Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location

[EN] A large volume of the water produced for public supply is lost in the systems between sources and consumers. An important-in many cases the greatest-fraction of these losses are physical losses, mainly related to leaks and bursts in pipes and in consumer connections. Fast detection and location of bursts plays an important role in the design of operation strategies for water loss control, since this helps reduce the volume lost from the instant the event occurs until its effective repair (run time). The transient pressure signals caused by bursts contain important information about their location and magnitude, and stamp on any of these events a specific "hydraulic signature". The present work proposes and evaluates three methods to disaggregate transient signals, which are used afterwards to train artificial neural networks (ANNs) to identify burst locations and calculate the leaked flow. In addition, a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy. The proposed methods are applied to two real distribution networks, and the results show good accuracy in burst location and characterization.Manzi, D.; Brentan, BM.; Meirelles, G.; Izquierdo Sebastián, J.; Luvizotto Jr., E. (2019). Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location. Water. 11(11):1-13. https://doi.org/10.3390/w11112279S1131111Creaco, E., & Walski, T. (2017). Economic Analysis of Pressure Control for Leakage and Pipe Burst Reduction. Journal of Water Resources Planning and Management, 143(12), 04017074. doi:10.1061/(asce)wr.1943-5452.0000846Campisano, A., Creaco, E., & Modica, C. (2010). RTC of Valves for Leakage Reduction in Water Supply Networks. Journal of Water Resources Planning and Management, 136(1), 138-141. doi:10.1061/(asce)0733-9496(2010)136:1(138)Campisano, A., Modica, C., Reitano, S., Ugarelli, R., & Bagherian, S. (2016). Field-Oriented Methodology for Real-Time Pressure Control to Reduce Leakage in Water Distribution Networks. Journal of Water Resources Planning and Management, 142(12), 04016057. doi:10.1061/(asce)wr.1943-5452.0000697Vítkovský, J. P., Simpson, A. R., & Lambert, M. F. (2000). Leak Detection and Calibration Using Transients and Genetic Algorithms. Journal of Water Resources Planning and Management, 126(4), 262-265. doi:10.1061/(asce)0733-9496(2000)126:4(262)Pérez, R., Puig, V., Pascual, J., Quevedo, J., Landeros, E., & Peralta, A. (2011). Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks. Control Engineering Practice, 19(10), 1157-1167. doi:10.1016/j.conengprac.2011.06.004Jung, D., & Kim, J. (2017). Robust Meter Network for Water Distribution Pipe Burst Detection. Water, 9(11), 820. doi:10.3390/w9110820Colombo, A. F., Lee, P., & Karney, B. W. (2009). A selective literature review of transient-based leak detection methods. Journal of Hydro-environment Research, 2(4), 212-227. doi:10.1016/j.jher.2009.02.003Choi, D., Kim, S.-W., Choi, M.-A., & Geem, Z. (2016). Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System. Water, 8(4), 142. doi:10.3390/w8040142Christodoulou, S. E., Kourti, E., & Agathokleous, A. (2016). Waterloss Detection in Water Distribution Networks using Wavelet Change-Point Detection. Water Resources Management, 31(3), 979-994. doi:10.1007/s11269-016-1558-5Guo, X., Yang, K., & Guo, Y. (2012). Leak detection in pipelines by exclusively frequency domain method. Science China Technological Sciences, 55(3), 743-752. doi:10.1007/s11431-011-4707-3Holloway, M. B., & Hanif Chaudhry, M. (1985). Stability and accuracy of waterhammer analysis. Advances in Water Resources, 8(3), 121-128. doi:10.1016/0309-1708(85)90052-1Sanz, G., Pérez, R., Kapelan, Z., & Savic, D. (2016). Leak Detection and Localization through Demand Components Calibration. Journal of Water Resources Planning and Management, 142(2), 04015057. doi:10.1061/(asce)wr.1943-5452.0000592Zhang, Q., Wu, Z. Y., Zhao, M., Qi, J., Huang, Y., & Zhao, H. (2016). Leakage Zone Identification in Large-Scale Water Distribution Systems Using Multiclass Support Vector Machines. Journal of Water Resources Planning and Management, 142(11), 04016042. doi:10.1061/(asce)wr.1943-5452.0000661Mounce, S. R., & Machell, J. (2006). Burst detection using hydraulic data from water distribution systems with artificial neural networks. Urban Water Journal, 3(1), 21-31. doi:10.1080/15730620600578538Covas, D., Ramos, H., & de Almeida, A. B. (2005). Standing Wave Difference Method for Leak Detection in Pipeline Systems. Journal of Hydraulic Engineering, 131(12), 1106-1116. doi:10.1061/(asce)0733-9429(2005)131:12(1106)Liggett, J. A., & Chen, L. (1994). Inverse Transient Analysis in Pipe Networks. Journal of Hydraulic Engineering, 120(8), 934-955. doi:10.1061/(asce)0733-9429(1994)120:8(934)Caputo, A. C., & Pelagagge, P. M. (2002). An inverse approach for piping networks monitoring. Journal of Loss Prevention in the Process Industries, 15(6), 497-505. doi:10.1016/s0950-4230(02)00036-0Van Zyl, J. E. (2014). Theoretical Modeling of Pressure and Leakage in Water Distribution Systems. Procedia Engineering, 89, 273-277. doi:10.1016/j.proeng.2014.11.187Izquierdo, J., & Iglesias, P. . (2004). Mathematical modelling of hydraulic transients in complex systems. Mathematical and Computer Modelling, 39(4-5), 529-540. doi:10.1016/s0895-7177(04)90524-9Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107-144. doi:10.1007/s10618-007-0064-zNavarrete-López, C., Herrera, M., Brentan, B., Luvizotto, E., & Izquierdo, J. (2019). Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework. Water, 11(2), 246. doi:10.3390/w11020246Meirelles, G., Manzi, D., Brentan, B., Goulart, T., & Luvizotto, E. (2017). Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks. Water Resources Management, 31(13), 4339-4351. doi:10.1007/s11269-017-1750-2Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1-4), 28-40. doi:10.1016/j.jhydrol.2011.06.013Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Hybrid SOM+ k -Means clustering to improve planning, operation and management in water distribution systems. Environmental Modelling & Software, 106, 77-88. doi:10.1016/j.envsoft.2018.02.013Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics - Theory and Methods, 3(1), 1-27. doi:10.1080/0361092740882710

Timber production assessment of a plantation forest: An integrated framework with field-based inventory, multi-source remote sensing data and forest management history

