Abstract It is known that several animals are sensitive to magnetic fields. Magnetoreception in animals is the ability to sense the geomagnetic field of the Earth and to use their vectorial properties for navigation and terrestrial location. Animals can also show magneto-sensitivity, unrelated to navigation and show changes in behavior during the application of magnetic fields. The aim of the present study was to analyze the behavior of the shrimp Litopennaeus vannamei under the presence of artificial magnetic fields of different intensities under laboratory conditions. Shrimps were chosen because of their economic importance and because they have been neglected in the study of magneto-sensitivity in animals. The laboratory experiments were performed with the use of a pair of circular coils and four aquariums (three treatments and a control, with 20 shrimps each). Our results show for the first time that L. vannamei shrimps are magneto-sensitive, showing a preference to swim against the magnetic field lines direction as is done by magnetotactic bacteria from the Southern Hemisphere. Also, it was observed that the shrimps remain still after the application of magnetic field intensities from 44 to 237 µT, which could be useful for maintaining shrimps in their captive areas. location magnetosensitivity, magnetosensitivity magneto sensitivity, sensitivity conditions three control 2 each. each . each) L magnetosensitive, magnetosensitive sensitive, magneto-sensitive Hemisphere Also 4 23 µT areas

Abstract: Mapping the submerged bottom is a hampered task when traditional vessels are inserted in shallow places that pose a danger to navigation. In this sense, current research has sought optical remote sensing to obtain bathymetry estimates in larger locations in less time. However, most studies employ a large sample of bathymetric points to estimate depth with orbital images. The need for a large amount of random bathymetric points can make these procedures less viable and unattractive. Thus, the present work proposes a methodology to estimate bathymetric depths from orbital images using sections of bathymetric points previously spaced in the study area, avoiding the need for a high amount of points collected through traditional bathymetric surveys. This work also compares this methodology using the NDWI index and the ANNs. Furthermore, the study showed that the points contained in the sections are quite efficient for extracting bathymetry with orbital images, especially through the implementation of neural networks, achieving a volume estimation accuracy within 4% of the actual volume of the reservoir in question. Abstract navigation sense time However unattractive Thus area surveys ANNs Furthermore networks 4 question

ORIENTATION: Volatile, uncertain, complex and ambiguous (VUCA) represents the environment in which business organisations operate, illuminated by the post-coronavirus disease 2019 (COVID-19) pandemic and the recent advent of artificial intelligence (AI). The VUCA environment epitomises pervasive unpredictability, shaping the challenges faced by businesses. RESEARCH PURPOSE: This article focuses on the dynamics of individuals within private higher education institutions (PHEIs), driven by a bottom line; they compete to attract students, face greater scrutiny from outside constituencies and subjected to higher accountability. It explores the hierarchical structure of management levels and how the interrelatedness of the business environments can facilitate the navigation of the work role identity in flux in the face of constant change. MOTIVATION FOR THE STUDY: The fast-paced and ever-changing nature of today's business landscape significantly affects employees' core identities, as the constant need to adapt and perform at their best creates feelings of insecurity and uncertainty. Consequently, it influences institutional and individual outcomes. RESEARCH APPROACH/DESIGN AND METHOD: This qualitative study collected data from qualified staff in senior-level positions within academia, with titles such as CEO or head of campus (HOC) (N = 15) at selected PHEIs. The data were analysed using ATLAS.ti. MAIN FINDINGS: The findings show an extended framework highlighting elements vital to the increased institutional brand image and improved professional identity. PRACTICAL/MANAGERIAL IMPLICATIONS: How individuals in the workplace experience and respond to constant unprecedented change affects the institutional brand and the role of management causes a significant influence. CONTRIBUTION/VALUE-ADD: Leaders can drive organisations to adapt, change and evolve to the conditions of their organisational environment.

ABSTRACT Over the past few years, there has been a steady increase in the use of aircraft vehicles, in particular unmanned aerial vehicles (UAV). UAV navigation is generally controlled by a human pilot. But the challenge for the scientific community is to carry out autonomous navigation. Some solutions have been proposed for the UAV autonomous navigation. Studies indicate as a solution to use data fusion and/or image processing navigation. Kalman Filter (KF) can be employed as a data fuser, but the KF has disadvantages. An alternative to the KF is based on artificial intelligence. Here, the KF is replaced by a self-configured neural network. This work investigates a way to select data for training the neural fuser, based on cross-validation techniques. The results are compared to the data fusion done by a KF. years UAV. . (UAV) pilot andor and or (KF fuser disadvantages intelligence Here selfconfigured self configured network crossvalidation cross validation techniques (UAV

