University of Las Palmas de Gran Canaria
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Boas práticas e necessidades de formação de profissionais em serviços de intervenção e apoio familiar
No full textLas intervenciones familiares desde el enfoque de la parentalidad positiva cuentan actualmente con un importante respaldo en España tanto a nivel legislativo como profesional. Gracias a una fructífera colaboración entre responsables políticos, personal investigador y profesionales, se ha llevado a cabo en los últimos años un importante esfuerzo porque estas intervenciones incorporen buenas prácticas basadas en la evidencia. Con ese objetivo, se diseñó la Guía de Buenas Prácticas en Parentalidad Positiva, un recurso de apoyo profesional que trata de fomentar procesos de innovación y mejora de la calidad de los servicios de atención familiar en España. En este estudio se analizaron las necesidades de formación profesional identificadas en los 54 planes de mejora de entidades públicas y no gubernamentales que llevan a cabo actuaciones de intervención familiar y que han obtenido el reconocimiento oficial a la promoción de la parentalidad positiva (otorgado el Ministerio de Derechos Sociales, Consumo y Agenda 2030 y la Federación Española de Municipios y Provincias), tras haber evaluado sus servicios y/o programas con del Protocolo on-line de la Guía de Buenas Prácticas. Los resultados obtenidos mostraron necesidades de formación profesional que tienen que ver tanto con la organización de los servicios como con la actuación del personal técnico en su trabajo con las familias. En este último ámbito, son especialmente relevantes las necesidades de formación relacionadas con la evaluación y con la metodología de trabajo grupal con familias desde un planteamiento positivo y fortalecedor, propio del enfoque de la parentalidad positiva. Estos resultados son discutidos destacando la importancia de identificar y atender las necesidades de formación profesional para mejorar la calidad de los servicios de intervención y apoyo familiar.Family interventions from a positive parenting perspective currently have significant support in Spain at both the legislative and professional levels. Thanks to fruitful collaboration between policy makers, researchers and professionals, a significant effort has been made in recent years to ensure that these interventions incorporate good practices based on evidence. With this objective, the Guide to Good Practices in Positive Parenting was designed, a professional support resource that seeks to promote innovation processes and improve the quality of family care services in Spain. This study analysed the professional training needs identified in the 54 improvement plans of public and non-governmental entities that carry out family intervention actions and that have obtained official recognition for the promotion of positive parenting (granted by the Ministry of Social Rights, Consumption and Agenda 2030 and the Spanish Federation of Municipalities and Provinces), after having evaluated their services and/or programs with the online Protocol of the Guide to Good Practices. The results obtained showed professional training needs that have to do with both the organization of the services and the performance of the technical staff in their work with families. In this last area, the training needs related to the evaluation and the methodology of group work with families from a positive and strengthening approach, typical of the positive parenting approach, are especially relevant. These results are discussed, highlighting the importance of identifying and addressing professional training needs to improve the quality of family intervention and support services.As intervenções familiares baseadas na abordagem parental positiva têm actualmente um apoio significativo em Espanha, tanto a nível legislativo como profissional. Graças a uma colaboração frutuosa entre decisores políticos, pessoal de investigação e profissionais, foi feito um esforço importante nos últimos anos para garantir que estas intervenções incorporam boas práticas baseadas em evidências. Com este objetivo foi concebido o Guia de Boas Práticas em Parentalidade Positiva, um recurso de apoio profissional que procura promover processos de inovação e melhorar a qualidade dos serviços de cuidados familiares em Espanha. Este estudo analisou as necessidades de formação profissional identificadas nos 54 planos de melhoria de entidades públicas e não governamentais que realizam ações de intervenção familiar e que obtiveram reconhecimento oficial para a promoção de uma parentalidade positiva (concedido pelo Ministério dos Direitos Sociais, Consumo e Agenda 2030 e a Federação Espanhola de Municípios e Províncias), depois de terem avaliado os seus serviços e/ou programas com o Protocolo online do Guia de Boas Práticas. Os resultados obtidos evidenciaram necessidades de formação profissional que têm a ver quer com a organização dos serviços, quer com o desempenho dos técnicos no seu trabalho com as famílias. Nesta última área são especialmente relevantes as necessidades de formação relacionadas com a avaliação e a metodologia de trabalho em grupo com as famílias a partir de uma abordagem positiva e fortalecedora, própria da abordagem parental positiva. Estes resultados são discutidos, destacando a importância de identificar e abordar as necessidades de formação profissional para melhorar a qualidade da intervenção familiar e dos serviços de apoio.9075160,25Q3ESCI0,0Q110,0ERIH PLU
Gamification in Higher Education: A Case Study in Educational Sciences
No full textTeachers are generally focused on optimizing the teaching–learning process and fostering high levels of student engagement, participation, and motivation. To address this challenge, this work presents a gamification experience implemented to teach content related to family involvement and educational programs in two courses —one at master’s degree and the other at bachelor’s degree level. A total of 354 students participated in the study and shared their perceptions regarding their learning, academic performance, participation, and motivation in relation to the gamification experience. The results indicate that students perceive the gamification experience as having a positive impact on all four areas. They expressed a preference for studying the subject through gamification rather than traditional methods. This paper highlights an experience that generates positive perceptions among students and encourages higher education instructors to incorporate gamification into their teaching methods and classroom dynamics.120,871Q1ESCI10,
