Dynamic Interaction Research Articles

Study on the effects of stroke ratio, velocity ratio, and duty cycle on the diffusion characteristics of pulsed buoyant jets in crossflow

Pulsed buoyancy jet diffusion dilution is characterized by low cost and high efficiency, so it is widely used in wastewater discharge dilution projects. This study, based on three-dimensional numerical simulations, investigates the effects of stroke ratio (1.71 ≤ L/D ≤ 8.57), velocity ratio (2.4 ≤ R ≤ 12), and duty cycle (0.17 ≤ η ≤ 0.83) on the diffusion of low-frequency pulsed buoyant triple jets. A large number of simulations were conducted for each parameter, and the concentration field, vorticity field, and flow field at different sections during the initial stage of the jet were analyzed. The results reveal the evolution patterns of the vortex structure in pulsed triple buoyant jets and their diffusion characteristics. The vortex structure at the initial stage of the jet exhibits continuous vortex ring characteristics, which later evolve into a vortex street and counter-rotating vortex pair (CVP) combination structure. Meanwhile, the buoyancy effect enhances the development and diffusion of the vortex structure in both the X and Z directions. As the stroke ratio decreases, the wastewater concentration distribution becomes more concentrated, and the dynamic interaction between the jets intensifies. The increase in duty cycle enhances the interaction between the jets, significantly improving the horizontal diffusion capacity, but the vertical penetration depth is less than that of continuous jets. The Strouhal number (St) is composed of the stroke ratio, velocity ratio, and duty cycle. These three parameters control the motion state of pulsed jets in the crossflow. The established relationships and equations arising from this research offer vital reference values for predicting sewage leakage and wastewater diffusion in engineering applications, contributing substantively to the theoretical framework governing multiple buoyant jets.

Mesoscale dynamics and its interaction with coastal upwelling in the northern Gulf of Guinea

Mesoscale dynamics is essential to understanding the physical and biological processes of the coastal ocean regions due to its ability to modulate water properties. However, on the shelf, interactions between eddies, coastal currents, and topography involve complex processes whose observation, understanding, and accurate simulation still pose a major challenge. The purpose of our work is to quantify the mesoscale eddies in the northern Gulf of Guinea, off West Africa (10°W–10°E, 2°N–7°N), and their dynamical interaction with the near-surface ocean particularly in the coastal upwelling that occurs in summer between 2°W and 2°E. We used a regional NEMO model simulation at 1/36° resolution over the 2007–2017 period with daily outputs. A total of 38 cyclonic and 35 anticyclonic eddy trajectories were detected over the 2007–2017 period in July–August–September (JAS), with a mean radius along their trajectories of 95 km and 125 km, respectively. The mean lifetime for cyclones and anticyclones is approximately 1 month with an associated sea-level amplitude between 1 and 2 cm. We then focused on the JAS upwelling period of the year 2016 and found a 73 km radius cyclonic eddy east of Cape Three Points (Ghana) with a lifetime of 1 month which interacted with the coastal upwelling. Indeed, the quasi-stationary eddy dwelled within the coastal upwelling region from mid-July to mid-August 2016. A Lagrangian study shows that the eddy waters come from the coastal upwelling, then mix with warmer offshore waters, and later are transported eastward by the Guinea Current. Using a heat budget analysis, we show that this eddy–coastal upwelling interaction has an impact on sea surface temperature (SST) with a double effect: i) the eddy expands offshore the cold and salty waters (23°C and 35.6) of the coastal upwelling from 14 to 26 July; and ii) from 27 July until its dissipation, the eddy weakens this upwelling by an easterly inflow of warm offshore waters. This study highlights how the eddy–upwelling interaction can modulate the coastal upwelling in the northern Gulf of Guinea.

Visualizing Dynamic Weak Interaction Networks of Fluorine Atoms within Proteins via NMR.

The utilization of fluorine probes in protein research has advanced our understanding of the nature of macromolecules. The diverse activities of proteins are governed by intricate weak interaction networks, yet the experimental detection presents a challenge. Herein, we have developed an NMR analytical method based on quantum coherent operations to characterize the weak chemical bonds of fluorine atoms within proteins that are bound to metal fluorides or labeled with fluorinated amino acids. Our approach offers a fast-screening method for identifying a weak interaction network without requiring crystallization.

