University of Las Palmas de Gran Canaria
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ANN Model presented in "Surrogate model based on ANN for the evaluation of the fundamental frequency of offshore wind turbines supported on jackets"
No full text<p>This repository contains the best of the models developed in the scientific article "Surrogate model based on ANN for the evaluation of the fundamental frequency of offshore wind turbines supported on jackets" by Román Quevedo-Reina, Guillermo M. Álamo, Luis A. Padrón, Juan J. Aznárez. (https://doi.org/10.1016/j.compstruc.2022.106917)</p>
<p>This is an enssemble model of 20 artificial neural networks with 22 neurons in the input layer, 4 hidden layers with 125 neurons per hidden layer, and 1 neuron in the output layer.</p>
<div>This model was trained using the dataset described in the paper above and available at: https://doi.org/10.5281/zenodo.14732047</div><div>The model is stored in the file "model.mat" and can be used by the included "class_model" class.</div>
<div> </div>
<div><strong>Model Contents</strong></div>
<div> </div>
<div>The "model.mat" file contains a Matlab variable named "model", which has the following properties:</div>
<ul>
<li>dlnet: A collection of 20 artificial neural networks (ANNs) generated in the study.</li>
<li>name_input: Names of the input variables for the model (described in the scientific article).</li>
<li>name_output: Name of the output variable of the model (fundamental frequency).</li>
<li>mean_input: Mean values of each input variable, obtained from the training dataset. Used for normalization.</li>
<li>std_input: Standard deviation of each input variable, obtained from the training dataset. Used for normalization.</li>
<li>mean_output: Mean value of the output variable, obtained from the training dataset. Used for normalization.</li>
<li>std_output: Standard deviation of the output variable, obtained from the training dataset. Used for normalization.</li>
</ul>
<div> </div>
<div><strong>How to Use the Model</strong></div>
<div> </div>
<div><em>Prerequisites</em></div>
<div>To use the model, you must have the Deep Learning Toolbox installed in Matlab.</div>
<div> </div>
<div><em>Evaluation Method</em></div>
<div>The model can be evaluated using the "eval" method included in the "class_model". This method provides the following options:</div>
<ul>
<li>output: Specifies how the individual ANN predictions should be aggregated. The options are:
<ul>
<li>"mean": Returns the mean of the predictions from the individual ANNs. (Default)</li>
<li>"std": Returns the standard deviation of the predictions from the individual ANNs.</li>
<li>"both": Returns both the mean and the standard deviation of the predictions from the individual ANNs, using the third dimension of the output matrix.</li>
<li>"all": Returns the predictions from all individual ANNs, using the third dimension of the output matrix.</li>
</ul>
</li>
<li>IDnet: Specifies which ANNs to use for the evaluation. The options are:
<ul>
<li>"all": Uses all ANNs embedded in the model. (Default)</li>
<li>any number: Uses only the ANN at the specified position.</li>
<li>any vector: Uses only the ANNs at the positions specified in the vector.</li>
</ul>
</li>
</ul>
<div> </div>
<div><em>Input Data Format</em></div>
<div>Prepare your input as a matrix where:</div>
<ul>
<li>Each row represents a sample.</li>
<li>The matrix must have 22 columns, corresponding to the 22 input variables, arranged in the same order as specified in name_input.</li>
</ul>
<div> </div>
<div><em>Example</em></div>
<div>Below is an example of how to load the model and use the eval method:
<div> </div>
<div>% Load the model</div>
<div>load('model.mat');</div>
<div> </div>
<div>% Prepare your input data (using case example)</div>
<div>load("case_example.mat"); % Load case example</div>
<div>inputData=example{:,:}; % Extract matrix of data</div>
<div> </div>
<div>% Evaluate the model (default)</div>
<div>output = model.eval(inputData);</div>
<div> </div>
<div>% Evaluate the model (specific options)</div>
<div>output = model.eval(inputData,'output','both','IDnet',1:10);</div>
</div>
Seabird biomonitoring indicates similar plastic pollution throughout the Canary Current
No full textMarine plastic pollution is an emerging global threat for biodiversity. Plastic ingestion is one of the most typical and studied consequences with petrels being a particularly vulnerable group. We studied the plastic ingestion by Cory's shearwater (Calonectris borealis) fledglings in three islands of the Canarian Archipelago (Tenerife, Gran Canaria and Lanzarote). Breeders from different islands show substantial spatial segregation in their foraging areas across the Canary Current Large Marine Ecosystem (CCLME), a critical marine biodiversity hotspot and an important fishing ground in the North Atlantic Ocean. Here, we used a combination of plastic ingestion parameters (number, maximum length, type and colour of plastics) and stable isotopes (δ13C and δ15N) to study potential differences in plastic ingestion loads and trophic niche between three colonies. Our findings reveal a high incidence of plastic ingestion (>90 %) among birds from the three islands. However, although adult birds show foraging and trophic niche segregation across the CCLME, no substantial differences were found in plastic ingestion. White and transparent threadlike fragments were predominantly ingested, likely originating from fishing activities in the CCLME. We provide a baseline for monitoring of plastic pollution in the CCLME and highlight Cory's shearwater as an effective bioindicator of pelagic ecosystems. Our findings emphasize the need for more standardized plastic monitoring programs covering other islands, and for management measures in the fishing activities conducted in the CCLME.91,4455,8Q1Q1SCIE11,
