Das inkludiernde De-Institutionalisierungsgebot im Lichte der Dramatik stationärer Pflege unter Corona-Bedingungen

Zusammenfassung Die Corona-Pandemie hat die Lebensqualität der Menschen in den Heimen als Einrichtungen der Langzeitpflege signifikant negativ geprägt. In der Dichte des stationären Settings wurden im Lichte eines „satt, sauber, trocken und still“-Dispositivs die ohnehin problematischen Strukturen der Entnormalisierung durch hospitalisierende Institutionalisierung eskalierend verstärkt. Selbstbestimmung und Teilhabe als Grundrechte im menschenrechtskonventionellen Sinne wurden pauschal ohne Risiko-bezogene Güterabwägungen zugunsten einer „Kasernierung“ zum Zwecke der Sicherung des „nackten Lebens“ dethematisiert. Die im Sektor ohnehin als Spuren struktureller Gewalt angelegten Mechanismen sozialer Ausgrenzung wurden im Krisenmanagement akzeleriert. Als Lehre aus diesen sozialen Praktiken muss über die Zukunft stationärer Settings radikal kritisch nachgedacht werden. Die Alternative ist eine Empowerment-orientierte investive Sozialpolitik als sozialraumorientierte Differenzierung der Wohnformen und ihren Care-Settings. Abstract: Normative Challenge of Including De-institutionalization of Long-term Nursing Under Conditions of the Corona Crisis The Corona pandemic is connected with a significant impact on the escalating reduction of the quality of life of old age in the settings of long-term nursing care. The heritage of given tradition of institutionalization as a culture of social exclusion as accelerating pathway towards a dispositive grammar of a regime of risk security and hygienic cleanliness of “naked life” in a limited activating atmosphere of “remain silent” dominating the valued of autonomy and inclusive participation as dimensions of dignity of human personhood. Social exclusion is a mode of performativity of structural violence. The critical result of the societal reflection about this social mechanism is to think about the alternative perspective of social investment in caring community-building as spatial social network supporting normal forms of living outside the institutions but within an inclusive normal social world of moral economy of social capital formation.

Deep learning for early warning signals of tipping points

Many natural systems exhibit tipping points where slowly changing environmental conditions spark a sudden shift to a new and sometimes very different state. As the tipping point is approached, the dynamics of complex and varied systems simplify down to a limited number of possible “normal forms” that determine qualitative aspects of the new state that lies beyond the tipping point, such as whether it will oscillate or be stable. In several of those forms, indicators like increasing lag-1 autocorrelation and variance provide generic early warning signals (EWS) of the tipping point by detecting how dynamics slow down near the transition. But they do not predict the nature of the new state. Here we develop a deep learning algorithm that provides EWS in systems it was not explicitly trained on, by exploiting information about normal forms and scaling behavior of dynamics near tipping points that are common to many dynamical systems. The algorithm provides EWS in 268 empirical and model time series from ecology, thermoacoustics, climatology, and epidemiology with much greater sensitivity and specificity than generic EWS. It can also predict the normal form that characterizes the oncoming tipping point, thus providing qualitative information on certain aspects of the new state. Such approaches can help humans better prepare for, or avoid, undesirable state transitions. The algorithm also illustrates how a universe of possible models can be mined to recognize naturally occurring tipping points.

Stability of Non-Hyperbolic Equilibrium Point for Polynomial System of Differential Equations

In this paper, a polynomial system of plane differential equations is observed. The stability of the non-hyperbolic equilibrium point was analyzed using normal forms. The nonlinear part of the differential equation system is simplified to the maximum. Two nonlinear transformations were used to simplify first the quadratic and then the cubic part of the system of equations.