Exploring the Impact of Battery Charge Reduction Rate and the Placement of Chargers on AGV Operation

This paper presents a simulation model to study the effect of the battery charging rate of Automated Guided Vehicles (AGVs) on the overall output of a toy car production environment. This paper uses Modular Petri Nets for modeling and the General Petri Net Simulator (GPenSIM) for model implementation on MATLAB and simulation. The main focus of this paper is to analyze the operational efficiency of AGVs under varying conditions, such as the impact of battery charge reduction rates and the strategic placement of Charging Stations within the production line. By employing Modular Petri Nets implemented with GPenSIM, this paper presents a detailed model that captures the dynamics (movements and interactions) of AGVs in a simulated manufacturing environment. The model is also extensible, as newer functionalities can be added to it as Petri Modules. This paper specifically focuses on two critical operational parameters: (a) the number of AGVs and their battery charge reduction rate; (b) the number of Charging Stations. In summary, the goal, aim, and novelty of this paper is to provide a simpler yet effective model to practitioners so that they can study and experiment without needing advanced mathematical skills.

Decoding Fictional Journalism: A Diary-Interview Approach to Uncovering Flemish Audience Interpretations of Journalism in Fiction

ABSTRACT Employing a diary-interview method, this study delved into how Flemish audiences make sense of fictional journalism series originating from various production contexts. Initially, it corroborated the prevalence of an overarching journalism discourse largely influenced by American fiction but extending to non-American narratives. However, participants’ engagement with this discourse did not exclude critical evaluation. They notably differentiated between American and European journalism fiction, viewing the latter as less sensationalized and more nuanced. Moreover, the interpretation of the series varied significantly based on participants’ media profiles. Consequently, the series tended to reinforce pre-existing opinions rather than catalyze change. Despite this tendency, participants did note a shift in their perspectives upon exposure to behind-the-scenes footage, prompting a more critical stance toward news coverage while also fostering a deeper appreciation for the challenges inherent in the profession. This study shed light on culturally diverse journalistic discourses in Flanders and their role in shaping Flemish journalistic discourse. It highlights a dynamic interaction between the narratives presented in fiction and the media profiles of their audiences. In a journalistic environment marked by declining trust and accusations of bias, locally produced fiction emerges as a vital force in shaping both existing and new journalistic discourses.

Stochastic modeling of plant-insect interaction dynamics with MEMS-based monitoring and noise effects

The dynamics of plant-insect interactions play a crucial role in the ecosystem, influenced by complex molecular signaling pathways. This study extends existing deterministic models of plant-insect systems by incorporating stochastic elements and molecular interactions, particularly focusing on the roles of Botrytis Induced Kinase-1 (BIK1) and Phyto Alexin Deficient-4 (PAD4) proteins. The model evaluates the effects of constant inhibition, pulsed inhibition, and adaptive feedback control on plant biomass (y1), insect herbivore density (y2), PAD4 levels (y3), and BIK1 levels (y4). Additionally, we examine the impact of different noise types, including deterministic, Gaussian, and Lévy noise, on system variability and stability. Results indicate that our stochastic model is superior as it shows a significant reduction in BIK1 levels, particularly under higher noise intensities, which enhances PAD4 activity and improves plant defense mechanisms. Moreover, moderate noise intensity (σ=0.05) provides an optimal balance, sustaining PAD4 levels while effectively controlling insect herbivore populations. We also integrate MEMS-based feedback mechanisms, which dynamically adjust plant biomass and molecular signaling, further stabilizing the system’s response to environmental variability.

Disrupted Sand Flows, Artisanal Fishers, and the Making of Coastal Protection in Southern India

<p>Flowing parallel to the sea, sand is subject to erosive, accretive, and extractive processes and is intertwined with the socio-ecological dynamics at the land–sea interface. Human interventions, climate change, and societal responses to it are constantly reshaping the morphology of coastal areas and thus disrupting sand flows, for example, through the construction of harbours or groins to prevent erosion. In this article, we ask how disrupted sand flows shape the interaction and social dynamics between different coastal actors in the making of coastal protection. Empirically, we ground our research in the Pondicherry region of southern India, characterised by a sandy morphology and numerous fishing communities. Building on the literature on “geosocialities,” we argue that engaging with the materialities of ocean sand and the social implications of sediment loss for artisanal fishers is crucial to reducing maladaptation. Following sand as a non-human actor unravels the social entanglements with ocean sand that underpin the implementation of protective measures and that shape access to sandy beaches for artisanal fishers. By exploring these contestations, we show how the reclamation of sand through groins is embedded in unequal power relations over shrinking beaches. While migration to other sandy beaches becomes a necessary means of adaptation, this leads to local conflicts over coastal space. We conclude by highlighting the need to understand coastal adaptation as a geophysical and socially intertwined process, in which ocean sand must be critically considered for future adaptation strategies.</p>

