The University of Las Palmas de Gran Canaria, also known as the ULPGC (Spanish Universidad de Las Palmas de Gran Canaria) is a Spanish university located in Las Palmas de Gran Canaria, the capital city of Gran Canaria island. It is the university with the most students in the Canary Islands. It consists of five campuses: four in Gran Canaria (Tafira, Obelisco, San Cristóbal and Montaña Cardones) and one in the island of Lanzarote, with Tafira being the largest. The University was created in 1989 after many years of petitions from the people of Gran Canaria. The university was incorporated through the University Reorganization Act of 1989. ULPGC was created as the aggregation of the teaching centres of former "Universidad Politécnica de Canarias", focused on engineering (industrial, civil, electronics and computer), and the centres from neighbouring Universidad de La Laguna that were located in Las Palmas province.This University of Las Palmas de Gran Canaria has an important university community of foreign students, being the first university in the Canary Islands and among the first in Spain to receive Erasmus students.The University of Las Palmas de Gran Canaria, in the academic year 2019/2020, has 1,648 teachers and researchers, 143 research staff in projects 109 research staff in training, 40 honorary doctors and 837 members and a total of 20,356 students.
Decarbonizing isolated island energy systems remains particularly challenging due to structural dependence on imported fossil fuels, fragmented infrastructure, and limited availability of dispatchable renewable resources. In parallel, these territories generate substantial quantities of residual lignocellulosic biomass that often remain underutilized despite their potential for carbon-negative valorization.This study evaluates the technical, environmental and techno-economic potential of biochar production from pruning biomass in isolated island systems, using the Canary Islands as a representative case study. Based on a previously quantified regional biomass resource exceeding 650,000 t/year of dry biomass, dominated by Pinus canariensis residues, an integrated assessment framework was developed combining conceptual slow pyrolysis modelling, carbon balance analysis, techno-economic evaluation and sensitivity assessment.Under the base-case scenario, 1 t of dry biomass produced approximately 350 kg of biochar, 300 kg of bio-oil and 350 kg of syngas. Net carbon removal reached approximately 0.66 t CO2eq/t biomass, equivalent to 9885 t CO2eq/year for a modular 15,000 t/year facility and up to 428,350 t CO2eq/year under a theoretical regional deployment scenario. The estimated gross production cost reached approximately €374/t biochar, with CAPEX, carbon credit price and biochar market value identified as the most influential economic variables. The results suggest that biochar deployment could represent a complementary negative-emission pathway for geographically constrained energy systems by integrating biomass valorization, carbon dioxide removal and territorial decarbonization strategies. Beyond the specific case analysed, the proposed framework offers a replicable methodology for evaluating biomass-based carbon removal systems in structurally constrained territories.
A new equation of state based on the lattice fluid framework with an explicit hydrogen-bonding contribution (HB-LF EoS) is developed for the description of pressure-volume-temperature (PVT) properties of deep eutectic solvents (DES). The model extends the Sanchez-Lacombe lattice fluid theory by incorporating a Veytsman-type association term that captures the role of hydrogen bonding in determining the volumetric behavior of these complex fluids. The equation of state was parameterized and validated against a comprehensive database of 1252 experimental high-pressure density data points spanning 7 representative DES systems, including both hydrophilic (choline chloride-based) and hydrophobic (menthol- and camphor-based) formulations, covering temperatures from 283.15 to 413.15 K and pressures up to 100 MPa. The model describes the experimental data with an overall average absolute relative deviation (AARD) of 0.11%, with maximum deviations below 0.25%. The physical parameters obtained-characteristic temperature, pressure, density, chain length, and hydrogen-bond energy and volume-are shown to correlate meaningfully with molecular structure and the number of hydrogen-bonding sites in each DES. The developed HB-LF EoS provides a physically grounded framework for predicting the volumetric properties of DES relevant to process design and industrial applications.
