Resultados de investigación

Table S1 compiles the principal analytical and semi-analytical studies addressing steady-state groundwater flow into open excavations beneath thin retaining structures (cofferdams). This synthesis is intended to place the present work within its historical and methodological context and to make explicit the gap that motivates it. Studies are listed in chronological order; where a contribution is known mainly through a later compilation, both the original work and the source in which it was reported are indicated. For each study, the table summarizes the reference, the cofferdam geometry considered, the methodology adopted (analytical, analogue, or numerically verified), and its main contribution. Four descriptors then indicate whether the formulation accounts for the excavation, for anisotropy of the hydraulic conductivity, and for a layered (heterogeneous) soil profile, and whether the flow domain is treated as bounded laterally and at depth; a final column reports the form in which the results are presented (mathematical expression, design curves, or tables). As the table shows, only a small number of works consider layered media, and none simultaneously incorporate layering and anisotropy under a bounded-domain assumption—precisely the configuration addressed in this study.

Radio access network optimization is a critical task in current cellular systems. For this purpose, Minimization of Drive Test (MDT) functionality provides mobile operators with georeferenced network performance statistics to tune radio propagation models in re-planning tools. However, some samples in MDT traces contain critical location errors due to the user equipment's energy-saving, thus making MDT data filtering vital to guarantee an adequate performance of MDT-driven algorithms. Supervised Learning (SL) allows to train automatic systems for detecting abnormal MDT measurements by using a labeled dataset. Unfortunately, labeling MDT data is a labor-intensive task, that can be alleviated by using Self-Supervised Learning (SSL). This work presents a novel SSL method to detect MDT measurements with abnormal position information in road scenarios. For this purpose, a dataset is first labeled by combining unlabeled MDT traces from high-mobility users and freely available land use maps, and then an SL classifier is trained. Model assessment is carried out using MDT data collected in a live Long-Term Evolution (LTE) network. Performance analysis includes the comparison of six well-known SL algorithms and 3 different sets of input features aiming to improve model accuracy, generalizability, and explainability, respectively. Results show that considering predictors regarding positioning error increases model accuracy, whereas omitting this information allows to cover a wider range of terminals. Likewise, Shapley Additive exPlanations (SHAP) analysis proves that the use of high-level predictors significantly improves model explainability., Proyecto 6G-GRADA, Ayuda para la formacion de doctores PRE2022-102119, Agencia Estatal de Investigacion, Unión Europea

Background Major depressive disorder affects more women than men, possibly due to sex-specific biological and environmental factors. Previous studies have shown that Galanin, as well as specific fragments of it, such as Galanin 1–15 [GAL(1–15)], play a crucial role in modulating depression in animal models by acting on brain regions like the hippocampus and dorsal raphe nuclei. Until now, its effects had only been studied in male rats, where GAL(1–15) produces depressive and anxiogenic behaviors in behavioral tests. This study analyzes, for the first time, the effects of GAL(1–15) on female rats and compares the expression of galaninergic and serotonergic genes between males and females to understand sex-dependent mechanisms in depression. Methods For this purpose, the effect of GAL(1–15) administered intranasally in female rats was assessed, using validated behavioral tests for depression: the Forced Swim Test (FST) and the Tail Suspension Test (TST), as well as the anxiety behavior test: the Open Field Test (OFT). Furthermore, the expression of galaninergic genes (GAL, GALR1, GALR2, GALR3) and the 5-HT1A receptor was compared in untreated male and female rats using qPCR in key brain regions implicated in depressive disorder: the dorsal raphe nucleus (DR), the dorsal hippocampus, and the prefrontal cortex (PFC). Results We demonstrated that GAL(1–15) intranasally administered in female rats strongly produced depressive behavior in the FST as well as in the TST. Moreover, in the anxiety test, intranasal GAL(1–15) at high doses produced anxiety behavior in the OFT. In addition, the qPCR study revealed that naïve female rats exhibited increased expression of the galaninergic system and 5-HT1A receptor compared to naïve male rats in the DR, dorsal hippocampus, and PFC, several nuclei implicated in depression. Conclusions.These results demonstrate the existence of sexual dimorphism in the galangergic system.These findings on sex-specific neurobiological variations are crucial for advancing toward more precise therapies tailored to the specific characteristics of each sex in the treatment of depression.

