Search Results (2,399)

The prototypical complex-forming reactions of OH + CO and OH + SO are critical elementary processes in atmospheric chemistry and combustion systems. The vibrational mode-specific quasi-classical trajectory (QCT) investigations for these two reactions were performed on full-dimensional globally accurate potential energy surfaces (PESs). Integral cross-sections (ICS), differential cross-sections (DCS), and product energy distributions for different vibrational excitation modes were calculated and analyzed. Although both exothermic oxygen-transfer reactions share similar topological features in their PES profiles, their energy landscapes exhibit pronounced differences, giving rise to entirely distinct dynamic and kinetic behaviors. This work will advance our understanding of how PES topology, collision energy, and mode-specific vibrational excitation jointly govern reactivity and energy dissipation in radical oxidation processes involving carbon- and sulfur-containing oxides. Full article

Nonsequential double ionization is a fundamental process in ultrafast strong-field physics, containing rich information about electron correlation dynamics. In few-cycle laser fields, the carrier-envelope phase becomes a key parameter for controlling electron behavior. However, this process involves nonlinear mechanisms such as multi-electron stochastic dynamics and complex Coulomb interactions, posing significant challenges to traditional analytical theories. To address this, this work develops a numerical experimental approach based on a Monte Carlo stochastic algorithm, transforming the quantum problem into a computable stochastic sampling task. Through statistical sampling and final-state analysis of tens of millions of quantum trajectories, the central regulatory role of the carrier-envelope phase is systematically revealed. The computational results show that this phase can not only independently regulate the yields of double ionization and frustrated double ionization but also control the branching ratio between them, with a peak-to-peak modulation amplitude of approximately 20%. Additionally, it enables fine-tuning of the electron momentum correlation distribution. The physical mechanisms underlying these regulatory effects are clearly elucidated through analysis of the quantum trajectories. This work not only clarifies the critical role of the carrier-envelope phase in few-cycle intense-field double ionization but also demonstrates the powerful capability of the Monte Carlo stochastic algorithm in revealing the intrinsic stochasticity of strong-field physics and achieving precise physical control, providing an example for the deep integration of intense-field physics and computational science. Full article

Background: Subclinical disordered eating behaviors (DEBs) are common among young women and are thought to result from complex interactions between psychological, dietary, and genetic factors. While chronic stress and unhealthy dietary habits have been implicated in the development of DEBs, the independent and interactive contributions of perceived stress, added sugar intake, and genetic susceptibility remain insufficiently understood. This pilot study investigated these associations using a two-stage case–control design. Methods: A total of 100 female university students (18–27 years) completed the Dutch Eating Behavior Questionnaire (DEBQ) during the first stage of the study. Based on DEBQ scores, 20 participants with the highest risk of DEBs and 20 with the lowest risk were selected for the second stage, forming case and control groups. Perceived stress was assessed using the Perceived Stress Scale (PSS-14), and added sugar intake was estimated using a semi-quantitative food frequency questionnaire (FFQ). Participants were also genotyped for four candidate polymorphisms (5-HTTLPR, rs6295, rs6265, and rs1800497). Group differences, correlation analyses, binary logistic regression models adjusted for BMI, and gene–environment interaction analyses were performed. Results: Perceived stress emerged as the strongest and most consistent predictor of belonging to the upper DEBQ quintile, with each one-point increase on the PSS-14 associated with a 44% increase in the odds of high-risk status after adjustment for BMI (OR = 1.44; 95% CI: 1.120–1.853; p = 0.004). Added sugar consumption did not withstand correction for multiple comparisons (p = 0.030, Bonferroni-adjusted α = 0.025). No significant main effects or gene–environment interactions were detected for any of the investigated polymorphisms or the cumulative genetic risk score. Conclusions: These preliminary findings highlight perceived stress as the important modifiable risk factor for subclinical disordered eating behaviors in young women, suggesting that stress-reduction and emotional regulation interventions may be more effective than dietary approaches in this population. However, the lack of significant associations for the genetic variants studied should be interpreted with caution given the limited sample size and statistical power. Replication in larger, longitudinal cohorts is warranted to confirm these findings and explore developmental trajectories. Full article

This review systematically investigates the developmental trajectory of Gas Metal Arc Welding (GMAW), beginning with its fundamental forms: Metal Inert Gas (MIG) and Metal Active Gas (MAG) welding. In the analysis of high-efficiency GMAW processes, a comparative evaluation was conducted across five major categories of efficiency enhancement strategies: single-wire high-efficiency welding, dual-wire high-efficiency welding, hybrid high-efficiency welding, Narrow-Gap Gas Metal Arc Welding, and Deep Penetration Gas Metal Arc Welding, accompanied by a detailed elaboration on auxiliary high-efficiency measures. This article systematically reviews the arc behavior, droplet transfer behavior, and the influence of residual stress distribution on weld formation and mechanical properties in high-efficiency gas-shielded welding processes utilizing various advanced welding methods. The mechanisms behind typical defects, such as pores, undercutting, and cracks, are elucidated, and corresponding mitigation strategies are summarized. The existing high-efficiency melting electrode gas-shielded welding technology has been widely applied in fields such as automobile manufacturing, shipbuilding, and pipeline welding, resulting in significant economic benefits. In response to the evolving demands of intelligent manufacturing, future research should prioritize the development of real-time monitoring, adaptive control systems, and cost-effective automation solutions for high-efficiency GMAW processes. By constructing high-precision process models, the advancement of efficient welding technology can progress towards greater intelligence and reliability, thereby providing more competitive solutions for industrial manufacturing. Full article

Green consumption is a crucial pathway for promoting ecological environmental improvement and fostering sustainable economic and social development. Although policies have been continuously implemented and residents’ environmental awareness is gradually increasing, there remains a significant gap in the transformation from cognition to behavior. Promoting green consumption is a systematic project that requires deep coordination among three key actors: the government, enterprises, and residents. However, existing studies have mostly focused on interactions between the government and enterprises, paying insufficient attention to the long-term strategic interactions among all three parties when residents are incorporated into the game system. To address this gap, this study develops a tripartite evolutionary game model that includes the government, enterprises, and residents to analyze the intrinsic driving forces behind the development of green consumption, thereby extending the research perspective to multi-agent dynamic strategic interaction. Through mathematical derivation of the replicator dynamic equations and the Jacobian matrix for each party, this study finds that the system has a unique evolutionarily stable equilibrium point, namely a tripartite synergistic state characterized by active government intervention, green production by enterprises, and green consumption by residents. Sensitivity analysis examines the impact of changes in key parameters on the evolutionary trajectory of the system, providing parameter-level evidence for differentiated policy design. Simulation results indicate that efforts should be directed toward building a collaborative governance system based on government guidance, enterprise responsibility, and resident participation, so as to promote the transformation of green consumption governance from single-dimensional management to pluralistic co-governance. Full article

Trajectory tracking remains one of the main challenges in mobile robotics, particularly when robots operate under real-world disturbances and modeling uncertainties. Model Predictive Control (MPC) has become one of the most effective solutions for this problem because of its ability to optimize future control actions while explicitly handling system constraints. However, despite the variety of predictive models reported in the literature, there is still limited experimental evidence regarding how predictive-model selection influences the closed-loop behavior of mobile robots. This work presents an experimental comparison between two MPC implementations: a Kinematic MPC and a multiple-input multiple-output MPC (MIMO-MPC) based on an experimentally identified state-space model. Both controllers were implemented on the same four-wheel differential-drive mobile robot running Robot Operating System 2 (ROS 2) and were configured with identical prediction horizons, weighting matrices, constraints, reference trajectories, and disturbance sequences, enabling a direct experimental comparison of the influence of predictive-model selection on closed-loop performance. Performance was assessed through trajectory-tracking accuracy, disturbance recovery, and control smoothness metrics under both nominal and externally perturbed operating conditions. The experimental evaluation was performed on real hardware at 50 Hz using a closed-loop trajectory of approximately 20 m. Under nominal conditions, the kinematic MPC achieved a lower lateral root mean square error (RMSE) (0.1031 m versus 0.1187 m) and maintained 93.23% of the trajectory within the $\pm 0.20$ m tolerance band. Under external perturbations, however, the MIMO-MPC reduced the heading RMSE from 0.7617 rad to 0.6503 rad, shortened the recovery time after the two largest disturbances by up to 35.6%, and decreased the angular-velocity rate root mean square (RateRMS) and jerk root mean square (JerkRMS) by approximately 24.7% and 24.8%, respectively. These results demonstrate that predictive-model selection has a significant influence on the transient behavior of MPC. While a conventional kinematic model provides excellent nominal tracking performance, an experimentally identified MIMO predictive model offers faster recovery and smoother control under disturbed conditions. The proposed experimental methodology provides practical evidence that can assist the selection of predictive models for MPC-based mobile robots according to the expected operating conditions. Full article

