A causal model to explain data reuse in science: a study in health disciplines

[EN] Investments in data infrastructures, data management, data repositories, and Open Data sharing policies and recommendations are viewed as increasingly important for scientific knowledge production. One of the underlying assumptions justifying these investments is that the more available Open Data becomes, then the greater the possibilities for creating new knowledge that can advance both science and human wellbeing. Yet efforts and investments in Open Data and other ways of data sharing only have value if data are actually reused. Recent scholarly efforts have brought forth some of the challenges and facilitators related to the reuse of data, in order to inform current and future policies and investments. However, despite these efforts, we still do not know why and how some researchers are successful in reusing data, despite the challenges they face, and why some researchers abandon the process of reusing data when facing such challenges. This dissertation aims to fill this gap by focusing on a causal explanation of the data reuse process, which it understands as being nested in broader patterns of researchers' motivations, scientific goals and decision-making strategies. The dissertation is comprised of three main elements. First, it proposes a heuristic model of the scientific actor, the bounded individual horizon (BIH) model, which understands that, on the one hand, researchers' work and careers are structured by their motivation to produce scientific contributions and rewards systems that prioritizes certain types of contributions. On the other hand, researchers' struggles to achieve their objective of creating new findings that accrue recognition and rewards occur within a frame of limited information and resources, conditioned by multiple institutional, social, and other factors. Second, the study proposes a mechanistic causal theoretical explanation that enables us to understand the data reuse process and its effects (outcomes). The data-reuse mechanism as it is called, enables us to understand how the satisficing behavior that characterizes scientific decision-making applies to the specific conditions and processes of data reuse. Third, a set of ten empirical case studies of data reuse in health research were conducted and are reported in the dissertation. These cases are analyzed and interpreted using the complementary theoretical lenses of the bounded individual horizon and the data-reuse mechanism approaches. The main findings explain that there is an apparent association between the extent and types of efforts required to reuse data, researchers' contextualized motivations, and broader goal-setting and decision-making frames. Access to data is a necessary condition for the reuse of data, yet is not sufficient for the reuse to happen. Characteristics of available data, including the context of their production, the extent of the preparation and stewarding of these data and their potential value in relation to researchers' motivations to make new scientific claims or generate background knowledge are found to be essential elements for understanding why some data reuse processes persist and succeed, while others do not. The thesis concludes that efforts and investments designed to reap the benefits of data reuse should also be expanded to include training researchers in data reuse, including to efficiently recognize opportunities, navigate the challenges of the reuse process, and be aware of and acknowledge the limitations of the use of secondary data. Without such investments, the promises and expectations linked to emerging data infrastructures, data repositories, data management guidelines and open science practices are argued to be far less likely to reach their full potential.[ES] Las inversiones en infraestructuras de datos, gestión de datos, repositorios de datos y políticas y recomendaciones de intercambio de Datos Abiertos (Open Data) se consideran cada vez más importantes para la producción del conocimiento científico. Una de las razones que justifica estas inversiones es que cuanto más Datos Abiertos haya, mayores serán las posibilidades de crear nuevo conocimiento que pueda hacer avanzar tanto la ciencia como el bienestar humano. Sin embargo, los esfuerzos y la inversión en Datos Abiertos y otras formas de compartirlos sólo tienen valor si se reutilizan realmente. Recientes trabajos académicos han puesto de manifiesto algunos de los retos y factores facilitadores relacionados con la reutilización de los datos, a fin de asesorar las políticas e inversiones actuales y futuras. Sin embargo, a pesar de esos esfuerzos, todavía desconocemos por qué y cómo algunos/as investigadores/as logran reutilizar los datos, a pesar de los retos a los que enfrentan, y por qué otros/as investigadores/as abandonan el proceso de reutilización de los datos. La presente tesis tiene por objeto llenar este vacío centrándose en una explicación causal del proceso de reutilización de los datos, que se entiende está inmersa en pautas de conducta más amplias que se relacionan con las motivaciones, los objetivos científicos y las estrategias de toma de decisiones de los/as investigadores/as. Este estudio consta de tres elementos principales. En primer lugar, propone un modelo heurístico del actor científico, el modelo del horizonte individual delimitado (BIH por su nombre en inglés, bounded individual horizon). En él se entiende que, por una parte, el trabajo y la carrera de los/as investigadores/as se estructuran en función de su motivación para producir contribuciones científicas y de los sistemas de recompensa que dan prioridad a determinados tipos de contribuciones. Por otra parte, los esfuerzos de los/as investigadores/as para lograr su objetivo de crear nuevos hallazgos que acumulen reconocimiento y recompensas se producen en un marco de información y recursos limitados, condicionados por múltiples factores institucionales, sociales y de otra índole. En segundo lugar, esta tesis propone una explicación teórica causal mecanicista que permite comprender el proceso de reutilización de los datos y sus efectos (resultados). El mecanismo de reutilización de datos (datareuse mechanism), como se denomina, nos permite comprender cómo la toma de decisiones científicas está caracterizada por una conducta que tiende a satisfacer esos objetivos en unas condiciones y procesos específicos de reutilización de datos. En tercer lugar, este estudio incluye los resultados