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For decades now, exports and import have grown more rapidly than domestic production. This is a strong indication that, besides the rapid growth of foreign trade in final goods, trade in intermediates is becoming increasingly important. For this reason, an input-output ap-proach is more appropriate for any analysis of diversification than a traditional approach based purely on macroeconomic data.
This article analyses economic diversification in Gulf Cooperation Council (GCC) countries using data from input-output tables which are an integral part of the national accounts. We compare the performance of the GCC economies with that of a reference case, Norway, which is considered to have successfully diversified its economy despite having a large oil resource base. It also assesses these countries’ relative progress on sustainable development using a measure of the World Bank, adjusted net savings, which evaluates the true rate of savings in an economy after accounting for investments in physical and human capital, de-pletion of natural resources, and damage from environmental pollution.
The article concludes that GCC countries have, contrary to expectation, collectively per-formed relatively well on diversification, but their performance on sustainable development varies.
Industrial growth and a rapidly growing world population have large impacts on the global environment and allocation of material resources. Most changes in the environment are brought about by human activities and these activities result in a flow of materials. The flows of resources from the natural environment to the economy are a prerequisite of production while flows of residuals from the economy to the environment are the consequence of production and consumption. A full understanding of these processes requires a complete description of the physical dimension of the economy and its interaction with the environment.
Sliding-mode observation with iterative parameter adaption for fast-switching solenoid valves
(2016)
Control of the armature motion of fast-switching solenoid valves is highly desired to reduce noise emission and wear of material. For feedback control, information of the current position and velocity of the armature are necessary. In mass production applications, however, position sensors are unavailable due to cost and fabrication reasons. Thus, position estimation by measuring merely electrical quantities is a key enabler for advanced control, and, hence, for efficient and robust operation of digital valves in advanced hydraulic applications. The work presented here addresses the problem of state estimation, i.e., position and velocity of the armature, by sole use of electrical measurements. The considered devices typically exhibit nonlinear and very fast dynamics, which makes observer design a challenging task. In view of the presence of parameter uncertainty and possible modeling inaccuracy, the robustness properties of sliding mode observation techniques are deployed here. The focus is on error convergence in the presence of several sources for modeling uncertainty and inaccuracy. Furthermore, the cyclic operation of switching solenoids is exploited to iteratively correct a critical parameter by taking into account the norm of the observation error of past switching cycles of the process. A thorough discussion on real-world experimental results highlights the usefulness of the proposed state observation approach.
This Chapter introduces parameterized, or parametric, Model Order Reduction (pMOR). The Sections are offered in a prefered order for reading, but can be read independently. Section 5.1, written by Jorge Fernández Villena, L. Miguel Silveira, Wil H.A. Schilders, Gabriela Ciuprina, Daniel Ioan and Sebastian Kula, overviews the basic principles for pMOR. Due to higher integration and increasing frequency-based effects, large, full Electromagnetic Models (EM) are needed for accurate prediction of the real behavior of integrated passives and interconnects. Furthermore, these structures are subject to parametric effects due to small variations of the geometric and physical properties of the inherent materials and manufacturing process. Accuracy requirements lead to huge models, which are expensive to simulate and this cost is increased when parameters and their effects are taken into account. This Section introduces the framework of pMOR, which aims at generating reduced models for systems depending on a set of parameters.
Stress is becoming an important topic in modern life. The influence of stress results in a higher rate of health disorders such as burnout, heart problems, obesity, asthma, diabetes, depressions and many others. Furthermore individual’s behavior and capabilities could be directly affected leading to altered cognition, inappropriate decision making and problem solving skills. In a dynamic and unpredictable environment, such as automotive, this can result in a higher risk for accidents. Different papers faced the estimation as well as prediction of drivers’ stress level during driving. Another important question is not only the stress level of the driver himself, but also the influence on and of a group of other drivers in the near area. This paper proposes a system, which determines a group of drivers in a near area as clusters and it derives the individual stress level. This information will be analyzed to generate a stress map, which represents a graphical view about road section with a higher stress influence. Aggregated data can be used to generate navigation routes with a lower stress influence to decrease stress influenced driving as well as improve road safety.