Search Results (12,794)

Effective turn-taking is fundamental to conversational interactions, shaping the fluidity of communication across human dialogues and interactions with spoken dialogue systems (SDS). Despite its apparent simplicity, conversational turn-taking involves complex timing mechanisms influenced by various linguistic, prosodic, and multimodal cues. This review synthesises recent theoretical insights and practical advancements in understanding and modelling conversational timing dynamics, emphasising critical phenomena such as voice activity (VA), turn floor offsets (TFO), and predictive turn-taking. We first discuss foundational concepts, such as voice activity detection (VAD) and inter-pausal units (IPUs), and highlight their significance for systematically representing dialogue states. Central to the challenge of interactive systems is distinguishing moments when conversational roles shift versus when they remain with the current speaker, encapsulated by the concepts of “hold” and “shift”. The timing of these transitions, measured through Turn Floor Offsets (TFOs), aligns closely with minimal human reaction times, suggesting biological underpinnings while exhibiting cross-linguistic variability. This review further explores computational turn-taking heuristics and models, noting that simplistic strategies may reduce interruptions yet risk introducing unnatural delays. Integrating multimodal signals, prosodic, verbal, visual, and predictive mechanisms is emphasised as essential for future developments in achieving human-like conversational responsiveness. Full article

This study investigated the diversity, distribution, and seasonal abundance of ambrosia and pine bark beetles (PBBs) in the Florida Panhandle, focusing on Leon and Gadsden Counties between July 2022 and October 2023. A total of 1657 specimens representing 24 species and 18 genera were captured using baited Lindgren funnel traps. Dominant species varied by location: Xyleborinus saxesenii, Cnestus mutilatus, and Xylosandrus crassiusculus were most abundant in Leon County, while Xylosandrus amputatus prevailed in Gadsden County. Three new county records were documented, including Xylosandrus amputatus and Ambrosiodmus lewisi for Leon County, and Cyclorhipidion distinguendum for Gadsden County. Additionally, three ambrosia beetle species within Platypodinae Euplatypus compositus, Myoplatypus flavicornis, and Euplatypus compositus were recorded across both counties. Seasonal patterns showed pronounced activity peaks during spring and early fall, corresponding with warmer and more humid conditions that support beetle reproduction and host colonization. Climatic analysis revealed that moisture-related variables, particularly relative humidity and precipitation, were the strongest predictors of beetle abundance, reflecting the ecological dependence of ambrosia beetles on fungal symbionts. Greater species richness observed in Leon County suggests that favorable microclimatic and habitat conditions enhance colonization dynamics. The documentation of new county records highlights the influence of shifting trade pathways, human movement, and environmental change on species introductions. The findings underscore the need for continuous surveillance and refined detection systems integrating ethanol-based lures and species-specific pheromones. As climate change continues to modify forest ecosystems, these results provide essential guidance for developing proactive monitoring and management strategies to protect forest health, biodiversity, and timber resources in the Florida Panhandle. Full article

Joining high-strength, cold-drawn Cu-Mg alloy conductors is a critical challenge for ensuring the reliability of high-speed railway catenary systems. This study investigates the evolution of mechanical properties and microstructure in Cu-0.43 wt% Mg alloy wires joined by multi-pass butt-upset cold welding without special surface preparation. High-integrity joints were achieved, exhibiting a peak tensile strength of 624 MPa (~96% of the base material’s strength). After four upsetting processes, the tensile strength of the weld can reach 90% of the original strength, and the gains from subsequent upsetting processes are negligible. Microstructural analysis revealed the joining process is governed by localized severe shear deformation, which forges a distinct gradient microstructure. This includes a transition zone of fine, equiaxed-like grains formed by dynamic recrystallization/recovery, and a central zone featuring a nano-laminar structure, high dislocation density, and deformation twins. A multi-stage dynamic bonding mechanism is proposed. It progresses from initial contact via thin film theory to bond consolidation through a “mechanical self-cleaning” process, where extensive radial plastic flow effectively expels surface contaminants. This work clarifies the fundamental bonding principles for pre-strained, high-strength alloys under multi-pass cold welding, providing a scientific basis to optimize this heat-free joining technology for industrial applications. Full article

