Emotional authenticity discrimination (EAD) is key for social interactions. The social cognition hypothesis suggests that emotion recognition relies on a dynamic visuo-motor interaction. However, the direction and role of this interaction in EAD are still unclear. Here, we investigate the impact of cortico-cortical paired associative stimulation (ccPAS) on the functional connectivity within the visuomotor networks involved in EAD of facial expressions. Sixty-four participants were randomly assigned to different ccPAS conditions targeting the backward and forward connectivity between the right inferior frontal gyrus (IFG) and the right posterior superior temporal sulcus (pSTS; IFG-pSTS, pSTS-IFG), and between the IFG and the right primary motor cortex (M1; IFG-M1, M1-IFG). The results revealed improvements in EAD selectively following the pSTS-IFG and the IFG-M1 modulation, across all emotional expressions. Interestingly, a selective increase for genuine emotions was found after IFG-M1 stimulation, suggesting a specific improvement to ecologically valid and socially relevant stimuli. These findings suggest that selectively enhancing the visual-to-motor and premotor-to-motor pathways facilitates EAD. In contrast, motor-to-visual feedback appears to play a negligible role. This study contributes to understanding the neural mechanisms underlying EAD and highlights the potential role of the ccPAS protocol in improving social cognitive abilities, with implications for developing clinical interventions.
Chronic social stressors negatively affect behavioral, physiological, and neuroendocrine functioning. Interventions such as listening to music may alter behavioral and physiological responses to social stressors. This study investigated the effects of music exposure (vs. ambient noise) during chronic social isolation on physiological and behavioral outcomes in socially monogamous prairie voles. Potential sex differences in responses to chronic social isolation with and without music exposure were also examined. Music exposure significantly reduced validated anxiety- and depression-related behavioral responses (increased exploration and grooming, and reduced immobility) and improved physiological measures (heart rate and heart rate variability) in chronically socially isolated prairie voles. Music exposure attenuated an endocrine indicator of stress relative to ambient noise conditions. Minor sex differences were observed; however, music protected against behavioral and physiological indicators of social isolation in both female and male prairie voles. This study demonstrates protective behavioral and physiological effects of music in a social context, and has translational potential for humans who experience chronic social isolation.
Precise force feedback is essential for teleoperated robots to perceive unknown environments and realize high-precision, safe motion control. However, harsh conditions and space limitations in demanding environments impede force sensor deployment, making sensorless force estimation imperative. This study proposes a residual spatial attention graph-temporal convolutional network (RSAG-TCN) that fuses spatial and temporal features for accurate sensorless force estimation in teleoperated robots. Comparative and ablation experiments were conducted in contact environments with varying stiffness. The comparative models include gated recurrent unit (GRU), long short-term memory (LSTM), PSTA-TCN, Densenet-Transformer, ASGRNN, and our prior MDELM. Experimental results showed that RSAG-TCN significantly outperformed all comparative models. Specifically, compared to the best-performing baseline (MDELM), RSAG-TCN reduced the root mean square error by 0.0236 N in soft contact environments and by 0.1040 N in hard contact environments. Ablation experiments further verified the necessity of the GCN and CAM-SAM modules for spatial feature extraction and key feature enhancement. Beyond offline accuracy evaluation, a human-in-the-loop experiment with six volunteers was conducted to demonstrate the proposed method's effectiveness in enhancing teleoperation transparency and stability in real-time operation. This work provides an efficient sensorless force estimation solution for teleoperation systems, offering technical support for high-precision robotic force control in extreme scenarios.
This paper presents a surface-enhanced Raman scattering sensing platform for sensitive detection of tetracycline (TTC) in food samples, in which dual-component signal enhancement is achieved by combining an aptamer-regulated catalytic switch with a structurally optimized substrate. The substrate consists of an ordered gold nanoparticle (AuNP) array conformally coated with a continuous MXene film via liquid-liquid interfacial assembly. A TTC-specific aptamer and catalytic nitrogen/silver co-doped carbon dots (CDN/Ag) are further integrated through controlled in-situ formation of AuNPs, enabling synergistic electromagnetic and chemical signal enhancement. The platform exhibits a wide linear range from 10-5 M to 10-12 M for TTC, with a limit of detection of 5.3 × 10-12 M, high selectivity, and robust performance in complex samples. Spike-recovery tests in milk yielded recoveries of 95.6%-126%, with relative standard deviations of 1.6%-5.1%, demonstrating the reliability and practical applicability of the sensor for food safety monitoring.
