Model Of Behavior Research Articles

Gaze-action coupling, gaze-gesture coupling, and exogenous attraction of gaze in dyadic interactions

AbstractIn human interactions, gaze may be used to acquire information for goal-directed actions, to acquire information related to the interacting partner’s actions, and in the context of multimodal communication. At present, there are no models of gaze behavior in the context of vision that adequately incorporate these three components. In this study, we aimed to uncover and quantify patterns of within-person gaze-action coupling, gaze-gesture and gaze-speech coupling, and coupling between one person’s gaze and another person’s manual actions, gestures, or speech (or exogenous attraction of gaze) during dyadic collaboration. We showed that in the context of a collaborative Lego Duplo-model copying task, within-person gaze-action coupling is strongest, followed by within-person gaze-gesture coupling, and coupling between gaze and another person’s actions. When trying to infer gaze location from one’s own manual actions, gestures, or speech or that of the other person, only one’s own manual actions were found to lead to better inference compared to a baseline model. The improvement in inferring gaze location was limited, contrary to what might be expected based on previous research. We suggest that inferring gaze location may be most effective for constrained tasks in which different manual actions follow in a quick sequence, while gaze-gesture and gaze-speech coupling may be stronger in unconstrained conversational settings or when the collaboration requires more negotiation. Our findings may serve as an empirical foundation for future theory and model development, and may further be relevant in the context of action/intention prediction for (social) robotics and effective human–robot interaction.

Beyond blueprints: unveiling sustainable housing ambitions – a TAM and EMGB fusion among paraprofessionals in urban construction landscape

PurposeThis study explores the factors influencing the investment intentions of potential home buyers among paraprofessionals in sustainable housing within urban construction. By combining the technology acceptance model (TAM) and the extended model of goal-directed behavior (EMGB), the research seeks to understand how technological perceptions, personal motivations and behavioral intentions intersect to shape these investment decisions.Design/methodology/approachA quantitative, cross-sectional and descriptive research design was employed in this study. The study surveyed 641 paraprofessionals as potential home buyers in various Indian cities using a multi-stage stratified sampling technique. It incorporated variables from the TAM and EMGB, modifying some aspects to include financial self-efficacy, hedonic motivation and anticipated regret, alongside adding location as a new factor to examine its impact. For data analysis, partial least squares structural equation modeling was utilized. The analysis focused on hypothesis testing to examine the relationships between the constructs of interest. Bootstrap t-values and effect sizes were used to assess these relationships’ significance and magnitude.FindingsThe study found that perceived usefulness and ease of use significantly enhance attitudes toward sustainable homes, while subjective norms have a minimal effect on such investments in India, emphasizing personal rather than societal influences. Financial self-efficacy, anticipated regret and hedonic motivation are key drivers, indicating that economic capacity and the pursuit of a satisfying lifestyle are crucial for investment intentions. Additionally, the importance of location is highlighted, with infrastructural aspects notably affecting sustainable housing appeal. These insights reveal unique dynamics in India’s sustainable housing sector, diverging from trends in developed countries.Originality/valueThe study lies in its unique fusion of the TAM and EMGB specifically tailored to the Indian urban construction context. It introduces financial self-efficacy, hedonic motivation and anticipated regret as novel variables within these frameworks, alongside emphasizing the significant role of location in sustainable housing decisions. This approach offers new insights into the psychological and socioeconomic factors driving sustainable housing investments in developing countries.

A rigorously simple quantitative model for free radical behavior in aerobic biological systems