Abstract: The design of efficient autonomous navigation systems for mobile robots or autonomous vehicles is fundamental to perform the programmed tasks. Basically, two kind of sensors are used in urban road following: LIDAR and cameras. LIDAR sensors are highly accurate but expensive and extra work is needed for human understanding of the point cloud scenes; however, visual content is understood better by human beings, which should be used to develop human-robot interfaces. In this work, a computer vision-based urban road following software tool called AutoNavi3AT for mobile robots and autonomous vehicles is presented. The urban road following scheme proposed in AutoNavi3AT uses vanishing point estimation and tracking on panoramic images to control the mobile robot heading on the urban road. To do that, Gabor filters, region growing, and particle filters were used. In addition, laser range data are also employed for local obstacle avoidance. Quantitative results were achieved using two kind of tests, one uses datasets acquired at the Universidad del Valle campus, and field tests using a Pioneer 3AT mobile robot. As a result, important improvements in the vanishing point estimation of 68.26 % and 61.46 % in average were achieved, which is useful for mobile robots and autonomous vehicles when they are moving on urban roads.

Resumen: El diseño de sistemas de navegación autónomos eficientes para robots móviles o vehículos autónomos es fundamental para realizar las tareas programadas. Básicamente, se utilizan dos tipos de sensores en el seguimiento de vías urbanas: LIDAR y cámaras. Los sensores LIDAR son muy precisos, pero costosos y se necesita trabajo adicional para la comprensión humana de las escenas de nubes de puntos; sin embargo, los seres humanos entienden mejor el contenido visual, lo que debería usarse para desarrollar interfaces humano-robot. En este trabajo, se presenta una herramienta de software de seguimiento de carreteras urbanas basada en visión artificial llamada AutoNavi3AT para robots móviles y vehículos autónomos. El esquema de seguimiento de vías urbanas propuesto en AutoNavi3AT utiliza la estimación del punto de fuga y el seguimiento de imágenes panorámicas para controlar el avance del robot móvil en la vía urbana. Para ello se utilizaron filtros Gabor, crecimiento de regiones y filtros de partículas. Además, los datos de alcance del láser también se emplean para evitar obstáculos locales. Los resultados cuantitativos se lograron utilizando dos tipos de pruebas, una utiliza conjuntos de datos adquiridos en el campus de la Universidad del Valle y pruebas de campo utilizando un robot móvil Pioneer 3AT. Como resultado, se lograron mejoras importantes en la estimación del punto de fuga de 68.26% y 61.46% en promedio, lo cual es útil para robots móviles y vehículos autónomos cuando se desplazan por vías urbanas.

Abstract: The use of mobile robots has increased for its application in various areas such as supply chains, factories, cleaning, disinfection, medical assistance, search, and exploration. It is a fact that most of these robots, if not all, use batteries to power themselves. During a mobile robot task execution, the battery's electric charge tends to deplete as a function of the energy load demands, which would cause the robot to shut down if the discharge is critical, leaving its task inconclusive. Therefore, it is of utmost importance that the robot learns when to charge its batteries, avoiding turning off. This work shows a reactive navigation scheme for a mobile robot that integrates a module for battery-level monitoring. A robot moves from a starting point to a destination according to the battery level. During the navigation, the robot decides when to change the course toward a battery charging station. This paper presents a rules-based reinforcement learning architecture with three entries; these entries correspond to the robot's battery level, the distance to the destination, and the distance to the battery charging station. According to the simulations, the robot learns to select an appropriate action to accomplish its task.

RESUMEN En las últimas décadas, debido a la creciente movilidad de personas y bienes, el rápido crecimiento de los usuarios de dispositivos móviles con servicios basados en la ubicación ha aumentado la necesidad de información geoespacial. En este contexto, el posicionamiento utilizando los datos recopilados por los Sistemas Globales de Satélite de Navegación (multi-GNSS) ha ganado más importancia en el campo de la geomática. La calidad de las soluciones está relacionada, entre otros factores, con el tipo de receptor utilizado en el trabajo. Para mejorar el posicionamiento con dispositivos de bajo costo y evitar gastos adicionales del usuario, este trabajo tiene como objetivo proponer la implementación de una Red Neural Artificial (ANN) para estimar los observables del operador GPS L2. Para esto, se seleccionó un modelo de red a través de la técnica de validación cruzada (CV), se estimaron las observaciones y se analizó la precisión de las soluciones. La técnica CV demostró que un Perceptrón multicapa con cuatro capas intermedias y uno con una capa intermedia son las configuraciones más apropiadas para este problema. El procesamiento RINEX de doble frecuencia (con datos artificiales) reveló mejoras significativas. Para algunas pruebas, fue posible cumplir con las regulaciones de georreferenciación de propiedad rural del Instituto Nacional de Colonización y Reforma Agraria (INCRA). Los resultados indican, por lo tanto, que la propuesta metodológica de la presente investigación es muy prometedora para aproximar la calidad de posicionamiento accesible utilizando un receptor de doble frecuencia.

