Search Results (883)

With the advancement of task-oriented reinforcement learning (RL), the capability of quadruped robots for motion generation and complex task completion has significantly improved. However, current control strategies require extensive domain expertise and time-consuming design processes to acquire operational skills and achieve multi-task motion control, often failing to effectively manage complex behaviors composed of multiple coordinated actions. To address these limitations, this paper proposes a motion policy generation method for quadruped robots based on multimodal motion primitives and imitation learning. A multimodal motion library was constructed using 3D engine motion design, motion capture data retargeting, and trajectory planning. A temporal domain-based behavior planner was designed to combine these primitives and generate complex behaviors. We developed a RL-based imitation learning training framework to achieve precise trajectory tracking and rapid policy deployment, ensuring the effective application of actions/behaviors on the quadruped platform. Simulation and physical experiments conducted on the Lite3 quadruped robot validated the efficacy of the proposed approach, offering a new paradigm for the deployment and development of motion strategies for quadruped robots. Full article

Background: Preterm birth is a significant risk factor for neurodevelopmental delays, but the appropriate use and timing of age correction for developmental assessment remain debated. Objective: This study investigated psychomotor development in preterm children at two years of age, with the aim of clarifying whether age correction remains necessary at this stage, particularly across different gestational age groups. Methods: A total of 161 preterm infants were assessed at a mean chronological age of 25.4 months (mean corrected age: 23.3 months) and compared with two control groups of typically developing children matched for gender and either corrected age (Control–Corr, N = 88) or chronological age (Control–Chron, N = 87). The preterm group was further stratified by gestational age: extremely preterm (<28 weeks), very preterm (28–31 weeks), and moderate-to-late preterm (32–36 weeks). Cognitive, Language (Receptive, Expressive), and Motor (fine, gross) scales of Bayley-III were analysed using t-tests and MANOVAs. Results: Using corrected age, preterm children showed a selective profile, with deficits in Receptive Language, borderline mean score in Gross Motor, and preserved performance in Cognitive, Expressive Communication, and Fine Motor. When compared with controls of the same age, significant differences emerged in the Cognitive, Language, and Gross Motor, but not Fine Motor, domains. In contrast, scoring by chronological age produced a generalised delay, with preterm children performing significantly worse than chronological-age controls across all domains. Subgroup analyses further showed that extremely preterm children already displayed marked Language vulnerabilities at corrected age, which became more severe with chronological scoring and extended to other domains. Very preterm children also fell into the deficit range in Cognitive, Language, and Gross Motor scales/subscales when chronological age was applied, whereas moderate-to-late preterm children performed comparatively better. Conclusions: Developmental assessment using corrected age remains essential at least until 24 months, especially for extremely and very preterm children, to avoid substantial overestimation of developmental difficulties. Chronological scoring, while helpful to highlight persistent vulnerabilities, may inflate delay classification if used too early. Tailoring correction strategies by gestational age and developmental domain could provide a more accurate and clinically meaningful representation of preterm children’s developmental trajectories. Full article

During the reentry terminal flight of lifting-body Reusable Launch Vehicles (RLVs) propelled by liquid fuel, the sloshing of liquid propellent presents new features that, if neglected, could lead to adverse flight oscillations or even worse. This paper focuses on liquid sloshing coupled flight dynamics, sloshing effect prediction, and the suppression of adverse flight oscillations. First, a transfer function model for unsteady aerodynamics is improved and applied to describe the sloshing force effect, being included in the rigid–liquid control coupled flight dynamics model. The frequency domain analysis results show that liquid sloshing tends to degrade the closed-loop stability margin of the vehicle and even induce less damped oscillations, which can be predicted through the frequency characteristics with the sloshing force effect included. Furthermore, three suppression control measures to mitigate adverse oscillation are addressed, which include enhancing the trajectory-tracking loop damping, separating the frequencies of the rigid body motion and the liquid sloshing, and especially introducing a compensation loop to counteract the sloshing effect. Simulations demonstrate that all the provided approaches help mitigate the sloshing effect, while the compensation control with sloshing frequency characteristics included works best. Full article

