Experiential restaurants: Playing while eating

The goal of this project was analysing if a restaurant based on experiential dinning would succeed in the city of Zaragoza. Having this objective in mind it was necessary to develop a market research. There were two basic ideas proposed, one for the long-run experience (Dynamism) and the other one for the short-run (Playing while eating). In order to cover the need of opinions and direct information, a focus group was made. The results form this technique made us split the research in two parts, one for each topic. At the end, we decided to follow the Playing while eating topic as there were more information available. After that decision, in order to go in depth with the topic we developed another focus group fully oriented to the playing topic. From that focus group we gathered a lot of useful information that had to be checked and compared using a representative sample, which made us develop a survey. Thanks to this survey we could analyse the data in a mathematical way (using averages and percentages) that allowed us to develop the final conclusions of the research and guided us to think about some recommendations for a future. The general conclusion of the research is that depending of the initial investment and the will to organize (as it would be complex) this project would succeed or not (following some of the recommendations of the project).<br /

Automatic supervision of gestures to guide novice surgeons during training

The final publication is available at Springer via http://dx.doi.org/10.1007/s00464-013-3285-9Background Virtual surgery simulators enable surgeons to learn by themselves, shortening their learning curves. Virtual simulators offer an objective evaluation of the surgeon’s skills at the end of each training session. The considered evaluation parameters are based on the analysis of the surgeon’s gestures performed throughout the training session. Currently, this information is usually known by surgeons only at the end of the training session, but very limited during the training performance. In this paper, we present a novel method for automatic and interactive evaluation of the surgeon’s skills that is able to supervise inexperienced surgeons during their training session with surgical simulators. Methods The method is based on the assumption that the sequence of gestures carried out by an expert surgeon in the simulator can be translated into a sequence (a character string) that should be reproduced by a novice surgeon during a training session. In this work, a string-matching algorithm has been modified to calculate the alignment and distance between the sequences of both expert and novice during the training performance. Results The results have shown that it is possible to distinguish between different skill levels at all times during the surgical training session. Conclusions The main contribution of this paper is a method where the difference between an expert’s sequence of gestures and a novice’s ongoing sequence is used to guide inexperienced surgeons. This is possible by indicating to novices the gesture corrections to be applied during surgical training as continuous expert supervision would do.Monserrat, C.; Lucas, A.; Hernández Orallo, J.; Rupérez Moreno, MJ. (2014). Automatic supervision of gestures to guide novice surgeons during training. Surgical Endoscopy. 28(4):1360-1370. doi:10.1007/s00464-013-3285-9S13601370284Ericsson KA (ed) (2009) Development of professional expertise: toward measurement of expert performance and design of optimal learning environments. Cambridge University Press, New YorkMcGaghie WC (2008) Research opportunities in simulation-based medical education using deliberate practice. Acad Emerg Med 15:995–1001Ericsson KA (2008) Deliberate practice and acquisition of expert performance: a general overview. Acad Emerg Med 15:988–994Issenberg SB, McGaghie WC, Petrusa ER et al (2005) Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach 27:10–28Porte MC, Xeoulis G, Reznick RK, Dubrowski A (2007) Verbal feedback from an expert is more effective than self-accessed feedback about motion efficiency in learning new surgical skills. Am J Surg 193:105–110. doi: 10.1016/j.amjsurg.2006.03.016Hall PAV, Dowling GR (1980) Approximate string matching. ACM computing surveys (CSUR) 18(2):381–402. doi: 10.1145/356827.356830Stylopoulos N, Cotin S, Maithel SK et al (2004) Computer-enhanced laparoscopic training system (CELTS): bridging the gap. Surg Endosc 