El propósito del estudio fue examinar el efecto de la práctica del aikido sobre el mindfulness y el estado de ansiedad, en estudiantes universitarios sin experiencia previa en artes marciales. Se utilizó un diseño cuasi experimental intra sujetos con mediciones Pre y Post tratamiento, con un grupo control activo (estudiantes de Educación Física. Se midió mindfulness con la escala MAAS y la ansiedad con la escala de Hamilton. Se aplicó un entrenamiento centrado en el aprendizaje y práctica de diversas técnicas de aikido (waza) y de la forma en que debían ser recibidas dichas técnicas (ukemi), por 11 semanas (2 sesiones semanales de 2 horas cada una). Grupo experimental: n=12, con edades entre 18 y 62 años. Grupo control: n=12 estudiantes, con edades entre 21 y los 34 años. Resultados: la práctica de aikido mostró tamaños de efecto significativos y de magnitud moderada tanto en mindfulness, como en la ansiedad. La edad no explica estos hallazgos. Se justifican estudios de seguimiento. The purpose of the study was to examine the effect of practicing aikido on mindfulness and anxiety state in university students with no previous experience in martial arts. We used an intra-subjects quasi-experimental design with Pre and Post treatment measurements, with an active control group (physical education students). Mindfulness was measured with the MAAS scale, whereas anxiety with the Hamilton scale. A training program focused on learning and practicing various aikido techniques (waza), and the way in which these techniques (ukemi) should be received, was implemented during 11 weeks (2 weekly sessions of 2 hours each). Experimental group: n = 12, with ages between 18 and 62 years old. Control group: n = 12 students, with ages between 21 and 34 years old. Results: the practice of aikido showed significant effect sizes of moderate magnitude in both mindfulness and anxiety. Age does not explain these findings. Follow-up studies are recommended.

In the realm of digital libraries, efficiently managing and accessing scientific publications necessitates automated bibliographic reference segmentation. This study addresses the challenge of accurately segmenting bibliographic references, a task complicated by the varied formats and styles of references. Focusing on the empirical evaluation of Conditional Random Fields (CRF), Bidirectional Long Short-Term Memory with CRF (BiLSTM + CRF), and Transformer Encoder with CRF (Transformer + CRF) architectures, this research employs Byte Pair Encoding and Character Embeddings for vector representation. The models underwent training on the extensive Giant corpus and subsequent evaluation on the Cora Corpus to ensure a balanced and rigorous comparison, maintaining uniformity across embedding layers, normalization techniques, and Dropout strategies. Results indicate that the BiLSTM + CRF architecture outperforms its counterparts by adeptly handling the syntactic structures prevalent in bibliographic data, achieving an F1-Score of 0.96. This outcome highlights the necessity of aligning model architecture with the specific syntactic demands of bibliographic reference segmentation tasks. Consequently, the study establishes the BiLSTM + CRF model as a superior approach within the current state-of-the-art, offering a robust solution for the challenges faced in digital library management and scholarly communication.

Large-scale historical corpora present persistent challenges for research, particularly in relation to the navigation, interpretation, and extraction of meaningful information across extensive and heterogeneous collections. The Legislación Mexicana corpus, a 42-volume compilation of legal dispositions spanning more than two centuries, exemplifies these challenges due to its size, structural complexity, and evolving terminology. Traditional approaches to working with such corpora have relied on close reading and the use of indexes. While effective within defined limits, these methods constrain the scope of inquiry and require significant time and expertise to produce meaningful results. This article presents the development of LegMexIA, an AI-enabled research assistant designed to extend interaction with the corpus beyond conventional retrieval methods. The system integrates traditional text retrieval and retrieval-augmented generation via a coordinated architecture that distinguishes between different types of user queries. Structured queries are processed using Elasticsearch and BM25 ranking, while exploratory and interpretive queries are routed through a retrieval-augmented generation workflow, where relevant text fragments are retrieved using vector similarity and assembled into contextual inputs for a language model. A key aspect of the system is the agentic decision layer that analyzes user intent and dynamically selects the appropriate processing strategy, in alignment with established reference practices in academic libraries and in a way that maintains the user’s role in verification. The article also addresses key considerations related to prompt design, technological sustainability, institutional constraints, and the implications of public deployment.

In the realm of digital libraries, efficiently managing and accessing scientific publications necessitates automated bibliographic reference segmentation. This study addresses the challenge of accurately segmenting bibliographic references, a task complicated by the varied formats and styles of references. Focusing on the empirical evaluation of Conditional Random Fields (CRF), Bidirectional Long Short-Term Memory with CRF (BiLSTM+CRF), and Transformer Encoder with CRF (Transformer+CRF) architectures, this research employs Byte Pair Encoding and Character Embeddings for vector representation. The models underwent training on the extensive Giant corpus and subsequent evaluation on the Cora Corpus to ensure a balanced and rigorous comparison, maintaining uniformity across embedding layers, normalization techniques, and Dropout strategies. Results indicate that the BiLSTM+CRF architecture outperforms its counterparts by adeptly handling the syntactic structures prevalent in bibliographic data, achieving an F1-Score of 0.96. This outcome highlights the necessity of aligning model architecture with the specific syntactic demands of bibliographic reference segmentation tasks. Consequently, the study establishes the BiLSTM+CRF model as a superior approach within the current state-of-the-art, offering a robust solution for the challenges faced in digital library management and scholarly communication.