Towards resilience in Industry 5.0: application of system dynamics in matrix-structured manufacturing system

In the face of increasing global disruptions, resilient manufacturing systems are critical to ensuring operational stability, a concept central to the Industry 5.0 vision. The Matrix-Structured Manufacturing System (MMS) is a key example of such resilience, designed to maintain functionality and recover quickly under major disruptions. This paper introduces a novel System Dynamics (SD)-based modelling and simulation framework to quantitively assess the performance indicators and resilience of MMS. By simulating key disruption scenarios, including partial equipment failures and order fluctuations with resource constraints, this study evaluates the system’s response and recovery capabilities using a Comprehensive Resilience Index (CRI). Unlike conventional approaches, the proposed SD model captures the dynamic interactions across various production configurations and simulates the operational logic governing them, which can also be applied to other production systems. The simulation results demonstrate the potential of SD to support decision-making by providing insights into the effectiveness of reconfiguration strategies and resource management. This work aims at offering robust methods for quantitative resilience assessment and strategic planning towards the practical implementation of resilient manufacturing in Industry 5.0 envisions.

Molecular insights into the interaction between a disordered protein and a folded RNA

Intrinsically disordered protein regions (IDRs) are well established as contributors to intermolecular interactions and the formation of biomolecular condensates. In particular, RNA-binding proteins (RBPs) often harbor IDRs in addition to folded RNA-binding domains that contribute to RBP function. To understand the dynamic interactions of an IDR-RNA complex, we characterized the RNA-binding features of a small (68 residues), positively charged IDR-containing protein, Small ERDK-Rich Factor (SERF). At high concentrations, SERF and RNA undergo charge-driven associative phase separation to form a protein- and RNA-rich dense phase. A key advantage of this model system is that this threshold for demixing is sufficiently high that we could use solution-state biophysical methods to interrogate the stoichiometric complexes of SERF with RNA in the one-phase regime. Herein, we describe our comprehensive characterization of SERF alone and in complex with a small fragment of the HIV-1 Trans-Activation Response (TAR) RNA with complementary biophysical methods and molecular simulations. We find that this binding event is not accompanied by the acquisition of structure by either molecule; however, we see evidence for a modest global compaction of the SERF ensemble when bound to RNA. This behavior likely reflects attenuated charge repulsion within SERF via binding to the polyanionic RNA and provides a rationale for the higher-order assembly of SERF in the context of RNA. We envision that the SERF-RNA system will lower the barrier to accessing the details that support IDR-RNA interactions and likewise deepen our understanding of the role of IDR-RNA contacts in complex formation and liquid-liquid phase separation.

Dynamic Analysis of Soil-Structure Interaction in Earthquake-Prone Areas

This study used a thorough experimental method to examine the dynamic interaction between soil and structures in earthquake-prone locations. The study challenge concentrated on how different soil types and configurations influence the diversity of structural reactions under seismic loading conditions. The research utilized a mixed methods approach, which involved quantitatively analyzing soil parameters and assessing structure dynamics. The methods employed included the creation of scaled replicas depicting common architectural structures situated on various soil types, including sandy, clayey, and mixed compositions. We used high-precision sensors to record ground motion characteristics such as Acceleration, velocity, and Displacement. The data was then evaluated using statistical methods such as ANOVA and regression analysis. The results revealed substantial differences in the structural reaction based on the type of soil and the parameters of the structure. Structures built on sandy soils saw greater peak accelerations (up to 0.170 g) but smaller displacements. On the other hand, structures on clayey soils had moderate accelerations (up to 0.140 g) but had bigger inter-story drifts. The varied soil layers, ranging from 1.500 Hz to 1.780 Hz, influenced the natural frequencies of the buildings. The damping ratios ranged from 5.000% to 7.800%, indicating that structural damping effectively reduces seismic forces. The results emphasized the critical importance of the interaction between soil and structures in seismic design and the necessity for customized engineering solutions based on the individual soil conditions at the site. Suggested measures include improving methods for soil characterization, optimizing structural dynamics using cutting-edge dampening technologies, and upgrading seismic design codes to enhance the ability of structures to withstand earthquakes in places prone to seismic activity.

