SensOre Ltd (ASX:S3N) is pleased to announce that SensOre and Gold Road (ASX: GOR) have reached agreement to restructure arrangements surrounding the Yilgarn Exploration Ventures (YEV) portfolio. SensOre has agreed to acquire Gold Road Resources' 40% minority interest in YEV for 800,000 SensOre shares. Yilgarn Exploration Ventures holds a portfolio of prospective gold assets in the Eastern Goldfields of Western Australia. SensOre aims to become the top performing minerals targeting company in the world through the deployment of AI and machine learning (ML) technologies, specifically its Discriminant Predictive Targeting (DPT) workflow. SensOre collects all available geological information in a terrane and places it in a multidimensional hypercube or data cube.

Muscle tissue drives nearly all movement in the animal kingdom, providing power, mobility, and dexterity. Technologies for measuring muscle tissue motion, such as sonomicrometry, fluoromicrometry, and ultrasound, have significantly advanced our understanding of biomechanics. Yet, the field lacks the ability to monitor muscle tissue motion for animal behavior outside the lab. Towards addressing this issue, we previously introduced magnetomicrometry, a method that uses magnetic beads to wirelessly monitor muscle tissue length changes, and we validated magnetomicrometry via tightly-controlled in situ testing. In this study we validate the accuracy of magnetomicrometry against fluoromicrometry during untethered running in an in vivo turkey model. We demonstrate real-time muscle tissue length tracking of the freely-moving turkeys executing various motor activities, including ramp ascent and descent, vertical ascent and descent, and free roaming movement. Given the demonstrated capacity of magnetomicrometry to track muscle movement in untethered animals, we feel that this technique will enable new scientific explorations and an improved understanding of muscle function. -- -- El tejido muscular es el motor de casi todos los movimientos del reino animal, ya que proporciona fuerza, movilidad y destreza. Las tecnolog\'ias para medir el movimiento del tejido muscular, como la sonomicrometr\'ia, la fluoromicrometr\'ia y el ultrasonido, han avanzado considerablemente la comprensi\'on de la biomec\'anica. Sin embargo, este campo carece de la capacidad de rastrear el movimiento del tejido muscular en el comportamiento animal fuera del laboratorio. Para abordar este problema, presentamos previamente la magnetomicrometr\'ia, un m\'etodo que utiliza peque\~nos imanes para rastrear de forma inal\'ambrica los cambios de longitud del tejido muscular, y validamos la magnetomicrometr\'ia mediante pruebas estrechamente controladas in situ. En este estudio validamos la precisi\'on de la magnetomicrometr\'ia en comparaci\'on con la fluoromicrometr\'ia usando un modelo de pavo in vivo mientras corre libremente. Demostramos el rastreo en tiempo real de la longitud del tejido muscular de los pavos que se mueven libremente ejecutando varias actividades motoras, incluyendo el ascenso y el descenso en rampa, el ascenso y el descenso vertical, y el movimiento libre. Dada la capacidad demostrada de la magnetomicrometr\'ia para rastrear el movimiento muscular en animales en un contexto m\'ovil, creemos que esta t\'ecnica permitir\'a nuevas exploraciones cient\'ificas y una mejor comprensi\'on de la funci\'on muscular.

ABSTRACT: Commercial drones are not yet dimensioned to perform indoor autonomous tasks, since they use GPS for their location in the environment. When it comes to a space with physical obstacles (walls, metal, etc.) between the communication of the drone and the satellites that allow the precise location of the same, there is great difficulty in finding the satellites or it generates interference for this location. This problem can cause an unexpected action of the drone, a collision and a possible accident can occur, The work to follow presents the development of a drone capable of operating in a physical space (indoor), without the need for GPS. In this proposal, a prototype of a system for detecting the distance (lidar) that the drone is from the walls is also developed, with the aim of being able to take this information as the location of the drone.