La apertura de la televisión privada en México

Hasta muy recientes tiempos, la televisión comercial en México mantenía una actitud de complacencia con los gobiernos del PRI en un pacto de do ut des por el que la televisión no criticaba al gobierno y éste otorgaba todo tipo de beneficios y privilegios al medio. Sin embargo, ya desde las últimas elecciones, la situación ha cambiado. La mayor competencia de medios televisivos, la alternancia democrática en México han forzado al primer grupo televisivo del país a dar una información más objetiva y veraz. El fundamento último de este cambio de actitud han sido los intereses económicos de la empresa.Until very recently, the commercial television in Mexico behaved in a complaisant way towards the Mexican PRI government in which seemed to be a do ut des pact, so that the medium didn’t critisize the Governemt and the Governemnt handed out all kinds of beneficts and privileges to the medium. Nevertheles the situation has changed since the last elections. Growing competence in the fiel of tv media and the political change in Mexico have compelled the first television group in the country to offer a more objective and true reporting. The ultimate reason for this change of behaviour has been the need for the group to bolster its economic benefits

Strategy realisation process: a modelling enabling approach

Changing conditions within an organisation’s environment necessitate enactment of the strategy realisation process to produce relevant coping strategic intents to successfully reconfigure current, or potential, process networks to better exploit potential opportunities or minimise impacts of a potential threats. Literature regarding strategy realisation has not produced a coherent approach to describe and decompose the subprocesses of the strategy realisation, i.e., several different approaches have been taken to enact some components however there is no formal decomposition of such process. A revision of the strategy realisation literature was conducted and a formal decomposition model for the strategy realisation process was conceived. Various modelling tools, methods and techniques were surveyed to enable the underpinning of the proposed strategy realisation conceptualisation. Utilising a combination of static, causal and simulation modelling methods and tools, a research methodology was proposed to underpin aspects of the enterprise which would facilitate the decision making process of the strategy realisation process. Two case studies were identified in which the proposed methodology could be implemented. In the first case study, two differing strategic intents were analysed within the same organisation under opposing economic conditions. The second case study observed the implementation of a different system configuration to achieve a strategic intent. The strategy realisation process was studied using the described conceptualisation and the enterprise was modelled. Key variables, set by senior management were observed and quantitative analysis was undertaken and reported. It was concluded that the use of modelling methods providing quantitative and qualitative analysis facilitated the decision process within an organisation. A new conceptualisation of the strategy realisation process and the integration of modelling methods, tools and techniques were devised

Exploring Neighborhood Effects and Socioeconomic Background in College Enrollment: A Longitudinal Analysis in St. Cloud, Minnesota

We follow the transition from high school to college and the characteristics of college enrollment from 2009 to 2017 in four cohorts of high school graduates in Saint Cloud Minnesota, using student records from the school district administrative system and the National Student Clearinghouse data on college registration. Residential addresses are geocoded at the census block group level to incorporate neighborhood effects. Logistic model, Two Step Least Squares, and survival analysis are applied to explore the effects of socioeconomic determinants in college enrollment, timing of enrollment and postsecondary education choices. Logistic models fail to reflect neighborhood effects across most specifications. High school grades, sex and family background have robust effects in these models. When GPA is considered endogenous to socioeconomic determinants, findings show neighborhood effects are robust and have a large impact on high school performance and college enrollment. Neighborhood educational attainment, unemployment, and income are strong predictors of enrollment and offset individual characteristics. Racial segregation is insignificant across most specifications. Evidence from survival models suggests that GPA, sex, and socioeconomic background are related to early enrollment. Students with better high school grades are more likely to enroll in 4 Year institutions and less likely to enroll in 2 Year institution, and have lower odds to enroll into For-Profit institutions. Results highlight the importance of neighborhood effects to explain educational outcomes and heterogeneous educational choices. It also stresses dynamic complementarities in education

