University of La Laguna Showcase

Se presenta el artículo original titulado "Adaptación al español y validación de la Escala de Continuidad de Vínculos (ECV) con el ser querido fallecido" , producto de una investigación que tuvo por objetivo adaptar al español la ECV y evaluar propiedades psicométricas de validez y fiabilidad. La información de las personas participantes está protegida bajo los principios de confidencialidad y privacidad de acuerdo con las consideraciones éticas vigentes.

New benchmark instances for the Cross-Dock Door Assignment Problem (CDAP), with instances from 8 origins and destinations and 4 inbound and outbound doors up to 50 origins and destinations and 30 inbound and outbound doors. The instances have been obtained following the criteria of the paper by Nassief et al. (2016). First of all, the number of pallets to be moved from a supplier to a customer is randomly generated by using a uniform distribution U[10, 50] until reaching density values set to 25, 35, 50, and 75%. Each inbound truck sends pallets at least to one outgoing truck and each outgoing truck receives pallets from at least one incoming truck. The distance matrix is generated with numbers from the interval [8, 8 + |I|- 1], meaning that a direct distance between two doors is equal to 8, and then an increment of 1 unit is added for the next indirect door. The number of considered incoming/outgoing trucks is 8, 9, 10, 11, 12, 15, 20, and 50. The number of considered inbound/outbound doors is 4, 5, 6, 7, 10, and 30. The door capacities are equal for each instance and calculated by dividing the total flow coming from all origins by the total number of inbound doors, and then adding a capacity slackness of 5, 10, 15, 20, and 30%. To generate this set of instances, we have considered the combinations of values reported in the paper by Sayed (2020). The instances are referred to as 00×00×00x00 (number of the incoming and outgoing trucks x number of inbound and outbound doors x capacity slackness associated with the inbound/outbound doors - 5, 10, 15, 20, and 30% - x density of the flow matrix - 25, 35, 50, and 75%).

This data set corresponds to the referenced work (https://doi.org/10.1038/s41598-022-12132-5), related to climate projections, for the Canary Islands, of some risk indicators derived from the Canadian Forest Fire Weather Index (FWI). Climate regionalized projections for the Canary Islands were performed, at 3 km spatial resolution, using as boundary conditions some of the results provided by the Coupled Model Intercomparison Project (CMIP5) initiative, and covering the recent past (1980–2009) and future (2070–2099) periods, under two Representative Concentration Pathways, 4.5 and 8.5. A dynamical downscaling technique has been applied, using the Weather and Research and Forecasting (WRF) model driven by the results of three CMIP5 models (GFDL-ESM2M, IPSL-CM5A-MR and MIROC-ESM). Each file, in netCDF format, has a name with the following nomenclature: canary_VAR_PERIOD_FREQ_mean_WRF_driven_by_GCM.nc where: * VAR is the variable (fot30, fwi, lofs) * PERIOD is the simulation period, and also indicates, for future periods, the corresponding emission scenario (1980_2009, 2070_2099_rcp45, 2070_2099_rcp85) *FREQ is the frequency for data: year (mean value for the annual values of the whole period) or monthly (mean value for each month, that is, the average for all the januaries in the period, …) * GCM is the global climate model (GCM) whose results have been used to drive the WRF simulations (GFDL_ESM2M, IPSL_CM5A_MR, MIROC_ESM) The three variables provided are: * fwi: monthly mean of daily values for the Canadian Forest Fire Weather Index (FWI). For this variable, results are also provided for a mid-century period (2030-2059). * fot30: percentage of days with an FWI greater than 30. This value is usually considered as an indicator of severe fire danger situations. * lofs: measures the extension in days of fire weather season. This starts/finishes each year when FWI exceeds/decreases the value 15 for more than two consecutive weeks. The provided values correspond to the average for each period analyzed.

This dataset corresponds to the referenced work (https://doi.org/10.1007/s10113-022-01880-9), related to climate projections, for the Canary Islands, of some tourism indices widely used in the literature (the Tourism Climate Index TCI and the Holiday Climate Index HCI). The HCI has been calculated for two types of tourist activities, resulting in two different indices: The HCI Beach index (HCIB) and the HCI Urban index (HCIU). Regional climate simulations, at a 3 km resolution, were computed using the Weather Research and Forecast (WRF) numerical weather prediction model driven by the results of three CMIP5 global climate models (GFDL-ESM2M, IPSL-CM5A-MR and MIROC-ESM) as boundary conditions. The simulations were performed for three 30-year periods, recent past (1980–2009), mid-century (2030–2059), and end-century (2070–2099). Two different future greenhouse gas emission scenarios (RCP4.5 and RCP8.5) have been used for the projections. Each file, in netCDF format, has a name with the following format: canary_VAR_PERIOD_monthly_sum_WRF_driven_by_GCM.nc where: * VAR is the variable (HCIB60, HCIB80, HCIU60, HCIU80, TCI60, TCI80) * PERIOD is the simulation period, and also indicates, for future periods, the corresponding emission scenario (1980_2009, 2030_2059_rcp45, 2030_2059_rcp85, 2070_2099_rcp45, 2070_2099_rcp85) * GCM is the global climate model (GCM) whose results have been used to drive the WRF simulations (GFDL_ESM2M, IPSL_CM5A_MR, MIROC_ESM) Each file contains 12 timesteps, one for each month (january, february, …). The content of the variables is the mean (over the 30-year period) percentage, for each month, of the days with a tourism index over 60 or 80. Thus, for example, HCIB60 is the percentage of days for which the HCIB index is over 60. For example, a value of 70 for january means that, in average (for the 30 januaries of the selected simulation), 70*31/100 = 21,7 days have an index over 60. For the three used indices values over 60 are considered good to ideal conditions for the corresponding tourist activity, and values over 80 excellent or ideal conditions. Thus, HCIU80 indicates the percentage of days per month in which the conditions for urban tourism are excellent or ideal.

