Resultados de investigación

This research was carried out with the financial assistance of the European Union under ENI CBC Mediterranean Sea Basin assistance of the European Union, through the project: Employing circular economy approach for OFMSW management within the Mediterranean countries. CEOMED (Ref.: A_B.4.2_0058). The contents of this document are the sole responsibility of CEBAS-CSIC and can under no circumstances be regarded as reflecting the position of the European Union or Programme management structures., Peer reviewed

All-day passive daytime radiative coolers offer a promising solution for energy-free cooling of buildings and devices. This Dataset is referring to this study, which investigates the use of various cellulose-derivative networks to achieve optimal and stable cooling performance. The results showed that the mixed cellulose ester network has a maximum solar reflectance of 97%, while cellulose acetate network has a maximum infrared emissivity of 96% in the atmospheric transparency window band, which is a near-perfect infrared emitter; the nitrocellulose network shows the highest cooling temperature, with a significant reduction of 14 °C from the ambient temperature and a power of 124 W·m−2 during the daytime and at night of 7.7 °C and 72.8 W·m−2. This study also analyzes the dampness's effect on the cooling performance of cellulose-derivative networks. The cooling performance of the nitrocellulose network drops ≈ 3 °C (from 14 to 11.3 °C) when the relative humidity of the day exceeds ≈ 30% is observed. These findings indicate that the capacity of a material to absorb water from the surrounding air significantly influences its performance as a passive cooler, primarily due to changes in its optical properties. This is an important insight, as it highlights the need to consider environmental factors like relative humidity and sample hydrophobicity for PDRC systems., Project 101087974 — COOLed. European Research Council (ERC), ADCP CV220524A_ATR_001 CV220524A_CCP CV220524A_SEM_001 CV220524A_SEM_002 CV220524A_SEM_003 CV220524B_ATR_001 CV220524B_CCP CV220524B_SEM_001 CV220524B_SEM_002 CV220524B_SEM_003 CV220524C_ATR_001 CV220524C_CCP CV220524C_SEM_001 CV220524C_SEM_002 CV220524C_SEM_003 IRE wet IRE RCT VISR wet VISR, Peer reviewed

[Description of methods used for collection/generation of data] [EN] Seasonal composites of Landsat satellite images (5, 7 and 8) between 1984 and the present. [ES] Compuestos estacionales de imágenes del satélite Landsat (5, 7 y 8) entre 1984 y el presente., [EN] CDC-VegChangeS is a vegetation change dataset based on the CDC algorithm, from 1984 to the present and for the entire Spanish terrestrial territory., [ES] CDC-VegChangeS es un conjunto de datos de cambio de vegetación basado en el algoritmo CDC, desde 1984 hasta la actualidad y para todo el territorio terrestre español., PTI ECOBIODIV., Peer reviewed

[Description of methods used for collection/generation of data] [EN] NDVI & EVI Seasonal composites of Landsat satellite images (5, 7 and 8) between 1984 and the present. [ES] Compuestos estacionales con NDVI y EVI de imágenes del satélite Landsat (5, 7 y 8) entre 1984 y el presente., [EN] LANDAST-VegTrends is a Landsat-based Vegetation Trends dataset from 1984 to the present and for the entire Spanish terrestrial territory., [ES] LANDAST-VegTrends es un conjunto de datos de Tendencias temporales de la Vegetación basadas en Landsat desde 1984 hasta la actualidad y para todo el territorio terrestre español., PTI ECOBIODIV., Peer reviewed

[Description of methods used for collection/generation of data] [EN] NDVI & EVI Seasonal composites of Landsat satellite images (5, 7 and 8) between 1984 and the present. [ES] Compuestos estacionales con NDVI y EVI de imágenes del satélite Landsat (5, 7 y 8) entre 1984 y el presente., [EN] LTR-VegChangeS is a vegetation change dataset based on the LandTrendr algorithm, from 1984 to the present and for the entire Spanish terrestrial territory., [ES] LTR-VegChangeS es un conjunto de datos de cambio de vegetación basado en el algoritmo LandTrendr, desde 1984 hasta la actualidad y para todo el territorio terrestre español., PTI ECOBIODIV., Peer reviewed

Top: Injected current into the cell soma consisting of increasingly larger current steps interleaved by resting periods. The red line represents the identified rheobase. Middle: Cell model membrane voltage as a response to the injected current. The red line represents the voltage threshold in the model. Bottom: After-spike currents of the model. Sharp vertical lines indicate the presence of a spike., Peer reviewed

Peer reviewed

(A) Left: Neuron’s membrane voltage response (top), input current response (middle), and firing rate (bottom) for a sample dVf neuron. The mean input current during visual flow (blue horizontal line) and the classification thresholds (black dots) are shown. Right: Responses of the different PSC sources for a sample dVf neuron, including total input current (red), recurrent current (olive), and LGN current (blue). Vertical dashed lines mark the visual flow period. (B) As in (A), but for a sample unclassified neuron. (C) As in (A), but for a sample hVf neuron., Peer reviewed

Peer reviewed

Peer reviewed