del estudio empírico de diez estudios de casos de reutilización de datos en ciencias de la salud. Estos casos se han analizado e interpretado utilizando el modelo teórico del horizonte individual delimitado y los enfoques del mecanismo de reutilización de datos. Los resultados principales explican que existe una aparente asociación entre el alcance el alcance y tipo de esfuerzo requerido para reutilizar datos, las motivaciones contextualizadas de los/as investigadores/as y marcos más amplios de fijación de objetivos y toma de decisiones. El acceso a los datos es una condición necesaria para su reutilización, pero no es suficiente para que ésta se produzca. Para comprender por qué algunos procesos de reutilización de datos persisten y tienen éxito, mientras que otros no,son elementos esenciales: las características de los datos disponibles, incluido el contexto de su producción; el grado de preparación y administración de esos datos; y su potencial valor en relación con las motivaciones de los investigadores para hacer nuevas afirmaciones científicas o generar conocimientos de base. Este estudio concluye que los esfuerzos e inversiones destinados a aprovechar los beneficios de la reutilización de los datos también deberían ampliarse para incluir la capacitación de los/as investigadores/as en materia de reutilización de datos. En particular, debe insistirse en la capacidad para reconocer eficientemente las oportunidades, sortear los problemas del proceso de reutilización y ser conscientes y reconocer las limitaciones de la utilización de datos secundarios. Sin estas inversiones, las promesas y expectativas vinculadas a las emergentes infraestructuras de datos, los repositorios de datos, las directrices de gestión de datos y las prácticas científicas abiertas tienen muchas menos probabilidades de alcanzar su pleno potencial.[CA] Les inversions en infraestructures de dades, gestió de dades, repositoris de dades i polítiques i recomanacions d'intercanvi de Dades Obertes (Open Data) es consideren cada vegada més importants per a la producció del coneixement científic. Un dels supòsits subjacents que justifiquen aquestes inversions és que com més disponibles siguen les Dades Obertes, majors seran les possibilitats de crear nou coneixement que pugui fer avançar tant la ciència com el benestar humà. No obstant això, els esforços i les inversions en les Dades Obertes i altres maneres de compartir dades només tenen valor si les dades es reutilitzen realment. Recents investigacions acadèmics han posat de manifest alguns dels reptes i dels factors facilitadors relacionats amb la reutilització de les dades, a fi d'informar les polítiques i inversions actuals i futures. No obstant això, encara desconeixem per què i com alguns/es investigador(e)s aconsegueixen reutilitzar les dades, malgrat els reptes als quals s’enfronten, i per què altres investigador(e)s abandonen el procés de reutilització de les dades quan s'enfronten a aquests reptes. La present tesi té com a objectiu omplir aquest buit centrant-se en una explicació causal del procés de reutilització de dades, que s'entén que està associada amb pautes més àmplies derivades de les motivacions, els objectius científics i les estratègies de presa de decisions d’els/les investigador(e)s. La tesi consta de tres elements principals. En primer lloc, proposa un model heurístic de l'actor científic, el model de l'horitzó individual delimitat (BIH pel nom anglès, bounded individual horizon), que entén que, d'una banda, el treball i la carrera d’els/les investigador(e)s s'estructuren en funció de la seua motivació per a produir contribucions científiques i dels sistemes de recompensa que prioritzen determinats tipus de contribucions. D'altra banda, els esforços d’els/les investigador(e)s per aconseguir el seu objectiu d’obtenir nous resultats que acumulin reconeixement i recompenses es produeixen en un marc d'informació i recursos limitats, condicionats per múltiples factors institucionals, socials i d'altra índole. En segon lloc, aquesta tesi proposa una explicació teòrica causal mecanicista que permet comprendre el procés de reutilització de les dades i els seus efectes (resultats). El mecanisme de reutilització de dades (data-reuse mechanism), com es denomina, ens permet comprendre com el comportament satisfactori que caracteritza la presa de decisions científiques s'aplica a les condicions i processos específics de reutilització de dades. En tercer lloc, aquesta tesi inclou l'estudi empíric d'un conjunt de deu estudis de casos de reutilització de dades en ciències de la salut, així com també els resultats d’aquest estudi.. Aquests casos s'han analitzat i interpretat utilitzant les lents teòriques complementàries de l'horitzó individual delimitat i els enfocaments del mecanisme de reutilització de dades. Les principals conclusions expliquen que existeix una aparent associació entre l'abast i els tipus d'esforços necessaris per a reutilitzar dades, les motivacions contextualitzades d’els/les investigador(e)s i els marcs més amplis de fixació d'objectius i presa de decisions. L'accés a les dades és una condició necessària per a la seua reutilització, però no és suficient perquè aquesta es produeixi. Es considera que les característiques de les dades disponibles, inclòs el context de la seua producció, el grau de preparació i administració d'aquestes dades i el seu potencial valor en relació amb les motivacions d’els/les investigador(e)s per a fer noves afirmacions científiques o generar coneixements de base, són elements essencials per a comprendre per què alguns processos de reutilització de dades persisteixen i tenen èxit, mentre que uns altres no. Aquest estudi conclou que els esforços i inversions destinats a aprofitar els beneficis de la reutilització de dades també haurien d'ampliar-se per a incloure la capacitació d’els/les investigador(e)s en matèria de reutilització de dades, en particular per a reconèixer eficientment les oportunitats, superar els problemes del procés de reutilització i ser conscients i reconèixer les limitacions de la reutilització de dades secundàries. Sense aquests esforços i inversions, les promeses i expectatives vinculades a les infraestructures, repositoris i directrius de gestió de dades i les pràctiques científiques obertes tenen moltes menys probabilitats d'aconseguir el seu ple potencial.Aleixos Borrás, MI. (2020). A causal model to explain data reuse in science: a study in health disciplines [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/153164TESI