Interlukein-6 as a Predictor for Pediatric Traumatic Brain Injury Recovery

Traumatic brain injury (TBI) is a leading cause of death and disability around the world, making it a major public health concern. The post-injury condition is marked by a complex, pathophysiological nature and wide-ranging physical, emotional, and cognitive symptoms. We hope to explore the connection, if any, between interleukin-6 (IL-6) levels and patient recovery following a head injury in pediatric patients. Essentially, we predict that higher IL-6 levels would correspond with more unfavorable patient outcomes following injury, and this relationship would be found to be more robust in TBI participants. To forward this objective, we used serum IL-6 samples and three surveys completed at baseline, 3 months, and 6 months since the initial hospital visit. We compared these values to a set of pediatric orthopedic controls to isolate TBI-specific biomarker-outcome interactions. Thus far, our findings indicate that IL-6 levels are a poor predictor for patient post-injury outcome for TBI cases and a moderately good predictor in orthopedic controls. Yet, on average, participants who reported worse outcomes had higher IL-6 levels, even across both groups. These findings allow for us to understand how certain biomarkers, such as IL-6, may play a role in predicting TBI-specific recovery in pediatric patients.Neuroscienc

Clinical microbiology with multi-view deep probabilistic models

Clinical microbiology is one of the critical topics of this century. Identification and discrimination of microorganisms is considered a global public health threat by the main international health organisations, such as World Health Organisation (WHO) or the European Centre for Disease Prevention and Control (ECDC). Rapid spread, high morbidity and mortality, as well as the economic burden associated with their treatment and control are the main causes of their impact. Discrimination of microorganisms is crucial for clinical applications, for instance, Clostridium difficile (C. diff ) increases the mortality and morbidity of healthcare-related infections. Furthermore, in the past two decades, other bacteria, including Klebsiella pneumoniae (K. pneumonia), have demonstrated a significant propensity to acquire antibiotic resistance mechanisms. Consequently, the use of an ineffective antibiotic may result in mortality. Machine Learning (ML) has the potential to be applied in the clinical microbiology field to automatise current methodologies and provide more efficient guided personalised treatments. However, microbiological data are challenging to exploit owing to the presence of a heterogeneous mix of data types, such as real-valued high-dimensional data, categorical indicators, multilabel epidemiological data, binary targets, or even time-series data representations. This problem, which in the field of ML is known as multi-view or multi-modal representation learning, has been studied in other application fields such as mental health monitoring or haematology. Multi-view learning combines different modalities or views representing the same data to extract richer insights and improve understanding. Each modality or view corresponds to a distinct encoding mechanism for the data, and this dissertation specifically addresses the issue of heterogeneity across multiple views. In the probabilistic ML field, the exploitation of multi-view learning is also known as Bayesian Factor Analysis (FA). Current solutions face limitations when handling high-dimensional data and non-linear associations. Recent research proposes deep probabilistic methods to learn hierarchical representations of the data, which can capture intricate non-linear relationships between features. However, some Deep Learning (DL) techniques rely on complicated representations, which can hinder the interpretation of the outcomes. In addition, some inference methods used in DL approaches can be computationally burdensome, which can hinder their practical application in real-world situations. Therefore, there is a demand for more interpretable, explainable, and computationally efficient techniques for highdimensional data. By combining multiple views representing the same information, such as genomic, proteomic, and epidemiologic data, multi-modal representation learning could provide a better understanding of the microbial world. Hence, in this dissertation, the development of two deep probabilistic models, that can handle current limitations in state-of-the-art of clinical microbiology, are proposed. Moreover, both models are also tested in two real scenarios regarding antibiotic resistance prediction in K. pneumoniae and automatic ribotyping of C. diff in collaboration with the Instituto de Investigación Sanitaria Gregorio Marañón (IISGM) and the Instituto Ramón y Cajal de Investigación Sanitaria (IRyCIS). The first presented algorithm is the Kernelised Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA). This algorithm uses a kernelised formulation to handle non-linear data relationships while providing compact representations through the automatic selection of relevant vectors. Additionally, it uses an Automatic Relevance Determination (ARD) over the kernel to determine the input feature relevance functionality. Then, it is tailored and applied to the microbiological laboratories of the IISGM and IRyCIS to predict antibiotic resistance in K. pneumoniae. To do so, specific kernels that handle Matrix-Assisted Laser Desorption Ionization (MALDI)-Time-Of-Flight (TOF) mass spectrometry of bacteria are used. Moreover, by exploiting the multi-modal learning between the spectra and epidemiological information, it outperforms other state-of-the-art algorithms. Presented results demonstrate