Abstract
Educational chatbots are digital tools designed to assist learners in various educational settings. These chatbots use natural language processing (NLP) and machine learning algorithms to simulate human conversation and respond to user queries in a way that facilitates learning. They can be integrated into various educational platforms such as learning management systems, educational apps, and websites to provide learners with a personalized and interactive learning experience. Our paper discusses different scenarios for educational purposes and suggests in total four scenarios for educational needs.

Abstract
The delivery of concrete is a crucial process in construction projects, and any delay or error can cause significant setbacks and added costs. Thus, effective real-time management of concrete delivery is essential to ensure timely and successful project completion. In this paper, we will discuss a practical and manufacturer-agnostic approach to real-time management of concrete delivery for construction named BET 4.0 that is being conceived with a close partnership with a construction company. This application provides the possibility to optimize the whole concreting process as it establishes the connection between all the relevant components and stakeholders involved in the construction process, namely the concrete plant, the transport, and the construction site, interfacing with all actors involved, and benefiting from real-time data produced by installed sensors in the several components such as machines, plants, or construction elements.

Abstract
The teaching of entrepreneurship has been progressively included in the curricula of several university courses to stimulate the development of empowering attitudes and an entrepreneurial mentality. However, a new form of entrepreneurship has emerged with a focus on sustainability and the creation of new projects that aim to reduce social asymmetries and contribute to a fairer and more balanced society. The role of universities is also to foster the emergence of these projects through the implementation of practices aimed at fostering social entrepreneurship among students. This study aims to understand the determinant dimensions that characterise the students' social entrepreneurial intention. For this purpose, a sample of 177 students attending a social entrepreneurship course in a higher education institution was employed. The findings indicate that individual, organisation, and context constructs are determinants of students' entrepreneurial intention. However, not all organisational factors contribute equally. Mentoring and social networks are relevant elements for the entrepreneurial intention of individuals, while curriculum and critical pedagogy are not recognised as determinants.

Abstract

Abstract
The transition to Industry 4.0 provoked a transformation of industrial manufacturing with a significant leap in automation and intelligent systems. This paradigm shift has brought about a mindset that emphasizes predictive maintenance: detecting future failures when current behaviour of industrial processes and machines is thought to be normal. The constant monitoring of industrial equipment produces massive quantities of data that enables the application of machine learning approaches to this task. This study uses deep learning-based models to build a data-driven predictive maintenance framework for the air production unit (APU), a crucial system for the proper functioning of a Metro do Porto train. This public transport system moves thousands of people every day and train failures lead to delays and loss of trust by clients. Therefore, it is essential not only to detect APU failures before they occur to minimize negative impacts, but also to provide explanations for the failure warnings that can aid in decision-making processes. We propose an autoencoder architecture trained with an adversarial loss, known as the Wasserstein Autoencoder with Generative Adversarial Network (WAE-GAN), designed to detect sensor failures in systems connected to the APU. Our model can detect APU failures up to two hours before they occur, allowing timely intervention of the maintenance teams. We further augment our model with an explainability layer, by providing explanations generated by a rule-based model that focuses on rare events. Results show that our model is able to detect APU failures without any false alarms, fulfilling the requisites of Metro do Porto for early detection of the failures.

Abstract
Data analysis has emerged as a cornerstone in facilitating informed decision-making across myriad fields, in particular in software development and project management. This integrative practice proves instrumental in enhancing operational efficiency, cutting expenditures, mitigating potential risks, and delivering superior results, all while sustaining structured organization and robust control. This paper presents ITC, a synergistic platform architected to streamline multi-organizational and multi-workspace collaboration for project management and technical documentation. ITC serves as a powerful tool, equipping users with the capability to swiftly establish and manage workspaces and documentation, thereby fostering the derivation of invaluable insights pivotal to both technical and business-oriented decisions. ITC boasts a plethora of features, from support for a diverse range of technologies and languages, synchronization of data, and customizable templates to reusable libraries and task automation, including data extraction, validation, and document automation. This paper also delves into the predictive analytics aspect of the ITC platform. It demonstrates how ITC harnesses predictive data models, such as Random Forest Regression, to anticipate project outcomes and risks, enhancing decision-making in project management. This feature plays a critical role in the strategic allocation of resources, optimizing project timelines, and promoting overall project success. In an effort to substantiate the efficacy and usability of ITC, we have also incorporated the results and feedback garnered from a comprehensive user assessment conducted in 2022. The feedback suggests promising potential for the platform's application, setting the stage for further development and refinement. The insights provided in this paper not only underline the successful implementation of the ITC platform but also shed light on the transformative impact of predictive analytics in information systems.