Acoustic Emissions in Rock Deformation and Failure: New Insights from Q-Statistical Analysis

We propose a new statistical analysis of the Acoustic Emissions (AE) produced in a series of triaxial deformation experiments leading to fractures and failure of two different rocks, namely, Darley Dale Sandstone (DDS) and AG Granite (AG). By means of q-statistical formalism, we are able to characterize the pre-failure processes in both types of rocks. In particular, we study AE inter-event time and AE inter-event distance distributions. Both of them can be reproduced with q-exponential curves, showing universal features that are observed here for the first time and could be important in order to understand more in detail the dynamics of rock fractures.

Noise and Financial Stylized Facts: A Stick Balancing Approach

In this work, we address the beneficial role of noise in two different contexts, the human brain and financial markets. In particular, the similitude between the ability of financial markets to maintain in equilibrium asset prices is compared with the ability of the human nervous system to balance a stick on a fingertip. Numerical simulations of the human stick balancing phenomenon show that after the introduction of a small quantity of noise and a proper calibration of the main control parameters, intermittent changes in the angular velocity of the stick are able to reproduce the most basilar stylized facts involving price returns in financial markets. These results could also shed light on the relevance of the idea of the "planetary nervous system", already introduced elsewhere, in the financial context.

Exploring the impact of mobility restrictions on the COVID-19 spreading through an agent-based approach

BACKGROUND

The recent health emergency caused by the COVID-19 pandemic forced people to change their mobility habits, with the reduction of non-essential travels and the promotion online activities. During the first phase of the emergency in 2020, governments considered several mobility restrictions to avoid the pandemic diffusion. However, it is difficult to quantify the actual effects of these restrictions on the virus spreading, especially due to the biased data available. Notwithstanding the big role of data analysis to understand the pandemic phenomenon, it is also important to have more general models capable of predicting the impact of different policy scenarios, including territorial parameters, independently from the available infection data. In this respect, this paper proposes an agent-based model to simulate the impact of mobility restrictions on the spreading of the COVID-19 at a large scale level, by considering different factors that can be attributed to the diffusion and lethality of the virus and population mobility patterns.

METHODS

The first step of the method includes a zonation of the study area, according to administrative boundaries. A risk index is calculated for each zone considering indicators which can influence the virus spreading and people lethality: mean winter temperature, housing concentration, healthcare density, population mobility, air pollution and the percentage of population over 60 years old. The agent-based model associates the risk index to the agents and determines their "status" ("susceptible", "infected", "isolated", "recovered" or "dead") by combining the risk index with the mean infection duration, using a SIR-based approach (i.e. susceptible-infective-removed).

RESULTS

The study is applied to Italy. Several scenarios based on different mobility restrictions have been simulated, including the one based on the official data (status quo). The main results show that characterizing zones with a risk index allows to adopt local policies with almost the same effectiveness as in the case of restrictions extended to the full study area; scenario simulations return an increase in terms of infected (+20%) and deaths (+25%) with respect to the status quo. These results underline the importance of finding a trade-off between socio-economic benefits and health impact.

CONCLUSIONS

The reproducibility of the proposed methodology and its scalability allow to apply it to different contexts and at a different administrative level, from the urban scale to a national one. Moreover, the model is able to provide a decision-support tool for the design of strategic plans to contrast pandemics based on respiratory diseases.