Timber production is the purpose for managing plantation forests, and its spatial and quantitative information is critical for advising management strategies. Previous studies have focused on growing stock volume (GSV), which represents the current potential of timber production, yet few studies have investigated historical process-harvested timber. This resulted in a gap in a synthetical ecosystem service assessment of timber production. In this paper, we established a Management Process-based Timber production (MPT) framework to integrate the current GSV and the harvested timber derived from historical logging regimes, trying to synthetically assess timber production for a historical period. In the MPT framework, age-class and current GSV determine the times of historical thinning and the corresponding harvested timber, by using a "space-for-time" substitution. The total timber production can be estimated by the historical harvested timber in each thinning and the current GSV. To test this MPT framework, an empirical study on a larch plantation (LP) with area of 43,946 ha was conducted in North China for a period from 1962 to 2010. Field-based inventory data was integrated with ALOS PALSAR (Advanced Land-Observing Satellite Phased Array L-band Synthetic Aperture Radar) and Landsat-8 OLI (Operational Land Imager) data for estimating the age-class and current GSV of LP. The random forest model with PALSAR backscatter intensity channels and OLI bands as input predictive variables yielded an accuracy of 67.9% with a Kappa coefficient of 0.59 for age-class classification. The regression model using PALSAR data produced a root mean square error (RMSE) of 36.5 m(3) ha(-1). The total timber production of LP was estimated to be 7.27 x 10(6) m(3), with 4.87 x 10(6) m(3) in current GSV and 2.40 x 10(6) m(3) in harvested timber through historical thinning. The historical process-harvested timber accounts to 33.0% of the total timber production, which component has been neglected in the assessments for current status of plantation forests. Synthetically considering the RMSE for predictive GSV and misclassification of age-class, the error in timber production were supposed to range from -55.2 to 56.3 m(3) ha(-1). The MPT framework can be used to assess timber production of other tree species at a larger spatial scale, providing crucial information for a better understanding of forest ecosystem service. (C) 2016 Elsevier B.V. All rights reserved.ArticleINTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION.52:155-165(2016)journal articl

Analisis Pengaruh Quality, Image, Brand Equity, dan Value terhadap Loyalitas Seller sebagai Salah Satu Partner E-marketplace di Lazada Indonesia

Penelitian ini bertujuan untuk mengetahui pengaruh dari beberapa faktor yaitu quality, image, brand equity dan value terhadap loyalitas seller sebagai salah satu partner e-marketplace di Lazada Indonesia. Sampel diambil dengan menggunakan metode purposive sampling, dengan jumlah sampel sebanyak 82 responden. Teknik pengumpulan data menggunakan kuesioner dan literatur. Metode analisis yang digunakan adalah metode analisis regresi berganda untuk mengetahui pengaruh antara variabel-variabel bebas terhadap variabel terikat. Hasil penelitian ini menunjukkan bahwa; 1). Kualitas e-marketplace tidak berpengaruh positif dan siginifikan terhadap loyalitas seller 2). Citra Perusahaan penyedia e-marketplace berpengaruh positif dan signifikan terhadap loyalitas seller 3). Ekuitas brand Perusahaan e-marketplace berpengaruh positif dan signifikan terhadap loyalitas seller 4). Nilai yang dimiliki oleh Perusahaan e-marketplace berpengaruh positif dan signifikan terhadap loyalitas seller 5). Kualitas Pelayanan, citra Perusahaan, ekuitas brand dan nilai Perusahaan secara bersama-sama berpengaruh positif dan signifikan terhadap loyalitas seller sebagai salah satu partner e-marketplace di Lazada Indonesia. Loyalitas seller sebagai salah satu partner e-marketplace di Lazada Indonesia terbukti dipengaruhi oleh keempat variabel yang diteliti yaitu sebesar 74% dan sisanya 26% dipengaruhi oleh faktor atau variabel-variabel lainnya.Kata Kunci: Quality, Image, Brand Equity, Value, Loyalitas Seller2 This study aims to determine the effect of e-service quality, image, brand equity, and value to seller's loyalty as a partner in Lazada Indonesia e-marketplace. Samples were taken by using purposive sampling method, with the total number of sample is 82 respondents. The technique of collecting data is using questionnaires and literatures. The analytical method that used in this research is multiple regression analysis to determine the effect of independent variables on the dependent variable. The results of this study indicate that; 1). E-service quality does not affect significantly on seller's loyalty. 2). Image has a possitive and significant effect on seller's loyalty. 3). Brand Equity has a possitive and significant effect on seller's loyalty. 4). Value has a possitive and significant effect on seller's loyalty. 5). E-Service quality, value, brand equity, and value jointly has a positive and significant effect on seller's loyalty as a partner in Lazada Indonesia e-marketplace. The seller's loyalty shown to be affected by the independent variables in this study at 74% and 26% is influenced by other factors or variables.Keywords: Quality, Image, Brand Equity, Value, Seller's Loyalty DAFTAR PUSTAKA Arikunto, Suharsimi. 2006. Prosedur Penelitian Suatu Pendekatan Praktik. Jakarta: Rineka Cipta. 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A systematic literature review of Total Quality Management (TQM) implementation in the organization

[EN] In today’s market situation and complex business environment, organization must be able to deliver the customer’s requirement and the expectations which are critical to the satisfaction such as high product quality, faster delivery and competitive cost. Organization need to apply a comprehensive concept and method on managing those requirements. The concept of Total Quality Management (TQM) is considered as one of a popular concept used to manage the quality of product and services comprehensively. This research is to observe is this concept and method still relevant to be use and effectively improved the business performance as well as customer satisfaction. It is a systematic literature review to the literatures from many industry sectors that were collected and reviewed in detail. The result show that this concept is still being used by many organizations around the world and its successfully help the organization to improve their competitiveness, business growth and the sustainability as well as increase employee’s morale.This article was completed thanks to the financial support from the university of Mercu Buana, Jakarta-Indonesia. It also completed with the purpose and motivation of the authors to have an innovate research thinking as well as the contribution to the future researcher.Permana, A.; Purba, H.; Rizkiyah, N. (2021). A systematic literature review of Total Quality Management (TQM) implementation in the organization. International Journal of Production Management and Engineering. 9(1):25-36. https://doi.org/10.4995/ijpme.2021.13765OJS253691Alanazi, M.H. (2020). The mediating role of primary TQM factors and strategy in the relationship between supportive TQM factors and organisational results: An empirical assessment using the MBNQA model. Cogent Business and Management, 7(1). https://doi.org/10.1080/23311975.2020.1771074Antunes, M.G., Mucharreira, P.R., Justino, M. do R.T., & Quirós, J.T. (2018). Total Quality Management Implementation in Portuguese Higher Education Institutions. Proceedings MDPI, 2(21), 1342. https://doi.org/10.3390/proceedings2211342Arifin, J. (2016). Penguatan Manajemen Syariah Melalui Total Quality Managementbagi Pelaku Lembaga Keuangan Syariah Di Kota Semarang. Jurnal At-Taqaddum, Volume 8, Nomor 2, November 2016, 8(2), 180. https://doi.org/10.21580/at.v8i2.1170Balasubramanian, M. (2016). Total Quality Management [TQM] in the Healthcare Industry - Challenges, Barriers and Implementation Developing a Framework for TQM Implementation in a Healthcare Setup. Science Journal of Public Health, 4(4), 271. https://doi.org/10.11648/j.sjph.20160404.11Benzaquen, J., Carlos, M., Norero, G., Armas, H., & Pacheco, H. (2019). Quality in private health companies in Peru: The relation of QMS & ISO 9000 