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Anion binding to nanographenes is governed by noncovalent interactions, particularly anion-π interactions in electron-deficient aromatic regions and CH---anion hydrogen bonding in electron-rich domains. These interactions are primarily driven by electrostatic effects, with the quadrupole moment of the aromatic system playing a central role in determining the strength and directionality of anion-π binding. The perpendicular component of the quadrupole moment (Qzz) correlates with binding energies for both anion-π and CH---anion interactions, though polycyclic systems present challenges due to competing interaction modes. In this study, we investigate the role of the local quadrupole moment in anion binding across 171 Cl--aromatic complexes, comparing various descriptors including aromaticity indices, Fukui functions, and electron density at ring critical points. We find that electrostatic descriptors, particularly the local quadrupole moment, provide a more consistent and robust explanation for binding energies than conventional descriptors. Specifically, two geometric descriptors derived from the local quadrupole moment-the scale factor ( S R max $$ {S}_R^{max} $$ ) and the ellipticity ( e c ' $$ {e}_c^{\prime } $$ )-show good correlation with binding strength, with S R max $$ {S}_R^{max} $$ reflecting π-acidity and ellipticity quantifying charge distribution anisotropy. These descriptors are validated across fluorinated naphthalenes and nanographenes, demonstrating their general applicability. Regression models based on S R max $$ {S}_R^{max} $$ and e c ' $$ {e}_c^{\prime } $$ effectively predict binding energies, with enhanced accuracy when combined with polarization-dependent penalty functions, especially for larger nanographene systems. While the predictive model is still somewhat constrained by polarization effects, its simplicity, robustness, and transferability across a wide range of systems offer distinct advantages over more complex, multilayered machine learning models. These results underscore the critical role of quadrupole moment anisotropy in anion-π interactions and offer a practical framework for predicting anion binding affinities and designing π-acidic receptors.

Musculoskeletal dynamics influence the progression and rehabilitation of movement-related conditions. However, estimating whole-body dynamics using accessible tools, like smartphone video, remains challenging. Physics-based and machine learning (ML)-based dynamic predictions each offer advantages, but both approaches struggle to achieve high accuracy and physical realism. Here, we created a hybrid ML-simulation framework to improve estimates of ground reaction forces, joint moments, and joint contact forces from smartphone video kinematics. We used ML models to predict ground forces and centers of pressure from video-based kinematics. The hybrid framework generates a dynamic simulation that tracks predicted forces and kinematics while encouraging dynamic consistency. We compared the hybrid model with kinematic-tracking simulations and with ML-predicted forces applied via inverse dynamics. Performance was evaluated using mean absolute error relative to lab-based inverse dynamics using marker and force plate data from 10 individuals walking. The ML and hybrid approaches reduced vertical ground force error by 40-44% compared to simulation. The hybrid model improved joint moment accuracy by 29-45% and joint contact force accuracy by 12-13% compared to simulation- or ML-only approaches, with the largest improvements in peak medial knee contact force (49%) and knee adduction moment impulse (30%). Our hybrid model improves the accuracy of dynamics from smartphone videos during walking, outperforming simulation for ground forces and both simulation- and ML-only approaches for joint moments and contact forces. These methods enable more accurate, scalable assessments of musculoskeletal dynamics, supporting out-of-lab studies and precision treatment of gait-related conditions.