The role of explanation in mathematics teaching is central to promoting student understanding, yet research has been limited by the challenges of analysing large volumes of classroom discourse. This study explores the use of AI to identify, segment, and classify explanatory textual sequences in secondary education mathematics classrooms. Drawing on textual linguistics and typologies of explanation in mathematics education, the study analyses 28 h of classroom recordings from six high school mathematics teachers. A two-step methodology was applied: (1) segmentation of explanatory sequences using Whisper and GPT-4, and (2) classification into interpretative, descriptive, and justificative types. The inter-rater agreement between GPT-4 and the coding team reached 92.4
Steroid hormones (SHs) have a high estrogenic potential, and urban wastewater is one of their main ways into the aquatic environment. Constructed wetlands (CWs) are considered one of the most sustainable alternatives for the treatment of wastewater from small communities. However, the use of gravel and sand implies a significant environmental impact associated with their extraction and transport. A more sustainable alternative is the use of plant residues, as they are abundant, inexpensive, and readily available, and they can improve the efficiency of hormone removal through sorption. Thus, the sorption of 15 SHs was studied on conventional, mineral substrates (gravel, sand, and volcanic ash) and alternative vegetal wastes, i.e., mulches from giant reed, palm tree, balsa wood, and pine needles. These materials were characterized by determining their Point of Zero Charge (pHPZC), ash content, content of leachable polycyclic aromatic hydrocarbons (PAH) and heavy metals, total surface area (BET), and pore characteristics. Results indicated that SH sorption on the mineral substrates was quite low, in most cases less than 10-15%. However, in the mulches it reached between 50 and 95%, except for corticosteroids (11-43%). The pseudo-second-order kinetics provided the best fit in all cases, with R2 values between 0.97 and 0.9999. Experiments with a contact time of 7 days showed that the palm tree was the only substrate that completely removed the three corticosteroids studied (cortisone, prednisone, and prednisolone). Additionally, a significant correlation was observed between removal due to sorption (%) and log octanol-water partition coefficient (log Kow). Freundlich isotherm provided a higher number of best fits than Langmuir. Lastly, to compare sand with palm mulch under more realistic experimental conditions, four lab-scale CWs (two with palm mulch and two with sand, with/without plants) were studied. The sand-based CWs achieved faster SH percentage removals, while after 24 h, SH mass removals were significantly higher in the palm mulch-based CWs.
Aims Vegetation mapping remains a slow and costly process, as traditional approaches rely heavily on expert fieldwork. Therefore, the objective of this study was to develop an efficient and scalable methodology for generating vegetation maps by leveraging computer vision techniques on aerial imagery.Location The study focused on the island of Gran Canaria, an ecologically rich territory with heterogeneous environments where vegetation mapping is essential for environmental conservation, ecosystem monitoring, and biodiversity assessment.Methods Deep semantic segmentation techniques were employed to delineate vegetation communities from high-resolution aerial imagery. A comparative analysis was conducted between widely used segmentation architectures. The methodology incorporated transfer learning with various backbones and evaluated performance across two versions of the dataset: One focused exclusively on vegetation communities and another that additionally included some non-ecological classes such as shadows, roads, and water bodies. Finally, an aggregation of the vegetation communities was performed based on biological similarity.Results The results obtained revealed clear performance differences between models, with Feature Pyramid Network (FPN) consistently achieving the highest Dice and IoU scores across all dataset configurations, reaching approximately 70% Dice and 59% IoU in both aggregated versions of the dataset. The analysis also highlighted the benefits of class aggregation for improving segmentation quality in highly fragmented vegetation types. A final discussion examined these findings and outlined the methodological limitations and practical implications for ecological mapping.Conclusions The findings confirmed that deep learning-based semantic segmentation enables the efficient generation of vegetation maps, even in ecologically complex territories. Although performance remained constrained by data availability and class complexity, the results demonstrated that these models can provide accurate and biologically meaningful representations of plant communities. The proposed framework therefore offers a solid foundation for supporting large-scale ecological monitoring and for guiding future developments toward more detailed, scalable, and data-rich vegetation mapping strategies.