Molecular Modeling of Vasodilatory Activity: Unveiling Novel Candidates Through Density Functional Theory, QSAR, and Molecular Dynamics

Cardiovascular diseases (CVD) pose a significant global health challenge, requiring innovative therapeutic strategies. Vasodilators, which are central to vasodilation and blood pressure reduction, play a crucial role in cardiovascular treatment. This study integrates quantitative structure– (QSAR) modeling and molecular dynamics (MD) simulations to predict the biological activity and interactions of vasodilatory compounds with the aim to repurpose drugs already known and estimateing their potential use as vasodilators. By exploring molecular descriptors, such as electronegativity, softness, and highest occupied molecular orbital (HOMO) energy, this study identifies key structural features influencing vasodilatory effects, as it seems molecules with the same mechanism of actions present similar frontier orbitals pattern. The QSAR model was built using fifty-four Food Drugs Administration-approved (FDA-approved) compounds used in cardiovascular treatment and their activities in rat thoracic aortic rings; several molecular descriptors, such as electronic, thermodynamics, and topographic were used. The best QSAR model was validated through robust training and test dataset split, demonstrating high predictive accuracy in drug design. The validated model was applied on the FDA dataset and molecules in the application domain with high predicted activity were retrieved and filtered. Thirty molecules with the best-predicted pKI50 were further analyzed employing molecular orbital frontiers and classified as angiotensin-I or β1-adrenergic inhibitors; then, the best scoring values obtained from molecular docking were used to perform a molecular dynamics simulation, providing insight into the dynamic interactions between vasodilatory compounds and their targets, elucidating the strength and stability of these interactions over time. According to the binding energies results, this study identifies novel vasodilatory candidates where Dasabuvir and Sertindole seem to have potent and selective activity, offering promising avenues for the development of next-generation cardiovascular therapies. Finally, this research bridges computational modelling with experimental validation, providing valuable insight for the design of optimized vasodilatory agents to address critical unmet needs in cardiovascular medicine.

Hybrid game model for electricity trading and pricing among multiple microgrids and consumers based on demand-side complex networks

Local energy markets (LEMs) are pivotal for enhancing renewable energy consumption and facilitating green electricity by linking multiple microgrids (MMGs) and consumers. Existing studies often overlook the evolution of consumer purchasing strategies influenced by social spatial structures and social learning dynamics. This paper addresses this gap by introducing a hybrid game model for LEMs electricity trading and dynamic pricing, integrating demand-side complex network structures. This model encompasses dynamic interactions between MMGs and electricity and carbon markets, employing non-cooperative games among MMGs, complex network evolutionary game among consumers, and Stackelberg games between MMGs and consumers. It promotes efficient transactions and adaptive pricing through distributed algorithms optimizing equilibrium solutions. Key findings include: (1) Compared to Power-to-Grid and Peer-to-Peer models, our model increased MMGs' revenues by 44.10 % and 10.60 %, reduced consumers’ costs by 8.76 % and 1.15 %, and cut carbon emissions by 93.32 % and 77.30 %, significantly boosting economic and environmental benefits. (2) Complex network analysis underscores the importance of social learning in adjusting power consumption strategies and pricing mechanisms. (3) Multidimensional consumer decision-making enhances renewable energy valuation, fosters price competition among MMGs, and accelerates low-carbon transitions in LEMs, providing crucial theoretical support for LEM structure design and policy to enhance renewable energy use.

Dynamic Characteristic Analysis of Multi-Virtual Synchronous Generator Systems Considering Line Impedance in Multi-Node Microgrid

With the increasing integration of distributed energy resources into modern power systems, virtual synchronous generators (VSGs) have been a promising approach to imitate the inertial response of synchronous generators, thereby enhancing microgrid stability in a dynamic state. When many VSGs are integrated into microgrids, the dynamic characteristics of the system become increasingly complex. Current studies typically assume that different VSGs are connected to a common coupling point, focusing on analyzing the interaction characteristics, which may overlook the widely distributed line impedances in microgrids with distance between different facilities. This may lead to incomplete understanding of the interaction dynamics when VSGs are distributed over long feeder lines. Therefore, this paper proposes and investigates a multi-node, multi-VSG model incorporating line impedances among different nodes, establishing transfer function models for multi-node load disturbances and the frequency responses of individual VSGs. The study explores the dynamic response characteristics of VSGs under varying parameter influences and proposes principles for designing VSG port impedance and inertia parameters to optimize system dynamic frequency characteristics. The findings, validated through simulations in PSCAD v46, provide insights for enhancing the flexibility and reliability of grids incorporating VSGs.