This study proposes an artificial intelligence (AI)-powered multimodal system designed to enhance the appreciation of traditional poetry, using Japanese haiku as the primary application domain. At the core of the system is an intelligent data analysis pipeline that extracts key emotional features from poetic texts. A fine-tuned Japanese BERT model is employed to compute three affective indices—valence, energy, and dynamism—which form a quantitative emotional representation of each haiku. These features guide a generative AI workflow: ChatGPT constructs structured image prompts based on the extracted affective cues and contextual information, and these prompts are used by DALL·E to synthesize stylistically consistent watercolor illustrations. Simultaneously, background music is automatically selected from an open-source collection by matching each poem’s affective vector with that of instrumental tracks, producing a coherent multimodal (text, image, sound) experience. A series of validation experiments demonstrated the reliability and stability of the extracted emotional features, as well as their effectiveness in supporting consistent cross-modal alignment. These results indicate that poetic emotion can be represented within a low-dimensional affective space and used as a bridge across linguistic and artistic modalities. The proposed framework illustrates a novel integration of affective computing and natural language processing (NLP) within cultural computing. Because the underlying emotional representation is linguistically agnostic, the system holds strong potential for cross-cultural extensions, including applications to Chinese classical poetry and other forms of traditional literature. Full article

Background/Objectives: This study investigated the relationships among the Landing Error Scoring System (LESS), markerless 3D (OpenCap)-derived knee valgus, and surface electromyography (EMG) of quadriceps and hamstrings. Methods: Thirty-two healthy male university students (26 athletes, 6 non-athletes) completed a drop-landing task. LESS was video-scored; knee valgus at peak knee flexion was computed with OpenCap; and bilateral rectus femoris (RF) and biceps femoris (BF) EMG was recorded. Phase-specific EMG was normalized to peak dynamic activity. Results: LESS showed an independent negative association with left knee valgus (p = 0.001). In the regression model, bilateral BF acceleration-phase activity was a significant predictor of knee valgus (p < 0.05). Exploratory comparisons indicated that athletes exhibited lower RF deceleration activity and smaller left-side valgus than non-athletes. Conclusions: These findings suggest that hamstring activity during the transition phase is linked to knee alignment. This study demonstrates the complementary value of integrating LESS with markerless 3D motion capture, although caution is warranted when generalizing group differences due to the unequal sample size. Full article

Recent advancements in multimodal large language models (MLLMs) have demonstrated remarkable capabilities in processing diverse data types, yet significant disparities persist between human cognitive processes and computational approaches to multimodal information integration. This research presents a systematic investigation into the parallels between human cross-modal chunking mechanisms and token representation methodologies in MLLMs. Through empirical studies comparing human performance patterns with model behaviors across visual–linguistic tasks, we demonstrate that conventional static tokenization schemes fundamentally constrain current models’ capacity to simulate the dynamic, context-sensitive nature of human information processing. We propose a novel framework for dynamic cross-modal tokenization that incorporates adaptive boundaries, hierarchical representations, and alignment mechanisms grounded in cognitive science principles. Quantitative evaluations demonstrate that our approach yields statistically significant improvements over state-of-the-art models on benchmark tasks (+7.8% on Visual Question Answering (p < 0.001, 5.3% on Complex Scene Description while exhibiting more human-aligned error patterns and attention distributions. These findings contribute to the theoretical understanding of the relationship between human cognition and artificial intelligence, while providing empirical evidence for developing more cognitively plausible AI systems. Full article