This study aims to enhance the precision of humanoid robots in imitating complex human "walking-grasping" coordinated movements. Addressing limitations in sample efficiency and reward function design in Generative Adversarial Imitation Learning (GAIL), we propose the Similarity Reward-Augmented Generative Adversarial Imitation Learning (SRA-GAIL) framework. The method integrates plantar thin-film resistive pressure sensors to measure the real-time pressure distribution at four key points on both feet, combined with roll/pitch angle data acquired from JY901S inertial measurement units (IMUs). A Lagrangian constraint optimization strategy is employed to achieve gait stability control based on the zero moment point (ZMP). Simultaneously, a visual similarity evaluation module is established using human demonstration trajectories captured by a Logitech C920E camera, augmented by grip force feedback from flexible thin-film pressure sensors on the hands. This enables the design of a multimodal sensor-fused similarity reward function. By incorporating Lagrangian constraint optimization and a maximum entropy reinforcement learning framework, Similarity Reward-Augmented Generative Adversarial Imitation Learning synchronously optimizes gait stability control-guided by zero moment point (ZMP) and roll/pitch data-and vision-based trajectory similarity evaluation. These components address motion stability constraints and trajectory similarity metrics, respectively, generating biomechanically plausible gait strategies. A spatiotemporal attention mechanism parses human motion trajectory features to drive the end-effector for high-precision trajectory tracking. To validate the proposed method, an imitation learning experimental system was constructed on a physical XIAOLI humanoid robot platform, integrating inertial measurement units (IMUs), plantar pressure sensors, and a vision system. Quantitative evaluations were conducted across multiple dimensions, including robot platform analysis, walking stability, object grasping success rates, and end-effector trajectory similarity. The results demonstrate that, compared to Generative Adversarial Imitation Learning (GAIL) and behavioral cloning, Similarity Reward-Augmented Generative Adversarial Imitation Learning achieves a stable object grasping success rate of 93.7% in complex environments, with a 23.8% improvement in sample efficiency. The method maintains a 96.5% compliance rate for zero moment point (ZMP) trajectories within the support polygon, significantly outperforming baseline approaches. This effectively addresses the bottleneck in robot policies adapting to dynamic changes in real-world environments.

This study aimed to estimate vertical ground reaction force (vGRF) and lower-limb joint moments during football cutting movements using a trunk-mounted inertial measurement unit (IMU) combined with a Random Forest model, and to validate the feasibility of this approach. IMU data collected during 45° cutting tasks were corrected using an Extended Kalman Filter (EKF). The model demonstrated good and consistent performance for vGRF (coefficient of determination, R2 = 0.766; correlation coefficient, r = 0.796) and sagittal plane moments of the ankle and knee (R2 = 0.661-0.689, r = 0.807-0.842). While Bland-Altman analysis indicated low bias and generally good agreement, precision at the individual-trial level and accuracy for non-sagittal plane moments somewhat reflected the inherent within-player trial-to-trial variability in movement execution, particularly in non-sagittal loading patterns. It should be noted that performance estimates under the current trial-based validation design may differ from those obtained using a subject-independent framework such as leave-one-subject-out cross-validation. This study demonstrates that a single trunk-mounted IMU can reliably estimate key lower-limb loading patterns, providing a practical foundation for wearable-based kinetic monitoring in applied football settings.

In this paper, we investigate the application of the relative entropy framework for safety assessments of steel elements with structural defects at the micro- and macro-scales. Mathematical theories developed by Bhattacharyya and by Kullback and Leibler (K-L) have been used for this purpose. This approach uses both expectations and variations, similar to the First-Order Reliability Method (FORM), but is extended to include 3rd- and 4th-order central probabilistic moments. It is necessary to use a hybrid computational technique that combines the Finite Element Method (FEM) software ABAQUS CAE 2017 with the implemented Gurson-Tvergaard-Needleman (GTN) damage model and the computer algebra system MAPLE. The iterative generalized stochastic perturbation technique has been used to determine the probabilistic moments of structural response, to utilize the Weighted Least Squares Method to approximate the structural response function, and to determine uncertainty in the stress, strain, and displacement state functions. This approach is based on relative entropy because of its universality. There is no need to assume a type of distribution of the state functions, in contrast to FORM, where a Gaussian distribution is required. This paper verifies whether relative entropy can serve as an alternative to FORM for determining reliability. The yield surface of the porous material with a random values of the void volume fraction f are also presented.

Rail infrastructure plays an important role in freight and passenger mobility, and the assessment of rail track structure depends critically on understanding how the rail interacts with the supporting foundation. When rail support degrades (e.g., due to ballast fouling, settlement, etc.), the rail exhibits greater localized deformation that can lead to serious deleterious conditions. Track modulus represents a fundamental diagnostic measure of rail support, encompassing the vertical stiffness characteristics of the foundation and its resistance against downward rail movement. Existing track modulus characterization methodologies typically comprise deflection measurements of railway track (e.g., tie deflections) under known loads. Track modulus estimations result from analyzing deflection and load under assumptions of a traditional Winkler foundation, which can oversimplify mechanic relationships. Specifically, in the context of rail-ballast-subgrade interaction, a tensionless foundation permits gap development which can occur as track structure separates from the supporting ballast; additionally, track modulus may vary along the track length as conditions vary spatially. This paper presents a general analytical solution of ballasted track support characterization based on an iterative algorithm for the static response of a finite beam resting on a tensionless Winkler foundation. The method relates to multiple loads (e.g., concentrated axle loads and distributed self-weight), deflection along the track, and track condition through singularity functions, superposition of discrete support springs, and moment-curvature relationships. The model estimates rail deflections, lift-off points and shear and moment diagrams along the track. The technique permits: (1) validations against benchmark solutions and previously published results, (2) estimations of track modulus from known loads and measured deflections, and ultimately, (3) a framework for designing and processing sensor data streams for use in analyses and evaluations of railway track structure.

The family of Maus's salts has been known for several centuries, but many aspects of their structures remain elusive, particularly because most examples are highly solvated and readily lose water. To examine the role of alkali cations and water on the structure, and to resolve some ambiguities of previously reported supercells, several new Maus's salt derivatives were synthesized and characterized by low-temperature X-ray diffraction. The parent Maus's salt, K5[Fe3O(SO4)6(H2O)3]·5H2O, grows in hexagonal P63/m, with trigonal symmetry of the Fe3+ clusters, conditions that allow for potential magnetic frustration. A new caesium ferric sulfate complex, Cs5[Fe4O2(HSO4)(SO4)6(H2O)3]·1.75H2O, was also synthesized under the same conditions. It is not a Maus's salt derivative but instead contains Fe3+ tetramers of corner- and edge-sharing iron octahedra, decorated by seven sulfate tetrahedra, crystallizing in monoclinic space group C2/c. Preliminary orientation-dependent magnetic data relative to the trigonal c axis were collected on single crystals of the parent Maus's salt. The data suggests very weak short-range antiferromagnetic spin reorientations with a subtle broad rolling feature having a local maximum near 60-70 K. Magneto-structural anisotropy and spin saturation moments, one-tenth those expected for high-spin Fe3+, suggest a splayed magnetic structure with spin moments predominantly in the trimer plane directed inward about the local z axis toward the shared oxygen between trimers.

Emotion dysregulation is a common issue across childhood and adolescence, including in autism and attention-deficit/hyperactivity disorder (ADHD). Yet most accounts remain descriptive-focusing on irritability, rapid escalation, slow return to baseline-without specifying the computational processes that stabilize emotions in context. We propose a predictive-coding account in which emotion regulation depends on how the brain predicts bodily and environmental signals, detects mismatches, between expectation and sensation, and determines how much confidence to assign those mismatches. Within this framework, dysregulation can arise when prediction errors are assigned excessive precision (i.e., incoming signals are overweighted relative to prior expectations, making them feel "too loud"), amplifying emotional reactivity or when prior expectations update too slowly (feelings get "stuck"), prolonging emotional states beyond contextual demands. We hypothesize that autism may involve heightened confidence assigned to surprising inputs together with sluggish updating of expectations, a combination that can produce both reactivity and emotional inertia. ADHD, by contrast, may be characterized by weaker expectations and unstable gain control, meaning the system's amplification of incoming signals fluctuates from moment to moment, contributing to rapid, reactive swings in affect. We outline candidate physiological correlates-including heart-rate variability dynamics, pupillary variability, mismatch negativity, and neural indices of interoceptive precision-that could evaluate these hypotheses. Framing intervention around restoring flexible inference rather than suppressing emotion offers a mechanistic direction for developmental research. These predictions await empirical validation in developmental samples.

Cognitive control fluctuates from moment to moment, even when the sensory and contextual demands of the task remain constant. However, traditional approaches used to measure cognitive control have largely treated such variability as noise and predominantly relied on overall performance metrics, such as accuracy and average response time. Using response time variability as an index of fluctuations in cognitive control, we examined how cognitive control consistency relates to the development of academic skills in children (N = 112) who were followed across kindergarten and first grade, an important period encompassing the transition and acclimation to formal schooling. Cognitive control consistency was assessed via response time variability in a Go/No-Go task, and math and literacy skills were assessed by the Applied Problems and Letter-Word Identification subtests of the Woodcock-Johnson III Tests of Achievement. More consistent cognitive control (indexed by lower response time variability) was associated with stronger math skills both concurrently in kindergarten and prospectively in first grade, above and beyond an accuracy-based metric (d'). In contrast, cognitive control consistency did not relate to literacy skills in kindergarten or first grade, suggesting potential domain-specificity in these relations during the early school years. Together, these findings demonstrate that consistency is an important aspect of cognitive control that uniquely contributes to math performance in early childhood.

