Abstract

Abstract
The evolution of electricity markets towards local energy trading models, including peer-To-peer transactions, is bringing by multiple challenges for the involved players. In particular, small consumers, prosumers and generators, with no experience on participating in competitive energy markets, are not prepared for facing such an environment. This paper addresses this problem by proposing a decision support solution for small players negotiations in local transactions. The collaborative reinforcement learning concept is applied to combine different learning processes and reached an enhanced final decision for players actions in bilateral negotiations. The reinforcement learning process is based on the application of the Q-Learning algorithm; and the continuous combination of the different learning results applies and compares several collaborative learning algorithms, namely BEST-Q, Average (AVE)-Q; Particle Swarm Optimization (PSO)-Q, and Weighted Strategy Sharing (WSS)-Q and uses a model to aggregate these results. Results show that the collaborative learning process enables players' to correctly identify the negotiation strategy to apply in each moment, context and against each opponent. © 2019 IEEE.

Abstract
Social media are essentially changing the way firms communicate, create and collaborate in and for innovation. In this special issue introductory article, we take stock of the robust multi-faceted nature of research and practice at the intersection of social media (SM) and innovation. We introduce the nine papers included in this special issue and highlight the rich variety of their contribution with reference to our organising framework. Diagnosing from a strategic perspective, we position SM strategy in and for innovation as an overlapping interaction between dynamic capabilities (sensing, seizing, reconfiguration) and the level of stakeholder engagement (macro, meso, micro). We explain how each interaction holds distinctive synergy in an open and collaborative innovation process. This organising framework shows how the malleable nature of SM creates opportunities for firms to engage widely distributed knowledge sources, enhance innovation capabilities and empower internal human resources towards an open and collaborative culture. Yet, we warn that all is not as rosy as it seems and a purposeful and coherent strategy that delivers distinctive ‘co-ownership’ experiences is quintessential ingredient to realise profits from SM use in innovation.

Abstract
Medical training is an intricate and long process, which is compulsory to medical practice and often lasts up to twelve years for some specialties. Health stakeholders recognise that an adequate planning is crucial for health systems to deliver necessary care services. However, proper planning needs to account for complexity related with the setting of medical school vacancies and of residency programs, which are highly influenced by multiple stakeholders with diverse perspectives and views, as well as by the specificities of medical training. Aiming at building comprehensive models with a potential to assist health decision-makers, this article develops a multi-methodological framework to assist the planning of medical training under such a complex environment. It combines the structuring of the objectives and specificities of the medical training problem with a Soft Systems Methodology through the CATWOE (Customer, Actor, Transformation, Weltanschauung, Owner, Environment) approach, and the formulation of a Mixed Integer Linear Programming model that considers all relevant aspects. Considering the specificities of countries based on a National Health Service structure, a multi -objective planning model emerges, informing on how many vacancies should be opened/closed per year in medical schools and in each specialty. This model aims at (i) minimizing imbalances between medical demand and supply; (ii) minimizing costs; and (iii) maximizing equity across medical specialties. A case study in Portugal is explored so as to illustrate the applicability of the proposed multi-methodology, showing the relevance of proper structuring for planning models having the potential to inform health decision-makers and planners in practice.

Abstract
In 1997, Eliyahu Goldratt proposed a method called critical chain project management (CCPM) to minimize the inefficiencies identified in traditional project management. The project management community accepted the proposed method as a viable alternative. However, to allow its implementation with a multiproject system, more research was necessary. Seeking to identify the key factors that influence the performance of the multiproject system applying the CCPM method, we performed a case study. Logistic regression analysis showed that applying the CCPM method in a multiproject system allows for better time estimation of activities and facilitates the allocation of critical resources.

Abstract
The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people's lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users' physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones.