This paper develops a guidance control law based on a parametric guiding vector field (GVF) and integrates it with a state-of-the-art acceleration and attitude control architecture for tailsitters. The resulting framework enables a direct comparison between traditional trajectory-tracking guidance and GVF-based path-following guidance using a realistic tailsitter model operating under windy conditions. Through extensive simulations, it is shown that for agile flight scenarios with wind and small initial position error, both guidance strategies achieve comparable tracking performance, indicating that the additional complexity introduced by the GVF formulation is not always justified. However, the GVF-based approach exhibits an advantage when initial deviation from the path is present, yielding smooth and well-behaved convergence toward the desired path. Two additional contributions support this evaluation. First, a modification of the parametric GVF is proposed that guarantees exponential stability of the tracking error dynamics for a single-integrator system. Second, the differential flatness transform of a tailsitter vehicle is extended to account for explicit knowledge of the wind velocity vector.

Children are adept statistical learners, capable of parsing streams of structured input into meaningful units, but the cognitive processes they engage during learning may differ from those of adults. To date, however, it is unclear how learners of different ages predict upcoming experience when navigating environments with complex structure, as well as how changes in predictive learning mechanisms influence structured knowledge acquisition. To address this question, we tested 106 children, adolescents, and adults, ages 8-22 years, on a predictive learning task, in which they experienced sequences of stimuli with a higher-order temporal structure. After an initial learning phase, participants' explicit knowledge of the relations between stimuli was probed via two additional task measures. We used a recently introduced computational model to characterize participants' response times during learning, and found that all participants relied on simple, recency-based prediction, anticipating that they would encounter stimuli they recently encountered in the past. With increasing age, however, participants demonstrated greater evidence of additionally relying on a more sophisticated learning mechanism, which captured a predictive representation of the conditional relations between stimuli. Though predictive learning changed with age, we found only weak evidence that these changes related to the acquisition of explicit knowledge of the environment. Our results suggest that the learning mechanisms through which people parse continuous streams of experience change with age, influencing their predictions about upcoming events. SUMMARY: Children, adolescents, and adults all learn to segment continuous streams of structured perceptual input, but they may do so via different learning processes. We examined how the learning mechanisms that enable people to predict upcoming experiences change and relate to structured knowledge acquisition across development. Computational modeling revealed that in a graph-learning task, younger participants relied on simple, recency-based prediction, while older participants tracked temporal relations between stimuli. The extent to which participants engaged in this sophisticated form of predictive learning only weakly related to their knowledge of the task's structure.

In the southern Gulf of Mexico, the oil and fishing industries coexist in a complex, historically unbalanced relationship that remains insufficiently documented. Constructing spatially explicit knowledge about these interactions is essential to inform decision-making, reduce conflicts among regional stakeholders and in the end, benefit the parties through marine territorial planning that enables stronger governance elements for a socioenvironmental responsible coexistence in the seascape. This study assessed the spatial overlap between the space used by industrial and small-scale fishing fleets and oil industry-related operations-including vessel traffic, marine infrastructure, and oil spills-through quantitative vulnerability and risk analyses. We integrated multi-source spatial data to evaluate both individual and cumulative vulnerabilities, as well as the risk of interactions between fishing fleets' space use and surface oil presence. Results highlighted two high-vulnerable and high-risk zones: (1) the eastern coast of Tabasco and western coast of Campeche, where small-scale fishing fleets face constant exposure to large vessels, oil platforms, and pipelines; and (2) industrial fishing fleets at the north of Ciudad del Carmen, which showed a higher probability of encountering surface oil. These findings represent a significant contribution to the understanding of risk in maritime spaces where small-scale fisheries and a recently declared marine protected area coexist with the oil industry. Our framework offers a foundation for integrated territorial management that seeks to reduce conflicts between oil extraction and fishing activities, while supporting broader conservation goals in one of Mexico's most ecologically and economically important marine regions.

Data-driven trajectory tracking control of UAV systems under a novel probability-selection event-triggered mechanism.

Human attention is often guided by contextual cues that signal where or how to focus in complex environments. Research on object contextual cueing has largely emphasized spatial regularities, but it remains unclear whether socially meaningful information can also provide such guidance. Across three experiments using face stimuli, we examined whether social cues can serve as contextual cues independently of spatial arrangements. In all experiments, participants searched for a target face among distractor faces, with spatial configurations fully randomized on every trial, and contextual information was defined by different types of social mappings. In Experiment 1, the cue was defined by consistent associations between a target identity and a specific set of distractor identities. In Experiment 2, the cue was the overall mood, quantified as the ratio of angry to happy faces. In Experiment 3, the cue was relational information, specifically facing-direction patterns within pairs of profile-view faces. Performance under Consistent Mapping conditions was compared with Variable Mapping conditions, in which these social regularities changed across trials. Bayesian analyses revealed reliable contextual cueing effects across all three experiments, with faster responses under Consistent than Variable Mapping conditions. The effect was strongest in Experiment 2, suggesting that global social cues may be encoded more efficiently than local relational cues. Post-experiment awareness tests indicated minimal explicit knowledge and no reliable association between awareness measures and contextual cueing. Together, these findings suggest that socially meaningful relational structure can provide stable predictive information that supports object-based contextual learning even when spatial regularities are absent.

Varying parameter ranges alters both partial rank correlation coefficient results and phenomenological behavior when modeling the epithelial mesenchymal transition.

Previous research has extensively investigated the impact of post-training sleep and wakefulness on procedural memory consolidation across development. However, results regarding how offline processes specifically affect the explicit and implicit components of newly acquired procedural skills in children and adults remain controversial. To address this issue, we investigated differences between 42 children (9.7 ± 1.8 years) and 58 young adults (21.7 ± 2.5 years) in the consolidation and explicit knowledge of a visuo-motor sequence learned in a Serial Reaction Time Task. Participants were assigned to either a nocturnal sleep or a daytime wakefulness offline interval condition between learning and retest (~11 h). Offline (between-session) and online (within-session) performance changes were assessed, and explicit sequence knowledge was quantified using the Process Dissociation Procedure (PDP). Results showed similar offline performance improvements in the two age groups and offline interval conditions. PDP results revealed that children had higher explicit knowledge of the sequence as compared to adults, irrespective of the offline interval condition. Furthermore, children exhibited a shift from online performance deterioration to online performance improvements that was consistent across sleep and wake conditions. The absence of a differential impact of sleep versus wakefulness intervals suggests that time-dependent mechanisms primarily drive procedural memory consolidation and the development of stronger related explicit knowledge in children. Our findings also highlight a developmental dissociation between the offline consolidation of procedural learning skills, which appears age-invariant and the resulting explicit knowledge, which developed more strongly in children.

Despite extensive development of computer-assisted language learning (CALL) tools and Intelligent Tutoring Systems (ITS) addressing grammar, learners often still struggle to apply complex morpho-syntactic rules in spontaneous speech. In particular, Arabic poses challenges in agreement (gender, number, case) that traditional instruction and rule-based feedback have only partially overcome. Moreover, recent reviews reveal that generative-AI research in language learning has focused overwhelmingly on written English tasks, leaving speaking and less-studied languages largely underexplored. To fill this gap, we conducted a longitudinal mixed-methods quasi-experiment comparing a GPT-4–based dialogic tutor to conventional explicit instruction for non-native Arabic learners’ oral mastery of agreement rules. Participants (N = 60 intermediate AFL learners) were assigned by intact class to either an AI-mediated speaking practice environment or traditional drill-based instruction. Pre-, post-, and delayed-post oral tests (elicited speech tasks targeting verb–subject, adjective–noun, and subject–predicate agreement) were analysed for accuracy in obligatory contexts, error density per 100 words, and fluency metrics (speech rate, pause ratio, response latency). System log data (feedback events, response times) and learner questionnaires (anxiety, perceived usefulness) provided additional insights. Results from ANCOVAs and mixed-effects models suggested that the AI group outperformed the control on agreement accuracy and showed greater improvements in fluency indices, with these gains largely maintained at a 4-week delay. Error density declined more sharply in the AI condition. Learning analytics indicated that log-derived features (e.g. accuracy, focus, and time-on-task) significantly predicted individual gains. Learners interacting with the AI tutor reported lower speaking anxiety and high technology acceptance, consistent with broader evidence that AI-mediated speaking support can enhance enjoyment and willingness to communicateLearners interacting with the AI tutor reported lower speaking anxiety and high technology acceptance, echoing Zhang et al. (2024) findings of boosted enjoyment and willingness to communicate under an AI speaking assistant. Qualitative comments further suggested that the dialogic agent may have functioned as a safe, personalised practice space. In sum, this study suggests that a generative-AI dialogic tutor may support the transition from explicit rule knowledge to more fluent use of Arabic agreement, thereby potentially scaffolding the proceduralisation of grammar. This work helps address the dearth of speaking-focused GenAI research, is consistent with cognitive load theory, and may extend skill-acquisition accounts by suggesting a possible role for GenAI in lowering the affective filter and enhancing noticing. Pedagogically, we frame our system as an Adaptive Learning Ecosystem that dynamically modulates task difficulty and feedback. We conclude with recommendations for curriculum designers on ethically integrating AI tutors – from scaffolding prompts to protecting student data – to harness GenAI affordances while preserving language-specific complexity.

Can't wait to relax: First-time evidence for a prospective relaxation of control as revealed by a future-based congruency sequence effect.

Foreign object detection (FOD) in autonomous driving environments poses a significant challenge due to the dual nature of anomalous objects: known objects appearing in inappropriate contexts or truly novel objects unseen during training. Existing methods typically address either closed-set semantic reasoning or open-set uncertainty estimation, limiting their ability to handle both types of anomalies effectively. This paper introduces HSAOSFOD (Hybrid Scene-Aware Open-set Foreign Object Detection), a unified lightweight framework that integrates closed-set and open-set paradigms through explicit modeling of a scene-object compatibility matrix and multi-signal open-set detection modules. The key contributions include: (1) a scene-object compatibility module that leverages domain priors to detect contextual mismatches of known objects, (2) a multi-signal fusion module that combines prototype-based novelty detection and uncertainty estimation, and (3) an efficient architectural design utilizing separable self-attention and depth wise convolutions, achieving a model size of only 4.33 million parameters. Extensive experiments on the Cityscapes and RailSem19 datasets for training, and Road Anomaly and Lost and Found datasets for evaluation, demonstrate that HSAOSFOD attains competitive performance with AUROC scores of 0.9506 on Road Anomaly and 0.6158 on Lost and Found, while preserving computational efficiency. Ablation studies confirm that the compatibility module (closed-set) contributes approximately 1.1% and the novelty detection head (open-set) contributes approximately 0.4% to average AUROC, together describing for a combined hybrid contribution of 1.5% over the base decoder alone. HSAOSFOD illustrates the potential of combining explicit domain knowledge with data-driven learning to produce efficient and interpretable hybrid models, delivering particularly strong results on context-sensitive anomalies.

EIK-Nav: Boosting zero-shot object navigation with explicit and implicit knowledge

Understanding artificial intelligence usage and knowledge sharing: The social ties perspective.

Flow cytometry (FC) is essential for the precise quantification and characterisation of individual cell populations in a larger heterogenous cell suspension. FC analysis provides a foundation for advanced clinical diagnostics and is a key component in many life-saving therapeutic strategies across a broad range of medical conditions. However, clinical, industrial and research laboratories alike face significant challenges in validating the metrological and biological accuracy of FC data analysis. Due to the inherent relative nature of FC data and the lack of definitive ‘ground truth’ associated with processed biological samples. This study specifically focuses on generating realistic fully synthetic flow cytometry cell clusters and demonstrating their suitability as substitutes for traditional FC data. The inherent model-based heritage of synthetic data enables the robust ability to generate distributionally-equivalent replicate datasets with explicit knowledge of cluster membership for each individual datapoint. Thereby, reducing the uncertainty issues associated with real cluster data and its analysis. This research uses meticulously optimised synthetic cluster-generating benchmarking software to simulate real monocyte clusters. A central component of the protocol is the ‘Rosetta-Routine’, a novel codebase which deciphers the statistical properties of real data and translates them into the computational coefficients required to generate accurate cluster-based synthetic replicates. This innovative approach ensures that the synthetic datasets faithfully represent the statistical characteristics of real-world data while retaining the benefits of computational traceability. This approach addresses a critical gap in current practices by enabling the ability to provide a controlled and reproducible validation framework for assessing clustering methods applied to analyse FC data. These features allow the ability to score and subsequently enhance the analysis confidence in many FC applications such as in diagnostics or in ‘mock-up’ training scenarios. Future synthetic-data-driven enhancements in FC analysis confidence will translate into more accurate clinical decision-making and subsequent overall improvements in patient care.

