Abstract
The development of flexible and efficient communication mechanisms is of paramount importance within the context of the Internet of Things (IoT) paradigm. IoT has been used for industrial, commercial, and residential applications, and the IEEE 802.15.4/ZigBee standard is one of the most suitable protocols for this purpose. This protocol is now frequently used to implement large-scale Wireless Sensor Networks (WSNs). In industrial settings, it is becoming increasingly common to deploy cluster-tree WSNs, a complex IEEE 802.15.4/ZigBee-based peer-to-peer network topology, to monitor and control critical processes such as those related to oil or gas, mining, or certain specific chemicals. The remote monitoring of critical events for hazards or disaster detection in large areas is a challenging issue, since the occurrence of events in the monitored environment may severely stress the regular operation of the network. This paper proposes theDynamic REconfiguration mechanism of cluster-Tree WSNs(DyRET), which is able to dynamically reconfigure large-scale IEEE 802.15.4 cluster-tree WSNs, and to assign communication resources to the overloaded branches of the tree based on the accumulated network load generated by each of the sensor nodes. A complete simulation assessment demonstrates the proposed mechanism's efficiency, and the results show that it can guarantee the required quality of service level for the dynamic reconfiguration of cluster-tree networks.

Abstract
This paper describes an application aimed at improving the current state of enforcement in the areas of food safety and economic activities in Portugal. More specifically, the application focuses on a flexible and interactive approach to generate inspection routes, to be followed by surveillance brigades with the aim of verifying Economic Operators’ compliance to national and European legislation on economic and food safety. The problem is modeled as a Multi-Depot Periodic Vehicle Routing Problem with Time Windows, and the algorithmic approaches employed seek to maximize either the number of inspected Economic Operators or a utility function that takes into account the utility gained from inspecting each Economic Operator. The generated solutions are shown in an intuitive platform, where human operators can visualize the solutions details (including georeferenced locations in a map) and fully customize them on time by manually removing or adding Economic Operators to be targeted. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Sustainability and eco-efficiency have been researched in multiple scientific papers since the last years. However the literature is not so abundant when applying those concepts to industrial assembly processes. This paper presents an innovate methodology to optimize aerospace assembly processes. Authors propose the introduction of a new element, the eco-efficiency, along with the traditional criteria, cost and time, currently used for optimization. Using a large Aero-Structure as an industrial case of study, the methodology analyzes the eco-efficiency of an assembly process in connection with a Life Cycle Assessment (LCA) to compute the environmental impact. Results are shown in a dashboard along with the relevant Key Process Indicator (KPI) to help the engineers to select the best assembly process. © 2020, IFIP International Federation for Information Processing.

Abstract
This paper presents an efficient, battery-powered, low-cost, and context-aware IoT edge computing solution tailored for monitoring a real enterprise cooking oil collecting infrastructure. The presented IoT solution allows the collecting enterprise to monitor the amount of oil deposited in specific barrels, deployed country-wide around several partner restaurants. The paper focuses on the specification, implementation, deployment and testing of ESP32/ESP8266-based end-node components deployed as an edge computing monitoring infrastructure. The achieved low-cost solution guarantees more than a year of battery life, reliable data communication, and enables automatic over-the-air end-node updates. The open-source software libraries developed for this project are shared with the community and may be applied in scenarios with similar requirements. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

Abstract
This paper proposes a conventional approach for pulmonary nodule segmentation, that uses the Sliding Band Filter to estimate the center of the nodule, and consequently the filter's support points, matching the initial border coordinates. This preliminary segmentation is then refined to try to include mainly the nodular area, and no other regions (e.g. vessels and pleural wall). The algorithm was tested on 2653 nodules from the LIDC database and achieved a Dice score of 0.663, yielding similar results to the ground truth reference, and thus being a promising tool to promote early lung cancer screening and improve nodule characterization.

Abstract
Progress in the field of Natural Language Processing (NLP) has been closely followed by applications in the medical domain. Recent advancements in Neural Language Models (NLMs) have transformed the field and are currently motivating numerous works exploring their application in different domains. In this paper, we explore how NLMs can be used for Medical Entity Linking with the recently introduced MedMentions dataset, which presents two major challenges: (1) a large target ontology of over 2M concepts, and (2) low overlap between concepts in train, validation and test sets. We introduce a solution, MedLinker, that addresses these issues by leveraging specialized NLMs with Approximate Dictionary Matching, and show that it performs competitively on semantic type linking, while improving the state-of-the-art on the more fine-grained task of concept linking (+4 F1 on MedMentions main task). © Springer Nature Switzerland AG 2020.