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Climate change is a global issue that must be considered and addressed immediately. Many articles have been published on climate change mitigation and adaptation. However, new methods are required to explore the complexities of climate change and provide more efficient and effective adaptation and mitigation policies. With the advancement of technology, machine learning (ML) and deep learning (DL) methods have gained considerable popularity in many fields, including climate change. This...
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Indoor thermal comfort in smart buildings has a significant impact on the health and performance of occupants. Consequently, machine learning (ML) is increasingly used to solve challenges related to indoor thermal comfort. Temporal variability of thermal comfort perception is an important problem that regulates occupant well-being and energy consumption. However, in most ML-based thermal comfort studies, temporal aspects such as the time of day, circadian rhythm, and outdoor temperature are...
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This study managed to create thermal comfort conditions at three temperatures (24°C-T24, 26°C-T26, and 28°C-T28) by adjusting clothing and air velocity. Thirty- six subjects (18 males and 18 females) were exposed to each of the three conditions for 4.5 h in a design balanced for order of presentation of conditions. During each exposure, they rated the physical environment, their comfort, the intensity of acute subclinical health symptoms, and their mental load, and they performed a...
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The relation between electroencephalogram signals, thermal comfort, and cognitive performance in neutral to hot indoor environment was investigated. The experiments were carried out at four temperatures: 26ºC, 30ºC, 33ºC, and 37ºC, and two relative humidity levels: 50% and 70%. Thirty-two subjects were exposed for 175 min. The electroencephalogram signals were measured for 30 min 25 min after the onset of exposure while the recruited subjects performed neurobehavioral tests and rated their...
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In a technology context dominated by data-intensive AI systems, the consequences of data processing are no longer restricted to the well-known privacy and data protection issues but encompass prejudices against a broader array of fundamental rights. Moreover, the tension between the extensive use of these systems, on the one hand, and the growing demand for ethically and socially responsible data use on the other, reveals the lack of a framework that can fully address the societal issues raised by AI.
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The recent turn in the debate on AI regulation from ethics to law, the wide application of AI and the new challenges it poses in a variety of fields of human activities are urging legislators to find a paradigm of reference to assess the impacts of AI and to guide its development. This cannot only be done at a general level, on the basis of guiding principles and provisions, but the paradigm must be embedded into the development and deployment of each application. To this end, this chapter...
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Having discussed in previous chapters the valuable contribution that an assessment model encompassing human rights, ethical and societal issues can provide to the development and regulation of AI, these concluding remarks address some of the challenges we face in implementing this approach in tangible reality. The focus on future global regulatory scenarios in the field of AI shows how the holistic HRESIA model, which includes the contextualisation of human rights and socio-ethical values in...
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Although the debate on AI regulation is still fluid at a global level and the European initiatives are in their early stages, three possible approaches to grounding AI regulation on human rights are emerging. One option is a principles-based approach, comprising guiding principles derived from existing binding and non-binding international human rights instruments, which could provide a comprehensive framework for AI. A different approach focuses more narrowly on the impacts of AI on...
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The extensive and frequently severe impact of AI systems on society cannot be fully addressed by the human rights legal framework. Many issues involve community choices or individual autonomy requiring a contextual analysis focused on societal and ethical values. The social and ethical consequences of AI represent a complementary dimension, alongside that of human rights, that must be properly investigated in AI assessment, to capture the holistic dimension of the relationship between humans...
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