A utilização da tecnologia na sala de aula como instrumento de visualização e algebrização dos objetos matemáticos

No presente relatório da Prática de Ensino Supervisionada são referidas opções de ensino, procedimentos e reações dos alunos ao processo de ensino. É dada uma grande ênfase ao ambiente de aprendizagem baseado na tecnologia e suportado por uma comunidade de aprendizagem, que tem lugar na própria sala de aula ou na sala de informática. A tecnologia é assumida como um recurso constante na maior parte das aulas através do recurso a tarefas escolhidas intencionalmente tendo em vista a possibilidade de introdução da tecnologia na sua resolução. Esta implementação assumiu várias formas, tais como a exploração de calculadoras, a manipulação do GeoGebra ou simplesmente através da apresentação de ficheiros acabados, o que constitui uma forma de obter uma boa visualização dos objetos matemáticos. A aplicação dos recursos tecnológicos foi progressivamente tornada mais intensiva, atingindo o seu culminar no Projeto de Estágio, designação atribuída a duas aulas concebidas explicitamente para a exploração da temática: “Estabelecimento de um Paralelismo entre a Geometria Tridimensional Dinâmica e as Funções”; Abstract: The Use of Technology in the Classroom as an Instrument of Visualization and Algebrization of the Mathematical Objects In this paper we refer to teaching options, procedures, and to students’ reactions to the teaching processes. We give a lot of reinforcement in the learning environment based on technology and supported by a community of learners, which take place in their own classroom or in the Informatics Class. Technology is assumed as a constant resource in most part of the classes through the intentional tasks’ choosing taking into account the possibility of technology introduction in their resolution. This implementation has assumed several forms, like calculators’ exploration, GeoGebra manipulation or simply by presenting finished files, which is a way of getting a great visualization of mathematical objects. The technological resources’ application turned itself progressively more intensive, presenting its center point on Practice Project, name who was gave to two classes conceived explicitly for the thematic exploration: “The establishment of a parallelism between Dynamic Tridimensional Geometry and the Functions”.

“Biomarkers in a One Health perspective: current knowledge of their ability to merge human and animal health

Biomarkers are nowadays essential tools to be one step ahead for fighting disease, enabling an enhanced focus on disease prevention and on the probability of its occurrence. Research in a multidisciplinary approach has been an important step towards the repeated discovery of new biomarkers. Biomarkers are defined as biochemical measurable indicators of the presence of disease or as indicators for monitoring disease progression. Currently, biomarkers have been used in several domains such as oncology, neurology, cardiovascular, inflammatory and respiratory disease, and several endocrinopathies. Bridging biomarkers in a One Health perspective has been proven useful in almost all of these domains. In oncology, humans and animals are found to be subject to the same environmental and genetic predisposing factors: examples include the existence of mutations in BR-CA1 gene predisposing to breast cancer, both in human and dogs, with increased prevalence in certain dog breeds and human ethnic groups. Also, breast feeding frequency and duration has been related to a decreased risk of breast cancer in women and bitches. When it comes to infectious diseases, this parallelism is prone to be even more important, for as much as 75% of all emerging diseases are believed to be zoonotic. Examples of successful use of biomarkers have been found in several zoonotic diseases such as Ebola, dengue, leptospirosis or West Nile virus infections. Acute Phase Proteins (APPs) have been used for quite some time as biomarkers of inflammatory conditions. These have been used in human health but also in the veterinary field such as in mastitis evaluation and PRRS (porcine respiratory and reproductive syndrome) diagnosis. Advantages rely on the fact that these biomarkers can be much easier to assess than other conventional disease diagnostic approaches (example: measured in easy to collect saliva samples). Another domain in which biomarkers have been essential is food safety: the possibility to measure exposure to chemical contaminants or other biohazards present in the food chain, which are sometimes analytical challenges due to their low bioavailability in body fluids, is nowadays a major breakthrough. Finally, biomarkers are considered the key to provide more personalized therapies, with more efficient outcomes and fewer side effects. This approach is expected to be the correct path to follow also in veterinary medicine, in the near future.

Load balancing for constraint solving with GPUs

Solving a complex Constraint Satisfaction Problem (CSP) is a computationally hard task which may require a considerable amount of time. Parallelism has been applied successfully to the job and there are already many applications capable of harnessing the parallel power of modern CPUs to speed up the solving process. Current Graphics Processing Units (GPUs), containing from a few hundred to a few thousand cores, possess a level of parallelism that surpasses that of CPUs and there are much less applications capable of solving CSPs on GPUs, leaving space for further improvement. This paper describes work in progress in the solving of CSPs on GPUs, CPUs and other devices, such as Intel Many Integrated Cores (MICs), in parallel. It presents the gains obtained when applying more devices to solve some problems and the main challenges that must be faced when using devices with as different architectures as CPUs and GPUs, with a greater focus on how to effectively achieve good load balancing between such heterogeneous devices.

Towards a Multi-device Constraints Solver

To reduce the amount of time needed to solve the most complex Constraint Satisfaction Problems (CSPs) usually multi-core CPUs are used. There are already many applications capable of harnessing the parallel power of these devices to speed up the CSPs solving process. Nowadays, the Graphics Processing Units (GPUs) possess a level of parallelism that surpass the CPUs, containing from a few hundred to a few thousand cores and there are much less applications capable of solving CSPs on GPUs, leaving space for possible improvements. This article describes the work in progress for solving CSPs on GPUs and CPUs and compares results with some state-of-the-art solvers, presenting already some good results on GPUs.