This study presents a sociotechnical framework for mitigating ethnic violent conflicts by integrating information and communication technologies (ICTs) with community-based social capital. Drawing on longitudinal case studies from three conflict-prone regions in Kenya, Mt. Elgon, Muhoroni, and the Turkana–West Pokot borderlands, the research examines how ICT-enabled peace networks, particularly the Early Warning and Early Response System (EWERS), mobilize bonding, bridging, and linking social capital to reduce violence. The study employs a multi-phase qualitative design, combining retrospective analysis, key informant interviews, focus group discussions, action participation, and thematic coding of EWERS data collected between 2009 and 2021. This approach enabled the reconstruction of system evolution, stakeholder dynamics, and community responses across diverse socio-political contexts. Findings demonstrate that embedding ICTs within trusted social structures fosters inter-ethnic collaboration, inclusive decision-making, and trust-building. EWERS facilitated confidential reporting, timely alerts, and coordinated interventions, leading to reductions in livestock theft, improved leadership accountability, emergence of inter-ethnic business networks, and enhanced visibility and response to gender-based violence. The system’s effectiveness was amplified by faith-based legitimacy, local governance integration, and adaptive training strategies. The study argues that ICTs can become effective enablers of peace when sensitively contextualized within local norms, relationships, and community trust. Operationalizing social capital through digital infrastructure strengthens community resilience and supports inclusive, sustainale peacebuilding. These insights offer a scalable model for ICT-integrated violence mitigation in low- and middle-income countries. This is among the first studies to operationalize bonding, bridging, and linking social capital within ICT-enabled peace networks in rural African contexts. By embedding digital infrastructure into trusted community relationships, the framework offers an analytical approach that can inform inclusive violence mitigation strategies across low- and middle-income settings. While the framework demonstrates potential for scalability, its outcomes depend on contextual adaptation and cannot be assumed to replicate uniformly across all environments. Full article

The liver is intricately innervated by sympathetic, parasympathetic, and sensory fibres, forming a dynamic neurovascular and neuroimmune network that regulates hepatic function and contributes to disease pathogenesis. While traditionally underexplored, hepatic innervation is now recognised as a key modulator of metabolic homeostasis, immune surveillance, and vascular tone. Historically, the liver was not considered a major target of neural regulation, but recent advances in neurology and imaging have revealed complex and dynamic interactions between neural circuits and hepatic functions. This review provides a comprehensive overview of liver innervation, detailing its anatomical organisation and functional roles in both physiological and pathological contexts. We investigate the role of liver innervation in shaping immune responses, particularly in the context of metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, and autoimmune liver diseases, including autoimmune hepatitis and primary biliary cholangitis. Special attention is given to the neuroimmune crosstalk that governs inflammation, fibrosis, malignancy, and tissue remodelling. Furthermore, we examine how neural inputs influence hepatic blood flow, sinusoidal endothelial function, and portal hypertension, highlighting the interplay between neural and vascular systems. We highlight neuromodulatory approaches, including vagus nerve stimulation and other agents to modulate liver inflammation, vascular dysfunction, and immune dysregulation. Finally, we discuss emerging research models, including liver organoids, Artificial Intelligence-based digital twins and biomaterials as innovative platforms designed to study neural-liver interactions and test new therapeutic strategies. By integrating neuromorphology, immunology, and hepatology, this review aims to advance our understanding of liver innervation as a central player in hepatic health and disease and to identify novel targets for therapeutic intervention. Full article

Skin aging, driven by oxidative stress, UV exposure, inflammation, and extracellular matrix degradation, necessitates the discovery of safer, multi-target natural products. We established an integrated pipeline combining UHPLC–MS/MS metabolomics, computational methods (network pharmacology, molecular docking, and dynamics simulation), and in vitro bioassays to efficiently discover and mechanistically characterize anti-aging compounds from novel botanical sources. We applied this pipeline to identify and evaluate Tephroseris flammea, a previously unassessed plant. Metabolomic profiling identified 21 compounds, including flavonoids, phenylpropanoids, and pyrrolizidine alkaloids. These compounds were linked via network pharmacology to 226 skin-aging-related targets, primarily involving inflammation (via AKT1, RELA) and matrix degradation (via MAPK3). Molecular docking and 100 ns molecular dynamics simulations confirmed stable ligand-target interactions with favorable binding energies. Validating these in silico predictions, the T. flammea extract demonstrated significant antioxidant activity and effectively suppressed key inflammatory mediators (IL-6, TNF-α, COX-2) and MMP-1 levels in UVB-exposed fibroblasts, notably without significant cytotoxicity. Collectively, this study validates the utility of our pipeline to mechanistically characterize complex botanicals, revealing that T. flammea contains multifunctional compounds modulating critical inflammatory and matrix-regulatory cascades. This work validates our pipeline for identifying novel, mechanistically defined ingredients from complex botanical sources. Full article

