Abstract
Decred is a cryptocurrency with its own blockchain and has several similarities with bitcoin but implements a governance model that resembles a company with thousands of investors. These stakeholders invest their coins, receive the right to direct the project as they see fit and are rewarded for doing so. Everyone else not invested may use the coin as means of exchange, trading it for goods or services or consuming other services provided by the blockchain as the digital notary. This paper investigates how Decred project created its own version of money and implemented security measures to improve governance and remove trusted third parties from money issuance and e-voting. This topic is particularly relevant to understand how blockchain technologies improve governance and avoid the tyranny of the majority. In order to reach our goal, we use multi-agent simulation and statistical modelling to verify to what extent Decred is capable of providing a predictable, scarce, trustworthy digital asset. We show that Decred increased blockchain security with its hybrid proof-of-work+proof-of-stake (PoW + PoS) security mechanism, making an attack more expensive. © 2025 Inderscience Enterprises Ltd.

Abstract
Introduction: Urban centers face increasing congestion and pollution due to population growth driven by jobs, education, and entertainment. Promoting active modes like walking and cycling offers healthier and less polluting alternatives. Understanding perceptions of comfort (green areas, commercial areas, crowd density, noise, thermal sensation, air quality, allergenics), safety and security (street illumination, traffic volume, surveillance, visual appearance, and speed limits) are crucial for encouraging active modes adoption. This study categorizes user groups based on these indicators, supporting policymakers in the development of targeted strategies. Methods: We developed a questionnaire to support our empirical study and collected 653 responses. We have analyzed the data using clustering methods such as Affinity Propagation, BIRCH, Bisecting K-means, HAC, K-means, Mini-Batch K-means, and Spectral clustering. The best performing method (K-means) was used to identify the user groups while a random forest model evaluated the relative importance of indicators for each group. Results: The study identified five user groups based on urban mobility indicators for safety and security, comfort, and distance and time. Conclusions: These groups, distinguished by sociodemographic features, include: Street Aesthetes (young men valuing visual appeal), Safety Seekers (employed men prioritizing speed limits), Working Guardians (employed men focused on surveillance and green spaces), Urban Explorers (young women valuing air quality and low traffic), and Comfort Connoisseurs (employed women prioritizing noise reduction and aesthetics).

Abstract
Background/Objectives: Boccia is an attractive and growing adapted sport. For approximately 30 years, this parasport was played together by male and female athletes, a fact that recently changed, to our best knowledge, without scientific support. Hence, this study aimed to analyse the relationship between gender participation and performance in Boccia international-level events. Methods: For data collection, four specific international-level Boccia events between 2012 and 2018 were selected as partials were available in the official competition websites (2708 partials, which represent a total of 32,496 ball throws). Results: We found that partials won by male athletes systematically increased between 2012 and 2018 but tended to stabilize between 2017 and 2018, contrary to females, with a growing trend from 2016 onwards. No differences were observed, considering the players' gender and the type of partials (adjusted, balanced, and unbalanced) in the Boccia classes BC1, BC2, and BC3. In BC4 differences were found, but with little variance or low association level (Cramer's Phi coefficient of 0.114). Conclusions: The results emphasize that based on performance, both men and woman can play Boccia together. Although, if the focus of separating genders in Boccia is toward growing and effective female participation and equal success and reward opportunities, this study highlights as a good perspective aiming regular practice of physical activity, exercise, and sport in people with disabilities, promoting their quality of life.

Abstract
Deep learning models are widely used in multivariate time series forecasting, yet, they have high computational costs. One way to reduce this cost is by reducing data dimensionality, which involves removing unimportant or low importance information with the proper method. This work presents a study on an explainability feature selection framework composed of four methods (IMV-LSTM Tensor, LIME-LSTM, Average SHAP-LSTM, and Instance SHAP-LSTM) aimed at using the LSTM black-box model complexity to its favour, with the end goal of improving the error metrics and reducing the computational cost on a forecast task. To test the framework, three datasets with a total of 101 multivariate time series were used, with the explainability methods outperforming the baseline methods in most of the data, be it in error metrics or computation time for the LSTM model training.

Abstract
With technological advancements, much data is being captured by sensors, smartphones, wearable devices, and so forth. These vast datasets are stored in data centres and utilized to forge data-driven models for the condition monitoring of infrastructures and systems through future data mining tasks. However, these datasets often surpass the processing capabilities of traditional information systems and methodologies due to their significant size. Additionally, not all samples within these datasets contribute valuable information during the model training phase, leading to inefficiencies. The processing and training of Machine Learning algorithms become time-consuming, and storing all the data demands excessive space, contributing to the Big Data challenge. In this paper, we propose two novel techniques to reduce large time-series datasets into more compact versions without undermining the predictive performance of the resulting models. These methods also aim to decrease the time required for training the models and the storage space needed for the condensed datasets. We evaluated our techniques on five public datasets, employing three Machine Learning algorithms: Holt-Winters, SARIMA, and LSTM. The outcomes indicate that for most of the datasets examined, our techniques maintain, and in several instances enhance, the forecasting accuracy of the models. Moreover, we significantly reduced the time required to train the Machine Learning algorithms employed.

Abstract
Spatio-temporal prediction tasks play a crucial role in facilitating informed decision-making through anticipatory insights. By accurately predicting future outcomes, the ability to strategize, preemptively address risks, and minimize their potential impact is enhanced. The precision in forecasting spatial and temporal patterns holds significant potential for optimizing resource allocation, land utilization, and infrastructure development. While existing review and survey papers predominantly focus on specific forecasting domains such as intelligent transportation, urban planning, pandemics, disease prediction, climate and weather forecasting, environmental data prediction, and agricultural yield projection, limited attention has been devoted to comprehensive surveys encompassing multiple objects concurrently. This article addresses this gap by comprehensively analyzing techniques employed in traffic, pandemics, disease forecasting, climate and weather prediction, agricultural yield estimation, and environmental data prediction. Furthermore, it elucidates challenges inherent in spatio-temporal forecasting and outlines potential avenues for future research exploration.