This paper addresses the global asymptotic stabilisation problem for unknown nonlinear systems via adaptive impulsive control. A novel adaptive law is proposed to update a time-varying positive-definite matrix parameter, which simultaneously governs the impulsive control gain and the impulsive triggering mechanism. The proposed strategy relies solely on sampled state information at impulsive instants, thereby eliminating the need for continuous state monitoring and reducing sensing and communication burdens. Global asymptotic stability and the exclusion of Zeno behaviour are rigorously established through Lyapunov-based analysis without requiring explicit knowledge of the continuous-time system dynamics. Simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

This article presents the results of a qualitative elicitation study focused on the linguistic reasoning of Slovak 3rd graders. We examined students’ linguistic reasoning in word-formation, particularly their ability to actively reflect on the meaning and structure of complex composite-suffixal adjective units. The research sample consisted of 26 3rd graders (8- to 9-year-old children) who had not yet been exposed to explicit knowledge of word-formation at school. The empirical data were collected via an individually administered research tool consisting of an expository text and open-ended oral tasks. The empirical data show that primary school students relied heavily on their implicit word-formation knowledge when reflecting on the meaning of a novel composite-suffixal word-form. They even recognised the constituents of the composite-suffixal structure. These findings contrast with the low frequency of these compounds in Slovak and in children’s language production. We surmise that the difficulty of the language material provided students with the necessary stimulus for linguistic exploration. Research findings provide evidence-based solutions for school practices that support the implementation of the new L1 curriculum in Slovakia. For an international audience, the article may offer inspiration for designing metalinguistic activities with complex words for primary school students.

Snakes play pivotal roles in many ecosystems. While some species, including medically important ones, are considered threatened by the IUCN, snakebite takes a heavy toll on rural agricultural populations in the developing world. Approximately 138,000 deaths and 400,000 disabilities result from snakebite annually and WHO has pledged to reduce the resulting health burden by 50% by 2030. Among a plethora of reasons for insufficient snakebite mitigation, one is limited explicit knowledge of how, where, and when humans and snakes interact, which limits the timely, accurate, and efficient deployment of resources. Here, we revise the list of medically important snakes based on recent taxonomic updates and use high-resolution data from a broad range of published and unpublished resources to compare expert-derived ranges with statistical geographical models of habitat suitability for all 508 most medically important snake species globally. Our study is the first to model every single medically important snake species including data deficient ones, at the highest resolution to date, and with the largest supporting occurrence dataset. We generate geographically explicit estimates of how much human and snake populations overlap (snake-human-overlap-index; SHOI), which is the most fundamental prerequisite for human-snake conflict to occur. Finally, we model the effects of climate change on snake distributions. We predict substantial, short- and long-term shifts in snake distributions, including range contractions for many threatened species and increased human exposure to species of major public health concern. In combination with other drivers of increased snake-human conflict, such as human behaviours and snake traits, our predictions can be used to decide where to stockpile which antivenom, how to ensure adequate capacity of individual health facilities, how to improve health care accessibility of remote at-risk communities, and where to focus conservation efforts for threatened snake species. Hence, we highlight the need for geographically targeted efforts to benefit both vulnerable human and snake populations, as part of a One-Health strategy.

This article introduces a novel compensation approach for actuator delays in a homogeneous Cooperative Adaptive Cruise Control (CACC) vehicle platoon. The Constant Time Headway (CTH) spacing policy is adopted, and a Predecessor-Follower (PF) communication topology is assumed. Vehicle dynamics are modeled with a third-order system that accounts for large, unknown actuator delays. To address these delays, we propose a compensation method based on the delay-independent Truncated Predictor Feedback (TPF). Specifically, a low-gain TPF-based controller is designed to achieve stabilization without requiring explicit knowledge of the actuator delay. It is rigorously proven that the proposed approach ensures both individual and string stability, maintaining zero steady-state error, regardless of the delay magnitude. Simulations and a comparative study with a baseline CTH-CACC controller are conducted to assess the theoretical findings. Results show that the proposed TPF-based controller compensates for significantly larger delay with neither its exact value, nor a delay estimate is required in the controller design.