This narrative review integrates longitudinal cohort studies, neuroimaging and biomarker research, and major clinical trials to examine how depression and cognitive decline interact across the dementia continuum. Depression and cognitive impairment frequently co-occur in late life and exhibit substantial clinical and biological overlap. Meta-analytic and large population-based cohort studies consistently show that late-life depression increases the risk of mild cognitive impairment and dementia, with stronger associations observed for vascular dementia than for Alzheimer’s disease. Neurobiological studies implicate cerebrovascular pathology, neuroinflammation, hypothalamic–pituitary–adrenal axis dysregulation, and fronto-subcortical circuit dysfunction as key mechanisms linking depressive symptoms to later cognitive decline. In a subset of older adults, new-onset depression—particularly when accompanied by executive dysfunction, subjective cognitive decline, or high white-matter hyperintensity burden—are associated with an increased likelihood of near-term cognitive decline and dementia, although evidence for a definitive prodromal state remains limited. Depression is also highly prevalent as part of the behavioral and psychological symptoms of dementia, occurring in 30–50% of individuals with Alzheimer’s disease and even higher proportions in dementia with Lewy bodies or frontotemporal dementia. Comorbid depression in dementia accelerates cognitive and functional decline, increases neuropsychiatric burden, and worsens quality of life for patients and caregivers. Therapeutically, antidepressant treatment may confer modest benefits on mood and selected cognitive domains (e.g., processing speed and executive function) in non-demented older adults, whereas in established dementia, antidepressant efficacy is limited. In contrast, cholinesterase inhibitors, memantine, and multimodal non-pharmacological interventions yield small but measurable improvements in depressive or apathy-related symptoms. Emerging disease-modifying therapies for Alzheimer’s disease have demonstrated cognitive benefits, but current trial data provide insufficient evidence regarding effects on depressive symptoms, highlighting an important gap for future research. These findings underscore the need for stage-specific, integrative strategies to address the intertwined trajectories of mood and cognition in aging. Full article

Background: Sleep quality (SQ) and physical activity (PA) are among the strongest behavioral determinants of healthy aging, while dietary behavior and psychological factors act as complementary modulators of these relationships. Although each domain has been studied extensively, their combined influence on subjective life expectancy (SLE)—an individual’s perceived likelihood of living to an advanced age—remains largely unexplored. Methods: This narrative review synthesizes evidence from sleep science, exercise physiology, behavioral medicine, and psychological aging. Literature published between January 2015 and 15 December 2025 was examined across PubMed, Scopus, and Web of Science using integrative keyword strategies. Studies addressing SQ, PA, circadian rhythms, psychological health, SLE, or aging-related outcomes were included. Results: The review identifies several converging pathways linking sleep and PA to aging trajectories. Sleep architecture, circadian stability, metabolic regulation, inflammatory balance, and autonomic function represent key biological mechanisms. PA contributes through improvements in mitochondrial efficiency, VO₂max, muscle metabolism, and anti-inflammatory signaling (IL-6 as a myokine). Across studies, both sleep and PA strongly influence psychological health, health perception, and future-oriented expectations, within a broader lifestyle context supported by nutritional status and dietary quality. SLE emerges as a central psychological mediator that shapes motivation, adherence to health behaviors, and long-term health outcomes. Contextual moderators—including age, gender, socioeconomic status, cultural norms, and wearable technology engagement—further influence these relationships. Conclusions: SQ and PA form the core behavioral components of a dynamic system that is further shaped by dietary behavior and psychological well-being and centered on SLE. Our proposed integrative model positions SLE as a key psychological link between lifestyle behaviors and longevity. This framework is hypothesis-generating and requires empirical validation through future longitudinal and interventional studies, underscoring the need for multidomain research integrating behavioral, biological, nutritional and psychological indicators of aging. Full article