Although previous research has confirmed an association between bullying victimization and executive function (EF) impairments, the long-term trajectory of these deficits remains controversial, specifically whether they persist as permanent scars into adulthood or are subject to a degree of neuroplastic recovery through brain maturation and subsequent learning experiences. Moreover, prior studies have largely focused on relatively stable, cross-task deficits, with limited examination of whether EF performance is dynamically modulated by victimization-related contextual cues. Accordingly, the present study examined the differences in EF performance among individuals with a history of bullying victimization under both general processing conditions and context-induced conditions through two experiments. Experiment 1a and Experiment 2a assessed general inhibitory control (IC) and working memory (WM) using the classic Stroop and N-back paradigms, respectively. Experiment 1b and Experiment 2b introduced victimization-related contextual cues into these paradigms to examine context-dependent alterations in cognitive control. The results showed that, under general conditions, the victimization group exhibited significantly longer reaction times (RTs) than the control group in both the Stroop and N-back tasks, indicating less efficient general cognitive processing. Further analyses revealed distinct patterns under context-specific conditions: in the adapted Stroop paradigm, the victimization group showed relatively faster RTs in victimization-related contexts, suggesting context-dependent differences in interference control; in contrast, in the WM task, RTs were prolonged in victimization-related contexts, suggesting greater contextual interference during WM processing. In conclusion, bullying victimization is associated not only with general differences in EF performance among college students but also with context-dependent alterations in cognitive processing. These findings highlight the context-dependent effects of bullying-related cues on EF and provide behavioral evidence that bullying victimization is associated with alterations in cognitive control processes, with potential implications for developing targeted intervention strategies. Full article

In some scenarios, motorsport simulators have been used to enable the controlled acquisition of dense telemetry with high similarity to real-world data, reducing cost when assessing driving performance. However, although popular, performance analyses traditionally treat human control as deterministic and overlook the stochasticity of driving behavior. In fact, existing coaching methods which improve driving performance have to deal with two distinct outcomes: a driver who restructures his race control strategy and a driver who merely repeats it faster. This article presents a Behavior-First framework for interpretable driver behavior analysis that separates them. We characterize control signals with two information-theoretic descriptors: Jensen–Shannon divergence, which quantifies distributional distance from a proficiency-matched reference and whose square root satisfies the triangle inequality, and Permutation Entropy to measure the ordinal complexity of the input sequence. A deterministic, physics-informed heuristic layer then identifies kinematic performance gaps and emits structured tokens that a Large Language Model translates into natural-language coaching narratives. We evaluated the framework in an exploratory case study. The three beginners who received generated coaching messages and the single uncoached comparison participant exhibited different lap-time and information-theoretic trajectories. Because the groups were small and non-randomized, these observations describe within-driver evolution and do not estimate a causal coaching effect. Full article

In this work, we develop a projection neural network based on a second-order dynamical model (SO-PDM) for solving inverse variational inequality problems (IVIPs) in Hilbert spaces. The proposed framework incorporates inertial and damping components, resulting in improved convergence behavior while ensuring feasibility through a projection operator. Under the Lipschitz continuity assumption on the operator, the proposed SO-PDM admits a unique global trajectory. Under the additional strong monotonicity assumption and suitable parameter conditions, convergence to the unique solution of the IVIP is established. A discrete-time formulation is derived via a finite-difference scheme, leading to a projection-based inertial algorithm with relaxation. Under suitable parameter conditions, the algorithm is shown to converge linearly to the unique solution of the IVIP, and under an additional parameter condition, the global asymptotic stability of the continuous-time SO-PDM is established via Lyapunov analysis. Furthermore, a numerical comparison in a higher-dimensional setting shows that the proposed algorithm converges faster and attains higher accuracy than the existing first-order projection method. Numerical experiments further confirm the effectiveness and stability of the proposed SO-PDM, including its application to sparse signal recovery in compressed sensing. Full article

Soft continuum robots exhibit highly nonlinear and configuration-dependent dynamics, making accurate trajectory tracking challenging under model uncertainty and external disturbances. This paper presents an adaptive Koopman-based model predictive control (MPC) framework for a tendon-driven soft continuum robot and evaluates its performance through comprehensive closed-loop simulations. A lifted linear Koopman model is identified from experimentally collected robot data and incorporated into an MPC formulation to provide computationally efficient prediction while capturing dominant nonlinear behavior. To compensate for plant–model mismatch and time-varying uncertainties, an online Recursive Least Squares (RLS) adaptation mechanism is integrated into the Koopman–MPC framework, enabling continuous model refinement during closed-loop operation without repeated offline retraining. The proposed controller is evaluated through comprehensive closed-loop simulations on circular, triangular, helical, and figure-eight trajectories under stochastic disturbances and structured parametric bias conditions using a Koopman model identified from experimentally collected robot data. Results demonstrate consistent improvements in tracking performance compared with fixed Koopman MPC while maintaining real-time computational feasibility. Under structured parametric bias, the proposed controller reduces the mean tracking error from 9.30 mm to 1.36 mm during circular trajectory tracking and achieves sub-millimeter accuracy in several operating conditions. These findings highlight the potential of online Koopman model adaptation for predictive control of soft continuum robots operating under uncertainty. Full article

Parent–child interaction frequency may vary during the transition into kindergarten and across activity content, but whether persistent or repeated low-frequency interaction is associated with preschool children’s emotional-behavioral and neurodevelopmental outcomes remains unclear. We examined whether three-wave overall and content-domain trajectories, together with cumulative low-level exposure at the overall, content-domain, and activity levels, were associated with final-follow-up outcomes. Among 526 children enrolled at baseline, 419 completed all three waves. Low exposure was defined as a score below the wave-specific 25th percentile, and adjusted logistic regression models estimated associations. Compared with the persistently normal trajectory, the persistently low overall trajectory was associated with higher odds of abnormal total emotional and behavioral difficulties (aOR = 3.46, 95% CI 1.01–11.86); each additional low-exposure wave was also associated with higher odds (aOR = 1.54, 95% CI 1.02–2.33), with similar findings for conduct problems. Nominal content-domain associations were concentrated in story-reading and environmental interaction trajectories, while nominal activity-level associations involved reading/picture-book viewing, learning about nature/animals/plants, and physical activity. Neurodevelopmental associations were fewer and less stable. By jointly characterizing temporal pattern, repeated low exposure, and activity content, this study illustrates the value of repeated and content-specific assessment alongside a single overall interaction measure. These dimensions may inform assessment in parenting support and early-childhood services, but intervention evidence is required before causal or activity-specific recommendations can be made. Full article

Background/Objectives: Triple-negative breast cancer (TNBC) exhibits substantial metabolic heterogeneity and plasticity, contributing to variable therapeutic responses. We investigated whether optical redox imaging (ORI) could characterize metabolic phenotypes, monitor responses to metabolic perturbation, and relate these responses to functional outcomes in TNBC cells. Methods: Four TNBC cell lines were treated with the lactate dehydrogenase A inhibitor FX11 or the glutaminase inhibitor CB-839, alone or in combination with paclitaxel. Intensity-based label-free ORI was performed and followed by imaging of fluorescent probes to assess mitochondrial membrane potential (MMP), re-active oxygen species (ROS), and cell number in the same dishes. Seahorse assays were used to evaluate mitochondrial respiration and glycolytic flux. Results: TNBC cell lines exhibited distinct basal redox phenotypes and differential responses to acute glycolytic and glutaminolytic perturbation. HCC1806 cells showed the strongest acute ORI responses to both FX11 and CB-839. Acute FX11 treatment induced a rapid reductive shift accompanied by ROS accumulation and loss of MMP, whereas CB-839 produced a more modest early reductive response without detectable ROS accumulation or MMP loss. Prolonged treatment revealed distinct temporal redox trajectories in HCC1806 cells: FX11-treated cells evolved from an acute reductive response toward a more oxidized state, while CB-839-treated cells transitioned from an early reductive shift to a sustained oxidized redox state accompanied by marked reductions in OCR and ECAR. Functionally, in two representative models (HCC1806 and MDA-MB-231), CB-839 reduced cell numbers and enhanced the anti-proliferative effect of paclitaxel, whereas FX11 had no significant effect on cell number despite inducing pronounced acute redox perturbations. Conclusions: Integrating ORI with metabolic flux assays and imaging-based functional measurements enables characterization of multiple dimensions of metabolic behavior, including basal phenotype, pathway-specific responsiveness, temporal redox responses, and treatment-associated outcomes. These findings support intensity-based wide-field ORI as a practical and accessible tool for probing metabolic heterogeneity and characterizing context-dependent metabolic responses in TNBC cells. Full article

