and consider a new mobile social-networking application with which users share pictures of “cool ” things they find in the real-world. Besides, we shift focus from items to people. Specifically, we focus on those who generate trends (trend makers) and those who spread them (trend spotters). We analyze

of a mobile application in which users find “cool ” items in the real world, take pictures of them, and share those pictures online. In this context, temporal dynamics matter, and users would greatly profit from ways of identifying the latest design trends. We propose a new way of recommending trending

There has been a rapidly growing trend towards the use of nanomaterials (NM) in healthcare and bioanalytical sciences during the last decade. It has led to a wide range of prospective applications of NM in biosensors, diagnostics, therapeutics, drug delivery, medicine, biomedical imaging, signal

plethora of applications that the maker community is interested in. This changes over time but recently, there is great interest in small robots, flying drones and quantifying one’s daily routines and habits. In this piece, we’ll be exploring some of these broad trends.

how the IBLT’s learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments.

presents and analyses the current trends in emerging DSS applications for the sector of forest policy and management. The research relates to the scientific journal articles that propose a DSS since 2007 and until the present. The DSS are registered and classified as to their characteristics

~133 million tweets show the effectiveness of the extracted features and learning approaches. The predicted popularity trend of events can be directly used for a variety of applications including recommendation systems, ad keywords bidding price decisions, stock trading decisions, dynamic ticket

well according to the current system state, but that will continue to be pro®table for the facility's lifetime, even as environmental factors change, populations shift, and market trends evolve. Finding robust facility locations is thus a dicult task, demanding that decision makers account

and collaborative task completion are essential, to be aware of this technology, its abilities, and the possible impacts to common workspaces and workers. The purpose of this chapter is to inform decision-makers of AR’s history, the completed research and current applications of AR, possible impacts to managers

— as they learn. A portfolio choice application compares the effect of changes in confidence under ambiguity versus changes in estimation risk under Bayesian learning. The former is shown to induce a trend towards more stock market participation and investment even when the latter does not. 1