To address the limited environmental perception capability and the difficulty of achieving consistent and efficient representation of the workspace feasible domain caused by high dust concentration, uneven illumination, and enclosed spaces in underground coal mines, this paper proposes a digital spatial construction and representation method for underground environments by integrating RGB-D depth vision with ORB-SLAM3. First, a ChArUco calibration board with embedded ArUco markers is adopted to perform high-precision calibration of the RGB-D camera, improving the reliability of geometric parameters under weak-texture and non-uniform lighting conditions. On this basis, a “dense–sparse cooperative” OAK-DenseMapper Pro module is further developed; the module improves point-cloud generation using a mathematical projection model, and combines enhanced stereo matching with multi-stage depth filtering to achieve high-quality dense point-cloud reconstruction from RGB-D observations. The dense point cloud is then converted into a probabilistic octree occupancy map, where voxel-wise incremental updates are performed for observed space while unknown regions are retained, enabling a memory-efficient and scalable 3D feasible-space representation. Experiments are conducted in multiple representative coal-mine tunnel scenarios; compared with the original ORB-SLAM3, the number of points in dense mapping increases by approximately 38% on average; in trajectory evaluation on the TUM dataset, the root mean square error, mean error, and median error of the absolute pose error are reduced by 7.7%, 7.1%, and 10%, respectively; after converting the dense point cloud to an octree, the map memory footprint is only about 0.5% of the original point cloud, with a single conversion time of approximately 0.75 s. The experimental results demonstrate that, while ensuring accuracy, the proposed method achieves real-time, efficient, and consistent representation of the 3D feasible domain in complex underground environments, providing a reliable digital spatial foundation for path planning, safe obstacle avoidance, and autonomous operation. Full article

With the rapid development of the automotive industry, autonomous driving has attracted growing research interest, among which path planning and trajectory tracking play a central role. To better understand the evolution, current status, and future directions of this field, this study conducts a comprehensive bibliometric analysis combined with latent Dirichlet allocation (LDA) topic modeling on publications related to autonomous vehicle path planning and trajectory tracking indexed in the Web of Science database. Multiple dimensions are examined, including publication trends, highly cited authors, leading institutions, research domains, and keyword co-occurrence patterns. The results reveal a sustained growth in research output, with trajectory planning, path optimization, trajectory tracking, and model predictive control (MPC) emerging as dominant topics, alongside a notable rise in learning-based approaches. In particular, reinforcement learning (RL) and deep reinforcement learning (DRL) have become increasingly prominent in complex decision-making and tracking control scenarios. The analysis further identifies core contributors and institutions, highlighting the leading roles of China and the United States in this research area. Overall, the findings provide a systematic overview of the knowledge structure and evolving research trends, offering valuable insights into key opportunities and challenges and supporting future research toward safer and more intelligent autonomous driving systems. Full article

Due to the significant potential of crowd flow prediction in the domains of commercial activities and public management, numerous researchers have commenced investing in pertinent investigations. The majority of existing studies employ recurrent neural networks, long short-term memory, and similar models to achieve their objectives. Despite the advancements in predictive modeling, the objective of many existing studies remains in the minimization of distance errors. This focus, however, introduces three notable limitations in prediction outcomes: (1) the predicted location may represent an average of multiple points rather than a distinct target, (2) the results may fail to reflect actual user behavior patterns, and (3) the predictions may lack geographic plausibility. To address these challenges, we developed a Transformer-based model integrated with adversarial network architecture. The Transformer component has shown considerable effectiveness in forecasting individual movement trajectories, while the discriminator within the adversarial framework guides the generator in refining outputs to better reflect user habits and spatial rationality. Full article

This paper presents a repeated cross-sectional longitudinal (trend) analysis of students’ self-perceived sustainability competence development across three waves surrounding participation in the Art Nouveau Path, a heritage-based mobile augmented reality game designed to foster sustainability competences, located in Aveiro, Portugal. In total, 1094 questionnaires were collected using a GreenComp-grounded instrument adapted from the GreenComp-based Questionnaire (GCQuest) to this context (25 items; 6-point Likert). Data were gathered at three stages: pre-intervention (S1-PRE; N = 221), immediately post-intervention (S2-POST; N = 439; n = 438 retained for scale scoring after applying a predefined completeness criterion), and follow-up (S3-FU; N = 434). Because responses were anonymous, waves were treated as independent samples rather than within-student trajectories. The Embodying Sustainability Values domain score and item-level response distributions were compared across waves using ordinal-appropriate non-parametric group comparisons, effect-size estimation, and descriptive threshold indicators. Results indicate an improvement from pre-intervention to post-intervention, followed by partial attenuation at follow-up while remaining above pre-intervention. Mean scores increased from 3.70 (S1-PRE) to 4.64 (S2-POST) and then stabilized at 4.13 (S3-FU). Findings, while exploratory, suggest that this heritage-based augmented reality game may have enhanced perceived sustainability competences. A structured program of follow-up activities is proposed to help sustain gains. Full article