On the role of chance in fencing tournaments: An agent-based approach

It is a widespread belief that success is mainly due to innate qualities rather than external forces. This is particularly true in sports competitions, where individual talent is usually considered the main, if not the only, ingredient to reach success. In this study, we explore the limits of this belief by quantifying the relative weight of talent and chance in fencing, a combat sport involving a weapon, with the help of both real data and agent-based simulations. Fencing competitions are structured as direct elimination tournaments, where randomness is explicitly present in some rules. We focused on épée, which is one of three disciplines. We collected data on international competition results and annual rankings, in the range 2008–2020, for male and female fencers under 20 years old (Junior category). Then, we built the model calibrated on our dataset and parametrized by just one free variable a, describing the importance of talent—and, consequently, of chance—in competitions (a = 1 indicates the ideal scenario where only talent matters, a = 0 the complete random one). Our agent-based approach can reproduce the main stylized facts observed in data, at the level of both single tournaments and the entire careers of a given community of épée fencers. We find that simulations approximate very well the data for both Junior Men and Women when talent weights slightly less than chance, i.e. when a is around 0.45. We conclude that the role of chance in fencing is unusually high and it probably represents an extreme case for individual sports. Our findings shed light on the importance of external factors in both athletes’ results in tournaments and throughout their career, making even more unfair the “winner-takes-all” disparities that often occur between the winner and the other classified competitors.

A configurable computer simulation model for reducing patient waiting time in oncology departments

Nowadays, the increase in patient demand and the decline in resources are lengthening patient waiting times in many chemotherapy oncology departments. Therefore, enhancing healthcare services is necessary to reduce patient complaints. Reducing the patient waiting times in the oncology departments represents one of the main goals of healthcare managers. Simulation models are considered an effective tool for identifying potential ways to improve patient flow in oncology departments. This paper presents a new agent-based simulation model designed to be configurable and adaptable to the needs of oncology departments which have to interact with an external pharmacy. When external pharmacies are utilised, a courier service is needed to deliver the individual therapies from the pharmacy to the oncology department. An oncology department located in southern Italy was studied through the simulation model and different scenarios were compared with the aim of selecting the department configuration capable of reducing the patient waiting times.

The Sound of Silence: Minorities, Abstention and Democracy

Despite the existence of an extensive literature, no definitive conclusion seems to emerge on the extent to which minorities are guaranteed by democratic rules in political systems. This paper contributes to this debate by proposing a modified Heigselmann and Krauss two-dimensional model of preferences in order to capture the role of abstention on minority representativeness. Regardless of the typology of abstention, simulation results show that voter abstention always benefits minorities.

Author Correction: A novel methodology for epidemic risk assessment of COVID-19 outbreak

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A novel methodology for epidemic risk assessment of COVID-19 outbreak

We propose a novel data-driven framework for assessing the a-priori epidemic risk of a geographical area and for identifying high-risk areas within a country. Our risk index is evaluated as a function of three different components: the hazard of the disease, the exposure of the area and the vulnerability of its inhabitants. As an application, we discuss the case of COVID-19 outbreak in Italy. We characterize each of the twenty Italian regions by using available historical data on air pollution, human mobility, winter temperature, housing concentration, health care density, population size and age. We find that the epidemic risk is higher in some of the Northern regions with respect to Central and Southern Italy. The corresponding risk index shows correlations with the available official data on the number of infected individuals, patients in intensive care and deceased patients, and can help explaining why regions such as Lombardia, Emilia-Romagna, Piemonte and Veneto have suffered much more than the rest of the country. Although the COVID-19 outbreak started in both North (Lombardia) and Central Italy (Lazio) almost at the same time, when the first cases were officially certified at the beginning of 2020, the disease has spread faster and with heavier consequences in regions with higher epidemic risk. Our framework can be extended and tested on other epidemic data, such as those on seasonal flu, and applied to other countries. We also present a policy model connected with our methodology, which might help policy-makers to take informed decisions.

Testing for top-down cascading effects in a biomass-driven ecological network of soil invertebrates

To investigate the structural changes of a food-web architecture, we considered real data coming from a soil food web in one abandoned pasture with former low-pressure agriculture management and we reproduced the corresponding ecological network within a multi-agent fully programmable modeling environment in order to simulate dynamically the cascading effects due to the removal of entire functional guilds.We performed several simulations differing from each other for the functional implications. At the first trophic level, we simulated a removal of the prey, that is, herbivores and microbivores, while at the second trophic level, we simulated a removal of the predators, that is, omnivores and carnivores. The five main guilds were removed either separately or in combination.The alteration in the food-web architecture induced by the removal of entire functional guilds was the highest when the entire second trophic level was removed, while the removal of all microbivores caused an alteration in the food-web structure of less than 5% of the total changes due to the removal of opportunistic and predatory species.Omnivores alone account for the highest shifts in time of the numerical abundances of the remaining species, providing computational evidence of the importance of the degree of omnivory in the stabilization of soil biota.