Post-translational modifications (PTMs) form a complex combinatorial "code" that orchestrates protein function and cellular signaling. However, deciphering this code by predicting PTM sites and linking sites to their regulatory enzymes remains a fundamental challenge. Here, we present COMPASS-PTM, a mechanism-aware, coarse-to-fine learning framework that unifies residue-level multi-label PTM prediction with enzyme-substrate assignment by jointly modeling PTM patterns and their catalytic regulators. COMPASS-PTM builds upon protein language models, integrating physicochemical descriptors and a crosstalk-aware prompting mechanism to learn biologically coherent patterns of cooperative and antagonistic modifications, while addressing the dual long-tail distribution inherent in PTM data. Across multiple proteome-scale benchmarks, COMPASS-PTM improves over the strongest evaluated baselines, with a 122% relative improvement in F1-score for multi-label site prediction and a 54% gain in zero-shot enzyme assignment. Furthermore, the model demonstrates interpretable generalization, recovering canonical kinase motifs and mechanistically linking missense variants to both local PTM disruptions and global rewiring of enzyme-substrate networks. By coupling statistical learning with explicit biochemical knowledge, COMPASS-PTM unifies site-level and enzyme-level prediction into a single framework that learns the grammar underlying protein regulation and signaling.

Contextual sentiment recognition is critical for applications such as intelligent customer service and mental health monitoring. However, existing models struggle with multimodal heterogeneity, knowledge scarcity, and inadequate capture of dynamic emotional transitions. To address these challenges, we propose a dual-branch neural encoding-decoding architecture integrated with dynamic knowledge guidance. The model processes multimodal features (text, speech, video) and contextual dependencies through separate branches, incorporating both explicit knowledge (personality traits, domain rules) and implicit knowledge distilled from large language models. A dynamic context window adapts based on emotional shifts to enhance real-time perception. Experiments on IEMOCAP, MELD, and DailyDialog datasets demonstrate that our full model achieves accuracies of 82.1%, 78.3%, and 76.2%, respectively, surpassing state-of-the-art benchmarks including fine-tuned GPT-4. The lightweight version (18.2M parameters) maintains high inference speed (950 samples/sec) while reducing deployment costs. Furthermore, the model exhibits strong cross-dataset generalization and practical utility. This work provides an efficient framework that effectively addresses core challenges in contextual sentiment recognition, balancing performance with practicality for real-world deployment.

Narrative extraction builds coherent ordered sequences of documents that trace how concepts develop over time, and is a growing area of information retrieval. In this work we focus on scientific literature, using a corpus of 3549 IEEE visualization research papers (1990–2022). A natural hypothesis is that augmenting embedding-based pathfinding with explicit domain knowledge should improve narrative quality. We present the Knowledge-Coherence Framework (KCF), which integrates structured metadata from OpenAlex into narrative extraction (building on the Narrative Trails algorithm), and conduct a systematic empirical investigation along three axes: (1) the effect of embedding model choice (MiniLM vs. SPECTER), (2) the effect of knowledge augmentation (with and without, plus sensitivity to the knowledge weight α), and (3) the reliability of LLM-based evaluation (cross-agreement among 13 large language models). Throughout, mathematical coherence denotes the geometric mean of angular and topic similarity between consecutive documents along a path—an automatic, model-computed quantity inherited from Narrative Maps and Narrative Trails—while narrative quality refers to the LLM-judged construct. Using up to 600 evaluation pairs, we find that embedding model choice has a large effect on mathematical coherence (SPECTER: 0.94 vs. MiniLM: 0.81) and that, contrary to expectations, knowledge augmentation does not improve LLM-judged narrative quality—it slightly decreases it for both embeddings. Notably, the two notions dissociate: SPECTER produces the most mathematically coherent paths, yet MiniLM paths receive the highest LLM narrative-quality scores (5.87 vs. 5.36 out of 10). Alpha sensitivity analysis over five values (α∈{0.0,0.3,0.5,0.7,1.0}, 500 pairs) confirms that LLM scores remain essentially flat while mathematical coherence steadily declines with increasing knowledge weight. Cross-model evaluation with 13 LLM judges shows high inter-model agreement (median Pearson r=0.71), supporting evaluation reliability. The main practical takeaways are that (i) embedding model choice, not knowledge augmentation, is the more consequential design decision, and (ii) mathematical coherence and LLM-judged narrative quality are distinct optimization targets that practitioners should not conflate.