Abstract
Partnerships for Science Education (PAFSE) is a case study of project-based learning applied to real-world problems connected with public health and sustainable development. The EU-funded project organizes science education activities at low secondary level engaging students and school stakeholders (universities, research centres, start-ups, enterprises, governmental organisations, NGOs) in health promotion and disease prevention actions that benefit the health and well-being of the community. Projects leaded by schools are addressed within educational scenarios co-created with partners interested in STEM education and developed under a relevant public health issue through their continuous engagement in open schooling approach.

Abstract

Abstract
Medical devices are rapidly evolving and becoming more interconnected with healthcare networks, overcoming resource constraints, and increasingly focused on patient well-being and needs. This work intends to identify future research themes in the area of cybersecurity in health by surveying the articles being developed and identifying their current limitations and future work. The developed analysis was based on the publications with the highest number of citations, enabling us to find several challenges and restrictions such as integrating devices in systems. Innovations and the emergence of new technologies with inherent security vulnerabilities, will continue to evolve, escalating the attackers interest in exploiting unknown cybersecurity risks within healthcare. It is mandatory to consider cybersecurity risks since the conception of the devices to reduce security flaws, ensure the patients with a better quality of life, and guarantee information security properties.

Abstract
The use of robots is widely spread across the industry. It is paramount that the robot end-effector tracks a pre-defined trajectory with the lowest energy loss. To contribute to the solution of this problem, the robot trajectory is defined using a tracking parameter which is optimised using the Matlab (R) fminunc function and the Particle Swam Optimisation algorithm. This approach was tested for a case study with the energy loss being reduced in approximately 96.15%.

Abstract

Abstract
Breast cancer is currently one of the main causes of death and tumoral diseases in women. Even if early diagnosis processes have evolved in the last years thanks to the popularization of mammogram tests, nowadays, it is still a challenge to have available reliable diagnosis systems that are exempt of variability in their interpretation. To this end, in this work, the design and development of an intelligent clinical decision support system to be used in the preventive diagnosis of breast cancer is presented, aiming both to improve the accuracy in the evaluation and to reduce its uncertainty. Through the integration of expert systems (based on Mamdani-type fuzzy-logic inference engines) deployed in cascade, exploratory factorial analysis, data augmentation approaches, and classification algorithms such as k-neighbors and bagged trees, the system is able to learn and to interpret the patient's medical-healthcare data, generating an alert level associated to the danger she has of suffering from cancer. For the system's initial performance tests, a software implementation of it has been built that was used in the diagnosis of a series of patients contained into a 130-cases database provided by the School of Medicine and Public Health of the University of Wisconsin-Madison, which has been also used to create the knowledge base. The obtained results, characterized as areas under the ROC curves of 0.95-0.97 and high success rates, highlight the huge diagnosis and preventive potential of the developed system, and they allow forecasting, even when a detailed and contrasted validation is still pending, its relevance and applicability within the clinical field.