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
This paper proposes an Artificial Neural Network (ANN) based approach to classify different contexts, with the goal of enhancing the management of residential energy resources. The increasing penetration of renewable based generation has completely changed the paradigm of the power and energy sector. The intermittent nature of these resources requires the system to incentivize the adaptability of consumers in order to guarantee the balance between generation and consumption. This leads to the emergence of several incentives with the objective of increasing the flexibility from the consumer's side. This, allied to the increasing price of electricity, leads to an increasing need for consumers to adapt their consumption in order to improve energy efficiency, decrease energy bills, and achieve a better use of their own generation resources. With this, several House Management Systems (HMS), and Building Energy Management Systems (BEMS) have emerged. These systems allow adapting the consumption (or suggesting changes in consumers' habits) according to several factors. However, in order to make this management truly smart, there is a need for adaptation to different contexts, so that changes can be done accordingly to the different situations that are faced at each time. This paper addresses this problem by proposing a novel methodology that enables classifying different situations in different contexts, according to different contextual variables. © 2017 IEEE.

Abstract
Procedural modeling techniques reduce the effort of creating large virtual cities. However, current methodologies do not allow direct user control over the generated models. Associated with this problem, we face the additional problem related to intrinsic ambiguity existing in user selections. In this paper, we propose to address this problem by using a genetic algorithm to generalize user-provided point-and-click selections of building elements. From a few user-selected elements, the system infers new sets of elements that potentially correspond to the user's intention, including the ones manually selected. These sets are obtained by queries over the shape trees generated by the procedural rules, thus exploiting shape semantics, hierarchy and geometric properties. Our system also provides a complete selection-action paradigm that allows users to edit procedurally generated buildings without necessarily explicitly writing queries. The pairs of user selections and procedural operations (the actions) are stored in a tree-like structure, which is easily evaluated. Results show that the selection inference is capable of generating sets of shapes that closely match the user intention and queries are able to perform complex selections that would be difficult to achieve in other systems. User studies confirm this result.

Abstract
Study of the adoption of non-traditional tools as support for Higher Education curricula in Electrical Engineering and Computers. We highlight the use of blended-learning, interactive and remote virtual laboratories, computer simulation, and methodologies, such as Active Learning and Problem Based Learning and their applications in the curricular units of the course. The study is a literature review with the systematization and presentation of the findings through a conceptual map. We concluded that the initiatives that have resorted to new technologies in engineering degrees, as well as reports of similar experiments on this topic, are reduced, not formalized in curricula, and ad hoc.

Abstract
The evolution and advances in technology bring with them a strong necessity of fast adaptation of the multiple industries and sectors, in order to meet market requirements. This paper is a case study based on qualitative research, which addresses the implementation of an automated system in a Portuguese pharmacy. The aim was to understand how these systems work, and what advantages and disadvantages exist as well as to understand what awaits us in the future in the pharmaceutical field through the analysis of a real example. A personal interview was performed, in January 2018, with the owner and director of the Giro pharmacy; a session of passive observation was also realized. The aim was to learn about and observe the implementation of an automated system for the distribution of products from storage. The advantage of the system is that it allows for a more personalized service, as the employee does not need to be absent at any time during the delivery of the service. Innovation and its source was also a topic during the interview. International contacts and observing how firms function in other countries which are technologically more advanced (e.g. in Germany) were revealed as being important.

Abstract
Cutting and Packing (C & P) problems arise in many industrial and logistics applications, whenever a set of small items, with different shapes, has to be assigned to large objects with specific shapes so as to optimize some objective function. Besides some characteristics common to combinatorial optimization problems, the distinctive feature of this field is the existence of a geometric subproblem, to ensure that the items do not overlap and are completely contained in the large objects. The geometric tools required to deal with this subproblem depend on the shapes (rectangles, circles, irregular) and on the specific conditions of the problem being solved. In this chapter, after an introduction that describes and classifies Cutting and Packing problems, we review the basic strategies that have appeared in the literature for designing constructive algorithms, local search procedures, and metaheuristics for problems with regular and irregular shapes.