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En México, la caña de azúcar (Saccharum officinarum L.) es uno de los principales cultivos que tiene una gran relevancia en el desarrollo agrícola y rural. Sin embargo, el esquema utilizado para su producción representa una fuerte problemática sobre la degradación del suelo, principalmente por el uso de productos químicos. En este sentido, el uso de biofertilizantes y diversas prácticas orgánicas representan fuertes alternativas para la mejora de la fertilidad del suelo. Por lo anterior, el presente trabajo tuvo como objetivo evaluar la efectividad de la aplicación de fertilizantes sintéticos y bioles en el crecimiento y producción de biomasa de caña de azúcar (S. officinarum) en La Candelaria, Tierra Blanca, Veracruz. Se evaluaron 6 tratamientos: Tratamiento 1, fertilización tradicional 20-10-20 ha-1 de N-P-K al momento de la siembra. Tratamiento 2, fertilización tradicional 20-10-20 ha-1 de N-P-K a 30 días después de la siembra. Tratamiento 3 fertilización tradicional al 50% 20-10-20 de NPK al momento de la siembra. Tratamiento 4 fertilización tradicional + biol a 30 días después de la siembra. Tratamiento 5 biol a 30 días después de la siembra y tratamiento 6 testigo sin aplicación. Los resultados muestran que no hay diferencias significativas entre las dosis de fertilización. Por lo tanto, el uso del biol puede ser una alternativa fertilizante para los productores de caña de azúcar y puede contribuir a la transición agroecológica del cultivo.

Heliconias belong to the group of tropical flowers, which generate economic benefits in the agricultural sector with their agronomic peculiarities. Besides providing the social actors involved with benefits aimed at sustainability in different tropical areas. Therefore, the objective of this study was to compare the physiological and agronomic characteristics of three commercial cultivars of Heliconias in three locations of a community in the municipality of Fortín, in the state of Veracruz, Mexico. Three sites were identified based on the type of agriculture: agroforestry system with Heliconias (site 1), agroforestry system - monoculture, with Heliconias (site 2) and monoculture (only Heliconias, site 3). A randomised complete block design with a split-plot arrangement (A x B) and six replications was used. Factors were: A) In situ cultivation sites, corresponding to three plots (sites) in the municipality of Coapichapan, Mexico, and B) Heliconias cultivars (Heliconia psittacorum, Heliconia wagneriana and H. bihai). The variables evaluated were tillers traits and inflorescence traits. The results show that in an agroforestry system with Heliconia, Heliconia bihai showed better performance in eight variables except for inflorescence number and bract number. In an agroforestry and monoculture system with Heliconia, Heliconia bihai shows better results in leaf length and inflorescence length. In an agroforestry system and monoculture with Heliconia, Heliconia wagneriana performed better in bract number, pseudostem width and leaf length. In monoculture, Heliconia bihai responded best to inflorescence length. Due to the tropical nature of this ornamental, the best growing conditions for Heliconia are in agroforestry systems. Due to its physiological characteristics, Heliconia bihai shows its agronomic potential in this type of agriculture even under monoculture conditions. Therefore, the conditions offered by an agroforestry system have a positive effect on the cultivation of Heliconia.

En el contexto actual de la transición agroecológica, se ha intensificado el uso de fertilizantes fermentados o bioles. El objetivo del trabajo fue comparar la concentración nutrimental de dos formulaciones de fertilizantes fermentados elaborados con insumos locales. Se diseñaron biodigestores artesanales y se utilizó estiércol de ovino y pasto Kikuyo (Pennisetum clandestinum). Se generaron dos formulaciones: Biol 1 (20% pasto kikuyo, 30% estiércol ovino y 50% agua) y Biol 2 (50% estiércol ovino y 50% agua), las cuales se fermentaron durante 100 días y se tuvieron tres repeticiones de cada formulación. En los bioles se determinó la materia orgánica y la concentración de los macronutrientes N, P y K, así como la concentración de los micronutrientes Fe, Cu, Mn y Zn. El Biol 1 tuvo mayor concentración de materia orgánica, N y Fe (P ≤ 0.05), es decir, se concluye que se mejoró la concentración nutrimental del biol cuando se agregó el pasto en el proceso de fermentación. El uso de insumos locales, de bajo costo y de fácil acceso puede ser una alternativa para producir bioles.

