Abstract
The 3rd International Congress on Blockchain and Applications 2021 will be held in Salamanca from 6 to 8 of October. This annual congress will reunite blockchain and artificial intelligence (AI) researchers, who will share ideas, projects, lectures, and advances associated with those technologies and their application domains. Among the scientific community, blockchain and AI are seen as a promising combination that will transform the production and manufacturing industry, media, finance, insurance, e-government, etc. Nevertheless, there is no consensus with schemes or best practices that would specify how blockchain and AI should be used together. Combining blockchain mechanisms and artificial intelligence is still a particularly challenging task. The BLOCKCHAIN’21 congress is devoted to promoting the investigation of cutting-edge blockchain technology, to exploring the latest ideas, innovations, guidelines, theories, models, technologies, applications and tools of blockchain and AI for the industry, and to identifying critical issues and challenges those researchers and practitioner must deal with in the future research. We want to offer researchers and practitioners the opportunity to work on promising lines of research and to publish their developments in this area. The technical program has been diverse and of high quality, and it focused on contributions to both, well-established and evolving areas of research. More than 44 papers have been submitted to 38 from over 20 different countries (Canada, France, Germany, India, Ireland, Italy, Jordan, Luxembourg, Malaysia, Malta, Morocco, Netherlands, Oman, Portugal, Slovenia, Spain, Sweden, United Arab Emirates, and USA). We would like to thank all the contributing authors, the members of the Program Committee, the sponsors (IBM, Indra, EurAI, AEPIA, AFIA, APPIA, and AIR Institute), and the Organizing Committee for their hard and highly valuable work. We are especially grateful for the funding supporting by project “XAI - XAI - Sistemas Inteligentes Auto Explicativos creados con Módulos de Mezcla de Expertos,” ID SA082P20, financed by Junta Castilla y León, Consejería de Educación, and FEDER funds. Their work contributed to the success of the BLOCKCHAIN’21 event and, finally, the Local Organization Members and the Program Committee Members for their hard work, which was essential for the success of BLOCKCHAIN’21.

Abstract
The development of foot ulcers is associated with the Diabetic Foot (DF), which is a problem detected in patientswith Diabetes Mellitus (DM). Several studies demonstrate that thermography is a technique that can be used to identify and monitor the DF problems, thus helping to analyze the possibility of ulcers arising, as tissue inflammation causes temperature variation. There is great interest in developing methods to detect abnormal plantar temperature changes, since healthy individuals generally show characteristic patterns of plantar temperature variation and that the plantar temperature distribution of DF tissues does not followa specific pattern, so temperature variations are difficult to measure. In this sequel, a methodology, that uses thermograms to analyze the diversity of thermal changes that exist in the plant of a foot and classifies it as being from an individual with possibility of ulcer arising or not, is presented in this paper. Therefore, the concept of clustering is used to propose binary classifiers with different descriptors, obtained using two clustering algorithms, to predict the risk of ulceration in a foot. Moreover, for each descriptor, a numerical indicator and a classification thresholder are presented. In addition, using a combination of two different descriptors, a hybrid quantitative indicator is presented. A public dataset (containing 90 thermograms of the sole of the foot healthy people and 244 of DM patients) was used to evaluate the performance of the classifiers; using the hybrid quantitative indicator and the k-means clustering, the following metrics were obtained: Accuracy = 80%, AUC = 87% and F-measure = 86%.

Abstract
Lithium-ion battery is one of the most used energy storage technologies in recent times. Enhancing the life span of the Lithium-ion battery is an issue that has technical and economic contributions. This paper addresses improving the life span of Lithium-ion batteries through the appropriate way of charging in the PV system. A modified control methodology is applied for the DC-DC converter-based charger that implements the maximum power point tracking algorithm with considering the battery-safe charging recommendations. The control methodology has been implemented in Matlab Simulink and effectively verified through the simulation results.

Abstract
The Gemini Infra-Red Multi-Object Spectrograph (GIRMOS) is a four-arm, Multi-Object Adaptive Optics (MOAO) IFU spectrograph being built for Gemini (commissioning in 2024). GIRMOS is being planned to interface with the new Gemini-North Adaptive Optics (GNAO) system, and is base lined with a requirement of 50% EE within a 0.100 spaxel at H-band. We present a design and forecast the error budget and performance of GIRMOS-MOAO working behind GNAO. The MOAO system will patrol the 20 field of regard of GNAO, utilizing closed loop GLAO or MCAO for lower order correction. GIRMOS MOAA will perform tomographic reconstruction of the turbulence using the GNAO WFS, and utilize order 16x16 actuator DMs operating in open loop to perform an additional correction from the Pseudo Open Loop (POL) slopes, achieving close to diffraction limited performance from the combined GNAO+MOAO correction. This high performance AO spectrograph will have the broadest impact in the study of the formation and evolution of galaxies, but will also have broad reach in fields such as star and planet formation within our Milky Way and supermassive black holes in nearby galaxies.

Abstract
This position paper provides an overview of the most important practices in the field of Artificial Intelligence (AI) used in educational contexts, with a focus on the main platforms used for teaching (LMS) to support the development of a research work at EduardoMondlane University (UEM) in Mozambique. To that end, definitions and descriptions of relevant terms, a brief historical overview of Artificial Intelligence (AI) in education and an overview of the common goals and practices of using computational methods in educational contexts are provided. The state of the art regarding the adaptation and use of Artificial Intelligence is presented and we discuss the potential benefits and the open challenges. The paper also presents the methodology and key steps which will be developed at UEM to achieve the research goals.

Abstract
With the increase of distance learning, in general, and e-learning, in particular, having a system capable of determining the engagement of students is of primordial importance, and one of the biggest challenges, both for teachers, researchers and policymakers. Here, we present a system to detect the engagement level of the students. It uses only information provided by the typical built-in web-camera present in a laptop computer, and was designed to work in real time. We combine information about the movements of the eyes and head, and facial emotions to produce a concentration indexwith three classes of engagement: very engaged, nominally engaged and not engaged at all. The system was tested in a typical e-learning scenario, and the results show that it correctly identifies each period of time where students were very engaged, nominally engaged and not engaged at all. Additionally, the results also show that the students with best scores also have higher concentration indexes.