the importance of heterogeneous models that can analyse epidemiological information and can automatically be adjusted for different data distributions. The implementation of this method in microbiological laboratories could significantly reduce the time required to obtain resistance results in 24-72 hours and, moreover, improve patient outcomes. The second algorithm is a hierarchical Variational AutoEncoder (VAE) for heterogeneous data using an explainable FA latent space, called FA-VAE. The FA-VAE model is built on the foundation of the successful KSSHIBA approach for dealing with semi-supervised heterogeneous multi-view problems. This approach further expands the range of data domains it can handle. With the ability to work with a wide range of data types, including multilabel, continuous, binary, categorical, and even image data, the FA-VAE model offers a versatile and powerful solution for real-world data sets, depending on the VAE architecture. Additionally, this model is adapted and used in the microbiological laboratory of IISGM, resulting in an innovative technique for automatic ribotyping of C. diff, using MALDI-TOF data. To the best of our knowledge, this is the first demonstration of using any kind of ML for C. diff ribotyping. Experiments have been conducted on strains of Hospital General Universitario Gregorio Marañón (HGUGM) to evaluate the viability of the proposed approach. The results have demonstrated high accuracy rates where KSSHIBA even achieved perfect accuracy in the first data collection. These models have also been tested in a real-life outbreak scenario at the HGUGM, where successful classification of all outbreak samples has been achieved by FAVAE. The presented results have not only shown high accuracy in predicting each strain’s ribotype but also revealed an explainable latent space. Furthermore, traditional ribotyping methods, which rely on PCR, required 7 days while FA-VAE has predicted equal results on the same day. This improvement has significantly reduced the time response by helping in the decision-making of isolating patients with hyper-virulent ribotypes of C. diff on the same day of infection. The promising results, obtained in a real outbreak, have provided a solid foundation for further advancements in the field. This study has been a crucial stepping stone towards realising the full potential of MALDI-TOF for bacterial ribotyping and advancing our ability to tackle bacterial outbreaks. In conclusion, this doctoral thesis has significantly contributed to the field of Bayesian FA by addressing its drawbacks in handling various data types through the creation of novel models, namely KSSHIBA and FA-VAE. Additionally, a comprehensive analysis of the limitations of automating laboratory procedures in the microbiology field has been carried out. The shown effectiveness of the newly developed models has been demonstrated through their successful implementation in critical problems, such as predicting antibiotic resistance and automating ribotyping. As a result, KSSHIBA and FA-VAE, both in terms of their technical and practical contributions, signify noteworthy progress both in the clinical and the Bayesian statistics fields. This dissertation opens up possibilities for future advancements in automating microbiological laboratories.La microbiología clínica es uno de los temas críticos de este siglo. La identificación y discriminación de microorganismos se considera una amenaza mundial para la salud pública por parte de las principales organizaciones internacionales de salud, como la Organización Mundial de la Salud (OMS) o el Centro Europeo para la Prevención y Control de Enfermedades (ECDC). La rápida propagación, alta morbilidad y mortalidad, así como la carga económica asociada con su tratamiento y control, son las principales causas de su impacto. La discriminación de microorganismos es crucial para aplicaciones clínicas, como el caso de Clostridium difficile (C. diff ), el cual aumenta la mortalidad y morbilidad de las infecciones relacionadas con la atención médica. Además, en las últimas dos décadas, otros tipos de bacterias, incluyendo Klebsiella pneumoniae (K. pneumonia), han demostrado una propensión significativa a adquirir mecanismos de resistencia a los antibióticos. En consecuencia, el uso de un antibiótico ineficaz puede resultar en un aumento de la mortalidad. El aprendizaje automático (ML) tiene el potencial de ser aplicado en el campo de la microbiología clínica para automatizar las metodologías actuales y proporcionar tratamientos personalizados más eficientes y guiados. Sin embargo, los datos microbiológicos son difíciles de explotar debido a la presencia de una mezcla heterogénea de tipos de datos, tales como datos reales de alta dimensionalidad, indicadores categóricos, datos epidemiológicos multietiqueta, objetivos binarios o incluso series temporales. Este problema, conocido en el campo del aprendizaje automático (ML) como aprendizaje multimodal o multivista, ha sido estudiado en otras áreas de aplicación, como en el monitoreo de la salud mental o la hematología. El aprendizaje multivista combina diferentes modalidades o vistas que representan los mismos datos para extraer conocimientos más ricos y mejorar la comprensión. Cada vista corresponde a un mecanismo de codificación distinto para los datos, y esta tesis aborda particularmente el problema de la heterogeneidad multivista. En el campo del aprendizaje automático probabilístico, la explotación del aprendizaje multivista también se conoce como Análisis de Factores (FA) Bayesianos. Las soluciones actuales enfrentan limitaciones al manejar datos de alta dimensionalidad y correlaciones no lineales. Investigaciones recientes proponen métodos probabilísticos profundos para aprender