finally, theoretical analysis and simulation shows that the choice of the appropriate temperature and gate-voltage combination the SET I-V curves demonstrates either a negative differential resistance region, a switching effect, or a simple resistance behavior.

We investigate whether it is possible to distinguish chaotic time series from random time series using network theory. In this perspective, we selected four methods to generate graphs from time series: the natural, the horizontal, the limited penetrable horizontal visibility graph, and the phase space reconstruction method. These methods claim that the distinction of chaos from randomness is possible by studying the degree distribution of the generated graphs. We evaluated these methods by computing the results for chaotic time series from the 2D Torus Automorphisms, the chaotic Lorenz system, and a random sequence derived from the normal distribution. Although the results confirm previous studies, we found that the distinction of chaos from randomness is not generally possible in the context of the above methodologies.

This article investigates the dynamical complexity and fractal characteristics changes of the Bitcoin/US dollar (BTC/USD) and Euro/US dollar (EUR/USD) returns in the period before and after the outbreak of the COVID-19 pandemic. More specifically, we applied the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) method to investigate the temporal evolution of the asymmetric multifractal spectrum parameters. In addition, we examined the temporal evolution of Fuzzy entropy, non-extensive Tsallis entropy, Shannon entropy, and Fisher information. Our research was motivated to contribute to the comprehension of the pandemic’s impact and the possible changes it caused in two currencies that play a key role in the modern financial system. Our results revealed that for the overall trend both before and after the outbreak of the pandemic, the BTC/USD returns exhibited persistent behavior while the EUR/USD returns exhibited anti-persistent behavior. Additionally, after the outbreak of COVID-19, there was an increase in the degree of multifractality, a dominance of large fluctuations, as well as a sharp decrease of the complexity (i.e., increase of the order and information content and decrease of randomness) of both BTC/USD and EUR/USD returns. The World Health Organization (WHO) announcement, in which COVID-19 was declared a global pandemic, appears to have had a significant impact on the sudden change in complexity. Our findings can help both investors and risk managers, as well as policymakers, to formulate a comprehensive response to the occurrence of such external events.

It is known that in thermal systems of finite size that are subject to second order phase transitions and until the spontaneous symmetry breaking is completed, the fluctuations of the order parameter obey to the dynamics of critical intermittency. Beyond the SSB, critical intermittency does not hold. Thus, it is not expected that the distribution of the waiting times in the order parameter timeseries would hold any power law. However we reveal for the first time that right after the SSB, power laws still exist within a small zone of temperatures. These power laws emerge due to another form of intermittency that determines the dynamics of the order parameter fluctuations in the beginning of the tricritical crossover, without this crossover ever being completed in a first order phase transition. In the work pesented hereby, we present and explain this change of the dynamics of the order parameter fluctuations, as the temperature drops under the temperature of the SSB. Finally, it is mentioned that such a phenomenon has been already observed in preseismic processes.

The COVID-19 pandemic has had an unprecedented impact on the global economy and financial markets. In this article, we explore the impact of the pandemic on the weak-form efficiency of the cryptocurrency and forex markets by conducting a comprehensive comparative analysis of the two markets. To estimate the weak-form of market efficiency, we utilize the asymmetric market deficiency measure (MDM) derived using the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) approach, along with fuzzy entropy, Tsallis entropy, and Fisher information. Initially, we analyze the temporal evolution of these four measures using overlapping sliding windows. Subsequently, we assess both the mean value and variance of the distribution for each measure and currency in two distinct time periods: before and during the pandemic. Our findings reveal distinct shifts in efficiency before and during the COVID-19 pandemic. Specifically, there was a clear increase in the weak-form inefficiency of traditional currencies during the pandemic. Among cryptocurrencies, BTC stands out for its behavior, which resembles that of traditional currencies. Moreover, our results underscore the significant impact of COVID-19 on weak-form market efficiency during both upward and downward market movements. These findings could be useful for investors, portfolio managers, and policy makers.

In this paper, considering critical phenomena and their phase transitions within the frame of the set of prime numbers, is attempted. Thus, the novel, theoretical and purely mathematical Model of Criticality based on Prime Numbers is introduced. This approach allows for the emergence of a parallelism between the physical concept of criticality and the corresponding concept in prime number theory. Based on this parallelism an application of the proposed model in determining the known magic numbers of Nuclear Physics is presented. This application introduces a physical meaning to the exceptional in properties set of the prime numbers and the corresponding prime number theory. Finally, going beyond the proposed model and its application, we suggest investigating other prime numbers as doubly magic numbers or candidate magic numbers for further experimental research.

Stock market prediction techniques are a major research area, thus, extracting time-dependent patterns for the existing predictive models is of major significance. In this work, we compare forecasting performance of the nonlinear model of recurrent neural networks (RNN) in two implementations, LSTM and CNN-LSTM, to the relatively novel approach of reservoir computing (RC), and in specific, the particular class of the echo state networks (ESN). This comparison focuses on exploiting data latent dynamics, in performing efficient training and high quality predictions of the evolution of real-world financial data. Applying a multivariate scheme to a stock market index without any stationarity techniques, a definite precedence of the ESN-RC over both types of RNN's in computational efficiency as well as prediction quality, emerges. Finally, the implemented approach is friendly to the trader, since specific values of a stock market timeseries provide with a frame allowing for in time forecasting, under real-world circumstances.