principles on TQM factor. International Journal of Healthcare Management, 0(0), 1-9. https://doi.org/10.1080/20479700.2019.1644472Bigliardi, B., & Galati, F. (2014). The implementation of TQM in R&D environments. Journal of Technology Management and Innovation, 9(2), 157-171. https://doi.org/10.4067/S0718-27242014000200012Bunglowala, A., & Asthana, N. (2016). A Total Quality Management Approach in Teaching and Learning Process. International Journal of Management (IJM), 7(5), 223-227. http://www.iaeme.com/MasterAdmin/uploadfolder/IJM_07_05_021/IJM_07_05_021.pdfBusu, M. (2019). Applications of TQM Processes to Increase the Management Performance of Enterprises in the Romanian Renewable Energy Sector. Processes MDPI. https://doi.org/10.3390/pr7100685Dahlgaard, J.J., Kristensen, K., & Kanji, G.K. (2002). Fundamentals of Total Quality Management: Process analysis and improvement Jens. Original illustrations © Taylor & Francis 2002. https://doi.org/10.4324/9780203930021Dewi, H.P., Lumbanraja, P., & Matondang, R. (2015). Implementation of Total Quality Management and Interpersonal Communication in Achieving Student Satisfaction through Service Quality at Yayasan Pendidikan Islam, Miftahussalam, Medan. International Journal of Research and Review, 2(6), 343-347. http://www.gkpublication.in/IJRR_Vol.2_Issue6_June2015/IJRR0066.pdfEltawy, N., & Gallear, D. (2017). Leanness and agility: A comparative theoretical view. Industrial Management and Data Systems, 117(1), 149-165. https://doi.org/10.1108/IMDS-01-2016-0032Fitriani, F. (2019). Persiapan Total Quality Management (Tqm). Adaara: Jurnal Manajemen Pendidikan Islam, 9(2), 908-919. https://doi.org/10.35673/ajmpi.v9i2.426Garcia-Alcaraz, J.L., Flor-Montalvo, F.J., Avelar-Sosa, L., Sánchez-Ramírez, C., & Jiménez-Macías, E. (2019). Human resource abilities and skills in TQM for sustainable enterprises. Sustainability MDPI, 11(22), 6488. https://doi.org/10.3390/su11226488George, S., & Weimerskirch, A. (1998). Total quality management: Strategies and techniques proven at todays' most successful companies (Second ed.). John Wiley & Sons, Inc.Green, F.B. (2006). Six-sigma and the revival of TQM. Total Quality Management and Business Excellence, 17(10), 1281-1286. https://doi.org/10.1080/14783360600753711Gómez-López, R., Serrano-Bedia, A.M., & López-Fernández, M.C. (2016). Motivations for implementing TQM through the EFQM model in Spain: an empirical investigation. Total Quality Management and Business Excellence, 27(11-12), 1224-1245. https://doi.org/10.1080/14783363.2015.1068688Haffar, M., Al-Karaghouli, W., & Ghoneim, A. (2013). An analysis of the influence of organisational culture on TQM implementation in an era of global marketing: The case of Syrian manufacturing organisations. International Journal of Productivity and Quality Management, 11(1), 96-115. https://doi.org/10.1504/IJPQM.2013.050570Hasan, K., Islam, M.S., Shams, A.T., & Gupta, H. (2018). Total Quality Management (TQM): Implementation in Primary Education System of Bangladesh. International Journal of Research in Industrial Engineering, 7(3), 370-380. https://doi.org/10.22105/riej.2018.128170.1041Houston, D. (2007). TQM and higher education: A critical systems perspective on fitness for purpose. Quality in Higher Education, 13(1), 3-17. https://doi.org/10.1080/13538320701272672Kaname, O. (2003). Handbook for TQM and QCC Vol 1. In Handbook (Vol. 1). Kantardjieva, M. (2015). The Relationship between Total Quality Management (TQM) and Strategic Management. Journal of Economics, Business and Management, 3(5), 537-541. https://doi.org/10.7763/JOEBM.2015.V3.242Kim, G.-S. (2016). Effect of Total Quality Management on Customer Satisfaction. International Journal of Engineering Sciences & Research Technology, 5(6), 507-514. https://doi.org/10.5281/zenodo.55618Kiruthiga, K. (2016). Major factors affecting the execution of total quality management in the construction industry in India. Journal of Chemical and Pharmaceutical Sciences, 9(2), E135-E140.Kumar, S., & Shanmuganathan, J. (2019). A structural relationship between TQM practices and organizational performance with reference to selected auto component manufacturing companies. International Journal of Management, 10(5). https://doi.org/10.34218/IJM.10.5.2019/009Kumar, U., Kumar, V., de Grosbois, D., & Choisne, F. (2009). Continuous improvement of performance measurement by TQM adopters. Total Quality Management & Business Excellence, 20(6), 603-616. https://doi.org/10.1080/14783360902924242Kuo, C. (2016). Effects of Total Quality Management Implementation and Supply Chain Management Capability on Customer Capital. The Journal of Global Business Management, 12(2), 47-60.Lawrence, J.J., & McCollough, M.A. (2004). Implementing Total Quality Management in the Classroom by Means of Student Satisfaction Guarantees. Total Quality Management and Business Excellence, 15(2), 235-254. https://doi.org/10.1080/1478336032000149063Mensah, J.O., Copuroglu, G., & Fening, F.A. (2012). Total Quality Management in Ghana: Critical Success Factors and Model for Implementation of a Quality Revolution. Journal of African Business, 13(2), 123-133. https://doi.org/10.1080/15228916.2012.693444Mercy, O., & Taiye, T.B. (2015). Strategic Imperatives of Total Quality Management and Customer Satisfaction in Organizational Sustainability. International Journal of Academic Research in Business and Social Sciences, 5(4), 1-22. https://doi.org/10.6007/IJARBSS/v5-i4/1538Mitreva, E., Cvetkovik, D., Filiposki, O., Taskov, N., & Gjorshevski, H. (2016). The Effects of Total Quality Management Practices on Performance within a Company for Frozen Food in the Republic of Macedonia. TEM Journal, 5(3), 339-346. https://doi.org/10.18421/TEM53-14Morath, C., & Doluschitz, R. (2009). Total Quality Management in the food industry - Current situation and potential in Germany. Applied Studies In Agribusiness And Commerce, 3(3-4), 83-87. https://doi.org/10.19041/APSTRACT/2009/3-4/18Musenze, I.A., & Thomas, M.S. (2020). Development and validation of a total quality management model for Uganda's local governments. Cogent Business and Management, 7(1), 1-22. https://doi.org/10.1080/23311975.2020.1767996Neyestani, B., & Juanzon, J.B.P. (2016). Developing an Appropriate Performance Measurement Framework for Total Quality Management (TQM) in Construction and Other Industries. IRA-International Journal of Technology & Engineering (ISSN 2455-4480), 5(2), 32. https://doi.org/10.21013/jte.v5.n2.p2Ngambi, M.T., & Nkemkiafu, A.G. (2015). 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A New Adaptive Image Interpolation Method to Define the Shoreline at Sub-Pixel Level