This study integrates deep reinforcement learning (DRL) with lean management for renewable energy project scheduling. The problem is formulated as a constrained Markov decision process with weather‑dependent dynamics and a composite reward such as schedule, cost, quality, and constraints. A four‑layer architecture (convolutional, recurrent, and attention layers) and a masked proximal policy optimization actor-critic enhanced with prioritized experience replay and meta-learning learn adaptive policies; four lean principles map directly onto the DRL loop. A three-stage feasibility filter-hard action masking, soft resource rescaling, and penalty fallback-ensures that every executed decision satisfies project-network, resource, weather-window, grid, and environmental constraints. Validation on 186 Northwest China projects shows 33.0% shorter duration, 29.2% lower cost, and 87.2% resource utilization while preserving quality. Compared with eight state-of-the-art deep-learning and classical baselines, the proposed approach delivers an average improvement of approximately 2.9 percentage points in schedule efficiency over the strongest deep‑learning baselines, and reduces mean absolute error by approximately 41%-50% on large‑scale projects (>200 activities). The proposed method retains ∼80% performance under 30% noise and ≥97.8% on CPU‑only hardware, supporting practical adoption by small-to-medium enterprises and on-site project offices.
At the level of smaller linguistic units, such as words and syllables, rhythm is characterized by a high degree of heterogeneity between languages. This suggests that it is distinct from nonhuman animal communication, where underlying isochrony is often observed. In this study, we examine speech rhythm using units similar to those previously examined for nonhuman animals: interonset intervals (IOIs), that is, sound elements surrounded by silence. Surprisingly, IOI durations are relatively similar across a comprehensive sample of 48 languages from all inhabited continents, with a median of approximately 2 s. IOI beats are also relatively similar at around 0.5 Hz, and human speech fits between exemplary animal species in terms of rhythmic variability and beat precision, that is, the extent to which an isochronous beat model fits the sequence. By revealing cross-linguistic regularities and comparing them to those of other animal species, our results help to position human speech within the broader spectrum of rhythmic vocal behavior observed in nature.
Accurate diagnosis of early-stage osteonecrosis of the femoral head (ONFH) remains challenging due to reliance on subjective radiological interpretation. This multicenter retrospective study developed an automated, multi-sequence MRI-based diagnostic model for early ONFH using data from 342 ONFH-affected femoral heads (FHs) from 282 patients and 265 healthy FHs from 233 healthy individuals obtained from two geographically distinct institutions. The dataset comprised three MRI sequences (T1WI, FS-T2WI, and Cor STIR) and 7844 annotated FH instances labeled as normal or abnormal. A You Only Look Once (YOLO) segmentation model was trained on both single-sequence and multi-sequence datasets, and a global attention mechanism (GAM) was integrated to enhance diagnostic performance, yielding the GAM-YOLO model. Model performance was compared with that of radiology residents using Fisher's exact test. The multi-sequence YOLO model achieved superior diagnostic accuracy (sensitivity 93.19%, specificity 94.65%, accuracy 94.04%) compared to single-sequence models. Incorporation of GAM further improved performance (sensitivity 97.00%, specificity 97.52%, accuracy 97.30%; p < 0.05). When applied to entire FH images, the GAM-YOLO model achieved sensitivity, specificity, and accuracy of 99.03%, 98.51%, and 98.82%, respectively, showing higher diagnostic performance than residents (p < 0.01). These findings suggest that the GAM-YOLO model offers a promising and objective tool for early ONFH diagnosis, demonstrating potential for clinical application.
Individuals with prenatal alcohol exposure (PAE) and fetal alcohol spectrum disorders (FASDs) present a range of neurodevelopmental deficits (e.g., inattention, hyperactivity, and executive dysfunction) which have shown marked overlap with attention-deficit/hyperactivity disorder (ADHD), making differential diagnosis challenging. While rates of comorbidity are high, evidence has suggested there are important distinctions between neurodevelopmental phenotypes. To understand these distinctions, we evaluated whether proposed neurobehavioral disorder associated with prenatal alcohol exposure (ND-PAE) criteria in the appendix of the Diagnostic and Statistical Manual for Mental Disorders (Fifth Edition) can differentiate FASD from ADHD. We conducted systematic searches across three databases (Medline, PsycINFO, PubMed) to identify studies comparing behavioral and cognitive functioning between FASD, ADHD, and healthy controls (HCs). Outcomes indicated FASD individuals have greater magnitude neurodevelopmental deficits than ADHD; however, no differences were found regarding the pattern of deficit because of methodological limitations, such as low I2. Moreover, outcomes supported adaptive and neurocognitive (executive functioning) criteria but did not support self-regulation criteria. These findings collectively underscore a need for clinicians to consider the magnitude of deficits presented to facilitate accurate recognition of PAE. Further research characterizing ND-PAE criteria and differences in the magnitude of deficits between disorders may ultimately support more accurate differential diagnosis.