Background Human life is based on oxygen respiration and an enzymatic, free radical-dependent water chemistry, whose billions of parallel reactions take place at pH ∼7.4 and a temperature of 37°C, in accordance with the laws of chemistry. The cellular metabolic processes occur over time periods covered by the half-lives of ROS (reactive oxygen species) for °OH to over 10 s for LOS (lipid oxygen species), indicating that mixtures of free radicals form the basic components for these processes. Summary and Key Messages The main source of radicals is the mitochondrial conversion of 1-5 % oxygen into "primary" ROS and "secondary" LOS. Every endogenous and exogenous radical generation, triggered by "natural background radiation", "natural environment" or "solar radiation" leads to qualitatively similar mixtures of "primary" ROS and "secondary" LOS or RNS (reactive nitrogen species). A Multilevel Antioxidant Regulation, Repair and Protection System (MARRPS) keeps these radical mixtures in a steady state. Depending on the total number of free radicals, different areas of radical action are defined. The Free Radical Ground State (FRGS) with "homeostasis" and "adaptive homeostasis", the Free Radical Threshold Value (FRTV) and Free Radical Pathological Conditions (FRPC). The quantitative ratio ROS > LOS comprehensively characterizes the "homeostasis" and "adaptive homeostasis" area of the FRGS. The total number of free radicals cannot be measured directly in the "homeostasis" area. "Adaptive homeostasis" is achieved when excess radicals are stable produced beyond "homeostasis" of the FRGS. The quantity that remains controllable in this range is a maximum of ∼3.58 10¹² radicals/mg, the value of the body constant FRTV. The sensitized MARRPS provides "semi-stable homeostatic" states characterized by dual stability with ROS > LOS and a stable total ROS/LOS and RNS count beyond the basal FRGS "homeostasis". If the total number of all radicals exceeds the FRTV, where LOS > ROS, this initiates uncontrolled radical chain reactions. The partial failure of the MARRPS in the FRPC area leads to pathological processes which are the starting point for a hundred different diseases. The design principle described by this simple model applies universally to all aerobic life.

Optimizing Delegation in Collaborative Human-AI Hybrid Teams

When humans and autonomous systems operate together as what we refer to as a hybrid team, we of course wish to ensure the team operates successfully and effectively. We refer to team members as agents. In our proposed framework, we address the case of hybrid teams in which, at any time, only one team member (the control agent) is authorized to act as control for the team. To determine the best selection of a control agent, we propose the addition of an AI manager (via Reinforcement Learning) which learns as an outside observer of the team. The manager learns a model of behavior linking observations of agent performance and the environment/world the team is operating in, and from these observations makes the most desirable selection of a control agent. From our review of current state of the art, we present a novel manager model for oversight of hybrid teams by our support for diverse agents and decision-maker operations across multiple time steps and decisions. In our model, we restrict the manager’s task by introducing a set of constraints. The manager constraints indicate acceptable team operation, so a violation occurs if the team enters a condition which is unacceptable and requires manager intervention. To ensure minimal added complexity or potential inefficiency for the team, the manager should attempt to minimize the number of times the team reaches a constraint violation and requires subsequent manager intervention. Therefore, our manager is optimizing its selection of authorized agents to boost overall team performance while minimizing the frequency of manager intervention. We demonstrate our manager’s performance in a simulated driving scenario representing the case of a hybrid team of agents composed of a human driver and autonomous driving system. We perform experiments for our driving scenario with interfering vehicles, indicating the need for collision avoidance and proper speed control. Our results indicate a positive impact on our manager, with some cases resulting in increased team performance up to \(\approx 187\%\) that of the best solo agent performance.

Wave propagation and soliton behavior in biomechanical tissues: A mathematical approach

This study presents a mathematical model for understanding wave propagation and soliton behavior in biomechanical tissues, explicitly focusing on the Achilles tendon. Utilizing the Korteweg-de Vries (KdV) equation, the research incorporates the Achilles tendons’ nonlinear elastic and viscoelastic properties to explore how mechanical waves propagate through this complex tissue. The tendon’s nonlinear elasticity leads to wave steepening, while its viscoelasticity introduces dispersive effects that counteract this steepening, resulting in the formation of solitons—stable, localized waves that maintain their shape as they propagate. Key findings from this study reveal that the formation and propagation of solitons are strongly influenced by the tendon’s mechanical properties. Numerical simulations show that stiffer tendons, characterized by a higher elasticity modulus, support faster soliton propagation, with wave speeds ranging from 18.9 m/s in damaged tendons to 28.6 m/s in stiffened tendons. Additionally, soliton amplitude increases with tissue stiffness, with the highest amplitude observed in stiffened tendons (5.1 mm) and the lowest in damaged tendons (3.2 mm). The study also demonstrates that energy dissipation due to the tendon’s viscoelasticity plays a critical role in soliton behavior. Damaged tendons exhibit the highest energy loss (18.6%), leading to shorter soliton propagation distances, while stiffer tendons retain more energy (96.1%) and allow solitons to travel further distances (up to 180 mm). Moreover, the balance between nonlinearity and dispersion is crucial for maintaining soliton stability. Excessive nonlinearity leads to unstable solitons, while higher levels of dispersion contribute to more stable waveforms.