A safety posture field framework for mobile manipulators based on human–robot interaction trend and platform-arm coupling motion

Mobile manipulators are increasingly deployed in industrial settings, such as material handling and workpiece loading, where they must safely interact with humans while efficiently completing tasks. Existing motion planning methods for mobile manipulators often struggle to ensure both safety and efficiency in dynamic human-robot interaction environments. This paper proposes a Safety Posture Field framework that addresses these limitations by firstly predicting human motion trends using the improved Long Short-Term Memory neural network and applying these predictions to potential field calculations for both the mobile platform and the robotic arm. During different stages of human-robot interaction, the mobile manipulator places varying emphasis on safety and efficiency while in motion. Additionally, when the robotic arm executes operations, a platform-arm coupling motion strategy is introduced when the potential field detects risks of singularity or local optima, preventing the robotic arm from becoming unstable or failing to reach the target pose in time. This strategy enhances the system's flexibility and operational stability. Comparative experiments in simulation and real-world settings confirm the ability of the framework to maintain high safety standards while improving task efficiency, making it suitable for industrial Human-Robot Interaction applications.

The resilience dynamics of energy ETF accessibility and stock market sentiment in China during the post-pandemic era

This paper investigates the dynamic interaction between energy ETF accessibility and stock market sentiment through the lens of resilience during the post COVID-19 pandemic period. We find that they tend to positively co-move in the long run. Adverse exogenous shocks on one of them not only have a persistent impact on itself but also cause non-diminish damage to the other. Compared to stock market sentiment resilience, energy ETF accessibility resilience is more constrained by the long-run co-movement. The co-movement is not significantly reshaped by possible structural time-breaks or Markovian regime shifts. Further exploration based on wavelet coherence analysis provides evidence for a stable and even gradually strengthening positive coherence between the two types of resilience in a volatile environment. Accordingly, our findings can offer valuable insights for financial regulators to achieve their designed objectives in the secondary markets of energy and stocks.

Significance of the Double Bond in the Acyl Chain of Cardiolipin Revealed by the Partial Unfolding Dynamics of Cytochrome c Using Femtosecond Transient Absorption Spectroscopy.

Cytochrome c (Cyt c) released from the mitochondrion acts as a trigger for the onset of apoptosis in which a double bond of cardiolipin (CL) is oxidized upon interaction with Cyt c. To understand the interaction dynamics of Cyt c with the double bond of CL, CLs having acyl chains with a systematic increase in the number of double bonds, 0 (18:0 CL), 1 (18:1 CL), and 2 (18,2 CL), were complexed with Cyt c, and their excited-state dynamics were studied using femtosecond time-resolved pump-probe spectroscopy. Steady-state and femtosecond transient absorption spectra revealed a systematic increase in the partial unfolding of Cyt c with an increase in double bonds in CL, as observed by the enhanced fluorescence intensity and lifetime of tryptophan due to variations in the resonance energy transfer and extended global conformational relaxation time constants. These studies reflect the significance of occurrence of global conformational changes of Cyt c by structural modification near the double bond of CL in the Cyt c-CL complex, which could be prerequisites for the apoptosis.

Interpretation of bacterial composition patterns and community assembly processes in the rhizosphere soil of tea trees in karst areas

Research background and purposeSoil microorganisms that are closely related to plants are important factors affecting plant health. Therefore, elucidating the abundant and rare bacterial species in soil associated with plant diseases is crucial for understanding ecological processes, maintaining the stability of microecological environments, and formulating microbial strategies that are consistent with modern agricultural development.ResultsTea leaf blight leads to an increase in bacterial diversity in the rhizosphere. Random processes dominate the assembly of abundant and rare taxa, while abundant taxa are also influenced by deterministic processes. In the co-occurrence network, the increase in bacterial community diversity mediated by tea cloud leaf blight enhances the stability of the network. Meanwhile, the proportion of positive correlation between rare taxa is relatively high, and the relationship between rare taxa and intermediate taxa is closer. This highly diverse bacteria community maintained the structure and stability of the community to a certain extent.ConclusionThe rare taxa in the rhizosphere and the rhizosphere bacterial community mediated by tea leaf blight have high diversity, which is of great significance for maintaining the stability of the rhizosphere bacterial ecological network. In the future, we will further explore the dynamic changes and interaction patterns of the species in the rhizosphere soil affected by tea tree diseases, and their ecological functions and importance in areas of habitat fragmentation. Overall, there are many microbial resources in the rhizosphere microbiota under the influence of plant diseases that can be used for agricultural practice. The results of this study will enrich the insights into ecodynamics of bacteria in karst areas, especially in karst tea gardens.