Calidad del empleo en el sector turístico
No full textIn many countries and regions, tourism activity is viewed as a means of increasing income and employment for local people. Research confirms that tourism development boosts employment. However, studies have neglected the quality of jobs. Job quality is a concern for policy makers and is considered a determinant of individual well-being. The quality of jobs in tourism is generally perceived to be low. Most evidence with respect to tourism job quality disregards job quality in specific tourism activities, non-tourism activities, several tourism occupations, and non-tourism occupations. These aspects are analysed using employment data from the European Union and two large databases on job characteristics (ONET) and working conditions (European Working Conditions Survey). The results indicate that job quality is low in accommodation and food and beverage service activities, but not in travel agency and tour operator services. With regard to occupations, job quality is low in most tourism occupations.4025160,187Q2Sello FECYTESCIQ176,770,0Q19,8ERIH PLU
Geographic and biological drivers shape anthropogenic extinctions in the Macaronesian vascular flora
No full textWhether species extinctions have accelerated during the Anthropocene and the extent to which certain species are more susceptible to extinction due to their ecological preferences and intrinsic biological traits are among the most pressing questions in conservation biology. Assessing extinction rates is, however, challenging, as best exemplified by the phenomenon of 'dark extinctions': the loss of species that disappear before they are even formally described. These issues are particularly problematic in oceanic islands, where species exhibit high rates of endemism and unique biological traits but are also among the most vulnerable to extinction. Here, we document plant species extinctions since Linnaeus' Species Plantarum in Macaronesia, a biogeographic region comprised of five hyperdiverse oceanic archipelagos, and identify the key drivers behind these extinctions. We compiled 168 records covering 126 taxa, identifying 13 global and 155 local extinction events. Significantly higher extinction rates were observed compared to the expected global background rate. We uncovered differentiated extinction patterns along altitudinal gradients, highlighting a recent coastal hotspot linked to socioeconomic changes in Macaronesian archipelagos from the 1960s onwards. Key factors influencing extinction patterns include island age, elevation, introduced herbivorous mammals, and human population size. Trait-based analyses across the floras of the Azores and Canary Islands revealed that endemicity, pollination by vertebrates, nitrogen-fixing capacity, woodiness, and zoochory consistently tended to increase extinction risk. Our findings emphasize the critical role of geography and biological traits, alongside anthropogenic impacts, in shaping extinction dynamics on oceanic islands. Enhancing our knowledge of life-history traits within island floras is crucial for accurately predicting and mitigating future extinction risks, underscoring the urgent need for comprehensive biodiversity assessments in island ecosystems.214,28510,8Q1Q1SCIE10,
Upgrading a horizontal surface flow constructed wetland with forest waste and aeration
No full textConstructed wetlands (CWs) are regarded as sustainable wastewater treatment systems for small to medium-sized communities. However, ponds and horizontal surface flow CWs (SF-CWs) can be an ideal environment for mosquitoes to thrive. In the current context of climate change, this may pose serious health problems for the population, which may predispose authorities against their use. A possible solution for existing SF-CWs is to convert them into sub-surface flow by filling them with conventional media, i.e. gravel and/or sand. However, the mining of these materials poses an enormous environmental threat. Thus, alternative, sustainable filling materials for CWs should be tested. Another constraint of CWs is their large footprint, which in many cases (lack of expensive land) limits their applicability. This work studies the effects of filling a full-scale SF-CW with a forest residue (palm tree branches) and aeration. The results indicate that in terms of percentage removal, filling increased that of E. coli and Total Coliforms, while the combination of filling and aeration resulted in a significant improvement in BOD5, turbidity, and ammonium. However, the analysis of surface loads removed indicated significant increases in E. coli and TC with the filling alone, and of BOD5, turbidity, E. coli, Total Coliformis, and ammonium for the filling + aeration combination. Studies at full-scale level on the use of forest residues as CW substrate and aeration are scarce, thus this work can serve as a guide for more sustainable designs.81,7718,0Q1Q1SCIE11,
Corpus-based Studies in Specialized Discourses
No full textCorpus-based Studies in Specialized DiscoursesThis book illustrates the complex ways language operates within specific professional and academic contexts. Through a series of meticulously conducted corpus-based studies, this volume explores the unique linguistic features and rhetorical strategies that characterize specialized discourses across various fields, including medicine, engineering, and the humanities. The book examines key aspects such as metadiscourse markers, rhetorical structures of research articles, and the customization of vocabulary tools to enhance language learning and professional communication. By revealing patterns and trends that traditional methods might overlook, this collection offers valuable insights for scholars, educators, and practitioners, making it an essential resource for understanding and improving communication within specialized domains.294Q
Towards Transferable Pollution Detection Methods in Aquatic Environments Using Hyperspectral Technology
No full textPrograma de Doctorado en Tecnologías de Telecomunicación e Ingeniería Computacional por la Universidad de Las Palmas de Gran CanariaThis thesis presents a suite of transferable methodologies for environmental
monitoring, focusing on detecting pollutants in aquatic ecosystems. Leveraging
remote sensing with hyperspectral imaging (HSI) and artificial intelligence (AI),
these approaches enable precise pollutant identification and adaptability across
diverse scenarios. Thus, supporting systematic observation over extensive
geographic regions or long-term datasets contributes to developing standardized
solutions for monitoring and protecting Earth’s ecosystems.
