Abstract
Purpose This paper aims to investigate the evolution, roots and influence of the rural entrepreneurship literature.Design/methodology/approachUsing a bibliometric exercise, the analysis starts with investigation of studies on entrepreneurship and gathering all (772) articles on rural entrepreneurship (from 1981 to 2020) found in both Scopus and Web of Science up to 15 August 2020. Citation analysis of the references/citations of 755 articles are listed in the abstract database, generating a citation database involving 46,432 references/citations. This paper considers 635 (out of the 772) articles on rural entrepreneurship (i.e. articles cited in one or more studies), generating a database of 10,767 studies influenced by the rural entrepreneurship literature.Findings This study discovers that the relative importance of rural entrepreneurship within the entrepreneurship literature has increased in the last few years, but rural entrepreneurship remains a European concern; the most frequently addressed topics include growth and development, institutional frameworks and governance and rurality, with theory building being rather understudied. Most of the studies on rural entrepreneurship are empirical, involving mainly qualitative analyses and targeting high income countries; rural entrepreneurship is rooted in the fields of economics and entrepreneurship and is relatively self-referential.Originality/value This study provides a comprehensive and updated investigation of evolution of the rural entrepreneurship literature. The assessment of the literature's scientific roots of rural entrepreneurship had not yet been tackled before. To the best of the author's knowledge this study can be considered as the first effort for identifying the scientific influence of the rural entrepreneurship literature.

Abstract
Background: Many progranunmg environments include automated feedback in the form of hints to help novices learn autonomously. Some experimental studies investigated the impact of automated liints in the immediate, performance and learning retention in that context. Automated feedback is also becoming a popular research topic in the context of formal specification languages, but so far no experimental studies have been conducted to assess its impact while learning such languages. Objective: We aim to investigate the impact of different types of automated hints while learning a formal specification language, not only in terms of immediate performance and learning retention, but also in the emotional response of the students. Method: We conducted a simple one-factor randomised experiment in 2 sessions involving 85 BSc students majoring in CSE. In the 1st session students were divided in 1 control group and 3 experimental groups, each receiving a different type of hint while learning to specify simple, requirements with the Alloy formal specification language. To assess the impact of hints on learning retention, in the 2nd session, 1 week later, students had no hints while formalising requirements. Before and after each session the students answered a standard self-reporting emotional survey to assess their emotional response to the experiment. Results: Of the 3 types of hints considered, only those pointing to the precise location of an error had a positive impact on the immediate performance and none had significant impact in learning retention. Hint availability also causes a significant impact on the emotional response, but no significant emotional :impact exists once hints are no longer available (i.e. no deprivation effects were detected). Conclusion: Although none of the evaluated hints had an impact on learning retention, learning a formal specification language with an environment that provides hints with precise error locations seems to contribute to a better overall experience without apparent drawbacks. Further studies are needed to investigate if other kind of feedback, namely hints combined with some sort of self explanation prompts, can have a positive impact in learning retention.

Abstract
Objective: The study aimed to demonstrate the development of a mobile app prototype, BarrierBeGone, a system that identifies potential barriers for individuals with mobility disabilities and promotes accessibility using gamification strategies. The main goal is to raise awareness about mobility and accessibility difficulties, especially for wheelchair users, and to promote more responsible behaviours. Method: The User-Centred Design methodology was employed, going through three phases: requirements gathering, design and development, and evaluation. Additionally, interviews with five individuals with mobility disabilities helped define the initial system requirements. The development of the barrier identification system was followed by usability tests with nine representative users. Results: The results of the usability tests of the "BarrierBeGone" barrier identification system were extremely positive. Stakeholders recognized the utility and simplicity of the platform, considering it a motivating factor for future use. Conclusion: The results support the effectiveness of the proposed educational tool in increasing awareness about accessibility and social inclusion in smart cities. This study makes a significant contribution to the field of urban planning and inclusive design. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

Abstract
The literature on the economics of location regarding creative activities is relatively scarce. Estimations, based on 369 newly created firms operating in creative industries in Portugal, which incorporate spatial effects of neighbouring regions in the location choices, yield the following results: (i) the concentration of creative and knowledge-based activities play an important role in location decisions of new creative establishments; (ii) creative firms tend to favour a diversified industrial tissue and related variety, in order to enjoy from inter-sectorial synergies; (iii) high education at a regional level has a highly significant, positive effect on location decisions, while lower educational levels of human capital negatively affect those decisions; (iv) tolerant/open environments attract creative activities; (v) creative firms tend to favour municipalities where the stock of knowledge and conditions for innovative activity are higher; (vi) municipality's attributes are more important in terms of firms' location decisions than the characteristics of nearby regions.

Abstract
Recent state-of-the-art forecasting methods are trained on collections of time series. These methods, often referred to as global models, can capture common patterns in different time series to improve their generalization performance. However, they require large amounts of data that might not be available. Moreover, global models may fail to capture relevant patterns unique to a particular time series. In these cases, data augmentation can be useful to increase the sample size of time series datasets. The main contribution of this work is a novel method for generating univariate time series synthetic samples. Our approach stems from the insight that the observations concerning a particular time series of interest represent only a small fraction of all observations. In this context, we frame the problem of training a forecasting model as an imbalanced learning task. Oversampling strategies are popular approaches used to handle the imbalance problem in machine learning. We use these techniques to create synthetic time series observations and improve the accuracy of forecasting models. We carried out experiments using 7 different databases that contain a total of 5502 univariate time series. We found that the proposed solution outperforms both a global and a local model, thus providing a better trade-off between these two approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Autonomous Surface Vehicles (ASVs) are bound to play a fundamental role in the maintenance of offshore wind farms. Robust navigation for inspection vehicles should take into account the operation of docking within a harbouring structure, which is a critical and still unexplored maneuver. This work proposes an end-to-end docking approach for ASVs, based on Reinforcement Learning (RL), which teaches an agent to tackle collision- free navigation towards a target pose that allows the berthing of the vessel. The developed research presents a methodology that introduces the concept of illegal actions to facilitate the vessel's exploration during the learning process. This method improves the adopted Actor-Critic (AC) framework by accelerating the agent's optimization by approximately 38.02%. A set of comprehensive experiments demonstrate the accuracy and robustness of the presented method in scenarios with simulated environmental constraints (Beaufort Scale and Douglas Sea Scale), and a diversity of docking structures. Validation with two different real ASVs in both controlled and real environments demonstrates the ability of this method to enable safe docking maneuvers without prior knowledge of the scenario.