[EN] This paper presents a new methodological process for detecting the instantaneous land-water border at sub-pixel level from mid-resolution satellite images (30 m/pixel) that are freely available worldwide. The new method is based on using an iterative procedure to compute Laplacian roots of a polynomial surface that represents the radiometric response of a set of pixels. The method uses a first approximation of the shoreline at pixel level (initial pixels) and selects a set of neighbouring pixels to be part of the analysis window. This adaptive window collects those stencils in which the maximum radiometric variations are found by using the information given by divided differences. Therefore, the land-water surface is computed by a piecewise interpolating polynomial that models the strong radiometric changes between both interfaces. The assessment is tested on two coastal areas to analyse how their inherent differences may affect the method. A total of 17 Landsat 7 and 8 images (L7 and L8) were used to extract the shorelines and compare them against other highly accurate lines that act as references. Accurate quantitative coastal data from the satellite images is obtained with a mean horizontal error of 4.38 +/- 5.66 m and 1.79 +/- 2.78 m, respectively, for L7 and L8. Prior methodologies to reach the sub-pixel shoreline are analysed and the results verify the solvency of the one proposed.This study is part of the PhD dissertation of E. Sanchez-Garcia, which was supported by a grant from the Spanish Ministry of Education, Culture and Sports (I + D + i 2013-2016). The authors also appreciate the financial support provided by the Spanish Ministry of Economy and Competitiveness (CGL2015-69906-R)Sánchez-García, E.; Balaguer-Beser, Á.; Almonacid-Caballer, J.; Pardo Pascual, JE. (2019). A New Adaptive Image Interpolation Method to Define the Shoreline at Sub-Pixel Level. 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(2014). Application of Empirical Wave Run-Up Formulas to the Polish Baltic Sea Coast. PLoS ONE, 9(8), e105437. doi:10.1371/journal.pone.0105437Roelvink, D., Reniers, A., van Dongeren, A., van Thiel de Vries, J., McCall, R., & Lescinski, J. (2009). Modelling storm impacts on beaches, dunes and barrier islands. Coastal Engineering, 56(11-12), 1133-1152. doi:10.1016/j.coastaleng.2009.08.006Kostrzewski, A., Zwoliński, Z., Winowski, M., Tylkowski, J., & Samołyk, M. (2015). Cliff top recession rate and cliff hazards for the sea coast of Wolin Island (Southern Baltic). Baltica, 28(2), 109-120. doi:10.5200/baltica.2015.28.10Terefenko, P., Zelaya Wziątek, D., Dalyot, S., Boski, T., & Pinheiro Lima-Filho, F. (2018). A High-Precision LiDAR-Based Method for Surveying and Classifying Coastal Notches. ISPRS International Journal of Geo-Information, 7(8), 295. doi:10.3390/ijgi7080295Terefenko, P., Paprotny, D., Giza, A., Morales-Nápoles, O., Kubicki, A., & Walczakiewicz, S. (2019). Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis. Remote Sensing, 11(7), 843. doi:10.3390/rs11070843Kolander, R., Morche, D., & Bimböse, M. (2013). Quantification of moraine cliff coast erosion on Wolin Island (Baltic Sea, northwest Poland). Baltica, 26(1), 37-44. doi:10.5200/baltica.2013.26.04Moore, L. J., Ruggiero, P., & List, J. H. (2006). Comparing Mean High Water and High Water Line Shorelines: Should Proxy-Datum Offsets be Incorporated into Shoreline Change Analysis? Journal of Coastal Research, 224, 894-905. doi:10.2112/04-0401.1Davidson, M., Van Koningsveld, M., de Kruif, A., Rawson, J., Holman, R., Lamberti, A., … Aarninkhof, S. (2007). The CoastView project: Developing video-derived Coastal State Indicators in support of coastal zone management. Coastal Engineering, 54(6-7), 463-475. doi:10.1016/j.coastaleng.2007.01.007Aarninkhof, S. G. ., Turner, I. L., Dronkers, T. D. ., Caljouw, M., & Nipius, L. (2003). A video-based technique for mapping intertidal beach bathymetry. Coastal Engineering, 49(4), 275-289. doi:10.1016/s0378-3839(03)00064-4Andriolo, U., Sánchez-García, E., & Taborda, R. (2019). Operational Use of Surfcam Online Streaming Images for Coastal Morphodynamic Studies. Remote Sensing, 11(1), 78. doi:10.3390/rs11010078Sánchez-García, E., Balaguer-Beser, A., & Pardo-Pascual, J. E. (2017). C-Pro: A coastal projector monitoring system using terrestrial photogrammetry with a geometric horizon constraint. ISPRS Journal of Photogrammetry and Remote Sensing, 128, 255-273. doi:10.1016/j.isprsjprs.2017.03.023Holman, R. A., & Stanley, J. (2007). The history and technical capabilities of Argus. Coastal Engineering, 54(6-7), 477-491. doi:10.1016/j.coastaleng.2007.01.003Sagar, S., Roberts, D., Bala, B., & Lymburner, L. (2017). Extracting the intertidal extent and topography of the Australian coastline from a 28 year time series of Landsat observations. Remote Sensing of Environment, 195, 153-169. doi:10.1016/j.rse.2017.04.009Luijendijk, A., Hagenaars, G., Ranasinghe, R., Baart, F., Donchyts, G., & Aarninkhof, S. (2018). The State of the World’s Beaches. Scientific Reports, 8(1). doi:10.1038/s41598-018-24630-6Li, J., & Roy, D. (2017). A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring. Remote Sensing, 9(9), 902. doi:10.3390/rs9090902Boak, E. H., & Turner, I. L. (2005). Shoreline Definition and Detection: A Review. Journal of Coastal Research, 214, 688-703. doi:10.2112/03-0071.1Gens, R. (2010). Remote sensing of coastlines: detection, extraction and monitoring. International Journal of Remote Sensing, 31(7), 1819-1836. doi:10.1080/01431160902926673Liu, H., Wang, L., Sherman, D. J., Wu, Q., & Su, H. (2011). Algorithmic Foundation and Software Tools for Extracting Shoreline Features from Remote Sensing Imagery and LiDAR Data. Journal of Geographic Information System, 03(02), 99-119. doi:10.4236/jgis.2011.32007Pardo-Pascual, J. E., Almonacid-Caballer, J., Ruiz, L. A., & Palomar-Vázquez, J. (2012). Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision. Remote Sensing of Environment, 123, 1-11. doi:10.1016/j.rse.2012.02.024Pardo-Pascual, J. E., Almonacid-Caballer, J., Ruiz, L. A., Palomar-Vázquez, J., & Rodrigo-Alemany, R. (2014). Evaluation of storm impact on sandy beaches of the Gulf of Valencia using Landsat imagery series. Geomorphology, 214, 388-401. doi:10.1016/j.geomorph.2014.02.020Almonacid-Caballer, J., Sánchez-García, E., Pardo-Pascual, J. E., Balaguer-Beser, A. A., & Palomar-Vázquez, J. (2016). Evaluation of annual mean shoreline position deduced from Landsat imagery as a mid-term coastal evolution indicator. Marine Geology, 372, 79-88. doi:10.1016/j.margeo.2015.12.015Sánchez-García, E., Pardo-Pascual, J. E., Balaguer-Beser, A., & Almonacid-Caballer, J. (2015). ANALYSIS OF THE SHORELINE POSITION EXTRACTED FROM LANDSAT TM AND ETM+ IMAGERY. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W3, 991-998. doi:10.5194/isprsarchives-xl-7-w3-991-2015Pardo-Pascual, J., Sánchez-García, E., Almonacid-Caballer, J., Palomar-Vázquez, J., Priego de los Santos, E., Fernández-Sarría, A., & Balaguer-Beser, Á. (2018). Assessing the Accuracy of Automatically Extracted Shorelines on Microtidal Beaches from Landsat 7, Landsat 8 and Sentinel-2 Imagery. Remote Sensing, 10(2), 326. doi:10.3390/rs10020326Almonacid-Caballer, J., Pardo-Pascual, J., & Ruiz, L. (2017). Evaluating Fourier Cross-Correlation Sub-Pixel Registration in Landsat Images. Remote Sensing, 9(10), 1051. doi:10.3390/rs9101051Liu, Q., Trinder, J., & Turner, I. (2016). A COMPARISON OF SUB-PIXEL MAPPING METHODS FOR COASTAL AREAS. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, III-7, 67-74. doi:10.5194/isprsannals-iii-7-67-2016Liu, Y., Wang, X., Ling, F., Xu, S., & Wang, C. (2017). Analysis of Coastline Extraction from Landsat-8 OLI Imagery. Water, 9(11), 816. doi:10.3390/w9110816Liu, Q., Trinder, J., & Turner, I. L. (2017). Automatic super-resolution shoreline change monitoring using Landsat archival data: a case study at Narrabeen–Collaroy Beach, Australia. Journal of Applied Remote Sensing, 11(1), 016036. doi:10.1117/1.jrs.11.016036Cipolletti, M. P., Delrieux, C. A., Perillo, G. M. E., & Cintia Piccolo, M. (2012). Superresolution border segmentation and measurement in remote sensing images. Computers & Geosciences, 40, 87-96. doi:10.1016/j.cageo.2011.07.015Liu, H., & Jezek, K. C. (2004). Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods. 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Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data