Transient Ischemic Attack and Mild Stroke Participant Perspectives on Barriers and Enablers to Participation in Cardiovascular Rehabilitation

Background Exercise-based cardiac rehabilitation reduces the risk factors and recurrence of cardiovascular disease in patients with coronary heart disease. Despite similar risk factors, people with transient ischemic attack (TIA) and mild stroke do not attend cardiac rehabilitation. Cardiovascular rehabilitation (CVR), including TIA, stroke, and coronary heart disease, could be a solution for increasing participation. Perspectives from TIA and stroke survivors on barriers and enablers to participation in CVR are limited, and implementation strategies for CVR are currently unknown. Objectives We sought to determine (1) What were the perspectives of participants with TIA and mild stroke on barriers and enablers to uptake, engagement, and participation in CVR? and (2) What implementation strategies may influence people with TIA and mild stroke to participate in CVR? Methods In-depth semistructured interviews were conducted with CVR participants. Inductive thematic analysis was completed before deductively mapping themes to implementation frameworks, the Capability, Opportunity, Motivation, and Behavior Model and Theoretical Domains Framework. Results Twenty participants aged 73 ± 11 years were interviewed. Most were men, stroke survivors, tertiary educated, and retired. Six themes were generated to explain the barriers and enablers to CVR participation. Themes and subthemes mapped to 8 Theoretical Domains Framework domains and 5 Capability, Opportunity, Motivation, and Behavior constructs. Prioritizing participants' physical and social opportunities, reflective motivation, and physical and psychological capabilities may improve their participation in CVR. Conclusions Barriers and enablers to CVR participation were similar to those in the coronary heart disease population. Increasing access and availability of CVR for people with TIA and mild stroke should be prioritized for future implementation.

Design, analysis, and demonstration of the COAST guidewire robot with middle tube rotation for endovascular interventions.

Minimally invasive procedures for endovascular interventions involve manual navigation of a guidewire. Endovascular interventions encompassing highly tortuous vessels would benefit from guidewires which exhibit higher dexterity. This paper introduces a version of the COAST (COaxially Aligned STeerable) guidewire system capable of exhibiting higher dexterity. The system presented in this paper consists of three coaxially aligned tubes with a tendon to actuate the middle tube. Furthermore, it is possible to independently rotate the middle tube with respect to the outer tube. This variation enables the guidewire to achieve curvature in different planes while avoiding rotation of the entire structure. We also present the simulated stability of the guidewire with different outer tube geometries and experimentally validate the model. Experimental analysis and modeling of the kinematic behavior of the system is presented. A model to calculate the curvature vs. tendon stroke relationship for the optimal notch geometry is presented with an average RMSE of 0.16mm. A control strategy addressing the snapping instabilities to ensure reliable operation is discussed. A custom phantom vessel and an aortic arch phantom model were used to demonstrate the ability of the system to safely navigate through tortuous pathways without exhibiting these elastic instabilities.

WOMAN: THE CATALYST FOR SOCIAL EDUCATION AND LOCAL WISDOM OF SAKAI COMMUNITY IN RIAU

This research concerns with the effort of the Sakai tribe in maintaining their local wisdom and social characters from generation to the next generation eventhough there is minimum outside social interaction. This research involved 9 key informants spread across 5 villages in Bengkalis Regency. They explained their lives in the Sakai people's village, how they modeled and educated the generations and communities to build a safe and peaceful life. The research findings confirm that in their daily lives, women interact a lot with children and other fellow women around the house. The socially visible modeling of women's behavior becomes an imitative model by their community. Patience, thoughtfulness, saving, restraint, not talkative, not getting angry often, using the mantra of overcoming minor problems, including demonstrating how they should behave into a social education slowly builds generational character and teach them how to survive in even difficult situations. This research concludes that Sakai women with their diversity have a strong role as a catalyst for social education and local wisdom of the Sakai people

Reversal Potentials of Tween 20 in ABC Transporter-Mediated Multidrug-Resistant Cancer and Treatment-Resistant Depression Through Interacting with Both Drug-Binding and ATP-Binding Areas on MDR proteins

