Numerical and Implementation Issues in Food Quality Modeling for Human Diseases Prevention

A. Galletti, R. Montella, L. Marcellino, A. Riccio, D. Di Luccio, A. Brizius, I. Foster

2017

Abstract

Monitoring nearshore sea water pollution using connected smart devices could be nowadays impracticable due to the aggressive saline environment, the network availability and the maintain and calibration costs. Accurate forecast of marine pollution is most needed to evaluate the adverse effects on coastal inhabitants’ health when fishes and mussels farming economically characterizes the local social background. In an operational context, numerical simulations are performed routinely on a dedicated computational infrastructure producing space and temporal high-resolution predictions of weather and marine conditions of the Bay of Naples. In this paper we present our results in developing a community open source Lagrangian pollutant transport and dispersion model, leveraging on hierarchical parallelism implying distributed memory, shared memory and GPGPUs. Some numerical details are also discussed. This system has been used to develop an alarm system to help local authorities in making decisions regarding the collection of mussels. The model setup and the simulation results will be improved using FairWind, an under development system dedicated to coastal marine crowdsourced data gathering and sharing, based on smart devices and Internet of Things afloat.

References

  1. Ascione, I., Giunta, G., Mariani, P., Montella, R., and Riccio, A. (2006). A grid computing based virtual laboratory for environmental simulations. In European Conference on Parallel Processing, pages 1085-1094. Springer.
  2. Barone, G., D'Ambra, P., di Serafino, D., Giunta, G., Murli, A., and Riccio, A. (2000). Application of a parallel photochemical air quality model to the campania region (southern italy). Environmental Modelling & Software, 15(6):503-511.
  3. Carracciuolo, L., D'Amore, L., and Mele, V. (2015). Toward a fully parallel multigrid in time algorithm in petsc environment: A case study in ocean models. In High Performance Computing & Simulation (HPCS), 2015 International Conference on, pages 595-598. IEEE.
  4. Croci, L., De Medici, D., Ciccozzi, M., Di Pasquale, S., Suffredini, E., and Toti, L. (2003). Contamination of mussels by hepatitis a virus: a public-health problem in southern italy. Food control, 14(8):559-563.
  5. Cuomo, S., Galletti, A., Giunta, G., and Marcellino, L. (2014a). A class of piecewise interpolating functions based on barycentric coordinates. Ricerche di Matematica, 63(11):87-102.
  6. Cuomo, S., Galletti, A., Giunta, G., and Marcellino, L. (2014b). A novel triangle-based method for scattered data interpolation. Applied Mathematical Sciences, 8(134):6717-6724.
  7. Cuomo, S., Galletti, A., Giunta, G., and Marcellino, L. (2015). Piecewise hermite interpolation via barycentric coordinates. Ricerche di Matematica, 64(2):303- 319.
  8. Cuomo, S., Galletti, A., Giunta, G., and Starace, A. (2013). Surface reconstruction from scattered point via rbf interpolation on gpu. In Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on, pages 433-440. IEEE.
  9. Fasshauer, G. E. (2007). Meshfree approximation methods with MATLAB, volume 6. World Scientific.
  10. Foster, I. (2011). Globus Online: Accelerating and democratizing science through cloud-based services. IEEE Internet Computing, 15(3):70.
  11. Galletti, A. and Maratea, A. (2016). A bound for the accuracy of sensors acquiring compositional data. Procedia Computer Science, 98:485 - 490. The 7th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2016)/The 6th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2016)/Affiliated Workshops.
  12. Giunta, G., Montella, R., Mariani, P., and Riccio, A. (2005). Modeling and computational issues for air/water quality problems: A grid computing approach. Nuovo Cimento C Geophysics Space Physics C, 28:215.
  13. Haidvogel, D. B., Arango, H. G., Hedstrom, K., Beckmann, A., Malanotte-Rizzoli, P., and Shchepetkin, A. F. (2000). Model evaluation experiments in the north atlantic basin: simulations in nonlinear terrainfollowing coordinates. Dynamics of Atmospheres and Oceans, 32(3):239-281.
  14. Haron, N. S., Mahamad, M. K., Aziz, I. A., and Mehat, M. (2009). Remote water quality monitoring system using wireless sensors. In Proceedings of the 8th WSEAS International Conference on Electronics, Hardware, Wireless and Optical Communications (EHAC'09), Cambridge, UK, pages 148-154.
  15. Laccetti, G., Lapegna, M., Mele, V., and Romano, D. (2013). A study on adaptive algorithms for numerical quadrature on heterogeneous gpu and multicore based systems. In International Conference on Parallel Processing and Applied Mathematics, pages 704- 713. Springer.