Precise knowledge of fuel conditions is important for predicting fire hazards and simulating fire growth and intensity across the landscape. We present a methodology to retrieve and map forest canopy fuel and other forest structural parameters using small-footprint full-waveform airborne light detection and ranging (LiDAR) data. Full-waveform LiDAR sensors register the complete returned backscattered signal through time and can describe physical properties of the intercepted objects. This study was undertaken in a mixed forest dominated by Douglas-fir, occasionally mixed with other conifers, in north-west Oregon (United States). We extracted two sets of LiDAR metrics using pulse detection and waveform modelling and then constructed several predictive models using forward stepwise multiple linear regression. The resulting models explained ~80% of the variability for many of the canopy fuel and forest structure parameters: aboveground biomass (R2 = 0.84), quadratic mean diameter (R2 = 0.82), canopy height (R2 = 0.79), canopy base height (R2 = 0.78) and canopy fuel load (R2 = 0.79). The lowest performing models included basal area (R2 = 0.76), stand volume (R2 = 0.73), canopy bulk density (R2 = 0.67) and stand density index (R2 = 0.66). Our results indicate that full-waveform LiDAR systems show promise in systematically characterising the structure and canopy fuel loads of forests, which may enable accurate fire behaviour forecasting that in turn supports the development of prevention and planning policies.This paper was developed as a result of two mobility grants funded by the Erasmus Mundus Programme of the European Commission under the Transatlantic Partnership for Excellence in Engineering (TEE Project) and the Generalitat Valenciana (BEST/2012/235). The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation in the framework of the project CGL2010-19591/BTE. In addition, the authors thank the Panther Creek Remote Sensing and Research cooperative program for the data provided for this research, Jim Flewelling (Seattle Biometrics) and George McFadden (Bureau of Land Management) for their help in data availability and preparation.Hermosilla Gómez, T.; Ruiz Fernández, LÁ.; Kazakova, AN.; Coops, N.; Moskal, LM. (2014). Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data. International Journal of Wildland Fire. 23(2):224-233. https://doi.org/10.1071/WF13086S224233232Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. doi:10.1109/tac.1974.1100705Andersen, H.-E., McGaughey, R. J., & Reutebuch, S. E. (2005). Estimating forest canopy fuel parameters using LIDAR data. Remote Sensing of Environment, 94(4), 441-449. doi:10.1016/j.rse.2004.10.013Arroyo, L. A., Pascual, C., & Manzanera, J. A. (2008). Fire models and methods to map fuel types: The role of remote sensing. Forest Ecology and Management, 256(6), 1239-1252. doi:10.1016/j.foreco.2008.06.048Ashworth, A., Evans, D. L., Cooke, W. H., Londo, A., Collins, C., & Neuenschwander, A. (2010). Predicting Southeastern Forest Canopy Heights and Fire Fuel Models using GLAS Data. Photogrammetric Engineering & Remote Sensing, 76(8), 915-922. doi:10.14358/pers.76.8.915Buddenbaum, H., Seeling, S., & Hill, J. (2013). Fusion of full-waveform lidar and imaging spectroscopy remote sensing data for the characterization of forest stands. International Journal of Remote Sensing, 34(13), 4511-4524. doi:10.1080/01431161.2013.776721Chuvieco, E., & Congalton, R. G. (1989). Application of remote sensing and geographic information systems to forest fire hazard mapping. 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Assessing canopy fuel stratum characteristics in crown fire prone fuel types of western North America. International Journal of Wildland Fire, 12(1), 39. doi:10.1071/wf02024Drake, J. B., Dubayah, R. O., Clark, D. B., Knox, R. G., Blair, J. B., Hofton, M. A., … Prince, S. (2002). Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sensing of Environment, 79(2-3), 305-319. doi:10.1016/s0034-4257(01)00281-4Erdody, T. L., & Moskal, L. M. (2010). Fusion of LiDAR and imagery for estimating forest canopy fuels. Remote Sensing of Environment, 114(4), 725-737. doi:10.1016/j.rse.2009.11.002Falkowski, M. J., Gessler, P. E., Morgan, P., Hudak, A. T., & Smith, A. M. S. (2005). Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling. Forest Ecology and Management, 217(2-3), 129-146. doi:10.1016/j.foreco.2005.06.013Flannigan, M. ., Stocks, B. ., & Wotton, B. . (2000). Climate change and forest fires. Science of The Total Environment, 262(3), 221-229. doi:10.1016/s0048-9697(00)00524-6García, M., Popescu, S., Riaño, D., Zhao, K., Neuenschwander, A., Agca, M., & Chuvieco, E. (2012). Characterization of canopy fuels using ICESat/GLAS data. Remote Sensing of Environment, 123, 81-89. doi:10.1016/j.rse.2012.03.018González-Olabarria, J.-R., Rodríguez, F., Fernández-Landa, A., & Mola-Yudego, B. (2012). Mapping fire risk in the Model Forest of Urbión (Spain) based on airborne LiDAR measurements. Forest Ecology and Management, 282, 149-156. doi:10.1016/j.foreco.2012.06.056Hall, S. A., Burke, I. C., Box, D. O., Kaufmann, M. R., & Stoker, J. M. (2005). Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. Forest Ecology and Management, 208(1-3), 189-209. doi:10.1016/j.foreco.2004.12.001Harding, D. J. (2005). ICESat waveform measurements of within-footprint topographic relief and vegetation vertical structure. Geophysical Research Letters, 32(21). doi:10.1029/2005gl023471Heinzel, J., & Koch, B. (2011). Exploring full-waveform LiDAR parameters for tree species classification. International Journal of Applied Earth Observation and Geoinformation, 13(1), 152-160. doi:10.1016/j.jag.2010.09.010Höfle, B., Hollaus, M., & Hagenauer, J. (2012). Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 134-147. doi:10.1016/j.isprsjprs.2011.12.003HYDE, P., DUBAYAH, R., PETERSON, B., BLAIR, J., HOFTON, M., HUNSAKER, C., … WALKER, W. (2005). Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems. Remote Sensing of Environment, 96(3-4), 427-437. doi:10.1016/j.rse.2005.03.005Keane, R. E., Burgan, R., & van Wagtendonk, J. (2001). Mapping wildland fuels for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling. International Journal of Wildland Fire, 10(4), 301. doi:10.1071/wf01028Kim, 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.2009.07.010Koetz, B., Morsdorf, F., Sun, G., Ranson, K. J., Itten, K., & Allgower, B. (2006). Inversion of a Lidar Waveform Model for Forest Biophysical Parameter Estimation. IEEE Geoscience and Remote Sensing Letters, 3(1), 49-53. doi:10.1109/lgrs.2005.856706Lefsky, M. A., Cohen, W. B., Acker, S. A., Parker, G. G., Spies, T. A., & Harding, D. (1999). Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests. 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E., & Bravo, V. A. (2004). Spatial models for estimating fuel loads in the Black Hills, South Dakota, USA. International Journal of Wildland Fire, 13(1), 119. doi:10.1071/wf02049Reitberger, J., Krzystek, P., & Stilla, U. (2008). Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees. International Journal of Remote Sensing, 29(5), 1407-1431. doi:10.1080/01431160701736448Riaño, D., Chuvieco, E., Salas, J., Palacios-Orueta, A., & Bastarrika, A. (2002). Generation of fuel type maps from Landsat TM images and ancillary data in Mediterranean ecosystems. Canadian Journal of Forest Research, 32(8), 1301-1315. doi:10.1139/x02-052Riaño, D. (2003). Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling. Remote Sensing of Environment, 86(2), 177-186. doi:10.1016/s0034-4257(03)00098-1Riaño, D., Chuvieco, E., Condés, S., González-Matesanz, J., & Ustin, S. L. (2004). Generation of crown bulk density for Pinus sylvestris L. from lidar. Remote Sensing of Environment, 92(3), 345-352. doi:10.1016/j.rse.2003.12.014Riaño, D., Chuvieco, E., Ustin, S. L., Salas, J., Rodríguez-Pérez, J. R., Ribeiro, L. M., … Fernández, H. (2007). Estimation of shrub height for fuel-type mapping combining airborne LiDAR and simultaneous color infrared ortho imaging. International Journal of Wildland Fire, 16(3), 341. doi:10.1071/wf06003SKOWRONSKI, N., CLARK, K., NELSON, R., HOM, J., & PATTERSON, M. (2007). Remotely sensed measurements of forest structure and fuel loads in the Pinelands of New Jersey. Remote Sensing of Environment, 108(2), 123-129. doi:10.1016/j.rse.2006.09.032Skowronski, N. S., Clark, K. L., Duveneck, M., & Hom, J. (2011). Three-dimensional canopy fuel loading predicted using upward and downward sensing LiDAR systems. Remote Sensing of Environment, 115(2), 703-714. doi:10.1016/j.rse.2010.10.012Van Leeuwen, M., & Nieuwenhuis, M. (2010). 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Application of fuzzy logic in performance management: a literature review

[EN] Performance management has become in a key success factor for any organization. Traditionally, performance management has focused uniquely in financial measures, mainly using quantitative measures, but two decades ago they were extended towards an integral view of the organization, appearing qualitative measures. This type of extended view and associated measures have a degree of uncertainty that needs to be bounded. One of the essential tools for uncertainty bounding is the fuzzy logic and, therefore,the main objective of this paper is the analysis of the literature about the application of fuzzy logic in performance measurement systems operating within uncertainty environments with the aim of categorizing, conceptualizing and classifying the works written so far. Finally, three categories are defined according to the different uses of fuzzy logic within performance management concluding that the most important application of fuzzy logic that counts with a higher number of studies is uncertainty bounding.Gurrea Montesinos, V.; Alfaro Saiz, JJ.; Rodríguez Rodríguez, R.; Verdecho Sáez, MJ. (2014). Application of fuzzy logic in performance management: a literature review. International Journal of Production Management and Engineering. 2(2):93-100. doi:10.4995/ijpme.2014.1859SWORD9310022Amini, S., & Jochem, R. (2011). A Conceptual Model Based on the Fuzzy Set Theory to Measure and Evaluate the Performance of Service Processes. 2011 IEEE 15th International Enterprise Distributed Object Computing Conference Workshops. doi:10.1109/edocw.2011.25Ammar, S. & Wright, R. (1995), "A Fuzzy Logic Approach to Performance Evaluation". Uncertainty Modeling and Analysis, 1995, and Annual Conference of the North American Fuzzy Information Processing Society. Proceedings of ISUMA - NAFIPS '95., pp. 246 - 251Ammar, S., & Wright, R. (2000). Applying fuzzy-set theory to performance evaluation. Socio-Economic Planning Sciences, 34(4), 285-302. doi:10.1016/s0038-0121(00)00004-5Arango, M.D., Jaimes, W.A. & Zapata, J.A. (2010) "Gestion cadena de abastecimiento - Logistica con indicadores bajo incertidumbre, caso aplicado sector panificador palmira" Ciencia e Ingeniería Neogranadina, Vol. 20-1, pp. 97-115.Beheshti, H. M., & Lollar, J. G. (2008). Fuzzy logic and performance evaluation: discussion and application. International Journal of Productivity and Performance Management, 57(3), 237-246. doi:10.1108/17410400810857248Behrouzi, F., & Wong, K. Y. (2011). Lean performance evaluation of manufacturing systems: A dynamic and innovative approach. Procedia Computer Science, 3, 388-395. doi:10.1016/j.procs.2010.12.065Chan, T.S., Ql, H.J. (2003), "An innovative performance measurement method for supply chain management". Sup-ply Chain Management: An International Journal Volume 8 Number 3, pp. 209-223.Chan, F. T. S., Qi, H. J., Chan, H. K., Lau, H. C. W., & Ip, R. W. L. (2003). A conceptual model of performance measurement for supply chains. Management Decision, 41(7), 635-642. doi:10.1108/00251740310495568Chen, C.-T., Lin, C.-T., & Huang, S.-F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102(2), 289-301. doi:10.1016/j.ijpe.2005.03.009Cheng, S., Hsu, B., & Shu, M. (2007). Fuzzy testing and selecting better processes performance. Industrial Management & Data Systems, 107(6), 862-881. doi:10.1108/02635570710758761Ferreira, A., Azevedo,S. &Fazendeiro, P. (2012) "A Linguistic Approach to Supply Chain Performance Assessment". IEEE International Conference on Fuzzy Sistems, pp.1-5.Lau, H. C. W., Kai Pang, W., & Wong, C. W. Y. (2002). Methodology for monitoring supply chain performance: a fuzzy logic approach. Logistics Information Management, 15(4), 271-280. doi:10.1108/09576050210436110Lalmazloumian M. & Yew K., (2012), "A Review of Modelling Approaches for Supply Chain Planning Under Un-certainty". 9th International Conference on Service Systems and Service Management (ICSSSM), pp. 197-203.Liao, M.-Y., & Wu, C.-W. (2010). Evaluating process performance based on the incapability index for measurements with uncertainty. Expert Systems with Applications, 37(8), 5999-6006. doi:10.1016/j.eswa.2010.02.005Lu, C. & Wei li, X. (2006), "Supply Chain Modeling Using Fuzzy Sets and Possibility Theory in an Uncertain Envi-ronment". The Sixth World Congress on Intelligent Control and Automation, Vol.2, pp. 3608-3612.Mahnam, M., Yadollahpour, M. R., Famil-Dardashti, V., & Hejazi, S. R. (2009). Supply chain modeling in uncertain environment with bi-objective approach. Computers & Industrial Engineering, 56(4), 1535-1544. doi:10.1016/j.cie.2008.09.038Muñoz, M. J., Rivera, J. M., & Moneva, J. M. (2008). Evaluating sustainability in organisations with a fuzzy logic approach. Industrial Management & Data Systems, 108(6), 829-841. doi:10.1108/02635570810884030Olugu, E. U., & Wong, K. Y. (2012). An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry. Expert Systems with Applications, 39(1), 375-384. doi:10.1016/j.eswa.2011.07.026Tabrizi, B. H., & Razmi, J. (2013). Introducing a mixed-integer non-linear fuzzy model for risk management in designing supply chain networks. Journal of Manufacturing Systems, 32(2), 295-307. doi:10.1016/j.jmsy.2012.12.001Theeranuphattana, A., & Tang, J. C. S. (2007). A conceptual model of performance measurement for supply chains. Journal of Manufacturing Technology Management, 19(1), 125-148. doi:10.1108/17410380810843480Unahabhokha, C., Platts, K., & Hua Tan, K. (2007). Predictive performance measurement system. Benchmarking: An International Journal, 14(1), 77-91. doi:10.1108/14635770710730946Van der Vorst, J. G. A. J., & Beulens, A. J. M. (2002). Identifying sources of uncertainty to generate supply chain redesign strategies. International Journal of Physical Distribution & Logistics Management, 32(6), 409-430. doi:10.1108/09600030210437951Wei, C., Liou, T., & Lee, K. (2008). An ERP performance measurement framework using a fuzzy integral approach. Journal of Manufacturing Technology Management, 19(5), 607-626. doi:10.1108/17410380810877285Xu Xiao Xia, L., Ma, B. & Lim, R. (2008) "Supplier Performance Measurement in a Supply Chain". 6th IEEE Inter-national Conference on Industrial Informatics, pp. 877-881

