Learning networks and moodle use in online courses: a social network analysis study

Dissertação para obtenção do Grau de Doutor em Ciências da Educação Especialidade em Tecnologias, Redes e Multimédia na Educação e FormaçãoThis research presents a case study on the interactions between the participants of the forums of four online undergraduate courses from the perspective of social network analysis (SNA). Due to lack of studies on social networks in online learning environments in higher education in Portugal we have choose a qualitative structural analysis to address this phenomenon. The context of this work was given by the new experiences in distance education (DE) that many institutions have been making. Those experiences are a function of the changes in educational paradigms and due to a wider adoption of Information and Communication Technologies (ICT) from schools as well as to the competitive market. Among the technologies adopted by universities are the Learning Management Systems (LMSs) that allow recording, storing and using large amounts of relational data about their users and that can be accessed through Webtracking. We have used this information to construct matrices that allowed the SNA. In order to deepen knowledge about the four online courses we were studying we have also collect data with questionnaires and interviews and we did a content analysis to the participations in the forums. The three main sources of data collection led us to three types of analysis: SNA, statistical analysis and content analysis. These types of analysis allowed, in turn, a three-dimensional study on the use of the LMS: 1) the relational dimension through the study of forums networks and patterns of interaction among participants in those networks, 2) the dimension relative to the process of teaching and learning through content analysis of the interviews; 3) and finally the dimension related to the participants' perceptions about the use of LMS for educational purposes and as a platform for creating social networks through the analysis of questionnaires.With the results obtained we carried out a comparative study between the four courses and tried to present a reflection on the Online Project of the University as well as possible causes that led to what was observed. We have finished with a proposal of a framework for studying the relational aspects of online learning networks aimed at possible future research in this area

Evaluation of Compton scattering sequence reconstruction algorithms for a portable position sensitive radioactivity detector based on pixelated Cd(Zn)Te crystals

We present extensive simulation studies on the performance of algorithms for the Compton sequence reconstruction used for the development of a portable spectroscopic instrument (COCAE), with the capability to localize and identify radioactive sources, by exploiting the Compton scattering imaging. Various Compton Sequence reconstruction algorithms have been compared using a large number of simulated events. These algorithms are based on Compton kinematics, as well as on statistical test criteria that exploit the redundant information of events having two or more photon interactions in the active detector's volume. The efficiency of the best performing technique is estimated for a wide range of incident gamma-ray photons emitted from point-like gamma sources.Comment: 16 pages, 17 figure

A Multi-Level Mathematical Model of the CO Catalytic Conversion Process

This paper presents a three-level modelling approach to the catalytic carbon monoxide oxidation in a temperature range between 400 K – 800 K. The first level involves the description of the chemical kinetics for the exothermic reactions on the catalyst surface. The second level models the thermal and hydrodynamic processes in the boundary diffusion layer between the catalyst surface and the reactive stream. Finally, the third modelling level focuses on the representation of the hydrodynamic and thermal properties for the bulk multi-component gas flow at various gas velocity and temperature ranges. The dynamic behaviour of the reactive system has been studied through simulated runs

An integrated machine learning and metaheuristic approach for advanced packed bed latent heat storage system design and optimization

To tackle the challenge of waste heat recovery in the industrial sector, this research presents a novel design and optimization framework for Packed Bed Latent Heat Storage Systems (PBLHS). This features a Deep Learning (DL) model, integrated with metaheuristic algorithms. The DL model was developed to predict PBLHS performance, trained using data generated from a validated Computational Fluid Dynamics (CFD) model. The model exhibited a high performance with an R2 value of 0.975 and a low Mean Absolute Percentage Error ( <9.14% ). To enhance the ML model's efficiency and optimized performance, various metaheuristic algorithms were explored. The Harmony Search algorithm emerged as the most effective through an early screening and underwent further refinement. The optimized algorithm demonstrated its capability by rapidly producing designs that showcased an improvement in total efficiency of up to 85% over available optimized experimental PBLHS designs. This research underscores the potential of ML-integrated approaches in laying the groundwork for generalized design frameworks for TES systems, offering efficient and effective solutions for waste heat recovery