Abstract
Original Equipment Manufacturers (OEMs) have sought new supply chain paradigms that allowed them to focus on core activities, i.e. overall product design and commercialisation. This pursuit led to partnerships with a new generation of tier-1 strategic suppliers acting as integrators. Integrators are not only responsible for system supply, but also for system design. However, critical integrators were not able to live up to their new roles, which led to costly delays in development and production. These failures highlight the ineptitude of current risk management practices employed by OEMs. To support OEMs in implementing a more differentiated and suitable approach to the use of integrators, this paper proposes a mathematical programming model for Supply Chain Design (SCD). Instead of looking at the introduction of integrators as a dichotomous decision, the model suggests the optimal number of integrators, i.e. systems, and individual part suppliers. We propose new measures for integration risk, which build upon current risk assessment practices. Robust optimisation is used to study the effect of uncertainty over baseline risk values. All approaches were tested using both randomly generated instances and real data from a large European OEM in the aerospace industry.

Abstract
The research areas of the Immersive Learning community cover many different interests and perspectives on teaching and learning with immersive technologies. Based on existing efforts to map the field of research, we gathered 35 participants at the iLRN 2022 conference during an open hybrid workshop. These volunteers formed expert groups focusing on five possible perspectives on Immersive Learning. The expert groups gathered and summarized possible research questions with regards to an “Agenda 2030”, meaning the most intriguing questions that should be addressed during the years to come. We let all participants vote on these research endeavors regarding their academic value and importance for the community. As a results, we gathered a total of 23 ranked questions. These questions were subsumed into ten topics forming a Research Agenda for Immersive Learning 2030 (RAIL.2030).

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Abstract
This work aimed to define a humane endpoint scoring system able to objectively identify signs of animal suffering in a rat model of type 2 diabetes. Sprague-Dawley male rats were divided into control and induced group. The induced animals drink a 10% fructose solution for 14 days. Then, received an administration of streptozotocin (40 mg/kg). Animals’ body weight, water and food consumption were recorded weekly. To evaluate animal welfare, a score sheet with 14 parameters was employed. Blood glucose levels were measured at three time points. After seven weeks of initiating the protocol, the rats were euthanized. The induced animals showed weight loss, polyuria, polyphagia, and polydipsia. According to our humane endpoints table, changes in animal welfare became noticeable after the STZ administration. None of the animals hit the critical score limit (four). Data showed that the most effective parameters to assess welfare in this type 2 diabetes rat induction model were dehydration, grooming, posture, abdominal visualization, and stool appearance. The glycemia was significantly higher in the induced group when compared to the controls (p < 0.01). Induced animals’ murinometric and nutritional parameters were significantly lower than the controls (p < 0.01). Our findings suggest that in this rat model of type 2 diabetes with STZ-induced following fructose consumption, our list of humane endpoints is suitable for monitoring the animals’ welfare.

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Reinforcement Learning (RL) is a well-suited paradigm to train robots since it does not require any previous information or database to train an agent. This paper explores using Deep Reinforcement Learning (DRL) to train a robot to navigate in maps containing different sorts of obstacles and which emulate hallways. Training and testing were performed using the Flatland 2D simulator and a Deep Q-Network (DQN) provided by OpenAI gym. Different sets of maps were used for training and testing. The experiments illustrate how well the robot is able to navigate in maps distinct from the ones used for training by learning new behaviours (namely following walls) and highlight the key challenges when solving this task using DRL, including the appropriate definition of the state space and reward function, as well as of the stopping criteria during training.

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