El deficiente manejo de los residuos sólidos urbanos se ha convertido en un grave problema de contaminación que afecta la calidad del aire, el agua y el suelo, con un impacto negativo para los seres vivos: plantas, animales y humanos. Es por ello de gran importancia el implementar estrategias de educación ambiental desde los primeros años de formación en los sistemas educativos. En México, la Secretaría del Medioambiente y Recursos Naturales (SEMARNAT) en coordinación con la Secretaría de Educación Pública (SEP) ha impulsado acciones de educación ambiental en las escuelas. El objetivo de la investigación cualitativa fue el identificar el conocimiento de los alumnos de 5°grado de educación básica en dos escuelas gubernamentales ubicadas en zonas con un nivel socioeconómico medio-bajo en la ciudad de Xalapa de Enríquez, en el estado de Veracruz sobre tópicos relacionados con la conservación del ambiente y el reciclaje como una estrategia para el manejo sustentable de los residuos urbanos. Se realizó diseño etnográfico que incluyó la encuesta y la observación como técnicas de recopilación de información para la descripción y análisis de los datos. Los resultados mostraron que los alumnos tienen un conocimiento básico sobre el manejo de los residuos y del reciclaje como acciones para la conservación de los recursos naturales, por lo que se recomendó implementar un programa de educación ambiental con el apoyo de estudiantes de la Universidad Veracruzana.

La preparación quirúrgica de machos celadores es la base para implementar esquemas de inseminación artificial en una explotación de ganado bovino bajo condiciones extensivas y/o estabuladas. Uno de los principales retos en este tipo de programas, es detectar de manera oportuna a las vacas en estro, por tanto; la función de un macho celador es facilitar esta labor, con una alta eficiencia. La correcta preparación quirúrgica y la adecuada selección de toros con las mejores características sexuales (mejor libido), mediante una técnica quirúrgica innovadora donde se consigue regresar al proceso fisiológico de la monta natural a diferencia de otras técnicas, donde el proceso quirúrgico es irreversible. El objetivo de este trabajo fue evaluar la técnica quirúrgica “penopexia perineal” en la preparación de toros marcadores para la empresa bovina. Para evaluar la técnica, se intervinieron quirúrgicamente doce bovinos de la raza suizo-cebú de 10 a 12 meses de edad, con un peso promedio de 280 Kg. Los parámetros evaluados fueron el tiempo de intervención, recuperación posoperatoria (procesos infecciosos) y retorno a la monta natural. Este estudio muestra un tiempo promedio total de 23.7 minutos y ocho días de recuperación posoperatoria, sin presencia de procesos infecciosos. Estos resultados nos permiten concluir que la utilización de esta técnica sería ventajosa en la preparación de toros marcadores.

RESUMEN Este artículo analiza los resultados del Servicio Social en una de las universidades más grandes de México, y toma como referencia los objetivos de su modelo educativo, el cual está orientado a la formación integral del estudiantado. El artículo se basa en un marco conceptual sobre el cambio curricular, la formación integral y la vinculación entre universidad y sociedad; también en el análisis de documentos institucionales y en entrevistas y cuestionarios aplicados al personal y a estudiantes. Se muestra que la ubicación del Servicio en los planes de estudio, la diversidad de perspectivas sobre sus metas y resultados, así como también una política institucional poco consistente han limitado los resultados de esta experiencia formativa tanto para los estudiantes, como para la universidad.*

