Abstract
In cutting processes, one of the strategies to reduce raw material waste is to generate leftovers that are large enough to return to stock for future use. The length of these leftovers is important since waste is expected to be minimal when cutting these objects in the future. However, in several situations, future demand is unknown and evaluating the best length for the leftovers is challenging. Furthermore, it may not be economically feasible to manage a stock of leftovers with multiple lengths that may not result in minimal waste when cut. In this paper, we approached the cutting stock problem with the possibility of generating leftovers as a two-stage stochastic program with recourse. We approximated the demand levels for the different items by employing a finite set of scenarios. Also, we modeled different decisions made before and after uncertainties were revealed. We proposed a mathematical model to represent this problem and developed a column generation approach to solve it. We ran computational experi-ments with randomly generated instances, considering a representative set of scenarios with a varying probability distribution. The results validated the efficiency of the proposed approach and allowed us to derive insights on the value of modeling and tackling uncertainty in this problem. Overall, the results showed that the cutting stock problem with usable leftovers benefits from a modeling approach based on sequential decision-making points and from explicitly considering uncertainty in the model and the solution method. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

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.

Abstract
We investigate the properties of time-dependent dissipative solitons for a cubic complex Ginzburg-Landau equation stabilized by nonlinear gradient terms. The separation of initially nearby trajectories in the asymptotic limit is predominantly used to distinguish qualitatively between time-periodic behavior and chaotic localized states. These results are further corroborated by Fourier transforms and time series. Quasiperiodic behavior is obtained as well, but typically over a fairly narrow range of parameter values. For illustration, two examples of nonlinear gradient terms are examined: the Raman term and combinations of the Raman term with dispersion of the nonlinear gain. For small quintic perturbations, it turns out that the chaotic localized states are showing a transition to periodic states, stationary states, or collapse already for a small magnitude of the quintic perturbations. This result indicates that the basin of attraction for chaotic localized states is rather shallow.

Abstract
There is a growing complexity, volatility, and unpredictability in the electric sector that hardens the decision-making process. To this end, the use of proper decision support tools and simulation platforms becomes essential. This paper presents the Multi-Agent based Real-Time INfrastructure for Energy (MARTINE) platform that allows real-time simulation and emulation of loads, resources, and infrastructures. MARTINE uses multi-agent systems that connect to physical resources and can represent additional simulated players that are not physically present in the simulation and emulation environment, enabling the creation of complex scenarios for testing and validation. MARTINE provides the seamless integration of real-time emulation with simulated and physical resources simultaneously in a unique simulation environment, which is only possible by supporting multi-agent systems. This work presents MARTINE's integration in a semantically interoperable multi-agent systems society developed for the test, study, monitoring, and validation of the power system sector. The use of ontologies and semantic web technologies eases the interoperability between the heterogeneous systems. The case study scenario demonstrates the use of MARTINE in simulating a local community electricity market that combines real-time data from physical devices with simulated data and the use of semantic web techniques to make the system interoperable, configurable, and flexible.& COPY; 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Abstract
This paper investigates the contribution of hydrogen electrolysers (HEs) for frequency related Ancillary Services (AS), namely Frequency Containment Reserve (FCR), Synthetic Inertia (SI) , Fast Frequency Response (FFR) in future operation scenarios in the Iberian Peninsula (IP) considering low system iner-tia. The proposed framework for analysis consists of a dynamic model developed in MATLAB/Simulink. Simulations show that an instantaneous inverter based resource (IBR) trip induced by a grid fault may lead to the occurrence of values of Rate of Change of Frequency (RoCoF) close to undesirable thresholds if the FCR is provided solely by the conventional generators. The obtained results illustrate that HEs can outperform conventional generators on the provision of FCR. Furthermore, the FCR is unable to unlock the full potential of fast responding HEs. This suggests the advantage of providing additional AS such as SI or FFR in critical periods. Simulations also show that the benefit of additional AS can be limited in specific conditions, especially depending on the evolution of HEs' ramping capabilities, but are still a relevant complement to other solutions designed to deal with low inertia in power systems such as synchronous compensators.& COPY; 2023 Elsevier Ltd. All rights reserved.

Abstract
The use of Unmanned Aerial Vehicles (UAVs) in different inspection tasks is increasing. This technology reduces inspection costs and collects high quality data of distinct structures, including areas that are not easily accessible by human operators. However, the reduced energy available on the UAVs limits their flight endurance. To increase the autonomy of a single flight, it is important to optimize the path to be performed by the UAV, in terms of energy loss. Therefore, this work presents a novel formulation of the Travelling Salesman Problem (TSP) and a path planning algorithm that uses a UAV energy model to solve this optimization problem. The novel TSP formulation is defined as Asymmetric Travelling Salesman Problem with Precedence Loss (ATSP-PL), where the cost of moving the UAV depends on the previous position. The energy model relates each UAV movement with its energy consumption, while the path planning algorithm is focused on minimizing the energy loss of the UAV, ensuring that the structure is fully covered. The developed algorithm was tested in both simulated and real scenarios. The simulated experiments were performed with realistic models of wind turbines and a UAV, whereas the real experiments were performed with a real UAV and an illumination tower. The inspection paths generated presented improvements over 24% and 8%, when compared with other methods, for the simulated and real experiments, respectively, optimizing the energy consumption of the UAV.