Systems Thinking for Sustainability

A sustainability framework based on nested adaptive socio-ecological systems is used to analyse historical examples of soil erosion and its implications for food production and security for a growing population. While there are examples of innovation in agriculture to address food availability, there are also cases of inadequate social responses leading to famine. The analysis highlights the value of considering these issues in a framework of linked biophysical and socio-economic systems. The socio-economic system generates management interventions to resolve biophysical limitations such as soil depletion on food production. The socio-economic system is also responsible when there are inadequate social responses. Currently, the biophysical system produces enough food to feed the world population. However, food distribution through the socio-economic system results in increasing numbers of undernourished people. Biophysical system modelling indicates that unless major changes are made to the current world system, overshoot because of resource depletion will lead to system collapse within the 21st century. To develop sustainability strategies, we need to analyse the socio-economic response to this biophysical vulnerability. A socio-ecological analysis also indicates that perspectives based on power relations that govern real-world decision making rather than sustainability interventions to address food security, need to be incorporated.

Environmental Conservation and Environmental Change

Chapter 1 introduces the content and structure of the book. It identifies the main characteristics of the Hotelling approach to conservation, and the nature of the conservation problems it can address. It summarizes the evidence for large-scale, systematic changes in biodiversity and ecological functioning across biomes. It identifies what elements of the biophysical system have and have not been conserved, how this differs from one society to the next, and what has been gained or lost in the process. Finally, the chapter also discusses the different ways in which the conservation problem has been analyzed by natural and social scientists.

The Psycho-biophysical Modeling of the some Stress Parameters

The human organism is a biophysical system. Stress represents a normal reaction of the organism which appears as a response to an aggression situation which requires an unusual and quick adaptation effort from the organism. Stress is a state of putting in alert, of mobilizing the forces of the organism in the occasion of an event which requires, in order to be kept under control, a big amount of energy in a very short time. This alert state or action preparation translated through physical and psychological manifestations. In higher-level living organisms the following forms of regulation are known: biological, nervous, hormonal, humoral and immune regulation. In the case of humans, psychic regulation also appears due to the existence of psychic activity. We study only two forms of the stress: fear and death. We present different biophysical modeling aspects. The stress in the human organism is a pertur­bation. This perturbation is regulates by negative feedback.

Benefits of object-oriented models and ModeliChart: modern tools and methods for the interdisciplinary research on smart biomedical technology

Computational models of biophysical systems generally constitute an essential component in the realization of smart biomedical technological applications. Typically, the development process of such models is characterized by a great extent of collaboration between different interdisciplinary parties. Furthermore, due to the fact that many underlying mechanisms and the necessary degree of abstraction of biophysical system models are unknown beforehand, the steps of the development process of the application are iteratively repeated when the model is refined. This paper presents some methods and tools to facilitate the development process. First, the principle of object-oriented (OO) modeling is presented and the advantages over classical signal-oriented modeling are emphasized. Second, our self-developed simulation tool ModeliChart is presented. ModeliChart was designed specifically for clinical users and allows independently performing