Background: This study aims to explore the perceptions of visually impaired patients and their families regarding rehabilitation. Methods: A qualitative, descriptive, and cross-sectional design was employed, incorporating data from semi-structured interviews conducted with patients and their families as part of a broader project. Data were analyzed using a content analysis approach to address the study’s objectives. Results: The findings identified three primary categories based on interviews with nine patients and six family members: (A) the impact of rehabilitation on autonomy and identity formation; (B) the significance of professional training, experience, and assistive technology in rehabilitation; and (C) the effects of rehabilitation on patients and their families. Conclusions: Patients and their families deemed rehabilitation essential for fostering autonomy and independence in daily activities. A multidisciplinary approach, emphasizing professional training that integrates health, rehabilitation, and education, was found to be vital.

This article presents a dataset and experimental evaluation of a parallelized variant of Junker’s QuickXPlain algorithm, designed to efficiently compute minimal conflict sets in constraint-based diagnosis tasks. The dataset includes performance benchmarks, conflict traces, and solution metadata for a wide range of configurable diagnosis problems based on real-world and synthetic CSP instances. Our parallel variant leverages multicore architectures to reduce computation time while preserving the completeness and minimality guarantees of QuickXPlain. All evaluations were conducted using reproducible scripts and parameter configurations, enabling comparison across different algorithmic strategies. The provided dataset can be used to replicate experiments, analyze scalability under varying problem sizes, and serve as a baseline for future improvements in conflict explanation algorithms. The full dataset, codebase, and benchmarking scripts are openly available and documented to promote transparency and reusability in constraint-based diagnostic systems research.

The ability to program has become an essential competence for individuals in an increasingly digital world. However, access to programming education remains unequal, particularly in non-WEIRD (Western, Educated, Industrialized, Rich, and Democratic) contexts. This study presents a dataset resulting from an educational intervention designed to foster programming competencies and computational thinking skills among primary school students aged 8 to 12 years in Milagro, Ecuador. The intervention integrated Scratch, a block-based programming environment that simplifies coding by eliminating syntactic barriers, and the CARAMBA recommendation system, which provided personalized learning paths based on students’ progression and preferences. A structured educational process was implemented, including an initial diagnostic test to assess logical reasoning, guided activities in Scratch to build foundational skills, a phase of personalized practice with CARAMBA, and a final computational thinking evaluation using a validated assessment instrument. The resulting dataset encompasses diverse information: demographic data, logical reasoning test scores, computational thinking test results pre- and post-intervention, activity logs from Scratch, recommendation histories from CARAMBA, and qualitative feedback from university student tutors who supported the intervention. The dataset is anonymized, ethically collected, and made available under a CC-BY 4.0 license to encourage reuse. This resource is particularly valuable for researchers and practitioners interested in computational thinking development, educational data mining, personalized learning systems, and digital equity initiatives. It supports comparative studies between WEIRD and non-WEIRD populations, validation of adaptive learning models, and the design of inclusive programming curricula. Furthermore, the dataset enables the application of machine learning techniques to predict educational outcomes and optimize personalized educational strategies. By offering this dataset openly, the study contributes to filling critical gaps in educational research, promoting inclusive access to programming education, and fostering a more comprehensive understanding of how computational competencies can be developed across diverse socioeconomic and cultural contexts.

Background: Quarantine is one of the most effective strategies to control outbreaks of communicable diseases. Individuals under isolation or quarantine experience both physical and mental effects. Therefore, given its widespread implementation around the world, it is pertinent to analyze this effect on physical and mental health. The psychosocial impact model, which divides four dimensions into two main points, exposure or protection and positive or negative responses, was used to analyze the psychosocial impact of quarantine. Objectives: The present study aimed to investigate the psychosocial impact of people exposed to or protected from quarantine. Methods: We conducted a search for primary articles in the Web of Science and Scopus databases, and after applying the inclusion and exclusion criteria, we meta-analyzed five of them. Results: Quarantined individuals were more likely to experience anxiety (K = 4; OR = 2.62) and depressive symptoms (K = 6; OR = 1.61) compared to those who did not undergo quarantine. Researchers discuss a twofold increase in the probability of anxiety or depression among those in quarantine. As a result, advancing interventions that reduce this impact is critical for both health and the global economy. Conclusions: In terms of economic variables, the non-moderation of GDP per capita and the moderation of the GINI index stand out, demonstrating that countries must move toward policies that promote the reconstruction of more resilient and inclusive societies.

FastDiag is a widely used algorithm for model-based diagnosis, computing minimal subsets of constraints whose removal restores consistency in knowledge-based systems. As applications grow in complexity, researchers have proposed parallel extensions such as Java-version FastDiagP and FastDiagP++ to accelerate diagnosis through speculative and multiprocessing strategies. This paper presents a reproducible and extensible framework for evaluating FastDiag and its parallel variants across a benchmark suite of feature models and ontology-like constraints. We analyze each variant in terms of recursion structure, runtime performance, and diagnostic correctness. Tracking mechanisms and structured logs enable the fine-grained comparison of recursive behavior and branching strategies. Technical validation confirms that parallel execution preserves minimality and structural soundness, while benchmark results show runtime improvements of up to 4× with FastDiagP++. The accompanying dataset, available as open source, supports educational use, algorithmic benchmarking, and integration into interactive configuration environments. The framework is primarily intended for reproducible benchmarking and teaching with open-source implementations that facilitate analysis and extension.

Artificial intelligence (AI) is not merely reshaping entrepreneurial practice—it is reprogramming its epistemic foundations. The purpose of this research note is to develop a conceptual perspective explaining how AI reconfigures the core assumptions of entrepreneurship. Specifically, it argues that AI transforms entrepreneurship from a human-centered process of discovery and design into a governed socio-technical system. Three foundational assumptions are placed under strain: creativity evolves into hybrid human–AI co-creation, uncertainty becomes partially computable, and agency is reconstituted as governed entrepreneurial judgment. Drawing on recent debates in entrepreneurship and innovation studies, the note reframes entrepreneurship as a practice of epistemic stewardship—one that balances algorithmic precision with moral intention. While AI enables hybrid co-creation across innovation processes, entrepreneurial judgment remains irreducibly human insofar as it entails responsibility and accountability. The argument concludes that the next frontier of entrepreneurship is not technological but philosophical: reimagining innovation as a responsible collaboration between human reasoning and algorithmic intelligence.