18(5):782–789. doi: 10.3233/978-1-60750-938-7-336Solis J, Oshima N, Ishii H, Matsuoka N et al (2009) Quantitative assessment of the surgical training methods with the suture/ligature training system WKS-2RII. In: IEEE international conference on robotics and automation, 2009 (ICRA ‘09), Kobe, pp 4219–4224. doi: 10.1109/ROBOT.2009.5152314Lin Z et al (2010) Objective evaluation of laparoscopic surgical skills using Waseda bioinstrumentation system WB-3. In: IEEE international conference on robotics and biomimetics (ROBIO), Tianjin, pp 247–252. doi: 10.1109/ROBIO.2010.5723335Chmarra MK, Klein S, Winter JCF, Jansen FW, Dankelman J (2010) Objective classification of residents based on their psychomotor laparoscopic skills. Surg Endosc 24(5):1031–1039. doi: 10.1007/s00464-009-0721-yLin HC, Shafran I, Yuh D, Hager GD (2006) Towards automatic skill evaluation: detection and segmentation of robot-assisted surgical motions. Comput Aided Surg 11(5):220–230. doi: 10.3109/10929080600989189Rosen J, Brown JD, Chang L, Sinanan MN, Hannaford B (2006) Generalized approach for modeling minimally invasive surgery as a stochastic process using a discrete Markov model. IEEE Trans Biomed Eng 53(3):399–413. doi: 10.1109/TBME.2005.869771Lahanas V, Loukas C, Nikiteas N, Dimitroulis D, Georgiou E (2011) Psychomotor skills assessment in laparoscopic surgery using augmented reality scenarios. In: 17th international conference on digital signal processing (DSP), Corfu. doi: 10.1109/ICDSP.2011.6004893Leong JJ et al (2006) HMM assessment of quality of movement trajectory in laparoscopic surgery. In: International conference on medical image computing and computer-assisted intervention (MICCAI’06), pp 752–759. doi: 10.3109/10929080701730979Megali G, Sinigaglia S, Tonet O, Dario P (2006) Modelling and evaluation of surgical performance using Hidden Markov models. IEEE Trans Biomed Eng 53(10):1911–1919. doi: 10.1109/TBME.2006.881784Huang J, Payandeh S, Doris P, Hajshirmohammadi I (2005) Fuzzy classification: towards evaluating performance on a surgical simulator. Stud Health Technol Inform 111:194–200Hajshirmohammadi I, Payandeh S (2007) Fuzzy set theory for performance evaluation in a surgical simulator. Presence 16(6):603–622. doi: 10.1162/pres.16.6.603Ukkonen E (1985) Algorithms for approximate string matching. Inf Control 64(1–3):100–118. doi: 10.1016/S0019-9958(85)80046-2Navarro G (2001) A guided tour to approximate string matching. ACM Comput Surv 33(1):31–88. doi: 10.1145/375360.375365Damerau FJ (1964) A technique for computer detection and correction of spelling errors. Commun ACM 7(3):171–176. doi: 10.1145/363958.363994Bergroth L, Hakonen H, Raita T (2000) A survey of longest common subsequence algorithms. In: Proceedings of the seventh international symposium on string processing information retrieval (SPIRE’00), A Coruña, p 39. doi: 10.1109/SPIRE.2000.878178Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334. doi: 10.1109/34.888718Simbionix™, Lap Mentor™. simbionix.com. http://simbionix.com/simulators/lap-mentor/library-of-modules/basic-skills/ . Accessed 31 Jan 2013Wagner RA, Fischer MJ (1974) Algorithms for approximate string matching. J ACM 21(1):168–173. doi: 10.1016/S0019-9958(85)80046-2Hirschberg DS (1975) A linear space algorithm for computing maximal common subsequences. Commun ACM 18(6):341–343. doi: 10.1145/360825.36086

Monitoring façade soiling as a maintenance strategy for the sensitive build heritage

The colour patterns generally found on the façades of architecturally sensitive buildings have an adverse impact on their aesthetics, to the detriment of their identity and potential economic value. A quantitative and qualitative study was conducted of the perception of aesthetic decay in the limestone on a heritage building. The study assessed building aesthetics between two façade cleaning operations, conducted in 1984-1986 and 2006-2008. Based on the calculation of the final or total soiling index, by means of in situ lightness measurement and three architectural design variables, the colour distribution of the façades was quantified in 2006 and a model was developed to monitor façade soiling over time. The proposed model, a tool for planning preventive façade maintenance on architecturally sensitive buildings, advocates for sustainable cleaning operations. Its premise that periodic cleaning should only be conducted in areas where the limestone is affected by aesthetic decay redounds to minimised intervention and lower building management costs

