University of Las Palmas de Gran Canaria
accedaCRIS (Universidad de Las Palmas de Gran Canaria)Not a member yet
103636 research outputs found
Sort by
The brown alga Rugulopteryx okamurae: insights into epifaunal diversity across marginal populations in Gran Canaria
No full textBiological invasions pose significant threats to biodiversity, primarily through the depletion of natural resources and the disruption of local ecosystems. Our study focuses on Rugulopteryx okamurae (E. Y. Dawson) I·K. Hwang, W.J. Lee, and H.S. Kim, an invasive alga proliferating in the Macaronesia and Mediterranean regions. This research examines the epifaunal assemblages associated with R. okamurae across eastern and northeastern zones in Gran Canaria, Canary Islands. A total of 11,398 individuals from 63 species within five taxonomic groups were collected, with Arthropoda dominating the samples (88.68 % of the overall abundance), particularly the amphipods Apohyale perieri and A. stebbingi. Significant differences in species richness and individual abundance were observed between populations from San Cristóbal, Jinámar and Ojos de Garza. The central populations exhibited the highest species richness, while the northern populations showed the lowest. Multivariate analyses revealed distinct community compositions between the edge and central populations, underscoring the invasive alga's capacity to support diverse and abundant epifaunal assemblages. Moreover, R. okamurae demonstrated remarkable dominance in the study area, achieving total coverage of up to 100 % at certain sampling sites. This extensive coverage highlights the alga's ability to sustain a rich and varied epifaunal community, characterized by both species abundance and high biodiversity. The findings emphasize the need for targeted conservation and management strategies to mitigate the spread of R. okamurae and protect local biodiversity from further disruption.110,5452,1Q2Q2SCIE11,
Estimating the western North Atlantic Subtropical Gyre zonal currents in 2021 through single- and three-box inverse models
No full textThe western North Atlantic Subtropical Gyre comprises the warm Gulf Stream (GS) and the cold Deep Western Boundary Current (DWBC), which are the main currents of the Atlantic Meridional Overturning Circulation (AMOC). Hydrographic sections conducted at 66°W (A22) and 52°W (A20) in the western North Atlantic Subtropical Gyre (NASG) have sampled these currents in 1997, 2003, 2012, and 2021. Both single- and three-box inverse models are used to compute mass, heat, and freshwater transport to the hydrographic data of 2021 with no significant differences between methodologies. This study reveals a substantial change in the GS mass transport at both 52°W and 66°W in spring 2021 compared to spring 2012 and summer 1997, but no changes compared to fall 2003. Conversely, the DWBC shows no significant modification between spring 2021 and previous cruises. Moreover, the North Brazil Current mass transport, which is sampled by the A20 section, presents the downstream weakening showcased previously. Heat and freshwater fluxes are not significantly different from zero, therefore the water volumes enclosed by the A20 and A22 sections do not reflect a net air-sea flux in 2021. Although the GS exhibits interdecadal variability, the lack of other changes over time in this area suggests a general stability in the environmental and forcing conditions. This study highlights the consistent mass transports of the main AMOC currents in the western NASG, particularly the GS and DWBC, emphasizing their stability and importance in broader climate and oceanographic processes.161,2983,8Q1Q1SCIE11,
Optimization of Energy Consumption in Voice Assistants Through AI-Enabled Cache Implementation: Development and Evaluation of a Metric
No full textntelligent systems developed under the Internet of Things (IoT) paradigm offer solutions for various social and productive scenarios. Voice assistants (VAs), as part of IoT-based systems, facilitate task execution in a simple and automated manner, from entertainment to critical activities. Lithium batteries often power these devices. However, their energy consumption can be high due to the need to remain in continuous listening mode and the time it takes to search for and deliver responses from the Internet. This work proposes the implementation of a VA through Artificial Intelligence (AI) training and using cache memory to minimize response time and reduce energy consumption. First, the difference in energy consumption between VAs in active and passive states is experimentally verified. Subsequently, a communication architecture and a model representing the behavior of VAs are presented, from which a metric is developed to evaluate the energy consumption of these devices. The cache-enabled prototype shows a reduction in response time and energy expenditure (comparing the results of cloud-based VA and cache-based VA), several times lower according to the developed metric, demonstrating the effectiveness of the proposed system. This development could be a viable solution for areas with limited power sources, low coverage, and mobility situations that affect internet connectivity
