Abstract
Background/objective: Playing a wind instrument implies rhythmic jaw movements where the embouchure applies forces with different directions and intensities towards the orofacial structures. These features are relevant when comparing the differences between a clarinettist and a saxophone player embouchure, independently to the fact that both belong to the single-reed instrument group, making therefore necessary to update the actual classification. Methods: Lateral cephalograms were taken to single-reed, double-reed and brass instrumentalists with the purpose of analyzing the relationship of the mouthpiece and the orofacial structures. Results: The comparison of the different wind instruments showed substantial differences. Therefore the authors purpose a new classification of wind instruments: Class 1 single-reed mouthpiece, division 1– clarinet, division 2 –saxophone; Class 2 double-reed instruments, division 1– oboe, division 2– bassoon; Class 3 cup-shaped mouthpiece, division 1– trumpet and French horn, division 2- trombone and tuba; Class 4 aperture mouthpieces, division 1– flute, division 2 – transversal flute and piccolo. Conclusions: Elements such as dental arches, teeth and lips, assume vital importance at a new nomenclature and classification of woodwind instruments that were in the past mainly classified by the type of mouthpiece and not taking into consideration its relationship with their neighboring structures. © 2019 Craniofacial Research Foundation

Abstract
In security-constrained unit commitment (SCUC) problems, one approach to decrease operation costs is using a transmission switching (TS) tool. In SCUC problems with TS, one of the main challenges is that there is no limitation for the number of switching of circuit breakers (CB) in the system. In this article, the reliability of CB is merged into the SCUC problem with the TS and is considered as a limiting factor for switching. With a more reliable CB, the overall reliability of the system will be increased. So, it can be concluded that the reliability of a CB affects the amount of load shedding. Reliability of a CB is a nonlinear equation based on the number of switching in a period. An approach is presented to linearize the switch reliability equation. In this article, the power flow model uses an improved linear ac optimal power flow and a dynamic thermal line rating (DTLR) model, which considers the weather conditions. Other than CB reliability, DTLR in SCUC problems affects the number of switching and, as a result, operation costs will be significantly decreased. The proposed model is empowered by Bender's decomposition and is tested on 6-bus and 118-bus IEEE test systems.

Abstract
This study proposes a model for (re-)designing machine layouts in already existing facilities with a multi-period time planning horizon. The model can be applied in several situations and at different moments of a layout life cycle, for example to design the initial layout of an existing facility, or to make some specific and local reconfigurations. This dynamic multiobjective model minimizes costs (production, material handling and reconfiguration costs), maximizes adjacency between machines, minimizes unsuitability (to combine characteristics of the machines and of the existing locations), and can allow changes between periods on the product mix or on the machine layout requirements (e.g., required area). The performance of the model was tested with a case study based on a real first-tier supplier of the automotive industry, thus showing the practical potential of the proposed approach.

Abstract
Intrusion Detection Systems (IDS) are used to prevent attacks by detecting potential harmful intrusion attempts. Currently, there are a set of available Open Source IDS with different characteristics. The Open Source Host-based Intrusion Detection System (OSSEC) supports multiple features and its implementation consists of Agents that collect and send event logs to a Manager that analyzes and tests them against specific rules. In the Manager, if certain events match a specific rule, predefined actions are triggered in the Agents such as to block or unblock a particular IP address. However, once an action is triggered, the systems administrator is not able to centrally check and obtain detailed information of the past event logs. In addition, OSSEC may assume false positive or negative detections and their triggered actions: previously harmless but blocked IP addresses by OSSEC have to be unblocked in order to reestablish normal operation or potential harmful IP addresses not previously blocked by OSSEC should be blocked in order to increase protection levels. These operations to override OSSEC actions must be manually performed in every Agent, thus requiring time and human resources. Both these limitations have a higher impact on large scale OSSEC deployments assuming tens or hundreds of Agents. This paper proposes an extension to OSSEC that improves the administrator analysis capability by maintaining, organizing and presenting Agent logs in a central point, and it allows for blocking or unblocking IP addresses in order to override actions triggered by false detections. The proposed extension aims to increase efficiency of time and human resources management, mainly considering large scale OSSEC deployments.

Abstract
This book presents, in an integrated form, both the analysis and synthesis of three different types of hidden Markov models. Unlike other books on the subject, it is generic and does not focus on a specific theme, e.g. speech processing. Moreover, it presents the translation of hidden Markov models’ concepts from the domain of formal mathematics into computer codes using MATLAB®. The unique feature of this book is that the theoretical concepts are first presented using an intuition-based approach followed by the description of the fundamental algorithms behind hidden Markov models using MATLAB®. This approach, by means of analysis followed by synthesis, is suitable for those who want to study the subject using a more empirical approach.

Abstract
Energy consumption forecasting is crucial in current and future power and energy systems. With the increasing penetration of renewable energy sources, with high associated uncertainty due to the dependence on natural conditions (such as wind speed or solar intensity), the need to balance the fluctuation of generation with the flexibility from the consumer side increases considerably. In this way, significant work has been done on the development of energy consumption forecasting methods, able to deal with different forecasting circumstances, e.g., the prediction time horizon, the available data, the frequency of data, or even the quality of data measurements. The main conclusion is that different methods are more suitable for different prediction circumstances, and no method can outperform all others in all situations (no-free-lunch theorem). This paper proposes a novel application, developed in the scope of the SIMOCE project (ANI vertical bar P2020 17690), which brings together several of the most relevant forecasting methods in this domain, namely artificial neural networks, support vector machines, and several methods based on fuzzy rule-based systems, with the objective of providing decision support for energy consumption forecasting, regardless of the prediction conditions. For this, the application also includes several data management strategies that enable training of the forecasting methods depending on the available data. Results show that by this application, users are endowed with the means to automatically refine and train different forecasting methods for energy consumption prediction. These methods show different performance levels depending on the prediction conditions, hence, using the proposed approach, users always have access to the most adequate methods in each situation.