Contribution: The NEWTON fabrication laboratory (Fab Lab) Education Initiative assesses the effectiveness of Fab Lab-based learning on K-12 students' attitude toward learning science, technology, engineering, art, and mathematics (STEAM). It considers two aspects: 1) students' motivation and affective state and 2) students' observations and perception of Fab Lab-based learning. Background: Fab Labs are described as small workshops equipped with a set of computer-controlled tools (e.g., 3-D printers) that offer personalized digital fabrication. They have been shown to have a positive impact on learners' academic and personal growth when used in extracurricular activities, but little research examines whether integrating Fab Labs into school curricula can help raise student interest in STEAM education. Research Question: Can Fab Lab-based learning foster students' interest in STEM in primary and secondary schools? Would students be keen to use Fab Labs in their science classes? Methodology: Two case studies were carried out in two different schools as part of the European Horizon 2020 NEWTON project. The study had 39 participants; three different surveys were used to assess different constructs. Findings: Results show that after using the Fab Lab-based learning: 1) students felt more interested in science classes as well as more engaged and less bored and 2) while some students struggled at first when manipulating Fab Lab tools, they adapted quickly and reported that they would like to use Fab Labs as part of their science classes.

Water is a major preoccupation for our generation since it is crucial in keeping a healthy ecosystem and supporting biodiversity. The state of aquatic systems and water bodies needs to be continiously monitored to make informed decisions and trigger sanitation when necessary. However, observing and tracking the evolution of many water bodies without disturbing and polluting the biotopes is expensive, not scalable, and thus infeasible. This paper presents a way to make sustainable measurements using a new low-cost, open-source, and autonomous monitoring system deployable in a broad network. The smart buoy is deployed and controlled by a central unit that uses lab-graded sensors to measure ambient factors. The custom electronic board offers sustainable electronics integration emphasizing power path and network connectivity. The smart buoy showed an average power consumption of 1.8mA and a cost of 932 euros per device. Currently, five spots have been monitored, which allowed the understanding of why biological events, such as a massive fish dead, occurred. The system is easily expandable and can be used in various applications to increase the knowledge of the underwater ecosystem.

Cloud and IoT technologies have the potential to support applica- tions that are not strictly limited to technical fields. This paper shows how digital fabrication laboratories (Fab Labs) can leverage cloud technologies to enable resource sharing and provide remote access to distributed expensive fabrication resources over the internet. We call this new concept Fabrication as a Service (FaaS), since each resource is exposed to the internet as a web ser- vice through REST APIs. The cloud platform presented in this paper is part of the NEWTON Horizon 2020 technology-enhanced learning project. The NEWTON Fab Labs architecture is described in detail, from system concep- tion and simulation to system cloud deployment and testing in NEWTON project small and large-scale pilots for teaching and learning STEM subjects.

Information about the paper titled "RAISING STUDENTS’ INTEREST IN STEM EDUCATION VIA REMOTE DIGITAL FABRICATION: AN IRISH PRIMARY SCHOOL CASE STUDY" at IATED Digital Library

Low-cost, high-performance embedded devices are proliferating and a plethora of new platforms are available on the market. Some of them either have embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm hardware accelerators. These enhanced hardware features enable new applications in which AI-powered smart objects can effectively and pervasively run in real-time distributed ML algorithms, shifting part of the raw data analysis and processing from cloud or edge to the device itself. In such context, Artificial Intelligence (AI) can be considered as the backbone of the next generation of Internet of the Things (IoT) devices, which will no longer merely be data collectors and forwarders, but really “smart” devices with built-in data wrangling and data analysis features that leverage lightweight machine learning algorithms to make autonomous decisions on the field. This work thoroughly reviews and analyses the most popular ML algorithms, with particular emphasis on those that are more suitable to run on resource-constrained embedded devices. In addition, several machine learning algorithms have been built on top of a custom multi-dimensional array library. The designed framework has been evaluated and its performance stressed on Raspberry Pi III- and IV-embedded computers.

Traffic congestion is, on a daily basis, responsible for a significant amount of economic and social costs. One of the critical examples is the obstruction of priority vehicles during fast trajectories, which potentially costs lives and property in case of delay that is too great. By means of visual sensing methods, solutions and schedules have already been proposed for adjusting traffic light sequences depending on a priority vehicle’s position. However, these mechanisms are computation and power intensive. Deploying and powering a large-scale network will have a crucial economical cost. Furthermore, these devices will not always have access to sufficient power. To provide a solution, we developed an acoustic and self-powered device that can detect priority vehicles and can be cost effectively deployed to define a sensor network. The device combines the detection of priority vehicles and the harvesting of sound energy through triboelectrification. This paper will introduce the use of triboelectric energy harvesting, specifically in a self-powered wireless sensor network for priority vehicle detection. Furthermore, it shows how to increase the power performance of such a generator. Finally, the results are analyzed.