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Soil Moisture (SM) is a key geophysical variable that enables a better understanding of the Earth’s hydrological processes. Missions like Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) have been the primary sources of SM estimations for years. The NASA-ISRO Synthetic Aperture Radar (NISAR) mission, planned to launch in 2024, will bring L-band and S-band SAR measurements that will be used for SM estimation. Consequently, investigations to link SMAP with NISAR to produce accurate SM retrievals at improved spatial resolutions will be a focus to many research initiatives. This investigation aims to set the basis to use polarimetric Global Navigation Satellite System-Reflectometry (GNSS-R) products to enhance the temporal resolution of NISAR’s L-band SAR data through its typical 12-day period. As a proof of concept, we model SMAP radar backscatter measurements from SMAP-Reflectometry (SMAP-R) measurements, using the 3 months of data collected by the SMAP radar while it was operational. The model is based on the sensitivity of polarimetric GNSS-R to roughness, vegetation, and SM, as well as on the complementary sensitivity existing between forward and backward scatter. Different regression models are implemented using single-, dual-, and full-polarization GNSS-R measurements synthetized from SMAP-R data. This study highlights the importance of the new constellations of polarimetric GNSS-R being built and shows how those frequent measurements can serve L-band SAR missions to improve their time resolution.

Abstract The Soil Moisture Active Passive (SMAP) mission has dramatically benefited our knowledge of the Earth’s surface processes. The SMAP mission was initially designed to provide complementary L-band measurements from a radiometer and a radar, producing geophysical measurements at a finer spatial resolution than the radiometer alone. Both instruments, sensitive to the geophysical parameters in the swath, provided independent measurements at different spatial resolutions. A few months after SMAP’s launch, the radar transmitter’s high-power amplifier suffered an anomaly, and the instrument could no longer return data. During recovery activities, the SMAP mission switched the radar receiver frequency facilitating the reception of Global Positioning System (GPS) signals scattered off the Earth’s surface, and enabling the radar to become the first spaceborne polarimetric Global Navigation Satellite System – Reflectometry (GNSS-R) instrument. With more than 7 years of continued measurements, SMAP GNSS-R data are the most extensive existing GNSS-R dataset and the only one providing GNSS-R polarimetric measurements. We demonstrate that the SMAP polarimetric GNSS-R reflectivity, derived from Stokes parameters mathematical formulation, can enhance the radiometer data over dense vegetation areas, recovering some of the original SMAP radar capability to contribute to the science products and pioneering the first polarimetric GNSS-R mission.

The Soil Moisture Active–Passive (SMAP) mission has greatly contributed to the use of remote sensing technologies for monitoring the Earth’s land surface and estimating geophysical parameters that influence the climate system. Since the SMAP mission switched its radar receiver to allow the reception of Global Positioning System (GPS) signals, Global Navigation Satellite System Reflectometry (GNSS-R) configuration has been enabled, providing full polarimetric forward scattering measurements of the Earth’s surface, also known as SMAP Reflectometry or SMAP-R. Polarimetric GNSS-R is beneficial for sensing land surface properties, especially for more accurate estimations of soil moisture (SM) in densely vegetated areas. In this study, we explore the opportunity to enhance SMAP mission soil moisture estimates using reflected GNSS signals. We achieve this by interpolating the sparse reflectivity data with terrain information to disaggregate radiometer brightness temperatures. Our main objective is to present a novel algorithm based on Graph Signal Processing (GSP) that uses reflectometry data to enhance SMAP radiometer observations and ultimately improve SM retrievals. By implementing methods from the GSP field, we formulate the reflectivity interpolation problem as a signal reconstruction on a graph, where the weights of the edges between the nodes are chosen as a function of geophysical information. Subsequently, using the retrieved reflectivity maps, we increase the resolution of the brightness temperature data, leading to an improvement in the SM estimates. Initial findings indicate that our GSP method presents a promising alternative for analyzing sparse remote sensing observations, leveraging Earth’s surface geophysical information. This approach results in a notable improvement, with a reduced Root Mean Square Error (RMSE) of 11.8% compared to SMAP data and a reduction in unbiased RMSE (uRMSE) by 14.7% over vegetated areas.

