Abstract
This work presents an experimental study on the effects of gamma radiation on Long Period Fiber Gratings (LPFGs) in a low-dose test campaign to evaluate their eventual degradation. The study was carried out with standard single-mode fibers where the grating was inscribed using the Electric-Arc Discharge (EAD) technique. Before the gamma campaign, a detailed optical characterization was performed with repeatability tests to verify the accuracy of the setup and the associated error sources. The gamma-induced changes up to a dose of 200 krad and the recovery after radiation were monitored with the Dip Wavelength Shift (DWS). The results show that the gamma sensitivity for a total dose of 200 krad is 11 pm/krad and a total DWS of 2.3 nm has been observed with no linear dependence. Post-radiation study shows that recovery from radiation-induced wavelength shift is nearly complete in about 4000 h. Experimental results show that the changes suffered under gamma irradiation of these LPFGs are temporary making them a good choice as sensors in space applications.

Abstract
Citizen engagement in building user-curated narratives of complex or long-lasting news stories has been the key foundation of the design and implementation of the Acropolis virtual environment. Previous user studies have shown, by positive evidence, that this goal can be pragmatically achieved, but the challenge now lies in assessing: a) the extent to which an environment like Acropolis can be used to empower citizens; and b) whether and how the tool could be used to support the work of professional curators. Findings from a focus group study highlighted the tool's potential to engage citizens with news, the usefulness of the environment to build virtual memories, and the convenience of using Acropolis to support professional journalistic work.

Abstract
In this study, two novel stochastic models are introduced to solve the dynamic sectorization problem, in which sectors are created by assigning points to service centres. The objective function of the first model is defined based on the equilibration of the distance in the sectors, while in the second one, it is based on the equilibration of the demands of the sectors. Both models impose constraints on assignments and compactness of sectors. In the problem, the coordinates of the points and their demand change over time, hence it is called a dynamic problem. A new solution method is used to solve the models, in which expected values of the coordinates of the points and their demand are assessed by using the Monte Carlo simulation. Thus, the problem is converted into a deterministic one. The linear and deterministic type of the model, which is originally non-linear is implemented in Python's Pulp library and in this way the generated benchmarks are solved. Information about how benchmarks are derived and the obtained solutions are presented.

Abstract
Businesses that are growing by supplying more services or reaching more customers, might need to create or relocate a facility location to expand their geographical coverage and improve their services. This decision is complex, and it is crucial to analyse their client locations, their journeys and be aware of the factors that may affect their geographical decision and the impact that they can have in the business strategy. Therefore, the decision-maker needs to ensure that the location is the most profitable site according to the business scope and future perspectives. In this paper, we propose a decision support system to help businesses on this complex decision that is capable of providing facility location suggestions based on their journeys analysis and the factors that the decision-makers consider more relevant to the company. The system helps the business managers to make better decisions by returning facility locations that have potential to maximise the company’s profit by reducing costs and maximise the number of covered customers by expanding their territorial coverage. To verify and validate the decision support system, a system evaluation was developed. Thus, a survey was responded by decision-makers in order to evaluate the efficiency, understandability, accuracy and effectiveness of the suggestions.

Abstract
There is nowadays a constant flux of data being generated and collected in all types of real world systems. These data sets are often indexed by time, space, or both requiring appropriate approaches to analyze the data. In univariate settings, time series analysis is a mature field. However, in multivariate contexts, time series analysis still presents many limitations. In order to address these issues, the last decade has brought approaches based on network science. These methods involve transforming an initial time series data set into one or more networks, which can be analyzed in depth to provide insight into the original time series. This review provides a comprehensive overview of existing mapping methods for transforming time series into networks for a wide audience of researchers and practitioners in machine learning, data mining, and time series. Our main contribution is a structured review of existing methodologies, identifying their main characteristics, and their differences. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified way and language. We first describe the case of univariate time series, which can be mapped to single layer networks, and we divide the current mappings based on the underlying concept: visibility, transition, and proximity. We then proceed with multivariate time series discussing both single layer and multiple layer approaches. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic. This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Fundamental Concepts of Data and Knowledge > Knowledge Representation

Abstract
Narrative extraction, understanding and visualization is currently a popular topic and an important tool for humans interested in achieving a deeper understanding of text. Information Retrieval (IR), Natural Language Processing (NLP) and Machine Learning (ML) already offer many instruments that aid the exploration of narrative elements in text and within unstructured data. Despite evident advances in the last couple of years the problem of automatically representing narratives in a structured form, beyond the conventional identification of common events, entities and their relationships, is yet to be solved. This workshop held virtually onApril 1^st, 2021 co-located with the 43^rd European Conference on Information Retrieval (ECIR’21) aims at presenting and discussing current and future directions for IR, NLP, ML and other computational fields capable of improving the automatic understanding of narratives. It includes a session devoted to regular, short and demo papers, keynote talks and space for an informal discussion of the methods, of the challenges and of the future of the area.