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The book "Globalization and Responsibility" consists of 8 chapters. The chapters in the book offer a decentered and dynamic terminology. They show that globalization consists of not only an objective process, but also of a lot of statements that define, describe and analyze the different experiences of the process. The chapters are written by authors and researchers from different academic disciplines, cultures and social contexts, therefore different experiences and scientific analyses on the consequences of globalization have been unified, starting from the multicultural and social epistemology to ethics of responsibility. Each chapter can be read separately, but in a complex, interconnected global universe of intertextuality of our world.
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The tattoo industry has evolved rapidly over the last decade, driven by innovation, precision engineering, and the growing expectations of artists and clients alike.
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Personal transformation has become one of the most searched-for promises of our time. We read books. We attend seminars. We hire coaches. We repeat affirmations. We redesign our goals. And yet, many people quietly confess the same thing:
Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings.
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