Integrated modeling with Top-Down approach in subsidiary industries

This article considers how conceptual design of industrial products is supported by current CAD systems. The case of subsidiary industries, or first tier suppliers, that must simultaneously deal with different customers and CAD platforms, receive special attention. Conceptual design is critical, since the large variety of fundamental product data managed (not just geometry) would be specified, modeled and interrelated (i.e. functional relations), to both simplify and ensure correctness and efficiency of the next design phases of current design, and make them easy to reuse, modify and redesign in the future. We give an approach to introduce conceptual design through top-down methodology and integrate it with final geometry. In this context, and in order to help subsidiary industries to improve their model quality, we propose the elaboration of product-oriented modeling guidelines, or “best modeling practices”, instead of CAD-oriented modeling guidelines. The approach has been validated by testing the conceptual design tools of two commercial high-end CAD systems at use in many subsidiary automotive industries

ParSketch: a Sketch-based Interface for a 2D Parametric Geometry Editor

ParSketch is a software prototype to evaluate the usability and functionality of a sketching interface aimed at defining 2D parametric sections. Currently, ParSketch interprets strokes which can be recognized as geometry (line, arc, circle, ellipse, or composed entities that are automatically segmented into those basic entities), or graphic gestures representing constraints (dimension, parallel, perpendicular, tangent, concentric, horizontal or vertical). From the functionality point of view, ParSketch compares to current commercial parametric CAD applications, as it offers many of the features provided by such applications. A theoretical analysis of the efficiency component of usability is provided that justifies the potential capability of sketching interfaces to compete with classical WIMP applications. Finally, a usability study is presented, which makes special emphasis in the satisfaction component of usability.The Spanish Ministry of Science and Education and the European Union (Project DPI2004-01373) supported this work. It was also partially supported by Fundació Caixa Castelló-Bancaixa under the Universitat Jaume I program for Research Promotion (Project P1-1B2004-02)

Special Issue: Image Analysis in Agriculture (Editorial)

Blasco, J.; Aleixos Borrás, MN.; Zude, M. (2014). Special Issue: Image Analysis in Agriculture (Editorial). Biosystems Engineering. 117:1-1. doi:10.1016/j.biosystemseng.2013.09.007S1111

Multispectral images of peach related to firmness and maturity at harvest

wo multispectral maturity classifications for red soft-flesh peaches (‘Kingcrest’, ‘Rubyrich’ and ‘Richlady’ n = 260) are proposed and compared based on R (red) and R/IR (red divided by infrared) images obtained with a three CCD camera (800 nm, 675 nm and 450 nm). R/IR histograms were able to correct the effect of 3D shape on light reflectance and thus more Gaussian histograms were produced than R images. As fruits ripened, the R/IR histograms showed increasing levels of intensity. Reference measurements such as firmness and visible spectra also varied significantly as the fruit ripens, firmness decreased while reflectance at 680 nm increased (chlorophyll absorption peak)

Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing

[EN] The use of remote sensing to map the distribution of plant diseases has evolved considerably over the last three decades and can be performed at different scales, depending on the area to be monitored, as well as the spatial and spectral resolution required. This work describes the development of a small low-cost field robot (Remotely Operated Vehicle for Infection Monitoring in orchards, XF-ROVIM), which is intended to be a flexible solution for early detection of Xylella fastidiosa (X. fastidiosa) in olive groves at plant to leaf level. The robot is remotely driven and fitted with different sensing equipment to capture thermal, spectral and structural information about the plants. Taking into account the height of the olive trees inspected, the design includes a platform that can raise the cameras to adapt the height of the sensors to a maximum of 200 cm. The robot was tested in an olive grove (4 ha) potentially infected by X. fastidiosa in the region of Apulia, southern Italy. The tests were focused on investigating the reliability of the mechanical and electronic solutions developed as well as the capability of the sensors to obtain accurate data. The four sides of all trees in the crop were inspected by travelling along the rows in both directions, showing that it could be easily adaptable to other crops. XF-ROVIM was capable of inspecting the whole field continuously, capturing geolocated spectral information and the structure of the trees for later comparison with the in situ observations.This work was partially supported by funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 727987 Xylella Fastidiosa Active Containment Through a multidisciplinary-Oriented Research Strategy (XF-ACTORS).Rey, B.; Aleixos Borrás, MN.; Cubero-García, S.; Blasco Ivars, J. (2019). Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing. Remote Sensing. 11(3). https://doi.org/10.3390/rs11030221113Martelli, G. P., Boscia, D., Porcelli, F., & Saponari, M. (2015). The olive quick decline syndrome in south-east Italy: a threatening phytosanitary emergency. European Journal of Plant Pathology, 144(2), 235-243. doi:10.1007/s10658-015-0784-7Olmo, D., Nieto, A., Adrover, F., Urbano, A., Beidas, O., Juan, A., … Landa, B. B. (2017). First Detection of Xylella fastidiosa Infecting Cherry (Prunus avium) and Polygala myrtifolia Plants, in Mallorca Island, Spain. Plant Disease, 101(10), 1820-1820. doi:10.1094/pdis-04-17-0590-pdnSaponari, M., Giampetruzzi, A., Loconsole, G., Boscia, D., & Saldarelli, P. (2019). Xylella fastidiosa in Olive in Apulia: Where We Stand. Phytopathology®, 109(2), 175-186. doi:10.1094/phyto-08-18-0319-fiVergara-Díaz, O., Zaman-Allah, M. A., Masuka, B., Hornero, A., Zarco-Tejada, P., Prasanna, B. M., … Araus, J. L. (2016). A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization. Frontiers in Plant Science, 7. doi:10.3389/fpls.2016.00666Thenkabail, P. S., & Lyon, J. G. (Eds.). (2016). Hyperspectral Remote Sensing of Vegetation. doi:10.1201/b11222Calderón, R., Navas-Cortés, J. A., Lucena, C., & Zarco-Tejada, P. J. (2013). High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing of Environment, 139, 231-245. doi:10.1016/j.rse.2013.07.031Gonzalez-Dugo, V., Hernandez, P., Solis, I., & Zarco-Tejada, P. (2015). Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping. Remote Sensing, 7(10), 13586-13605. doi:10.3390/rs71013586Hernández-Clemente, R., Navarro-Cerrillo, R., Ramírez, F., Hornero, A., & Zarco-Tejada, P. (2014). A Novel Methodology to Estimate Single-Tree Biophysical Parameters from 3D Digital Imagery Compared to Aerial Laser Scanner Data. Remote Sensing, 6(11), 11627-11648. doi:10.3390/rs61111627Colaço, A. F., Molin, J. P., Rosell-Polo, J. R., & Escolà, A. (2018). Application of light detection and ranging and ultrasonic sensors to high-throughput phenotyping and precision horticulture: current status and challenges. Horticulture Research, 5(1). doi:10.1038/s41438-018-0043-0Ma, Q., Su, Y., Luo, L., Li, L., Kelly, M., & Guo, Q. (2018). Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data. Ecological Indicators, 95, 298-310. doi:10.1016/j.ecolind.2018.07.050Ma, Q., Su, Y., & Guo, Q. (2017). Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9), 4225-4236. doi:10.1109/jstars.2017.2711482Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., … Dandekar, A. M. (2014). Advanced methods of plant disease detection. A review. Agronomy for Sustainable Development, 35(1), 1-25. doi:10.1007/s13593-014-0246-1Calderón, R., Navas-Cortés, J., & Zarco-Tejada, P. (2015). Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas. Remote Sensing, 7(5), 5584-5610. doi:10.3390/rs70505584Zarco-Tejada, P. J., Camino, C., Beck, P. S. A., Calderon, R., Hornero, A., Hernández-Clemente, R., … Navas-Cortes, J. A. (2018). Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nature Plants, 4(7), 432-439. doi:10.1038/s41477-018-0189-7Aasen, H., Honkavaara, E., Lucieer, A., & Zarco-Tejada, P. (2018). Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote Sensing, 10(7), 1091. doi:10.3390/rs10071091Vicent, A., & Blasco, J. (2017). When prevention fails. Towards more efficient strategies for plant disease eradication. New Phytologist, 214(3), 905-908. doi:10.1111/nph.14555Wang, X., Singh, D., Marla, S., Morris, G., & Poland, J. (2018). Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies. Plant Methods, 14(1). doi:10.1186/s13007-018-0324-5Bourgeon, M. A., Gée, C., Debuisson, S., Villette, S., Jones, G., & Paoli, J. N. (2016). « On-the-go » multispectral imaging system to characterize the development of vineyard foliage with quantitative and qualitative vegetation indices. Precision Agriculture, 18(3), 293-308. doi:10.1007/s11119-016-9489-yUnderwood, J. P., Hung, C., Whelan, B., & Sukkarieh, S. (2016). Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors. Computers and Electronics in Agriculture, 130, 83-96. doi:10.1016/j.compag.2016.09.014Zampetti, E., Papa, P., Di Flaviano, F., Paciucci, L., Petracchini, F., Pirrone, N., … Macagnano, A. (2017). Remotely Controlled Terrestrial Vehicle Integrated Sensory System for Environmental Monitoring. Sensors, 338-343. doi:10.1007/978-3-319-55077-0_43Hiremath, S. A., van der Heijden, G. W. A. M., van Evert, F. K., Stein, A., & ter Braak, C. J. F. (2014). Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter. Computers and Electronics in Agriculture, 100, 41-50. doi:10.1016/j.compag.2013.10.005Pérez-Ruiz, M., Gonzalez-de-Santos, P., Ribeiro, A., Fernandez-Quintanilla, C., Peruzzi, A., Vieri, M., … Agüera, J. (2015). Highlights and preliminary results for autonomous crop protection. Computers and Electronics in Agriculture, 110, 150-161. doi:10.1016/j.compag.2014.11.010Weiss, M., Baret, F., Smith, G. J., Jonckheere, I., & Coppin, P. (2004). Review of methods for in situ leaf area index (LAI) determination. Agricultural and Forest Meteorology, 121(1-2), 37-53. doi:10.1016/j.agrformet.2003.08.001Hosoi, F., & Omasa, K. (2006). Voxel-Based 3-D Modeling of Individual Trees for Estimating Leaf Area Density Using High-Resolution Portable Scanning Lidar. IEEE Transactions on Geoscience and Remote Sensing, 44(12), 3610-3618. doi:10.1109/tgrs.2006.881743Stein, M., Bargoti, S., & Underwood, J. (2016). Image Based Mango Fruit Detection, Localisation and Yield Estimation Using Multiple View Geometry. Sensors, 16(11), 1915. doi:10.3390/s16111915Saponari, M., Boscia, D., Altamura, G., Loconsole, G., Zicca, S., D’Attoma, G., … Martelli, G. P. (2017). Isolation and pathogenicity of Xylella fastidiosa associated to the olive quick decline syndrome in southern Italy. Scientific Reports, 7(1). doi:10.1038/s41598-017-17957-

