Abstract
Ensuring a sustainable transition process, whether at a global or local level, involves designing an energy mix appropriate to the user's needs. The aim is to identify the optimal future strategy that maximizes both socio-economic benefits and sustainability. To address these challenges, multiple modeling tools are now available to assess different scenarios before implementation. However, modeling tools are generally designed for economic optimization. This is the case with the Open-Source Energy Modeling System (OSeMOSYS) a CLEWs tool. This paper proposes an optimization methodology in terms of sustainability, introducing Life Cycle Assessment (LCA) as a global estimator. In particular, the effects of energy payback times (EPBT) on the selection of transition scenarios will be evaluated. To ensure the reproducibility of the study, we present an exercise that uses data for a fictitious country that shares features of both a developing and a developed country (Atlantis).

Abstract
This paper presents the development of an acquisition system and data logger from an existing set of three continuous stirred-tank reactors in series. The reactors are currently used in chemical engineering educational laboratories to perform kinetic and tracer experiments. In this sense, to accomplish the store data process, the volumetric flow rate and the concentration of tracer, reactants and/or products of the reaction must be acquired as a function of time. In the original experimental setup, only the signal conditioning system was operational, while the acquisition, visualization, and control systems were obsolete and damaged. Thus, a new system composed of an interface and real-time acquisition data is proposed alongside preserving the main reactor structure. A graphical user interface and the automation of the various actuators were developed based on worldwide usage and low cost, respectively. This system, based on a common 8-bit microcontroller and an application developed in Lazarus, allows the storage of the acquired data in a time-series database. In this way, students can analyze the results later or in real time. Moreover, remote access allows controlling the reactor and getting data by the Internet of Things (IoT) resources. Additionally, the proposed system using IoT allows data to be shared with the community as a dataset. © 2023 IEEE.

Abstract
Long-term operation of robots creates new challenges to Simultaneous Localization and Mapping (SLAM) algorithms. Long-term SLAM algorithms should adapt to recent changes while preserving older states, when dealing with appearance variations (lighting, daytime, weather, or seasonal) or environment reconfiguration. When also operating robots for long periods and trajectory lengths, the map should readjust to environment changes but not grow indefinitely. The map size should depend only on updating the map with new information of interest, not on the operation time or trajectory length. Although several studies in the literature review SLAM algorithms, none of the studies focus on the challenges associated to lifelong SLAM. Thus, this paper presents a systematic literature review on long-term localization and mapping following the Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines. The review analyzes 142 works covering appearance invariance, modeling the environment dynamics, map size management, multisession, and computational topics such as parallel computing and timing efficiency. The analysis also focus on the experimental data and evaluation metrics commonly used to assess long-term autonomy. Moreover, an overview over the bibliographic data of the 142 records provides analysis in terms of keywords and authorship co-occurrence to identify the terms more used in long-term SLAM and research networks between authors, respectively. Future studies can update this paper thanks to the systematic methodology presented in the review and the public GitHub repository with all the documentation and scripts used during the review process.

Abstract
In this paper the design of a Sustainability Plan for the Salve brewery is modelled. To support transition for decarbonization of the company ensuring a triple zero production (energy, waste, and transport), the critical points during the production process have been identified from a SWOT analysis. Using the CLEWs tools (through the OSeMOSYS software), five scenarios are designed and evaluated according to the company energetic needs. Both historical set of real consumption data of the company, as well as the growth expectations raised by the owners have served as the basis for the work. To simplify the decision making to the company, both investment and total associated costs as well as the corresponding CO2 emissions has been estimated in the period 2023–2030. As main results, an energetic scenario including the use of bagasse waste, the implementation of photovoltaic solar energy and avoiding the fossil fuels, allows to achieve the planned objective with an economic contribution assumable by the company. In addition, several potential business niches associated with the circular economy have been identified.

Abstract
The modern manufacturing environment has adjusted to technological improvements. With Virtual Reality applications geared for factory training are becoming increasingly common. The industry is seeking ways to lower downtimes, resource component waste, risk of possible work accidents and decrease expenses, which can be achieved by engaging in new techniques of training professionals. This article evaluates a VR training application developed within the scope of the R&D project, aimed at training personnel in vehicle antenna production lines. We included the following variables: previous experience with VR technology, cybersickness, immersive tendencies, presence, system usability and satisfaction. Both the system usability scores and satisfaction were considered acceptable. We also found positive correlations between several variables, highlighting the possible influence of attention and familiarity with VR technology on the user experience. In contrast, a negative correlation raised questions about participants' expectations regarding VR technology and their resulting experience.

Abstract
Emerging wearable technology able to monitor electrocardiogram (ECG) continuously for long periods of time without disrupting the patient's daily life represents a great opportunity to improve suboptimal current diagnosis of paroxysmal atrial fibrillation (AF). However, its integration into clinical practice is still limited because the acquired ECG recording is often strongly contaminated by transient noise, thus leading to numerous false alarms of AF and requiring manual interpretation of extensive amounts of ECG data. To improve this situation, automated selection of ECG segments with sufficient quality for precise diagnosis has been widely proposed, and numerous algorithms for such ECG quality assessment can be found. Although most have reported successful performance on ECG signals acquired from healthy subjects, only a recent algorithm based on a well-known pre-trained convolutional neural network (CNN), such as AlexNet, has maintained a similar efficiency in the context of paroxysmal AF. Hence, having in mind the latest major advances in the development of neural networks, the main goal of this work was to compare the most recent pre-trained CNN models in terms of classification performance between high- and low-quality ECG excerpts and computational time. In global values, all reported a similar classification performance, which was significantly superior than the one provided by previous methods based on combining hand-crafted ECG features with conventional machine learning classifiers. Nonetheless, shallow networks (such as AlexNet) trended to detect better high-quality ECG excerpts and deep CNN models to identify better noisy ECG segments. The networks with a moderate depth of about 20 layers presented the best balanced performance on both groups of ECG excerpts. Indeed, GoogLeNet (with a depth of 22 layers) obtained very close values of sensitivity and specificity about 87%. It also maintained a misclassification rate of AF episodes similar to AlexNet and an acceptable computation time, thus constituting the best alternative for quality assessment of wearable, long-term ECG recordings acquired from patients with paroxysmal AF.