A general phenomenological model of the kinetics of thermally induced structural phase transitions in multi-phase alloys is introduced for arbitrary numbers of lattice phases and transitions. The model is based on a system of ODEs yielding the temporal evolution of lattice phase fractions caused by temperature variation described by a heat balance equation. The kinetics of each transition are modeled by temperature rates of phase fractions rather than sigmoids, where an extra multiplicative parameter describing the shape of the transition rate curves is introduced. The model is applied to the electrical behavior of thin NiTi filaments by relating its resistivity to the relative proportions of three main structural phases, namely Martensite, Austenite and an intermediate phase, known as R-phase. The model yields resistivity time-series for successive heating/cooling cycles. Computer simulations are compared to previously published resistivity measurements on filaments self-heated by time-varying currents of various frequencies and passively heated samples.

In this work, first, it is confirmed that a recently introduced symbolic time-series-analysis method based on the prime-numbers-based algorithm (PNA), referred to as the “PNA-based symbolic time-series analysis method” (PNA-STSM), can accurately determine the exponent of the distribution of waiting times in the symbolic dynamics of two symbols produced by the 3D Ising model in its critical state. After this numerical verification of the reliability of PNA-STSM, three examples of how PNA-STSM can be applied to the category of systems that obey the dynamics of the on–off intermittency are presented. Usually, such time series, with on–off intermittency, present bimodal amplitude distributions (i.e., with two lobes). As has recently been found, the phenomenon of on–off intermittency is associated with the spontaneous symmetry breaking (SSB) of the second-order phase transition. Thus, the revelation that a system is close to SSB supports a deeper understanding of its dynamics in terms of criticality, which is quite useful in applications such as the analysis of pre-earthquake fracture-induced electromagnetic emission (also known as fracture-induced electromagnetic radiation) (FEME/FEMR) signals. Beyond the case of on–off intermittency, PNA-STSM can provide credible results for the dynamics of any two-symbol symbolic dynamics, even in cases in which there is an imbalance in the probability of the appearance of the two respective symbols since the two symbols are not considered separately but, instead, simultaneously, considering the information from both branches of the symbolic dynamics.

Predicting major downturns in financial markets is a popular topic among researchers. Improving the models used for this could benefit individuals, investment banks and financial institutions. The latest developments in econophysics provide additional forecasting tools that may aid this endeavor. This paper introduces an innovative method to identify early warnings for major declines in the Standard & Poor's 500 (S&P 500) index. This method performs a nonlinear analysis of the logarithmic returns of the index and then uses the moving Lyapunov exponent as a dynamic indicator of stability. The results show that the fluctuating behavior of the moving Lyapunov exponent forms spikes, which may act as warning signals since they precede all significant events that have caused major drops in the S&P 500 index over the past 20 years, including the dot-com bubble, the Great Recession and the Covid-19 pandemic.

In this paper, we present a new method for successfully simulating the dynamics of COVID-19, experimentally focusing on the third wave. This method, namely, the Method of Parallel Trajectories (MPT), is based on the recently introduced self-organized diffusion model. According to this method, accurate simulation of the dynamics of the COVID-19 infected population evolution is accomplished by considering not the total data for the infected population, but successive segments of it. By changing the initial conditions with which each segment of the simulation is produced, we achieve close and detailed monitoring of the evolution of the pandemic, providing a tool for evaluating the overall situation and the fine-tuning of the restrictive measures. Finally, the application of the proposed MPT on simulating the pandemic's third wave dynamics in Greece and Italy is presented, verifying the method's effectiveness.

Random telegraph noise (RTN) owns its very name to its assumed stochastic nature. In this paper, we follow up previous works that questioned this stochastic nature, and we investigate this assumption using experimentally measured noise coming from properly biased Ni/HfO2 unipolar Resistive RAM memristor nanodevices. We have used established, well–known tools from nonlinear theory to examine the current–noise temporal series. Evaluation results show that this series appears to exhibit not a stochastic, but a deterministic chaotic behavior, also demostrating interesting fractal characteristics in 2D and 3D phase space projections. The presented results clearly advocate for a strong component of complex (chaotic) fluctuation of deterministic origin, instead of a typical (fully stochastic) RTN. This result could pave the path for an enhanced understanding of the mechanisms behind RTN emergence, as well as improve its noise models.

