帕罗奥多研究中心

Background: Glucocorticoids increase in response to the hypothalamic–pituitary–adrenal axis stimulation, and their metabolites can be measured in dolphins’ feces. Aim: This study aimed to assess the welfare of bottlenose dolphins under human care by measuring fecal glucocorticoid metabolites. Methods: Our study consisted of measuring glucocorticoid metabolites concentration by enzyme immunoassay in fecal samples from five bottlenose dolphins housed in a dolphinarium. Dolphins were sampled once a month over a year, and one day before and two days after the three stressful events. Results: We confirmed the validation of an extraction technique and an enzyme immunoassay to measure fecal glucocorticoid metabolites and we observed an increase in their concentration after the stressful events, which provides a biological validation of this method. In parallel, we confirmed that males had a higher concentration of fecal glucocorticoid metabolites than females, with a basal concentration of around 80 and 50 ng/g of dried feces, respectively. Conclusion: Our study confirms that fecal glucocorticoid metabolites measurement is a relevant indicator of stress response in bottlenose dolphins under human care, although it needs to take into account the sex and reproductive status of the animals.

Ensuring high standards of animal welfare is not only an ethical duty for zoos and aquariums, but it is also essential to achieve their conservation, education, and research goals. While for some species, animal welfare assessment frameworks are already in place, little has been done for marine animals under human care. Responding to this demand, the welfare committee of the European Association for Aquatic Mammals (EAAM) set up a group of experts on welfare science, cetacean biology, and zoo animal medicine across Europe. Their objective was to develop a comprehensive tool to evaluate the welfare of bottlenose dolphins (Tursiops truncatus), named Dolphin-WET. The tool encompasses 49 indicators that were either validated through peer review or management-based expertise. The first of its kind, the Dolphin-WET is a species-specific welfare assessment tool that provides a holistic approach to evaluating dolphin welfare. Inspired by Mellor’s Five Domains Model and the Welfare Quality®, its hierarchical structure allows for detailed assessments from overall welfare down to specific indicators. Through combining 37 animal-based and 12 resource-based indicators that are evaluated based on a two- or three-level scoring, the protocol offers a detailed evaluation of individual dolphins. This approach allows for regular internal monitoring and targeted welfare management, enabling caretakers to address specific welfare concerns effectively.

Due to the growing demand for robust autonomous systems, automating maintenance and fault mitigation activities has become essential. If an unexpected fault occurs during the travel, the system should be able to manage that fault autonomously and continue its mission. Thus, a robust fault mitigation system is needed that can quickly reconfigure itself in an optimal way. This paper presents a novel digital twin-based fault mitigation strategy that uses hierarchical control architecture. Here, a computationally efficient high-fidelity hybrid engine model is developed to simulate actual engine behavior. This hybrid engine model includes a neural network model representing the cylinder combustion process and well-studied physics-based analytical equations describing the remaining subsystems. This architecture uses a feedback controller on top of the control calibration map, generated offline using the hybrid model, to mitigate faults and modeling errors. The fault mitigation strategies are calibrated and validated through model-in-loop (MIL) and hardware-in-loop (HIL) simulations for various operating points using the Navistar 7.6 liters six-cylinder engine. The effectiveness of the proposed architecture in handling injector nozzle clogging, intake manifold leaks, and pressure shift faults is illustrated. The results demonstrate that the proposed architecture can completely overcome faults and maintain the desired torque in a few seconds. Moreover, the average accuracy of 96% is observed for the engine model compared to experimental data. It is anticipated that the proposed end-to-end architecture will be easily deployable on unmanned marine vessels and can be extended to accommodate other component faults.

This study examined the impact of aflatoxin contamination on rice, a widely consumed staple food. Various forms and types of rice from three regions in Pakistan between 2019 and 2022 were investigated for their relationship with processing, infestation severity and physicochemical characteristics. Semi-polished rice displayed the highest aflatoxin levels employing significant variability, while parboiled rice was also found to be contaminated. Nearly 22% of polished rice samples and 3% of brown rice samples exceeded the EU limit. Nonbasmati rice varieties, particularly IRRI-6 (47.3 mu g/kg), showed twice as much contamination as basmati rice. The coastal Zone-IV exhibiting significantly higher aflatoxin levels (avg. 14.40 mu g/kg). Environmental conditions played a significant role, with moisture in brown rice and protein in parboiled rice exhibiting positive and negative correlations (p < 0.01) with aflatoxin levels, respectively. Principal Component Analysis (PCA) indicated that semi-finished and non-basmati rice were more associated with non-compliant rice. The first principal component, explaining 57.8% of variance, was positively loaded with aflatoxin, moisture, and insect infestation, while negatively loaded with protein, amylose, ash, immature, chalky, and foreign matter. These findings are insightful for local and international stakeholders for mitigating aflatoxin contamination in rice.

We demonstrate an end-to-end framework to improve the resilience of man-made systems to unforeseen events. The framework is based on a physics-based digital twin model and three modules tasked with real-time fault diagnosis, prognostics and reconfiguration. The fault diagnosis module uses model-based diagnosis algorithms to detect and isolate faults and generates interventions in the system to disambiguate uncertain diagnosis solutions. We scale up the fault diagnosis algorithm to the required real-time performance through the use of parallelization and surrogate models of the physics-based digital twin. The prognostics module tracks fault progression and trains the online degradation models to compute remaining useful life of system components. In addition, we use the degradation models to assess the impact of the fault progression on the operational requirements. The reconfiguration module uses PDDL-based planning endowed with semantic attachments to adjust the system controls to minimize the fault impact on the system operation. We define a resilience metric and use a fuel system example to demonstrate how the metric improves with our framework.