Drug efflux transporters, especially those belonging to the ATP-binding cassette (ABC) transporter superfamily, play a crucial role in various drug resistance issues, including multidrug resistance (MDR) in cancer and treatment-resistant depression (TRD) in individuals with major depressive disorder. Key transporters in this context include P-glycoprotein (P-gp), multidrug resistance protein 1 (MRP1), and breast cancer resistance protein (BCRP). This study aimed to investigate the modulatory effects of polyoxyethylene (20) sorbitan monolaurate (Tween 20) on these efflux transporters in vitro and to evaluate its potential for overcoming drug resistance in two models: an in vitro cancer MDR model and an in vivo TRD model. The findings indicated that 0.001% Tween 20 significantly inhibited the efflux actions of all three transporters. Additionally, 0.005% Tween 20 effectively reversed resistance to paclitaxel, vincristine, doxorubicin, and mitoxantrone in various cancer MDR cell lines. In the in vivo depression-like behavior model, 0.01% Tween 20 markedly enhanced the antidepressant and anxiolytic effects of fluoxetine. Given its strong inhibitory effects on P-gp, MRP1, and BCRP, along with its capacity to reverse drug resistance both in vitro and in vivo, Tween 20 is a compelling candidate for tackling transporter-mediated drug resistance.

How Metacognitions Contribute to Compulsive Online Shopping: An Exploratory Study.

Compulsive Online Shopping (COS) is considered a technological addiction, characterized by excessive engagement in online shopping behaviors that can cause economic, social, and emotional impairments in an individual's life. Among the theoretical models aimed at conceptualizing addictive behaviors, the metacognitive model has gained increased attention. However, no previous study has investigated the role of metacognitions in COS. The current study was aimed at clarifying the contribution of metacognitions about online shopping as potential mediating variables in the relationship between some well-established psychological correlates (i.e., boredom proneness, impulsivity, materialism, negative affect) and COS. A sample of 254 participants (mean age = 34.79 ± 11.45; Females = 84.3%) was recruited using convenience sampling. The hypothesized model produced a good fit to the data and accounted for 48% of COS variance. All the correlates (i.e., boredom proneness, impulsivity, materialism, and negative affect) were significantly and positively associated with Positive Metacognitions About Emotional And Cognitive Regulation, which in turn predicted COS. Boredom proneness and impulsivity were also positively associated with Negative Metacognitions About Uncontrollability And Cognitive Harm of online shopping, which in turn predicted COS. All the indirect effects were significant. The present findings add to the argument that the metacognitive model of addictive behaviors may applied to the understanding of COS and open the possibility of applying metacognitive techniques to the treatment of COS.

Long-Run Volatility Memory Dynamics and Inter-Market Linkages in GCC Equity Markets: Application of DCC-FIGARCH Models

Abstract The study investigates volatility persistence, long-term memory and time-varying conditional correlations among the stock markets of the Gulf Cooperation Council (GCC) countries. Daily equity index data between 2012 and 2024 were analyzed using univariate fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models to examine long-memory behavior and multivariate dynamic conditional correlation (DCC) models to assess conditional correlations between these markets. For each of the GCC equity markets, the analysis highlighted large degrees of long-memory and volatility persistence. Finally, the DCC model shows that strong and dynamic Intermarket links among the GCC, especially between KSA and UAE, exist and reflect significant volatility spillover from good economic ties. This study fills the gap in the literature by providing a comprehensive understanding of long-run volatility memory and inter-market associations in the GCC stock markets.

The Influence of Social Media Marketing and Customer Experience on Consumer Purchase Intentions

In this study the impact of social media marketing and customer experience was investigated on consumer purchase intentions, using Mixue Ice Cream & Tea as a case study. Data were collected through an online survey of 501 Nanjing, China respondents who had purchased products from Mixue Ice Cream & Tea. Structural equation modeling was used for hypothesis testing. The findings revealed that social media marketing and customer experience significantly and positively influenced purchase intentions, with trust and brand awareness as key mediators. The results underscore the importance of innovative social media strategies and high-quality customer experiences in fostering trust and enhancing brand awareness. Practical implications include recommendations for enhancing social media engagement, customer service, and transparent business practices. Future research should expand geographically, incorporate additional variables, and explore emerging technologies. This study contributes to the literature by offering a comprehensive model of consumer behavior and actionable insights for the ready-to-drink tea industry.

