Linear algebra: selected problems

Linear Algebra—Selected Problems is a unique book for senior undergraduate and graduate students to fast review basic materials in Linear Algebra. Vector spaces are presented first, and linear transformations are reviewed secondly. Matrices and Linear systems are presented. Determinants and Basic geometry are presented in the last two chapters. The solutions for proposed excises are listed for readers to references.

Creativity and digital spaces in the intercultural society

In Permanent Transit: Discourses and Maps of the Intercultural Experience builds interdisciplinary approaches to the study of migrations, traffics, globalization, communication, regulations, arts, literature, and other intercultural processes, in the context of past and present times. The book offers a convergence of perspectives, combining conceptual and empirical work by sociologists, anthropologists, historians, linguists, educators, lawyers, media, specialists, and literary studies writers, in their shared attempt to understand the many routes of the intercultural experience. In Permanent Transit: Discourses and Maps of the Intercultural Experience builds interdisciplinary approaches to the study of migrations, traffics, globalization, communication, regulations, arts, literature, and other intercultural processes, in the context of past and present times. The book offers a convergence of perspectives, combining conceptual and empirical work by sociologists, anthropologists, historians, linguists, educators, lawyers, media, specialists, and literary studies writers, in their shared attempt to understand the many routes of the intercultural experience.

Simulation of the magnetic induction vector of a magnetic core to be used in FFC NMR relaxometry

This paper is a contribution for the assessment and comparison of magnet properties based on magnetic field characteristics particularly concerning the magnetic induction uniformity in the air gaps. For this aim, a solver was developed and implemented to determine the magnetic field of a magnetic core to be used in Fast Field Cycling (FFC) Nuclear Magnetic Resonance (NMR) relaxometry. The electromagnetic field computation is based on a 2D finite-element method (FEM) using both the scalar and the vector potential formulation. Results for the magnetic field lines and the magnetic induction vector in the air gap are presented. The target magnetic induction is 0.2 T, which is a typical requirement of the FFC NMR technique, which can be achieved with a magnetic core based on permanent magnets or coils. In addition, this application requires high magnetic induction uniformity. To achieve this goal, a solution including superconducting pieces is analyzed. Results are compared with a different FEM program.

Transient Spaces: unsettling boundaries and norms at the cultural event Noc Noc, Guimarães, Portugal

Cities are increasingly expected to be creative, inventive and to exhibit intense expressivity. In the past decades many cities have experienced growing pressure to produce and stage cultural events of different sorts and to develop new strategies that optimize competitive advantages, in order to promote themselves and to boost and sell their image. Often these actions have relied on heavy public investment and major private corporation sponsoring, but it is not always clear or measured how successful and reproductive these investments have been. In the context of strained public finances and profound economic crisis of European peripheral countries, events that emerge from local communities and have low budgets, which manage to create significant fluxes of visitors and visibility, assume an increased interest. In order to reflect and sketch possible answers, we look to an emerging body of literature concerning creative cities, and we focus on the organisation of a particular cultural event and its impact and assimilation into a medium size Portuguese city. This paper looks at the two editions (2011 and 2012) of one of such events – Noc Noc – organized by a local association in the city of Guimarães, Portugal. Inspired by similar events, Noc Noc is based on creating transient spaces of culture which are explored by artists and audiences, by transforming numerous homes into ephemeral convivial and playful social ‘public’ environments. The event is based on a number of cultural venues/homes scattered around the old and newer city, which allows for an informal urban exploration and an autonomous rambling and getting lost along streets. This strategy not only disrupts the cleavages between public and private space permitting for various transgressions, but it also disorders normative urban experiences and unsettles the dominant role of the city council as the culture patron of the large majority of events. Guimarães, an UNESCO World Heritage City was the European Capital of Culture in 2012, with a public investment of roughly 73 million euro. By interviewing a sample of people who have hosted these transitory art performances and exhibitions, sometimes doubling as artists, the events’ organizers and by experience both editions of the event, this paper illustrates how urban citizens’ engagement and motivations in a low budget cultural event can strengthen community ties. Furthermore, it also questions the advantages of large scale high budget events, and how this event may be seen as unconscious counter movement against a commodification of cultural events and everyday urban experience at large, engaging with the concepts of staging and authenticity.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

Solar Intensity Forecasting using Artificial Neural Networks and Support Vector Machines

This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches.

Short-term Wind Speed Forecasting using Support Vector Machines

Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.