Dynamic Mechanism Research Articles

Double adaptive event-triggered strategy for leader-following group consensus of heterogeneous multi-agent systems in cooperative–competitive networks

This article investigates adaptive event-triggered control for leader-following group consensus of hybrid heterogeneous multi-agent systems (HMASs) composed of single- and double-integrator agents under cooperative–competitive relationships. By using fully distributed control protocol and adaptive protocol, the leader-following group consensus can be achieved without global information. Additionally, a novel adaptive double dynamic event-triggered mechanism (DDETM) is proposed, in which two independent adaptive event-triggered conditions are devised to maintain events of communication and updates of controllers. Furthermore, adaptive parameters and internal dynamic variables are employed to reduce the number of triggers and exclude Zeno phenomenon. By utilizing Lyapunov stability theory, some sufficient conditions are acquired for the leader-following group consensus of the HMASs. Finally, simulation examples are given to prove the effectiveness of the proposed method.

A day-ahead wind speed correction method: Enhancing wind speed forecasting accuracy using a strategy combining dynamic feature weighting with multi-source information and dynamic matching with improved similarity function

Forecasting error of day-ahead wind speed (WS) seriously affects wind power integration and power system security and stability. In this regard, this paper fully considers the spatiotemporal correlation of wind farms (WFs) in different geographical locations, and proposes a day-ahead WS combined correction method that integrates multi-source station dynamic information weighting. Different from the previous WS correction methods, this paper fully considers the dynamic correlation of WS between the WFs, introduces an improved weighted similarity function to screen and dynamically weight the information of WFs with dynamic correlation, and introduces the dynamic weighting feature into the WS correction process. A combined decomposition mechanism is proposed, which combines sequential variational mode decomposition (SVMD) and feature mode decomposition (FMD) models to extract the most relevant trend components and non-stationary components of forecasted and measured WS. A combined correction model is introduced, and a combined architecture of Non-stationary Transformer combined with bidirectional long short-term memory network (Ns-Transformer-BILSTM) is used to correct the stationary WS component. A dynamic matching mechanism of fluctuation components considering improved similarity is proposed for the correction of non-stationary components. The proposed method is applied to several regional WFs in China. The experimental results show that the average correction of NRMSE, NMAE and R can reach 2.4 % ∼ 3.7 %, 2.0 % ∼ 3.0 % and 3.3 % ∼ 9.7 %, respectively. The NRMSE and NMAE corresponding to the corrected WS of certain individual WFs can be reduced by 10 % and 9 %, respectively, and R can be increased by 33 %.

Effects of Surface Roughness Obtained by Milling on Interface Bonding Quality for 316H during Metal Additive Forging

The metallurgical bonding quality of bonded joints is affected by the substrate surface condition in metal additive forging process, and the surface roughness serves as a critical indicator for the surface condition. Nevertheless, the effect of surface roughness obtained by milling on interface bonding quality(IBQ) remains unclear, resulting in the lack of effective evaluation criteria for surface roughness in the milling process. This study prepared samples with various surface roughness through milling, and introduced three parameters, Ra, Rc, and Rsm, to quantify surface roughness. The interfacial morphology, elemental distribution, and mechanical properties were used to characterize the IBQ, and the influence of surface roughness on the IBQ was finally revealed. The results show that a reduction in surface roughness obtained by milling leads to an improvement in interfacial bonding, more uniform elemental distribution, and enhanced mechanical properties, all of which were related to the size of the initial voids and the dynamic recrystallization mechanism of the bonding interface. And the interface bonding process with various surface roughness involves grain boundary bulging and continuous dynamic recrystallization mechanisms. Finally, the Ra 1.2 μm was recommended as the evaluation criteria in milling process, and a high-feed face milling cutter equipped with wiper edge was suggested for machining to achieve high-quality and high-efficiency cutting of the substrate. This study provide a theoretical basis and practical guidance for the efficient acquisition of substrate surfaces suitable for interface bonding in metal additive forging process.

Dynamic impact of green finance on renewable energy development: Based on scale, structure, and efficiency perspectives

Utilizing China's provincial panel data from 2003 to 2021, this paper employs a dynamic panel quantile regression model and mediating test model to investigate the dynamic evolution characteristics, type heterogeneity, and mechanism of green finance (GF) driving renewable energy development (RED) in China, considering the scale, structure, and efficiency of GF. The findings of this study are as follows. While the scale, structure, and efficiency of GF all contribute to the promotion of RED, the impact of green financial structure is particularly significant. As renewable energy continues to develop, the impact of green financial scale and green financial structure gradually diminishes, while the effect of green financial efficiency increases. In comparison to hydropower and solar power, GF has the strongest promotion effect on wind power, and the marginal effect of green financial scale and green financial structure on wind power follows a U-shaped trajectory. The driving of GF on RED is mainly through direct effect, supplemented by indirect effect. In the mechanism by which GF impacts RED, the channels associated with green attributes are relatively smooth. Based on these research conclusions, this paper offers policy recommendations aimed at enhancing RED through GF from perspectives encompassing scale, structure, and efficiency.