Perfect Information vs Random Investigation: Safety Guidelines for a Consumer in the Jungle of Product Differentiation

We present a graph-theoretic model of consumer choice, where final decisions are shown to be influenced by information and knowledge, in the form of individual awareness, discriminating ability, and perception of market structure. Building upon the distance-based Hotelling's differentiation idea, we describe the behavioral experience of several prototypes of consumers, who walk a hypothetical cognitive path in an attempt to maximize their satisfaction. Our simulations show that even consumers endowed with a small amount of information and knowledge may reach a very high level of utility. On the other hand, complete ignorance negatively affects the whole consumption process. In addition, rather unexpectedly, a random walk on the graph reveals to be a winning strategy, below a minimal threshold of information and knowledge.

Modeling financial markets by self-organized criticality

We present a financial market model, characterized by self-organized criticality, that is able to generate endogenously a realistic price dynamics and to reproduce well-known stylized facts. We consider a community of heterogeneous traders, composed by chartists and fundamentalists, and focus on the role of informative pressure on market participants, showing how the spreading of information, based on a realistic imitative behavior, drives contagion and causes market fragility. In this model imitation is not intended as a change in the agent's group of origin, but is referred only to the price formation process. We introduce in the community also a variable number of random traders in order to study their possible beneficial role in stabilizing the market, as found in other studies. Finally, we also suggest some counterintuitive policy strategies able to dampen fluctuations by means of a partial reduction of information.

Reducing financial avalanches by random investments

Building on similarities between earthquakes and extreme financial events, we use a self-organized criticality-generating model to study herding and avalanche dynamics in financial markets. We consider a community of interacting investors, distributed in a small-world network, who bet on the bullish (increasing) or bearish (decreasing) behavior of the market which has been specified according to the S&P 500 historical time series. Remarkably, we find that the size of herding-related avalanches in the community can be strongly reduced by the presence of a relatively small percentage of traders, randomly distributed inside the network, who adopt a random investment strategy. Our findings suggest a promising strategy to limit the size of financial bubbles and crashes. We also obtain that the resulting wealth distribution of all traders corresponds to the well-known Pareto power law, while that of random traders is exponential. In other words, for technical traders, the risk of losses is much greater than the probability of gains compared to those of random traders.

Are random trading strategies more successful than technical ones?

In this paper we explore the specific role of randomness in financial markets, inspired by the beneficial role of noise in many physical systems and in previous applications to complex socio-economic systems. After a short introduction, we study the performance of some of the most used trading strategies in predicting the dynamics of financial markets for different international stock exchange indexes, with the goal of comparing them to the performance of a completely random strategy. In this respect, historical data for FTSE-UK, FTSE-MIB, DAX, and S & P500 indexes are taken into account for a period of about 15–20 years (since their creation until today).

Noise, synchrony, and correlations at the edge of chaos

We study the effect of a weak random additive noise in a linear chain of N locally coupled logistic maps at the edge of chaos. Maps tend to synchronize for a strong enough coupling, but if a weak noise is added, very intermittent fluctuations in the returns time series are observed. This intermittency tends to disappear when noise is increased. Considering the probability distribution functions (pdfs) of the returns, we observe the emergence of fat tails which can be satisfactorily reproduced by q-Gaussians' curves typical of nonextensive statistical mechanics. The interoccurrence times of these extreme events are also studied in detail. Similarities with the recent analysis of financial data are also discussed.