Real-Time PCR based test for the early diagnosis of Haplosporidium pinnae affecting fan mussel Pinna nobilis

All sequence files are available from the Genbank database (accession numbers MK142774-MK142779). The protocol is available from protocols.io (DOI: dx.doi.org/10. 17504/protocols.io.xmyfk7w).Noble pen shell or fan mussel, Pinna nobilis Linnaeus (1758), protected since 1992, was incorporated into the Spanish Catalogue of Threatened Species (Category: Vulnerable, Royal Decree 139/2011). The status is presently in the process of being catalogued as critically endangered, pending approval by Spanish Government (https://www.mapama.gob.es/ es/biodiversidad/participacion-publica/Borrador_OM_situacion_critica.aspx). The International Union for the Conservation of Nature (IUCN) alerted the countries of the Mediterranean basin to the “emergent situation” due to serious mortality events suffered by the fan mussel, putting it in serious risk of extinction. Thus, emergency actions have been implemented by Spanish authorities in which several research institutes from all over the country are involved. The parasite, Haplosporidium pinnae, was recently characterized by histology, TEM, SEM and molecular biology techniques and it was considered responsible for the mass mortality of P. nobilis in the Mediterranean Sea. In this context, the aim of this study has been to develop species-specific quantitative PCR (qPCR) protocol carrying out a fast, specific and effective molecular diagnose of H. pinnae. In this sense, the detection limit for qPCR was equal to 30 copies of SSU rDNA / ng of DNA using plasmid alone and when 100ng DNA of non-infected oyster were added. The qPCR assay revealed that 94% of the 32 analysed mantle tissues of fan mussel were infected by H. pinnae, showing a high sensitivity and specificity for its detection (100% if we don’t consider negative and too much degraded samples). This technique will allow us to make quicker follow-ups of the disease, allowing us to get a better understanding of its evolution in order to help in the rescue of P. nobilis populationsProject MAPAMA ref. 28-5310LIFE IPE INTERMARES(LIFE15 IPE ES 012)IFAPA-UCV 014/2018 and co-funded by PHENOFISH ProjectState Plan for Scientific and Technical Research and Innovation 2013-2016, MINECO, ref. PTA2015-14709-IMinisterio de Economía, Industria y Competitividad, Gobierno de España, INIA CCAA (DOC INIA 8-2013)Project BF/HEM 15-1662Ciencias del Ma

Hexahidrita-epsomita en el deterioro de piedra dolomítica del patrimonio arquitectónico

