Insoluble soybean polysaccharides: Obtaining and evaluation of their O/W emulsifying properties

The aims of this work were to obtain different samples of insoluble soybean polysaccharides (ISPS) from defatted soy flour and to study their potential application as O/W emulsifier. In this regard, the insoluble residue (okara) resulting from an aqueous extraction (60 °C, pH 9.0), was submitted to an acidic extraction (pH 3.5, 120 °C) without or with a pretreatment (high pressure homogenization or sonication treatment). The insoluble residues of these extractions were dried (oven, 70 °C or vacuum post-treatment with 2-propanol, 40 °C) yielding different ISPS samples. Aqueous dispersions of ISPS samples (1?2% w/w, pH 3 and 7), were used to prepare coarse and fine O/W emulsions. Emulsion stability against creaming and coalescence processes, and the rheological behavior were analyzed. ISPS samples obtained by okara pretreatment and vacuum dried post-treatment with 2-propanol allow to produces emulsions with high values of flocculation degree, increasing the stability of the particle size, and allowing the formation of stronger gel-like emulsions. These pretreatments expose internal sites of the polysaccharide and protein structures, increasing their superficial hydrophobicity and, therefore, allow a strong absorption of the macromolecules at the oil-water interface and/or the formation of external layers, increasing the rigidity of the interfacial film and contributing to the formation of hydrated flocs, Also, these treatments could solubilize certain compounds in okara that would interfere negatively in the formation of the interfacial film. Particularly, sample obtained by high pressures homogenization of the okara presented the best emulsifying properties and it was not significantly affected by variations in the pH of the emulsion. The results of this research work demonstrate a high potential of application of the ISPS samples as O/W emulsifier, under acid and neutral conditions, increasing the added value of an important by-product of the soybean industry.Fil: Porfiri, María Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Laboratorio de Investigación en Funcionalidad y Tecnología de Alimentos; ArgentinaFil: Vaccaro, J.. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Laboratorio de Investigación en Funcionalidad y Tecnología de Alimentos; ArgentinaFil: Stortz, Carlos Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones en Hidratos de Carbono. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones en Hidratos de Carbono; ArgentinaFil: Navarro, Diego Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones en Hidratos de Carbono. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones en Hidratos de Carbono; ArgentinaFil: Wagner, Jorge Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Laboratorio de Investigación en Funcionalidad y Tecnología de Alimentos; ArgentinaFil: Cabezas, Dario Marcelino. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Laboratorio de Investigación en Funcionalidad y Tecnología de Alimentos; Argentin

Infraestructuras azules urbanas como herramienta de conservación de aves

Los humedales se relacionan directamente con la presencia de aves, que no solo dan placer como parte del paisaje urbano, rural o silvestre, sino además cumplen importantes servicios ecológicos. Su integración a una ciudad sustentable permitiría agrandar y conectar áreas verdes y servicios ecológicos importantes. En este contexto, las infraestructuras azules constituyen elementos intrínsecamente relacionados con las infraestructuras verdes, en los que las componentes o procesos relacionados con el agua cuentan con una especial relevancia para entender su funcionamiento y los servicios que aportan. Una planificación y gestión adecuada del agua y de sus ecosistemas asociados resulta imprescindible para la mejora integrada de los procesos territoriales; no sólo por las cuestiones ligadas al recurso (dotación y tratamiento del agua, producción alimenticia, recarga de acuíferos o control de inundaciones), sino también por sus efectos psicológicos y emocionales en los ciudadanos. Las infraestructuras azules se convierten en nodos de corredores ecológicos interurbanos, sirviendo como conducto a los desplazamientos, y facilitando el intercambio genético (Gurrutxaga San Vicente & Lozano Valencia, 2008) de fauna entre parches, que de otra forma se encontrarían aislados (Bennett, 2003). Es así como se define como Corredor Biológico Interurbano (CBI) a la extensión territorial que proporciona conectividad entre paisajes, ecosistemas, hábitats modificados o naturales. Esto beneficia el mantenimiento y recambio genético, y la propagación de especies favoreciendo las migraciones y conexión de ecosistemas. El objetivo de este proyecto fue analizar la función de las infraestructuras azules en el tramo inferior del río Limay para la protección de especies amenazadas de aves.Facultad de Informátic

A high-performance IoT solution to reduce frost damages in stone fruits

[EN] Agriculture is one of the key sectors where technology is opening new opportunities to break up the market. The Internet of Things (IoT) could reduce the production costs and increase the product quality by providing intelligence services via IoT analytics. However, the hard weather conditions and the lack of connectivity in this field limit the successful deployment of such services as they require both, ie, fully connected infrastructures and highly computational resources. Edge computing has emerged as a solution to bring computing power in close proximity to the sensors, providing energy savings, highly responsive web services, and the ability to mask transient cloud outages. In this paper, we propose an IoT monitoring system to activate anti-frost techniques to avoid crop loss, by defining two intelligent services to detect outliers caused by the sensor errors. The former is a nearest neighbor technique and the latter is the k-means algorithm, which provides better quality results but it increases the computational cost. Cloud versus edge computing approaches are analyzed by targeting two different low-power GPUs. Our experimental results show that cloud-based approaches provides highest performance in general but edge computing is a compelling alternative to mask transient cloud outages and provide highly responsive data analytic services in technologically hostile environments.This work was partially supported by the Fundación Séneca del Centro de Coordinación de la Investigación de la Región de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants TIN2016-78799-P (AEI/FEDER, UE) and RTC-2017-6389-5. Finally, we thank the farmers for the availability of their resources to be able to asses and improve the IoT monitoring system proposed.Guillén-Navarro, MA.; Martínez-España, R.; López, B.; Cecilia-Canales, JM. (2021). A high-performance IoT solution to reduce frost damages in stone fruits. Concurrency and Computation: Practice and Experience (Online). 33(2):1-14. https://doi.org/10.1002/cpe.529911433

Adherence to supportive periodontal treatment in relation to patient awareness

To evaluate the risk profile of noncompliant patients in relation to adherence to supportive periodontal therapy in order to identify factors associated with this profile, and be able to prevent the abandonment of perio- dontal therapy. Ma

Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network

[EN] Wireless acoustic sensor networks are nowadays an essential tool for noise pollution monitoring and managing in cities. The increased computing capacity of the nodes that create the network is allowing the addition of processing algorithms and artificial intelligence that provide more information about the sound sources and environment, e.g., detect sound events or calculate loudness. Several models to predict sound pressure levels in cities are available, mainly road, railway and aerial traffic noise. However, these models are mostly based in auxiliary data, e.g., vehicles flow or street geometry, and predict equivalent levels for a temporal long-term. Therefore, forecasting of temporal short-term sound levels could be a helpful tool for urban planners and managers. In this work, a Long Short-Term Memory (LSTM) deep neural network technique is proposed to model temporal behavior of sound levels at a certain location, both sound pressure level and loudness level, in order to predict near-time future values. The proposed technique can be trained for and integrated in every node of a sensor network to provide novel functionalities, e.g., a method of early warning against noise pollution and of backup in case of node or network malfunction. To validate this approach, one-minute period equivalent sound levels, captured in a two-month measurement campaign by a node of a deployed network of acoustic sensors, have been used to train it and to obtain different forecasting models. Assessments of the developed LSTM models and Auto regressive integrated moving average models were performed to predict sound levels for several time periods, from 1 to 60 min. Comparison of the results show that the LSTM models outperform the statistics-based models. In general, the LSTM models achieve a prediction of values with a mean square error less than 4.3 dB for sound pressure level and less than 2 phons for loudness. Moreover, the goodness of fit of the LSTM models and the behavior pattern of the data in terms of prediction of sound levels are satisfactory.This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18.Navarro, JM.; Martínez-España, R.; Bueno-Crespo, A.; Cecilia-Canales, JM.; Martínez, R. (2020). Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network. Sensors. 20(3):1-16. https://doi.org/10.3390/s20030903S116203Hornikx, M. (2016). Ten questions concerning computational urban acoustics. Building and Environment, 106, 409-421. doi:10.1016/j.buildenv.2016.06.028Murphy, E., & King, E. A. (2010). Strategic environmental noise mapping: Methodological issues concerning the implementation of the EU Environmental Noise Directive and their policy implications. Environment International, 36(3), 290-298. doi:10.1016/j.envint.2009.11.006Arana, M., San Martin, R., San Martin, M. L., & Aramendía, E. (2009). Strategic noise map of a major road carried out with two environmental prediction software packages. Environmental Monitoring and Assessment, 163(1-4), 503-513. doi:10.1007/s10661-009-0853-5Garg, N., & Maji, S. (2014). A critical review of principal traffic noise models: Strategies and implications. Environmental Impact Assessment Review, 46, 68-81. doi:10.1016/j.eiar.2014.02.001Steele, C. (2001). A critical review of some traffic noise prediction models. Applied Acoustics, 62(3), 271-287. doi:10.1016/s0003-682x(00)00030-xLi, B., Tao, S., Dawson, R. W., Cao, J., & Lam, K. (2002). A GIS based road traffic noise prediction model. Applied Acoustics, 63(6), 679-691. doi:10.1016/s0003-682x(01)00066-4VAN LEEUWEN, H. J. A. (2000). RAILWAY NOISE PREDICTION MODELS: A COMPARISON. Journal of Sound and Vibration, 231(3), 975-987. doi:10.1006/jsvi.1999.2570Lui, W. K., Li, K. M., Ng, P. L., & Frommer, G. H. (2006). A comparative study of different numerical models for predicting train noise in high-rise cities. Applied Acoustics, 67(5), 432-449. doi:10.1016/j.apacoust.2005.08.005Van Leeuwen, J. J. A. (1996). NOISE PREDICTIONS MODELS TO DETERMINE THE EFFECT OF BARRIERS PLACED ALONGSIDE RAILWAY LINES. Journal of Sound and Vibration, 193(1), 269-276. doi:10.1006/jsvi.1996.0267Oerlemans, S., & Schepers, J. G. (2009). Prediction of Wind Turbine Noise and Validation against Experiment. International Journal of Aeroacoustics, 8(6), 555-584. doi:10.1260/147547209789141489Tadamasa, A., & Zangeneh, M. (2011). Numerical prediction of wind turbine noise. Renewable Energy, 36(7), 1902-1912. doi:10.1016/j.renene.2010.11.036Maisonneuve, N., Stevens, M., & Ochab, B. (2010). Participatory noise pollution monitoring using mobile phones. Information Polity, 15(1,2), 51-71. doi:10.3233/ip-2010-0200Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393-422. doi:10.1016/s1389-1286(01)00302-4Peckens, C., Porter, C., & Rink, T. (2018). Wireless Sensor Networks for Long-Term Monitoring of Urban Noise. Sensors, 18(9), 3161. doi:10.3390/s18093161Alías, F., & Alsina-Pagès, R. M. (2019). Review of Wireless Acoustic Sensor Networks for Environmental Noise Monitoring in Smart Cities. Journal of Sensors, 2019, 1-13. doi:10.1155/2019/7634860Mydlarz, C., Salamon, J., & Bello, J. P. (2017). The implementation of low-cost urban acoustic monitoring devices. Applied Acoustics, 117, 207-218. doi:10.1016/j.apacoust.2016.06.010Navarro, J. M., Tomas-Gabarron, J. B., & Escolano, J. (2017). A Big Data Framework for Urban Noise Analysis and Management in Smart Cities. Acta Acustica united with Acustica, 103(4), 552-560. doi:10.3813/aaa.919084Längkvist, M., Karlsson, L., & Loutfi, A. (2014). A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters, 42, 11-24. doi:10.1016/j.patrec.2014.01.008Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018). Recurrent Neural Networks for Multivariate Time Series with Missing Values. Scientific Reports, 8(1). doi:10.1038/s41598-018-24271-9Kim, H.-G., & Kim, J. Y. (2017). Environmental sound event detection in wireless acoustic sensor networks for home telemonitoring. China Communications, 14(9), 1-10. doi:10.1109/cc.2017.8068759Luque, A., Romero-Lemos, J., Carrasco, A., & Barbancho, J. (2018). Improving Classification Algorithms by Considering Score Series in Wireless Acoustic Sensor Networks. Sensors, 18(8), 2465. doi:10.3390/s18082465Zhang, Y., Fu, Y., & Wang, R. (2018). Collaborative representation based classification for vehicle recognition in acoustic sensor networks. Journal of Computational Methods in Sciences and Engineering, 18(2), 349-358. doi:10.3233/jcm-180794Cobos, M., Perez-Solano, J. J., Felici-Castell, S., Segura, J., & Navarro, J. M. (2014). Cumulative-Sum-Based Localization of Sound Events in Low-Cost Wireless Acoustic Sensor Networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12), 1792-1802. doi:10.1109/taslp.2014.2351132Sevillano, X., Socoró, J. C., Alías, F., Bellucci, P., Peruzzi, L., Radaelli, S., … Zambon, G. (2016). DYNAMAP – Development of low cost sensors networks for real time noise mapping. Noise Mapping, 3(1). doi:10.1515/noise-2016-0013Segura-Garcia, J., Navarro-Ruiz, J., Perez-Solano, J., Montoya-Belmonte, J., Felici-Castell, S., Cobos, M., & Torres-Aranda, A. (2018). Spatio-Temporal Analysis of Urban Acoustic Environments with Binaural Psycho-Acoustical Considerations for IoT-Based Applications. Sensors, 18(3), 690. doi:10.3390/s18030690Bello, J. P., Silva, C., Nov, O., Dubois, R. L., Arora, A., Salamon, J., … Doraiswamy, H. (2019). SONYC. Communications of the ACM, 62(2), 68-77. doi:10.1145/3224204Socoró, J., Alías, F., & Alsina-Pagès, R. (2017). An Anomalous Noise Events Detector for Dynamic Road Traffic Noise Mapping in Real-Life Urban and Suburban Environments. Sensors, 17(10), 2323. doi:10.3390/s17102323Yu, L., & Kang, J. (2009). Modeling subjective evaluation of soundscape quality in urban open spaces: An artificial neural network approach. The Journal of the Acoustical Society of America, 126(3), 1163-1174. doi:10.1121/1.3183377Lopez-Ballester, J., Pastor-Aparicio, A., Segura-Garcia, J., Felici-Castell, S., & Cobos, M. (2019). Computation of Psycho-Acoustic Annoyance Using Deep Neural Networks. Applied Sciences, 9(15), 3136. doi:10.3390/app9153136Mansourkhaki, A., Berangi, M., Haghiri, M., & Haghani, M. (2018). A NEURAL NETWORK NOISE PREDICTION MODEL FOR TEHRAN URBAN ROADS. Journal of Environmental Engineering and Landscape Management, 26(2), 88-97. doi:10.3846/16486897.2017.1356327Pedersen, K., Transtrum, M. K., Gee, K. L., Butler, B. A., James, M. M., & Salton, A. R. (2018). Machine learning-based ensemble model predictions of outdoor ambient sound levels. 2019 International Congress on Ultrasonics. doi:10.1121/2.0001056Torija, A. J., Ruiz, D. P., & Ramos-Ridao, A. F. (2012). Use of back-propagation neural networks to predict both level and temporal-spectral composition of sound pressure in urban sound environments. Building and Environment, 52, 45-56. doi:10.1016/j.buildenv.2011.12.024Garg, N., Soni, K., Saxena, T. K., & Maji, S. (2015). Applications of AutoRegressive Integrated Moving Average (ARIMA) approach in time-series prediction of traffic noise pollution. Noise Control Engineering Journal, 63(2), 182-194. doi:10.3397/1/376317Tong, W., Li, L., Zhou, X., Hamilton, A., & Zhang, K. (2019). Deep learning PM2.5 concentrations with bidirectional LSTM RNN. Air Quality, Atmosphere & Health, 12(4), 411-423. doi:10.1007/s11869-018-0647-4Krishan, M., Jha, S., Das, J., Singh, A., Goyal, M. K., & Sekar, C. (2019). Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India. Air Quality, Atmosphere & Health, 12(8), 899-908. doi:10.1007/s11869-019-00696-7Noriega-Linares, J. E., Rodriguez-Mayol, A., Cobos, M., Segura-Garcia, J., Felici-Castell, S., & Navarro, J. M. (2017). A Wireless Acoustic Array System for Binaural Loudness Evaluation in Cities. IEEE Sensors Journal, 17(21), 7043-7052. doi:10.1109/jsen.2017.2751665Raspberry PI https://www.raspberrypi.orgLegates, D. R., & McCabe, G. J. (1999). Evaluating the use of «goodness-of-fit» Measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233-241. doi:10.1029/1998wr90001

Use of ecosystem health indicators for assessing anthropogenic impacts on freshwaters in Argentina: a review

Indicators of ecosystem health are effective tools to assess freshwater ecosystem impairment. However, they are scarcely used as a monitoring tool by local environmental agencies in Argentina. Here, we review the literature to analyze the use of ecosystem health indicators in freshwaters from Argentina. We found 91 scientific articles relating to the use of ecological indices to assess the impact of different environmental stressors in aquatic environments published between 1996 and 2019. We generated Google Earth map where we deployed the sampling sites and type of indices reported by each article. As biological indices were the most used, we also surveyed bioindication experts to gather information on their application. We found that most studies were concentrated mainly in Pampas (34%), Dry Chaco (20%), Espinal (12%), and Patagonian Steppe (10%) ecoregions. Biological indices (mainly with invertebrates) were more used than geomorphological or physico-chemical indices. Indices resulted useful to evaluate the impact of stressors in 63% of cases, being land use the most studied stressor. However, sampling design varied greatly among studies, making their comparison difficult. The information compiled here could help to the design of monitoring protocols, the adoption of regional indices, and the creation of a national inventory of ecosystem health status, which are mandatory to propose well-grounded conservation and management policies for freshwaters in Argentina.Fil: Rocha, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Universidad Nacional de Luján. Instituto de Ecología y Desarrollo Sustentable; ArgentinaFil: Hegoburu, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Universidad Nacional de Luján. Instituto de Ecología y Desarrollo Sustentable; ArgentinaFil: Torremorell, Ana María. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Universidad Nacional de Luján. Instituto de Ecología y Desarrollo Sustentable; ArgentinaFil: Feijoó, Claudia Silvina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Universidad Nacional de Luján. Instituto de Ecología y Desarrollo Sustentable; ArgentinaFil: Navarro, Enrique. Consejo Superior de Investigaciones Científicas; EspañaFil: Fernandez, Hugo Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Biodiversidad Neotropical. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Biodiversidad Neotropical. Instituto de Biodiversidad Neotropical; Argentin

Evaluación de la eficacia del registro multicanal del Potencial Evocado Auditivo de estado estable a Múltiples frecuencias (PEAeeMf)

Steady-state Auditory Evoked Potentials at multiple frequencies (ASSR-Mf) have reached a wide diffusion in the objective evaluation of hearing and have usually been recorded in a simple set-up of a bipolar lead, so there are no reports that characterize them in a multi-channel record setup. Objectives: To evaluate the efficacy of the new multi-channel recording methodology of the ASSR-Mf with high spatial resolution and to determine the effect of the physical parameters of acoustic stimulation on its amplitude and phase. Methods: A sample of 47 audiologically healthy young adults is studied. Monaural stimulation in the right ear with tones of 500 and 4000 Hz, amplitude modulated at 40 and 80 Hz. Recording is performed with the 10/20 montage and a modified 10/10 montage focused on the parietotemporal region of the left hemisphere. Results: The PEAeeMf obtains a high detection percentage in all the recording leads, similar to those obtained in the standard recording, the recording lead has a significant effect on the detection and amplitude of the potential by modulation at 40 Hz, while the carrier and modulating frequencies have a significant effect on the amplitude and phase of the potential. Conclusions: The multi-channel extended recording setup is effective for obtaining the MFEPE, so this design can be used with veracity for the design of optimized electroaudiometric evaluation protocols and for the study of the topography of auditory responses.Los Potenciales Evocados Auditivos de estado estable a múltiples frecuencias (PEAeeMf) han alcanzado una amplia difusión en la evaluación objetiva de la audición y usualmente han sido registrados en un montaje simple de una derivación bipolar, por lo que no existen reportes que los caractericen en un montaje de registro multicanal. Objetivos: Evaluar la efi cacia de la nueva metodología de registro multicanal del PEAeeMf de alta resolución espacial y determinar el efecto de los parámetros físicos de la estimulación acústica en su la amplitud y fase. Métodos: Se estudia una muestra de 47 adultos jóvenes audiológicamente sanos. Estimulación monoaural en el oído derecho con tonos de 500 y 4000 Hz, modulados en amplitud a 40 y 80 Hz. El registro se realiza con el montaje 10/20 y un montaje 10/10 modifi cado focalizado en la región parietotemporal del hemisferio izquierdo. Resultados: El PEAeeMf obtiene un alto porciento de detección en todas las derivaciones de registro, similares a las obtenidas en el registro estándar, la derivación de registro tiene un efecto signifi cativo en la detección y amplitud del potencial por modulación a 40 Hz, mientras que la frecuencias portadora y moduladora obtienen un efecto signifi cativo en la amplitud y fase del potencial. Conclusiones: El montaje de registro extendido de múltiples canales resulta efi caz para la obtención del PEAeeMf, por lo que este diseño puede ser utilizado con veracidad para el diseño de protocolos de evaluación electroaudiométrica optimizados y para el estudio de la topografía de las respuestas auditivas

Enhancing the context-aware FOREX market simulation using a parallel elastic network model

[EN] Foreign exchange (FOREX) market is a decentralized global marketplace in which different participants, such as international banks, companies or investors, can buy, sell, exchange and speculate on currencies. This market is considered to be the largest financial market in the world in terms of trading volume. Indeed, the just-in-time price prediction for a currency pair exchange rate (e.g., EUR/USD) provides valuable information for companies and investors as they can take different actions to improve their business. The trading volume in the FOREX market is huge, disperses, in continuous operations (24 h except weekends), and the context significantly affects the exchange rates. This paper introduces a context-aware algorithm to model the behavior of the FOREX Market, called parallel elastic network model (PENM). This algorithm is inspired by natural procedures like the behavior of macromolecules in dissolution. The main results of this work include the possibility to represent the market evolution of up to 21 currency pair, being all connected, thus emulating the real-world FOREX market behavior. Moreover, because the computational needs required are highly costly as the number of currency pairs increases, a hybrid parallelization using several shared memory and message passing algorithms studied on distributed cluster is evaluated to achieve a high-throughput algorithm that answers the real-time constraints of the FOREX market. The PENM is also compared with a vector autoregressive (VAR) model using both a classical statistical measure and a profitability measure. Specifically, the results indicate that PENM outperforms VAR models in terms of quality, achieving up to 930xspeed-up factor compared to traditional R codes using in this field.This work was jointly supported by the Fundación Séneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under Grant 20813/PI/18 and by the Spanish MEC and European Commission FEDER under Grants TIN2016-78799-P and TIN2016-80565-R (AEI/FEDER, UE).Contreras, AV.; Llanes, A.; Herrera, FJ.; Navarro, S.; López-Espin, JJ.; Cecilia-Canales, JM. (2020). Enhancing the context-aware FOREX market simulation using a parallel elastic network model. The Journal of Supercomputing. 76(3):2022-2038. https://doi.org/10.1007/s11227-019-02838-1S20222038763Bahrepour M, Akbarzadeh-T MR, Yaghoobi M, Naghibi-S MB (2011) An adaptive ordered fuzzy time series with application to FOREX. Expert Syst Appl 38(1):475–485Bank for International Settlements. https://www.bis.org/ . Accessed 13 Feb 2013Bhattacharyya S, Pictet OV, Zumbach G (2002) Knowledge-intensive genetic discovery in foreign exchange markets. IEEE Trans Evolut Comput 6(2):169–181Bank of International Settlements (2016) Triennial central bank survey: foreign exchange turnover in April 2016, BaselCaporale GM, Gil-Alana L, Plastun A (2017) Searching for inefficiencies in exchange rate dynamics. Comput Econ 49(3):405–432De Grauwe P, Markiewicz A (2013) Learning to forecast the exchange rate: two competing approaches. J Int Money Finance 32:42–76Fama E (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25(2):383–417Fama EF (1965) The behavior of stock-market prices. J Bus 38(1):34–105Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25(2):383–417Fuglebakk E, Reuter N, Hinsen K (2013) Evaluation of protein elastic network models based on an analysis of collective motions. J Chem Theory Comput 9(12):5618–5628Hanssens DM, Parsons LJ, Schultz RL (2003) Market response models: econometric and time series analysis, vol 12. Springer, New YorkKamruzzaman J, Sarker RA (2003) Forecasting of currency exchange rates using ANN: a case study. In: Proceedings of the 2003 International Conference on Neural Networks and Signal Processing, 2003, vol 1. IEEE, pp 793–797Kamruzzaman J, Sarker RA, Ahmad I (2003) SVM based models for predicting foreign currency exchange rates. In: Third IEEE International Conference on Data Mining, 2003. ICDM 2003, IEEE, pp. 557–560Karplus M, McCammon JA (2002) Molecular dynamics simulations of biomolecules. Nat Struct Mol Biol 9(9):646–652Kleen A (2015) Intel PMU profiling tools. https://github.com/andikleen/pmu-tools/tree/d70840ba . Accessed 15 Mar 2019Kuo RJ, Chen C, Hwang Y (2001) An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets Syst 118(1):21–45LeBaron B, Arthur WB, Palmer R (1999) Time series properties of an artificial stock market. J Econ Dyn Control 23(9):1487–1516Li Q, Chen Y, Wang J, Chen Y, Chen H (2017) Web media and stock markets: a survey and future directions from a big data perspective. IEEE Trans Knowl Data Eng 30:381–399Luetkepohl H (2009) Econometric analysis with vector autoregressive models. In: Belsley DA, Kontoghiorghes EJ (eds) Handbook of computational econometrics. Wiley, New York, pp 281–319Makovskỳ P (2014) Modern approaches to efficient market hypothesis of FOREX—the central European case. Proc Econ Finance 14:397–406Meade N (2002) A comparison of the accuracy of short term foreign exchange forecasting methods. Int J Forecast 18(1):67–83Meese RA, Rogoff K (1983) Empirical exchange rate models of the seventies: do they fit out of sample? J Int Econ 14(1–2):3–24Mockus J, Raudys A (2010) On the efficient-market hypothesis and stock exchange game model. Expert Syst Appl 37(8):5673–5681Nassirtoussi AK, Aghabozorgi S, Wah TY, Ngo DCL (2014) Text mining for market prediction: a systematic review. Expert Syst Appl 41(16):7653–7670Neely C, Weller P, Dittmar R (1997) Is technical analysis in the foreign exchange market profitable? A genetic programming approach. J Financial Quant Anal 32(4):405–426Pincak R (2013) The string prediction models as invariants of time series in the FOREX market. Phys A: Stat Mech Appl 392(24):6414–6426Samuelson PA (2016) Proof that properly anticipated prices fluctuate randomly. In: The World Scientific Handbook of Futures Markets, pp 25–38Sarantis N, Stewart C (1995) Structural, VAR and BVAR models of exchange rate determination: a comparison of their forecasting performance. J Forecast 14(3):201–215Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117Sims CA (1980) Macroeconomics and reality. Econ: J Econ Soc. 48:1–48Ţiţan AG (2015) The efficient market hypothesis: review of specialized literature and empirical research. Proc Econ Finance 32:442–449Yao J, Tan CL (2000) A case study on using neural networks to perform technical forecasting of FOREX. Neurocomputing 34(1):79–9

Evaluation of the 3-D finite difference implementation of the acoustic diffusion equation model on massively parallel architectures

The diffusion equation model is a popular tool in room acoustics modeling. The 3-D Finite Difference (3D-FD) implementation predicts the energy decay function and the sound pressure level in closed environments. This simulation is computationally expensive, as it depends on the resolution used to model the room. With such high computational requirements, a high-level programming language (e.g., Matlab) cannot deal with real life scenario simulations. Thus, it becomes mandatory to use our computational resources more efficiently. Manycore architectures, such as NVIDIA GPUs or Intel Xeon Phi offer new opportunities to enhance scientific computations, increasing the performance per watt, but shifting to a different programming model. This paper shows the roadmap to use massively parallel architectures in a 3D-FD simulation. We evaluate the latest generation of NVIDIA and Intel architectures. Our experimental results reveal that NVIDIA architectures outperform by a wide margin the Intel Xeon Phi co-processor while dissipating approximately 50 W less (25%) for large-scale input problems.Ingeniería, Industria y Construcció