Single Photon Avalanche Diodes: Towards the Large Bidimensional Arrays

Single photon detection is one of the most challenging goals of photonics. In recent years, the study of ultra-fast and/or low-intensity phenomena has received renewed attention from the academic and industrial communities. Intense research activity has been focused on bio-imaging applications, bio-luminescence, bio-scattering methods, and, more in general, on several applications requiring high speed operation and high timing resolution. In this paper we present design and characterization of bi-dimensional arrays of a next generation of single photon avalanche diodes (SPADs). Single photon sensitivity, dark noise, afterpulsing and timing resolution of the single SPAD have been examined in several experimental conditions. Moreover, the effects arising from their integration and the readout mode have also been deeply investigated.

Analysis of self-organized criticality in the Olami-Feder-Christensen model and in real earthquakes

We perform an analysis on the dissipative Olami-Feder-Christensen model on a small world topology considering avalanche size differences. We show that when criticality appears, the probability density functions (PDFs) for the avalanche size differences at different times have fat tails with a q-Gaussian shape. This behavior does not depend on the time interval adopted and is found also when considering energy differences between real earthquakes. Such a result can be analytically understood if the sizes (released energies) of the avalanches (earthquakes) have no correlations. Our findings support the hypothesis that a self-organized criticality mechanism with long-range interactions is at the origin of seismic events and indicate that it is not possible to predict the magnitude of the next earthquake knowing those of the previous ones.

Detecting complex network modularity by dynamical clustering

Based on cluster desynchronization properties of phase oscillators, we introduce an efficient method for the detection and identification of modules in complex networks. The performance of the algorithm is tested on computer generated and real-world networks whose modular structure is already known or has been studied by means of other methods. The algorithm attains a high level of precision, especially when the modular units are very mixed and hardly detectable by the other methods, with a computational effort O(KN) on a generic graph with N nodes and K links.

Glassy phase in the Hamiltonian mean-field model

We study the relaxation dynamics of a Hamiltonian system of N fully coupled XY spins. The thermodynamics of the system predicts a ferromagnetic and a paramagnetic phase. Starting from out-of-equilibrium initial conditions, the dynamics at constant energy drives the system into quasistationary states (QSSs) characterized by dynamical frustration. We introduce the spin polarization as an order parameter which allows us to interpret the dynamically generated QSS regime as a glassy phase of the model.

Optical bistability of an aqueous aerosol particle detected through light scattering: theory and experiment

We present a catastrophe-based graphic model for understanding the optical bistability (OB) of an aqueous aerosol particle detected in light scattering. The model is shown to be in good agreement with hidden resonance experiments and indicates that OB in such particles can occur at an incident power level of <50 x 10(-9) W. This threshold is controlled principally by the quality factor of morphological resonances and thermophysical properties of the particle. A catastrophe scheme for the bistability of solid particles is anticipated.

Optical bistability of an aerosol particle

The optical bistability of an aerosol particle is reported for the first time to the authors' knowledge. The mechanism for the effect is associated with the photothermal evaporation that accompanies the visible absorption by structure resonances.

[5 years of activity in a small coronary unit. Experiences and considerations]

Five years' experience at the coronary unit of the S. Angelo Hospital medical division is described. Practical considerations are offered with regard to the management of arrhythmia with drugs, electroconversion and electrostimulation.

Far-infrared high-resolution Fourier transform spectrometer: applications to H(2)O, NH(3), and NO(2) lines

A Michelson-type Fourier transform spectrometer has been constructed for high resolution work in the far ir. The instrument has a theoretical resolution of 0.007 cm(-1) and can be used with a tunable band pass filter system for the reduction of sampling points in a specific spectral region. Separations of 0.05 cm(-1) in the inversion spectrum of NH(3) in the 200-cm(-1) spectral region have been observed with about 1000 sampling points and splittings of 0.04 cm(-1) in the NO(2) spectrum at about 100 cm(-1).