ABSTRACT In recent decades, due to the increasing mobility of people and goods, the rapid growth of users of mobile devices with location-based services has increased the need for geospatial information. In this context, positioning using data collected by the Global Navigation Satellite Systems (multi-GNSS) has gained more importance in the field of geomatics. The quality of the solutions is related, among other factors, to the receiver's type used in the work. To improve the positioning with low-cost devices and to avoid additional user expenses, this work aims to propose the implementation of an Artificial Neural Network (ANN) to estimate the GPS L2 carrier observables. For this, a network model was selected through the cross-validation (CV) technique, the observations were estimated, and the accuracy of the solutions was analyzed. The CV technique demonstrated that a Multilayer Perceptron with four intermediate layers and one with one intermediate layer are the most appropriate configurations for this problem. The dual-frequency RINEX processing (with artificial data) revealed significant improvements. For some tests, it was possible to comply with the rural property georeferencing regulations of the Brazilian National Institute of Colonization and Agrarian Reform (INCRA). The results indicate, therefore, that the methodological proposal of the present investigation is very promising for approximating the quality of positioning reachable using a dual-frequency receiver.

This work presents an artificial intelligence approach to solve the problem of finding a path and creating a map in unknown environments using Reinforcement Learning (RL) and Simultaneous Localization and Mapping (SLAM) for a differential mobile robot along with an optimal control system. We propose the integration of these approaches (two of the most widely used ones) for the implementation of robot navigation systems with an efficient method of control composed by a neural identifier and an inverse optimal control in order to obtain a robust and autonomous system of navigation in unknown and dynamic environments.

This contribution reassesses progress in the development of satellite laser ranging (SLR) technology and its scientific and societal applications in South Africa. We first highlight the current global SLR tracking stations within the framework of the International Laser Ranging Service (ILRS) and the artificial satellites currently being tracked by these stations. In particular, the present work focuses on analysing SLR measurements at Hartebeesthoek Radio Astronomy Observatory (HartRAO), South Africa, based on the MOBLAS-6 SLR configuration. Generally, there is a weak geometry of ILRS stations in the southern hemisphere and the SLR tracking station at HartRAO is the only active ILRS station operating on the African continent. The SLR-derived products - such as station positions and velocities, satellite orbits, components of earth's gravity field and their temporal variations, earth orientation parameters - are collected, merged, achieved and distributed by the ILRS under the Crustal Dynamic Data Information System. These products are used in various research fields such as detection and monitoring of tectonic plate motion, crustal deformation, earth rotation, polar motion, and the establishment and monitoring of International Terrestrial Reference Frames, as well as modelling of the spatio-temporal variations of the earth's gravity field. The MOBLAS-6 tracking station is collocated with other geodetic techniques such as very long baseline interferometry and Global Navigation Satellite Systems, thus making this observatory a fiducial geodetic location. Some applications of the SLR data products are described within the context of earth system science.

La navegación autónoma de robots es uno de los principales problemas entre los robots debido a su complejidad y dinamismo, ya que depende de las condiciones ambientales como la interacción entre ellos mismo, personas o cualquier cambio sin previo aviso en el entorno. En este trabajo, hemos propuesto la aplicación de técnicas evolutivas para la navegación autónoma de robots. El objetivo de este artículo es desarrollar el control de movimiento de un robot para alcanzar una meta en un entorno desconocido, donde los comportamientos de bajo nivel son obtenidos a través de redes de neuronas artificiales y posteriormente han sido evolucionados mediante algoritmos evolutivos. Para este propósito ROS y el TurtleBot 2 han sido utilizados. El documento finaliza con una discusión crítica de los resultados experimentales.

The autonomous navigation of robots is one of the main problems among the robots due to its complexity and dynamism as it depends on environmental conditions as the interaction between themselves, persons or any unannounced change in the environment. In this work, we have proposed the application of evolutionary techniques for autonomous navigation of robots. The objective of this work is to develop the motion control for a robot to reach a goal in an unknown environment, where the low level behaviors are obtained through artificial neural networks and subsequently have been evolved by evolutionary algorithms. For this purpose ROS and robot TurtleBot 2 are used. The paper ends with a critical discussion of the experimental results.