This article seeks to provide a snapshot of the security of Wi-Fi access points in the metropolitan area of A Coruña. First, we discuss the options for obtaining a tool that allows the collection and storage of auditable information from Wi-Fi networks, from location to signal strength, security protocol or the list of connected clients. Subsequently, an analysis is carried out aimed at identifying password patterns in Wi-Fi networks with WEP, WPA and WPA2 security protocols. For this purpose, a password recovery tool called Hashcat was used to execute dictionary or brute force attacks, among others, with various word collections. The coverage of the access points in which passwords were decrypted is displayed on a heat map that represents various levels of signal quality depending on the signal strength. From the handshakes obtained, and by means of brute force, we will try to crack as many passwords as possible in order to create a targeted and contextualized dictionary both by geographical location and by the nature of the owner of the access point. Finally, we will propose a contextualized grammar that minimizes the size of the dictionary with respect to the most used ones and unifies the decryption capacity of the combination of all of them.

Single-pass soil moisture retrieval has been a key objective of Global Navigation Satellite System-Reflectometry (GNSS-R) for the last decade. Achieving this goal will allow small satellites with GNSS-R payloads to perform such retrievals at high temporal resolutions. Properly modeling the soil surface roughness is key to providing high-quality soil moisture estimations. In the present work, the Physical Optics and Geometric Optics models of the Kirchhoff Approximation are implemented to the coherent and incoherent components of the reflectometry measurements collected by the SMAP radar receiver (SMAP-Reflectometry or SMAP-R). Two surface roughness products are retrieved and compared for a single-polarization approach, critical for single-polarization GNSS-R instruments that target soil moisture retrievals. Then, a polarization decoupling model is implemented for a dual-polarization retrieval approach, where the ratio between two orthogonal polarizations is evaluated to estimate soil moisture. Differences between linear and circular polarization ratios are evaluated using this decoupling parameter, and the theoretical soil moisture error with varying decoupling parameters is analyzed. Our results show a 1-sigma soil moisture error of 0.08 cm3/cm3 for the dual-polarization case for a fixed polarization decoupling value used for the whole Earth, and a 2-sigma error of 0.08 cm3/cm3 when the measured reflectivity and the VOD are used to estimate the polarization decoupling parameter.

The global navigation satellite system-reflectometry (GNSS-R) field has experienced an exponential growth as it is becoming relevant to many applications and has captivated the attention of an elevated number of research scholars, research centers and companies around the world. Primarily based on the contents of two Special Issues dedicated to the applications of GNSS-R to Earth observation, this review article provides an overview of the latest advances in the GNSS-R field. Studies are reviewed from four perspectives: (1) technology advancements, (2) ocean applications, (3) the emergent land applications, and (4) new science investigations. The technology involved in the GNSS-R design has evolved from its initial GPS L1 LHCP topology to include the use of other GNSS bands (L2, L5, Galileo, etc.), as well as consider RHCP/LHCP-receiving polarizations in order to perform polarimetric studies. Ocean applications have included developments towards ocean wind speed retrievals, swell and altimetry. Land applications have evolved considerably in the past few years; studies have used GNSS-R for soil moisture, vegetation opacity, and wetland detection and monitoring. They have also determined flood inundation, snow height, and sea ice concentration and extent. Additionally, other applications have emerged in recent years as we have gained more understanding of the capabilities of GNSS-R.

This review presents a state of the art in artificial intelligence applied to urban planning and particularly to land-use predictions. In this review, different articles after the year 2016 are analyzed mostly focusing on those that are not mentioned in earlier publications. Most of the articles analyzed used a combination of Markov chains and cellular automata to predict the growth of urban areas and metropolitan regions. We noticed that most of these simulations were applied in various areas of China. An analysis of the publication of articles in the area over time is included.