Movies evoke dynamic emotional experiences that fluctuate moment-to-moment. While fMRI has mapped these fluctuations, the real-time continuous oscillatory dynamics underlying naturalistic viewing remain less understood. In this study, 25 adults watched an emotionally rich short film while EEG, continuous subjective arousal annotations and pupil diameter were recorded. Inter-subject correlation (ISC) analyses revealed robust synchronization in behavioural and pupillary arousal across the cohort. Leveraging these shared, group-level arousal trajectories to probe individual-level cortical processing, we mapped the neural networks correlating with these two arousal signals. Both pupillary and subjective arousal negatively correlated with low-frequency power in occipitoparietal regions, reflecting bottom-up sensory gain control and attentional gating. Furthermore, high-arousal epochs were marked by low-frequency desynchronization in the precuneus; this cortical activation likely indexes the rapid retrieval of episodic memories required to update the viewer's situational model during plot shifts. Finally, while both measures tracked low-frequency acoustic features in the auditory cortex, subjective arousal was more prominently associated with extended top-down semantic networks and central theta activity. These findings highlight that while pupillary arousal heavily reflects bottom-up sensory intensity, subjective reporting captures active cognitive integration. Together, this demonstrates how emotional arousal acts as a dynamic control signal, orchestrating a complex interplay of sensory gating, memory updating and top-down evaluation to make sense of the unfolding narrative.

Osteosarcoma patients who have undergone prosthetic lower limb reconstruction following tumour resection walk at a slower preferred speed than healthy controls. In addition, they demonstrate lower peak isokinetic knee extension torques, which may explain the observed differences in sagittal plane knee joint kinematics during walking. However, mechanisms for this and the associated knee joint kinetics are not well understood, particularly at matched walking speeds. This observational case-control study compared sagittal plane lower limb walking gait characteristics between patients who have undergone prosthetic reconstruction and matched healthy controls across their preferred and matched walking speeds. Data were collected from 18 control participants and 17 patients while walking on a force-instrumented treadmill at the different speeds. Spatiotemporal variables, peak knee flexion angle, peak knee extensor moments (torques), and peak knee joint power were compared between groups. Patients walked with a slower preferred speed than the control group. When comparing lower limb gait mechanics at matched speeds, patients demonstrated lower magnitude peak knee extensor moments than the control group, and these differences were greater at faster speeds. At the fastest walking speed, patients also displayed lower peak knee joint power compared to the control group. The differences in knee joint kinetics observed between groups may be due to an inability amongst patients to generate the magnitudes of knee extensor force that the control group can generate.

Chronic insomnia is a prevalent condition associated with significant psychosocial and economic burden. When comorbid with Generalized Anxiety Disorder (GAD), symptom severity is intensified resulting in treatment pathways becoming more complex. Despite the impact, insomnia is often normalized, under recognized as a medical condition, and managed through fragmented care systems. A mixed-methods study was conducted in the United States to explore the patient journey of chronic insomnia with and without comorbid GAD. Qualitative insights were generated through 29 semi-structured online interviews with patients (n = 16), family members (n = 5), and healthcare professionals (n = 8), supported by secondary data review. Quantitative symptom data were collected using the Insomnia Severity Index (ISI) and Generalized Anxiety Disorder-7 (GAD-7) questionnaires to characterize the sample. A structured three step approach was applied: data immersion, qualitative research, and thematic analysis. Findings revealed substantial delays in help seeking and diagnosis, often spanning years or decades, driven by symptom normalization, stigma, and reliance on self-management remedies. Diagnosis was experienced as a moment of relief and validation, though expectations were frequently unmet due to brief consultations, limited information, and reliance on over-the-counter (OTC) options. Treatment was characterized by trial-and-error approaches, with healthcare professionals (HCP) acknowledging an overreliance on pharmacological interventions and systemic barriers to the implementation of cognitive behavioral therapy for insomnia (CBT-I). Ongoing management highlighted adaptation to insomnia as a chronic condition, emotional strain on families, and fragmented care across mental health and sleep services. Across all groups, the comorbidity of insomnia and GAD was recognized as exacerbating symptom severity and complicating management. This study uniquely captures the lived experience of chronic insomnia and comorbid GAD from the perspectives of patients, family members, and healthcare professionals across the US care spectrum. The findings underscore unmet psychological needs, systemic barriers to nonpharmacological interventions, and the absence of integrated, patient centered care models. Addressing these gaps requires improved recognition of insomnia as a medical condition, expansion of access to CBTI, and inclusion of family perspectives in long term management strategies.