Unmanned Aerial Vehicles (UAVs) are emerging as a fundamental part of Flying Ad Hoc Networks (FANETs). However, owing to the limited energy capacity of UAV batteries, wireless power transfer (WPT) technologies have recently gained interest from researchers, offering recharging possibilities for FANETs. Based on this background, this study highlights the need for wireless charging to enhance the operational endurance of FANETs in Internet-of-Things (IoT) environments. This review investigates WPT power replenishment to explore the dynamic usage of UAVs in two ways. The former is for using a UAV as a mobile charger to recharge the ground nodes, whereas the latter is for WPT applications in in-flight (UAV-to-UAV) charging. For the two research domains, we describe the different methods of WPT and its latest advancements through the academic and industrial research literature. We categorized the results based on the power transfer range, efficiency, wireless charger topology (ground or in-flight), coordination among multiple UAVs, and trajectory optimization formulation. A crucial finding is that in-flight UAV charging can extend the endurance by three times compared to using standalone batteries. Furthermore, the integration of IoT for the deployment of a clan of UAVs as a FANET is rigorously emphasized. Our data findings also indicate the present and future forecasting graphs of UAVs and IoT-integrating UAVs in the global market. Existing systems have scalability issues beyond 20 UAVs; therefore, future research requires edge computing for WPT scheduling and blockchains for energy trading. Full article

Expressway networks represent evolving complex systems whose topological properties significantly impact regional development. This paper presents a decision support framework for addressing the expressway infrastructure sequencing problem using computational intelligence. We develop a novel framework that models expressways as L-space networks and evaluates how construction sequences create path-dependent evolutionary trajectories, introducing network science principles into infrastructure planning decisions. Our decision support framework quantifies project impacts on accessibility, connectivity, and reliability using nine topological metrics and a hybrid weighting mechanism that combines domain expertise with entropy-based uncertainty quantification. The system employs a hybrid TOPSIS algorithm that relies on geometric symmetry to simulate network evolution, capturing emergent properties in which each decision restructures possibilities for subsequent choices—a computational challenge that conventional planning approaches have not addressed. The system was validated with real-world Chongqing expressway planning data, demonstrating its ability to identify sequences that maximize synergistic network effects. Results reveal how topologically equivalent projects produce dramatically different system-wide outcomes depending on implementation order. Analysis shows that network science-informed sequencing substantially enhances system performance by exploiting structural synergies. This research advances decision support frameworks by bridging complex network theory with computational decision-making, creating a novel analytical tool that enables transportation authorities to implement evidence-based infrastructure sequencing strategies beyond the reach of conventional planning methods. Full article

The first 1000 days of life, from conception through the second year, represents a uniquely sensitive period for neurodevelopment. During this time, multiple physiological systems undergo rapid and coordinated maturation. Among these, the brain, gut, and sleep system form a tightly interconnected triad, exerting reciprocal influences on each other and playing a pivotal role in shaping lifelong cognitive, emotional, and behavioral trajectories. Disruptions in any one of these domains can reverberate across the others, amplifying developmental vulnerabilities. A key modifiable factor that can modulate this gut–brain–sleep triad is nutrition. In this review, we synthesize current evidence on the interconnected development of the brain, gut, and sleep systems and examine the role of key nutrients in shaping these pathways. We also identify critical gaps in the literature and highlight opportunities for future research to better understand how early-life nutritional interventions can optimize neurodevelopmental outcomes. Full article