representaciones jerárquicas de los datos, que pueden capturar relaciones no lineales intrincadas entre características. Sin embargo, algunas técnicas de aprendizaje profundo (DL) se basan en representaciones complejas, dificultando así la interpretación de los resultados. Además, algunos métodos de inferencia utilizados en DL pueden ser computacionalmente costosos, obstaculizando su aplicación práctica. Por lo tanto, existe una demanda de técnicas más interpretables, explicables y computacionalmente eficientes para datos de alta dimensionalidad. Al combinar múltiples vistas que representan la misma información, como datos genómicos, proteómicos y epidemiológicos, el aprendizaje multimodal podría proporcionar una mejor comprensión del mundo microbiano. Dicho lo cual, en esta tesis se proponen el desarrollo de dos modelos probabilísticos profundos que pueden manejar las limitaciones actuales en el estado del arte de la microbiología clínica. Además, ambos modelos también se someten a prueba en dos escenarios reales relacionados con la predicción de resistencia a los antibióticos en K. pneumoniae y el ribotipado automático de C. diff en colaboración con el IISGM y el IRyCIS. El primer algoritmo presentado es Kernelised Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA). Este algoritmo utiliza una formulación kernelizada para manejar correlaciones no lineales proporcionando representaciones compactas a través de la selección automática de vectores relevantes. Además, utiliza un Automatic Relevance Determination (ARD) sobre el kernel para determinar la relevancia de las características de entrada. Luego, se adapta y aplica a los laboratorios microbiológicos del IISGM y IRyCIS para predecir la resistencia a antibióticos en K. pneumoniae. Para ello, se utilizan kernels específicos que manejan la espectrometría de masas Matrix-Assisted Laser Desorption Ionization (MALDI)-Time-Of-Flight (TOF) de bacterias. Además, al aprovechar el aprendizaje multimodal entre los espectros y la información epidemiológica, supera a otros algoritmos de última generación. Los resultados presentados demuestran la importancia de los modelos heterogéneos ya que pueden analizar la información epidemiológica y ajustarse automáticamente para diferentes distribuciones de datos. La implementación de este método en laboratorios microbiológicos podría reducir significativamente el tiempo requerido para obtener resultados de resistencia en 24-72 horas y, además, mejorar los resultados para los pacientes. El segundo algoritmo es un modelo jerárquico de Variational AutoEncoder (VAE) para datos heterogéneos que utiliza un espacio latente con un FA explicativo, llamado FA-VAE. El modelo FA-VAE se construye sobre la base del enfoque de KSSHIBA para tratar problemas semi-supervisados multivista. Esta propuesta amplía aún más el rango de dominios que puede manejar incluyendo multietiqueta, continuos, binarios, categóricos e incluso imágenes. De esta forma, el modelo FA-VAE ofrece una solución versátil y potente para conjuntos de datos realistas, dependiendo de la arquitectura del VAE. Además, este modelo es adaptado y utilizado en el laboratorio microbiológico del IISGM, lo que resulta en una técnica innovadora para el ribotipado automático de C. diff utilizando datos MALDI-TOF. Hasta donde sabemos, esta es la primera demostración del uso de cualquier tipo de ML para el ribotipado de C. diff. Se han realizado experimentos en cepas del Hospital General Universitario Gregorio Marañón (HGUGM) para evaluar la viabilidad de la técnica propuesta. Los resultados han demostrado altas tasas de precisión donde KSSHIBA incluso logró una clasificación perfecta en la primera colección de datos. Estos modelos también se han probado en un brote real en el HGUGM, donde FA-VAE logró clasificar con éxito todas las muestras del mismo. Los resultados presentados no solo han demostrado una alta precisión en la predicción del ribotipo de cada cepa, sino que también han revelado un espacio latente explicativo. Además, los métodos tradicionales de ribotipado, que dependen de PCR, requieren 7 días para obtener resultados mientras que FA-VAE ha predicho resultados correctos el mismo día del brote. Esta mejora ha reducido significativamente el tiempo de respuesta ayudando así en la toma de decisiones para aislar a los pacientes con ribotipos hipervirulentos de C. diff el mismo día de la infección. Los resultados prometedores, obtenidos en un brote real, han sentado las bases para nuevos avances en el campo. Este estudio ha sido un paso crucial hacia el despliegue del pleno potencial de MALDI-TOF para el ribotipado bacteriana avanzado así nuestra capacidad para abordar brotes bacterianos. En conclusión, esta tesis doctoral ha contribuido significativamente al campo del FA Bayesiano al abordar sus limitaciones en el manejo de tipos de datos heterogéneos a través de la creación de modelos noveles, concretamente, KSSHIBA y FA-VAE. Además, se ha llevado a cabo un análisis exhaustivo de las limitaciones de la automatización de procedimientos de laboratorio en el campo de la microbiología. La efectividad de los nuevos modelos, en este campo, se ha demostrado a través de su implementación exitosa en problemas críticos, como la predicción de resistencia a los antibióticos y la automatización del ribotipado. Como resultado, KSSHIBA y FAVAE, tanto en términos de sus contribuciones técnicas como prácticas, representan un progreso notable tanto en los campos clínicos como en la estadística Bayesiana. Esta disertación abre posibilidades para futuros avances en la automatización de laboratorios microbiológicos.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Juan José Murillo Fuentes.- Secretario: Jerónimo Arenas García.- Vocal: María de las Mercedes Marín Arriaz