Cerebral Palsy (CP) is a leading cause of childhood motor disability and is frequently assessed through clinical gait analysis using marker-based motion capture systems. However, these systems present challenges, such as errors regarding marker placement and soft tissue artifact. Markerless systems are a potential alternative that offer practical and technical benefits to perform gait analysis, by using computer vision and deep learning algorithms to overcome those limitations. are there any differences between the joint angles, moments and powers, using both markerless and marker-based motion capture systems, when assessing gait kinematics and kinetics of children diagnosed with CP? Fifteen children diagnosed with Cerebral Palsy (11 males, 4 females, aged 13.66&#x202f;&#xb1;&#x202f;1.72 years old) were submitted to a clinical gait analysis, captured with standard marker-based and markerless motion capture systems. The kinematic and kinetic variables were averaged and compared between the systems using RMSD and a two-tail paired sample t-test (&#x3b1;=0.05). there is a consistent waveform and good agreement in sagittal plane kinematics, with Root Mean Square Difference (RMSD) <&#x202f;6.0&#xba;, particularly for knee flexion. Nevertheless, hip flexion and pelvic tilt showed systematic offsets, and the transverse plane obtained more inconsistent measurements between the systems (RMSD > 10.0&#xba;), except for pelvic rotation. Markerless system exhibits great potential for clinical gait analysis, and results suggest that joint kinematics in the sagittal plane are highly comparable. Accuracy improvements for estimations in other anatomical planes and regarding joint kinetics are still necessary, especially for the use of this technology in clinical settings.

Lodging is a major constraint limiting grain yield in dry direct seeding rice (DDSR), yet the key traits and phenotypic relationships governing lodging resistance in japonica varieties adapted to this system remain poorly understood. This study evaluated 79 japonica accessions over two years in Shenyang, Northeast China, to dissect phenotypic variation in lodging index and identify ideotypes for breeding. Based on hierarchical clustering, varieties were classified into strong lodging resistance (SLR), medium lodging resistance (MLR), and weak lodging resistance (WLR) types, with SLR varieties achieving lodging indices 27.4-31.8% lower than those of MLR and 63.2-83.8% lower than those of WLR varieties. SLR varieties reduced lodging risk by coordinately balancing whole-plant bending moment and stem breaking resistance: plant height and center-of-gravity height were 5.2-10.7% lower, while basal internode bending stress was 27.9-81.9% higher than in other types. Structural equation modeling identified culm dry weight, inner diameter, and culm phenotype index as primary determinants of lodging variation. Notably, despite 11.0-13.7% fewer spikelets per panicle, SLR varieties maintained grain yields comparable to those of WLR varieties through compensatory increases in grain-filling rate (6.7-7.3%) and 1000-grain weight (8.1-8.7%). These findings demonstrate that optimizing basal internode structure and enhancing culm tissue density can simultaneously improve lodging resistance and preserve yield potential, providing a practical framework for breeding lodging-resistant, high-yielding japonica varieties for DDSR systems in Northeast China.

Quantum correlations in Bell and prepare-and-measure experiments are central resources for probing nonclassicality and enabling device-based quantum information protocols. In the absence of shared public randomness (i.e., without run-to-run mixing), even qubit correlation sets are typically nonconvex, making standard convex characterizations inadequate. Here we derive qubit-specific constraints from uncertainty relations, yielding a state-independent consistency test for observed statistics in both prepare-and-measure and Bell scenarios. The test captures explicit nonconvex boundaries in representative correlation families and enables correlation-based device inference by constraining (and sometimes uniquely determining) unitary-invariant measurement parameters even away from extreme points. Moreover, incorporating the inferred qubit constraints as additional conditions in a moment-matrix relaxation strengthens separability tests and can certify entanglement even for Bell-local correlations within the independent-device model. These tools provide a practical route to characterize and leverage low-dimensional quantum devices, including certification, randomness generation, and entanglement verification.

