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This chapter discussed the implementation of the digital twin (DT) idea into construction. Through the adoption of DTs into construction practices, construction professionals have been able to project an identical virtual concept of sections of the project execution right from the onset. In the introduction and discussing of its origin, the DT was further assessed about its applications in construction beneficial in enhancing project delivery. Other sections like barriers, drivers and benefits of the DT in construction summarised what this chapter represents in terms of discussing the new involvement of digital tools in construction execution, management and sustainability.

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.

The Internet of Things (IoT) is a cloud system that saves energy by being involved in the decision-making process of machines. By this means, machines create an environment of direct interaction without the need for explicit instructions. In this chapter, answers have been sought to the questions of what kind of research has been done on IoT-based technological devices, and how IoT-based technologies are effective in sustainable food production. The systematic literature review examined the scientific research on IoT and Food in the range of 2010–2022 in Scopus. The general framework of the research has been carried out in the context of 6Ws (who, when, where, what, why, and how), which is one of the systematic literature reviews. The results obtained have been analyzed and interpreted in the MAXQDA 2020 qualitative data analysis program. In the findings obtained, it has been determined that IoT and food have gained importance worldwide, especially in England, India, and China. Furthermore, it has been determined that most of the studies on IoT are based on case studies, and all the articles examined are collected in three main focus points. Subcodes have been created under the main codes ‘Food Supply Chain, Smart and Sustainable Agriculture ve Waste Management’, and problem points have been tried to be customized.

Energy production and distribution is undergoing a revolutionary transition with the advent of disruptive technologies such as the Internet of Energy (IoE), 5G and artificial intelligence (AI). IoE essentially involves automating and enhancing the energy infrastructure: the power grid from grid operators to energy generators and distribution utilities. The IoE also relies on powerful connectivity networks such as 5G, big data analytics and AI to optimise its operation. By incorporating the technology that employs ubiquitous devices such as smartphones, tablets or smart electric vehicles, it will be possible to fully exploit the potential of IoE using 5G networks. 5G networks will provide high speed connections between devices such as drones, tractors and cloud networks, to transfer huge amounts of sensor data. Additionally, there are many sources of isolated data across the main energy production units (generation, transmission and distribution), and the data is increasing at phenomenal rates. By applying AI to these data, major improvements can be brought at each stage of the energy production chain. Tying renewable energy to the telecommunications sector and leveraging on the potential of data analytics is something which is gaining major attention among researchers and industry experts. This chapter therefore explores the combination of three of the most promising technologies i.e. IoE, 5G and AI for achieving affordable and clean energy, which is SDG 7 in the UN Sustainable Development Goals (SDGs).

Purpose: The study aims to analyse and discuss the effect of COVID-19 on businesses. The chapter discusses the various machine learning (ML) tools and techniques, which can help in better decision making by businesses in the present world.

Need for the Study: COVID-19 has increased the role of VUCA elements in the business environment, and there is a need to address the challenges faced by businesses in such environment. ML and artificial learning can help businesses in facing such challenges.

Methodology: The focus and approach of the chapter are in the context of using artificial intelligence (AI) and ML techniques for decision making during the COVID-19 pandemic in a VUCA business environment.

Findings: The key findings and their implications emphasise the importance of understanding and implementing AI and ML techniques in business strategies during times of crisis.

Practical Implications: The chapter’s content is in the context of using AI and ML techniques during the COVID-19 pandemic and in a VUCA business environment.

In this chapter, a general introduction on artificial intelligence (AI) is given as well as an overview of the advances of AI in different engineering disciplines, including its effectiveness in driving the United Nations Sustainable Development Goals (UN SDGs). This chapter begins with some fundamental definitions and concepts on AI and machine learning (ML) followed by a classification of the different categories of ML algorithms. After that, a general overview of the impact which different engineering disciplines such as Civil, Chemical, Mechanical, Electrical and Telecommunications Engineering have on the UN SDGs is given. The application of AI and ML to enhance the processes in these different engineering disciplines is also briefly explained. This chapter concludes with a brief description of the UN SDGs and how AI can positively impact the attainment of these goals by the target year of 2030.

