Abstract
Built-in sensors in most modern smartphones open multiple opportunities for novel context-aware applications. Although the Human Activity Recognition field seized such opportunity, many challenges are yet to be addressed, such as the differences in movement by people doing the same activities. This paper exposes empirical research on Online Semi-supervised Learning (OSSL), an under-explored incremental approach capable of adapting the classification model to the user by continuously updating it as data from the user's own input signals arrives. Ultimately, we achieved an average accuracy increase of 0.18 percentage points (PP) resulting in a 82.76% accuracy model with Naive Bayes, 0.14 PP accuracy increase resulting in a 83.03% accuracy model with a Democratic Ensemble, and 0.08 PP accuracy increase resulting in a 84.63% accuracy model with a Confidence Ensemble. These models could detect 3 stationary activities, 3 active activities, and all transitions between the stationary activities, totaling 12 distinct activities.

Abstract
Many water boiler manufacturers are not able to detect the occurrence of failures in the machines they produce before they can pose inconvenience and sometimes danger for costumers and workers. Moreover, the number of boilers that have to be monitored, are many times in the range of the thousands or even millions, proportionaly to the number of costumers a company possesses. The detection of these failures in real time, would provide a significant improvement to the perception that consumers have of a certain company, since, if these failures occur, maintenance services can be deployed almost as soon as a failure happens. In this paper, an application prototype capable of monitoring and preventing failures in domestic water boilers, on the y, is presented. This application evaluates measurements which are performed by sensors within the boilers, and identifies the ones that greatly differ from those received previously, as new data arrives, detecting tendencies which might illustrate the occurrence of a failure. The incremental local outlier factor is used with an approach based on the interquatile range measure to detect the outlier factors that should be analysed.

Abstract
Built-in sensors in most modern smartphones open multipleopportunities for novel context-aware applications. Although the HumanActivity Recognition field seized such opportunity, many challengesare yet to be addressed, such as the differences in movement by peopledoing the same activities. This paper exposes empirical research onOnline Semi-supervised Learning (OSSL), an under-explored incrementalapproach capable of adapting the classification model to the userby continuously updating it as data from the users own input signalsarrives. Ultimately, we achieved an average accuracy increase of 0.18percentage points (PP) resulting in a 82.76% accuracy model with NaiveBayes, 0.14 PP accuracy increase resulting in a 83.03% accuracy modelwith a Democratic Ensemble, and 0.08 PP accuracy increase resultingin a 84.63% accuracy model with a Confidence Ensemble. These modelscould detect 3 stationary activities, 3 active activities, and all transitionsbetween the stationary activities, totaling 12 distinct activities

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
The Assembly Line Part Feeding Problem (ALPFP) is a complex combinatorial optimisation problem concerned with the delivery of the required parts to the assembly workstations in the right quantities at the right time. Solving the ALPFP includes simultaneously solving two sub-problems, namely tour scheduling and tow-train loading. In this article, we first define the problem and formulate it as a multi-objective mixed-integer linear programming model. Then, we carry out a complexity analysis, proving the ALPFP to be NP-complete. A modified particle swarm optimisation (MPSO) algorithm incorporating mutation as part of the position updating scheme is subsequently proposed. The MPSO is capable of finding very good solutions with small time requirements. Computational results are reported, demonstrating the efficiency and effectiveness of the proposed MPSO.

Abstract