1,452,381 research outputs found

    Calculation of biomass volume of citrus trees from an adapted dendrometry

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    A methodology and computational algorithms, to calculate volumes and the total biomass contained in citrus trees from an adapted dendrometry were developed. The methodology could be used as a tool to manage resources from the orchards, establishing adequate predictive models for assessing parameters such as income from raw materials for the cultivation, fruit production, CO2 sink, and waste materials (i.e. residual wood) used for energy or industry. Dendrometry has been traditionally applied to forest trees. However, little research has been conducted on fruit trees due to their heterogeneous structure. To develop the process of biomass quantification it was necessary to perform systems of measurement, enabling to determine volumes of the analysed trees. Firstly, form factors and volume functions for the branches were calculated. These volume functions gave 0.97 coefficient of determination from base diameter and length. The relationships between apparent crown volume and actual volume in the crown (i.e. no hollows) of the trees were established, with 0.80 coefficient of determination. Occupation factor and the distribution of biomass in the crown strata were evaluated. These results could be correlated with production and quality of the fruit, with the amount of residual biomass coming from pruning, and with LIDAR data what may produce a simple, quick and accurate way to predict biomass.This research were developed by the project AGL2010-15334 funded by the Ministry of Science and Innovation of Spain funds.Velázquez Martí, B.; Estornell Cremades, J.; López Cortés, I.; Marti Gavila, J. (2012). Calculation of biomass volume of citrus trees from an adapted dendrometry. Biosystems Engineering. 112(4):285-292. https://doi.org/10.1016/j.biosystemseng.2012.04.011S285292112

    Reverse engineering applied to biomodelling and pathological bone manufacturing using FDM technology

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    [EN] Reverse engineering and medical image-based modeling technologies allow manufacturing of 3D biomodels of anatomical structures of human body. These techniques are based on anatomical information from scanning data such as CT and MRI, whose scanners are used for scanning data acquisition of the external and internal geometry of anatomical structures. These 3D biomodels have many medical applications such surgical training, preoperative planning, surgical simulation, diagnosis and treatments. 3D virtual models of human body structures based on CT are increasingly being used in clinical practice. A data processing methodology is required to obtain an accurate 3D model suitable for manufacturing using AM, and specially the FDM technologies. This study shows a step-by-step methodology to process the CT information in bounded uncertainty conditions in order to obtain the STL models of the degenerated bone components, and to manufacture the 3D biomodels for surgery analysis with optimal design and details, and with an adequate accuracy to ensure proper results by surgeons analysis.The authors wish to acknowledge the support of Ms. Jerica Risent and Mr. Joan Ortiz of Ford Motor Company for his assistance in the scanning of printed models. This work was supported by the Polisabio Funding (UPV-Fisabio 2017)Laura Piles; Miguel J. Reig; Vte. Jesús Seguí; Rafael Pla; Fernando Martínez; José Miguel Seguí (2019). Reverse engineering applied to biomodelling and pathological bone manufacturing using FDM technology. Procedia Manufacturing. 41:739-746. https://doi.org/10.1016/j.promfg.2019.09.065S73974641Van Eijnatten, M., Berger, F. H., de Graaf, P., Koivisto, J., Forouzanfar, T., & Wolff, J. (2017). Influence of CT parameters on STL model accuracy. Rapid Prototyping Journal, 23(4), 678-685. doi:10.1108/rpj-07-2015-0092Lalone, E. A., Willing, R. T., Shannon, H. L., King, G. J. W., & Johnson, J. A. (2015). Accuracy assessment of 3D bone reconstructions using CT: an intro comparison. Medical Engineering & Physics, 37(8), 729-738. doi:10.1016/j.medengphy.2015.04.010Stull, K. E., Tise, M. L., Ali, Z., & Fowler, D. R. (2014). Accuracy and reliability of measurements obtained from computed tomography 3D volume rendered images. Forensic Science International, 238, 133-140. doi:10.1016/j.forsciint.2014.03.005Van Eijnatten, M., van Dijk, R., Dobbe, J., Streekstra, G., Koivisto, J., & Wolff, J. (2018). CT image segmentation methods for bone used in medical additive manufacturing. Medical Engineering & Physics, 51, 6-16. doi:10.1016/j.medengphy.2017.10.008Javaid, M., & Haleem, A. (2018). Additive manufacturing applications in medical cases: A literature based review. Alexandria Journal of Medicine, 54(4), 411-422. doi:10.1016/j.ajme.2017.09.003D.V.C. Stoffelen, K. Eraly, P. Debeer, The use of 3D printing technology in reconstruction of a severe glenoid defect: a case report with 2.5 years of follow-up, Journal of Shoulder Elbow Surgery, 24 (2015) e218-e22

