Abstract
The advancements in information and communication technology in the past decades have been converging into a new communication paradigm in which everything is expected to be interconnected. The Internet of Things, more than a buzzword, is becoming a reality, and is finding its way into the industrial domain, enabling what is now dubbed as the Industry 4.0. Among several standards that help in enabling Industry 4.0, the IEEE 802.15.4e standard addresses requirements such as increased robustness and reliability. Although the standard seems promising, the technology is still immature and rather unproven. Also, there has been no thorough survey of the standard with emphasis on the understanding of the performance improvement in regards to the legacy protocol IEEE 802.15.4. In this survey, we aim at filling this gap by carrying out a performance analysis and thorough discussions of the main features and enhancements of IEEE 802.15.4e. We also provide a literature survey concerning the already proposed add-ons and available tools. We believe this work will help to identify the merits of IEEE 802.15.4e and to contribute towards a faster adoption of this technology as a supporting communication infrastructure for future industrial scenarios.

Abstract
Deterministic Synchronous Multichannel Extension (DSME) is a prominent MAC behavior first introduced in IEEE 802.15.4e that supports deterministic guarantees using its multisuperframe structure. DSME also facilitates techniques like multi-channel and CAP reduction that help to increase the number of available guaranteed timeslots in a network. However, no tuning of these functionalities in dynamic scenarios is supported in the standard. In this paper, we present an effective multisuperframe tuning technique that also helps to utilize CAP reduction in an effective manner improving flexibility and scalability, while guaranteeing bounded delay.

Abstract
The development of Metadata Application Profiles is done in several phases. According to the Me4MAP method, one of these phases is the creation of the domain model. This paper reports the validation process of a domain model developed under the project POSTDATA - Poetry Standardization and Linked Open Data. The development of the domain model ran with two steps of construction and two of validation. The validation steps drew on the participation of specialists in European poetry and the use of real resources. On the first validation we used tables with information about resources related properties and for which the experts had to fill certain fields like, for examples, the values. The second validation used a XML framework to control the input of values in the model. The validation process allowed us to find and fix flaws in the domain model that would otherwise have been passed to the Description Set Profile and possibly would only be found after implementing the application profile in a real case.

Abstract
EMPOWER SSE is a Fundação para a Ciência e Tecnologia (FCT, Portugal) and Department of Science & Technology (DST, India), financed research project that aims to use the Linked Open Data Framework to empower the Social and Solidarity Economy (SSE) Agents. It is a collaborative project between India and Portugal that is focused on defining a Semantic Web framework to consolidate players of the informal sector, enabling a paradigm shift. The Indian economy can be categorized into two sectors: formal and informal. The informal sector economy differs from the formal as it is an unorganized sector and comprised of economic activities that are not covered by formal arrangements such as taxation, labor protections, minimum wage regulations, unemployment benefits, or documentation. The major economy in India depends on the skilled labor of this informal sector such e.g. daily labor, farmers, electricians, food production, and small-scale industries (Kalyani, 2016). The informal sector is mainly made of skilled people that follow their family job traditions, sometimes they are not even formally trained. This sector struggles with the lack of information, data sharing needs and interoperability issues across systems and organizational boundaries. In fact, this sector does not have any visibility to the society not having the possibility to do business as most of the agents of this sector do not reach the end of the chain. This blocks them from getting proper exposure and a better livelihood.

Abstract

Abstract
In this paper a load forecasting methodology for 2days-ahead based on functional clustering and on ensemble learning is presented. Due to the longitudinal nature of the load diagrams, these are segmented using a functional clustering procedure to group together similar daily load curves concerning its phase and amplitude. Next, ensemble learning of extreme learning machine models, developed for several load curves groups, is made to fully integrate the advantages of all models and improve the accuracy of the final load forecasting. The quality of this methodology is illustrated with a real case study concerning load consumption patterns of clients with different economic activities from a Portuguese energy trading company. The forecasting results for 2days-ahead are good for practical use, yielding a R-2 = 0.967.