Modified REL-Based Piecewise Path Loss Modeling Approach for Shore-to-Ship Communication at 5.6 GHz

The need for reliable and uninterrupted communication systems in the marine environment has become critically important with increasing maritime activities, environmental monitoring, and the spread of autonomous systems. However, the complex structure of electromagnetic wave propagation in a sea environment limits the accuracy of the existing propagation models. Thus, the Modified Round Earth Loss (REL) model was first developed in this study to estimate the path loss more accurately in shore-to-ship communication. Subsequently, a piecewise modeling approach based on the principle of two-segment data modeling was proposed. In the Modified REL model, unlike the traditional REL model, the paths and gains of the direct and reflected waves were not considered equal in the calculations. Moreover, unlike in the classical approach, the receiver height was not taken as a fixed value; the estimated best receiver height value for each measurement was included in the calculations as a representation of the effect of roughness in the sea environment. Thus, the model is better adapted to various environmental conditions. In addition, the proposed piecewise model divides the propagation medium into two regions using a break point calculated by Fresnel zone theory. The Modified REL model was used for the first region and the log-distance model was used for the second region. This method allows for more accurate modeling of signal behaviors, especially at different distances. Experimental measurements and performance evaluations conducted using four different shore-to-ship communication scenarios show that the Modified REL model shows an average improvement of up to 3% in Root Mean Square Error (RMSE) values compared to the classical REL model. Additionally, the proposed piecewise model improves the fitting error of the Modified REL model, which models the data as a single whole, by an average of 22.25%. These findings emphasize the necessity of propagation models that are sensitive and adaptable to environmental changes for maritime communication.

The Convergence of Artificial Intelligence and Human Marketing: A Framework for Enhanced Customer Insights and Personalization

This article examines the transformative integration of artificial intelligence and human expertise in modern marketing analytics, focusing on customer insight generation and personalization strategies. We present a comprehensive framework for human-AI collaboration in marketing operations through a mixed-methods approach combining quantitative analysis of customer data from 127 retail organizations and qualitative interviews with 34 marketing professionals. The findings reveal that organizations implementing AI-enhanced customer analytics in conjunction with human-driven creative strategy achieved a 47% improvement in customer engagement metrics and a 31% increase in conversion rates compared to traditional approaches. The article demonstrates that while AI excels at real-time pattern recognition and predictive modeling of customer behavior, human marketers provide crucial emotional intelligence and contextual interpretation that significantly enhance the effectiveness of personalization efforts. The article introduces the Dual Intelligence Marketing Framework (DIMF), which outlines optimal integration points between AI capabilities and human expertise throughout the customer journey. The framework addresses critical challenges in implementation, including technical infrastructure requirements, talent adaptation, and ethical considerations. This article contributes to both theoretical understanding and practical application of AI in marketing, offering actionable insights for organizations seeking to leverage the complementary strengths of artificial and human intelligence in their marketing operations.

AI in Biomedical Imaging and Diagnostics

Advances in artificial intelligence (AI) and synthetic biology have profoundly influenced biomedical research, creating transformative opportunities in imaging, diagnostics, and therapeutic engineering. In biomedical imaging, AI-driven algorithms enhance precision and accuracy, enabling automated analysis of complex datasets, real-time imaging insights, and identification of disease biomarkers. Meanwhile, synthetic biology redefines cellular engineering, particularly in T-cell research, by enabling customized functionalities, such as precision-targeted antigen recognition and tunable immune responses. The integration of AI into T-cell engineering amplifies these capabilities, facilitating the design and optimization of synthetic circuits, predictive modeling of cellular behaviors, and dynamic monitoring of therapeutic outcomes. This interdisciplinary approach is revolutionizing diagnostics and immunotherapy by streamlining the identification of disease-specific markers, improving diagnostic accuracy, and enabling real-time modulation of T-cell functionality within the tumor microenvironment. By combining AI-powered insights with synthetic biology's ability to engineer living systems, this research aims to address critical challenges in disease treatment, including tumor heterogeneity and immune evasion. This work explores the synergistic application of AI and synthetic biology in biomedical imaging and T-cell engineering, highlighting state-of-the-art technologies, their therapeutic potential, and the future landscape of personalized medicine.