Speech and music both unfold over time. However, these dynamics are perceived quite differently: music as rhythmic, and speech as quasi-rhythmic. Here, we aim to identify whether this distinction between rhythm and quasi-rhythm is sourced from the raw acoustic waveform by analyzing the modulation spectrum of the acoustic envelope. We analyzed several large corpora on three scales: the coarsest scale containing recordings of each corpus, the intermediate scale of individual speakers/songs, and the finest scale of individual sentences or short musical segments. We confirm previous findings that speech and music modulation spectra differ in their center frequency, which reflects how quickly the sound envelope fluctuates, but find that the modulation bandwidth, which captures how periodic the speech/music envelope was, is comparable for music and speech. Furthermore, the bandwidth is largely preserved across the three scales, indicating that the corpus-level temporal irregularity is dominated by the irregularity within a few seconds of speech/music recordings. These results demonstrate that the perceived rhythmicity of music may not directly reflect the temporal periodicity present in its acoustic structure.
The development of three-dimensional (3D) information encryption and transmission is constrained by the massive data bandwidth involved and the inherent limits of first-order optical parameters. Here, we present a novel scheme that integrates the spatial coherence structure of the optical field with holographic coding to enable secure transmission of encrypted 3D objects and concealed information. By exploiting the second-order coherence of partially coherent light, 3D information is encoded into the spatial coherence structure. Joint transmission is then achieved through two-dimensional (2D) intensity information. Meanwhile, the inherent concealment of the spatial coherence structure enables the synchronous transmission of hidden information. The incorporation of hidden information enhances system security, ensures transmission integrity, and facilitates the detection of degradation, loss, or tampering. This method achieves both efficient encrypted transmission and high-fidelity 3D reconstruction, markedly enhancing physical-layer security and resistance to attacks. This work provides a coherence structure-based framework for secure and efficient transmission of 3D data.
Progressive collapse is initiated by local component failure and evolves into global structural instability through continuous damage propagation. Rapid prediction of the structural response before an actual collapse-inducing event occurs is important for collapse-resistant design, pre-disaster consequence assessment, and safety evaluation. However, challenges remain in predicting three-dimensional and multi-component collapse scenarios, including geometry representation, topology changes, and generalization across different failure conditions. To address these issues, this study proposes a graph neural network model for fast prediction of progressive collapse in steel-framed buildings after component failure. The model adopts a graph encoder-decoder architecture to represent component geometry and structural connectivity, employs multi-scale message passing to infer global damage patterns from local failures, and incorporates a multi-source feature joint training strategy for single-, double-, and multi-column failure scenarios. Validation results show that the model achieves 94% accuracy compared with simulation data and a 6900-fold acceleration, enabling real-time collapse prediction for pre-disaster consequence forecasting. This approach improves the balance between simulation accuracy and efficiency and provides a new pathway for large-scale collapse analysis.
Persistent postural-perceptual dizziness (PPPD) patients with visual dependency rely more on visual cues and may have altered oculomotor responses to visual motion. This study examined early eye movement responses to large-field motion stimuli-the ocular following responses (OFRs)-in 30 PPPD patients and 29 healthy controls. Participants viewed horizontal motion in peripheral and/or central visual fields while eye movements were recorded. OFR velocity 200 ms after stimulus onset and suppression efficacy were analyzed across four central field sizes from 5° to 42° radius. Both groups exhibited strong OFRs to stimuli in the central visual field that increased with the size of the stimulation area. When the central visual field was stationary and the surrounding area moved, patients showed significantly higher eye velocity than controls. Both groups could partially suppress OFRs, but healthy controls were more effective at suppressing eye movements during peripheral stimulation. Patients also showed greater visual dependency on the dynamic subjective visual vertical (2.2° vs. 1.2°), which correlated with OFR magnitude. Overall, PPPD patients were less able to keep their eyes still during peripheral visual motion, producing greater retinal slip and disrupting volitional fixation-an abnormality that may contribute to dizziness and discomfort in visually complex environments.
