Although there are various models of epidemic diseases, there are a few individual-based models that can guide susceptible individuals on how they should behave in a pandemic without its appropriate ...treatment. Such a model would be ideal for the current coronavirus disease 2019 (COVID-19) pandemic. Thus, here, we propose a topological model of an epidemic disease, which can take into account various types of interventions through a time-dependent contact network. Based on this model, we show that there is a maximum allowed number of persons one can see each day for each person so that we can suppress the epidemic spread. Reducing the number of persons to see for the hub persons is a key countermeasure for the current COVID-19 pandemic.
Short- and long-distance circadian communication is essential for integration of temporal information. However, a major challenge in plant biology is to decipher how individual clocks are ...interconnected to sustain rhythms in the whole plant. Here we show that the shoot apex is composed of an ensemble of coupled clocks that influence rhythms in roots. Live-imaging of single cells, desynchronization of dispersed protoplasts, and mathematical analysis using barycentric coordinates for high-dimensional space show a gradation in the strength of circadian communication in different tissues, with shoot apex clocks displaying the highest coupling. The increased synchrony confers robustness of morning and evening oscillations and particular capabilities for phase readjustments. Rhythms in roots are altered by shoot apex ablation and micrografting, suggesting that signals from the shoot apex are able to synchronize distal organs. Similarly to the mammalian suprachiasmatic nucleus, shoot apexes play a dominant role within the plant hierarchical circadian structure.
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•Shoot apex clocks function as the suprachiasmatic nucleus neurons in mammals•Circadian coupling defines the high degree of synchrony among shoot apex clocks•The shoot apex clocks influence the circadian activity in roots•Different plant organs exhibit variations in clock precision and circadian synchrony
The plant shoot apex clocks resemble the suprachiasmatic nucleus in mammals in their coupling properties and their capacity to synchronize circadian rhythms in distal organs.
•We used infinite-dimensional delay coordinates to predict solar irradiance.•Time series prediction up to 6h ahead was improved.•Especially, prediction for 7am at 5am has been improved.
Short-term ...prediction for renewable energy outputs up to 6h is important especially for preparing backup power plants such as thermal power plants and hydro power plants to keep the voltage and the frequency in a power grid constant. However, short-term prediction of solar irradiance for the morning is especially difficult because measurements of solar irradiance before sunrise are zeros and useless for the prediction. Here we propose to use recently derived infinite-dimensional delay coordinates for predicting solar irradiance after sunrise based on a time series before the sunrise. As a result, one can make time series prediction by taking into account the long history of previous changes of solar irradiance. We demonstrate that the examined short-term time series prediction has effectively predictive skills because its prediction errors are smaller by about 85% best compared with the 24h clear sky index persistence.
A pseudo-basis using a recurrence plot Shiro, Masanori; Hirata, Yoshito
The European physical journal. ST, Special topics,
02/2023, Letnik:
232, Številka:
1
Journal Article
Recenzirano
Odprti dostop
We examine how much of columns of a recurrence plot can be represented by bit operations of a subset of the columns, which is here called a pseudo-basis. Such bit operations include AND, OR, and NOT. ...We find that the ratio of the columns that cannot be represented by such bit operations decreases exponentially when one increases the number of columns included in the subset. Our results would be a fundamental reasoning on how one can express a time series generated from a nonlinear system.
Records for observing dynamics are usually complied by a form of time series. However, time series can be a challenging type of dataset for deep neural networks to learn. In deep neural networks, ...pairs of inputs and outputs are usually fed for constructive mapping. Such inputs are typically prepared as static images in successful applications. And so, here we propose two methods to prepare such inputs for learning the dynamical properties behind time series. In the first method, we simply array a time series in the shape of a rectangle as an image. In the second method, we convert a time series into a distance matrix using delay coordinates, or an unthresholded recurrence plot. We demonstrate that the second method performs well in inferring a slow driving force from observations of a forced system within which there are symmetry and almost invariant subsets.
Delay embedding-a method for reconstructing dynamical systems by delay coordinates-is widely used to forecast nonlinear time series as a model-free approach. When multivariate time series are ...observed, several existing frameworks can be applied to yield a single forecast combining multiple forecasts derived from various embeddings. However, the performance of these frameworks is not always satisfactory because they randomly select embeddings or use brute force and do not consider the diversity of the embeddings to combine. Herein, we develop a forecasting framework that overcomes these existing problems. The framework exploits various "suboptimal embeddings" obtained by minimizing the in-sample error via combinatorial optimization. The framework achieves the best results among existing frameworks for sample toy datasets and a real-world flood dataset. We show that the framework is applicable to a wide range of data lengths and dimensions. Therefore, the framework can be applied to various fields such as neuroscience, ecology, finance, fluid dynamics, weather, and disaster prevention.
Forecasting the aftershock probability has been performed by the authorities to mitigate hazards in the disaster area after a main shock. However, despite the fact that most of large aftershocks ...occur within a day from the main shock, the operational forecasting has been very difficult during this time-period due to incomplete recording of early aftershocks. Here we propose a real-time method for efficiently forecasting the occurrence rates of potential aftershocks using systematically incomplete observations that are available in a few hours after the main shocks. We demonstrate the method's utility by retrospective early forecasting of the aftershock activity of the 2011 Tohoku-Oki Earthquake of M9.0 in Japan. Furthermore, we compare the results by the real-time data with the compiled preliminary data to examine robustness of the present method for the aftershocks of a recent inland earthquake in Japan.
Forecasting aftershock probabilities, as early as possible after a main shock, is required to mitigate seismic risks in the disaster area. In general, aftershock activity can be complex, including ...secondary aftershocks or even triggering larger earthquakes. However, this early forecasting implementation has been difficult because numerous aftershocks are unobserved immediately after the main shock due to dense overlapping of seismic waves. Here we propose a method for estimating parameters of the epidemic type aftershock sequence (ETAS) model from incompletely observed aftershocks shortly after the main shock by modeling an empirical feature of data deficiency. Such an ETAS model can effectively forecast the following aftershock occurrences. For example, the ETAS model estimated from the first 24 h data after the main shock can well forecast secondary aftershocks after strong aftershocks. This method can be useful in early and unbiased assessment of the aftershock hazard.
Key Points
A method for estimating the ETAS model from an early aftershock sequence
The ETAS model can be well estimated from the first 1‐day data of aftershocks
The estimated ETAS model well forecasts the complex aftershock activity
Networks are widely used as a tool for describing diverse real complex systems and have been successfully applied to many fields. The distance between networks is one of the most fundamental concepts ...for properly classifying real networks, detecting temporal changes in network structures, and effectively predicting their temporal evolution. However, this distance has rarely been discussed in the theory of complex networks. Here, we propose a graph distance between networks based on a Laplacian matrix that reflects the structural and dynamical properties of networked dynamical systems. Our results indicate that the Laplacian-based graph distance effectively quantifies the structural difference between complex networks. We further show that our approach successfully elucidates the temporal properties underlying temporal networks observed in the context of face-to-face human interactions.
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