Abstract
Laser scanners are widely used in mobile robotics localization systems but, despite the enormous potential of its use, their high price tag is a major drawback, mainly for hobbyist and educational robotics practitioners that usually have a reduced budget. The Neato XV-11 Laser Scanner is a very low cost alternative, when compared with the current available laser scanners, being this fact the main motivation for its use. The modeling of a hacked Neato XV-11 Laser Scanner allows to provide valuable information that can promote the development of better designs of robot localization systems based on this sensor. This paper presents, as an example, the performance enhancement of a Neato XV-11 Laser Scanner applied to mobile robot self-localization, being used as case study the Perfect Match Algorithm applied to the Robot@Factory competition. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
Distributed power sources will become increasingly ubiquitous in the near future. In this power production paradigm, photovoltaic conversion systems will play a fundamental role due to the growing tendency of energy price, and an opposed trend for the photovoltaic panels. This will lead to increased pressure for the installation of this particular renewable energy source in home buildings. In particular, on-grid photovoltaic systems where the generated power can be injected directly to the main power grid. This strategy requires the use of DC-AC inverters whose output is synchronized, in phase, with the main grid voltage. In order to provide steady output in the presence of load disturbances, the inverter must work in closed-loop. This work presents a new way to design an inverter controller by resorting to the CDM design technique. The obtained results suggest that the controller achieved with this method, although simpler than other methods, leads to an acceptable and robust closed-loop response. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

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
The most current forecasts point to a decrease in the amount of potable water available. This increase in water scarcity is a problem with which sustainable agricultural production is facing. This has led to an increasing search for technical solutions in order to improve the efficiency of irrigation systems. In this context, this work describes the architecture of an agent-based network and the cyberphysical elements which will be deployed in a strawberry fertigation production plant. The operation of this architecture relies on local information provided by LoRA based wireless sensor network that is described in this paper. Using the information provided by the array of measurement nodes, cross-referenced with local meteorological data, grower experience and the actual crop vegetative state, it will be possible to better define the amount of required irrigation solution and then to optimise the water usage. © Springer Nature Switzerland AG 2019.

Abstract
In this paper, a numerical simulation of a semi-active neuroemotional based control system for vibration reduction of a 3-story framed building structure under seismic excitation is presented. The Brain Emotional Learning Based Intelligent Controller (BELBIC) is used to design a closed-loop control system that determines the required control action (emotional response) based on the desired and actual system response (sensory input). In this case, the control signal is used to adjust in real time the damping force of a MagnetoRheological (MR) damper to reduce the system response. The results obtained from the numerical simulation validate the effectiveness of the brain emotional learning semi-active controller in improving the overall response of the structural system. © 2019 The authors.