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The consideration of alternative sources of material for construction is imperative to reduce the environmental impacts as two-fifths of the carbon footprint of materials is attributed to the construction industry. One alternative material with improved biodegradable attributes which can contribute to carbon offset is bamboo. The commercialisation of bamboo in modern infrastructures has significant potential to address few of the Sustainable Development Goals (SDGs) itemised by the United Nations, namely SDG 9 about industry, innovation and infrastructure. Other SDGs covering sustainable cities and communities, responsible consumption and production and climate action are also indirectly addressed when utilising sustainable construction materials. Being a natural material however, the full commercialisation of materials such as bamboo is constrained by a lack of durability. Besides fracture mechanisms arising from load-induced cracks and thermal modification, the durability of bamboo material is greatly impaired by biotic and abiotic factors, which equally affect its natural rate of degradation, hence fracture behaviour. In first instance, this chapter outlines the various factors leading to the durability limitations in bamboo material due to load-induced cracks and natural degradation based on recent findings in this field from the author's own work and from past literature. Secondly, part of this chapter is devoted to a new approach of processing the surge of information about the varied aspects of bamboo durability by considering the powerful technique of artificial intelligence (AI), specifically the artificial neural network (ANN) for prediction modelling. Further use of AI-enabled technologies could have an impactful outcome on the life cycle assessment of bamboo-based structures to address the growing challenges outlined by the United Nations.

This chapter offers an overview of the applications of artificial intelligence (AI) in the textile industry and in particular, the textile colouration and finishing industry. The advent of new technologies such as AI and the Internet of Things (IoT) has changed many businesses and one area AI is seeing growth in is the textile industry. It is estimated that the AI software market shall reach a new high of over US$60 billion by 2022, and the largest increase is projected to be in the area of machine learning (ML). This is the area of AI where machines process and analyse vast amount of data they collect to perform tasks and processes. In the textile manufacturing industry, AI is applied to various areas such as colour matching, colour recipe formulation, pattern recognition, garment manufacture, process optimisation, quality control and supply chain management for enhanced productivity, product quality and competitiveness, reduced environmental impact and overall improved customer experience. The importance and success of AI is set to grow as ML algorithms become more sophisticated and smarter, and computing power increases.

Globalization has increased the consumer's demand for safe and quality foods. To make food available to consumers from farm to fork, packaging plays a crucial role. The objective of packaging is to shield the foodstuff from degrading and to serve as the medium of communication between the processing industry and the consumers. Conventionally, several materials are used in the packaging such as laminates, plastics, glass, metal, etc., but with the advent of technology, newer and novel smart packaging technologies have entered this field. Smart packaging in the form of active and intelligent packaging not only acts as a barrier to external influences but also prevents internal deterioration. Oxygen scavengers, moisture controllers, antioxidants, CO₂ absorber/emitter, antimicrobial agents, etc., are some of the vital active packaging systems. On the other hand, an intelligent packaging system contains internal or external indicators and sensors that monitor the condition of packed food and gives information about its quality during storage and transportation. It seems that these interventions in packaging have very positive effects on the whole industry, but it is observed that this advancement in the packaging has also raised questions about its disposal. To overcome this issue, industries have started using smart packaging design along with the sustainable packaging trend. Communication with the recycling bodies at the time of development will ensure the smart packaging fit to be recycled. Considering such standards for smart packaging will not only create a healthy bond between industries and consumers but will also help in sustainable development. This chapter mainly focuses on the advancement of the packaging system associated with the agri-food sector. It also discusses how the implementation of these technological advancements will help the industries toward sustainable development.

Process-oriented guided-inquiry learning (POGIL) is a student-centered instructional strategy to actively engage students in the classroom in promoting content mastery, critical thinking, and process skills. The students organize into groups of three to four, and each group member works collaboratively to construct their understanding as they proceed through the embedded learning cycle in the POGIL activity. Each group member has a specific role and actively engages in the learning process. The roles rotate periodically, and each student has the opportunity to develop essential process skills, such as leadership skills, oral and written communication skills, team-building skills, and information-processing skills. The student groups are self-managed, and the instructor serves as a facilitator of student learning. A POGIL activity typically contains a model that the students deconstruct using a series of guided, exploratory questions. The students develop concepts (concept invention) as the group members reach a valid, consensus conclusion. The students apply their concepts to new problems completing the learning cycle. The authors implemented POGIL instruction in several chemistry courses at Jackson State University and Tuskegee University. They share their initial findings, experiences, and insights gained using a new instructional strategy.

Purpose – Description of the effects of the revolution in neuroscience and other areas of biology that can help to explain the roots of some portion of violent crime. The chapter reconsiders the role of brain chemistry in social behavior and violent behavior. To illustrate the interdisciplinary complexities entailed when linking brain chemistry to policy decisions concerning violent crime, this analysis has four main stages: first, why might SiFs (H2SiF6 and Na2SiF6, jointly called “silicofluorides” or SiFs) be dangerous? Second, what biochemical effects of SiF could have toxic consequences for humans? Third, on this basis a research hypothesis predicts children in communities using SiF should have increased uptake of lead from environmental sources and higher rates of behavioral dysfunctions known to be caused by lead neurotoxicity.

Design/methodology/approach – To illustrate the implications of the new issues involved, this chapter focuses on a public policy that inadvertently seems to increase rates of violent crime. Since violent behavior is one of the effects of lead neurotoxicity, the hypothesis is tested using multiple sources of data including rates of violent crime studied using a variety of multivariate statistical techniques (including analysis of variance, multiple regression, and stepwise regression).

Findings – Various data sources point to greater violence among individuals with greater exposure to SiFs.

Originality/value – Testing hypotheses linking neurotoxins to violent behavior reveals the generally unsuspected value of analyzing human social behavior and public policy from the perspective of evolutionary psychology.