This manuscript focuses on the need for tailoring flood assessment products to decision making within the humanitarian sector. Decision-makers often struggle to extract all of the information contained in scientific products, either because they come from different fields of expertise or because they have different needs that are not captured in the results or the processing of the data. Here we define the key elements of a flood assessment product designed for the humanitarian sector. From a remote sensing perspective, in order to assess flooding, the measurement sampling properties, i.e., spatial resolution and temporal repeat, are key. We have therefore implemented a methodology through the processing and interpretation of the measurements from the Cyclone Global Navigation Satellite System (CYGNSS) mission. CYGNSS measurements are usually parametrized in various possible observables. Those observables are then linked to the surface characteristics, such as, in this case, the presence of inundation in the CYGNSS footprint. Our methodology includes the variability of the pixels in landscapes with infrastructure, rivers, agricultural fields, rural areas, and other elements characteristic of the agricultural-urban interface. We provide an original methodology that uses CYGNSS mission bistatic radar measurements and an artificial intelligence classification algorithm based on statistical properties of the land pixels through a k-means clustering strategy to detect and monitor flooding events, as well as to characterize the land surface prior to and post flooding events. The novel methodology to derive a flooding product is then evaluated towards the needs of the humanitarian sector by a cognizant link (a translator) between technologists or scientists and decision-makers. The inclusion of humanitarian needs into product development following the advice of a cognizant link is novel to the applications developed employing GNSS bistatic radar data.

The study of Near-Earth Asteroids (NEA) is crucial for human safety. Small hazardous asteroids with small radar cross sections are not easy to detect, track, and characterize due to the small signal-to-noise ratio (SNR) of the radar echo. This manuscript describes the results obtained for the application of a feed-forward neural network (FFNN) denoising methodology to NEA data obtained from the Goldstone Solar System Radar (GSSR). We demonstrate an increase in the signal level of up to ×4 the original value—in terms of sigma above the mean noise—when applying the FFNN denoising technique to radar Z-score normalized Binary Phase Code (BPC) images. This improvement benefits better radar detection of NEAs in general. Reducing the noise background level for antennas that have lower aperture, e.g., 34 m dishes, enables the use of FFNN denoising to improve visual detections on those noisier conditions. In addition, reducing noise level benefits shorter integration times of the data to obtain adequate signal levels. When talking about detection of small bodies crossing the antenna beam, since the asteroids or debris can move across the beam quite fast, it is relevant to reduce the integration time to allow for an increased number of independent pieces of information crossing the target through the antenna beam. The increased distance between the signal level and the noise level enables a better detection of the small-bodies at shorter integration times and therefore would be very useful for the detection of objects in the cis-lunar space.

Image Enhancement (IE) is an image processing procedure in which the image’s original information is improved, highlighting specific features to ease post-processing analyses by a human or machine. State-of-the-art image enhancement pipelines apply solutions to fixed and static constraints to solve specific issues in isolation. In this work, an IE system for image marketing is proposed, more precisely, real estate marketing, where the objective is to enhance the commercial appeal of the images, while maintaining a level of realism and similarity with the original image. This work proposes a generic image enhancement pipeline that combines state-of-the-art image processing filters, Machine Learning methods, and Evolutionary approaches, such as Genetic Programming (GP), to create a dynamic framework for Image Enhancement. The GP-based system is trained to optimize 4 metrics: Neural Image Assessment (NIMA) technical and BRISQUE, which evaluate the technical quality of the images; and NIMA aesthetics and PhotoILike, that evaluate the commercial attractiveness. It is shown that the GP model was able to find the best image quality enhancement (0.97 NIMA Aesthetics), while maintaining a high level of similarity with the original images (Structural Similarity Index Measure (SSIM) of 0.88). The framework has better performance according to the image quality metrics than the off-the-shelf image enhancement tool and the framework’s isolated parts.

Automatic prediction of the aesthetic value of images has received increasing attention in recent years. This is due, on the one hand, to the potential impact that predicting the aesthetic value has on practical applications. Even so, it remains a difficult task given the subjectivity and complexity of the problem. An image aesthetics assessment system was developed in recent years by our research group. In this work, its potential to be applied in commercial tasks is tested. With this objective, a set of three portals and three real estate agencies in Spain were taken as case studies. Images of their websites were taken to build the experimental dataset and a validation method was developed to test their original order with another proposed one according to their aesthetic value. So, in this new order, the images that have the high aesthetic score by the AI system will occupy the first positions of the portal. Relevant results were obtained, with an average increase of 52.54% in the number of clicks on the ads, in the experiment with Real Estate portals. A statistical analysis prove that there is a significant difference in the number of clicks after selecting the images with the AI system.