Abstract
Despite documentation being considered the primary challenge to agile methods in safety-critical software systems development [1], agile would be of particular interest to improve changeability while providing efficiency and effectiveness to all the phases of software development. In this work, we created mechanisms for automating document processing and management to improve the efficiency and effectiveness of documentation activities of safety-critical software systems development, most concretely in the aerospace domain. The implemented tools were co-designed and validated iteratively in the concrete industrial context of Critical Software (CSW) projects, within a wider research work towards continuous certification [3]. We interviewed Critical Software professionals to validate our solution, collected feedback on the implemented tools and got insights for future work. The tools were also the target of synthetic tests that allowed us to conclude that document automation is possible in the critical-safety software development industry and carries several benefits. The developed tools are not yet qualified in compliance with the DO-330 standard (Tools Qualification).

Abstract

Abstract
Parkinson's disease (PD) is a neurodegenerative disorder that impairs people's mobility. Due to its erratic nature and complexity, the progression of the disease differs from person to person, making it difficult to keep track of the patient's progress. These factors, together with the limited number of annual clinical appointments, create the need to have a tool that can help patients and healthcare professionals better manage Parkinson's outside of the clinical environment. PDapp strives to address this need combining mHealth features with the capabilities of the iHandU appcessory, a novel and seamless wearable device designed to measure wrist rigidity, bradykinesia (slow movement), and tremor, thus enabling continuous effective follow-up, while connecting patients and clinicians remotely. The PDapp system is comprised of a mobile application where patients can manage their medication, self-perform various symptom tests, and maintain clinicians informed of relevant events; a specialized web dashboard for clinicians to monitor all their patient's history and recent events; and a cloud database that exhibits existing data in real-time. The first prototype integrates all these components and provides a promising proof-of-concept that, with a few additions, can be a system that brings value to Parkinson's management. This application design and functionalities were developed jointly with clinicians, addressing their problems and needs. The collected feedback was very positive stating that its usability and simplicity is completely suitable for patients to use. PDapp will introduce a complete and innovative methodology to follow-up PD patient's disease progression and support clinicians during appointments and patients at home, guiding medication adjustment for better disease management. This system is intended as one more step to the PD mHealth ecosystem, improving follow-up and disease therapy yet reducing clinicians' workload.

Abstract
Collaborative robots are being increasingly used by manufacturing companies due to their potential to help companies cope with market volatility. Before introducing this technology, companies face the decision phase where they determine the investment feasibility. Decision models for cobot adoption can assist decision-makers in this task, but they require previous identification of decision criteria. Since existing literature overlooked this issue, this study aims to provide a list of decision criteria that can be considered in the cobot adoption decision process. These criteria were identified by a literature review of the benefits, advantages, and disadvantages of cobot adoption. Results show that flexibility, competitiveness, ergonomics, quality, safety, space, mobility, ease of programming, technical features, human-robot collaboration, and productivity are important aspects to consider when deciding whether to invest in cobots. The findings of this study provide a better understanding of the decision process for cobot adoption by listing decision criteria along with some indicators, which is an important input for the design of a decision-making process.

Abstract

Abstract
Low Power Wide Area Networks (LPWAN) are used worldwide in several Internet of Things (IoT) applications that rely on large-scale deployments. Despite most of these networks include their own security mechanisms with built-in encryption, they are still vulnerable to a range of attacks that can be performed using low-cost Software Defined Radio (SDR) hardware and low-complexity techniques. This work provides an experimental setup implemented to exploit physical layer vulnerabilities with SDR techniques. Several attack vectors typically related to LPWAN within the IoT ecosystem are implemented and tested such as Global Positioning (GPS) Spoofing, Replay Attacks, Denial-of-Service (DoS) and Jamming, in environments that rely specifically on LoRaWAN networks. The results show that, in LoRAWAN networks, a set of vulnerabilities can be exploited leading to the incorrect functioning of the executed applications, and possible damage to the systems in which they operate. It was possible to verify that, depending on the type of activation method used between the devices and the LoRaWAN server, the communications and the devices can be compromised.