Abstract
This paper describes a new development within the conceptual framework BAYSE (Bayesian State Estimation), which enables the full integration of the SCADA (Supervisory Control and Data Acquisition) data with PMU (phasor measurement units) data. It is based on Bayesian inference principles and extends the concept of the prior distributions to accommodate a broad set of past state conditions, under a sliding window approach. By choosing an appropriate window length, the method enhances accuracy under stationary conditions, with a reduced impact under system changes. The work also submits a rectangular coordinates transformation procedure, based on the Jacobian method, to consistently integrate polar coordinates estimations with the PMU linear model (in rectangular coordinates). The paper presents the new approach in proof-of concept mode over a didactic test-bed, using real PMU time series, to emphasize the enhanced accuracy and good asymptotic properties.

Abstract
Discourse markers carry information about the discourse structure and organization, and also signal local dependencies or epistemological stance of speaker. They provide instructions on how to interpret the discourse, and their study is paramount to understand the mechanism underlying discourse organization. This paper presents a new language resource, an ISO-based annotated multilingual parallel corpus for discourse markers. The corpus comprises nine languages, Bulgarian, Lithuanian, German, European Portuguese, Hebrew, Romanian, Polish, and Macedonian, with English as a pivot language. In order to represent the meaning of the discourse markers, we propose an annotation scheme of discourse relations from ISO 24617-8 with a plug-in to ISO 24617-2 for communicative functions. We describe an experiment in which we applied the annotation scheme to assess its validity. The results reveal that, although some extensions are required to cover all the multilingual data, it provides a proper representation of discourse markers value. Additionally, we report some relevant contrastive phenomena concerning discourse markers interpretation and role in discourse. This first step will allow us to develop deep learning methods to identify and extract discourse relations and communicative functions, and to represent that information as Linguistic Linked Open Data (LLOD).

Abstract
This paper addresses an integrated framework for the dynamic capacity withholding assessment of an independent system operator that determines the mid-term maintenance scheduling of generation companies and day-ahead scheduling of wholesale market participants. The main contribution of this research is that two dynamic capacity-withholding indices are proposed for mid-term and day-ahead scheduling of generation companies that estimate the dynamic capacity withholding opportunities of generation units in an ex-ante manner. The proposed framework is another contribution of this research that uses a four-stage optimization process that the system operator can detect and prevent the formation of withholding groups. The optimal maintenance scheduling from the generation companies viewpoint is assessed in the first-stage problem that considers different mid-term withholding opportunities. The optimal mid-term maintenance scheduling is carried out in the second-stage problem that recognizes and rejects the dynamic capacity withholding of generation companies. The optimal scheduling of day-ahead generation companies considering their dynamic capacity withholding is the third contribution of this paper that optimizes the scheduling of generation units for day-ahead horizon considering responsive loads. The proposed method is applied to 30-bus, 57-bus and 118-bus IEEE test systems. A full competition algorithm is also carried out to evaluate the competition states of generation companies. The proposed algorithm detected that the dynamic capacity withholding might lead to increase of nodal price by about 279.22%, 764.43%, and 851.2% for 30-bus, 57-bus, and 118-bus IEEE test systems with respect to the non-capacity withholding conditions, respectively.

Abstract
Fire is a traditional method used for the elimination of residual materials from agricultural and forestry activities, but its improper and negligent use increases the risk to cause significant social, economic, and environmental impacts. Translating the nature and magnitude of the risk, a mental model approach has been used for a deeper understanding of what can be done to reduce future socioeconomic and environmental impacts. Considering the rural fire risk context, in this study we applied the mental model approach to (1) explore the different perspectives of experts on rural fire risks, focusing on the burning problem, and (2) systematize the aggregated knowledge of experts on the subject. Semi-structured interviews that included real-time interactions with the proposed mental model on the MURAL platform were remotely conducted with twenty-eight experts with in-depth involvement and experience in fire and forest management in Portugal, including actors from industry, academics, third sector, and public entities. The experts' contributions were periodically validated by the research team and included in the reference mental model to translate the aggregated knowledge of the expert community. The data collected was transcribed, coded, and analyzed following a thematic analysis approach with NVivo software support. After nine iterations, the expert's mental model was concluded encompassing the burning motivators, alternative solutions, associated risks before and during the burning, underlying causes, natural elements and factors, and impacts. Overall, the study provides an in-depth and holistic understanding of rural fire risks, focusing on burning, which is essential for identifying incorrect beliefs and existing risk factors that are often neglected. The participatory and dynamic process of actors' knowledge systematization on burning risks through a mental model allowed the identification of divergent perspectives between expert groups, especially concerning the effectiveness of the current legislation on the use of fire, indicating the need to improve the dialogue between experts. Moreover, the study shows how risk communication campaigns do not replace technical monitoring closer to the population and recognize the importance and need of sensitization and communication actions to disseminate the use of available alternatives to overcome traditional habits of rural communities. Still, it indicates that successful alternative solutions of burning may be achieved with strong local and sectoral involvement. Finally, this research provides a baseline to help decision-makers and stakeholders identify the most relevant variables about rural fire risks to build effective risk communications strategies and mitigate social and environmental impacts in the future.

Abstract
Life in society has initiated a search for comfort and security in social centers. This search generated revolutions within the knowledge about the technologies involved, making the environments automated and integrated. Along with this increase, ecological concerns have also arisen, which have been involved since the design of intelligent buildings, remaining through the years of their use. Based on these two pillars, the present study aims to monitor three central systems inside the apartments of the Apolo Building (Braganca city, Portugal). The electrical energy consumption, water flow, and waste disposal systems are integrated through a single database. The data is sent remotely via WiFi through the microcontroller. For better visualization and analytics of the data, a web application is also developed, which allows for real-time monitoring. The obtained results demonstrate to the consumer his behavior regarding household expenses. The idea of showing the consumer their expenditure is to create an ecological awareness. Through the data collected and the environmental alternatives found, it is possible to observe whether there was a behavior change when receiving this data, either in the short or long term.

Abstract
Information and communication technologies have contributed decisively to the evolution of the agriculture field, allowing the exchange of information between various platforms, the automation of processes, and the storage and processing of large volumes of data. This study proposes the development of an application for mobile devices that facilitates the process of managing and monitoring the production of an urban farming. The application can also be used by less experienced farmers by providing relevant information on the best products per season according to soil characteristics and climate. The results of the developed application demonstrated the application’s potential to increase crops visibility, increase efficiency throughout the value chain, access to real-time business and financial data, turns possible to customize the application according to the users’ profile, increase the efficiency of marketing processes, and contribute to reducing transport costs. © 2022 by IGI Global. All rights reserved.