Electric vehicles (EVs) are moving toward greater intelligence and personalization, but the dynamic adaptability of current vehicle seats no longer meets drivers' comfort and safety needs. To address insufficient posture adaptation and low recognition accuracy for occluded joints, this study proposes an intelligent seat adaptive adjustment model on the basis of human pose recognition. Kinect V2 was used to collect drivers' posture data, and features were extracted through a cascaded pose analysis network (PSN) integrating a spatial transformer network, squeeze-and-excitation module, and convolutional block attention module. The network recognized 16 joint points with an average accuracy of 0.87, a PCK@0.2 (percentage of correct keypoints within 20% of limb length) of 0.92, and a mean per joint position error (MPJPE) of 3.2 mm. Considering biomechanical and vehicle physical constraints, a seat adjustment model was established and optimized using the analytic hierarchy process (AHP) to coordinate seat fore-aft position, height, and backrest angle. Simulated driving experiments suggested preliminary effectiveness: Subjective comfort increased by about 30%, lumbar muscle activity decreased by 39.5%, and posture estimation plus parameter generation remained within 0.3 s. These findings indicate preliminary feasibility for intelligent seat posture assistance under simulated conditions, whereas practical safety validation in real driving conditions remains for future work.
Pain reflects both sensory input and predictive processes shaped by expectations. Individuals with high autistic traits (HATs) often exhibit atypical pain responses, potentially due to alterations in anticipatory processing. This study investigated pain anticipation in HAT individuals and examined the modulatory effects of oxytocin using behavioral, computational, and pharmacological approaches. In experiment 1, HAT and low autistic trait (LAT) individuals completed a cue-based pain anticipation task. Anticipatory processing was characterized through two components: decision-related processes and evaluative responses. HAT individuals showed altered decision-making, characterized by greater caution under uncertainty and reduced processing efficiency under certain high-pain conditions. They also exhibited more negative evaluative responses, which were associated with poorer psychological health. In experiment 2, HAT participants received intranasal oxytocin or placebo. Oxytocin selectively influenced decision-related processes under uncertainty, increasing evidence accumulation and reducing nondecision time while also increasing the tendency to choose high-pain outcomes under ambiguous conditions. In contrast, oxytocin showed limited effects on evaluative measures. These findings suggest that pain anticipation in HAT individuals involves partially distinct decision-related and evaluative components and that oxytocin exerts process-specific effects on anticipatory processing.
Dance-based interventions are increasingly used to support mental health and well-being across clinical, community, and educational contexts. However, the review-level evidence remains conceptually and methodologically fragmented. This quality-sensitive umbrella review synthesized 90 review articles identified across five databases. Systematic reviews and meta-analyses were appraised using AMSTAR 2, while scoping, narrative, literature, and clinical reviews were assessed using adapted review-appraisal criteria. Findings from systematic reviews and meta-analyses were used primarily to interpret effectiveness evidence, whereas nonsystematic reviews were used to map conceptual definitions, practice traditions, and measurement issues. The synthesis showed that the field is fragmented at three interrelated levels. First, intervention labels and delivery models are inconsistent, ranging from clearly defined dance/movement therapy to broad and underspecified uses of dance. Second, effectiveness is conceptualized differently across intervention traditions, with therapy-oriented reviews emphasizing symptom reduction and non-therapy-oriented reviews foregrounding well-being, vitality, social connection, and embodied experience. Third, outcome assessment relies predominantly on generic psychological scales, with limited use of movement-sensitive or dance-specific measures. Quality-sensitive evidence suggests that dance-based interventions show promise for well-being, social connection, emotional regulation, and selected symptom outcomes, but confidence remains limited by low review quality, heterogeneous populations, inconsistent intervention reporting, and insufficient attention to primary-study overlap. Future research should move beyond asking whether dance works in general and instead specify what kind of dance, for whom, through which mechanisms, and according to which model of mental health and well-being.
Harmony and rhythm perception have traditionally been treated as separate perceptual phenomena in auditory cognitive science. Yet, both involve inherently oscillatory and periodic structures. Converging evidence suggests that harmonic and rhythmic patterns that can be described in terms of (approximate) low-order integer relationships are often perceived differently from those characterized by more complex relations. In harmony, small integer frequency ratios (e.g., 2:1 for the octave, 3:2 for the perfect fifth) are related to perceived consonance and ease of auditory encoding. Similarly, rhythmic patterns whose component durations can be expressed by simple ratios (e.g., 2:1, 3:1) as well as polyrhythms based on simple ratios (e.g., 2:3, 3:4) are more readily detected, produced, and remembered than those based on more complex ratios. In this paper, we ask whether these perceptual and processing advantages reflect underlying shared mechanisms across harmonic and rhythmic domains. Furthermore, we explore whether and how these advantages extend into the motor domain. Grounding on neural resonance theory, we propose an integrated ratio-based framework that links the perceptual salience of low-order integer relationship in rhythm and harmony to enhanced sensorimotor synchronization.
