SUMMARY Many different control schemes have been proposed in the technical literature to control the special class of underactuated systems, the- so-called brachiation robots. However, most of these schemes are limited with regard to the method by which the robot executes the brachiation movement. Moreover, many of these control strategies do not take into account the energy of the system as a decision variable. To observe the behavior of the system’s, energy is very important for a better understanding of the robot dynamics while performing the motion. This paper discusses a variety of energy-based strategies to better understand how the system’s energy may influence the type of motion (under-swing or overhand) the robot should perform.

Edge computing is a new development paradigm that brings computational power to the network edge through novel intelligent end-user services. It allows latency-sensitive applications to be placed where the data is created, thus reducing communication overhead and improving security, mobility and power consumption. There is a plethora of applications benefiting from this type of processing. Of particular interest is emerging edge-based image classification at the microscopic level. The scale and magnitude of the objects to segment, detect and classify are very challenging, with data collected using order of magnitude in magnification. The required data processing is intense, and the wish list of end-users in this space includes tools and solutions that fit into a desk-based device. Taking heavy-lift classification models initially built in the cloud to desk-based image analysis devices is a hard job for application developers. This work looks at the performance limitations and energy consumption footprint in embedding deep learning classification models in a representative edge computing device. Particularly, the dataset and heavy-lift models explored in the case study are phytoplankton images to detect Harmful Algae Blooms (HAB) in aquaculture at early stages. The work takes a deep learning model trained for phytoplankton classification and deploys it at the edge. The embedded model, deployed in a base form alongside optimised options, is submitted to a series of system stress experiments. The performance and power consumption profiling help understand system limitations and their impact on the microscopic grade image classification task.

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This study determined whether supplementing the diet of laying hens in the final third of laying with vitamin D improves bird health and egg performance and quality. The hens were separated into four groups with six repetitions each and five animals per repetition to test three doses of vitamin D supplementation (50, 100, and 150 mg/kg). The vitamin D supplementation had no effect on the hens performance; however, when we individually evaluated each repetition, we found that the birds of T100 and T150 showed significant increases (P<0.001) in the percentage of the laying rate. Regarding egg quality, the T150 group presented a better result of shell resistance, and the T150 I had a better range of colors and color "a". The yolk percentage was higher in the supplemented groups than the control (T0). On day 21, serum cholesterol levels were lower in supplemented birds groups than in control. At 42 days, the highest vitamin D supplementation for T150 birds resulted in a higher serum albumin concentration, whereas birds in the T100 group had a higher protein concentration. The supplementation had positive and timely effects on the birds' metabolism, reflected in the higher laying rate and the better egg quality.