ABSTRACT
A nonlinear-dynamical algorithm for city planning is proposed as an impulse pattern formulation (IPF) for predicting relevant parameters such as health, artistic freedom, or financial developments of different social or political stakeholders over the cause of a planning process. The IPF has already shown high predictive precision at low computational cost in musical instrument simulations, brain dynamics, and human-human interactions. The social and political IPF consists of three basic equations of system state developments, self-adaptation of stakeholders, two adaptive interactions, and external impact terms suitable for respective planning situations. Typical scenarios of stakeholder interactions and developments are modeled by adjusting a set of system parameters. These include stakeholder reaction to external input, enhanced system stability through self-adaptation, stakeholder convergence due to adaptive interaction, as well as complex dynamics in terms of fixed stakeholder impacts. A workflow for implementing the algorithm in real city planning scenarios is outlined. This workflow includes machine learning of a suitable set of parameters suggesting best-practice planning to aim at the desired development of the planning process and its output.
ABSTRACT
HIV infection of CD4+ peripheral blood lymphocytes leads to a loss of MHC class I molecules on the surface of the infected cells as detectable by monoclonal antibody staining and flow cytometry. Incubation of the infected cells at 26 degrees C or treatment at 37 degrees C with peptides leads to upregulation of MHC class I to levels equal to those found on uninfected cells cultured under the same conditions. The data suggest that, after HIV infection, the mechanisms responsible for peptide generation, peptide transport and thus stable association between peptides and MHC class I molecules are severely affected.