Background: The rapid rise in antimicrobial resistance has become one of the 10 most pressing health problems worldwide in recent years. Antibiotic stewardship offers hope in the fight against antibiotic resistance, but it is currently still falling short of expectations. A better understanding of the dynamics of the interaction between antibiotic consumption and the emergence and spread of resistance is urgently needed. Methods: We discuss a simple dynamic model based on a differential equation to describe the increase in the proportion of a pathogen’s antimicrobial resistance to an antibiotic as a function of the time-dependent consumption of that antibiotic. Furthermore, we investigate the association of heterogeneity in the consumption of antibiotics with the rate of resistant pathogens. Data basis is the hospital information system and the patient data-management system of a German hospital, restricted to the intensive care unit. To quantify heterogeneity, we discuss and compare different entropy measures. Results: For some pathogen–antibiotic pairs, the consumption-dependent dynamic model for the growth in the proportion of antimicrobial resistance provides acceptable predictions, while for others, the model is less suitable. Cross-resistance and complex interactions with other pathogens and antibiotics may be responsible for this, suggesting that the observed dynamic behavior should be complementary, described using heterogeneity models. Time courses of Shannon entropy, the Antibiotic Heterogeneity Index, and the negative Gini Index correlate positively with the time series of the resistance rate. Thus, an increase in heterogeneity correlates with a decreasing resistance rate. However, a time-delayed cross-correlation of a differential entropy measure with resistance share suggests a functional dependence that can be utilized for antibiotic stewardship. Conclusions: Evidence is provided that the amount of consumption of certain antibiotics drives the corresponding proportions of pathogens’ resistance to these antibiotics; however, the model predictions of these univariable models are generally not sufficiently good, pointing to a more complex interaction dynamics. Therefore, we switch to the level of structural features and show that the degree of constantly mixing of the shares of antibiotic consumption has a control function regarding the incidence of resistance. Controlling differential consumption heterogeneity, therefore, appears to be a feasible operational basis for antibiotic stewardship. Experimental studies are demanded to identify functional dependencies; however, the integration of clinical expertise with model-based prediction appears to be a feasible antibiotic stewardship strategy. Full article

This paper develops a mathematical approach to the analysis of the stability of economic equilibria in nonsmooth models. The $\lambda$-Hölder apparatus of subdifferentials is used, which extends the class of systems under study beyond traditional smooth optimization and linear approximations. Stability conditions are obtained for solutions to intertemporal choice problems and capital accumulation models in the presence of nonsmooth dependencies, threshold effects, and discontinuities in elasticities. For $\lambda$-Hölder production and utility functions, estimates of the sensitivity of equilibria to parameters are obtained, and indicators of the convergence rate of trajectories to the stationary state are derived for $\lambda >1$. The methodology is tested on a multisectoral model of economic growth with technological shocks and stochastic disturbances in capital dynamics. Numerical experiments confirm the theoretical results: a power-law dependence of equilibrium sensitivity on the magnitude of parametric disturbances is revealed, as well as consistency between the analytical $\lambda$-Hölder convergence rate and the results of numerical integration. Stochastic disturbances of small variance do not violate stability. The results obtained provide a rigorous mathematical foundation for the analysis of complex economic systems with nonsmooth structures, which are increasingly used in macroeconomics, decision theory, and regulation models. Full article