Community parks are public spaces frequently used by older adults for outdoor physical activity. This makes age-friendly renewal increasingly important in urban planning. However, the mechanisms through which landscape characteristics influence serious leisure participation via leisure constraint negotiation remain insufficiently understood, particularly in mountainous cities. We surveyed 939 physically active older adults in 66 community parks in Chongqing, China and adopted a mixed-methods approach that reorganized the hierarchical/negotiation model by redefining the order of latent variables and incorporating landscape characteristics. Our results showed that older adults' serious leisure in community parks in a mountainous city is primarily shaped by a constraint-driven negotiation process, with structural constraints positioned at the first level, before intrapersonal and interpersonal constraints. Older adults of advanced age, those with higher economic status, and those visiting mountainous community parks experienced greater negotiation difficulty. Community parks exhibit seven structural constraints, and differences in landscape preferences among older adult groups lead to nonlinear relationships between structural constraints and landscape characteristics. Accordingly, targeted strategies such as improving accessibility, enhancing facility diversity, and optimizing spatial configuration are recommended to support age-friendly renewal while preserving distinctive landscape characteristics. These findings provide practical implications for similar cities and support healthier, more inclusive urban environments.
The intricate interplay among cerium (Ce) alloying, phase transformation kinetics, and mechanical behavior in a high-carbon nanobainitic bearing steel was systematically investigated. Steels with Ce contents of 0, 0.032, and 0.054 wt.% were subjected to isothermal bainitic treatments, revealing that Ce acts as a critical phase transformation regulator and microstructure refiner. Contrary to expectations, low-level Ce addition significantly suppressed the bainitic transformation completion, leading to incomplete reaction and a two- to fourfold increase in the volume fraction of retained austenite (RA). This abundant, stable RA is identified as the primary source for a substantial and continuous improvement in impact toughness, even as the fracture mode remained quasi-cleavage. Furthermore, the dual effect of Ce was revealed: addition of 0.032 wt.% Ce refined prior austenite grains from 57 to 40 µm and modified inclusions into fine oxysulfides, whereas a higher content of 0.054 wt.% resulted in grain coarsening and the formation of large, detrimental Ce-phosphide inclusions. These negative microstructural changes partially offset the toughness gains, explaining the diminished rate of improvement at higher Ce levels. These findings provide a new strategy for designing high-toughness nanobainitic steels by controlling the RA content through Ce alloying.
Urban microbial communities are undergoing directional homogenization across soils, wildlife gut microbiota, and human exposure pathways, yet no monitoring framework exists to track this loss or govern the probiotic city interventions now scaling without ecological oversight. I argue that the urban microbiome constitutes an "invisible commons": a shared, depletable resource whose depletion goes unaccounted for because it has never been made measurable. The meta-omics community has the tools to change this. I propose five monitoring priorities and six immediately deployable indicators and show that the governance architecture required to convert these measurements into decisions already exists in conservation biology. What is missing is not new science but the decision to apply existing methods to this domain. With antibiotic resistance causing over one million deaths annually and probiotic city interventions advancing without resistance gene screening, that decision is overdue.
Moving in unison to a musical beat can blur boundaries between individual movements, fostering prosociality. However, structured patterns of sounds and silences in rhythms (meter) allow various ways of moving to the beat. How do these different rhythmic interpretations affect social connectedness? Unacquainted dyads drummed in unison, and at more complex integer multiple and polyrhythmic ratios. Coordination requires continuously tracking who does what and when. We varied trackability by manipulating task-sharing (the same or separate drum pads, and audible or inaudible partner drumming). More complex ratios decreased social connectedness. However, coordination revealed a dichotomy between unison and the more complex ratios: in unison drumming, participants automatically tracked and adjusted to each other's actions, but both integer multiples and polyrhythms separated the two participants' actions in time, allowing one to be stable despite their partner being variable. Hearing each other increased participants' mutual reliance on each other's actions, but by itself did not increase connectedness. However, sharing a drum pad and audio significantly boosted self-other merging ratings. Notably, more stable coordination facilitated integrated self-other representations. Our findings are consistent with the premise that meter provides a shared cognitive-motor scaffold that enables group cohesion, while simultaneously allowing individual expression through multi-part rhythm production.
