Abstract
Software transactional memory (STM) is a synchronisation paradigm which improves the parallelism and composability of modern applications executing on a multi-core architecture. However, to abort and retry a transaction multiple times may have a negative impact on the temporal characteristics of a real-time task set. This paper addresses this issue: It provides a framework in which an upper-bound on the worst-case response time of each task is derived, assuming that tasks are scheduled by following either the Non-Preemptive During Attempt (NPDA), Non-Preemptive Until Commit (NPUC) or Stack Resource Policy for Transactional Memory (SRPTM) policy.

Abstract
The importance of liquefied natural gas (LNG) is rising as demand for it grows rapidly and steadily due to growth in energy demand, the transition to a low carbon economy and the longer distances over which natural gas is now traded. Given its importance, this work proposes an optimization model that assists to decide on when and where LNG should be delivered by coordinating tanker type, assignment and routing, inventory management, contract obligations, arbitrage and uncommitted LNG. The model maximizes the profit mainly by taking advantage of price differences between different markets. The contributions of this work are twofold. First, following the analysis of expenses and revenues, a new mixed integer programming model for LNG liquefaction and shipping is proposed from a corporate finance perspective. Furthermore, a solution approach for it is implemented and tested. Second, the model is used to derive a short term trade policy for the Middle Eastern LNG producers regarding the spot sale of their uncommitted product to Japan or to the UK, namely to: dispatch to whichever market has the higher current spot price, regardless of the variability of the transport expenses.

Abstract
We present an exact method, based on an arc-flow formulation with side constraints, for solving bin packing and cutting stock problems-including multi-constraint variants-by simply representing all the patterns in a very compact graph. Our method includes a graph compression algorithm that usually reduces the size of the underlying graph substantially without weakening the model. Our formulation is equivalent to Gilmore and Gomory's, thus providing a very strong linear relaxation. However, instead of using column-generation in an iterative process, the method constructs a graph, where paths from the source to the target node represent every valid packing pattern. The same method, without any problem-specific parameterization, was used to solve a large variety of instances from several different cutting and packing problems. In this paper, we deal with vector packing, bin packing, cutting stock, cardinality constrained bin packing, cutting stock with cutting knife limitation, bin packing with conflicts, and other problems. We report computational results obtained with many benchmark test datasets, some of them showing a large advantage of this formulation with respect to the traditional ones.

Abstract
The tourist behaviour has changed significantly over the last decades due to technological advancement (e.g., ubiquitous access to the Web) and Web 2.0 approaches (e.g., Crowdsourcing). Tourism Crowdsourcing includes experience sharing in the form of ratings and reviews (evaluation-based), pages (wiki-based), likes, posts, images or videos (social-network-based). The main contribution of this paper is a tourist-centred off-line and on-line analysis, using hotel ratings and reviews, to discover and present relevant trends and patterns to tourists and businesses. On the one hand, online, we provide a list of the top ten hotels, according to the user query, ordered by the overall rating, price and the ratio between the positive and negative Word Clouds reviews. On the other hand, off-line, we apply Multiple Linear Regression to identify the most relevant ratings that influence the hotel overall rating, and generate hotel clusters based on these ratings. © 2016 ACM.

Abstract
The commercialization of research done by universities and research institutes is seen as a key element of entrepreneurship, justifying a better understanding of the entrepreneurial process within those institutions. Despite efforts in recent decades, there remains a widespread ignorance about who are the individuals with a greater vocation for research and marketing of knowledge. Focusing on a relatively unexplored context - the field of life sciences in a moderate innovator country, Portugal - this study adds new empirical evidence to the precedents of academic entrepreneurship. The results, based on 247 responses, evidence that entrepreneurship (including patent production, consultancy work and creation of start-up companies) is still very incipient. The econometric analysis of the "Index of entrepreneurial activity," shows that: (1) the phenomenon of academic entrepreneurship is within everyone's reach, but embraced by very few; and (2) academic entrepreneurship feeds on the exchange of knowledge and resources among universities and research institutes and their socio-economic environment. Thus, to increase academic entrepreneurship in life science in Portugal, the mechanisms to facilitate the interactions between universities and the business community must be strengthened, promoting the development of business clusters with the integration of the academic world, which will enable the transformation of knowledge into market products and services.

Abstract