One of the most hyped concepts in the manufacturing industry is ‘Industry 4.0’. The ‘Industry 4.0’ concept is grabbing the attention of every manufacturing industry across the globe because of its immense applications. This phenomenon is an advanced version of Industry 3.0, combining manufacturing processes and the latest Internet of Things (IoT) technologies. The main advantage of this paradigm shift is efficiency and efficacy in the manufacturing process with the help of advanced automated technologies. The concept of ‘Industry 4.0’ is contemporary, so it falls under exploratory study. Therefore, the research methodology is thematic narration grounded on secondary data (online) analysis. In this light, this chapter aims to explain ‘Industry 4.0’ in terms of concepts, theories and models based on the Web of Science (WoS) database. The data include research manuscripts, book chapters, blogs, white papers, news items and proceedings. The study details the latest technologies behind the ‘Industry 4.0’ phenomenon, different business intelligence technologies and their practical implications in some manufacturing industries. This chapter mainly elaborates on Industry 4.0 frameworks designed by (1) PwC (2) IBM (3) Frost & Sullivan.

Various mass shooters have explained their violent actions as a response to failing at dominant forms of masculinity, including rejection from women and negative social comparisons to other men. The affect control theory of self (ACT-Self) posits that interactions that violate one's sense of self cause inauthenticity. This disequilibrium motivates behaviors that restore self-meanings, which may partially explain the link between challenges to the self and compensatory violence.

In Study 1, we use ACT-Self to examine the relationship between inauthenticity, violent fantasies, and physical aggression in the autobiography of one mass shooter. We quantify self-sentiments and inauthenticity using ACT-Self measures and methods, and perform a thematic analysis of the shooter's interpretations of and responses to disconfirming events. In Study 2, we examine the relationship between these same concepts in a survey of 18-to-32-year-old men (N = 847).

Study 1 shows that the shooter's inability to achieve popularity, wealth, sex, and relationships with beautiful women (compared to other men) produced inauthenticity that he resolved through violent fantasies, increasingly aggressive behavior, and ultimately, mass violence. Study 2 finds that inauthenticity arising from reflected appraisals from women predicts self-reported violent fantasies and physical aggression in a convenience sample of men in emerging adulthood.

This work leverages a formal social psychological theory to examine the link between self-processes and violence. Our findings suggest that men's inauthenticity, particularly produced by reflected appraisals from women, is positively associated with violent fantasies and acts. Further work is needed to assess whether this relationship is causal and for whom.

This chapter addresses one of the most crucial areas for critical thinking: the morality of turbulent markets around the world. All of us are overwhelmed by such turbulent markets. Following Nassim Nicholas Taleb (2004, 2010), we distinguish between nonscalable industries (ordinary professions where income grows linearly, piecemeal or by marginal jumps) and scalable industries (extraordinary risk-prone professions where income grows in a nonlinear fashion, and by exponential jumps and fractures). Nonscalable industries generate tame and predictable markets of goods and services, while scalable industries regularly explode into behemoth virulent markets where rewards are disproportionately large compared to effort, and they are the major causes of turbulent financial markets that rock our world causing ever-widening inequities and inequalities. Part I describes both scalable and nonscalable markets in sufficient detail, including propensity of scalable industries to randomness, and the turbulent markets they create. Part II seeks understanding of moral responsibility of turbulent markets and discusses who should appropriate moral responsibility for turbulent markets and under what conditions. Part III synthesizes various theories of necessary and sufficient conditions for accepting or assigning moral responsibility. We also analyze the necessary and sufficient conditions for attribution of moral responsibility such as rationality, intentionality, autonomy or freedom, causality, accountability, and avoidability of various actors as moral agents or as moral persons. By grouping these conditions, we then derive some useful models for assigning moral responsibility to various entities such as individual executives, corporations, or joint bodies. We discuss the challenges and limitations of such models.

The fifth industrial revolution, known as Industry 5.0, envisions an industry that is innovative, resilient, socio-centric, and competitive while minimizing negative environmental and social impacts, respecting people, the planet, and prosperity. Industry 5.0 is replacing earlier advancements and it is successful because it reaches the pinnacle of perfection. Additionally, machine work saves human workers time and effort. It is built on the concept of fusing digitalization elements from the fourth industrial revolution with Sustainable Development Goals through human-centric solutions, bio-inspired technologies, and secure data transfer. Industry 5.0 mentions about the various opportunities, constraints, and potential directions for future research. Industry 5.0 places less emphasis on technology and focus on human collaboration for progress, it supposed to have a shift in existing paradigm. Industry 5.0 is necessary in contemporary business with the paid technology advancements in order to get competitive advantages as well as economic growth for the manufacturing and it has three drivers: “green transition”, “digital transition”, and “competitive transition”. The goal of green transition is to prevent climate change and environmental degradation. This necessitates changes to current economic growth strategies. The goal of digital transition is to support the circular economy by modernizing digital strategies in terms of digital skills, data, technologies, and infrastructure. Competitive transition aims to convert marketing policies, regulations, standards to increase people's prosperity and business value. It focuses on business and marketing rules that are fair, competitive, innovative, and adaptable.