This data set corresponds to the referenced work (https://doi.org/10.1038/s41612-023-00358-7), related to climate projections, for the Canary Islands, of some aridity and drought indices. The meteorological variables used to calculate these indices are also provided, as they may be useful for other studies. Regional climate simulations, at 3 km resolution, were computed using the WRF model driven by three CMIP5 global climate models (GFDL-ESM2M, IPSL-CM5A-MR and MIROC-ESM). The simulations were performed for three 30-year periods, recent past (1980–2009), mid-century (2030–2059), and end-century (2070–2099) and two future greenhouse gas emission scenarios (RCP4.5 and RCP8.5). Each file name has the following nomenclature: canary_VAR_PERIOD_FREQ_mean_WRF_driven_by_GCM.nc where: * VAR is the variable (aridity, evspsblpot, pr, spi3, spi12, spei3, spei12, tasmin, tasmax, sfcWind) * PERIOD is the simulation period, and also indicates, for future periods, the corresponding emission scenario (1980_2009, 2030_2059_rcp45, …) *FREQ is the frequency for data: year (mean value of annual values for the whole period) or monthly (mean value for each month, that is, the average for all the januaries, …) * GCM is the global climate model (GCM) whose results have been used to drive the WRF simulations (GFDL_ESM2M, IPSL_CM5A_MR, MIROC_ESM) The variables provided are: * aridity: Aridity index (UNEP, 1992), defined as P/PET where PET is the mean annual potential evapotranspiration and P is the average annual precipitation for the same period. * evspsblpo: mean (over the period) of the monthly potential evapotranspiration (PET). The Penman–Monteith (PM) method was used to calculate PET. Units are kg m-2 s-1. To convert, for example, the january value to the total mean evapotranspiration for that month, in mm, the original value must be multiplied by 60*60*24*31 = 2678400 (seconds in that month). * pr: precipitation. Units are kg m-2 s-1. Therefore, the unit conversion factor is the same as for evapotranspiration. * spi3: Standardized Precipitation Index computed from precipitation amounts for an accumulation period of 3 months. SPI values below ‒1 indicate rainfall deficits (drier than normal), while SPI values above 1 indicate excess rainfall (wetter than normal). The lower the SPI, the more intense the drought. Values are provided only for future periods, since, being a standardized index, the average over the same period in which the cumulative probability function is calculated (1980-2009) is zero. * spi12: same as spi3 but for an accumulation period of 12 months. * spei3: Standardized Precipitation-Evapotranspiration Index, computed from “precipitation minus PET” amounts for an accumulation period of 3 months. * spei12: same as spei3 but for an accumulation period of 12 months. * tasmin: monthly mean of daily minimum temperature, in K. * tasmax: monthly mean of daily maximum temperature, in K. * sfcWind: monthly mean 10 m wind speed, m s-1

En esta base de datos se presentan los recursos desarrollados en la investigación de la adaptación de la artesanía semi-industrial de las baldosas hidráulicas del taller al aula. Por un lado, se comparten tutoriales para el diseño paramétrico y fabricación digital de trepas, así como al archivo .stl de prueba. Por otro lado, se adjuntan los elementos de bastidor y estantería de baldosas para su reproducción en impresión 3D y corte láser respectivamente. También se disponen los planos del sistema constructivo que se validará en el aula en una fase posterior de la investigación. Todos los elementos han sido diseñados en el software Fusion 360 con licencia educativa. La elaboración de baldosas hidráulicas es una artesanía semi-industrial en declive, que en la investigación se adapta para su uso como medio de capacitación transversal en el aula. Se inicia dando acceso tanto a la realización de estas piezas en entornos sin el equipamiento tradicional del oficio, como al proceso completo de diseño y fabricación de las piezas. Palabras clave: Cultura maker, artesanía, educación, diseño, fabricación digital.

The instances at hand constitute problem instances for the Pickup and Delivery Problem with split loads and transshipments. They have been used for the computational experiments in "Wolfinger, D., & Salazar-González, J. J. (2021). The pickup and delivery problem with split loads and transshipments: A branch-and-cut solution approach. European Journal of Operational Research, 289(2), 470-484."

Dataset artificially generated through the Generative Adversarial Networks of students in postgraduate degrees at the University of La Laguna on levels of use of digital tools and frequency of personal activities with ICT.

Dataset of Computer Aided Drug Design (CADD) operations for article, titled "DRUG DESIGNING OF NOVEL β-TUBULIN BINDING MICROTUBULE DISSASSEMBLY INHIBITORS"

12 urban parks on the island of Tenerife were evaluated, combining the assessment of a trained observer and the perception of users, to analyse and categorise the environmental quality of the parks. The findings conclude that users are good evaluators of public spaces; that the Public Space Characteristics Observation Questionnaire (PSCOQ) tool allows the classification of public spaces and that physical order is capable of predicting the environmental quality and the restorative capacity of spaces, as perceived by users. The PSCOQ observation tool makes it possible to detect the strengths and weaknesses of public spaces so they can be improved and adapted to the needs of users