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Purpose – This paper proposes a new procedure for measuring affective responses during social interaction using facial thermographic imaging.

Methodology – We first describe the results of several small pilot experiments designed to develop and refine this new measure that reveal some of the methodological advantages and challenges offered by this measurement approach. We then demonstrate the potential utility of this measure using data from a laboratory experiment (N=114) in which we used performance feedback to manipulate identity deflection and measured several types of affective responses – including self-impressions and emotions.

Findings – We find warming of the brow (near the corrugator muscle) and cheek (near the zygomatic major muscle) related most strongly to emotion valence and self-potency, with those whose brows and cheeks warmed the most feeling less positive emotion and less potent self-impressions. Warming in the eye area (near the orbicularis oculi) related most closely to undirected identity deflection and to positive self-sentiments. Positive self-views and strong identity disruptions both contributed to warming of the eyes.

Implications – The rigor of contemporary sociological theories of emotion exceeds our current ability to empirically test these theories. Facial thermographic imaging may offer sociologists new assessments of affect and emotion that are ecologically valid, socially unreactive, temporally sensitive, and accurate. This could dramatically improve our ability to test and develop affect based theories of social interaction.

Currently, Mauritius is adopting landfilling as the main waste management method, which makes the waste sector the second biggest emitter of greenhouse gas (GHG) in the country. This presents a challenge for the island to attain its commitments to reduce its GHG emissions to 30% by 2030 to cater for SDG 13 (Climate Action). Moreover, issues like eyesores caused by littering and overflowing of bins and low recycling rates due to low levels of waste segregation are adding to the obstacles for Mauritius to attain other SDGs like SDG 11 (Make Cities & Human Settlements Inclusive, Safe, Resilient & Sustainable) and SDG 12 (Guarantee Sustainable Consumption & Production Patterns). Therefore, together with an optimisation of waste collection, transportation and sorting processes, it is important to establish a solid waste characterisation to determine more sustainable waste management options for Mauritius to divert waste from the landfill. However, traditional waste characterisation is time consuming and costly. Thus, this chapter consists of looking at the feasibility of adopting machine learning to forecast the solid waste characteristics and to improve the solid waste management processes as per the concept of smart waste management for the island of Mauritius in line with reducing the current challenges being faced to attain SDGs 11, 12 and 13.

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

Neuroimaging research has substantially enhanced our understanding of the neurobiological mechanisms of typical and atypical learning in children. These developments can advance the design of novel approaches to diagnosis and intervention for learning disabilities. Despite the promise of educational neuroscience, there are still walls between neuroscience and special education researchers such that more collaboration and understanding are needed between these disciplines. This chapter attempts to break down the walls by discussing how neuroimaging techniques can be incorporated into special education research. We also present arguments as to why neuroscience is “the next big thing” in special education research and the obstacles that must be overcome in order for neuroscience to be incorporated into education research. To describe how neurobiology might impact special education, we focus primarily on reading disability. We believe that educational neuroscience can aid in the identification and intervention of other learning disorders as well.

Groups have the ability to create something new and novel that does not exist at the individual level. This chapter examines group communication as the driver of this creation process, using the input–process–output model. Group processes are often understudied and consigned to a “black box” between inputs and outputs. How advances in methodology and analysis software have increased the ability to study group communication processes and emergent states within this black box is highlighted. Four different areas of research are then briefly reviewed to showcase ways to focus on process. These four areas include structuration, shared mental models, transactive memory, and collective intelligence. The chapter concludes with a focus on future trends and a call for more interdisciplinary research with a theoretical focus.