University of Las Palmas de Gran Canaria
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Evaluation of a robust system for hyperspectral data compression under neutron and proton effects
No full textThe use of hyperspectral sensors in space missions is steadily increasing. These sensors generate large volumes of data in the form of hyperspectral cubes (images captured in hundreds of different wavelengths) which require the adoption of data compression solutions onboard. In this work we evaluate error mitigation for CCSDS-123 hyperspectral data lossless compression IP. Realizing that a data compressor has to be used with some data interfaces and possibly a processor, the evaluation is performed for entire systems built around the SHyLoC CCSDS-123 IP core. To this purpose, we designed hardened systems that include redundant IP cores, input and output data stream interfaces, and processors. The hardened systems were implemented in a commercial SoC device and tested with atmospheric neutrons and protons. Experimental results are presented and discussed for two architectures using dual and triple IP core redundancy, respectively.0,5371,9Q2Q
Efectividad Y Seguridad De Los Anticoagulantes En La Profilaxis Del Tromboembolismo Venoso: Revisión Sistemática Y Metanálisis En Red
No full textFrom the object to the subject of law: critical analysis of the legal personality of the Mar Menor
No full textLa investigación examina el caso del Mar Menor, primer ecosistema europeo en recibir personalidad jurídica mediante la Ley 19/2022. Impulsada por una Iniciativa Legislativa Popular, reconoce al ecosistema como sujeto de derechos, incluyendo su derecho a existir y evolucionar naturalmente. La ley establece una estructura tripartita de gobernanza: Comité de Representantes, Comisión de Seguimiento y Comité Científico, para defender los intereses de la laguna. Sin embargo, el estudio revela desafíos significativos en su implementación, desde cuestiones de constitucionalidad hasta la imprecisión en la definición de derechos, planteando dudas sobre si esta nueva figura jurídica puede proteger efectivamente uno de los ecosistemas más importantes del Mediterráneo.The research examines the revolutionary case of the Mar Menor, the first European ecosystem to receive legal personality through Law 19/2022. This legal innovation, driven by a Popular Legislative Initiative, recognizes the ecosystem as a subject of rights, including its right to exist and evolve naturally. The law establishes a tripartite governance structure: Committee of Representatives, Monitoring Commission and Scientific Committee, to defend the interests of the lagoon. However, the study reveals significant challenges in its implementation, from questions of constitutionality to imprecision in the definition of rights, raising doubts about whether this new legal figure can effectively protect one of the most important lagoon ecosystems in the Mediterranean.0,0Q24,
Ángeles Mateo del Pino (ed.), "Tecnologías del yo: Mujer, sujeto y subjetividad". Berlín: Peter Lang, 2022. 262 páginas. ISBN: 978-3-631-83706-1
No full text35735530,173Q1ESCIQ232,070,0Q19,9ERIH PLU
David Viñas Piquer (ed.), "La Teoría en la ficción literaria española del siglo XXI". Madrid: Iberoamericana Vervuert, 2023. 197 páginas. ISBN: 978-84-9192-382-4
No full text36235940,173Q1ESCIQ232,070,0Q19,9ERIH PLU
Gioconda Belli, "Parir el alba". Sel. de Gioconda Belli y María José Bruña Bragado. Introd. y ed. de María José Bruña Bragado. Salamanca: Ediciones Universidad de Salamanca, 2023. 336 páginas. ISBN: 978-84-1311-849-9.
No full text36536330,173Q1ESCIQ232,070,0Q19,9ERIH PLU
ANN model presented in "ANN-based surrogate model for the structural evaluation of jacket support structures for offshore wind turbines"
No full text<p>This repository contains the best of the models developed in the scientific article "ANN-based surrogate model for the structural evaluation of jacket support structures for offshore wind turbines" by Román Quevedo-Reina, Guillermo M. Álamo, Juan J. Aznárez. (https://doi.org/10.1016/j.oceaneng.2024.119984)</p>
<p>This is an enssemble model of 20 artificial neural networks with 74 neurons in the input layer, 5 hidden layers with 250 neurons per hidden layer, and 26 neuron in the output layer.</p><div>The model is stored in the file "model.mat" and can be used by the included "class_model_classification" 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: Names of the partial checks predicted by the model (indicated in the scientific article and detailed in http://dx.doi.org/10.1016/j.oceaneng.2024.117802).</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>
</ul>
<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_classification". This method provides the following options:</div>
<div> </div>
<ul>
<li>output: Specifies how the global feasibility is computed from the partial checks.
