1,721,074 research outputs found
Urban noise: physical and biomedical considerations in an european context
No full textUrban noise has ancient origins but today many new serious consequences are known. Urbanization, industrialization and transports diffusion transformed the environment. In noisy cities people often suffer from hearing impairment, insomnia and high blood pressure. The young and workers are the groups at risk. Nowadays new clinical techniques allow the early detection of noise-induced diseases and new techniques are used to monitor the environment. With the aid of models we are able to predict the noise levels in many different areas of the city and create 3-D coloured maps (noise mapping). It is mandatory to comply with regulations in order to reduce the urban noise levels under the fixed limits and to protect the people’s health. A healthy diet rich in anti-oxidants is recommended in order to protect the inner ear cells from noise injury. Finally, some European projects are briefly reporte
Business cycle modeling between financial crises and black swans: Ornstein-Uhlenbeck stochastic process vs Kaldor deterministic chaotic model
No full textBusiness cycles are oscillations in the economy because of recessions and expansions. In this paper we investigate the oscillation of the gross domestic product as a result of its relations with the other main macroeconomic variables such as capital, consumption, and investment. There is a long-standing debate about chaos and non-linear dynamics in economy and even the usefulness of those concepts has been questioned. Stochastic modeling has proven to be able to simulate reality fairly well. However, a stochastic behavior implies that reality is about exogenous randomness, while a chaotic behavior means that reality is deterministic and non-linearities are endogenous. Here we compare an Ornstein-Uhlenbeck stochastic process with a Kaldor-Kalecki deterministic chaotic model to understand which one fits better real data. We show that our chaotic model is able to represent reality as well as the stochastic model taken into consideration. Furthermore, our model may reproduce an extreme event (black swans)
Recurrence quantification analysis on a Kaldorian business cycle model
No full textBusiness cycles denote oscillations in economy as a result of downturns and expansions. The macroeconomic variable under our investigation is income as derived by the dynamic interaction with capital, consumption and investment. In this paper, a Kaldorian business cycle model is used to simulate real dynamics so that nonlinear techniques such as recurrence quantification analysis, Poincaré Plot and related quantifiers can be applied. Analysis of chaos brings evidences on fractal dimension and entropy measures for both real data and model’s simulations. The final goal is to discover whether real and simulated business cycle dynamics have similar characteristics and validate the model as a suitable tool to simulate reality
Recurrence quantification analysis of business cycles
No full textThis paper investigates, by means of recurrence quantification analysis, the characteristics of trade cycles and economic development. Trade cycles are complex phenomena oscillating because of economic downturns and expansions. In this paper the features of the underlying dynamics are studied over an extensive dataset e.g. Levy and Chen, OECD, BEA, etc. It is shown that recurrence quantification analysis can be suitably applied to economics and, therefore, may help in anticipating transitions from laminar (i.e. regular) to turbulent (i.e. chaotic) phases such as USA GDP in 1949, 1953, etc. Moreover, recurrence quantification analysis detects differences between macroeconomic variables, and highlights hidden features of economic dynamics
RQA CORRELATIONS ON REAL BUSINESS CYCLES TIME SERIES
No full textTrade cycles are complex phenomena which oscillate because of economic downturns and expansions.
Recurrence quantification analysis (RQA) detects state changes without necessitating any a priori mathematical assumption and highlights hidden features of the dynamics both at equilibrium and near transition phases.
This paper aims to understand some potential application of recurrence quantification analysis in detecting
recessions
Earlier appraisal of seismic and volcanic events by means of recurrence quantification analysis of AE time-series: preliminary results
No full textRecurrence Quantification Analysis (RQA) appears one of most promising non linear time series techniques for
the analysis of complex systems [1]. Recently, it has been applied to investigate acoustic emissions from both
rocky samples [2] and complex seismic processes dynamics [3].
Friction induced vibrations may occur whenever two objects, once put in contact, slide with respect to each other.
Typical examples are active faults inside seismogenic zones, train wheels running along tight curves with their
narrow-banded noise, friction in bearings, and events at microscopic scale in molecular physics.
Within this context, the application of RQA to Passive Acoustic Emission (AE) signals released, at ultrasonic
frequencies, by stressed rocks in the Earth’s crust beyond a specific threshold (event) is presented. The data
records are constituted by AE time-series collected nearby active tectonic and volcanic sites in Italy, Greece and
Argentina. The AE data were gathered, with 30 sec of sampling rate, by piezoelectric transducers, operating at
two ultrasonic frequencies (typically 25 and 150 kHz), fixed to a rock [4, 5, 6]. Usually, the data set is very huge
and the AE signal amplitude changes with to the acoustic impedance, associated with local rock stress conditions
and particularly sensitive to fracture density and water content. The evolution features of the quiescence and
activation status of the crustal structure is examined by applying the RQA method to the AE time-series focusing
on characteristic recurrence patterns, disregarding the signal amplitude. RQA is a quite simple processing method
which considers few parameters describing the whole complexity of a signal. The RQA parameters are simply
reckoned from the so-called “Recurrence Plot” [7] and are used to monitor quantitative changes in dynamics of
temporal distribution [2], loss of synchronization of dynamic mechanism or spatial irregularities occurring along
time [8]. In particular, this work aims at defining few descriptors that are able to explain the main characteristics
of the AE signals and identifying anomalies to be related to crustal stress modifications or paroxysmal volcanic
activities in the monitored seismic and volcanic areas [4,5]
Kaldor–Kalecki Business Cycle Model: An 80-Year Multidisciplinary Retrospective
No full textBusiness cycles exhibit complex fluctuations driven by economic downturns and expansions. Understanding whether these fluctuations follow deterministic chaos is crucial for economic modeling and policy planning. This study investigates the presence of deterministic chaos in business cycles by analyzing real-world economic data and simulations based on the Kaldor–Kalecki model. Using advanced analytical techniques, including recurrence quantification analysis (RQA), principal component analysis (PCA), and wavelet entropy, we assess whether endogenous economic collapses can be simulated and detected in real data. The findings reveal that transitions from laminar (regular) to turbulent (chaotic) phases occur in both simulated and historical economic data, such as U.S. GDP downturns in 1949, 1953, and 1958. Moreover, statistical analyses confirm that the simulated and real-world data are indistinguishable, reinforcing the model’s validity. These insights suggest that different economies, despite following distinct developmental trajectories, may be governed by a shared deterministic dynamic. By identifying such structures in business cycles, this study contributes to improving economic forecasting and crisis detection methodologies, offering valuable implications for policymakers and economists
- …