Real world efficacy and safety of the advanced hybrid closed-loop system MiniMed 780G (SmartGuard) in children under 7 years of age
No full textObjective To evaluate the safety and efficacy of the Medtronic 780G SmartGuard (TM) AID system in children under 7 years of age with type 1 diabetes (T1D). Methods Retrospective analysis of data from children living with T1D under 7 years of age using the MiniMed 780G (TM) across three pediatric endocrinology units in the Canary Islands. Metabolic control parameters were analyzed from 14 days of pretreatment to 12 months of follow-up. Results The study included 61 children under 7 years of age, 35 in Group 1 and 26 in Group 2. In Group 1, there was a significant increase in time in range (TIR) (13%, p = 0,000), along with a significant decrease in time above range (TAR) (7% for TAR1 and 3% for TAR2; p = 0,000). These improvements persisted for up to 1 year of follow-up. In Group 2, there was a significant increase in the TIR (7%; p = 0,000) and a significant decrease in the TAR (7%; p = 0,000 for TAR1 and 6.5%; p = 0,001 for TAR2). These improvements persisted for up to 6.5 months of follow-up. No significant changes were observed in the time below range (TBR) or variation coefficient (CV) in either group. No events of severe hypoglycemia or diabetic ketoacidosis occurred. Efficacy and safety were maintained in children with a TDD <8 units/day. Conclusion The use of the Medtronic 780G (TM) SmartGuard (TM) system in children under 7 years of age with T1D is effective and safe, with benefits persisting for up to 6-12 months. The safety profile is maintained in children receiving a TDD <8 units/day.70,9093,9Q1Q2SCIE10,
Ensayos sobre economía política y aplicada: la corrupción política en España
No full textPrograma de Doctorado en Turismo, Economía y Gestión por la Universidad de Las Palmas de Gran Canari
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 textThe model is stored in the file "model.mat" and can be used by the included "class_model" class.
Model Contents
The "model.mat" file contains a Matlab variable named "model", which has the following properties:
dlnet: A collection of 20 artificial neural networks (ANNs) generated in the study.
name_input: Names of the input variables for the model (described in the scientific article).
name_output: Name of the output variable of the model (fundamental frequency).
mean_input: Mean values of each input variable, obtained from the training dataset. Used for normalization.
std_input: Standard deviation of each input variable, obtained from the training dataset. Used for normalization.
mean_output: Mean value of the output variable, obtained from the training dataset. Used for normalization.
std_output: Standard deviation of the output variable, obtained from the training dataset. Used for normalization.
How to Use the Model
Prerequisites
To use the model, you must have the Deep Learning Toolbox installed in Matlab.
Evaluation Method
The model can be evaluated using the "eval" method included in the "class_model". This method provides the following options:
output: Specifies how the individual ANN predictions should be aggregated. The options are:
"mean": Returns the mean of the predictions from the individual ANNs. (Default)
"std": Returns the standard deviation of the predictions from the individual ANNs.
"both": Returns both the mean and the standard deviation of the predictions from the individual ANNs, using the third dimension of the output matrix.
"all": Returns the predictions from all individual ANNs, using the third dimension of the output matrix.
IDnet: Specifies which ANNs to use for the evaluation. The options are:
"all": Uses all ANNs embedded in the model. (Default)
any number: Uses only the ANN at the specified position.
any vector: Uses only the ANNs at the positions specified in the vector.
Input Data Format
Prepare your input as a matrix where:
Each row represents a sample.
The matrix must have 22 columns, corresponding to the 22 input variables, arranged in the same order as specified in name_input.
Example
Below is an example of how to load the model and use the eval method:
% Load the model
load('model.mat');
% Prepare your input data (using case example)
load("case_example.mat"); % Load case example
inputData=example{:,:}; % Extract matrix of data
% Evaluate the model (default)
output = model.eval(inputData);
% Evaluate the model (specific options)
output = model.eval(inputData,'output','both','IDnet',1:10)
José María Cardesín Díaz (dir.), Revuelta popular y violencia colectiva en la Guerra de la Independencia, Centro de Estudios Políticos y Constitucionales, Madrid, 2024, 346 págs., ISBN: 978-84-259-2033-2
No full text57156750,144Q2ESCI10,0ERIH PLU
