Fernando Sanchez Lasheras

Dr. Fernando Sanchez Lasheras

Universidad de Oviedo, Oviedo, Asturias, Spain
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Research Focus & Metrics

Academic Career Length

16Years (2011-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2019 in Journal of Computational and Applied Mathematics
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Journal Article
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COCelestino Ordóñez
Celestino Ordóñez
Fernando Sanchez LasherasFernando Sanchez Lasheras
Fernando Sanchez Lasheras
JRJavier Roca-Pardiñas
Javier Roca-Pardiñas
FJFrancisco Javier de Cos Juez
Francisco Javier de Cos Juez

29 October 2014 in Sensors
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Journal Article
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CTConcepción Crespo Turrado
Concepción Crespo Turrado
CMCarmen Meizoso-López
Carmen Meizoso-López
Fernando Sanchez LasherasFernando Sanchez Lasheras
Fernando Sanchez Lasheras
BGBenigno Antonio Rodríguez Gómez
Benigno Antonio Rodríguez Gómez
Jose Luis Calvo-RolleJose Luis Calvo-Rolle
Jose Luis Calvo-Rolle
FJFrancisco Javier De Cos Juez
Francisco Javier De Cos Juez
Global solar broadband irradiance on a planar surface is measured at weather stations by pyranometers. In the case of the present research, solar radiation values from nine meteorological stations of the MeteoGalicia real-time observational network, captured and stored every ten minutes, are considered. In this kind of record, the lack of data and/or the presence of wrong values adversely affects any time series study. Consequently, when this occurs, a data imputation process must be performed in order to replace missing data with estimated values. This paper aims to evaluate the multivariate imputation of ten-minute scale data by means of the chained equations method (MICE). This method allows the network itself to impute the missing or wrong data of a solar radiation sensor, by using either all or just a group of the measurements of the remaining sensors. Very good results have been obtained with the MICE method in comparison with other methods employed in this field such as Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR). The average RMSE value of the predictions for the MICE algorithm was 13.37% while that for the MLR it was 28.19%, and 31.68% for the IDW.

1 September 2019 in Journal of Computational and Applied Mathematics
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PNPaulino José García Nieto
Paulino José García Nieto
EGEsperanza García-Gonzalo
Esperanza García-Gonzalo
Fernando Sanchez LasherasFernando Sanchez Lasheras
Fernando Sanchez Lasheras
JPJ.P. Paredes–Sánchez
J.P. Paredes–Sánchez
PFPedro Riesgo Fernández
Pedro Riesgo Fernández

22 November 2016 in BioMed Research International
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EPEsteban Pérez-Pevida
Esteban Pérez-Pevida
Aritza Brizuela-VelascoAritza Brizuela-Velasco
Aritza Brizuela-Velasco
DCDavid Chávarri-Prado
David Chávarri-Prado
AJAntonio Jiménez-Garrudo
Antonio Jiménez-Garrudo
Fernando Sanchez LasherasFernando Sanchez Lasheras
Fernando Sanchez Lasheras
FMFrancesca Monticelli
Francesca Monticelli
The objective of the present study is to evaluate how the elastic properties of the fabrication material of dental implants influence peri-implant bone load transfer in terms of the magnitude and distribution of stress and deformation. A three-dimensional (3D) finite element analysis was performed; the model used was a section of mandibular bone with a single implant containing a cemented ceramic-metal crown on a titanium abutment. The following three alloys were compared: rigid (Y-TZP), conventional (Ti-6Al-4V), and hyperelastic (Ti-Nb-Zr). A 150-N static load was tested on the central fossa at 6° relative to the axial axis of the implant. The results showed no differences in the distribution of stress and deformation of the bone for any of the three types of alloys studied, mainly being concentrated at the peri-implant cortical layer. However, there were differences found in the magnitude of the stress transferred to the supporting bone, with the most rigid alloy (Y-TZP) transferring the least stress and deformation to cortical bone. We conclude that there is an effect of the fabrication material of dental implants on the magnitude of the stress and deformation transferred to peri-implant bone.

1 June 2012 in Expert Systems with Applications
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Fernando Sanchez LasherasFernando Sanchez Lasheras
Fernando Sanchez Lasheras
JAJavier De Andrés
Javier De Andrés
PLPedro Lorca
Pedro Lorca
FJFrancisco Javier De Cos Juez
Francisco Javier De Cos Juez

Hélder OliveiraHélder Oliveira
Hélder Oliveira
Aritza Brizuela-VelascoAritza Brizuela-Velasco
Aritza Brizuela-Velasco
José Vicente Ríos-SantosJosé Vicente Ríos-Santos
José Vicente Ríos-Santos
Fernando Sanchez LasherasFernando Sanchez Lasheras
Fernando Sanchez Lasheras
Bernardo Ferreira de LemosBernardo Ferreira de Lemos
Bernardo Ferreira de Lemos
Mariano Herrero-ClimentMariano Herrero-Climent
Mariano Herrero-Climent
Implant design evolved alongside the development of implant therapy. The purpose of this finite element analysis (FEA) study was to analyze the influence of different implant designs on the stress and strain distribution to the implants and surrounding bone. Three implant designs with the same length and diameter were used. The three-dimensional geometry of the bone was simulated with a cortical bone of three different thicknesses and two medullar bone densities: low density (150 Hounsfield units) and high density (850 Hounsfield units). A 30° oblique load of 150 N was applied to the implant restoration. Displacement and stress (von Mises) results were obtained for bone and dental implants. The strain and stress distributions to the bone were higher for the tissue-level implant for all types of bone. The maximum principal strain and stress decreased with an increase in cortical bone thickness for both cancellous bone densities. The distribution of the load was concentrated at the coronal portion of the bone and implants. All implants showed a good distribution of forces for non-axial loads, with higher forces concentrated at the crestal region of the bone–implant interface. Decrease in medullar bone density negatively affects the strain and stress produced by the implants.