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The potential of the developing zebrafish model for toxicology and drug discovery is limited by inefficient approaches to manipulating and chemically exposing zebrafish embryos—namely, manual placement of embryos into 96- or 384-well plates and exposure of embryos while still in the chorion, a barrier of poorly characterized permeability enclosing the developing embryo. We report the automated dechorionation of 1600 embryos at once at 4 h postfertilization (hpf) and placement of the dechorionated embryos into 96-well plates for exposure by 6 hpf. The process removed ≥95% of the embryos from their chorions with 2% embryo mortality by 24 hpf, and 2% of the embryos malformed at 120 hpf. The robotic embryo placement allocated 6-hpf embryos to 94.7% ± 4.2% of the wells in multiple 96-well trials. The rate of embryo mortality was 2.8% (43 of 1536) from robotic handling, the rate of missed wells was 1.2% (18 of 1536), and the frequency of multipicks was <0.1%. Embryo malformations observed at 24 hpf occurred nearly twice as frequently from robotic handling (16 of 864; 1.9%) as from manual pipetting (9 of 864; 1%). There was no statistical difference between the success of performing the embryo placement robotically or manually.

For a successful rollout of electric vehicles (EVs), it is required to establish an adequate charging infrastructure. The adequate access to such infrastructure would help to mitigate concerns associated with limited EV range and long charging times. Battery swapping stations are poised as effective means of eliminating the long waiting times associated with charging the EV batteries. These stations are mediators between the power system and their customers. In order to successfully deploy this type of stations, a business and operating model is required, that will allow it to generate profits while offering a fast and reliable alternative to charging batteries. This paper proposes an optimization framework for the operating model of battery swapping stations. The proposed model considers the day-ahead scheduling process. Battery demand uncertainty is modeled using inventory robust optimization, while multi-band robust optimization is employed to model electricity price uncertainty. The results show the viability of the proposed model as a business case, as well as the effectiveness of the model to provide the required service.

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