Aquatic pollution threatens biodiversity and ecosystem services, especially oil
spills and plastic waste. Oil spills spread quickly across water surfaces, blocking
sunlight and endangering aquatic biodiversity. Plastic waste accumulates
in water bodies, breaking into microplastics that marine organisms ingest,
disrupting food chains. Both pollutants are especially critical to monitor because
they have profound, long-term impacts on ecosystems and food webs. Dye
tracers such as rhodamine can be used as a proxy in oil spill simulations due
to their similar dispersion behaviour in water. This aids in the development of
detection and monitoring techniques for real oil spill events. However, there is
a need for efficient and transferable aquatic pollution monitoring technologies.
HSI is a powerful tool for identifying aquatic pollutants due to its rich spectral
detail. However, the massive data it generates is costly and complex to process,
posing key challenges. Dimensionality reduction techniques—such as spectral
indices and band selection—can trim redundant data, reducing storage and
computational demand while maintaining accuracy. Furthermore, the scarcity of
labelled datasets hinders AI model training. Unsupervised learning approaches
offer a promising solution, enabling models to extract meaningful patterns from
non-labelled data, making HSI more adaptable across diverse environments.
This thesis aims to pioneer efficient and transferable HSI methodologies
for detecting and monitoring critical aquatic pollutants. It focuses on
developing novel approaches that streamline data analysis and improve
transferability across diverse environments. The research progresses from wellestablished, straightforward methods such as spectral indexes to cutting-edge
AI methodologies to enhance pollutant detection. Each chapter focuses on
different HSI technology needs, overcoming the challenges of data complexity,
dimensionality, and the scarcity of labelled datasets. This creates a cohesive
framework that addresses the demands of large-scale environmental monitoring
with adaptable HSI methods. Chapter 2 reduces HSI data complexity by introducing the Normalized
Difference O il I ndex ( NDOI), a n ew s pectral i ndex d esigned t o i mprove oil
spill detection in coastal areas. The study compares the performance of
several spectral indices utilizing images from multiple satellite and airborne
sensors—AVIRIS, HICO, and MERIS—captured during the Deepwater Horizon
disaster in the Gulf of Mexico in 2010. Traditional indices often misclassify
other elements, such as suspended sediments, leading to inaccurate results.
The NDOI avoids sand-in-suspension false positives, offering a m ore reliable
response in coastal areas. NDOI is suitable for detecting oil spills thicker than
50 microns, with an average oil F1-score of 83%, and estimating its thickness
and oil volume exceeding 90% accuracy. It also provides rapid detection of oil
spills due to its simple calculation compared with other spectral indices and
IA models, which is crucial for quick responses to environmental crises. This
development directly contributes to optimizing the use of optical sensors for fast
and efficient pollutant detection.
Chapter 3 tackles the challenge of hyperspectral data’s high dimensionality
by presenting a new dimensionality reduction methodology. The spectral
band selection method identifies t he m ost r elevant b ands f or d etecting specific
pollutants, with testing conducted on plastics and rhodamine. This minimises
redundant or irrelevant bands, reducing computational cost and resource
demands. This methodology has been applied to laboratory images and
outdoor experiments, focusing on analyzing the impact of background effects on
identifying target objects. The methodology successfully transferred influential
spectral bands between datasets with 80-90% accuracy, indicating the potential
for developing specialized sensors with these common bands to enable detection
across various environments. However, the transfer of pre-trained classification
models remains an area for further research, particularly regarding semitransparent objects or solutions influenced by background reflections in complex
environments like optically shallow waters. Refined p ost-processing approaches
suggest that model transfer could be feasible, potentially reducing the need for
labelled data or in-situ validation, thus preserving resources and enabling a more
generalizable classifier.