  16. Madduri, R., Chard, K., Chard, R., Lacinski, L., Rodriguez, A., Sulakhe, D., Kelly, D., Dave, U., and Foster, I. (2015). The Globus Galaxies platform: Delivering science gateways as a service. Concurrency and Computation: Practice and Experience, 27(16):4344- 4360. CPE-15-0040.
  17. Montella, R., Coviello, G., Giunta, G., Laccetti, G., Isaila, F., and Blas, J. G. (2011). A general-purpose virtualization service for HPC on cloud computing: an application to GPUs. In International Conference on Parallel Processing and Applied Mathematics, pages 740-749. Springer.
  18. Montella, R., Di Luccio, D., Troiano, P., Brizius, A., and Foster, I. (2016a). WaComM: A parallel Water quality Community Model for pollutant transport and dispersion operational predictions. In International Workshop on Numerical Algorithms and Methods for Data Analysis and Classification, 2016. NAMDAC'16. IEEE. IEEE.
  19. Montella, R., Ferraro, C., Kosta, S., Pelliccia, V., and Giunta, G. (2016b). Enabling android-based devices to high-end gpgpus. In Algorithms and Architectures for Parallel Processing, pages 118-125. Springer.
  20. Montella, R., Giunta, G., and Riccio, A. (2007). Using grid computing based components in on demand environmental data delivery. In Proceedings of the second workshop on Use of P2P, GRID and agents for the development of content networks, pages 81-86. ACM.
  21. Montella, R., Kelly, D., Xiong, W., Brizius, A., Elliott, J., Madduri, R., Maheshwari, K., Porter, C., Vilter, P., Wilde, M., et al. (2015). FACE-IT: A science gateway for food security research. Concurrency and Computation: Practice and Experience, 27(16):4423-4436.
  22. Montella, R., Luccio, D. D., Ferraro, C., Izzo, F., Troiano, P., and Giunta, G. (2016c). Fairwind: a marine data crowdsourcing platform based on internet of things and mobile/cloud computing technologies. In 8th International Workshop on Modeling the Ocean (IWMO), Bologna, Italy.
  23. Ortega, G., Puertas, A., de Las Nieves, F. J., and MartinGarzón, E. (2016). Gpu computing to speed-up the resolution of microrheology models. In Algorithms and Architectures for Parallel Processing, pages 457- 466. Springer.
  24. Pham, Q., Malik, T., Foster, I. T., Di Lauro, R., and Montella, R. (2012). SOLE: Linking Research Papers with Science Objects. In IPAW, pages 203-208. Springer.
  25. Rodean, H. C. (1996). Stochastic Lagrangian models of turbulent diffusion, volume 45. Springer.
  26. Shchepetkin, A. F. and McWilliams, J. C. (2003). A method for computing horizontal pressure-gradient force in an oceanic model with a nonaligned vertical coordinate. Journal of Geophysical Research: Oceans, 108(C3).
  27. Shchepetkin, A. F. and McWilliams, J. C. (2005). "the regional oceanic modeling system (roms): a split-explicit, free-surface, topography-followingcoordinate oceanic model". Ocean Modelling, 9(4):347 - 404.
  28. Skamarock, W. C., Klemp, J. B., and Dudhia, J. (2001). Prototypes for the wrf (weather research and forecasting) model. In Preprints, Ninth Conf. Mesoscale Processes, J11-J15, Amer. Meteorol. Soc., Fort Lauderdale, FL.
  29. Suffredini, E., Lanni, L., Arcangeli, G., Pepe, T., Mazzette, R., Ciccaglioni, G., and Croci, L. (2014). Qualitative and quantitative assessment of viral contamination in bivalve molluscs harvested in italy. International journal of food microbiology, 184:21-26.
  30. Wilkin, J. L., Arango, H. G., Haidvogel, D. B., Lichtenwalner, C., Glenn, S. M., and Hedström, K. S. (2005). A regional ocean modeling system for the long-term ecosystem observatory. Journal of Geophysical Research: Oceans, 110(C6).
Download


Paper Citation


in Harvard Style

Galletti A., Montella R., Marcellino L., Riccio A., Di Luccio D., Brizius A. and Foster I. (2017). Numerical and Implementation Issues in Food Quality Modeling for Human Diseases Prevention . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: SmartMedDev, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 526-534. DOI: 10.5220/0006297905260534


in Bibtex Style

@conference{smartmeddev17,
author={A. Galletti and R. Montella and L. Marcellino and A. Riccio and D. Di Luccio and A. Brizius and I. Foster},
title={Numerical and Implementation Issues in Food Quality Modeling for Human Diseases Prevention},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: SmartMedDev, (BIOSTEC 2017)},
year={2017},
pages={526-534},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006297905260534},
isbn={978-989-758-213-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: SmartMedDev, (BIOSTEC 2017)
TI - Numerical and Implementation Issues in Food Quality Modeling for Human Diseases Prevention
SN - 978-989-758-213-4
AU - Galletti A.
AU - Montella R.
AU - Marcellino L.
AU - Riccio A.
AU - Di Luccio D.
AU - Brizius A.
AU - Foster I.
PY - 2017
SP - 526
EP - 534
DO - 10.5220/0006297905260534