Rhodolith Beds in Brazil—A Natural Heritage in Need of Conservation
No full textAim: Brazil harbours the largest known extent of rhodolith beds (RBs) in the world, a habitat whose ecological and economic importance have been widely overlooked. This creates a dire situation that is likely to worsen with the rapidly expanding human activities, considering that less than 5% of Brazil's ocean area is fully protected. We assessed the importance of Brazilian RBs for supporting biodiversity, at a country-wide level, and identified multi-criteria hotspots that, in face of lack of protection and presence of anthropogenic threats, could safeguard conservation seascapes across Southwestern Atlantic waters. Location: Southwestern Atlantic Ocean. Methods: We performed a systematic review of studies on Brazilian RBs to retrieve information regarding their spatial distribution and associated biodiversity. Multi-criteria hotspots were identified based on the areas where high species diversity co-occurs with a high presence of endemic, threatened and commercially important species. Furthermore, we assessed how well RBs are covered by marine protected areas (MPAs), as well as their spatial overlap with multiple threats. Results: Existing records for Brazilian RBs indicate > 1000 different species, mostly fish and algae, including significant numbers of endemic, threatened and commercially important species. Most of the RBs are either unprotected or only partially protected, including the majority of the biodiversity hotspots identified by our analysis. Among the main potential threats to RBs, bottom trawling ranks highest, while the expansion of seabed mining and oil and gas activities may sharply increase the risk of cumulative impacts on RBs in the near future. Main Conclusions: Our large-scale quantitative assessment confirms the significant role of RBs as biodiversity hotspots. This information could be leveraged to help meet the twin goals of RB conservation, through the establishment of highly-protected MPAs in hotspot areas, and their sustainable use through an ecosystem-based approach that accounts for vulnerabilities of RBs to multiple threats.141,7874,6Q1Q1SCIE10,
"Assessment of potential eutrophication in coastal waters of Gran Canaria: Impact on plankton community under CO<sub>2</sub> depletion"
No full textPopulation growth in coastal tourist areas is leading to enhanced waste production, raising concerns about potential nutrient release increases and the resulting impact on marine ecosystems through eutrophication. Knowledge of the specific impacts of eutrophication on plankton communities in many of these regions is limited, highlighting the need for further research and appropriate environmental management strategies. To help address these gaps, we conducted a 30-day mesocosm study in the coastal waters of Gran Canaria, Canary Islands, a major European tourist destination, and the third most densely populated autonomous community in Spain. With the aim of assessing the effects of nutrient input on biomass, primary production (PP) and recycling processes by phytoplankton, zooplankton, and bacterioplankton, we simulated three nutrient discharge intensities (Low, Medium, and High), with daily additions of 0.1, 1, and 10 μmol L−1 of nitrate, respectively, along with phosphate and silicate. We observed that PP, chlorophyll a (Chl-a), and biomass increased linearly with nutrient input, except in the High treatment, where CO2 depletion (2500 μmol L−1) resulted in reduced PP. Despite limitations in nitrogen (Control, Low, and Medium) or carbon (High) availability across treatments, which led to stabilized or decreased PP rates and dissolved organic carbon (DOC) concentrations, bacterial degradation remained active in all treatments. This microbial activity resulted in an accumulation of recalcitrant chromophoric dissolved organic matter (CDOM), indicating the resilience of carbon recycling processes under varying nutrient conditions. Furthermore, a clear succession was evident in all enriched treatments, transitioning from an oligotrophic condition dominated by pico- and nanophytoplankton to a eutrophic state primarily composed of diatoms. However, under CO2 depletion, diatoms experienced a decline in the High treatment, leading to the proliferation of potentially mixotrophic dinoflagellates. Microzooplankton was less sensitive than mesozooplankton to the decrease in prey availability and high pH caused by CO2 depletion. Interestingly, the Medium treatment showed high efficiency in terms of PP, despite reaching CO2 levels near of 1.0 μmol L−1 by the end of the experiment. PP rates increased from 10 to 100 μg C·L−1·d−1 during the first week and remained stable as diatoms predominated throughout the study period. These findings provide valuable insights into the responses of plankton communities to varying nutrient inputs and emphasize the importance of considering the effects of DIC depletion, along with changes in total alkalinity, in eutrophication scenarios as well as in ocean alkalinity enhancement experiments aimed at reducing carbon dioxide emissions.160,8763,3Q1Q1SCIE11,
Evaluación de la enfermedad hepática relacionada con Fibrosis quística en una cohorte pediátrica.