Elucidating the role of a basic helix loop helix (bHLH) transcription factor (TF) during bud break in hybrid aspen.

openIn extreme weather conditions, perennial trees have developed mechanisms that help them to survive these environmental cues. Temperature and day length regulates the bud burst in Aspen in the spring. The molecular basis of basic helix loop helix (bHLH) transcription factors mediated control of bud break is poorly understood. Here we identify and elucidate a transcription factor involved in the control of bud break in hybrid aspen. The transcription factor T89CIB is closely related to Arabidopsis Floral inducer AtCIB1 acting, activating, and regulated in a similar manner as we can infer until now. To understand where is localized, how it is regulated, and the effect on bud break. Two constructs 35S: T89CIB and 35S: AtCIB1 were made, and subcellular localization of the transcription factors was performed by Confocal Laser Scanning Microscopy. Furthermore, to explain the regulation of the proteins, Nicotiana benthamiana plants were infiltrated and exposed under dark and blue light conditions, finally, COL-O and cry2 lines were transformed with T89 CIB using Floral dip as a transformation method in Arabidopsis Thaliana. We elucidate the nuclear localization and demonstrate the role of blue light and dark as positive and negative regulators respectively. And the possible interaction and phenotype of T89CIB and Cryptochrome 2 (CRY2). Thus, our results reveal the basis of the T89CIB transcription factor and support the idea of the importance of this protein as part of the pathways activated during bud break in Hybrid Aspen. Elucidating these pathways will give a better understanding of the survival of Perennial trees under harsh conditions.In extreme weather conditions, perennial trees have developed mechanisms that help them to survive these environmental cues. Temperature and day length regulates the bud burst in Aspen in the spring. The molecular basis of basic helix loop helix (bHLH) transcription factors mediated control of bud break is poorly understood. Here we identify and elucidate a transcription factor involved in the control of bud break in hybrid aspen. The transcription factor T89CIB is closely related to Arabidopsis Floral inducer AtCIB1 acting, activating, and regulated in a similar manner as we can infer until now. To understand where is localized, how it is regulated, and the effect on bud break. Two constructs 35S: T89CIB and 35S: AtCIB1 were made, and subcellular localization of the transcription factors was performed by Confocal Laser Scanning Microscopy. Furthermore, to explain the regulation of the proteins, Nicotiana benthamiana plants were infiltrated and exposed under dark and blue light conditions, finally, COL-O and cry2 lines were transformed with T89 CIB using Floral dip as a transformation method in Arabidopsis Thaliana. We elucidate the nuclear localization and demonstrate the role of blue light and dark as positive and negative regulators respectively. And the possible interaction and phenotype of T89CIB and Cryptochrome 2 (CRY2). Thus, our results reveal the basis of the T89CIB transcription factor and support the idea of the importance of this protein as part of the pathways activated during bud break in Hybrid Aspen. Elucidating these pathways will give a better understanding of the survival of Perennial trees under harsh conditions

