Subsidiary strategy: The embeddedness component

This paper inductively derives a model that develops the concept of subsidiary embeddedness as the canvas within which subsidiary strategy can take place. Our model identifies three hierarchical levels of embeddedness: Operational embeddedness relates to the interlocking day-to-day relations. Capability embeddedness deals with the development of competitive capabilities for the multinational as a whole. Strategic embeddedness deals with subsidiary participation in the MNC strategy setting. We deem these three types of embeddedness as ways to develop subsidiary strategic alternatives. In as such, different types of subsidiary embeddedness imply different subsidiary roles. Embeddedness, as it was inductively derived from a revelatory case study, is not merely an outcome of the institutional setting, but a resource a subsidiary can manage by means of manipulating dependencies or exerting influence over the allocation of critical resources. A subsidiary can modify its embeddedness to change its strategic restraints. Therefore, the development of subsidiary embeddedness becomes an integral part of subsidiary strategy.Multinational management; subsidiary; strategy; organization;

Conservación del género Dipteryx spp “Shihuahuaco” en bosques de producción permanente en la provincia deTahuamanu en Madre de Dios

Universidad Nacional Agraria La Molina. Escuela de Posgrado. Maestría en Conservación de Recursos ForestalesLa presente investigación tiene por objetivo analizar el modelo de concesiones forestales en bosques de producción permanente en cuanto a la conservación del género Dipteryx spp en la provincia de Tahuamanu mediante el desarrollo de una metodología de estudio poblacional. Para ello, se ha recopilado y organizado bases de datos de diferentes evaluaciones del género, se realizó un modelamiento para la determinación de áreas de existencia potencial del género Dipteryx spp en las regiones de Ucayali y Madre de Dios mediante el uso del modelo predictivo Maxent. Se realizó un análisis de la deforestación antes del año 2000 y entre el año 2000 y 2020 en las áreas de la Reserva Territorial Tahuamanu, en Bosques de Producción Permanente y en el área de influencia de la carretera interoceánica. Se realizó una evaluación forestal en Bosques de Producción Permanente en tres sectores: 1) Parcela de corta de una concesión forestal la cual no ha tenido ningún tipo de aprovechamiento forestal. 2) Parcela de corta aprovechada hace 10 años mediante la aplicación de los estándares de manejo forestal (MF) y certificación forestal. 3) Parcela de corta en una concesión forestal donde se cumplió la normativa, sin embargo, ha sufrido una serie de invasiones a sus áreas por parte de agricultores por falta de control y vigilancia. No se encontraron diferencias significativas en los tres escenarios evaluados; sin embargo, se encontró mayor regeneración natural en el bosque aprovechado bajo estándares de manejo forestal y certificación con individuos en el 33,3 por ciento de las subparcelas, frente al 16,7 por ciento de presencia de regeneración en subparcelas sin manejo forestal. La densidad poblacional encontrada fue extrapolada a las áreas boscosas encontradas en el modelamiento y en el análisis de la deforestación. Se encontró una estructura poblacional importante por debajo del diámetro mínimo de corta en la parcela aprovechada bajo estándares de manejo forestal y certificación forestal garantizando una siguiente cosecha, así como una importante regeneración natural en los espacios donde se ha incrementado la disponibilidad de luz para el desarrollo de los árboles jóvenes.The present research aims to analyze the model of forest concessions in permanent production forests in terms of the conservation of the genus Dipteryx spp in the province of Tahuamanu through the development of a population study methodology. For this end, databases of different evaluations of the genus were compiled and organized, and a modeling was carried out to determine areas of potential existence of the genus Dipteryx spp in the regions of Ucayali and Madre de Dios using the Maxent predictive model. An analysis of deforestation before the year 2000 and between the years 2000 and 2020 was carried out in the areas of the Tahuamanu Territorial Reserve, in Permanent Production Forests and in the area of influence of the interoceanic highway. A forestry evaluation was conducted in three sectors in Permanent Production Forests: 1) Cutting plot of a forest concession which has not had any type of forest use. 2) Plot of felling harvested 10 years ago through the application of forest management (FM) and forest certification standards. 3) Cutting plot in a forestry concession where the regulations were complied with, however, it has suffered a series of invasions of its areas by farmers due to lack of control and surveillance. No significant differences were found in the three scenarios evaluated; however, greater natural regeneration was found in the forest harvested under forest management standards and certification with individuals in thirty three point three percent of the subplots, compared to sixteen point seven percent of regeneration presence in subplots without forest management. The population density found was extrapolated to the forested areas found in the modeling and in the analysis of deforestation. An important population structure below the minimum cutting diameter was found in the plot harvested under forest management and forest certification standards guaranteeing a following harvest, as well as an important natural regeneration in the areas where the availability of light for the development of young trees has increased

