Sea surface salinity and temperature are essential climate variables in monitoring and modeling ocean health. Multispectral ocean color satellites allow the estimation of these properties at a resolution of 10 to 300 m, which is required to correctly represent their spatial variability in coastal waters. This paper investigates the effect of pre-applying an unsupervised classification in the performance of both temperature and salinity inversion. Two methodologies were explored: clustering based solely on spectral radiances, and clustering applied directly to satellite images. The former improved model generalization by identifying similar water clusters across different locations, reducing location dependency. It also demonstrated results correlating cluster type with salinity and temperature distributions thereby enhancing regression model performance and improving a global ocean color sea surface temperature regression model RMSE error by 10%. The latter approach, applying clustering directly to satellite images, incorporated spatial information into the models and enabled the identification of front boundaries and gradient information, improving global sea surface temperature models RMSE by 20% and sea surface salinity models by 30%, compared to the initial ocean color model. Beyond improving algorithm performance, optical water classification can be used to monitor and interpret changes to water optics, including algal blooms, sediment disturbance or other climate change or antropogenic disturbances. For example, the clusters have been used to show the impact of a category 4 hurricane landfall on the Mississippi estuarine region.

This paper focuses on the feature-based visual-inertial odometry (VIO) in dynamic illumination environments. While the performance of most existing feature-based VIO methods is degraded by the dynamic illumination, which leads to unstable feature association, we propose a tightly-coupled VIO algorithm termed RAFT-VINS, integrating a Lite-RAFT tracker into the visual inertial navigation system (VINS). The key module of this odometry algorithm is a lightweight optical flow network designed for accurate feature tracking with real-time operation. It guarantees robust feature association in dynamic illumination environments and thereby ensures the performance of the odometry. Besides, to further improve the accuracy of the pose estimation, a moving consistency check strategy is developed in RAFT-VINS to identify and remove the outlier feature points. Meanwhile, a tightly-coupled optimization-based framework is employed to fuse IMU and visual measurements in the sliding window for efficient and accurate pose estimation. Through comprehensive experiments in the public datasets and real-world scenarios, the proposed RAFT-VINS is validated for its capacity to provide trustable pose estimates in challenging dynamic illumination environments. Our codes are open-sourced on https://github.com/USTC-AIS-Lab/RAFT-VINS.

The increasing prevalence of embedded software in today’s vehicles is leading to growing complexity, which can only be managed effectively through the use of reliable interdisciplinary engineering processes. With this in mind, systems engineering (SE) is currently being introduced on a large scale into the automotive industry. Pilot projects have demonstrated the potential for implementing changes, but these have not yet been accompanied by viable implementation concepts for SE. In the context of the proposed application-based research, the SETup automotive method (Systems Engineering Transformation under piloting in the automotive industry) is presented, which comprises a step-by-step procedure of introducing SE into large automotive companies. By introducing SE by pilot projects first, both an in-process tailoring of all processes, methods, tools and structures (PMTS) required for the introduction and an in-process validation of the pilot scheme elaborated by the pilot projects are achieved. The presented method builds upon fundamental approaches to change management, which have been developed over many years in both research and practice. It has been validated by the industrial practice of SE transformation at German car manufacturers and suppliers. As a result, decision-makers, transformation managers and systems engineers are provided with a scientifically based and field-tested set of steps for the introduction of SE in their own company.

The path navigation of robot in an entirely known space is presented by various researchers in the recent times. The navigational complexity arises when a robot moves in a completely unknown and complex environment from one defined start to a designated desired location. As the success of the nature-inspired algorithms in the unclear navigational problem is better, therefore, an improved butterfly optimization algorithm (IBOA) to determine the optimal feasible path for a humanoid robot navigating through a platform cluttered with both known and unfamiliar barriers is presented in this study. The BOA is inspired by the food-gathering habits of butterflies, where the sense of smell is the vital parameter in the global optimal search. However, the performance of this technique in the complex environment is poor, as a result, the chances of being trapped in local minima are more. Hence, the BOA is improved by using a nonlinear weight reduction strategy in updating the position of the butterflies in every iteration. The simulation is carried out in the Webots platform by considering variable-legged robot, NAO, in an unfamiliar environment. The outcomes derived from the simulation and real assessments demonstrate the potential of the proposed technique and compare with other existing algorithms, which highlights the potential and efficacy of the proposed IBOA algorithm.

In today’s ultra-connected world, personal and emotional narratives are omnipresent in media. This study examines how the emotional framing of second-hand testimonies about difficult or controversial past events influences attitudes. A sample of 154 Belgian participants, aged 18–77, evaluated their attitudes regarding Second World War (WWII) collaboration with Nazi Germany and the post-war repression before and after reading either the positively framed or negatively framed version of an ecologically valid interview. The narrative revolved around a son recounting his father’s past as a former collaborator joining the German forces during WWII. Results revealed a significant influence of the narrative’s emotional frame on attitudes towards collaboration and repression. The positively framed interview promoted more understanding attitudes towards collaboration and nuanced views on repression, while the opposite occurred with the negatively framed story, where participants viewed collaboration less favourably and regarded repression as justified and moral. Nevertheless, the role of emotions needs further investigation, exploring the medium of presentation of the narrative and considering the development of first-person narratives to elicit stronger emotional reactions.