These data derived from the proyect PID2020-115954RB-I00: Interacción entre la virulencia de nodavirus y la respuesta inmune innata en dorada y lubina (INTERNODASS)

Emotion regulation (ER) is a core transdiagnostic mechanism implicated in the development and maintenance of anxiety and depression. Although insomnia also plays a key role in these internalizing conditions, it has received comparatively less empirical attention. Moreover, the extent to which insomnia, above and beyond ER, contributes to anxiety and depressive symptoms at different severity levels remains poorly understood. A cross-sectional sample of 2,352 adolescents (11–18 years old) completed validated measures assessing ER, insomnia, and symptoms of anxiety and depression. Based on their scores on the Depression, Anxiety and Stress Scales (DASS-21; Lovibond & Lovibond, 1995), participants were classified into four groups: clinical anxiety, subclinical anxiety, clinical depression, and subclinical depression. Within each severity group, hierarchical multiple regression analyses were conducted to determine the unique and incremental contribution of ER and insomnia to anxiety and depressive symptoms, after controlling for age and sex. ER significantly predicted anxiety and depression only within the clinical groups. Insomnia emerged as a significant predictor of anxiety in both subclinical and clinical groups, and of depression in the clinical group, over and above ER and demographic covariates. Across models, insomnia accounted for a greater proportion of explained variance compared to ER. These findings underscore the central role of sleep difficulties and ER as transdiagnostic mechanisms of internalizing psychopathology, particularly at higher symptom levels. They also highlight the importance of prioritizing insomnia-focused interventions, given the robust contribution of sleep disturbances to anxiety and depression across severity groups.

README – SPAREMETAL DATASET 3 Title: Experimental and predictive data from sustainable machining of light alloys (SPAREMETAL project) Project: PID2021-125988OB-I00 Sistema experto para la mejora de la integridad superficial en el mecanizado sostenible de aleaciones ligeras Institution: University of Malaga (Universidad de Málaga) Principal Investigator: Lorenzo Sevilla Hurtado (ORCID: 0000-0002-2236-5807) Contributors: Francisco Javier Trujillo Vilches Carolina Bermudo Gamboa Sergio Martín Béjar Manuel Herrera Fernández Tobias Andersson Daniel Svensson Yezika Sánchez Hernández Description: This dataset contains experimental and processed data obtained during dry machining processes of light alloys (UNS A97075 aluminum alloy and Ti6Al4V titanium alloy). The data were collected within the SPAREMETAL research project and include both online monitoring data and offline characterization results. This is dataset 3 (there are 4 in total for this project), which analyzes the effect of cutting parameters on slenderness tests. Cutting forces, Temperature, Macrogeometrical deviations, Surface Roughness and Energy consumption were analysed. Data include: - Online monitoring data: cutting forces, temperature, vibrations, energy consumption - Offline measurements: surface roughness (2D/3D), microhardness, geometrical deviations - Derived datasets: aggregated databases linking input machining parameters and output variables - Predictive models: artificial neural networks (ANN) developed in MATLAB/Simulink Folder structure: 01. RAW DATA: Folder containing the raw data for the output variables; 02. PROCESSED DATA: Folder with the processed databases of the output variables; 03. MODELS: Developed mathematical models for the output variables as a function of the input variables; 04. Documentation: Documentation associated with the Project; File formats: - CSV, XLSX, FEL, DWD, ATS, DXD (data) - MAT, M, SLX, MALAPP, MLX, EXE (models) - PDF, DOCX (documentation) - PNG, TIFF, JPG (images) Methodology: The dataset was generated through controlled machining experiments using CNC turning equipment. Input parameters include cutting speed, feed rate and depth of cut. Output variables were measured using dynamometers, thermographic cameras, accelerometers and optical metrology systems. Units: All variables are expressed in SI units unless otherwise specified in metadata files. Reuse: Data can be reused for: - Validation of machining models - Development of predictive algorithms - Comparative studies in manufacturing engineering License: Creative Commons Attribution 4.0 (CC BY 4.0) Funding: Spanish State Research Agency (AEI) – Projects of Knowledge Generation 2021 Contact: Lorenzo Sevilla Hurtado University of Malaga Email: (lsevilla@uma.es) Date of publication: 2026 Version: v2.0, Este dataset contiene datos experimentales y procesados obtenidos durante procesos de mecanizado en seco de aleaciones ligeras (aleación de aluminio UNS A97075 y aleación de titanio Ti6Al4V). Los datos se recopilaron en el marco del proyecto de investigación SPAREMETAL e incluyen tanto datos de monitorización en tiempo real como resultados de caracterización fuera de línea. Se trata del dataset 3 (existen 4 para este proyecto), en el que se analiza el efecto de los parámetros de corte en los ensayos de esbeltez de la aleacion UNS A97075. Se analizaron las fuerzas de corte, la temperatura, las desviaciones macrogeométricas, la rugosidad superficial y el consumo de energía para esta aleación. Datos generados/recogidos: En el proyecto SPAREMETAL se han generado datos experimentales derivados de procesos de mecanizado en seco de aleaciones ligeras (UNS A97075 y Ti6Al4V), incluyendo: • Datos de monitorización online (fuerzas de corte, temperatura, vibraciones, consumo energético) • Datos de monitorización offline (rugosidad, desviaciones geométricas, microdureza, fatiga) • Bases de datos integradas de parámetros de corte y variables de salida • Modelos predictivos basados en redes neuronales artificiales • Archivos de simulación (MATLAB/Simulink) • Documentación metodológica y protocolos experimentales Formatos y estándares utilizados: • CSV, XLSX, FEL, DWD, ATS, DXD (data) • MAT, M, SLX, MALAPP, MLX, EXE (models) • Documentación: PDF, DOCX • Imágenes: TIFF, PNG Valor para otros investigadores: Los datos permiten: • Reproducir experimentos de mecanizado sostenible • Validar modelos predictivos • Desarrollar nuevos algoritmos de optimización, Ministerio de Ciencia e Innovación