Product data quality and collaborative engineering

[EN] We survey the impact of product data quality within an extended enterprise framework and present a linguistic model, which focuses on three levels: morphological, syntactic, and semantic.The Spanish Government national R&D Feder program partially sponsored this work as project number 1FD97 0784 “Implementing Design and Manufacturing Advanced Technologies in a Concurrent Engineering Environment. Application to an Automotive Components Manufacturing Company.” We also thank Radiadores Ordoñez, who helped us check the effectiveness of our approachContero, M.; Company Calleja, P.; Vila, C.; Aleixos Borrás, MN. (2002). Product data quality and collaborative engineering. IEEE Computer Graphics and Applications. 22(3):32-42. doi:10.1109/MCG.2002.999786S324222

Complete and Automated Generation of Configurable Virtual Prototypes of Products Based on Parameterization Tools and Rules. Application to a Case Study

[EN] In engineering, the 3D model of a product is essential. A 3D model allows making modifications by editing the characteristic modeling functions, which implies knowing in detail the process followed in the modeling. In addition, the flexibility in the configuration is limited, since the modifications are made on geometry and parameters that are dependent on each other. In this work, a parametric modeling approach allows the generation of 3D models with different specifications by modifying a reduced number of parameters. To demonstrate the functionality, an application has been developed, using Autodesk Inventor iLogic, for the modeling of an engine with V-cylinder arrangement. Taking as input key parameters (number of cylinders,¿), it can generate virtual prototypes with different configurations, facilitating the selection of the best product configuration by allowing to evaluate different alternatives.Veliz Vega, V.; Albert Gil, FE.; Aleixos Borrás, MN. (2022). Complete and Automated Generation of Configurable Virtual Prototypes of Products Based on Parameterization Tools and Rules. Application to a Case Study. Lecture Notes in Mechanical Engineering (Online). 294-301. https://doi.org/10.1007/978-3-030-92426-3_3429430