Improving Distributed Decision Making in Inventory Management: A Combined ABC-AHP Approach Supported by Teamwork

[EN] The need of organizations to ensure service levels that impact on customer satisfaction has required the design of collaborative processes among stakeholders involved in inventory decision making. The increase of quantity and variety of items, on the one hand, and demand and customer expectations, on the other hand, are transformed into a greater complexity in inventory management, requiring effective communication and agreements between the leaders of the logistics processes. Traditionally, decision making in inventory management was based on approaches conditioned only by cost or sales volume. These approaches must be overcome by others that consider multiple criteria, involving several areas of the companies and taking into account the opinions of the stakeholders involved in these decisions. Inventory management becomes part of a complex system that involves stakeholders from different areas of the company, where each agent has limited information and where the cooperation between such agents is key for the system's performance. In this paper, a distributed inventory control approach was used with the decisions allowing communication between the stakeholders and with a multicriteria group decision-making perspective. This work proposes a methodology that combines the analysis of the value chain and the AHP technique, in order to improve communication and the performance of the areas related to inventory management decision making. This methodology uses the areas of the value chain as a theoretical framework to identify the criteria necessary for the application of the AHP multicriteria group decision-making technique. These criteria were defined as indicators that measure the performance of the areas of the value chain related to inventory management and were used to classify ABC inventory of the products according to these selected criteria. Therefore, the methodology allows us to solve inventory management DDM based on multicriteria ABC classification and was validated in a Colombian company belonging to the graphic arts sector.Pérez Vergara, IG.; Arias Sánchez, JA.; Poveda Bautista, R.; Diego-Mas, JA. (2020). Improving Distributed Decision Making in Inventory Management: A Combined ABC-AHP Approach Supported by Teamwork. Complexity. 2020:1-13. https://doi.org/10.1155/2020/6758108S1132020Poveda-Bautista, R., Baptista, D. C., & García-Melón, M. (2012). Setting competitiveness indicators using BSC and ANP. International Journal of Production Research, 50(17), 4738-4752. doi:10.1080/00207543.2012.657964Castro Zuluaga, C. A., Velez Gallego, M. C., & Catro Urrego, J. A. (2011). Clasificación ABC Multicriterio: Tipos de Criterios y efectos en la asignación de pesos. ITECKNE, 8(2). doi:10.15332/iteckne.v8i2.35Morash, E. A., & Clinton, S. R. (1998). Supply Chain Integration: Customer Value through Collaborative Closeness versus Operational Excellence. Journal of Marketing Theory and Practice, 6(4), 104-120. doi:10.1080/10696679.1998.11501814Fabbe-Costes, N. (2015). Évaluer la création de valeurdu Supply Chain Management. Logistique & Management, 23(4), 41-50. doi:10.1080/12507970.2015.11758621Flores, B. E., & Clay Whybark, D. (1986). Multiple Criteria ABC Analysis. International Journal of Operations & Production Management, 6(3), 38-46. doi:10.1108/eb054765Partovi, F. Y., & Burton, J. (1993). Using the Analytic Hierarchy Process for ABC Analysis. International Journal of Operations & Production Management, 13(9), 29-44. doi:10.1108/01443579310043619Balaji, K., & Kumar, V. S. S. (2014). Multicriteria Inventory ABC Classification in an Automobile Rubber Components Manufacturing Industry. Procedia CIRP, 17, 463-468. doi:10.1016/j.procir.2014.02.044Ramanathan, R. (2006). ABC inventory classification with multiple-criteria using weighted linear optimization. Computers & Operations Research, 33(3), 695-700. doi:10.1016/j.cor.2004.07.014Van Kampen, T. J., Akkerman, R., & Pieter van Donk, D. (2012). SKU classification: a literature review and conceptual framework. International Journal of Operations & Production Management, 32(7), 850-876. doi:10.1108/01443571211250112Flores, B. E., Olson, D. L., & Dorai, V. K. (1992). Management of multicriteria inventory classification. Mathematical and Computer Modelling, 16(12), 71-82. doi:10.1016/0895-7177(92)90021-cGajpal, P. P., Ganesh, L. S., & Rajendran, C. (1994). Criticality analysis of spare parts using the analytic hierarchy process. International Journal of Production Economics, 35(1-3), 293-297. doi:10.1016/0925-5273(94)90095-7Scala, N. M., Rajgopal, J., & Needy, K. L. (2014). Managing Nuclear Spare Parts Inventories: A Data Driven Methodology. IEEE Transactions on Engineering Management, 61(1), 28-37. doi:10.1109/tem.2013.2283170Hadad, Y., & Keren, B. (2013). ABC inventory classification via linear discriminant analysis and ranking methods. International Journal of Logistics Systems and Management, 14(4), 387. doi:10.1504/ijlsm.2013.052744Altay Guvenir, H., & Erel, E. (1998). Multicriteria inventory classification using a genetic algorithm. European Journal of Operational Research, 105(1), 29-37. doi:10.1016/s0377-2217(97)00039-8Rezaei, J., & Dowlatshahi, S. (2010). A rule-based multi-criteria approach to inventory classification. International Journal of Production Research, 48(23), 7107-7126. doi:10.1080/00207540903348361Hatefi, S. M., Torabi, S. A., & Bagheri, P. (2013). Multi-criteria ABC inventory classification with mixed quantitative and qualitative criteria. International Journal of Production Research, 52(3), 776-786. doi:10.1080/00207543.2013.838328Ishizaka, A., Pearman, C., & Nemery, P. (2012). AHPSort: an AHP-based method for sorting problems. International Journal of Production Research, 50(17), 4767-4784. doi:10.1080/00207543.2012.657966Yu, M.-C. (2011). Multi-criteria ABC analysis using artificial-intelligence-based classification techniques. Expert Systems with Applications, 38(4), 3416-3421. doi:10.1016/j.eswa.2010.08.127Tsai, C.-Y., & Yeh, S.