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>
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<div><strong>Model Contents</strong></div>
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<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>
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<div><strong>How to Use the Model</strong></div>
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<div><em>Prerequisites</em></div>
<div>To use the model, you must have the Deep Learning Toolbox installed in Matlab.</div>
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<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>
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<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>
Host Genetic Factors in Q Fever Susceptibility
No full textSeveral indirect findings suggest that host-related factors influence susceptibility to Coxiella burnetii infection. We decided to explore the influence of genetic factors related to both innate and adaptive immunity in acute Q fever susceptibility. TLR2 (Arg753Gln) and TLR4 (Asp299Gly, Thr399Ile) polymorphisms, along with HLA-DRB1 alleles, were analyzed for 38 patients with acute Q fever, 38 matched controls, and 121 blood donors. No significant associations were found for TLR polymorphisms. However, HLA-DRB1*04 was more frequent in patients. HLA-DRB1 variants may play a role in Q fever susceptibility, supporting the need for further investigation into their potential implications for vaccination and risk assessment.60,8433,3Q2Q2SCIE10,
Existence Of A Unique Mild Solution To A Fractional Thermostat Model Via A Rus'S Fixed Point Theorem
No full textThe purpose of this paper is to study the existence of a unique solution of a nonlinear fractional boundary value problem which can be considered as the fractional version of the thermostat model. Our analysis is based on a Rus's fixed point theorem involving two metrics.552539140,3990,9Q3Q2SCIE10,
Molecular response with asciminib 20 mg QD in a patient intolerant to multiple TKIs
No full textThis case highlights a young CML patient who achieved a major molecular response (MMR) after just four months with a subtherapeutic dose of asciminib. The patient could not tolerate full-dose asciminib, or the full dose of two previous tyrosine kinase inhibitors (TKI) due to myelotoxicity, but blood counts recovered rapidly upon TKI suspension and reduced dosing enabled sustained treatment. This is the second reported case of the use of 20 mg QD asciminib, four times below the recommended dose, and the first to demonstrate efficacy at this dose. The study emphasizes asciminib's potential for overcoming treatment challenges associated with multi-resistant/intolerant CML patients.3009300730,9123,0Q1Q2SCIE11,
Exploring textural features for handwriting-based personality assessment: an experimental study
No full textPersonality trait identification through handwriting analysis presents a challenging area within automated document recognition based on Artificial Intelligence solutions. Recent studies relied on solutions automating graphonomic processes, while others address only a few local features, conversely few studies offer solutions based on textural features. In this work, we propose an automated approach for personality trait identification that treats a scripter’s handwriting as a texture by leveraging a diverse set of textural features, including LCP, oBIFCs, LPQ, LBP, among others. The approach is validated on FFM-annotated datasets using cost-effective classifiers such as XGBoost, Random Forest, Gradient Boost, SVM, and Naive Bayes. Our empirical study enabled the judicious selection of the most suitable textural features for each personality trait. Subsequently, we constructed a comprehensive personality trait identification solution by combining multiple textural features and integrating top-performing classifiers. The experimental results demonstrated the validity of our hypothesis, achieving performance improvements of more than 10% on both datasets.80,5582,0Q2Q3SCIE10,
Impact of glucagon-like peptide-1 receptor agonists on hypertension management: a narrative review
No full textPurpose of review The increasing prevalence of hypertension, alongside the growing use of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) for conditions beyond type 2 diabetes, underscores the need for understanding if there is a role for these medications in blood pressure management. This review addresses the timely opportunity to assess how GLP-1 RAs could influence blood pressure control, potentially broadening therapeutic strategies for cardiovascular risk management. Recent findings Emerging literature indicates that GLP-1 RAs influence blood pressure through various mechanisms, such as sympathetic nervous modulation, vasodilation, and diuretic effects. Clinical trials demonstrate modest yet statistically significant reductions in systolic blood pressure (SBP), with less consistent effects on diastolic blood pressure (DBP). The advent of dual GLP-1 and glucose-dependent insulinotropic polypeptide (GIP) receptor agonists presents enhanced possibilities for managing hypertension. Summary The implications of these findings suggest that GLP-1 RAs have potential as adjunctive therapies for hypertension, especially in patients already receiving these agents for other cardiometabolic conditions. The blood pressure-lowering effects, often independent of weight loss or glucose control, warrant further investigation to determine their precise role within hypertension treatment algorithms and encourage integration into clinical practice.0,642,0Q2Q3SCIE11,
Caracterísiticas Epidemiológicas De Los Pacientes Con Dermatitis De Contacto Localizada En La Cara En El Área Sur De Gran Canaria.
No full textInfección Del Líquido De Diálisis Peritoneal, Análisis Descriptivo E Impacto En La Evolución
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