Dynamic pricing and demand forecasting: Integrating time-series analysis, regression models, machine learning, and competitive analysis

Abstract. Dynamic pricing is a critical strategy for businesses seeking to optimize revenue and stay competitive in fluctuating markets. This paper explores the integration of various demand forecasting techniques, including time-series analysis, regression models, and machine learning algorithms, with competitive analysis methodologies to enhance dynamic pricing strategies. Time-series analysis focuses on decomposing data into trend, seasonality, and random fluctuations, using ARIMA models for accurate demand prediction. Regression models delve into the complexities of variable interactions, extending beyond linear relationships to include advanced techniques like Ridge, Lasso, and Elastic Net regression. Machine learning algorithms, such as decision trees, random forests, gradient boosting, and neural networks, revolutionize demand forecasting by uncovering complex patterns in large datasets. Competitive analysis incorporates market scanning, price elasticity estimation, and competitor behavior modeling to inform dynamic pricing decisions. Optimization algorithms, including linear programming, genetic algorithms, and simulated annealing, are employed to refine pricing strategies, while revenue management techniques like yield management and overbooking ensure maximum revenue from perishable goods and services. This comprehensive approach enables businesses to dynamically adjust prices in real-time, maintaining competitiveness and maximizing profitability in an ever-evolving market landscape.

Modeling and Compensation of Stiffness-Dependent Hysteresis Coupling Behavior for Parallel Pneumatic Artificial Muscle-Driven Soft Manipulator

Modeling and Compensation of Stiffness-Dependent Hysteresis Coupling Behavior for Parallel Pneumatic Artificial Muscle-Driven Soft Manipulator

Threats on SNS Use in Crisis With Conditional Analysis With Risk Mitigation and Fear of Missing Out

In response to the evolving e-commerce landscape, the study investigates changing patterns of social networking site (SNS) usage during a pandemic. Grounded in the interaction of the person-affect-cognition-execution model for addictive behaviors and the appraisal and coping theory, it explores the depth and extent of SNS usage. Specifically, the study examines the moderated mediating effect of perceived stress (PS) and fear of missing out, with risk mitigation behavior as a moderator. With data from a nationwide survey in Korea with 760 responses, results show that risk mitigation behavior moderates the relationship between infection threat and PS, while PS and fear of missing out mediate the effect of infection threat on SNS usage. The moderated mediation of PS was significant, shedding light on the complex psychological mechanisms that drive SNS behavior during global crises. The findings deepen understanding of how stress and coping strategies shape SNS engagement in response to crisis.

Approaches for Behavior Intensity Estimation in Groups of Heterogeneous Individuals: Precision and Applicability for Data with Uncertainty

In socially oriented areas, there arises the problem of assessing the cumulative characteristics of behavior, such as intensity, that are realized in groups of individuals. All individuals vary in their behavior and the available data is limited and may be associated with significant uncertainty: only a few episodes may be known and only a few individualsi the group may be observed. Mathematical models of behavior are used for estimation of key characteristics of the behavior. One of them is based on the gamma–Poisson point process, that reflects the heterogeneity of individuals in a form of a mixing distribution. This general model allows to formulate several methods of frequency estimation: the Cox regression, estimation of the copula parameter, and a posteriori inference in Bayesian belief networks. The aim of the paper is to assess their determine the precision of these methods based on the Kantorovich–Rubinstein distance between estimates and the true distribution of the desired parameter. The analysis of assumptions of those methods allows to formulate rules, that allow to chose the appropriate method in various sutuations of data availability. It has been shown that the copula-based approach provides the most accurate estimates and has the mild assumptions for the number of observed objects, but it cannot take into account external factors that may influence the behavior. Among methods that can take into account process covariants, estimates based on a posteriori inference in hybrid Bayesian belief networks have the highest precision. The paper considers a method for quantification of a hybrid BBNs with the approximation of mixtures of truncated exponents, that is data-demanding at the stage of calculating a priori estimates. However, it is noted that there are other approaches to setting hybrid BSDs in which a priori estimates can be set completely expertly.

Human Dynamics Research in GIScience: challenges and opportunities

The Symposium on Human Dynamics Research, first organized at the 2015 AAG Meeting in Chicago, celebrated its 10th anniversary at the 2024 AAG Meeting in Honolulu, marking a decade of transformative advancements in the field. Over the past decade, the focus of human dynamics research has shifted from traditional spatial-temporal analyses to sophisticated modeling of human behavior in a hybrid physical-virtual world. This evolving field now examines the intricate interdependencies between physical and digital environments, addressing critical issues such as urban resilience, public health, social equity, and community sustainability. The symposium emphasized the growing importance of interdisciplinary collaboration, advanced data-driven analytical platforms, and innovative theoretical frameworks to better understand human interactions across these spaces. As human dynamics continue to shape global urban systems, these advancements are pivotal for future research and real-world problem-solving, offering novel insights into the interconnectedness of mobility, technology, and societal well-being in a rapidly changing world.