Fractional calculus extends conventional differentiation and integration to non-integer orders, facilitating a more suitable modeling framework for complicated dynamical processes characterized by memory and long-range dependence. The fractional and variable-order fractional Genesio-Tesi systems have recently attracted significant interest owing to their rich nonlinear dynamics and the added flexibility introduced by variable fractional orders. This work comparatively studies the stability and chaotic behavior of constant-order versus variable-order formulations of the fractional Genesio–Tesi system. The study of the system dynamics is carried out by numerical simulations, including time series, bifurcation diagrams, Lyapunov exponents, and phase portraits. We identify further stability boundaries and chaotic regimes through analytical investigations based on the Jacobian eigenvalue spectrum. It is found that variable-order derivatives intensify sensitivity and transient responses, disclosing chaotic patterns that contribute to a more profound understanding of fractional nonlinear dynamics. Full article

Spiking Neural Networks (SNNs) have drawn increasing attention for their event-driven and energy-efficient characteristics. However, achieving accurate and efficient inference within limited time-steps remains a major challenge. This paper proposes an efficient spiking Transformer framework that integrates cross-time-step knowledge distillation, multi-scale resolution processing, and attention-based token pruning to enhance both temporal modeling and energy efficiency. The cross-time-step distillation mechanism enables earlier time steps to learn from later ones, which improves inference consistency and accuracy, leading to better performance. Meanwhile, the multi-scale processing module dynamically adjusts input resolution and reuses features across scales, while the attention-based token pruning adaptively removes redundant tokens to reduce computational overhead. Extensive experimental results on static datasets (CIFAR-10/100 and ImageNet-1K) and dynamic event-based datasets (DVS128-Gesture and CIFAR10-DVS) demonstrate that the proposed method achieves higher accuracy and more than 1.4× inference speedup compared to baseline SNN–Transformer models. This framework provides a promising solution for developing energy-efficient and high-performance neuromorphic vision systems. Full article

This paper proposes a dynamic compensation method for laser ranging based on pulse width for the miniaturization and high-precision requirements of the initiation device in precision-guided aircraft. The study aims to improve the measurement accuracy of the laser ranging unit in the initiation device system and ensure the accuracy and reliability of its fixed-distance initiation decision. The variation in echo pulse width is analyzed by studying laser echo characteristics. The pulse width and the detection distance exhibit an approximately linear negative correlation within the middle range of the applicable distance range. A dynamic compensation method is proposed based on a dual-correction approach using a static lookup table and dynamic compensation. This method establishes the mapping relationship between pulse width and distance deviation, and achieves distance correction by adding distance deviation compensation to the basic value from the static lookup table. The dynamic compensation system integrated with calibration and correction is designed and implemented, and the feasibility of the dynamic compensation method is verified by testing. The relative error between the calculated correction distance and the actual distance is small, and the average relative error is about 1.33%. The proposed method provides key technical support for the establishment of miniaturized and intelligent initiation devices. Full article

Mitochondrial dysfunction is a defining hallmark of aging that connects redox imbalance, metabolic decline, and inflammatory signaling across organ systems. The mitochondrial deacetylase SIRT3 preserves oxidative metabolism and proteostasis, yet its age-related decline transforms metabolically demanding organs into sources of pro-senescent cues. This review synthesizes evidence showing how SIRT3 loss in select “driver tissues”—notably liver, adipose tissue, vascular endothelium, bone-marrow macrophages, and ovary—initiates systemic aging through the release of cytokines, oxidized metabolites, and extracellular vesicles. We discuss molecular routes and mediators of senescence propagation, including the senescence-associated secretory phenotype (SASP), mitochondrial-derived vesicles, and circulating mitochondrial DNA, as well as sex-specific modulation of SIRT3 by hormonal and intrinsic factors. By integrating multi-tissue and sex-dependent data, we outline a framework in which SIRT3 activity defines the mitochondrial threshold separating local adaptation from systemic aging spread. Targeting SIRT3 and its NAD⁺-dependent network may offer a unified strategy to restore mitochondrial quality, dampen chronic inflammation, and therefore recalibrate the aging dynamics of an organism. Full article