The global engineering/construction industry is huge. In 2017, it was estimated to be an $8.8 trillion industry (Market Research Hub, 2016). The US construction industry in 2017 was estimated at $1.2 trillion (Wilcox, 2018). Because the industry is comprised of a myriad of projects to build new facilities or to repair or upgrade existing ones, it is often the location for bribery, fraud, and corruption. Government leaders in Panama, Brazil, and Spain have been removed from office for receiving bribes and kickbacks from projects in their countries. Engineering firms in the United States and Canada have been sanctioned for giving bribes to secure projects. These are the facts.

In an era of corporate mistrust, creating sustainable ethical corporations goes beyond implementing governance, risk, and compliance (GRC) strategy. It requires an ongoing intensified spotlight to make the highest ethical standards the norm, and ruthless intolerance of anything less. Corporations are at a tipping point seeking to build sustainable businesses while striving to avoid a front-page scandal. They are placing greater scrutiny on values as business enabler, leadership accountability, and building ethical decision-making as an integrated business process. The next generation of ethical systems is at our corporate doorstep. As Albert Einstein famously said, “we cannot solve problems by using the same kind of thinking we used when we created them.” Today’s workplace has an unprecedented four generations working alongside each other. Globalization and the flattened twenty-first-century economy have pivotally shifted the norms of communication, information sharing, and collaboration. Greater visibility through mass media and social media has revealed new consumer and corporate behaviors. With greater transparency at our fingertips, trust has become the new currency, evidenced in the backlash as trust in public officials and corporate leaders steadily declines. The Edelman Trust Barometer has been studying trust across four institutions since 2012: businesses, government, nongovernmental organizations (NGOs), and media. Their 2017 report reveals that trust has declined broadly across all four institutions and that trust is in crisis around the world.

Large-scale aggregate analyses of anonymized data can yield valuable results and insights that address public health challenges and provide new avenues for scientific discovery. These methods can extend our knowledge and provide new tools for enhancing health and well-being. However, they raise questions about how to best address potential threats to privacy while reaping benefits for individuals and for society as a whole. The use of machine learning to make leaps across informational and social contexts to infer health conditions and risks from nonmedical data provides representative scenarios for reflections on directions with balancing innovation and regulation.

Medicine has a dichotomous personality, some of it is science and some of it is art. The science of medicine focuses on the technical skills and proficiency; whereas the art of medicine examines the ethical decision-making, professionalism, and relationships we foster to provide care to patients, comfort to families, and compassion to colleagues. It is often referred to as bedside manner, but it extends beyond that. It is communication, honesty, and respect.

Pencil, paper and a scientific calculator are often adequate when analysing small amounts of data. However, spreadsheets are favoured especially when large amounts of data are involved. This chapter explores some of the basic features of spreadsheets and show how they may be applied to the analysis and presentation of experimental data. The Excel spreadsheet program by Microsoft is highlighted due to its availability, power, and longevity. Specific features of Excel are discussed such as the LINEST function for fitting equations, and the Analysis ToolPak which contains a number of useful analysis tools.

Errors in data are a part of life for experimenters in science and engineering. This chapter considers the types of errors, including random and systematic error that can occur during an experiment and methods by which uncertainties arising from such errors can be combined. Many worked examples are included in this chapter, as well as exercises for the student to complete

This chapter is an overview of experimentation and explains why experiments are important. The role of the laboratory notebook for keeping a faithful record of work is emphasised. Guidelines are given for keeping a laboratory notebook. Examples pages from the author's own notebook are included.

This chapter introduces the technique of fitting equations to data using least squares. Both unweighted fitting and weighted fitting are considered. Worked examples are included in the chapter. The technique described can be extended to situations where equations have more than two parameters. Discussion is confined to cases where there is a linear relationship between x and y and the errors in measured quantities are limited to the y quantity.

Graphs are a powerful and concise way to communicate information. Representing data from an experiment in the form of an x-y graph allows relationships to be examined, scatter in data to be assessed and allows for the rapid identification of special or unusual features. A well laid out graph containing all the components discussed in this chapter can act as a 'one stop' summary of a whole experiment. Someone studying an account of an experiment will often examine the graph(s) included in the account first to gain an overall picture of the outcome of an experiment. The importance of graphs, therefore, cannot be overstated as they so often play a central role in the communication of the key findings of an experiment. This chapter contains many examples of graphs and includes exercises and end of chapter problems which reinforce the graph-plotting principles.