Viable Intertwined Supply Network: Modelling and Dynamic Analysis Using Artificial Neural Networks

The viability of intertwined supply networks (ISNs) has recently been studied as a critical topic in operations management. Modelling the viability of ISNs is considered a promising tool to meet the demands of extraordinary events, such as the Russo-Ukrainian War and the COVID-19 pandemic. To enhance the viability of ISNs, the structures of ISN must be modelled, and the behavioral dynamics of interactions between firms in a changing environment should be analyzed. In this study, a trophic chain-based dynamic formulation of ISN viability is presented, and a solution methodology for dynamic analysis of the ISN viability model is designed. The concept of artificial neural networks (ANNs) in ISN analysis is introduced to predict and analyze future behavioral strategies in buyer-supplier relations. The dynamic model of ISN is represented by a system of nonlinear differential equations and described in terms of three dynamic values: suppliers Xτ, focal firms Yτ, and market demand Zτ. The stochastic numerical simulations are performed for the dynamics of ISN model by employing ANNs with a scaled conjugate gradient neural network (SCGNN) in a more advanced and efficient manner. Two numerical cases are investigated to evaluate the performance of the proposed approach. The validation, correctness, and reliability of the proposed stochastic SCGNN technique are analyzed by selecting 78% of the data for training, 12% for validation, and 10% for testing. The correctness of the scheme is authenticated through the overlapping of the proposed and state-of-the-art results. Moreover, the statistical analysis is presented graphically in terms of mean square error, state transitions, function fitness, error histograms. The regression coefficient values are calculated as 1 for each scenario presents the perfect model. Finally, a comparison of numerical results, which shows the overlapping is examined and the absolute error is performed between 10-05 to 10-07. The small mean square error performances enhance the correctness of the scheme. These results indicate that the dynamical model can effectively analyze the ISN structures and help researchers and practitioners ensure the survival of supply chains during extraordinary events.

Multi-output discrete grey model tailored for electricity consumption forecast

To address the limitation of many conventional grey forecasting methods that prioritize temporal dynamics analysis while often overlooking the essential spatial characteristics, we developed a multi-output discrete grey model tailored for forecasting electricity consumption in China's Yangtze River Delta, incorporating spatial effects. Specifically, we design a dynamic spatial interaction matrix to precisely capture the spatial correlations among neighboring areas, and integrate multiple sets of fixed-effect parameters to adeptly address spatial heterogeneity. Additionally, we implement regularization technique to prevent overfitting and utilize the Bayesian Optimization algorithm for hyper-parameter adjustment, considerably enhancing the model's stability of parameter estimation and its resilience to noise. Finally, a comprehensive case study, benchmarked against seven other models, highlights our model's outstanding multi-step predictive accuracy and robustness against input data uncertainty, exceeding the performance of existing methods.

The sperm hook as a functional adaptation for migration and self-organized behavior.

In most murine species, spermatozoa exhibit a falciform apical hook at the head end. The function of the sperm hook is not yet clearly understood. In this study, we investigate the role of the sperm hook in the migration of spermatozoa through the female reproductive tract in Mus musculus (C57BL/6), using a deep tissue imaging custom-built two-photon microscope. Through live reproductive tract imaging, we found evidence indicating that the sperm hook aids in the attachment of spermatozoa to the epithelium and facilitates interactions between spermatozoa and the epithelium during migration in the uterus and oviduct. We also observed synchronized sperm beating, which resulted from the spontaneous unidirectional rearrangement of spermatozoa in the uterus. Based on live imaging of spermatozoa-epithelium interaction dynamics, we propose that the sperm hook plays a crucial role in successful migration through the female reproductive tract by providing anchor-like mechanical support and facilitating interactions between spermatozoa and the female reproductive tract in the house mouse.