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.

Participation is a prevalent topic in many areas, and data-driven projects are no exception. While the term generally has positive connotations, ambiguities in participatory approaches between facilitators and participants are often noted. However, how facilitators can handle these ambiguities has been less studied. In this paper, we conduct a systematic literature review of participatory data-driven projects. We analyse 27 cases regarding their openness for participation and where participation most often occurs in the data life cycle. From our analysis, we describe three typical project structures of participatory data-driven projects, combining a focus on labour and resource participation and/or rule- and decision-making participation with the general set-up of the project as participatory-informed or participatory-at-core. From these combinations, different ambiguities arise. We discuss mitigations for these ambiguities through project policies and procedures for each type of project. Mitigating and clarifying ambiguities can support a more transparent and problem-oriented application of participatory processes in data-driven projects.

Spoken term discovery (STD) is challenging when a large volume of spoken content is generated without annotations. Unsupervised approaches resolve this challenge by directly computing pattern matches from the acoustic feature representation of the speech signal. However, this approach produces a lot of false alarms due to inherent speech variabilities, leading to performance degradation in the STD task. To overcome these challenges and improve performance, we propose a two-stage approach. First, we identify an acoustic feature representation that emphasizes spoken content irrespective of the variability challenge. Second, we employ the proposed diagonal pattern search to capture spoken term matches in an unsupervised way without any transcriptions. The proposed approach validated using Microsoft Speech Corpus for Low-Resource languages reveals that an 18% gain in hit ratio and 37% reduction in the false alarm ratio was achieved compared with the state-of-the-art methods.

In response to the auxiliary requirements for the treatment and prevention of lumbar diseases, based on the biomechanical characteristics of the human waist, a novel unpowered rigid-flexible coupling waist exoskeleton with multiple degrees of freedom and its human-exoskeleton parallel wearable equivalent research prototype are proposed, further focusing on the encompassing kinematic compatibility and dynamic load-bearing effectiveness of the biomimetic coordination, an in-depth analysis is performed on the multi-body dynamic dimensional synthesis and its methodological research. Initially, based on the rigid-flexible coupling characteristics and experimental biomechanical data of the lumbar region in the sagittal plane, an accurate multi-body system dynamics model of the research prototype, which incorporates the rigid-flexible coupling characteristics, is systematically constructed. Subsequently, to effectively quantify the biomimetic coordination of the exoskeleton, a novel comprehensive optimization index, termed biomimetic load-bearing comfort, is proposed. Finally, by utilizing this index, the exoskeleton is optimized in dimension by employing a thorough combination of multi-dimensional spatial search algorithm and compression factor particle swarm algorithm. The simulation results validate the correctness and effectiveness of both the dynamic dimensional synthesis and its methodology. Furthermore, the study also reveals that the optimized exoskeleton’s passive working mode showcases favorable biomimetic coordination. These results are crucial for progressing the research on the biomimetic load-bearing capacities of other exoskeletons.

The rapid advancement of large language models (LLMs) has enabled their integration into a wide range of scientific disciplines. This article introduces a comprehensive benchmark dataset specifically designed for testing recent LLMs in the hydrology domain. Leveraging a collection of research articles and hydrology textbooks, we generated a wide array of hydrology-specific questions in various formats, including true/false, multiple-choice, open-ended, and fill-in-the-blank. These questions serve as a robust foundation for evaluating the performance of state-of-the-art LLMs, including GPT-4o-mini, Llama3:8B, and Llama3.1:70B, in addressing domain-specific queries. Our evaluation framework employs accuracy metrics for objective question types and cosine similarity measures for subjective responses, ensuring a thorough assessment of the models’ proficiency in understanding and responding to hydrological content. The results underscore both the capabilities and limitations of artificial intelligence (AI)-driven tools within this specialized field, providing valuable insights for future research and the development of educational resources. By introducing HydroLLM-Benchmark, this study contributes a vital resource to the growing body of work on domain-specific AI applications, demonstrating the potential of LLMs to support complex, field-specific tasks in hydrology.

In a recent proof mining application, the proof-theoretical analysis of Dykstra’s cyclic projections algorithm resulted in quantitative information expressed via primitive recursive functionals in the sense of Gödel. This was surprising as the proof relies on several compactness principles and its quantitative analysis would require the functional interpretation of arithmetical comprehension. Therefore, a priori one would expect the need of Spector’s bar-recursive functionals. In this paper, we explain how the use of bounded collection principles allows for a modified intermediate proof justifying the finitary results obtained, and discuss the approach in the context of previous eliminations of weak compactness arguments in proof mining.

Cable-driven parallel robots (CDPRs) have been widely used as motion executers for their large workspace and lower inertia. However, there are few studies on structural optimization design considering its stability. This paper proposes a stability optimization method based on force-position workspace for a reconfigurable cable-driven parallel robot (RCDPR). First, the structural optimization analysis of RCDPR is carried out. Then, the forces distribution algorithm based on the feasibility of real-time control is determined, and the boundary contour algorithm (BCA) of the RCDPR force feasible workspace (FFW) on the central plane is proposed. Second, the stiffness and cables driving force space (CFS) models of RCDPR are established. Subsequently, the stability evaluation function is established to optimize the structure of RCDPR, which uses FFW and main task feasible workspace (MFW) as carriers and stiffness and CFS as weights. Finally, an experimental prototype of the developed robot is constructed, and motion performance and workspace verification experiments are conducted. The results demonstrate that the developed RCDPR has good motion accuracy and stable workspace, and the results also verify the feasibility of the stability evaluation function and BCA.

In this paper, we delve into Notation3 Logic (N3), an extension of Resource Description Framework (RDF), which empowers users to craft rules introducing fresh blank nodes to RDF graphs. This capability is pivotal in various applications such as ontology mapping, given the ubiquitous presence of blank nodes directly or in auxiliary constructs across the Web. However, the availability of fast N3 reasoners fully supporting blank node introduction remains limited. Conversely, engines like VLog or Nemo, though not explicitly designed for Semantic Web rule formats, cater to analogous constructs, namely existential rules.

We investigate the correlation between N3 rules featuring blank nodes in their heads and existential rules. We pinpoint a subset of N3 that seamlessly translates to existential rules and establish a mapping preserving the equivalence of N3 formulae. To showcase the potential benefits of this translation in N3 reasoning, we implement this mapping and compare the performance of N3 reasoners like EYE and cwm against VLog and Nemo, both on native N3 rules and their translated counterparts. Our findings reveal that existential rule reasoners excel in scenarios with abundant facts, while the EYE reasoner demonstrates exceptional speed in managing a high volume of dependent rules.

Additionally to the original conference version of this paper, we include all proofs of the theorems and introduce a new section dedicated to N3 lists featuring built-in functions and how they are implemented in existential rules. Adding lists to our translation/framework gives interesting insights on related design decisions influencing the standardization of N3.