    Estimation of wood volume and height of olive tree plantations using airborne discrete-return LiDAR data

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    The aim of this study is to analyze methodologies based on airborne LiDAR (light detection and ranging) technology of low pulse density points (0.5m(-2)) for height and volume quantification of olive trees in Viver (Spain). A total of 29 circular plots, each with a radius of 20m, were sampled and their volumes and heights were obtained by dendrometric methods. For these estimations, several statistics derived from LiDAR data were calculated in each plot. Regression models were used to predict volume and height. The results showed good performance for estimating volume (R-2=0.70) and total height (R-2=0.67).The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion (Ministry for Science & Innovation) within the framework of the project AGL2010-15334 and by the Vice-Rectorate for Research of the Universitat Politecnica de Valencia [Grant PAID-06-12-3297; SP20120534].Estornell Cremades, J.; Velázquez Martí, B.; López Cortés, I.; Salazar Hernández, DM.; Fernández-Sarría, A. (2014). Estimation of wood volume and height of olive tree plantations using airborne discrete-return LiDAR data. GIScience and Remote Sensing. 51(1):17-29. https://doi.org/10.1080/15481603.2014.883209S1729511Estornell, J., Ruiz, L. A., Velázquez-Martí, B., & Fernández-Sarría, A. (2011). Estimation of shrub biomass by airborne LiDAR data in small forest stands. Forest Ecology and Management, 262(9), 1697-1703. doi:10.1016/j.foreco.2011.07.026García, M., Riaño, D., Chuvieco, E., & Danson, F. M. (2010). Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sensing of Environment, 114(4), 816-830. doi:10.1016/j.rse.2009.11.021Hyyppa, J., Kelle, O., Lehikoinen, M., & Inkinen, M. (2001). A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Transactions on Geoscience and Remote Sensing, 39(5), 969-975. doi:10.1109/36.921414Kim, Y., Yang, Z., Cohen, W. B., Pflugmacher, D., Lauver, C. L., & Vankat, J. L. (2009). Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data. Remote Sensing of Environment, 113(11), 2499-2510. doi:10.1016/j.rse.2009.07.010Moorthy, I., Miller, J. R., Berni, J. A. J., Zarco-Tejada, P., Hu, B., & Chen, J. (2011). Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data. Agricultural and Forest Meteorology, 151(2), 204-214. doi:10.1016/j.agrformet.2010.10.005Næsset, E. (2004). Accuracy of forest inventory using airborne laser scanning: evaluating the first nordic full-scale operational project. Scandinavian Journal of Forest Research, 19(6), 554-557. doi:10.1080/02827580410019544Popescu, S. C. (2007). Estimating biomass of individual pine trees using airborne lidar. Biomass and Bioenergy, 31(9), 646-655. doi:10.1016/j.biombioe.2007.06.022Popescu, S. C., Wynne, R. H., & Nelson, R. F. (2002). Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, 37(1-3), 71-95. doi:10.1016/s0168-1699(02)00121-7Velázquez-Martí, B., Estornell, J., López-Cortés, I., & Martí-Gavilá, J. (2012). Calculation of biomass volume of citrus trees from an adapted dendrometry. Biosystems Engineering, 112(4), 285-292. doi:10.1016/j.biosystemseng.2012.04.011Velázquez-Martí, B., Fernández-González, E., Estornell, J., & Ruiz, L. A. (2010). Dendrometric and dasometric analysis of the bushy biomass in Mediterranean forests. Forest Ecology and Management, 259(5), 875-882. doi:10.1016/j.foreco.2009.11.027Velázquez-Martí, B., Fernández-González, E., López-Cortés, I., & Salazar-Hernández, D. M. (2011). Quantification of the residual biomass obtained from pruning of trees in Mediterranean olive groves. Biomass and Bioenergy, 35(7), 3208-3217. doi:10.1016/j.biombioe.2011.04.042Yu, X., Hyyppä, J., Kaartinen, H., & Maltamo, M. (2004). Automatic detection of harvested trees and determination of forest growth using airborne laser scanning. Remote Sensing of Environment, 90(4), 451-462. doi:10.1016/j.rse.2004.02.00