Este artículo presenta el desarrollo de un sistema de inspección y vigilancia el cual procesa las imágenes de las cámaras abordo con el fin de realizar una navegación autónoma o semiautónoma, según como el usuario lo especifique. El dispositivo aéreo utilizado es un cuadrotor, al que se le implementaron varias estrategias de control servo-visual para determinar la orientación, la posición del mismo y así seguir una trayectoria demarcada. Las imágenes de la segunda cámara del robot aéreo son transmitidas por la red permitiendo la inspección remota del lugar que se desea confrontar. Se realizaron las verificaciones correspondientes a los controladores, demostrando que el robot aéreo sigue diversos tipos de trayectorias satisfactoriamente.

This paper presents the development of an inspection system and monitoring which processes the onboard camera images in order to perform an autonomous or semi-autonomous navigation, depending on how the user specified. The aerial device used is a quadrotor, which is implemented several strategies servovisual control to determine the orientation, the position thereof and thus follow a path demarcated. The images of the second aerial robot camera are transmitted over the network allowing remote inspection of the place you want to confront. Checks were made corresponding to the controllers, showing that the robot follows diverse types of air paths satisfactorily.

RESUMEN La visión artificial intenta capturar información relevante del medio ambiente, utilizando cámaras como sensores de ciertas características (formas, colores, texturas, etc.), para el funcionamiento adecuado de algunos mecanismos. Con el fin de obtener una imagen idéntica al entorno real, es necesario generar imágenes estereoscópicas que nos permita obtener la profundidad y así conseguir una representación en 3D. En este documento se plasma cómo se realizó la integración de un sistema de visión estereoscópica artificial a un robot móvil, con el objetivo de reconocer y seguir el centro de un camino. Dicho sistema se encarga de la adquisición, procesamiento y caracterización de imágenes, utilizando procedimientos fuera de línea para calibrar las cámaras y los métodos en línea para generar el mapa de disparidad, aplicación del operador Canny y la Transformada de Hough, empleando las librerías de OpenCV en un proyecto de Consola Win32 en C++. Es importante mencionar que los algoritmos desarrollados en este trabajo fundamentalmente son para ambientes estructurados. Los ambientes pueden clasificarse en: estructurados, no estructurados y semi-estructurados.

ABSTRACT An artificial vision tries to capture relevant information from environment using cameras as sensors of certain characteristics (shapes, colors, textures, etc.) for a proper functioning of some mechanisms. In order to get an identical image to real environment, it is required to generate stereoscopic images that allow us to get the depth and thus a 3D representation. This paper shows an artificial stereoscopic vision system incorporated to a mobile robot to recognize and follow the center of a path. Such a system handles the capture, processing and characterization of images using offline procedures such to standardize cameras and online methods for a disparity mapping generation, application of the Canny edge detector and the Hough transform, using the OpenCV libraries in Win32 Console project in C++. It`s important to mention that algorithms developed in this work are fundamentally for structured environments. Environments can classified as follows: structured, non-structured and semi-structured.

Recifes artificiais e estruturas correlatas têm sido usadas ao redor do mundo como ferramentas de manejo para melhorar a pesca e turismo e mitigar áreas impactadas, além de outras funções. No entanto, projetos envolvendo recifes artificiais requerem uma série de estudos e um planejamento adequado para que sua implantação não cause danos ao meio ambiente, como alterações severas nas comunidades biológicas marinhas locais e adjacentes assim como nos padrões oceanográficos locais. Existe ainda, no caso de estruturas mal planejadas, o risco de perdas de partes desta estrutura devido às correntes, subsidência ou movimentos não desejados, danos a rotas de navegação e conflitos entre diferentes grupos de usuários. Assim, qualquer tentativa de implantar projetos usando recifes artificiais e estruturas correlatas deve ser seguida de uma reflexão sobre sua relevância, planejamento e monitoramento eficiente e uma avaliação aprofundada quanto seu impacto em potencial.

Artificial reefs and correlated structures have been largely used all over the world as management tools to enhance fisheries and tourism and mitigate damaged areas, among others functions. Nevertheless, projects involving artificial reefs require a series of studies and an adequate planning so its implementation will not cause any damage to the environment, such as severe alterations on the local and surrounding marine biological communities as well as alterations on the local oceanographic patterns. There is, still, on not well planned ventures, the risk of lost of parts of the structures due to scouring, subsidence or undesirable movement, damage to navigation routes, and conflicts among different groups of users. Thus, any attempt to implement projects using artificial reefs and correlated structures must be followed by reflection about its relevance, efficient planning and monitoring and a deep evaluation of its potential impact.