Background: Natural and living systems are dynamical systems that demonstrate complex behavior, which appears to be deterministic chaotic, characterized and governed by entropy increase and loss of information throughout their entire lifespan. Lipidic nanoparticles, such as liposomes, as artificial biomembranes, have long been considered appropriate models for studying various membrane phenomena that cell systems exhibit. By utilizing these models, we can better comprehend cellular functions, stability, as well as factors that might alter the cell physiology, leading to severe disease states. In addition, liposomes are well-established drug and vaccine delivery nanosystems, which are present in the market, playing a significant role; therefore, due to their importance, issues concerning their effectiveness and stability are research topics that are constantly investigated and updated. Methods: In this study, the emergent deterministic chaotic behavior of liposomes is described, while evaluation in accordance to their colloidal physical stability, by utilizing established nonlinear dynamics tools, is presented. Two liposomes of different composition and physical stability were developed and a chaotic evaluation on the time series of their size and polydispersity was conducted. Results: The utilized models revealed instability, loss of information and order loss for both liposomes in due time, with important differentiations. An initial interpretation of the results is apposed, whereas the foundations for further investigating possible exploitation of the demonstrated nonlinearity and adaptability of artificial biomembranes is laid, with projection on biosystems. Conclusion: The present approach is expected to impact the application of lipidic nanoparticles and liposomes in various crucial fields, such as drug and vaccine delivery, providing useful information for both the academia and industry.

Purpose: The purpose of the study is to evaluate the performance of private hospitals and identify conditions that secure sustainable financing of the sector. Design/Methodology/Approach: The Data Envelopment Analysis (DEA) was used as the main tool to measure efficiency and effectiveness among fifteen (15) major private hospitals in Greece. Audited financial statement data were analyzed as a basis for the assessment of their performance. An input oriented model was applied due to the fact that assets and employee expenses are more likely to be under the control of management in private hospitals, compared to revenues and CFFO. The latter were used as outputs that represent measures of effectiveness and efficiency respectively which secure sustainability. We opted for the Variable Return to Scale (VRS) version of DEA (in connection with the CRS one), since hospital are systems extremely depended on the human capital and the knowledge management, as a means of creating value and are characterized by non-linear dynamics. Findings: The great majority of the hospitals in the sample exhibit increasing and decreasing returns scale. Inefficiencies found to emanate from a non-optimal scale of the hospitals rather, than from management's lack of capability to transform inputs to outputs. Practical Implications: The study aspires to frame options and help management to make informed choices that promote sustainable development of the private sector, which are also applicable to the public one. It is essential for public authorities to judge the meaningful performance of the private hospitals, to administer accordingly the level of its subsidies through public insurance funds, the claw back and rebate policies in a period of fiscal austerity and act accordingly to attract or deter the inflow of scalable private funds in healthcare to promote human wellbeing. Originality/Value: Performance differences, can be leveraged to guide improvements in the operation of the private hospitals and reforms in the health care system.

Recently, it has been successfully shown that the temporal evolution of the fraction of COVID-19 infected people possesses the same dynamics as the ones demonstrated by a self-organizing diffusion model over a lattice, in the frame of universality. In this brief, the relevant emerging dynamics are further investigated. Evidence that this nonlinear model demonstrates critical dynamics is scrutinized within the frame of the physics of critical phenomena. Additionally, the concept of criticality over the infected population fraction in epidemics (or a pandemic) is introduced and its importance is discussed, highlighting the emergence of the critical slowdown phenomenon. A simple method is proposed for estimating how far away a population is from this "singular" state, by utilizing the theory of critical phenomena. Finally, a dynamic approach applying the self-organized diffusion model is proposed, resulting in more accurate simulations, which can verify the effectiveness of restrictive measures. All the above are supported by real epidemic data case studies.

In this brief, the spontaneous symmetry breaking (SSB) of the φ 4 theory in phase space, is studied. This phase space results from the appropriate system of Poincaré maps, produced in both the Minkowski and the Euclidean time. The importance of discretization in the creation of phase space, is highlighted. A series of interesting, novel, unknown behaviors are reported for the first time; among them the most characteristic is the change in stability. In specific, the stable fixed points of the φ 4 potential appear as unstable ones, in phase space. Additionally, in the Euclidean-time phase space a unique instability in the position of the critical point, can be created. This instability is further proposed to host tachyonic field in Euclidean space.

Purpose:The aim of the study is to demonstrate the value of the financial performance , in assessing the degree of resilience and agility of a fruitful hotel strategy in a turbulent and disruptive era.Design/Methodology/Approach: Data Envelopment Analysis (DEA) was performed for 2017-2019 period, in conjunction with the asset turnover and operating profit to assets ratios, were used as the main tool to measure resilience and preparedness, that are manifested in enduring efficiency and effectiveness performance of operations.The latter two features of performance together represent credible resilience engines, since are inextricably intertwined to enhance capability with flexibility, growth and prudence in confronting uncertainty decisively.Αudited financial data were exploited to assess performance endurance among those dimensions.Αn input oriented model was employed based on total assets as the crucial input, while revenues and operating profits were utilized as outputs.Findings: The DEA window analysis results, portrayed both, the low scale efficiency (SE) and the deficient pure technical efficiency (PTE) as contributors to low global efficiency (TE).Adequate revenues turnover and operating profits with respect to total assets, are the essential ingredients to secure resilience and the crucial aspects of effectiveness and efficiency performance of a victorious strategy.Originality/Value: Performance differences among hotels, can be exploited to guide strategic management interventions to enhance the value creation process and resilience through versatility and sustainability, which are reflected in the effectiveness and efficiency measures.Performance measurement and evaluation unveils management options for informed choices to benefit the key stakeholders.