Sistema distribuido para medida y procesado en tiempo real de sensores IoT

Internet of things (IoT) is a term we use today to describe all those technologies that enables interconnection of digital with physical world. IoT is present in our everyday life allowing to connect all kind of elements. They can be domestic devices, industrial or merchant ones, in such a way that the human can interact and modify them. In IoT, things are enabled to collect information from their surroundings, to later process it, analyze it and give it value and thus be able to create applications and offer innovative services. This services and applications are bit by bit changing the world, making it better and changing the concept of technology as we know it today. This project aims to implement an IoT application using a wireless sensor network (WSN). Each sensor collects some information, in our case, temperature, battery level and the values of two external sensors, chosen by the user, connected to the mote to proceed with its analysis and processing. The information will be presented to the user through two interfaces: a web page and an Android application. The user can view data in different formats for further analysis. To carry out the network implementation, a Raspberry Pi 3 Model B and four Zolertia Z1 motes were used. Z1 motes feature a low power microcontroller MSP430F2617. Contiki was used as operating system for motes. Raspberry Pi plays the role of a border router that allows the communication between the WSN and external IP network. To send the information from the nodes, we have developed a serie of programs that allow the sensor to send data using UDP as a transport protocol. On the other hand, to receive the information sent from the nodes, we have developed a UDP server. Finally, we have gone one step forward and we offer to the user to choose what kind of sensor want to work with

Métodos geológicos no invasivos en la solución de problemas geotécnicos de estabilidad de taludes: Estudio de caso

The soil geotechnical conditions for the foundation of civil structures are evaluated, through the application of geophysical techniques (vertical electrical probes) and geochemical (analysis of stable isotopes of water), to recognize the hydrogeological conditions of the subsoil. The dominant rocky substratum are clayey and sandy phyllites of the Palmarito (Paleozoic - Permian) geologic unit; These rocks, when they decompose, form clays, silts and sands from fine to very fine grains. The location site of the work is projected on old deposits of alluvial fans (Quaternary) partially stabilized, with a mixture of gravel-silty gravel residual soils (GC), with a thickness greater than 15 m. The measured groundwater levels (2 m) correspond to a discontinuous layer of fine to very fine saturated sand. Through geochemical and geophysical methods, it was possible to know the distribution of groundwater for the design of the drainages and the surface of the rotational sliding of the dry stratum superior to the phreatic level (2m) is recognized. The corrective stabilization of the slope allowed reaching safety factor values ​​greater than or equal to (FS = 1.5).Se evalúan las condiciones geotécnicas de suelos para la fundación de estructuras civiles, a través de la aplicación de  técnicas  geofísicas  (Sondeos  eléctricos  verticales)  y  geoquímicos  (análisis  de  isótopos  estables  de  agua),  para reconocer las condiciones hidrogeológicas del subsuelo. El sustrato rocoso dominante son filitas arcillas y  arenosas  de  la  unidad  geológica  Palmarito  (Paleozoico  -  Pérmico);  estas  rocas  al  descomponerse  forman  arcillas, limos y arenas de grano fino a muy fino. El sitio de ubicación de la obra se proyecta sobre antiguos depósitos  de  abanicos  aluviales  (Cuaternarios)  parcialmente  estabilizados,  con  mezcla  de  suelos  residuales  gravo arcillosos-limosos (GC), con espesor mayor a 15 m. Los niveles freáticos medidos (2 m), corresponden con un estrato discontinuo de arena de grano fino a muy fino saturada. A través de métodos geoquímicos y geofísico se pudo conocer la distribución de aguas subterráneas para el diseño de los drenajes y se reconoce la superficie del deslizamiento rotacional del estrato seco superior al nivel freático (2m). Los correctivos de estabilización del talud permitieron alcanzar valores de factor de seguridad superiores o iguales a (FS = 1.5