The persistence of ordered experience in a quantum-branching universe raises fundamental questions about how continuity is maintained across multiple possible outcomes. The Frame Survival Model (FSM) is a theoretical framework grounded in quantum decoherence, and is applicable to any system—biological or artificial—capable of sustaining integrated, survival-compatible states. FSM models reality as a sequence of discrete “Hyperframes”—complete matter–energy configurations defined by quantum decoherence events. At each transition, a system either proceeds along a survival-compatible path or terminates its trajectory within that branch. When applied to consciousness, FSM formalizes subjective continuity as “threading” through a network of compatible Hyperframes, yielding an observer-relative path through the multiverse. The same formalism extends to other coherent, path-dependent processes, making FSM relevant to physics, information science, and the life sciences. By providing operational definitions for survival filtering, informational coherence, and frame-to-frame stability, FSM unifies continuity across domains and re-contextualizes longstanding paradoxes—including subjective death, quantum immortality, and identity persistence—without invoking new physics. It further suggests experimentally approachable implications, such as modulation of perceived time by changes in decoherence rates, positioning FSM as both a general continuity principle and a testable framework for applied fields such as cognitive neuroscience. Full article

Background/Objectives: Early childhood caries (ECC) and saliva have been studied across disparate domains, including microbiome, fluoride, immune, oxidative-stress, and neuroendocrine research. However, the ECC–saliva literature has not previously been mapped as a connected system using modern natural language processing (NLP). This study treats PubMed titles and abstracts as data to identify major themes, emerging topics, and candidate neuroimmune axes in ECC–saliva research. Methods: Using the NCBI E-utilities API, we retrieved 298 PubMed records (2000–2025) matching (“early childhood caries” [Title/Abstract]) AND saliva [Title/Abstract]. Text was cleaned with spaCy and embedded using a transformer encoder; BERTopic combined UMAP dimensionality reduction and HDBSCAN clustering to derive thematic topics. We summarised topics with class-based TF–IDF, constructed keyword co-occurrence networks, defined an internal topic-level Novelty Index (semantic distance plus temporal dispersion), and mapped high-novelty topics to gene ontology and Reactome pathways using g:Profiler. Prophet was used to model temporal trends and forecast topic-level publication trajectories. Finally, we generated a fully synthetic neuroimmune salivary dataset, based on realistic ranges from the literature, to illustrate how the identified axes could be operationalised in future ECC cohorts. Results: Seven coherent ECC–saliva topics were identified, including classical microbiome and fluoride domains as well as antioxidant/redox, proteomic, peptide immunity, and Candida–biofilm themes. High-novelty topics clustered around total antioxidant capacity, glutathione peroxidase, superoxide dismutase, and peptide-based host defence. Keyword networks and ontology enrichment highlighted “Detoxification of Reactive Oxygen Species”, “cellular oxidant detoxification”, and cytokine-mediated signalling as central processes. Temporal forecasting suggested plateauing growth for classical epidemiology and fluoride topics, with steeper projected increases for antioxidant and peptide-immunity themes. A co-mention heatmap revealed a literature-level Candida–cytokine–neuroendocrine triad (e.g., Candida albicans, IL-6/TNF, cortisol), which we propose as a testable neuro-immunometabolic hypothesis rather than a confirmed mechanism. Conclusions: AI-assisted topic modelling and network analysis provide a reproducible, bibliometric map of ECC–saliva research that highlights underexplored antioxidant/redox and neuroimmune salivary axes. The synthetic neuroimmune dataset and modelling pipeline are illustrative only, but together with the literature map, they offer a structured agenda for future ECC cohorts and mechanistic studies. Full article

This paper presents a simulation framework for analysing race car overtaking manoeuvres under aerodynamic wake effects using optimal control theory. The proposed formulation integrates wake-dependent aerodynamic disturbances into a spatial-domain optimal control problem, enabling simultaneous optimisation of racing line and control inputs. A planar vehicle model representative of a modern FIA Formula 3 car is employed and calibrated using real telemetry data obtained from Campos Racing. Wake effects are modelled as distance- and offset-dependent aerodynamic loss factors that influence drag, downforce, and aerodynamic balance of the following vehicle. The framework is implemented using the Dymos optimal control library and applied to single-car and two-car overtaking scenarios on a closed circuit. Simulation results demonstrate that wake effects significantly modify optimal braking points, corner entry trajectories, and corner-exit strategies. Moreover, we show that optimal overtaking requires deliberate lateral deviations from the wake core to recover downforce and traction. The study highlights the importance of incorporating aerodynamic interaction effects into trajectory optimisation when analysing performance-critical motorsport manoeuvres. Full article