Ecology, Sustainability, and Scientific Education

Environmental education is a new discipline, which has been gaining increasing importance in recent decades. It is a fundamental tool to sensitize citizens and communities to greater responsibility and attention to environmental issues and good governance of the territory. The educational component allows us to transform scientific languages into simple languages that can be appropriated and understood by the different social groups, and the environmental component that tries to reconstruct. These relationships are created by man with nature, but through cultural parameters. The growing concern for environmental devastation has its roots in the decade of the 1970s. Countless efforts have been made to draw the attention of populations to this topic. Among these attempts come out different approaches such as sustainable development that uses environmental education and local sustainability as tools to prevent damages. This chapter describes the key theoretical and government references that support environmental education, focusing on their relationship and the rising of a new sector of work.

Risk and Benefit of Effective Techniques and Technologies in Education

Technological innovation is increasingly penetrating the world, and its implementation has the potential to bring a change in the learning organization and pedagogical approach; therefore, it is vital to understand where technology can alleviate problems and not be a hindrance or a gadget. Change factors in the knowledge dynamic today are on the one hand the passage from individual knowledge to global knowledge, with multi-user connections, but without losing the subjective prospective, and on the other hand, the passage from a received multimediality (such as television and cinema) to an interactive multimediality (such as videogames or virtual reality), to a built multimediality (such as socialnetworking knowledge management tool), up to a reflective multimediality (dynamic, intelligent, and semantic UI). This chapter has the purpose to describe possible benefits or risks, barriers or precautions, using effective technologies in education, resulting from the long history of aware applications.

Talent Management

Toyota South Africa Motors (TSAM) is the leading original equipment manufacturer in South Africa. Business strategy and talent management alignment is therefore the key for business success. For the past couple of years, there has been a frequent executive management turnover and several promotions to senior management positions. The research question was to ascertain the relationship between talent management strategy and the achievement of business objectives. The quantitative research study included a deductive approach. Primary data was collected using a questionnaire survey personally administered by the researchers. Results for two talent management pillars were positive but require some improvements. However, results for succession planning reveal this pillar requires urgent attention to maintain competitive advantage and realize the vision of becoming the mother plant in Africa.

Orientism Management Strategy for Training Policy-Makers in Emergency Decision-Making

Policy-making is asked to define criteria and decisional support to respond to emergencies and a complex mix of social, environmental, economic, and cultural problems. Politics, especially at the highest levels, requires preparing professionals able to manage the opportunities offered by new technologies in the context of security and management. Risk analysis is an increasingly urgent necessity and challenge. Research data needs to be packaged into effective policy tools that will help policy-makers make an evidence-informed policy. The objective of this chapter is to offer a framework of analysis (Orientism Management OM Framework) useful for training policy-makers to develop a multi-perspective informed policy. It intends orienting the variety of backgrounds, interests, knowledge, skills, and the whole personality of the trainees, individually or within a work team.

A Framework for Risk Management in AI System Development Projects

It has been well-known that the chance of successfully delivering a software project within an allocated time and budget is very low. Most of the researches in this area have concluded that “user's requirements” of the systems is one of the most difficult risks to deal with in this case. Interestingly, until today, regardless of amount of effort put into this area, the possibility of project failure is still very high. The issue with requirement can be significantly increased when developing an artificial intelligence (AI) system, where one would like the systems to autonomously behave. This is because we are not only dealing with user's requirements, but we must also be able to deal with “system's behavior” that, in many cases, do not even exist during software development. This chapter discusses a preliminary work on a framework for risk management for AI systems development projects. The goal of this framework is to help project management in minimizing risk that can lead AI software projects to fail due to the inability to finish the projects on time and within budget.

Future of Jobs and Humans in a Tech-Centric World

Modern technologies have presented the world with a plethora of new opportunities, from the mundane (reduced input rigor) through to new outcomes that eluded our collective wisdom and knowledge. These changes have confronted organizations with new risk types, spanning the strategic through to operational, resources through to circularity and sustainability. One poignant impact being seen today is the morphing between virtual and physical inputs (tech tools and humans), resulting in a demand for organizations to reinvigorate their business models in a manner that encompasses both types of inputs seamlessly. However, attendant issues with skills, job types, and tasks have come around full circle. One the one end, there is the fear of technology automating humans out of a job altogether (alongside the promise of enhancing individuals to pursue new skills in a cognitive world), while on the other end there are significant socio-economic concerns surrounding job losses and consequential impacts on the consumption-production continuum.

System Dynamics Modeling for Public Health Bed Capacity Planning

The system dynamics approach was developed by Jay Forrester from MIT during 1950s to analyze the complex behavior in administration with computer simulation in social sciences. System dynamics is a form of the systems approach as a methodology to understand the dynamic behavior of complex systems. The basis of system dynamics is to understand how system structures cause system behavior and system events. This chapter aims to develop a system dynamic model. System dynamics approach was adopted to build a bed capacity management model using a software package, Vensim.

Bio-Ethics of Innovation

The word innovation could mean everything new capable of solving in an easier and better way any problem we find in our lives. Any innovation has always created risks, but specifically, now, the innovation related to the information technology poses major problems because of the pervasiveness everywhere, anytime. It can happen that we are not any more capable of doing very basic things without the help of a computer, and unfortunately, too many persons feel strongly that we can solve the crisis we are living now just by innovating. This chapter explores the bio-ethics of innovation.

The Coming Human/Machine Symbiosis, Effects on Society, and Business Planning

Although the future of society is hazy, a workable vision of the future can be developed from existing computer modeling of key parameters like population and sea level. This vision is referred to in this chapter as the driven future. The future of humans and artificial intelligence (AI) is then reconsidered from within the context of this driven future. Specific examples of how AIs might be utilized to address real human problems in this future are developed. The modifications to the way humans and AIs interact to facilitate addressing major problems is then discussed, including the human/machine symbiosis and the application of corporate persons directly to AIs.

Scientific Intelligence, Decision Making, and Cyber-Security

Because of the huge amount of data and information in the decision-making and strategic choices processes, basing decisions on information directly collected from the sources is not conceivable. A decision-making analyst becomes a fundamental pillar in both the corporate field and the institutional world. This role is becoming increasingly complex and specialized, critical within the cycle of the intelligence analysis, for the relationships that bind it to the other stakeholders, and for the methodological and technological tools that support it. The purpose of this chapter is to explore the milestones of the intelligence analysis deriving from a close collaboration between social sciences, cognitive science, computer engineering, and ICT in order to respond to the different needs in the field of risk management, safety, investigations, and applied intelligence.