Intelligent optimization of horizontal wellbore trajectory based on reinforcement learning

Ultra-long horizontal wells are crucial for improving the production and developmental benefits of shale oil and gas wells. Currently, advanced downhole semi-closed-loop steering drilling technology depends on a dual communication mechanism between the surface and downhole, as well as the empirical decisions of human experts. However, it is difficult to acutely control the trajectory of ultra-long horizontal open hole drilling in a reservoir owing to geological uncertainties related to shale oil and gas, the uncertainty of the drilling tool's building capacity, and the lag of measurement information while drilling. This report proposes an adaptive target detection and design method for ultra-long horizontal well trajectories based on reinforcement learning. The developed approach enables dynamic identification of reservoir sequences according to logging-while-drilling data and prediction of horizontal targets in real-time, with a recognition accuracy of 90.1%. Additionally, measurement-while-drilling data are used to accurately characterize the depth, inclination, and azimuth of the target-entering process. The dynamic interaction mechanism between the bit and the target environment is simulated by defining the bit action, reward function, and an update mechanism. The drilling engineering conditions restrict the interactions, such that the bit automatically decides the direction in which to drill the targets, demonstrating 100% accuracy for the wellbore trajectory toward the target. The experimental application results indicate that the developed method can be applied to determine the dynamic target area of a horizontal well in real time and make intelligent downhole decisions regarding ultra-long horizontal section borehole trajectory adjustments while drilling and measuring. The drilling rate of high-quality reservoirs is therefore significantly improved. The results discussed herein provide insights to support further developments of downhole full-closed-loop intelligent autonomous decision-making borehole trajectory control.

Dynamic formation of gradient structure by microparticle impact — A crystal plasticity material point method study

Metallic materials with unique microstructure can achieve desirable strength-ductility synergy. However, effectively fabricating and precisely controlling microstructure distribution in metals remain challenging. Microparticle impact, the key process of cold spray technique, can lead to a gradient structure in the particle, which may also serve as a promising additive manufacturing technology. To investigate the dynamic formation mechanism of heterogeneous microstructure and its significant influencing factors, the crystal plasticity material point method (CPMPM) is developed, especially for microstructure formation under a high strain rate and large deformation. Our work provides a quantitative analysis of evolution of structural gradient during impact. It is found that decreasing grain size can afford a larger structural gradient and there is negligible influence on the compression ratio of particles. It suggests that microstructure distribution can be tailored by optimizing the impact process without influencing vertical deformation of particles.

Integrating Population-Based Biobanks: Catalyst for Advances in Precision Health

Precision health extends beyond the scope of precision medicine and involves a broader range of activities, including the prediction, prevention, treatment, and management of diseases. Tailored to specific populations, precision health offers personalized treatment and preventive measures considering genetics, lifestyle behaviors, social determinants of health, and environmental factors. Precision medicine focuses on the personalized treatment of diseases, whereas precision health aims to promote health and prevent diseases using tools such as big data and advanced analytics to predict health risks and prevent diseases at the population level. Biobanks play a crucial role in achieving precision health because they provide well-characterized biological samples and related data for disease prediction, diagnosis, and treatment. Challenges in integrating different biobanks include data format consistency, privacy concerns, and legal constraints. Standardized methodologies and digitalization can mitigate these challenges. The integration of biobanks can facilitate comprehensive analyses across multiple datasets to achieve various research goals. This study proposes strategies to address these challenges, including the development of a dynamic consent mechanism for population-based biobanks using digitalization and blockchain technology. This study recommends the following: 1) integrating population-based biobanks, 2) introducing dynamic consent tools for human biobanks, and 3) using large human biobanks with dynamic consent for research on diverse diseases. These recommendations can increase the utility of biobanks in realizing precision health. A case study implemented at Taoyuan Tiansheng Hospital demonstrated the effectiveness of these recommendations for achieving precision health and enhancing the value of biobanks. Through a comprehensive examination of precision health and biobanks, this study provides valuable insights for researchers, healthcare professionals, and policymakers in the precision healthcare sector.

Dynamic response and damage mechanisms of 2D plain weave hybrid composites under the out-of-plane punch shear loading

A modified theoretical method for the punch shear test is derived by considering the influence of stress wave reflections caused by cross section discontinuities in the incident and transmitted bars. The dynamic punch shear behavior of 2D plain weave carbon/aramid hybrid composites was researched. The failure strength and strain are sensitive to strain rate. The stress-strain curves were obtained by the modified method. Significant shear deformation was observed under a single loading. An effective punch shear mesoscopic simulation model that accounts for the strain rate effect of the component materials was developed using the multi-scale analysis method. The model reveals the damage characteristics in the component materials. Damage modes are affected by hybridization and strain rates. The high elongation at the fracture of aramid fibers at high strain rates plays a crucial role in absorbing impact energy.

