Editorial - Evolving Trends in Supply Chain Management: Complexity, New Technologies, and Innovative Methodological Approaches

Ministerio de Ciencia e Innovación DPI201680750

Artificial Intelligence in Engineering Risk Analytics

Risks exist in every aspect of our lives, and can mean different things to different people. While negative in general they always cause a great deal of potential damage and inconvenience for stakeholders. Recent engineering risks include the Fukushima nuclear plant disaster from the 2011 tsunami, a year that also saw earthquakes in New Zealand, tornados in the US, and floods in both Australia and Thailand. Earthquakes, tornados (not to mention hurricanes) and floods are repetitive natural phenomenon. But the October 2011 floods in Thailand were the worst in 50 years, impacting supply chains including those of Honda, Toyota, Lenovo, Fujitsu, Nippon Steel, Tesco, and Canon. Human-induced tragedies included a clothing factory fire in Bangladesh in 2012 that left over 100 dead. Wal-Mart and Sears supply chains were downstream customers. The events of Bhopal in 1984, Chernobyl in 1986, Exxon Valdez in 1989, and the Gulf oil spill of 2010 were tragic accidents. There are also malicious events such as the Tokyo Sarin attach in 1995, The World Trade Center and Pentagon attacks in 2001, and terrorist attacks on subways in Madrid (2004), London (2005), and Moscow (2010). The news brings us reports of such events all too often. The next step up in intensity is war, which seems to always be with us in some form somewhere in the world. Complex human systems also cause problems. The financial crisis resulted in recession in all aspects of the economy. Risk and analytics has become an important topic in today’s more complex, interrelated global environment, replete with threats from natural, engineering, economic, and technical sources (Olson and Wu, 2015)

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Handbook of Computational Intelligence in Manufacturing and Production Management

Artificial intelligence (AI) is simply a way of providing a computer or a machine to think intelligently like human beings. Since human intelligence is a complex abstraction, scientists have only recently began to understand and make certain assumptions on how people think and to apply these assumptions in order to design AI programs. It is a vast knowledge base discipline that covers reasoning, machine learning, planning, intelligent search, and perception building. Traditional AI had the limitations to meet the increasing demand of search, optimization, and machine learning in the areas of large, biological, and commercial database information systems and management of factory automation for different industries such as power, automobile, aerospace, and chemical plants. The drawbacks of classical AI became more pronounced due to successive failures of the decade long Japanese project on fifth generation computing machines. The limitation of traditional AI gave rise to development of new computational methods in various applications of engineering and management problems. As a result, these computational techniques emerged as a new discipline called computational intelligence (CI)

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

Electrical and Computer Engineering Annual Report 2016

Faculty Directory Faculty Highlights Faculty Fellow Program Multidisciplinary Research Fills Critical Needs Better, Faster Technology Metamaterials: Searching for the Perfect Lens The Nontraditional Power of Demand Dispatch Space, Solar Power\u27s Next Frontier Kit Cischke, Award-Winning Senior Lecturer Faculty Publications ECE Academy Class of 2016 Staff Profile: Michele Kamppinen For the Love of Teaching: Jenn Winikus Graduate Student Highlights Undergraduate Student Highlights External Advisory Committee Contracts and Grants Department Statistics AAES National Engineering Awardhttps://digitalcommons.mtu.edu/ece-annualreports/1002/thumbnail.jp

Guest editorial: special issue on complex systems in finance and economics

The papers in this special section focus on the development of models and data analysis systems for use in the ever growing complex financial markets. Both finance and economics are complex domains, in which multiple components such as investors, trading venues, or intermediary firms frequently interact to generate aggregate outcomes that may be desirable or undesirable, intended or unintended. The behavior of the underlying elements is often adaptive and the aggregate dynamics can be highly nonlinear. The resulting complexity can therefore be difficult to measure, model, and control. The recent financial crisis revealed how interconnections between institutions can provide feedback loops and propagation channels across the financial system, nationally and globally, spilling into the real economy. There is a great need for advances in the ways in which financial and economic systems are modeled, simulated, designed, controlled, and regulated. The techniques and hybrid approaches emerging from the ongoing efforts of the systems community can help address the challenge

Risk perception and decision making in the supply chain: theory and practice

For over sixty years, academics and practitioners from different backgrounds, including psychology, sociology, and management, have studied the perception of risk and how different decision making affects daily life and business activities. Although it is almost six hundred years since Machiavelli stressed the importance of calculation of risk and effective response to it, approaches to risk measurement and assessment, and to decision making in risky situations, continue to develop and evolve. In the business world, managers strive to find ways to understand how different internal and external factors influence risk, how to judge and interpret the available evidence on the possibility of loss, and how to take individual actions to manage the risk (Slovic 2000). In this decade, a number of risk management frameworks (e.g. IS031000) have been proposed and employed in different areas. These frameworks provide foundations and building blocks for managers to collect available data to analyse risk. Most importantly, such frameworks allow managers to gather knowledge intellectually, to properly judge their experience and to assess the current situation, so as to enter into the most appropriate decision

Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities