Evaluación del impacto ambiental atmosférico por dióxido de azufre mediante un modelo de dispersión en una ciudad de la sierra del país

El objetivo del presente informe es evaluar la aplicación de modelos de dispersión de contaminantes atmosféricos en la ciudad de La Oroya, centrándose específicamente en el área conocida como Oroya Antigua. Esta zona, debido a su proximidad a la fundición, se encuentra especialmente expuesta a los gases emitidos por esta fuente de contaminación. Además de analizar las condiciones climáticas y meteorológicas, el estudio incluyó la evaluación de la calidad del aire y la identificación de emisiones atmosféricas. Los resultados indican que las estaciones de monitoreo actuales, especialmente las meteorológicas, podrían no reflejar con precisión la dinámica atmosférica real y, por ende, la magnitud de la contaminación en La Oroya. Sin embargo, estas estaciones constituyen la base para la implementación de modelos de dispersión que permitan establecer una red de monitoreo más objetiva para evaluar los impactos de la contaminación. Se sugiere que esta fase sea continuada a través de un convenio con la empresa responsable o mediante la prestación de servicios especializados

Características epidemiológicas y clínicas de los pacientes con cáncer colorectal en el Hospital Médico Quirúrgico y de Oncología del ISSS en el periodo 2014 a 2015.

Más de 1.2 millones de pacientes son diagnosticados con cáncer colorectal cada año, y más de 600 000 mueren de la enfermedad. El cáncer colorectal es la tercera causa más común de muerte globalmente. En El Salvador no se cuenta con un centro de registro nacional que permita integrar la información epidemiológica acerca de los pacientes diagnosticados con cáncer sin embargo en 2014 se creó una iniciativa que permitió realizar un diagnóstico situacional del cáncer en el país de 2009 a 2013 concluyendo que el cáncer colorectal se encuentra como la 1° causa de letalidad de todas las neoplasias diagnosticadas en este periodo. En el siguiente trabajo de investigación se expondrán las características epidemiológicas como: sexo, edad, procedencia, factores de riesgo, signos, síntomas entro otros para describir a los pacientes que adolecen de cáncer de colon en el Hospital Médico Quirúrgico y de Oncología en el periodo de 2014 y 2015

Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms

[EN] Internet of Things (IoT) is becoming a new socioeconomic revolution in which data and immediacy are the main ingredients. IoT generates large datasets on a daily basis but it is currently considered as "dark data", i.e., data generated but never analyzed. The efficient analysis of this data is mandatory to create intelligent applications for the next generation of IoT applications that benefits society. Artificial Intelligence (AI) techniques are very well suited to identifying hidden patterns and correlations in this data deluge. In particular, clustering algorithms are of the utmost importance for performing exploratory data analysis to identify a set (a.k.a., cluster) of similar objects. Clustering algorithms are computationally heavy workloads and require to be executed on high-performance computing clusters, especially to deal with large datasets. This execution on HPC infrastructures is an energy hungry procedure with additional issues, such as high-latency communications or privacy. Edge computing is a paradigm to enable light-weight computations at the edge of the network that has been proposed recently to solve these issues. In this paper, we provide an in-depth analysis of emergent edge computing architectures that include low-power Graphics Processing Units (GPUs) to speed-up these workloads. Our analysis includes performance and power consumption figures of the latest Nvidia's AGX Xavier to compare the energy-performance ratio of these low-cost platforms with a high-performance cloud-based counterpart version. Three different clustering algorithms (i.e., k-means, Fuzzy Minimals (FM), and Fuzzy C-Means (FCM)) are designed to be optimally executed on edge and cloud platforms, showing a speed-up factor of up to 11x for the GPU code compared to sequential counterpart versions in the edge platforms and energy savings of up to 150% between the edge computing and HPC platforms.This work has been partially supported by the Spanish Ministry of Science and Innovation, under the Ramon y Cajal Program (Grant No. RYC2018-025580-I) and under grants RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5 and by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18.Cecilia-Canales, JM.; Cano, J.; Morales-García, J.; Llanes, A.; Imbernón, B. (2020). Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms. Sensors. 20(21):1-19. https://doi.org/10.3390/s20216335S1192021Gebauer, H., Fleisch, E., Lamprecht, C., & Wortmann, F. (2020). Growth paths for overcoming the digitalization paradox. Business Horizons, 63(3), 313-323. doi:10.1016/j.bushor.2020.01.005Guillén, M. A., Llanes, A., Imbernón, B., Martínez-España, R., Bueno-Crespo, A., Cano, J.-C., & Cecilia, J. M. (2020). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing, 77(1), 818-840. doi:10.1007/s11227-020-03288-wWang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144-156. doi:10.1016/j.jmsy.2018.01.003Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic Markets, 25(3), 179-188. doi:10.1007/s12525-015-0196-8Pramanik, M. I., Lau, R. Y. K., Demirkan, H., & Azad, M. A. K. (2017). Smart health: Big data enabled health paradigm within smart cities. Expert Systems with Applications, 87, 370-383. doi:10.1016/j.eswa.2017.06.027Weber, M., & Podnar Žarko, I. (2019). A Regulatory View on Smart City Services. Sensors, 19(2), 415. doi:10.3390/s19020415Ghosh, A., Chakraborty, D., & Law, A. (2018). Artificial intelligence in Internet of things. CAAI Transactions on Intelligence Technology, 3(4), 208-218. doi:10.1049/trit.2018.1008Monti, L., Vincenzi, M., Mirri, S., Pau, G., & Salomoni, P. (2020). RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning. Sensors, 20(19), 5583. doi:10.3390/s20195583Kumar, P., Sinha, K., Nere, N. K., Shin, Y., Ho, R., Mlinar, L. B., & Sheikh, A. Y. (2020). A machine learning framework for computationally expensive transient models. Scientific Reports, 10(1). doi:10.1038/s41598-020-67546-wMittal, S., & Vetter, J. S. (2015). A Survey of CPU-GPU Heterogeneous Computing Techniques. ACM Computing Surveys, 47(4), 1-35. doi:10.1145/2788396Singh, D., & Reddy, C. K. (2014). A survey on platforms for big data analytics. Journal of Big Data, 2(1). doi:10.1186/s40537-014-0008-6Khayyat, M., Elgendy, I. A., Muthanna, A., Alshahrani, A. S., Alharbi, S., & Koucheryavy, A. (2020). Advanced Deep Learning-Based Computational Offloading for Multilevel Vehicular Edge-Cloud Computing Networks. IEEE Access, 8, 137052-137062. doi:10.1109/access.2020.3011705Satyanarayanan, M. (2017). The Emergence of Edge Computing. Computer, 50(1), 30-39. doi:10.1109/mc.2017.9Capra, M., Peloso, R., Masera, G., Roch, M. R., & Martina, M. (2019). Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World. Future Internet, 11(4), 100. doi:10.3390/fi11040100Lu, H., Gu, C., Luo, F., Ding, W., & Liu, X. (2020). Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning. Future Generation Computer Systems, 102, 847-861. doi:10.1016/j.future.2019.07.019Mimmack, G. M., Mason, S. J., & Galpin, J. S. (2001). Choice of Distance Matrices in Cluster Analysis: Defining Regions. Journal of Climate, 14(12), 2790-2797. doi:10.1175/1520-0442(2001)0142.0.co;2Gimenez, C. (2006). Logistics integration processes in the food industry. International Journal of Physical Distribution & Logistics Management, 36(3), 231-249. doi:10.1108/09600030610661813Chang, P.-C., Liu, C.-H., & Fan, C.-Y. (2009). Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry. Knowledge-Based Systems, 22(5), 344-355. doi:10.1016/j.knosys.2009.02.005Zheng, B., Yoon, S. W., & Lam, S. S. (2014). Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Systems with Applications, 41(4), 1476-1482. doi:10.1016/j.eswa.2013.08.044Woodley, A., Tang, L.-X., Geva, S., Nayak, R., & Chappell, T. (2019). Parallel K-Tree: A multicore, multinode solution to extreme clustering. Future Generation Computer Systems, 99, 333-345. doi:10.1016/j.future.2018.09.038Kwedlo, W., & Czochanski, P. J. (2019). A Hybrid MPI/OpenMP Parallelization of $K$ -Means Algorithms Accelerated Using the Triangle Inequality. IEEE Access, 7, 42280-42297. doi:10.1109/access.2019.2907885Liu, B., He, S., He, D., Zhang, Y., & Guizani, M. (2019). A Spark-Based Parallel Fuzzy $c$ -Means Segmentation Algorithm for Agricultural Image Big Data. IEEE Access, 7, 42169-42180. doi:10.1109/access.2019.2907573Baydoun, M., Ghaziri, H., & Al-Husseini, M. (2018). CPU and GPU parallelized kernel K-means. The Journal of Supercomputing, 74(8), 3975-3998. doi:10.1007/s11227-018-2405-7Li, Y., Zhao, K., Chu, X., & Liu, J. (2013). Speeding up k-Means algorithm by GPUs. Journal of Computer and System Sciences, 79(2), 216-229. doi:10.1016/j.jcss.2012.05.004Cuomo, S., De Angelis, V., Farina, G., Marcellino, L., & Toraldo, G. (2019). A GPU-accelerated parallel K-means algorithm. Computers & Electrical Engineering, 75, 262-274. doi:10.1016/j.compeleceng.2017.12.002Al-Ayyoub, M., Abu-Dalo, A. M., Jararweh, Y., Jarrah, M., & Sa’d, M. A. (2015). A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation. The Journal of Supercomputing, 71(8), 3149-3162. doi:10.1007/s11227-015-1431-yAit Ali, N., Cherradi, B., El Abbassi, A., Bouattane, O., & Youssfi, M. (2018). GPU fuzzy c-means algorithm implementations: performance analysis on medical image segmentation. Multimedia Tools and Applications, 77(16), 21221-21243. doi:10.1007/s11042-017-5589-6Timón, I., Soto, J., Pérez-Sánchez, H., & Cecilia, J. M. (2016). Parallel implementation of fuzzy minimals clustering algorithm. Expert Systems with Applications, 48, 35-41. doi:10.1016/j.eswa.2015.11.011Cebrian, J. M., Imbernón, B., Soto, J., García, J. M., & Cecilia, J. M. (2020). High-throughput fuzzy clustering on heterogeneous architectures. Future Generation Computer Systems, 106, 401-411. doi:10.1016/j.future.2020.01.022Cecilia, J. M., Timon, I., Soto, J., Santa, J., Pereniguez, F., & Munoz, A. (2018). High-Throughput Infrastructure for Advanced ITS Services: A Case Study on Air Pollution Monitoring. IEEE Transactions on Intelligent Transportation Systems, 19(7), 2246-2257. doi:10.1109/tits.2018.2816741Sriramakrishnan, P., Kalaiselvi, T., & Rajeswaran, R. (2019). Modified local ternary patterns technique for brain tumour segmentation and volume estimation from MRI multi-sequence scans with GPU CUDA machine. Biocybernetics and Biomedical Engineering, 39(2), 470-487. doi:10.1016/j.bbe.2019.02.002Fang, Y., Chen, Q., & Xiong, N. (2019). A multi-factor monitoring fault tolerance model based on a GPU cluster for big data processing. Information Sciences, 496, 300-316. doi:10.1016/j.ins.2018.04.053Rodriguez, M. Z., Comin, C. H., Casanova, D., Bruno, O. M., Amancio, D. R., Costa, L. da F., & Rodrigues, F. A. (2019). 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Mobile crowdsensing approaches to address the COVID-19 pandemic in Spain

[EN] Mobile crowdsensing (MCS) is a technique where people with computing and sensing devices such as smartphones collectively share data that are of potential interest to the rest of society. MCS includes two different trends (i) mobile sensing, which shares raw data generated from the sensors that are embedded in mobile devices, and (ii) social sensing, which uses the information shared by people in online social networks (OSNs). In this study, the authors present the timeline evolution of the COVID¿19 pandemic in Spain, and summarise the MCS research efforts that are being undertaken by the Spanish community to address COVID¿19 outbreak. Indeed, the COVID¿19 pandemic is putting today's society at risk; lockdown and social distancing measures proposed by governments are dramatically affecting economies. In this regard, MCS tools can become a powerful solution to provide smart quarantine strategies in periods of a steep decrease of infections, or new outbreaks.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 the Spanish Ministry of Science, Innovation and Universities under grants RTI2018-096384-B-I00 and RTC-2017-6389-5.Cecilia-Canales, JM.; Cano, J.; Hernández-Orallo, E.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2020). Mobile crowdsensing approaches to address the COVID-19 pandemic in Spain. IET Smart Cities. 2(2):1-6. https://doi.org/10.1049/iet-smc.2020.0037S1622World Health Organization:‘Novel coronavirus (2019‐ncov): Situation report 91’ [accessed 30‐April‐2020]Instituto de Salud.Carlos.III:‘Situación de covid‐19 en españa’ [accessed 30‐April‐2020].https://covid19.isciii.es/LiR.RiversC.TanQ.et al.: ‘The demand for inpatient and ICU beds for COVID‐19 in the US: lessons from Chinese cities’ medRxiv 2020 pp.1–12 doi:10.1101/2020.03.09.20033241World Health Organization:‘Critical preparedness readiness and response actions for COVID‐19: interim guidance 22 March 2020’Kissler, S. M., Tedijanto, C., Goldstein, E., Grad, Y. H., & Lipsitch, M. (2020). Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science, 368(6493), 860-868. doi:10.1126/science.abb5793International Labour Organization: ‘The socioeconomic impact of COVID‐19 in fragile settings: peace and social cohesion at risk’ https://www.ilo.org/global/topics/employment‐promotion/recovery‐and‐reconstruction/WCMS_741158/langen/index.htm [accessed 30‐April‐2020]Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N. Y., Huang, R., & Zhou, X. (2015). Mobile Crowd Sensing and Computing. ACM Computing Surveys, 48(1), 1-31. doi:10.1145/2794400AdolphC.AmanoK.Bang JensenB.et al.: ‘Pandemic politics: timing state‐level social distancing responses to COVID‐19’ medRxiv 202

Hydrolysis of the phosphoanhydride linkage of cyclic ADP-ribose by the Mn2+-dependent ADP-ribose/CDP-alcohol pyrophosphatase

Cyclic ADP-ribose (cADPR) metabolism in mammals is catalyzed by NAD glycohydrolases (NADases) that, besides forming ADP-ribose, form and hydrolyze the N(1)-glycosidic linkage of cADPR. Thus far, no cADPR phosphohydrolase was known. We tested rat ADP-ribose/CDP-alcohol pyrophosphatase (ADPRibase-Mn) and found that cADPR is an ADPRibase-Mn ligand and substrate. ADPRibase-Mn activity on cADPR was 65-fold less efficient than on ADP-ribose, the best substrate. This is similar to the ADP-ribose/cADPR formation ratio by NADases. The product of cADPR phosphohydrolysis by ADPRibase-Mn was N(1)-(5-phosphoribosyl)-AMP, suggesting a novel route for cADPR turnover.info:eu-repo/semantics/publishedVersio

Technology adoption and extension strategies in Mediterranean Agriculture: the case of family farms in Chile

Extension services play a crucial role by improving skills and access to information thatresult in greater farm level innovations, especially on family farms which are the predominant formof agriculture in the world. This study analyzed the connection between strategies implemented byextension services and technology adoption on family farms. Using the case of the Servicio de AsesoríaTécnica (SAT) Program, we developed a bottom-up adoption index (AI) for vegetable and berryfarmers in three regions of Central ChileThis research was funded by FONDECYT, grant number 1171122.This research was funded by FONDECYT, grant number 1171122.Postprint (published version

Contribution of protein domains to the activities of the human enzyme and molecular dynamics simulation of domain movements