Flavored Non-Minimal Left-Right Symmetric Model Fermion Masses and Mixings

A complete study on the fermion masses and flavor mixing is presented in a non-minimal left-right symmetric model (NMLRMS) where the ${\bf S}_{3}\otimes {\bf Z}_{2}\otimes {\bf Z}^{e}_{2}$ flavor symmetry drives the Yukawa couplings. In the quark sector, the mass matrices possess a kind of the generalized Fritzsch textures that allow us to fit the CKM mixing matrix in good agreement to the last experimental data. In the lepton sector, on the other hand, a soft breaking of the $\mu\leftrightarrow \tau$ symmetry provides a non zero and non maximal reactor and atmospheric angles, respectively. The inverted and degenerate hierarchy are favored in the model where a set of free parameters is found to be consistent with the current neutrino data.Comment: 23 pages, 9 figures. references added, typos corrected, JCGI added his current institution;conclusions rewrote and unchanged results. Version published in European Physical Journal

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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Diseño una estrategia de gestión de conocimiento fundamentada en la cultura organizacional y la gestión del conocimiento del Centro para la Investigación de Recursos Acuáticos de Nicaragua, con el apoyo de una herramienta WEB, en el periodo del primer semestre del año dos mil dieciséis

El presente estudio realizado se llevó a cabo en el Centro para la Investigación de Recursos Acuáticos de Nicaragua - CIRA, donde se tomaron elementos de suma importancia a nivel organizacional, que sirvieron como una fuente de apoyo para poder alcanzar los objetivos planteados. El CIRA no cuenta con una estrategia de Gestión del Conocimiento (GC) que les permita crearlo y distribuirlo de manera eficiente en el centro; de ahí surge la necesidad de diseñar estrategia fundamentada en rasgos culturales con el apoyo una herramienta web (E-portafolio). Para el diseño tanto de la estrategia como de la herramienta web se realizó basada en el análisis de la cultura organizacional y la GC. Para el análisis de la cultura organizacional se aplicó el cuestionario de Denison y una guía de análisis documental. En el caso del análisis de la GC, se realizó una entrevista a los directivos del centro. La estrategia de GC propuesta se basa en el Modelo KMAT, sin perder de vista los resultados del análisis de la cultura y la GC

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

Diagnóstico participativo y prácticas pre-profesionales en comunidades de El Crucero, Managua, Nicaragua

El programa para el Desarrollo Participativo Integral Rural (DEPARTIR) trabajó con familias, estudiantes y docentes investigadores de la Universidad Nacional Agraria (UNA), Universidad Agropecuaria de Viena (BOKU) y la Casa de los Tres Mundos de Granada (CTM) en comunidades rurales de Nicaragua con el apoyo del Programa Austriaco de Educación Superior e Investigación (APPEAR), así como la Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO). Durante el período 2006-2015 el programa tuvo intervención en 17 comunidades, desarrollado siete ediciones de diagnósticos y priorizadas propuestas en conjunto con las familias. El objetivo de este estudio fue comunicar la apreciación y valoración del curso teórico-práctico desarrollado por DEPARTIR a estudiantes y analizar información básica recopilada por estudiantes en cinco comunidades rurales de El Crucero, Managua. Fueron aplicadas encuestas en la unidad familiar de producción (UFP) para conocer las condiciones socieconómicas con la participación activa de estudiantes y familias rurales e intervención de la UNA. Los estudiantes realizaron prácticas pre-profesionales en comunidades rurales interactuaron con las familias, redactaron informes sobre la situación actual, los cuales fueron evaluados a través de escala Likert y estadístico de Cronbach (análisis de evaluación del curso por estudiantes e informes redactados por estudiantes). Posterior a esto, fueron muestreadas encuestas con información de las comunidades rurales de El Callao, Las Pilas 1, Las Pilas 2, Santa Julia y Daniel Téller (municipio de El Crucero, Managua), y relacionadas mediante herramientas estadísticas univariadas y multivariadas.La descripción de pirámide poblacional, nivel de analfabetismo e índice de calidad de vida de la vivienda caracterizó a estos municipios. Las variables más representativas fueron: área de UFP (X1), personas en el hogar (X2), sexo (X3), edad (X4), tenencia de la tierra (X5), organización (X6), religión (X7), meses de acceso a la carretera (X8) e índice calidad de vida de la vivienda (X9). Este diagnóstico participativo realizado por un grupo interdisciplinario desarrolló competencias en los estudiantes, despertó la cognición al indagar el contexto de las familias rurales; además, esta experiencia fue acreditada como Práctica Pre-profesional en el Plan de Estudios por objetivos. La experiencia e información obtenida a partir de los diagnósticos realizados por DEPARTIR, puede ser retomada y utilizada en la docencia, investigación y extensión para la conformación de proyectos de desarrollo comunitario de carácter interdisciplinario en comunidades rurales de Nicaragua. La población comunitaria es joven, con bajos niveles de analfabetismo, pero con índice de calidad de vida en vivienda moderada. pirámide poblacional es progresiva, Asimismo, la relación de las comunidades estuvo discriminada en un 77% por las variables X3, X7, X8 y X

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