The social identity of migrant children is not merely a demographic label but is actively constructed and constrained by institutional structures. In China, a rapidly growing migrant population faces stringent institutional barriers, namely cross-regional enrollment policies with restrictive eligibility conditions, which exclude migrant children from local high school education. This study investigates how these structural factors shape the educational decisions and, consequently, the social identity formation of migrant children. Using data from the China Migrants Dynamic Survey and a cohort-based difference-in-differences (DID) approach, we employ an innovative strategy to identify students’ high school enrollment locations by leveraging information on migration timing and birth cohorts. Our findings reveal that strict institutional barriers significantly reduce the likelihood of high school attendance in destination areas, forcing families into a critical trade-off between children’s educational opportunities and family co-residence, which serves as a key site where migrant identity is negotiated. Residence requirements and school-type restrictions emerge as the most binding constraints. Importantly, these policies reinforce the role of household economic capital in accessing education while only partially mitigating disparities linked to cultural capital, suggesting a stratified reproduction of migrant social identities. Mechanism analysis indicates that early co-migration of children serves as a key behavioral channel, illustrating how structural barriers reshape family strategies. Ultimately, this study demonstrates that the educational decisions of migrant families are strongly constrained by institutional arrangements, which in turn shape the salience, meaning, and developmental trajectory of their migrant social identity. Reducing access thresholds and providing more flexible pathways are essential for fostering more equitable identity outcomes. Full article

Predicting target intent in complex, dynamic multi-agent environments remains a formidable challenge due to incomplete perception and the highly dynamic nature of multi-target interactions. Conventional approaches—such as D-S evidence theory, expert systems, and Recurrent Neural Networks (RNNs)—are often constrained by data incompleteness and rigid behavioral assumptions, limiting their adaptability to dynamic high-value target identification and multi-target situational awareness on the ground. To address these challenges, a novel framework termed Threat Field–Gated Recurrent Unit (TF-GRU) is proposed. The TF-GRU framework integrates threat field modeling with a dynamic repair mechanism to enhance intent prediction under partial perception. Specifically, threat field modeling associates target attributes with intentions through the construction of static and dynamic threat fields, effectively capturing the temporal and semantic relationships among multiple targets. A particle filtering and dynamic time warping fusion strategy (PF-DTW) is employed to repair data gaps via short-term filtering and long-term trajectory matching, further refined by a neighborhood-angle constraint for accurate multi-target state estimation. In addition, trajectory and threat field features are processed using a Mish activation function and a threat-adaptive gating mechanism, which dynamically regulate information flow within the recurrent unit to model behavioral evolution. Experimental evaluations demonstrate that TF-GRU significantly enhances intent prediction accuracy under incomplete data conditions, thereby improving comprehensive situational awareness and supporting high-confidence decision-making in dynamic multi-target scenarios. Full article

Adverse childhood experiences (ACEs) affect a third of all children in the United States. However, less is understood about how specific aspects of ACEs, such as household dysfunction, influence the behavioral development of children as they enter adolescence and whether collective efficacy can buffer this relationship. This study used longitudinal data from the Adolescent Brain Cognitive Development (ABCD) study (n = 11,868, baseline M_age = 9.9, SD_age = 0.6, 52% male; 52% White, 20% Hispanic, 15% Black, 2% Asian, and 11% Other race/ethnicity). Multiple regression analyses suggested that baseline household dysfunction positively predicted externalizing behaviors two and three years later, and that these associations were attenuated by high levels of collective efficacy at the two-year follow-up. These findings suggest the joint roles of household- and neighborhood-level contexts in shaping adolescent behavioral health and indicate that collective efficacy can serve as a promotive and protective resilience factor in the trajectory of externalizing behaviors. Full article