    Considerations about quality in model-driven engineering

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    The final publication is available at Springer via http://dx.doi.org/10.1007/s11219-016-9350-6The virtue of quality is not itself a subject; it depends on a subject. In the software engineering field, quality means good software products that meet customer expectations, constraints, and requirements. Despite the numerous approaches, methods, descriptive models, and tools, that have been developed, a level of consensus has been reached by software practitioners. However, in the model-driven engineering (MDE) field, which has emerged from software engineering paradigms, quality continues to be a great challenge since the subject is not fully defined. The use of models alone is not enough to manage all of the quality issues at the modeling language level. In this work, we present the current state and some relevant considerations regarding quality in MDE, by identifying current categories in quality conception and by highlighting quality issues in real applications of the model-driven initiatives. We identified 16 categories in the definition of quality in MDE. From this identification, by applying an adaptive sampling approach, we discovered the five most influential authors for the works that propose definitions of quality. These include (in order): the OMG standards (e.g., MDA, UML, MOF, OCL, SysML), the ISO standards for software quality models (e.g., 9126 and 25,000), Krogstie, Lindland, and Moody. We also discovered families of works about quality, i.e., works that belong to the same author or topic. Seventy-three works were found with evidence of the mismatch between the academic/research field of quality evaluation of modeling languages and actual MDE practice in industry. We demonstrate that this field does not currently solve quality issues reported in industrial scenarios. The evidence of the mismatch was grouped in eight categories, four for academic/research evidence and four for industrial reports. These categories were detected based on the scope proposed in each one of the academic/research works and from the questions and issues raised by real practitioners. We then proposed a scenario to illustrate quality issues in a real information system project in which multiple modeling languages were used. For the evaluation of the quality of this MDE scenario, we chose one of the most cited and influential quality frameworks; it was detected from the information obtained in the identification of the categories about quality definition for MDE. We demonstrated that the selected framework falls short in addressing the quality issues. Finally, based on the findings, we derive eight challenges for quality evaluation in MDE projects that current quality initiatives do not address sufficiently.F.G, would like to thank COLCIENCIAS (Colombia) for funding this work through the Colciencias Grant call 512-2010. This work has been supported by the Gene-ralitat Valenciana Project IDEO (PROMETEOII/2014/039), the European Commission FP7 Project CaaS (611351), and ERDF structural funds.Giraldo-Velásquez, FD.; España Cubillo, S.; Pastor López, O.; Giraldo, WJ. (2016). Considerations about quality in model-driven engineering. Software Quality Journal. 1-66. https://doi.org/10.1007/s11219-016-9350-6S166(1985). Iso information processing—documentation symbols and conventions for data, program and system flowcharts, program network charts and system resources charts. ISO 5807:1985(E) (pp. 1–25).(2011). Iso/iec/ieee systems and software engineering – architecture description. 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    Water footprint for a South African platinum mine

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    A dissertation submitted to the Faculty of Engineering and Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering. June, 2015The volume of water consumed by a platinum mine located in South Africa was quantified in two ways: (1) using WaterMiner software to complete the Water Accounting Framework (WAF), and (2) using the Water Footprint Network (WFN) method. The WAF was developed by the Minerals Council of Australia and the Sustainable Minerals Institute at the University of Queensland, and the WFN method was developed by Hoekstra et al. (2011). The process steps included in the study were, two concentrator plants, a smelter plant and a tailings dam. The mining step and the external water footprint associated with electricity and chemicals were not included. Flow rate, production rate and rainfall data were obtained from the mining company and average monthly historic evaporation rates was obtained from a South African Department of Water Affairs report (DWAF, 1985). Unknown flow rates around flotation plants, cyclones and thickeners were calculated by closing the mass balance and using densities and percent solids for flows out of this equipment. The measured flow rates, calculated flow rates, rainfall and evaporation data were entered into WaterMiner and the results used to complete the WAF. The measured flow rates, calculated flow rates, rainfall and evaporation data were used to calculate the water footprint for the operation. When using the WAF, it was found that 12 686 ML/year of water was consumed, while the WFN method showed that 10 649 ML/year of blue water was consumed. The difference in the values calculated was due to the water inputs included in each method. The WAF included water entrained in ore and water obtained from third parties whereas the blue water footprint only included water consumed from surface or ground water sources. The yearly average total water footprint per kilogram of platinum group metal was 806 m3/kg PGM. Of this, 228 m3/kg PGM was blue water and 578 m3/kg PGM was grey water. Concentrator plant 1 had the largest blue water footprint (124 m3/kg PGM) and the tailings dam the smallest (4 m3/kg PGM). The largest loss of water was through tailings dam evaporation. Methods that could be implemented by the mining company to reduce the volume of water consumed on site may include covering the tailings dam to reduce evaporation or to add a pre-concentration step to concentrator plant 2. The blue water footprint can be reduced to 204 m3/kg PGM (10% reduction) if the tailings dam is covered and evaporation is reduced. The blue water footprint can be reduced to 216 m3/kg PGM (5% reduction) if a pre-concentration step is included in concentrator plant 2
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