Our results extend the scope of the diversity-induced resonance effect.Given the complex temporal evolution of epileptic seizures, understanding their dynamic nature might be beneficial for clinical diagnosis and treatment. Yet, the mechanisms behind, for instance, the onset of seizures are still unknown. According to an existing classification, two basic types of dynamic onset patterns plus a number of more complex onset waveforms can be distinguished. Here, we introduce a basic three-variable model with two time scales to study potential mechanisms of spontaneous seizure onset. We expand the model to demonstrate how coupling of oscillators leads to more complex seizure onset waveforms. Finally, we test the response to pulse perturbation as a potential biomarker of interictal changes.Emergence of extremism in social networks is among the most appealing topics of opinion dynamics in computational sociophysics in recent decades. Most of the existing studies presume that the initial existence of certain groups of opinion extremities and the intrinsic stubbornness in individuals' characteristics are the key factors allowing the tenacity or even prevalence of such extreme opinions. We propose a modification to the consensus making in bounded-confidence models where two interacting individuals holding not so different opinions tend to reach a consensus by adopting an intermediate opinion of their previous ones. We show that if individuals make biased compromises, extremism may still arise without a need of an explicit classification of extremists and their associated characteristics. With such biased consensus making, several clusters of diversified opinions are gradually formed up in a general trend of shifting toward the extreme opinions close to the two ends of the opinion range, which may allow extremism communities to emerge and moderate views to be dwindled. Furthermore, we assume stronger compromise bias near opinion extremes. It is found that such a case allows moderate opinions a greater chance to survive compared to that of the case where the bias extent is universal across the opinion space. As to the extreme opinion holders' lower tolerances toward different opinions, which arguably may exist in many real-life social systems, they significantly decrease the size of extreme opinion communities rather than helping them to prevail. Brief discussions are presented on the significance and implications of these observations in real-life social systems.The problem of distinguishing deterministic chaos from non-chaotic dynamics has been an area of active research in time series analysis. Since noise contamination is unavoidable, it renders deterministic chaotic dynamics corrupted by noise to appear in close resemblance to stochastic dynamics. As a result, the problem of distinguishing noise-corrupted chaotic dynamics from randomness based on observations without access to the measurements of the state variables is difficult. We propose a new angle to tackle this problem by formulating it as a multi-class classification task. The task of classification involves allocating the observations/measurements to the unknown state variables in order to find the nature of these unobserved internal state variables. We employ signal and image processing based methods to characterize the different system dynamics. A deep learning technique using a state-of-the-art image classifier known as the Convolutional Neural Network (CNN) is designed to learn the dynamics. https://www.selleckchem.com/products/vvd-214.html The time series are transformed into textured images of spectrogram and unthresholded recurrence plot (UTRP) for learning stochastic and deterministic chaotic dynamical systems in noise. We have designed a CNN that learns the dynamics of systems from the joint representation of the textured patterns from these images, thereby solving the problem as a pattern recognition task. The robustness and scalability of our approach is evaluated at different noise levels. Our approach demonstrates the advantage of applying the dynamical properties of chaotic systems in the form of joint representation of UTRP images along with spectrogram to improve learning dynamical systems in colored noise.Cardiac alternans, beat-to-beat alternations in action potential duration, is a precursor to fatal arrhythmias such as ventricular fibrillation. Previous research has shown that voltage driven alternans can be suppressed by application of a constant diastolic interval (DI) pacing protocol. However, the effect of constant-DI pacing on cardiac cell dynamics and its interaction with the intracellular calcium cycle remains to be determined. Therefore, we aimed to examine the effects of constant-DI pacing on the dynamical behavior of a single-cell numerical model of cardiac action potential and the influence of voltage-calcium (V-Ca) coupling on it. Single cell dynamics were analyzed in the vicinity of the bifurcation point using a hybrid pacing protocol, a combination of constant-basic cycle length (BCL) and constant-DI pacing. We demonstrated that in a small region beneath the bifurcation point, constant-DI pacing caused the cardiac cell to remain alternans-free after switching to the constant-BCL pacing, thus introducing a region of bistability (RB). The size of the RB increased with stronger V-Ca coupling and was diminished with weaker V-Ca coupling. Overall, our findings demonstrate that the application of constant-DI pacing on cardiac cells with strong V-Ca coupling may induce permanent changes to cardiac cell dynamics increasing the utility of constant-DI pacing.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.https://www.selleckchem.com/products/vvd-214.html