Peer reviewe

Risk Assessment of Hip Fracture Based on Machine Learning

[EN] Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%. In order to improve this accuracy, this paper proposes the use of Machine Learning (ML) models trained with data from a biomechanical model that simulates a sideways-fall. Machine Learning (ML) models are models able to learn and to make predictions from data. During a training process, ML models learn a function that maps inputs and outputs without previous knowledge of the problem. The main advantage of ML models is that once the mapping function is constructed, they can make predictions for complex biomechanical behaviours in real time. However, despite the increasing popularity of Machine Learning (ML) models and their wide application to many fields of medicine, their use as hip fracture predictors is still limited. This paper proposes the use of ML models to assess and predict hip fracture risk. Clinical, geometric, and biomechanical variables from the finite element simulation of a side fall are used as independent variables to train the models. Among the different tested models, Random Forest stands out, showing its capability to outperform BMD-DXA, achieving an accuracy over 87%, with specificity over 92% and sensitivity over 83%.This study was partially funded by the FPI grant (FPI-SP20170111) from the Universitat Politecnica de Valencia obtained by Eduardo Villamor.Galassi, A.; Martín-Guerrero, JD.; Villamor, E.; Monserrat Aranda, C.; Rupérez Moreno, MJ. (2020). Risk Assessment of Hip Fracture Based on Machine Learning. Applied bionics and biomechanics (Online). 2020:1-13. https://doi.org/10.1155/2020/8880786S1132020World Health OrganizationAssessment of fracture risk and its application to screening for postmenopausal osteoporosis. Report of a WHO Study Group1994http://www.who.int/iris/handle/10665/39142, http://apps.who.int//iris/handle/10665/39142Cooper, C., Campion, G., & Melton, L. J. (1992). Hip fractures in the elderly: A world-wide projection. Osteoporosis International, 2(6), 285-289. doi:10.1007/bf01623184El Maghraoui, A., & Roux, C. (2008). DXA scanning in clinical practice. QJM, 101(8), 605-617. doi:10.1093/qjmed/hcn022Testi, D., Viceconti, M., Cappello, A., & Gnudi, S. (2002). Prediction of Hip Fracture Can Be Significantly Improved by a Single Biomedical Indicator. Annals of Biomedical Engineering, 30(6), 801-807. doi:10.1114/1.1495866Nguyen, N. D., Frost, S. A., Center, J. R., Eisman, J. A., & Nguyen, T. V. (2008). Development of prognostic nomograms for individualizing 5-year and 10-year fracture risks. Osteoporosis International, 19(10), 1431-1444. doi:10.1007/s00198-008-0588-0Bolland, M. J., Siu, A. T., Mason, B. H., Horne, A. M., Ames, R. W., Grey, A. B., … Reid, I. R. (2011). Evaluation of the FRAX and Garvan fracture risk calculators in older women. Journal of Bone and Mineral Research, 26(2), 420-427. doi:10.1002/jbmr.215Fountoulis, G., Kerenidi, T., Kokkinis, C., Georgoulias, P., Thriskos, P., Gourgoulianis, K., … Vlychou, M. (2016). Assessment of Bone Mineral Density in Male Patients with Chronic Obstructive Pulmonary Disease by DXA and Quantitative Computed Tomography. International Journal of Endocrinology, 2016, 1-6. doi:10.1155/2016/6169721Pellicer-Valero, O. J., Rupérez, M. J., Martínez-Sanchis, S., & Martín-Guerrero, J. D. (2020). Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations. Expert Systems with Applications, 143, 113083. doi:10.1016/j.eswa.2019.113083Martínez-Martínez, F., Rupérez-Moreno, M. J., Martínez-Sober, M., Solves-Llorens, J. A., Lorente, D., Serrano-López, A. J., … Martín-Guerrero, J. D. (2017). A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time. Computers in Biology and Medicine, 90, 116-124. doi:10.1016/j.compbiomed.2017.09.019Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98. doi:10.7861/futurehosp.6-2-94Kruse, C., Eiken, P., & Vestergaard, P. (2016). Clinical fracture risk evaluated by hierarchical agglomerative clustering. Osteoporosis International, 28(3), 819-832. doi:10.1007/s00198-016-3828-8Ho-Le, T. P., Center, J. R., Eisman, J. A., Nguyen, T. V., & Nguyen, H. T. (2017). Prediction of hip fracture in post-menopausal women using artificial neural network approach. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2017.8037784Dall’Ara, E., Eastell, R., Viceconti, M., Pahr, D., & Yang, L. (2016). Experimental validation of DXA-based finite element models for prediction of femoral strength. Journal of the Mechanical Behavior of Biomedical Materials, 63, 17-25. doi:10.1016/j.jmbbm.2016.06.004Enns-Bray, W. S., Bahaloo, H., Fleps, I., Pauchard, Y., Taghizadeh, E., Sigurdsson, S., … Helgason, B. (2019). Biofidelic finite element models for accurately classifying hip fracture in a retrospective clinical study of elderly women from the AGES Reykjavik cohort. Bone, 120, 25-37. doi:10.1016/j.bone.2018.09.014Testi, D., Viceconti, M., Baruffaldi, F., & Cappello, A. (1999). Risk of fracture in elderly patients: a new predictive index based