We introduce a new benchtop microgravity simulator (MGS) that is scalable and easy to use. Its working principle is similar to that of random positioning machines (RPM), commonly used in research laboratories and regarded as one of the gold standards for simulating microgravity. The improvement of the MGS concerns mainly the algorithms controlling the movements of the samples and the design that, for the first time, guarantees equal treatment of all the culture flasks undergoing simulated microgravity. Qualification and validation tests of the new device were conducted with human bone marrow stem cells (bMSC) and mouse skeletal muscle myoblasts (C2C12). bMSC were cultured for 4 days on the MGS and the RPM in parallel. In the presence of osteogenic medium, an overexpression of osteogenic markers was detected in the samples from both devices. Similarly, C2C12 cells were maintained for 4 days on the MGS and the rotating wall vessel (RWV) device, another widely used microgravity simulator. Significant downregulation of myogenesis markers was observed in gravitationally unloaded cells. Therefore, similar results can be obtained regardless of the used simulated microgravity devices, namely MGS, RPM, or RWV. The newly developed MGS device thus offers easy and reliable long-term cell culture possibilities under simulated microgravity conditions. Currently, upgrades are in progress to allow real-time monitoring of the culture media and liquids exchange while running. This is of particular interest for long-term cultivation, needed for tissue engineering applications. Tissue grown under real or simulated microgravity has specific features, such as growth in three-dimensions (3D). Growth in weightlessness conditions fosters mechanical, structural, and chemical interactions between cells and the extracellular matrix in any direction.

Automated agricultural robots are becoming more common with the decreased cost of sensor devices and increased computational capabilities of single-board computers. Weeding is one of the mundane and repetitive tasks that robots could be used to perform. The detection of weeds in crops is now common, and commercial solutions are entering the market rapidly. However, less work is carried out on combatting weeds in pastures. Weeds decrease the grazing yield of pastures and spread over time. Mowing the remaining weeds after grazing is not guaranteed to remove entrenched weeds. Periodic but selective cutting of weeds can be a solution to this problem. However, many weeds share similar textures and structures with grazing plants, making their detection difficult using the classic RGB (Red, Green, Blue) approach. Pixel depth estimation is considered a viable source of data for weed detection. However, systems utilizing RGBD (RGB plus Depth) are computationally expensive, making them nonviable for small, lightweight robots. Substituting one of the RGB bands with depth data could be a solution to this problem. In this study, we examined the effect of band substitution on the performance of lightweight YOLOv8 models using precision, recall and mAP50 metrics. Overall, the RDB band combination proved to be the best option for YOLOv8 small and medium detection models, with 0.621 and 0.634 mAP50 (for a mean average precision at 50% intersection over union) scores, respectively. In both instances, the classic RGB approach yielded lower accuracies of 0.574 and 0.613.