Abstract
Transthyretin-associated Familial Amyloid Polyneuropathy (TTR-FAP) is a chronic fatal disease with a high incidence in Portugal. It is therefore relevant to provide professionals and citizens with a tool that enables a detailed geographical and territorial study. For this reason, we have developed an web based application that brings together techniques applied to spatial data that allow the study of the historical progression and growth of cases in patients' residential areas and areas of origin as well as an epidemic forecast. The tool enables the exploration of geographical longitudinal data at national, district and county levels. High density regions and periods can be visually identified according to parameters selected by the user. The visual evaluation of the data and its comparison across different time spans of the disease era can have an impact on more informed decision making by those working with patients to improve their quality of life, treatment or follow-up. The tool is available online for data exploration and its code is available on GitHub for adaptation to other geospatial scenarios.

Abstract
Digital twins have been emerging as a hybrid approach that combines the benefits of simulators with the realism of experimental testbeds. The accurate and repeatable set-ups replicating the dynamic conditions of physical environments, enable digital twins of wireless networks to be used to evaluate the performance of next-generation networks. In this paper, we propose the Position-based Machine Learning Propagation Loss Model (P-MLPL), enabling the creation of fast and more precise digital twins of wireless networks in ns-3. Based on network traces collected in an experimental testbed, the P-MLPL model estimates the propagation loss suffered by packets exchanged between a transmitter and a receiver, considering the absolute node's positions and the traffic direction. The P-MLPL model is validated with a test suite. The results show that the P-MLPL model can predict the propagation loss with a median error of 2.5 dB, which corresponds to 0.5x the error of existing models in ns-3. Moreover, ns-3 simulations with the P-MLPL model estimated the throughput with an error up to 2.5 Mbit/s, when compared to the real values measured in the testbed.

Abstract
The transition to a circular water economy by water reuse has many drivers and barriers. Facing them requires integrated approaches where new water users and producers are considered under a water–land nexus. Policies, regulations, and plans foreseeing water circularity are critical factors for a successful transition. However, little is known about how policy and planning contexts consider water circularity. This chapter develops an analytical framework based on a typical decision-making arena and a set of enabling factors used to analyze selected policy and planning documents, namely circular economy action plans, water and spatial planning frameworks, and water and spatial plans. This analytical framework is then applied to assess how the current policy and planning the set-up of three municipalities located in southern European countries, namely Almendralejo (Spain), Lecce (Italy), and Omis (Croatia), are prepared to facilitate new water circular economy initiatives. The findings bring to the fore very different settings. Although the policy, regulations, and plans recognize the water–land nexus, the integration of circularity codes still needs to be fostered. © 2023 Elsevier Inc. All rights reserved.

Abstract
Stress is a common feeling in people's day-to-day life, especially at work, being the cause of several health problems and absenteeism. Despite the difficulty in identifying it properly, several studies have established a correlation between stress and perceivable human features. The problem of detecting stress has attracted significant attention in the last decade. It has been mainly addressed through the analysis of physiological signals in the execution of specific tasks in controlled environments. Taking advantage of technological advances that allow to collect stress-related data in a non-invasive way, the goal of this work is to provide an alternative approach to detect stress in the workplace without requiring specific controlled conditions. To this end, a video-based plethysmography application that analyses the person's face and retrieves several physiological signals in a non-invasive way was used. Moreover, in an initial phase, additional information that complements and labels the physiological data was obtained through a brief questionnaire answered by the participants. The data collection pilot took place over a period of two months, having involved 28 volunteers. Several stress detection models were developed; the best trained model achieved an accuracy of 86.8% and a F1 score of 87% on a binary stress/non-stress prediction.

Abstract
In cutting processes, one of the strategies to reduce raw material waste is to generate leftovers that are large enough to return to stock for future use. The length of these leftovers is important since waste is expected to be minimal when cutting these objects in the future. However, in several situations, future demand is unknown and evaluating the best length for the leftovers is challenging. Furthermore, it may not be economically feasible to manage a stock of leftovers with multiple lengths that may not result in minimal waste when cut. In this paper, we approached the cutting stock problem with the possibility of generating leftovers as a two-stage stochastic program with recourse. We approximated the demand levels for the different items by employing a finite set of scenarios. Also, we modeled different decisions made before and after uncertainties were revealed. We proposed a mathematical model to represent this problem and developed a column generation approach to solve it. We ran computational experi-ments with randomly generated instances, considering a representative set of scenarios with a varying probability distribution. The results validated the efficiency of the proposed approach and allowed us to derive insights on the value of modeling and tackling uncertainty in this problem. Overall, the results showed that the cutting stock problem with usable leftovers benefits from a modeling approach based on sequential decision-making points and from explicitly considering uncertainty in the model and the solution method. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Abstract