This research work proposes the development of a mobile robot prototype through Artificial Intelligence techniques allowing autonomous navigation in indoor type test environments. In this research work, a perception system composed of five perception sonars array that provides an environment detection system was designed. The sonar signals were captured by the embedded software which determined the control signals from the drive system by means of Artificial Intelligence techniques. The design of the differential drive systems with two servomotors and a supply circuit to assure batteries voltage stabilization are also presented.

Este trabajo propone el desarrollo de un prototipo de robot móvil para la investigación de técnicas de Inteligencia Artificial que permita la navegación autónoma en entornos de prueba tipo indoor. Se diseña un sistema de percepción compuesto por un arreglo de cinco sonares que proveen una detección del entorno, las señales de los sonares son capturadas por el software embebido que mediante técnicas de Inteligencia Artificial determinan las señales de control del sistema motriz. También, se presentan los diseños de los sistemas de tracción diferencial, con dos servomotores, y de alimentación con un circuito de estabilización de la tensión de las baterías.

A informação da altitude fornecida pelo sistema GNSS ( Global Navigation Satellite System ) é puramente geométrica, e na maioria dos trabalhos de engenharia a altitude deve estar referenciada ao geóide. Com um número suficiente de Referências de nível (Rn's) com coordenadas horizontais e verticais conhecidas, quase sempre, é possível ajustar-se, pelo Método dos Mínimos Quadrados, expressões matemáticas que permitem interpolar as alturas geoidais. O objetivo deste trabalho foi avaliar a eficiência das Redes Neurais Artificiais (RNAs) no processo de predição de alturas geoidais tendo como área de estudo o Estado de São Paulo. As informações utilizadas basearam-se em um conjunto de 157 Referências de nível (Rn's) distribuídas uniformemente em todo Estado. Para estas Rn's são conhecidas suas coordenadas horizontais (latitude e longitude) e verticais (altitudes geométrica e ortométrica e altura geoidal). Das 157 Rn's, 115 foram utilizadas para o treinamento da RNA e 42 no processo de simulação para avaliar a eficiência do modelo proposto. A eficiência baseou-se em determinar as discrepâncias (erro) entres as alturas geoidais conhecidas e as obtidas pelo modelo neural. Como contribuição da pesquisa comparou-se também os valores simulados com o Earth Gravitational Model 2008 (EGM2008) e também com o MAPGEO2004. Em termos de resultados a RNA proporcionou um erro absoluto médio de 0,19 m ± 0,14 m com uma forte correlação (R² = 0,9871) com os valores tomados como verdadeiros. Estatisticamente os testes realizados mostraram que não houve diferença entre as médias das alturas geoidais conhecidas e as fornecidas pelo modelo neural para um nível de significância de 5%. Comparando-se os resultados com o EGM2008 e MAPGEO2004 a RNA proporcionou uma redução no erro de 0,07 m e 0,44 m, respectivamente.

The information of height provided by the GNSS (Global Navigation Satellite System) is purely geometrical, and in most engineering papers, the height must be referenced to the geoid. Provided we have a sufficient number of Bench Marks (BMs) with known horizontal and vertical coordinates, it is nearly always possible to adjust mathematical expressions that allow for the interpolation of geoidal heights. The aim of this paper is to evaluate the efficiency of Artificial Neural Network (ANN) in the process of predicting geoidal heights, having the State of São Paulo as the area of study. The information used is based on a set of 157 BMs, evenly distributed all across the State. The horizontal coordinates (latitude and longitude) and the vertical coordinates (geometrical, orthometrical and geoidal heights) of these BMs are known. From the 157 BMs, 115 were used for the training of RNA and 42 in the process of simulation to assess the efficiency of the model proposed. Efficiency is based in determining the discrepancies (error) between known geoidal heights and those which were obtained by the neural model. As a contribution to this research, we have compared the values simulated with the Earth Gravitational Model 2008 (EGM2008) and with the MAPGEO2004 as well. In terms of results, the RNA produced a mean absolute error of 0.19 m ± 0.14 m and a strong correlation (R² = 0.9871) with the values taken as true. Statistically, the tests showed that there was no difference between known geoidal heights and those which were provided by the neural model for a level of significance of 5%. When we compare these results with the EGM2008 and MAPGEO2004, the RNA has an error reduction of 0.07 and 0.44 m, respectively.