No full textBackground: Cystic fibrosis (CF) is an autosomal recessive, chronic, potentially lethal genetic disease. CF manifestations are due to mutations in the CF transmembrane receptor transporter (CFTR) gene which codes for a protein (CFTR) that acts as an anion transporter, mainly chlorine, at epithelial cells where it is expressed. Cystic fibrosis related liver disease (CFRLD) includes a spectrum of hepatobiliary manifestations whose diagnosis and follow-up remains a challenge.
Methods: Cross-sectional, descriptive study from 10 Spanish Cystic fibrosis Units. Clinical and biochemical data obtained. Patients categorized into 3 groups according to liver involvement based on ESPGHAN 2017 criteria. Liver stiffness assessed by transient elastography (TE) and findings from abdominal ultrasound recorded. Statistics performed using SPSS v25.0.
Results: We obtained hepatic TE data from 155 pediatric CF patients. Forty-four classified as CFRLD, 38 (86%) had CFRLD without cirrhosis and 6 (14%) had cirrhosis. Fourteen patients without CFRLD (12%) had ultrasound abnormalities. Mean liver elastography value (kPa) was 4.7 (3.5-5.3) in non-CFRLD and 6.09 (4.4-6.7) in CFRLD (p=0.01;Tstudent [T]).
Conclusions: CFRLD is common in children with CF. Transient elastography is a useful method for diagnosis and follow-up, as higher values of TE are found in patients with CFRLD.Antecedentes: La fibrosis quística (FQ) es una enfermedad genética autosómica recesiva, crónica y potencialmente letal. Las manifestaciones de la FQ se deben a mutaciones en el gen del transportador del receptor transmembrana de la FQ (CFTR), que codifica una proteína (CFTR) que actúa como transportador de aniones, principalmente cloro, en las células epiteliales donde se expresa. La enfermedad hepática relacionada con la fibrosis quística (EHFQ) incluye un espectro de manifestaciones hepatobiliares cuyo diagnóstico y seguimiento sigue siendo un reto.
Métodos: Estudio descriptivo transversal de 10 Unidades de fibrosis quística españolas. Obtención de datos clínicos y bioquímicos. Pacientes categorizados en 3 grupos según afectación hepática en base a criterios ESPGHAN 2017. Rigidez hepática evaluada mediante elastografía (TE) y se registran hallazgos de la ecografía abdominal. Estadística realizada mediante SPSS v25.0.
Resultados: Se obtuvieron datos de TE hepática de 155 pacientes pediátricos con FQ. Cuarenta y cuatro se clasificaron como CFRLD, 38 (86%) tenían fenotipo de afectación hepática sin cirrosis y 6 (14%) tenían cirrosis. Catorce pacientes sin CFRLD (12%) presentaban anomalías ecográficas. El valor medio de la elastografía hepática (kPa) fue de 4,7 (3,5-5,3) en los pacientes sin CFRLD y de 6,09 (4,4-6,7) en los pacientes con CFRLD (p=0,01;Tstudent [T]).
Conclusiones: La CFRLD es frecuente en niños con FQ. La elastografía hepática es un método útil para el diagnóstico y seguimiento, ya que se encuentran valores más altos de TE en pacientes con CFRLD.70,2632,2Q3Q3SCIE11,
El ciberacoso sexual en el trabajo: un problema vinculado a las personalidades psicopáticas, maquiavélicas y narcisistas
No full textLos derechos de participación política de la ciudadanía canaria
No full textEntre los principios que inspiran el vigente Estatuto de Autonomía de Canarias (en adelante, EACan), aprobado mediante Ley
Orgánica 1/2018, de 5 de noviembre, su Preámbulo se refiere, en
términos muy generales, a la “consolidación y mejora de la calidad
de nuestro sistema democrático”. Una de las vías tradicionales para
la profundización democrática del Estado constitucional ha sido la
afirmación y extensión progresiva de los derechos de participación
política de la ciudadanía. En ese sentido, el EACan incluye entre los
derechos subjetivos de su Título I una lista relativamente extensa de
derechos de participación política (art. 31) que se proclaman a favor
de los ciudadanos que ostentan la condición de canarios (art. 6).
No obstante, como podremos comprobar en los próximos apartados
del presente capítulo, este listado de derechos políticos, que se re-
produce de modo muy similar en otros Estatutos de Autonomía de
tercera generación (sirvan como ejemplo, el art. 30.1 del Estatuto
de Autonomía de Andalucía y el art. 29 del Estatuto de Autonomía
de Cataluña), viene, en gran medida, a reiterar derechos de participación política que ya habían sido previamente reconocidos por el
ordenamiento jurídico nacional y/o autonómic