<ul>
<li>"global": Global feasibility is computed as the conjunction of independent events. (Default)</li>
<li>"global_min": Global feasibility is equal to the minimum of the partial checks.</li>
<li>"global_geomean": Global feasibility is the geometric mean of the partial checks.</li>
<li>"partial": Returns the partial checks predictions.</li>
</ul>
</li>
<li>ensemble: Specifies how the individual ANN predictions should be aggregated (more details in the scientific article). The options are:
<ul>
<li>"mean": Mean of the predictions from the individual ANNs.</li>
<li>"voting": Proportion of individual ANNs whose predictions are greater than 0.5. (Default)</li>
<li>"bayes": The combined probability of feasibility is updated by the evidences given by individual ANNs, following the Bayesian inference.</li>
<li>"none": Returns the prediction of individual ANNs.</li>
</ul>
</li>
<li>ensembledvar: Specifies how the individual ANN predictions are agregated on partial checks predictions or on global feasibility prediction:
<ul>
<li>"partial": Ensembled is made on partial checks predictions. (Default)</li>
<li>"global": Ensembled is made on global feasibility predictions.</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 74 columns, corresponding to the 74 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>
<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','partial','IDnet',1:10);</div>
Dissemination of antibiotic-resistant bacteria associated with microplastics collected from Monastir and Mahdia coasts (Tunisia)
No full textThe exponential use of plastics and their recalcitrant nature leads to their significant accumulation in the environment. The occurrence of plastic wastes is considered as a serious environmental problem. Additionally, plastic wastes can break down into smaller pieces called microplastics (MPs), leading to further interactions with the environment and living organisms. In this study, sixty-six strains were isolated from microplastic particles collected on different coastal areas of Monastir and Mahdia (Tunisia). The different bacterial isolates were identified according to some biochemical tests such as catalase, oxidase, and were subjected to molecular characterization. Amplification of the internal transcribed spacer (ITS) revealed the presence of 31 ITS haplotypes. The partial sequencing of the 16S ribosomal DNA of representative strains was analyzed. The majority of bacterial isolates (84.31 %) belonged to Gamma-proteobacteria (84.78 %), while the remaining isolates were affiliated to Firmicutes (15.21 %). The microplastic-associated bacterial isolates belonged to 10 genera, namely Acinetobacter, Pseudomonas, Bacillus, Staphylococcus, Shewanella, Aeromonas, Vibrio, Stutzerimonas, Exiguobacterium, Enterobacter. Among the well-represented Acinetobacter genus, the most common species identified was Acinetobacter johnsonii. Susceptibility patterns of these strains were studied against 21 antibiotics commonly used in Tunisia. A high level of antibiotic resistance was observed for Penicillin G (97.82 %) and Temocillin (86.95 %). S26 strain presented the highest multidrug resistance with a multiple antibiotic resistance (MAR) index of 0.71.110,7533,3Q2Q2SCIE11,
Performance evaluation of nanofiltration membranes for SWRO brine valorisation: Insights from a pilot plant under real conditions
No full textThe success of a desalination brine valorisation process heavily depends on the effectiveness of the pre-treatment stage, which purifies the brine by reducing the concentration of ions that could form insoluble compounds and hinder system performance. This study focused on testing an innovative nanofiltration (NF) pilot plant operating with 144 m3/d of actual brine from an upstream seawater reverse osmosis (SWRO) pilot plant located in the Canary Islands (North Atlantic Ocean). The NF membranes tested (NE8040-40) played a critical role, demonstrating exceptional rejection rates for divalent ions, therefore also increasing their concentration in the divalent-rich stream, setting the stage for future valorisation of the related by-products. Simultaneously, these NF membranes allowed the achievement of a high-purity monovalent-rich stream with Na+ and Cl– concentrations reaching approximately 98 %, potentially enabling the generation of valuable commercial products in subsequent stages. The NF system was thoroughly assessed under different conditions of flow, pressure, and recovery rates, incorporating extensive process monitoring with comprehensive sensor data.141141,5338,6Q1Q1SCIE11,