on bone mineral density and finite element analysis. Computer Methods and Programs in Biomedicine, 60(1), 23-33. doi:10.1016/s0169-2607(99)00007-3Yang, L., Palermo, L., Black, D. M., & Eastell, R. (2014). Prediction of Incident Hip Fracture with the Estimated Femoral Strength by Finite Element Analysis of DXA Scans in the Study of Osteoporotic Fractures. Journal of Bone and Mineral Research, 29(12), 2594-2600. doi:10.1002/jbmr.2291Luo, Y., Ahmed, S., & Leslie, W. D. (2018). Automation of a DXA-based finite element tool for clinical assessment of hip fracture risk. Computer Methods and Programs in Biomedicine, 155, 75-83. doi:10.1016/j.cmpb.2017.11.020Terzini, M., Aldieri, A., Rinaudo, L., Osella, G., Audenino, A. L., & Bignardi, C. (2019). Improving the Hip Fracture Risk Prediction Through 2D Finite Element Models From DXA Images: Validation Against 3D Models. Frontiers in Bioengineering and Biotechnology, 7. doi:10.3389/fbioe.2019.00220Nishiyama, K. K., Ito, M., Harada, A., & Boyd, S. K. (2013). Classification of women with and without hip fracture based on quantitative computed tomography and finite element analysis. Osteoporosis International, 25(2), 619-626. doi:10.1007/s00198-013-2459-6Jiang, P., Missoum, S., & Chen, Z. (2015). Fusion of clinical and stochastic finite element data for hip fracture risk prediction. Journal of Biomechanics, 48(15), 4043-4052. doi:10.1016/j.jbiomech.2015.09.044Ferizi, U., Besser, H., Hysi, P., Jacobs, J., Rajapakse, C. S., Chen, C., … Chang, G. (2018). Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data. Journal of Magnetic Resonance Imaging, 49(4), 1029-1038. doi:10.1002/jmri.26280Villamor, E., Monserrat, C., Del Río, L., Romero-Martín, J. A., & Rupérez, M. J. (2020). Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning. Computer Methods and Programs in Biomedicine, 193, 105484. doi:10.1016/j.cmpb.2020.105484Rossman, T., Kushvaha, V., & Dragomir-Daescu, D. (2015). QCT/FEA predictions of femoral stiffness are strongly affected by boundary condition modeling. Computer Methods in Biomechanics and Biomedical Engineering, 19(2), 208-216. doi:10.1080/10255842.2015.1006209Si, H. (2015). TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator. ACM Transactions on Mathematical Software, 41(2), 1-36. doi:10.1145/2629697Morgan, E. F., & Keaveny, T. M. (2001). Dependence of yield strain of human trabecular bone on anatomic site. Journal of Biomechanics, 34(5), 569-577. doi:10.1016/s0021-9290(01)00011-2Morgan, E. F., Bayraktar, H. H., & Keaveny, T. M. (2003). Trabecular bone modulus–density relationships depend on anatomic site. Journal of Biomechanics, 36(7), 897-904. doi:10.1016/s0021-9290(03)00071-xBayraktar, H. H., Morgan, E. F., Niebur, G. L., Morris, G. E., Wong, E. K., & Keaveny, T. M. (2004). Comparison of the elastic and yield properties of human femoral trabecular and cortical bone tissue. Journal of Biomechanics, 37(1), 27-35. doi:10.1016/s0021-9290(03)00257-4Wirtz, D. C., Schiffers, N., Pandorf, T., Radermacher, K., Weichert, D., & Forst, R. (2000). Critical evaluation of known bone material properties to realize anisotropic FE-simulation of the proximal femur. Journal of Biomechanics, 33(10), 1325-1330. doi:10.1016/s0021-9290(00)00069-5Eckstein, F., Wunderer, C., Boehm, H., Kuhn, V., Priemel, M., Link, T. M., & Lochmüller, E.-M. (2003). Reproducibility and Side Differences of Mechanical Tests for Determining the Structural Strength of the Proximal Femur. Journal of Bone and Mineral Research, 19(3), 379-385. doi:10.1359/jbmr.0301247Orwoll, E. S., Marshall, L. M., Nielson, C. M., Cummings, S. R., Lapidus, J., … Cauley, J. A. (2009). Finite Element Analysis of the Proximal Femur and Hip Fracture Risk in Older Men. Journal of Bone and Mineral Research, 24(3), 475-483. doi:10.1359/jbmr.081201Maas, S. A., Ellis, B. J., Ateshian, G. A., & Weiss, J. A. (2012). FEBio: Finite Elements for Biomechanics. Journal of Biomechanical Engineering, 134(1). doi:10.1115/1.4005694Choi, W. J., Cripton, P. A., & Robinovitch, S. N. (2014). Effects of hip abductor muscle forces and knee boundary conditions on femoral neck stresses during simulated falls. Osteoporosis International, 26(1), 291-301. doi:10.1007/s00198-014-2812-4Van den Kroonenberg, A. J., Hayes, W. C., & McMahon, T. A. (1995). Dynamic Models for Sideways Falls From Standing Height. Journal of Biomechanical Engineering, 117(3), 309-318. doi:10.1115/1.2794186Robinovitch, S. N., McMahon, T. A., & Hayes, W. C. (1995). Force attenuation in trochanteric soft tissues during impact from a fall. Journal of Orthopaedic Research, 13(6), 956-962. doi:10.1002/jor.1100130621Dufour, A. B., Roberts, B., Broe, K. E., Kiel, D. P., Bouxsein, M. L., & Hannan, M. T. (2011). The factor-of-risk biomechanical approach predicts hip fracture in men and women: the Framingham Study. Osteoporosis International, 23(2), 513-520. doi:10.1007/s00198-011-1569-2BowyerK. W.ChawlaN. V.HallL. O.KegelmeyerW. P.SMOTE: synthetic minority over-sampling techniqueCoRRhttps://arxiv.org/abs/1106.181

