Abstract
Wireless Sensor Networks (WSNs) based on the Long-Range radio modulation (LoRa) can use the LoRaWAN protocol as the medium access layer. However, this protocol only supports a single-hop star topology. As a consequence, devices can not use retransmissions along the network to extend their coverage area or to circumvent signal attenuation with distance, obstacles or interference from other radio sources. This paper proposes a multi-hop LoRaWAN wide-scale WSN based on a scheduled cluster-tree topology. This methodology can expand the spatial coverage of the network, decrease collisions, and improve overall network performance. A multi-hop cluster-tree topology eliminates the need for adjustments of LoRa radio parameters as an attempt to expand the single-hop coverage limitation. Simulation results show that the scheduled cluster-tree topology can scale the network coverage and significantly improve communication and energy consumption performances.

Abstract
Storytelling is gaining popularity due to its expected ability to earn consumers’ attention and generate positive outcomes such as brand awareness, trust, and customer engagement. However, the effects of digital storytelling campaigns on brands are still insufficiently researched, especially among certain segments such as young adults. Therefore, the main aim of this article is to explore young adults’ views on digital storytelling campaigns, focusing on the determinants of interaction, the impacts on consumer behavior, and the outcomes for brands. By adopting a qual-itative approach, eight focus groups were conducted. Participants were 40 Portuguese consumers and social network site users, aged 19 to 37. The study demonstrates that being posted by a friend makes the content more attractive to one’s attention and increase its chances of further interaction (i.e., liking, sharing and commenting). The study also demonstrates that despite the expected positive emotional impacts of digital storytelling campaigns highlighted in the literature, they can also generate mistrust whenever it is not clear for the consumer how the topic chosen for the story relates to the brand and its products. Moreover, these campaigns may also fail to improve brand’s image if the brand is not conveniently featured in the campaign.

Abstract
Background The small sample sizes available within many very preterm (VPT) longitudinal birth cohort studies mean that it is often necessary to combine and harmonise data from individual studies to increase statistical power, especially for studying rare outcomes. Curating and mapping data is a vital first step in the process of data harmonisation. To facilitate data mapping and harmonisation across VPT birth cohort studies, we developed a custom classification system as part of the Research on European Children and Adults born Preterm (RECAP Preterm) project in order to increase the scope and generalisability of research and the evaluation of outcomes across the lifespan for individuals born VPT. Methods The multidisciplinary consortium of expert clinicians and researchers who made up the RECAP Preterm project participated in a four-phase consultation process via email questionnaire to develop a topic-specific classification system. Descriptive analyses were calculated after each questionnaire round to provide pre- and post- ratings to assess levels of agreement with the classification system as it developed. Amendments and refinements were made to the classification system after each round. Results Expert input from 23 clinicians and researchers from the RECAP Preterm project aided development of the classification system's topic content, refining it from 10 modules, 48 themes and 197 domains to 14 modules, 93 themes and 345 domains. Supplementary classifications for target, source, mode and instrument were also developed to capture additional variable-level information. Over 22,000 individual data variables relating to VPT birth outcomes have been mapped to the classification system to date to facilitate data harmonisation. This will continue to increase as retrospective data items are mapped and harmonised variables are created. Conclusions This bespoke preterm birth classification system is a fundamental component of the RECAP Preterm project's web-based interactive platform. It is freely available for use worldwide by those interested in research into the long term impact of VPT birth. It can also be used to inform the development of future cohort studies.

Abstract
Robotic manipulation, an area inside the field of industrial automation and robotics, consists of using a robotic arm to guide and grasp a desired object through actuators such as a vacuum or fingers, among others. Some objects, such as fragile ceramic pieces, require special attention to the force and the gripping method exerted on them. For this purpose, two grippers were developed, where one of them is a rotary vacuum gripper and the other is an impact gripper with three fingers, each one equipped with a load sensor capable of evaluating the values of load exerted by the grip actuators onto the object to be manipulated. The vacuum gripper was developed for the purpose of glazing a coffee saucer and the gripper with three fingers was developed for the purpose of polishing a coffee cup. Being that the impact gripper with sensorial feedback reacts to the excess and lack of grip force exerted, both these grippers were developed with success, handling with ease the ceramic pieces proposed.

Abstract
Background: The multimedia capturing of live lectures has increased within higher education institutions, even in the pre-COVID-19 period. Despite student satisfaction, the video lecture capture (VLC) influence on students' attainment and achievement of intended learning outcomes is controversial. Methods: To explore the impact of VLC, a cross-sectional study across 2016/17 (n=209 students) and 2017/18 (n=206 students) was conducted in the course of Mechanistic Toxicology in Pharmaceutical Education. Results: The results showed that 73% and 90% of the assessed students entirely viewed the videos of theoretical (550 minutes) and practical/laboratory classes (250 minutes), respectively. VLC impacted student attainment and the achievement of intended learning outcomes on the capacity to understand the subjects and apply knowledge. Conclusion: The effectiveness of VLC is to be considered under the framework of constructive alignment and the specificities of the course.

Abstract
The amount of data produced by distributed devices, such as smart devices and the IoT, is increasing continuously. The cost of transmitting data and also distributed computing power raise interest in distributed data mining (DDM). However, in a pure DDM scenario, data availability may not be enough to generate reliable models in a distributed environment. So, the ability to exchange data efficiently and effectively will become a crucial component of DDM. In this paper, we propose the concept of the Machine Learning Data Market (MLDM), a framework for the exchange of data among autonomous agents. We consider a set of learning agents in a cooperative distributed ML, where agents negotiate data to improve the models they use locally. In the proposed data market, the system's predictive accuracy is investigated, as well as the economic value of data. The question addressed in this paper is: How data exchange among the agents will improve the accuracy of the learning model. Agent budget is defined as a limitation of negotiation. We defined a multi-agent system with negotiation and assessed it against the multi-agent system baseline and the single-agent system. The proposed framework is analyzed based on the different sizes of batch data collected over time to find out how this changes the effect of the negotiation on the accuracy of the model. The results indicate that even simple negotiation among agents increases their learning accuracy.