Abstract
Classic Collaborative Filtering (CF) algorithms rely on the assumption that data are static and we usually disregard the temporal effects in natural user-generated data. These temporal effects include user preference drifts and shifts, seasonal effects, inclusion of new users, and items entering the systemand old ones leavinguser and item activity rate fluctuations and other similar time-related phenomena. These phenomena continuously change the underlying relations between users and items that recommendation algorithms essentially try to capture. In the past few years, a new generation of CF algorithms has emerged, using the time dimension as a key factor to improve recommendation models. In this overview, we present a comprehensive analysis of these algorithms and identify important challenges to be faced in the near future.(C) 2015 John Wiley & Sons, Ltd.

Abstract
Intelligence involves knowing information about some competitive factors like competitors' profitability and turnover rate. Information technology can help organizations to seize the information available. In this chapter we will present the solution architected and developed for a Portuguese company with a starting scenario that showed characteristics like dispersed information, concentration of knowledge in a single individual, no defined architecture for data or information, simple changes involving huge efforts and lack of agility. We found that business decision makers had problems of relying on the results, as the system was like a black box and often provided not accurate data. We will describe how we solved the problem, designing and implementing a business intelligence solution and the impact of having an information architecture.

Abstract
Global competition and the customers demand for customized products with shorter due dates, marked the introduction of the Extended Enterprise. In this Extended Manufacturing Environment (EME), lean, virtual, networked and distributed enterprises collaborate to respond to the market demands. In this paper we study the influence of the batch size on Flexible Flow Shop makespan minimization problem FFC vertical bar vertical bar C-max for two multi-sites approaches, the FSBF (Flow Shop Based Factories) and the PMBF (Parallel-Machines Based Factories). The computational study demonstrates how the performance of the PMBF model decreases with the increase of batch size and determines the batch sizes in which the performance is similar.

Abstract
Locally advanced breast cancer (LABC) constitutes a heterogeneous entity that includes advanced-stage primary tumours, cancers with extensive nodal involvement and inflammatory breast carcinomas. Although the definition of LABC can be broadened to include some large operable breast tumours, we use this term to strictly refer to inoperable cancers that are included in the above-mentioned categories. The prognosis of such tumours is often unfavourable; despite aggressive treatment, many patients eventually develop distant metastases and die from the disease. Advances in systemic therapy, including radiation treatment, surgical techniques and the development of new targeted agents have significantly improved clinical outcomes for patients with this disease. Notwithstanding these advances, LABC remains an important clinical problem, particularly in developing countries and those without widely adapted breast cancer awareness programmes. The optimal management of LABC requires a multidisciplinary approach, a well-coordinated treatment schedule and close cooperation between medical, surgical and radiation oncologists. In this Review, we discuss the current state of the art and possible future treatment strategies for patients with LABC.

Abstract
A novel dead reckoning algorithm conceived for localization of small inspection rail vehicles in Global Navigation Satellite System (GNSS) denied environments is presented. This work focus on simplifying the rail vehicle localization task, taking into account restrictions on movement imposed by the railroad tracks. Considering that dead reckoning techniques accumulate errors over time, leading to increasing global uncertainty, a method was designed to correct the estimates and also smooth trajectory errors backwards in time, through visualization of global landmarks. Results show the effectiveness of this approach in reducing long-term position errors. The current document reports real railroad experiments, featuring a specially designed non-motorized mobile modeling vehicle.

Abstract
In the fiercely competitive era induced by expansion of open business archetypes, the managerial aspects of Extended Manufacturing Environments (EMEs) are experiencing growing concerns. There is no scope of leaving a possible operational improvement unexplored. For enhanced operational efficiency and capacity utilization the balancing and scheduling problems of EMEs are, therefore, rightfully considered and an integer programme is proposed in this paper. The model is designed in a spread sheet and solved through What'sBest optimizer. The model capabilities is assessed through a test problem. The results have demonstrated that the model is capable of defining optimized production schedules for EMEs.