Impact of Nonlocal Interaction on Chimera States in Nonlocally Coupled Stuart–Landau Oscillators

We investigate the existence of collective dynamical states in nonlocally coupled Stuart–Landau oscillators with symmetry breaking included in the coupling term. We find that the radius of nonlocal interaction and nonisochronicity parameter play important roles in identifying the swing of synchronized states through amplitude chimera states. Collective dynamical states are distinguished with the help of strength of incoherence. Different transition routes to multi-chimera death states are analyzed with respect to the nonlocal coupling radius. In addition, we investigate the existence of collective dynamical states including traveling wave state, amplitude chimera state and multi-chimera death state by introducing higher-order nonlinear terms in the system. We also verify the robustness of the given notable properties for the coupled system.

Youth Culture and Indian Boarding Schools

Because of their perceived malleability, young people have been central to efforts by modern settler states to erase and displace Indigenous populations in order to control land and resources. In Canada and the United States between the late nineteenth and mid-twentieth centuries, these efforts (which were always contested) focused largely on Indigenous child removal and assimilatory schooling. Well-known as sites of abuse, hunger, disease, and premature death, state-funded and state-run Indian boarding schools in both countries were also shaped by the development of distinctive peer cultures. This chapter uses secondary literature, archival sources, memoir, and oral history to better understand the lived experience of Indigenous young people at these carceral and genocidal institutions. At Indian boarding schools on both sides of the Canada-U.S. border, Indigenous students used language, labor, performance, mischief, and sport to invest in and support distinctly Indigenous youth cultures characterized by imagination, resistance, solidarity, and critique.

Absorbing Markov chains for analyzing COVID-19 infections

Recently, there are many works that proposed modeling approaches to describe the random movement of individuals for COVID-19 infection. However, these models have not taken into account some key aspects for disease such the prediction of expected time of patients remaining at certain health state before entering an absorption state (e.g., exit out of the system for ever such as death state or recovery). Therefore, we propose a dynamical model approach called the absorbing Markov chains for analyzing COVID-19 infections. From this modeling approach, we seek to focus and predict two states of absorption: recovery and death, as these two conditions are considered as important indicators in assessment of the health level. Based on the absorbing Markov model, the study suggested that there is a gradually increase in the predicted death number, while a decrease in the number of recovered individuals.

Interplay of random inputs and adaptive couplings in the Winfree model

<p style='text-indent:20px;'>We study a structural robustness of the complete oscillator death state in the Winfree model with random inputs and adaptive couplings. For this, we present a sufficient framework formulated in terms of initial data, natural frequencies and adaptive coupling strengths. In our proposed framework, we derive propagation of infinitesimal variations in random space and asymptotic disappearance of random effects which exhibits the robustness of the complete oscillator death state for the random Winfree model.</p>

EVEN THE GODS DIE ... THE STATE FUNERAL AND NATIONAL MOURNING FOR THE ALBANIAN COMMUNIST DICTATOR ENVER HOXHA

This paper focuses on the death, state funeral, and national mourning for the Albanian communist dictator Enver Hoxha (1908–1985). This exploration of the emotional experiences and cultural strategies of coping with the death of a deathless leader aims to shed light on the performative roles of the ruling party, the workers, and the class enemies, as well as on the methods of ruling in a dictatorial system.

Death Behavior of Mean-Field Coupled van der Pol Oscillators Induced by Time-Delayed Feedback

Explosive death in coupled nonlinear oscillators has been an active area of extensive research in nonlinear dynamics in the recent decades. Depending on proper choice of network topology, coupling scenarios, and feedback strength, explosive death can be revealed. In this work, for the first time, we report the effect of delayed feedback on the death behavior in an ensemble of identical mean-field coupled van der Pol oscillators. In both systems with or without time delay, the normalized amplitude exhibits an abrupt transition between the oscillatory state and the death state. Intriguingly, the presence of time delay in the coupling may induce the normalized amplitude of all oscillators in the network to experience a step-like descent with small jumps in approaching the death state, pulling back the forward and backward transition points. The backward transition point has been explicitly obtained, which is confirmed by the numerical results.

An Efficient Suicide based Dataset using Machine Learning Algorithms

A study is presented on analyzing the major factors that affect the number of suicides in different parts of India from year 2000 to 2012 and using them to predict the number of suicides in the future. By analyzing the data and predicting the major causes of suicides it can help government to know which part of population is most affected, so that the government can provide required steps to avoid suicides. The Indian government records the database of each suicide occurs in India. Along with the age-group, cause of death, state of victim, this data was made public by crime branch bureau of the data analytics purpose. Relationship will be made between the different features of suicide so that a linear relationship can be formed with the help of linear regression and other machine learning algorithms will be used to develop a model for the prediction of number of suicides in the future. It has been found that the results obtained by machine learning algorithms are more accurate when compared with the traditional algorithms.