Physical parameter sensitivity of system eigenvalues and physical model reduction

The identification of subsystems and/or components that is related to a given eigenvalue of the overall system is a challenging and important topic. The use of special structure of the system matrices obtained busing bond graphs can result in identifying subsystems and/or components that affect a given eigenvalue of an overall system. This paper, by making use of a set of theorems and definitions proposes an efficient procedure for this purpose. The basic procedure is based upon the calculation of sensitivity of eigenvalues. The so-called "effect" matrices are produced that indicates the relative importance of physical parameters on a selected eigenvalue. In addition to the relative importance, the effect matrix is used for an efficient physical model reduction procedure. Furthermore, reasons of different dynamic behavior of a system can be explained. Use of effect matrices also improves the physical model reduction method based on decomposition procedures. Three examples are given to illustrate the approach and its consequences

Self-determination theory in HCI: shaping a research agenda

Self-determination theory (SDT) has become one of the most frequently used and well-validated theories used in HCI research, modelling the relation of basic psychological needs, intrinsic motivation, positive experience and wellbeing. This makes it a prime candidate for a ‘motor theme’ driving more integrated, systematic, theory-guided research. However, its use in HCI has remained superficial and disjointed across various application domains like games, health and wellbeing, or learning. This workshop therefore convenes researchers across HCI to co-create a research agenda on how SDT-informed HCI research can maximise its progress in the coming years

Deindustrialization in cities of the global south

Recent research by economists has shown that deindustrialization is more severe in Sub-Saharan Africa and Latin America than it ever was in the Organisation for Economic Co-operation and Development (OECD). Nevertheless, most research on deindustrialization is focused on the former centres of Fordist manufacturing in the industrial heartlands of the North Atlantic. In short, there is a mismatch between where deindustrialization is researched and where it is occurring, and the objective of this paper is to shift the geographical focus of research on deindustrialization to the Global South. Case studies from Argentina, India, Tanzania and Turkey demonstrate the variegated nature of deindustrialization beyond the North Atlantic. In the process, it is demonstrated that cities in the Global South can inform wider theoretical discussions on the impacts of deindustrialization at the urban scale

An appeal to the global health community for a tripartite innovation: an ‘‘Essential Diagnostics List,’’ ‘‘Health in All Policies,’’ and ‘‘See-Through 21st Century Science and Ethics"

Diagnostics spanning a wide range of new biotechnologies, including proteomics, metabolomics, and nanotechnology, are emerging as companion tests to innovative medicines. In this Opinion, we present the rationale for promulgating an ‘‘Essential Diagnostics List.’’ Additionally, we explain the ways in which adopting a vision for ‘‘Health in All Policies’’ could link essential diagnostics with robust and timely societal outcomes such as sustainable development, human rights, gender parity, and alleviation of poverty. We do so in three ways. First, we propose the need for a new, ‘‘see through’’ taxonomy for knowledge-based innovation as we transition from the material industries (e.g., textiles, plastic, cement, glass) dominant in the 20th century to the anticipated knowledge industry of the 21st century. If knowledge is the currency of the present century, then it is sensible to adopt an approach that thoroughly examines scientific knowledge, starting with the production aims, methods, quality, distribution, access, and the ends it purports to serve. Second, we explain that this knowledge trajectory focus on innovation is crucial and applicable across all sectors, including public, private, or public–private partnerships, as it underscores the fact that scientific knowledge is a co-product of technology, human values, and social systems. By making the value systems embedded in scientific design and knowledge co-production transparent, we all stand to benefit from sustainable and transparent science. Third, we appeal to the global health community to consider the necessary qualities of good governance for 21st century organizations that will embark on developing essential diagnostics. These have importance not only for science and knowledge based innovation, but also for the ways in which we can build open, healthy, and peaceful civil societies today and for future generations

Model reduction in the physical domain

This paper is concerned with obtaining physical-based low-order approximations of linear physical systems. Low-order models possess some advantages, including the reduction of computational difficulty and understanding of the physics of the original system in a simpler manner. Previously, a number of methods have been suggested to develop suitable low-order approximations. However, most of these approaches do not reflect the relation between the mathematical model and the physical subsystems. Specifically, these techniques do not indicate which of the physical subsystems should be retained or eliminated in the reduced-order model. The proposed model reduction method is based on identifying subsystem types of a physical system using the bond graph method. These subsystems are then removed or retained based on the information from the physical system decomposition procedures and partial fraction expansion residues to obtain a reduced-order model. The physical model reduction procedure is verified on physical linear systems