Bone nonunion is a challenging clinical issue that hinders fracture healing. Warm acupuncture, as a traditional Chinese medicine therapy, has shown potential in promoting tissue repair, but its effect on bone nonunion healing and the underlying mechanism remain unclear. This study aims to investigate the effect of warm acupuncture on the healing of bone nonunion in rats and predict its potential mechanism. Sprague-dawley (SD) rats were randomly divided into a blank control group (BC group), a tibial bone nonunion model group (M group), and a warm acupuncture treatment group (WA group). BC group was not treated. Bone nonunion modeling was performed on M and WA groups. After modeling, M group was tied up. WA group was treated with warm acupuncture after restraint, once a day for 4 weeks. After treatment, collected blood samples, performed Digital Radiography (DR), Micro computed tomography (Micro-CT), Hematoxylin-Eosin staining (HE staining) and assess the bone morphogenetic protein 2 (BMP-2) protein expression by Western blot. After treatment, the phosphorus (P), calcium (Ca), and alkaline phosphatase (ALP) levels and the BMP-2 protein expression level were significantly higher in WA group than in BC group and M group. The difference between WA group and M group in terms of fracture scab measurement was statistically significant. The recovery of fracture structure in WA group was better than that in M group. WA group was better than M group in bending strength, bending stiffness, bending load, bending deflection, bending moment and deflection. Bone volume fraction, trabecular thickness, and number of trabeculae were higher in WA group than in M group. Warm acupuncture can enhance the serum Ca, ALP, and P concentrations and increase the expression of BMP-2 in rats, thus improving bone scab production, promoting the healing of fractures and increasing the biomechanical strength in rats.

Architected polymer composites use spatially organized phases to achieve targeted property combinations. Shape forming elements (SFEs) are modular coextrusion die inserts that impose internal architectures by reshaping multiple melt streams. This study evaluates three SFE designs (Jacks, I-Beam, and Barn Door) that position a liquid crystalline polymer (LCP) and an amorphous polyamide (APA) in distinct core-shell configurations. Polymer clay prototyping and ANSYS Polyflow simulations were used to screen flow behavior, followed by extrusion at two puller speeds and characterization via optical microscopy and tensile testing. Microscopy revealed that abrupt area transitions and viscosity contrast disrupt encapsulation and distort designed features. Regression analysis showed that LCP content governs stiffness and strength, while higher puller speed enhances reinforcement through molecular orientation. Cross sectional geometries were quantified using interfacial perimeter, moments of inertia, and polar dispersion ratios, and correlated to tensile performance. Increased interfacial length reduced modulus, strength, and ductility. Modulus improved with LCP orientation and confinement, strength increased when LCP was placed at vertical extremities, and elongation was maximized by horizontally distributing LCP within a thick APA shell. These results demonstrate that SFEs enable tunable tradeoffs between stiffness, strength, and ductility.

Plant-based diets are generally associated with a reduced risk of chronic diseases; however, the relationship between a vegan diet and genome integrity remains insufficiently characterized. In this cross-sectional study, we assessed primary DNA damage in peripheral blood cells of vegans and omnivores. A total of 62 apparently healthy adults were included: 31 vegans (median vegan diet duration 4.5 years) and 31 omnivores matched for sex and smoking status. DNA damage was assessed using the alkaline comet assay under standardized conditions and expressed as tail intensity (% tail DNA), tail length, tail moment, and total comet area. Tail intensity was significantly higher in vegans than in omnivores (B = 1.98; 95% CI 0.19 to 3.76; p = 0.031) after adjustment for age, physical activity, body mass index (BMI), and alcohol consumption. Within the vegan group, longer duration of adherence to a vegan diet was positively associated with tail intensity, independent of age (B = 0.23; 95% CI 0.03 to 0.43; p = 0.026). These findings suggest that adherence to a vegan diet and its duration may be associated with higher levels of primary DNA damage; however, these results should be interpreted with caution given the observational design and modest sample size.