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Internet of Things (IoT) and artificial intelligence are two leading technologies that bought revolution to each and every field of humans using in daily life by making everything smarter than ever. IoT leads to a network of things which creates a self-configuring network. Improving farm productivity is essential to meet the rapidly growing demand for food. In this chapter, the authors have introduced a smart greenhouse by integration of two leading technologies in the market (i.e., Machine Learning and IoT). In proposed model, several sensors are used for data collection and managing the environment of greenhouse. The idea is to propose an IoT and Machine Learning based smart nursery that helps in healthy growing and monitoring of the seed. The structure will be a dome-like structure for observation and isolation of an egg with various sensors like pressure, humidity, temperature, light, moisture, conductivity, air quality, etc. to monitor the nursery internal environment and maintain the control and flow of water and other minerals inside the nursery. The nursery will have a solar panel from which it stores the electricity generated from the sun, a small fan to control the flow of air and pressure. A camera will also be equipped inside the nursery that will use computer vision technology to monitor the health of the plant and will be trained on the past data to notify the user if the plant is diseased or need attention.

A Digital Twin (DT) is a digital replica of an artefact which is updated on real-time or semi–real-time basis. In 2017, Gartner listed DT as one of the top 10 emerging technologies of the year. Since then, there have been numerous attempts to develop architecture and reference models for DTs, and in some studies, DT construction for real-world case studies is reported. This chapter attempts to provide a contextualised background on DT for smart cities. It also discusses various stakeholders involved in devising and/or employing DTs in a smart city. The chapter concludes with a set of recommendations for the training requirements of final DT users.

This paper describes the application of, enhancements to, and use of surface fire spread models in predicting and mitigating fire risk in the Wildland–Urban Interface (WUI). Research and fire management strategies undertaken in the East Bay Hill region (containing the 1991 Tunnel Fire) of the San Francisco Bay area over the past decade are reported. We ascertain that surface fire spread modeling has impacted policy and decision making, resulting in a regional strategic plan where large landowners and public agencies are able to implement fire mitigation practices. Although these practices involve extensive fuel management within a buffer zone between the wildland and residential properties, the residential property owners are still at risk, as no strategy within neighborhoods can be accurately mapped using the current scale of the data and models. WUI fires are eventually extinguished by fire fighters on the ground, up close, and at the backyard scale. We argue that large-scale (backyard scale) mapping and modeling of surface fire spread is necessary to engage the individual homeowner in a fuels management strategy. We describe our ongoing research and strategies, and suggest goals for future research and development in the area of large-scale WUI fire modeling and management.

Internet of things (IoT) and Big Data, which are among the pioneers of Industry 4.0 technologies, have gained great importance in recent years. Within the scope of Industry 4.0, organizations are trying to undertake digital transformation by adapting these two important technologies to their business processes. Undoubtedly, while this transformation provides great advantages for organizations in terms of management, organization, and marketing, it also carries disadvantages such as difficulties and complexity regarding the privacy of the collected data and systems. However, IoT and Big Data Analytics play a role as restructuring factors for products, services, and especially business processes. This study discusses the impact of IoT and Big Data Analytics on the digital transformation of organizations from the perspective of corporate culture, marketing, and management. Simultaneously, the effects of the COVID-19 epidemic that the world has experienced recently, on the business of institutions, are also discussed. By adopting IoT and Big Data Analytics, the attitudes, benefits, and challenges of the institutions that are or are not willing to realize digital transformation during the epidemic process are examined, and a projection is tried to be made to the post-COVID-19 period. While the study specifically highlights the positive effects of IoT and Big Data Analytics on the business, it sheds light on available opportunities and provides useful implications for managers and marketers.

We examine the challenges of commercialization of university-developed technology and the synergistic relationship of the university's technology transfer office with business-school-based entrepreneurship education programs. We postulate that business schools can effectively augment the university technology transfer office in developing and growing successful startups, through catalyzing the process of startup creation and by actively assisting in the formation of multi-disciplinary leadership teams for spinout companies. The assistance of the business school's alumni and entrepreneur networks can also be leveraged for both mentoring and investment. The challenges of an effective program include securing early marketing input, building effective leadership teams, negotiating the terms of technology licenses, and developing the enthusiasm and cooperation of faculty researchers. At Carnegie Mellon, we have developed an integrated entrepreneurship education program focused on opportunity recognition and strategy development, team building and leadership development, and resource acquisition and allocation. Our program actively assists in launching and supporting the resulting spinout companies by connecting entrepreneurs with value-added investors, support networks, and partners. In addition, we monitor and mentor the spinout companies through their startup and growth stages. Our program includes an aggressive cross-campus initiative in which we teach entrepreneurship courses in the science, engineering, and computer science schools (in addition to the business school) and conduct seminar series to reach faculty and graduate students within those areas of the university. We are aided in the program by the enlightened technology transfer policies that Carnegie Mellon adopted in 2001. The rationale and objectives of those policies are explained in a lengthy appendix. We illustrate the effectiveness of the model through discussion of three recent spinout companies. We conclude that university entrepreneurship education programs can significantly enhance the effectiveness of university technology transfer programs. To optimize that result, the entrepreneurship education program should extend beyond the walls of the business school and should actively assist in the creation of well thought-out business plans and the formation of well-balanced leadership teams actively monitored and mentored by the business school and its alumni and entrepreneur networks. Additionally, it is necessary to tailor the program to the specific character and needs of the region.

Using a series of case studies, we show that global mindset is at the heart of global growth and opportunity for entrepreneurial ventures. We review how having an entrepreneurial mindset and international experience influence the rapidity of internationalization by discussing the entrepreneurial process and how the global mindset of founders of born global firms influences their choices in the competitive landscape. This chapter closes with a discussion of a continuum — globalization frustrated (focusing on firms with entrepreneurs that have global mindsets but cannot internationalize) to globalization mandated (focusing on firms that are forced to be global).