Abstract
A buildings resilience to seismic activity can be increased by providing ways for the structure to dynamically counteract the effect of the Earth's crust movements. This ability is fundamental in certain regions of the globe, where earthquakes are more frequent, and can be achieved using different strategies. State-of-the-art anti-seismic buildings have, embedded on their structure, mostly passive actuators such as base isolation, Tuned Mass Dampers (TMD) and viscous dampers that can be used to reduce the effect of seismic or even wind induced vibrations. The main disadvantage of this type of building vibration reduction strategies concerns their inability to adapt their properties in accordance to both the excitation signal or structural behaviour. This adaption capability can be promoted by adding to the building active type actuators operating under a closed-loop. However, these systems are substantially larger than passive type solutions and require a considerable amount of energy that may not be available during a severe earthquake due to power grid failure. An intermediate solution between these two extremes is the introduction of semi-active actuators such as magneto-rheological dampers. The inclusion of magneto-rheological actuators is among one of the most promising semi-active techniques. However, the overall performance of this strategy depends on several aspects such as the actuators number and location within the structure and the vibration sensors network. It can be the case where the installation leads to a non-collocated system which presents additional challenges to control. This paper proposes to tackle the problem of controlling the vibration of a non-collocated three-storey building by means of a brain-emotional controller tuned using an evolutionary algorithm. This controller will be used to adjust the stiffness coefficient of a magneto-rheological actuator such that the building's frame oscillation under earthquake excitation, is mitigated. The obtained results suggest that, using this control strategy, it is possible to reduce the building vibration to secure levels.

Abstract
The optimisation of forest fuels supply chain involves several entities actors, and particularities. To successfully manage these supply chains, efficient tools must be devised with the ability to deal with stakeholders dynamic interactions and to optimize the supply chain performance as a whole while being stable and robust, even in the presence of uncertainties. This work proposes a framework to coordinate different planning levels and event-based models to manage the forest-based supply chain. In particular, with the new methodology, the resilience and flexibility of the biomass supply chain is increased through a closed-loop system based on the system forecasts provided by a discrete-event model. The developed event-based predictive model will be described in detail, explaining its link with the remaining elements. The implemented models and their links within the proposed framework are presented in a case study in Finland and results are shown to illustrate the advantage of the proposed architecture.

Abstract

Abstract
This paper deals with the modelling and control of an oven for reflowing electronic surface-mounted devices. The modelling is done taking into account that the system has a second-order dynamic behaviour whose parameters were obtained through a genetic algorithm. It is also shown that this system exhibits a high pure time-delay which makes the design of the control system very difficult. In particular, the performance of a PI controller is evaluated with respect to tracking two different types of reference signals. It is observed that the performance of such a controller is severely affected by the huge inertia of the system. In order to mitigate this effect, tests were performed with a different architecture known as the Smith predictor. It is concluded that, although with differences, neither the PI controller nor the Smith allows the tracking of the typical reference in a reflow process.

Abstract
This article presents the results regarding a systematic literature review procedure on digital twins applied to precision agriculture. In particular, research and development activities aimed at the use of digital twins, in the context of predictive control, with the purpose of improving soil quality. This study was carried out through an exhaustive search of scientific literature on five different databases. A total of 158 articles were extracted as a result of this search. After a first screening process, only 11 articles were considered to be aligned with the current topic. Subsequently, these articles were categorised to extract all relevant information, using the preferred reporting items for systematic reviews and meta-analyses methods. Based on the obtained results, there are two main conclusions to draw: First, when compared with industrial processes, there is only a very slight rising trend regarding the use of digital twins in agriculture. Second, within the time frame in which this work was carried out, it was not possible to find any published paper on the use of digital twins for soil quality improvement within a model predictive control context.

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.