Abstract
Sectorization is the partition of a set or region into smaller parts, taking into account certain objectives. Sectorization problems appear in real-life situations, such as school or health districting, logistic planning, maintenance operations or transportation. The diversity of applications, the complexity of the problems and the difficulty in finding good solutions warrant sectorization as a relevant research area. Decision Support Systems (DSS) are computerised information systems that may provide quick solutions to decision-makers and researchers and allow for observing differences between various scenarios. The paper is an overview of the development of a DSS for Sectorization, its extent, architecture, implementation steps and benefits. It constitutes a quite general system, for it handles various types of problems, which the authors grouped as (i) basic sectorization problems; (ii) sectorization problems with service centres; (iii) re-sectorization problems; and (iv) dynamic sectorization problems. The new DSS is expected to facilitate the resolution of various practitioners’ problems and support researchers, academics and students in sectorization. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This paper proposes a novel approach for the provision of non-frequency ancillary service (AS) by consumers connected to low-voltage distribution networks. The proposed approach considers an asymmetric pool-based local market for AS negotiation, allowing consumers to set a flexibility quantity and desired price to trade. A case study with 98 consumers illustrates the proposed market-based non-frequency AS provision approach. Also, three different strategies of consumers' participation are implemented and tested in a real low-voltage distribution network with radial topology. It is shown that consumers can make a profit from the sale of their flexibility while contributing to keeping the network power losses, voltage, and current within pre-defined limits. Ultimately, the results demonstrate the value of AS coming directly from end-users.

Abstract
Escape rooms offer an immersive and engaging learning experience that encourages critical thinking, problem solving and teamwork. Although they have shown promising results in promoting student engagement in the teaching-learning process, they continue to operate as independent systems that are not fully integrated into educational environments. This work aims to detail the integration of educational escape rooms, based on international standards, with the typical central component of an educational setting - the learning management system (LMS). In order to proof this concept, we present the integration of a math escape room with the Moodle LMS using the Learning Tools Interoperability (LTI) specification. Currently, this specification comprises a set of Web services that enable seamless integration between learning platforms and external tools and is not limited to any specific LMS which fosters learning interoperability. With this implementation, a single sign-on ecosystem is created, where teachers and students can interact in a simple and immersive way. The major contribution of this work is to serve as an integration guide for other applications and in different domains.

Abstract
This position paper builds on the authors' previous work on paraconsistent transition systems to propose a modelling framework for quantum circuits with explicit representation of decoherence.

Abstract
Pulse oximeters are medical devices used to assess peripheral arterial oxygen saturation (SpO(2)) noninvasively. In contrast, the gold standard requires arterial blood to be drawn to measure the arterial oxygen saturation (SaO(2)). Devices currently on the market measure SpO(2) with lower accuracy in populations with darker skin tones. Pulse oximetry inaccuracies can yield episodes of hidden hypoxemia (HH), with SpO(2) >= 88%, but SaO(2) < 88%. HH can result in less treatment and increased mortality. Despite being flawed, pulse oximeters remain ubiquitously used; debiasing models could alleviate the downstream repercussions of HH. To our knowledge, this is the first study to propose such models. Experiments were conducted using the MIMIC-IV dataset. The cohort includes patients admitted to the Intensive Care Unit with paired (SaO(2), SpO(2)) measurements captured within 10min of each other. We built a XGBoost regression predicting SaO(2) from SpO(2), patient demographics, physiological data, and treatment information. We used an asymmetric mean squared error as the loss function to minimize falsely elevated predicted values. The model achieved R-2 = 67.6% among Black patients; frequency of HH episodes was partially mitigated. Respiratory function was most predictive of SaO(2); race-ethnicity was not a top predictor. This singlecenter study shows that SpO(2) corrections can be achieved with Machine Learning. In future, model validation will be performed on additional patient cohorts featuring diverse settings.

Abstract
When crowd workers provide their contributions in a shared working environment, they may be influenced by the inputs of other contributors in implicit ways. Stigmergy in crowdsourcing consists of tracking changes in work activities to guide crowd workers based on the digital traces left by other workers. In such scenarios, there is no direct communication between the contributors. Still, the traceable changes they left during their actions act as a mediating element that clearly affects the final work product. From a behavior analysis perspective, the properties recorded in event logs can be of practical value in observing the behavioral traces produced by crowd workers when performing microtasks. This form of task fingerprinting has been explored for over a decade to better understand performance-related data and user navigational behavior in crowdsourcing markets. In line with this, the goal of this paper is to study the feasibility of task fingerprinting alongside the stigmergic effect occurring in a crowdsourcing setting through a user event logger. To this end, a case study was conducted using a real-world scenario of extreme weather phenomena represented on interactive maps. Each user could observe the traces of other crowd members while providing annotations. Twelve experts in weather forecasting were recruited to participate in this study to annotate extreme weather events. The results indicate that it is possible to use task fingerprinting for tracking the stigmergic effect in such activities with gains in terms of implicit coordination. Furthermore, the task fingerprinting allowed to map participants with similar behavioral traces, suggesting an increase in the accuracy of annotation clusters.