Mammalian triokinase, which phosphorylates exogenous dihydroxyacetone and fructose-derived glyceraldehyde, is neither molecularly identified nor firmly associated to an encoding gene. Human FMN cyclase, which splits FAD and other ribonucleoside diphosphate-X compounds to ribonucleoside monophosphate and cyclic X-phosphodiester, is identical to a DAK-encoded dihydroxyacetone kinase. This bifunctional protein was identified as triokinase. It was modeled as a homodimer of two-domain (K and L) subunits. Active centers lie between K1 and L2 or K2 and L1: dihydroxyacetone binds K and ATP binds L in different subunits too distant (≈ 14 Å) for phosphoryl transfer. FAD docked to the ATP site with ribityl 4'-OH in a possible near-attack conformation for cyclase activity. Reciprocal inhibition between kinase and cyclase reactants confirmed substrate site locations. The differential roles of protein domains were supported by their individual expression: K was inactive, and L displayed cyclase but not kinase activity. The importance of domain mobility for the kinase activity of dimeric triokinase was highlighted by molecular dynamics simulations: ATP approached dihydroxyacetone at distances below 5 Å in near-attack conformation. Based upon structure, docking, and molecular dynamics simulations, relevant residues were mutated to alanine, and kcat and Km were assayed whenever kinase and/or cyclase activity was conserved. The results supported the roles of Thr(112) (hydrogen bonding of ATP adenine to K in the closed active center), His(221) (covalent anchoring of dihydroxyacetone to K), Asp(401) and Asp(403) (metal coordination to L), and Asp(556) (hydrogen bonding of ATP or FAD ribose to L domain). Interestingly, the His(221) point mutant acted specifically as a cyclase without kinase activity.info:eu-repo/semantics/publishedVersio

COVIDSensing: Social Sensing strategy for the management of the COVID-19 crisis

[EN] The management of the COVID-19 pandemic has been shown to be critical for reducing its dramatic effects. Social sensing can analyse user-contributed data posted daily in social-media services, where participants are seen as Social Sensors. Individually, social sensors may provide noisy information. However, collectively, such opinion holders constitute a large critical mass dispersed everywhere and with an immediate capacity for information transfer. The main goal of this article is to present a novel methodological tool based on social sensing, called COVIDSensing. In particular, this application serves to provide actionable information in real time for the management of the socio-economic and health crisis caused by COVID-19. This tool dynamically identifies socio-economic problems of general interest through the analysis of people¿s opinions on social networks. Moreover, it tracks and predicts the evolution of the COVID-19 pandemic based on epidemiological figures together with the social perceptions towards the disease. This article presents the case study of Spain to illustrate the tool.This work is derived from R&D project RTI2018-096384-B-I00, as well as the Ramon y Cajal Grant RYC2018-025580-I, funded by MCIN/AEI/10.13039/501100011033 and ERDF A way of making Europe, by the Spanish Agencia Estatal de Investigación (grant number PID2020- 112827GB-I00/ AEI/10.13039/501100011033), and by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Proyectos AICO/2020, Spain, under Grant AICO/2020/302.Sepúlveda, A.; Periñán-Pascual, C.; Muñoz, A.; Martínez-España, R.; Hernández-Orallo, E.; Cecilia-Canales, JM. (2021). COVIDSensing: Social Sensing strategy for the management of the COVID-19 crisis. Electronics. 10(24):1-17. https://doi.org/10.3390/electronics10243157S117102

LADEA: A Software Infrastructure for Audio Delivery and Analytics

[EN] The LoRa technology enables long distance links with reduced power consumption at low cost, the main limitation being the low bandwidth that it offers. With LoRa, remote locations, like rural areas, can benefit from connectivity based services that would otherwise be impossible. In this work, we describe a LoRa architecture that can include generic external data sources using an MQTT-based interface. We particularly focus on audio sources aiming to two basic services: a voice messaging system that allows users who cannot read or write to send voice notes, and an audio compression service to extract the main features from the audio input to use it for developing intelligent ML-based audio analytics.This work has been partially supported by the Spanish Ministry of Science and Innovation, under the Ramon y Cajal Program (Grant No. RYC2018-025580-I) and under grants RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5, by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by the "Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020", Spain, under Grant AICO/2020/302.Nakamura, K.; Hernandez, D.; Cecilia-Canales, JM.; Manzoni, P.; Zennaro, M.; Cano, J.; Tavares De Araujo Cesariny Calafate, CM. (2021). LADEA: A Software Infrastructure for Audio Delivery and Analytics. Mobile Networks and Applications (Online). 26(5):2048-2054. https://doi.org/10.1007/s11036-021-01747-z2048205426