-W. (2008). A multiple objective particle swarm optimization approach for inventory classification. International Journal of Production Economics, 114(2), 656-666. doi:10.1016/j.ijpe.2008.02.017Aydin Keskin, G., & Ozkan, C. (2013). Multiple Criteria ABC Analysis with FCM Clustering. Journal of Industrial Engineering, 2013, 1-7. doi:10.1155/2013/827274Lolli, F., Ishizaka, A., & Gamberini, R. (2014). New AHP-based approaches for multi-criteria inventory classification. International Journal of Production Economics, 156, 62-74. doi:10.1016/j.ijpe.2014.05.015Raja, A. M. L., Ai, T. J., & Astanti, R. D. (2016). A Clustering Classification of Spare Parts for Improving Inventory Policies. IOP Conference Series: Materials Science and Engineering, 114, 012075. doi:10.1088/1757-899x/114/1/012075Zowid, F. M., Babai, M. Z., Douissa, M. R., & Ducq, Y. (2019). Multi-criteria inventory ABC classification using Gaussian Mixture Model. IFAC-PapersOnLine, 52(13), 1925-1930. doi:10.1016/j.ifacol.2019.11.484Babai, M. Z., Ladhari, T., & Lajili, I. (2014). On the inventory performance of multi-criteria classification methods: empirical investigation. International Journal of Production Research, 53(1), 279-290. doi:10.1080/00207543.2014.952791Schneeweiss, C. (2003). Distributed decision making––a unified approach. European Journal of Operational Research, 150(2), 237-252. doi:10.1016/s0377-2217(02)00501-5Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83. doi:10.1504/ijssci.2008.017590Cakir, O., & Canbolat, M. S. (2008). A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology. Expert Systems with Applications, 35(3), 1367-1378. doi:10.1016/j.eswa.2007.08.041Liu, J., Liao, X., Zhao, W., & Yang, N. (2016). A classification approach based on the outranking model for multiple criteria ABC analysis. Omega, 61, 19-34. doi:10.1016/j.omega.2015.07.004Douissa, M. R., & Jabeur, K. (2016). A New Model for Multi-criteria ABC Inventory Classification: PROAFTN Method. Procedia Computer Science, 96, 550-559. doi:10.1016/j.procs.2016.08.233Lolli, F., Balugani, E., Ishizaka, A., Gamberini, R., Rimini, B., & Regattieri, A. (2018). Machine learning for multi-criteria inventory classification applied to intermittent demand. Production Planning & Control, 30(1), 76-89. doi:10.1080/09537287.2018.1525506Kartal, H., Oztekin, A., Gunasekaran, A., & Cebi, F. (2016). An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Computers & Industrial Engineering, 101, 599-613. doi:10.1016/j.cie.2016.06.004López-Soto, D., Angel-Bello, F., Yacout, S., & Alvarez, A. (2017). A multi-start algorithm to design a multi-class classifier for a multi-criteria ABC inventory classification problem. Expert Systems with Applications, 81, 12-21. doi:10.1016/j.eswa.2017.02.048Dweiri, F., Kumar, S., Khan, S. A., & Jain, V. (2016). Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Systems with Applications, 62, 273-283. doi:10.1016/j.eswa.2016.06.030Bruno, G., Esposito, E., Genovese, A., & Simpson, M. (2016). Applying supplier selection methodologies in a multi-stakeholder environment: A case study and a critical assessment. Expert Systems with Applications, 43, 271-285. doi:10.1016/j.eswa.2015.07.016Poza, C. (2020). A Conceptual Model to Measure Football Player’s Market Value. A Proposal by means of an Analytic Hierarchy Process. [Un modelo conceptual para medir el valor de mercado de los futbolistas. Una propuesta a través de un proceso analítico jerárquico]. RICYDE. Revista internacional de ciencias del deporte, 16(59), 24-42. doi:10.5232/ricyde2020.05903Guarnieri, P., Sobreiro, V. A., Nagano, M. S., & Marques Serrano, A. L. (2015). The challenge of selecting and evaluating third-party reverse logistics providers in a multicriteria perspective: a Brazilian case. 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Neutrosophic Completion Technique for Incomplete Higher-Order AHP Comparison Matrices

[EN] After the recent establishment of the Sustainable Development Goals and the Agenda 2030, the sustainable design of products in general and infrastructures in particular emerge as a challenging field for the development and application of multicriteria decision-making tools. Sustainability-related decision problems usually involve, by definition, a wide variety in number and nature of conflicting criteria, thus pushing the limits of conventional multicriteria decision-making tools practices. The greater the number of criteria and the more complex the relations existing between them in a decisional problem, the less accurate and certain are the judgments required by usual methods, such as the analytic hierarchy process (AHP). The present paper proposes a neutrosophic AHP completion methodology to reduce the number of judgments required to be emitted by the decision maker. This increases the consistency of their responses, while accounting for uncertainties associated to the fuzziness of human thinking. The method is applied to a sustainable-design problem, resulting in weight estimations that allow for a reduction of up to 22% of the conventionally required comparisons, with an average accuracy below 10% between estimates and the weights resulting from a conventionally completed AHP matrix, and a root mean standard error below 15%.The authors acknowledge the financial support of the Spanish Ministry of Economy and Business, along with FEDER funding (DIMALIFE Project: BIA2017-85098-R).Navarro, IJ.; Martí Albiñana, JV.; Yepes, V. (2021). Neutrosophic Completion Technique for Incomplete Higher-Order AHP Comparison Matrices. Mathematics. 9(5):1-19. https://doi.org/10.3390/math905049611995Worrell, E., Price, L., Martin, N., Hendriks, C., & Meida, L. O. (2001). CARBON DIOXIDE EMISSIONS FROM THE GLOBAL CEMENT INDUSTRY. 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Fully automatic smartphone-based photogrammetric 3D modelling of infant¿s heads for cranial deformation analysis

[EN] Image-based and range-based solutions can be used for the acquisition of valuable data in medicine. However, most of these methods are not valid for non-static patients. Cranial deformation is a problem with high prevalence among infants and image-based solutions can be used to assess the degree of deformation and monitor the evolution of patients. However, it is required to deal with infants normal movement during the assessment in order to avoid sedation. Some high-end multiple-sensor image-based solutions allow the achievement of accurate 3D data for medical applications under unpredicted dynamic conditions in consultation. In this paper, a novel, single photogrammetric smartphone-based solution for cranial deformation assessment is presented. A coded cap is placed on the infant's head and a guided smartphone app is used by the user to acquire the information, that is later processed on a server to obtain the 3D model. The smartphone app is designed to guide users with no knowledge of photogrammetry, computer vision or 3D modelling. The processing is fully automatic offline. The photogrammetric tool is also non-invasive, reacting well with quick and sudden infant's movements. Therefore, it does not require sedation. This paper tackles the accuracy and repeatability analysis tested both for a single user (intrauser) and multiple non-expert user (interuser) on 3D printed head models. The results allow us to confirm an accuracy below 1.5 mm, which makes the system suitable for clinical practice by medical staff. The basic automatically-derived anthropometric linear magnitudes are also tested obtaining a mean variability of 0.6 +/- 0.6 mm for the longitudinal and transversal distances and 1.4 +/- 1.3 mm for the maximum perimeter.This project is funded by Instituto de Salud Carlos III and European Regional Development Fund (FEDER), project number PI18/00881, and by Generalitat Valenciana, grant number ACIF/2017/056.Barbero-García, I.; Lerma, JL.; Mora Navarro, JG. (2020). Fully automatic smartphone-based photogrammetric 3D modelling of infant¿s heads for cranial deformation analysis. ISPRS Journal of Photogrammetry and Remote Sensing. 166:268-277. https://doi.org/10.1016/j.isprsjprs.2020.06.013S268277166Aldridge, K., Boyadjiev, S. A., Capone, G. T., DeLeon, V. B., & Richtsmeier, J. T. (2005). Precision and error of three-dimensional phenotypic measures acquired from 3dMD photogrammetric images. American Journal of Medical Genetics Part A, 138A(3), 247-253. doi:10.1002/ajmg.a.30959Argenta, L. 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