The dataset consists of a table of 364 inscriptions from Baetica in which one or more divinities are mentioned. It records information on the inscriptions, the deities mentioned, the dedicants involved, their classification according to the categories used in the article, and the locality where each inscription was found. In addition to the raw data, the file contains three further sheets with the numerical matrices used for the network analyses. These matrices were generated in Excel through a series of pivot tables and subsequently imported into Visone 2.28, a free software package for network analysis, in order to produce the network visualisations and calculations. They correspond to two one-mode networks, applied respectively to divinities and dedicants, and one two-mode network connecting dedicants and deities., This repository contains the dataset supporting the quantitative network analysis presented in the book chapter "Esclavitud y dependencia en las manifestaciones religiosas de la Bética: una aproximación desde la teoría de redes", which examines the relationships between dedicants and divinities in the religious epigraphy of the Roman province of Baetica. Particular attention is paid to slavery, dependence, gender, and socio-juridical status as categories for exploring patterns of religious interaction among different groups of devotees.

This dataset contains scripts and associated data used to deploy, configure, and operate an Open Radio Access Network (O-RAN) environment based on the srsRAN software stack. It includes Python and shell scripts for automating network deployment, managing system components, and executing experiments. The dataset also incorporates tools for parsing network logs and a simulation framework that enables real-time reproduction of previously recorded experiments. This allows controlled and reproducible analysis of network behaviour under consistent conditions

Datos de investigación del Proyecto PID2021-126283NB-I00 correspondientes a arrendamientos por mayor de rentas regias ordinarias de los años 1480-1494 en toda la Corona de Castilla procedentes del vaciado de información de fuentes del Archivo General de Simancas. Se incluyen datos correspondientes a los siguientes ítems: 1. Año de arrendamiento 2. Precio de arrendamiento 3. Partido fiscal 4. Arrendador/es mayor/es 5. Vecindad de arrendador mayor 6. Oficio/cargo 7. Vínculos familiares 8. Fiador/es 9. Vecindad de fiador/es 10. Cargos/oficios de los fiadores 11. Cantidad fiada 12. Fuente