Abstract
Quality management practices are widely implemented by companies, as they constitute a competitive advantage. Nowadays it is almost mandatory to follow quality standards, in order to make a product available on the market. However, facing new production paradigms, such as Industry 4.0, questions arise about how quality management processes could benefit and adapt in the era of digital technologies. Following a literature review approach, this paper lead to the development of a table that links the relationship between quality management practices and Industry 4.0 technologies that improve quality, as it aimed. (C) 2021 The Authors. Published by Elsevier B.V.

Abstract
As our dependence on automated systems grows, so does the need for guaranteeing their safety, cybersecurity, and privacy (SCP). Dedicated methods for verification and validation (V&V) must be used to this end and it is necessary that the methods and their characteristics can be clearly differentiated. This can be achieved via method classifications. However, we have experienced that existing classifications are not suitable to categorise V&V methods for SCP of automated systems. They do not pay enough attention to the distinguishing characteristics of this system type and of these quality concerns. As a solution, we present a new classification developed in the scope of a large-scale industry-academia project. The classification considers both the method type, e.g., testing, and the concern addressed, e.g., safety. Over 70 people have successfully used the classification on 53 methods. We argue that the classification is a more suitable means to categorise V&V methods for SCP of automated systems and that it can help other researchers and practitioners.

Abstract
The advancement of technology and techniques applied to robotics contributes to increasing the quality of life and safety of humanity. One of the most widespread applications of mobile robotics is related to monitoring indoor environments. However, due to factors such as the size of the environment impacting the monitoring response, battery autonomy, and autonomous navigation in environments with unknown obstacles, they are still significant challenges in the diffusion of mobile robotics in these areas. Strategy adopting multiple robots can overcome these challenges. This work presents an approach to use multi-robots in hazardous environments with gas leakage to perform spatial mapping of the gas concentration. Obstacles arranged in the environment are unknown to robots, then a fuzzy control approach is used to avoid the collision. As a result of this paper, spatial mapping of an indoor environment was carried out with multi-robots that reactively react to unknown obstacles considering a point gas leak with Gaussian dispersion.

Abstract
Quality management practices are widely implemented by companies, as they constitute a competitive advantage. Nowadays it is almost mandatory to follow quality standards, in order to make a product available on the market. However, facing new production paradigms, such as Industry 4.0, questions arise about how quality management processes could benefit and adapt in the era of digital technologies. Following a literature review approach, this paper lead to the development of a table that links the relationship between quality management practices and Industry 4.0 technologies that improve quality, as it aimed. © 2021 The Authors. Published by Elsevier B.V.

Abstract
Daily activities have been increasingly supported by intelligent devices and applications. Smart devices are constantly communicating through the Internet of Things (IoT) networks, either by sending collected data and notifying the actions taken or by receiving instructions for actions to be taken. Most of this communication requires the confidentiality of data through the usage of encryption algorithms, being the Advanced Encryption Standard (AES) algorithm one of the most used. However, how do the operation modes of AES algorithm perform in a resource-constraint device? This paper aims to evaluate the impact on the time to encrypt and decrypt different sized messages in IoT devices when using each one of the five AES modes of operation and the three key sizes defined. The test scenario was implemented using two programming languages, running on a Raspberry Pi device. The results achieved infers that Python was quicker and had a more homogeneous result set than JavaScript implementation in most AES operation modes. These results help to understand the trade-off between IoT devices' security needs and delays in communication caused by the selection of the AES algorithm operation mode.

Abstract
Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients. Low dose computed tomography has been suggested as a potential screening tool but manual screening is costly and time-consuming. This has fuelled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules. In spite of promising results, the application of automatic methods to clinical routine is not straightforward and only a limited number of studies have addressed the problem in a holistic way. With the goal of advancing the state of the art, the Lung Nodule Database (LNDb) Challenge on automatic lung cancer patient management was organized. The LNDb Challenge addressed lung nodule detection, segmentation and characterization as well as prediction of patient follow-up according to the 2017 Fleischner society pulmonary nodule guidelines. 294 CT scans were thus collected retrospectively at the Centro Hospitalar e Universitrio de So Joo in Porto, Portugal and each CT was annotated by at least one radiologist. Annotations comprised nodule centroids, segmentations and subjective characterization. 58 CTs and the corresponding annotations were withheld as a separate test set. A total of 947 users registered for the challenge and 11 successful submissions for at least one of the sub-challenges were received. For patient follow-up prediction, a maximum quadratic weighted Cohen's kappa of 0.580 was obtained. In terms of nodule detection, a sensitivity below 0.4 (and 0.7) at 1 false positive per scan was obtained for nodules identified by at least one (and two) radiologist(s). For nodule segmentation, a maximum Jaccard score of 0.567 was obtained, surpassing the interobserver variability. In terms of nodule texture characterization, a maximum quadratic weighted Cohen's kappa of 0.733 was obtained, with part solid nodules being particularly challenging to classify correctly. Detailed analysis of the proposed methods and the differences in performance allow to identify the major challenges remaining and future directions data collection, augmentation/generation and evaluation of under-represented classes, the incorporation of scan-level information for better decision making and the development of tools and challenges with clinical-oriented goals. The LNDb Challenge and associated data remain publicly available so that future methods can be tested and benchmarked, promoting the development of new algorithms in lung cancer medical image analysis and patient followup recommendation.