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Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik’s wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation’s success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

The United Nations have announced 17 Sustainable Development Goals and 169 targets, which are indivisible and integrated, and which balance the economic, social, and environmental dimensions of sustainable development. This indicates that the performance of successful nations is generally good across many sustainability indicators. Our results, based on multi-criteria and statistical analysis across 173 countries, suggest an interconnection between a country’s sustainability 12 indicators and success. This article focuses on the Country Success and Sustainability (CSS) Maps and Models of the World, which show that improvements in environmental, social, and economic sustainability indicators lead to improvements in the country's success, and vice versa. The CSS Models explain 98.2% of national success and 80.8% of the three dimensions of average sustainability dispersions. When a nation’s success increases by 1%, the 12 indicators of the three dimensions of sustainability improve by 0.85% on average. The human development index and GDP per capita were the success variables with the most substantial impact on 12 sustainability indicators in 173 countries. Calculations made using equal and different weights of 17 criteria show a deviation of 5.34% for the priorities of these 173 countries.

Most people around the world have felt the effects of climate change on their quality of life. This study sought to achieve the maximum efficiency for climate change actions with the minimum negative impact on the well-being of countries and cities. The Climate Change and Country Success (C3S) and Climate Change and Cities’ Quality of Life (C3QL) models and maps of the world created as part of this research showed that as economic, social, political, cultural, and environmental metrics of countries and cities improve, so do their climate change indicators. For the 14 climate change indicators, the C3S and C3QL models indicated 68.8% average dispersion dimensions in the case of countries and 52.8% in the case of cities. Our research showed that increases in the success of 169 countries saw improvements in 9 climate change indicators out of the 12 considered. Improvements in country success indicators were accompanied by a 71% improvement in climate change metrics.