Chapter 4 addresses the scarcity of labelled data in HSI and AI applications
through the spectral loss function (Sl), which enhances HSI segmentation in
unsupervised neural networks. The loss function is tested on HSI benchmark
datasets, such as Pavia University, Salinas Valley, Indian Pines, and University of
Houston, and a case study using an AVIRIS image from the Deepwater Horizon
catastrophe. Sl was introduced in the currently best-performing unsupervised
segmentation neural network, enhancing evaluation metrics performance by up
to 6%. The proposed method also outperforms well-established techniques,
such as spectral indices. For example, spectral indices rely on few spectral
bands to produce a numerical value for each pixel, requiring an expert to establish a threshold for class determination. In contrast, the unsupervised
neural network can directly assign different classes to varying oil thicknesses
based on their complete spectral response, making them fully transferable across
environments. Therefore, the unsupervised approach can generate ground-truth
data, reducing manual labour. This is especially relevant for remote areas such
as the open ocean, where manual labelling is challenging and resource-intensive.
This contribution expands the scope of AI-driven detection techniques to operate
without labelled datasets, thereby enhancing adaptability.
This thesis concludes with a synthesis of the main insights from each
chapter, a reflection on the implications and limitations identified, and
suggestions for future research directions. The thesis successfully develops
new transferable HSI methods that enhance the detection and monitoring
of aquatic pollutants, overcoming critical knowledge gaps. Spectral indices,
such as NDOI, provide a quick and efficient solution for rapid, low-resource
decision-making but rely on manual thresholding. Band selection methods
help identify critical spectral bands, which can improve model transfer and
generalization across environments. Unsupervised methods complement these
techniques by addressing non-labelled datasets, providing a foundation for
large-scale monitoring. This research contributes to more efficient, adaptable,
and transferable environmental monitoring technologies by addressing critical
challenges related to data complexity, dimensionality reduction, and the scarcity
of labelled datasets. The strengths and weaknesses of this suite of methods
should be carefully considered to select the most appropriate approach based on
each study’s specific characteristics.
These advancements hold promise for designing next-generation sensors for
UAVs and space missions, prioritizing data efficiency and precision. Moreover,
the techniques directly apply to environmental management, including early
spill detection, beach cleanup coordination, and supporting data-driven policies.
Future work will focus on further automating hyperspectral monitoring
techniques to minimize manual intervention. Efforts will be directed toward
improving algorithm transferability by incorporating more variability in training
data and advancing post-processing techniques. Additionally, the scalability of
emerging tools, such as cloud computing, will be explored to improve efficient
large-scale monitoring
Predicción de variables oceanográficas basada en métodos de aprendizaje profundo
No full textEste Trabajo Fin de Grado se centra en desarrollar un modelo basado en redes neuronales profundas para predecir variables oceanográficas, especialmente la temperatura potencial del agua. A partir de un conjunto de datos de reanálisis oceánico de Copernicus, se adapta
un modelo preentrenado en meteorología, llamado Aurora, para un contexto marino. Este modelo, originalmente diseñado para pronosticar variables atmosféricas, se ajusta gracias a técnicas de congelación y descongelación de capas, además de un cuidadoso preprocesamiento
y normalización de los datos.
El objetivo principal es mejorar la precisión en la predicción de dinámicas oceánicas, reduciendo el coste computacional característico de los métodos numéricos tradicionales. Se emplea métricas como el error cuadrático medio y el sesgo para comprobar la fiabilidad de
las predicciones a corto y medio plazo. Los resultados muestran una evolución prometedora, con menor error en zonas de mar abierto y mayor complejidad cerca de la costa. Este enfoque, basado en el aprendizaje profundo, sienta las bases para incorporar más variables o regiones en el futuro y supone un paso adelante en la integración de la ciencia de datos en la investigación oceánica.This bachelor’s project focuses on developing a deep neural network model to predict oceanographic
variables, particularly the potential temperature of seawater. Using reanalysis data
from Copernicus, We adapt a meteorologically pre-trained model named Aurora to marine
settings. Originally built for atmospheric forecasting, Aurora is fine-tuned through layer
freezing and unfreezing, alongside careful preprocessing and data normalization.
The main goal is to enhance the accuracy of oceanic predictions while reducing the computational
cost of traditional numerical methods. We use metrics like mean squared error and
bias to assess reliability over short and medium time horizons. The results show promising
performance, with lower errors in open ocean regions and more complexity near coastal areas.
This deep learning approach lays the groundwork for adding more variables or expanding to
broader domains, marking a step forward in integrating data science into oceanic research