La caliza de Morata de Tajuña, Comunidad de Madrid: una piedra tradicional de construcción en la capital a principios del siglo XX.

A partir del estudio sobre la procedencia y la calidad de la caliza empleada en la construcción -a principios del siglo XX- de un emblemático inmueble madrileño, se analizan los principales factores que en esta época favorecieron su utilización. Se trata de una piedra tradicionalmente empleada en la región pero apenas conocida en la capital y que por entonces se explotaba en las canteras Cornicabra, ubicadas en el paraje del Valhondo de Morata de Tajuña (Comunidad de Madrid). Por un lado, esta caliza resultaba una piedra económicamente muy ventajosa, debido principalmente a la favorable situación por entonces de las canteras y de las vías de comunicación. Por otro, debido a su origen geológico y posteriores procesos diagenéticos, presenta una muy elevada calidad y durabilidad, resultando un material muy apropiado para configurar elementos portantes especialmente resistentes a la acción del agua y al propio paso del tiempo. Considerando una época en la que la piedra tradicional de la región estaba siendo sustituida por piedras procedentes de otros lugares, el prestigio del que históricamente ha disfrutado la caliza extraída en las canteras de Colmenar de Oreja y el efímero empleo en la capital de la caliza de Morata de Tajuña como piedra de cantería, el carácter tradicional de la caliza objeto de estudio resulta ciertamente excepcional

Interactive evaluation of surgery skills in surgery simulators: A new method based on string matching algorithms

The final publication is available at Springer via http://dx.doi.org/10.1007/s11548-013-0881-zMonserrat Aranda, C.; Lucas, A.; Hernández-Orallo, J.; Rupérez Moreno, MJ.; Alcañiz Raya, ML. (2013). Interactive evaluation of surgery skills in surgery simulators: A new method based on string matching algorithms. International Journal of Computer Assisted Radiology and Surgery. 8(1 Supplement):373-374. doi:10.1007/s11548-013-0881-zS37337481 Supplemen

Heritage value of building materials: Former Workers Hospital of Maudes, Madrid (Spain) case study

Building materials used at the Former Workers Hospital of Maudes, Madrid (Spain) were studied. The study addressed the information both achieved from documental resources and characterization techniques. Documentary work has enabled to know about the architect thought, the ideology of the project or the grounds that conditioned such materials selection; it also permitted to learn about materials provenance and/or its elaboration. Analytical studies provided information about petrographic features of the materials and their composition; limestone provenance was confirmed and new data on material manufacture were provided. Such information, which deserves to be known and disseminated, provides a significant heritage value to materials that shape cultural assets. Studies with a multidisciplinary approach represent a commitment to improve the knowledge and conservation of heritage