Dynamic response mechanism of layered coatings under impacts: Insights from the perspective of stress wave

Precision machining operations often lead to the failure of protective coatings on cutting tools due to common issues such as cracking, delamination, and peeling from cyclic impacts. While material selection and structural design are crucial for enhancing impact resistance, they primarily focus on static performance with limited consideration from the dynamic sights. This paper presents a novel dynamic design method for coatings, viewed through the lens of stress waves. We investigate the propagation behavior of stress waves in TaN/TiN and CrN/TiN coatings with layered structures. Our findings indicate that the attenuation of stress waves is dominated by the physical properties on both sides of the interface and the stride length. For interfaces with similar physical properties, the attenuation of stress waves is insensitive to the stride length, while for interfaces with different physical properties, the attenuation is regulated by the ratio of single-layer thickness to the full width at half maximum of the stress wave. These insights offer a strategy for extending the life of coatings and improving process safety under dynamic shocks.

Analysis of grain structure, precipitation and hardness heterogeneities, supported by a thermal model, for an aluminium alloy 7075 deposited by solid-state multi-layer friction surfacing

Thermomechanical cycles during multi-layer friction surfacing (MLFS) cause microstructural and mechanical heterogeneities in the deposited high-strength Al alloy, 7075. The thermal profile and heat accumulation were investigated in this study using a multilayer numerical thermal model of the MLFS process; additionally, these variables were linked to experimentally observed microstructural heterogeneities. Compared with the feedstock, grain sizes decreased by 55–80%. The mean grain size at the bottom and top areas of a given layer was finer than that in the middle of the layer because of the enhanced recrystallisation, which resulted from the friction and shear deformation experienced by the deposited material. The differences in the thermal cycle and plastic strain rate of the bottom and top areas along the layers resulted in a gradual increase in the grain size at the bottom of each layer and a reduction in the grain size at the top of each layer. The grain growth and continuous dynamic recrystallisation mechanisms are governed by the temperature and strain rate, those mechanisms determine the intra- and inter- layer grain sizes. The accumulated heat, owing to subsequent experimental deposition, resulted in excessive growth of the precipitates in the bottom layers. The strengthening of the solid-solution and Guinier-Preston zones significantly increased the microhardness of the top layer. Post-deposition T6 heat treatments confirmed the restoration of a uniform distribution of microhardness.

The Impact of Task Difficulty on Neural Modulation Throughout A Visuomotor Multi-day Practice Training

The effectiveness of rehabilitation is contingent upon the motor recovery process which typically involves long-term motor skill re-acquisition. Given that the learning process can be modulated by task difficulty, elucidating the underlying neural mechanism is essential for optimizing rehabilitation prescription to suit different patient conditions. This study aimed to investigate the impact of task difficulty on cortical response during force-control training via electroencephalography (EEG). An 8-day visuomotor force-tracking training experiment was conducted. Healthy right-handed participants (N=33) were recruited and randomly assigned to 3 groups, and each group was tasked with a different level of difficulty. The task difficulty was manipulated by variation in force-production complexity and execution sequence assignments, with real-time visual feedback provided to participants for self-output adjustment. Behavioral performance was quantitatively assessed using a pre-defined score metric related to performance accuracy. The EEG signals were collected, and corresponding event-related desynchronization (ERD) and relative functional connectivity (FC) during the task execution were analyzed within the alpha- (8-13Hz) and beta- (15-30Hz) bands. A post-training experiment was also performed to evaluate the near-transfer capability of learning. Results showed all the behavioral performances improved during practice, while higher task difficulty level was affiliated with better post-training near-transfer ability. The dynamic neural response to training could be mediated by changes in difficulty level, where increased task complexity corresponded with the heightened activities in the beta-band priorly within the right dorsolateral prefrontal area. Additionally, stronger alpha-band functional connectivity was observed to be predominantly associated with the left motor area (LMA) during challenging tasks, and the intensification in connectivity persisted selectively post-training which appeared to be acritical factor for skill transfer performance improvement. These findings illustrated the dynamic neural mechanism through which task difficulty affects behavioral performance during long-term motor training with accurate force-control purpose. The selectively strengthened functional connectivity may contribute to facilitating new task execution after training interventions. Therefore, beneficial neural modulation can be expected to be feasible by well-designed task difficulty strategies for effective motor rehabilitation.

Dynamic behavior of metal droplets impacting on porous surfaces

During the operation of solid rocket motors, the behavior of condensed particles impacting the wall will have a remarkable influence on the structure and performance of the engine. Especially when the aircraft is under overload flight conditions, the condensed particles will form a local high concentration particle flow under the action of inertia force, continuously scouring the surface of the insulation layer, seriously affecting the thermal protection structure and the work safety of the engine. Therefore, it is an essential issue to master the behavior mode of the condensate particle impinging the wall and clarify its dynamic characteristics and evolutionary mechanism. In this paper, the dynamic behavior of aluminum droplets impacting on the porous surface is experimentally investigated by preparing the porous wall, the influence mechanism of the porous structure on the spreading process of aluminum droplets is clarified, and the effects of the droplet's initial parameters as well as surface environment are analyzed. Combined with the fluid of volume method, the flow process of droplets on the porous surface is simulated. With the variation of the dimensionless parameters M and N, the main behavior patterns of the droplets obtained so far are rebound, adhesion, partial rebound, partial adhesion, and porous seepage. The presence of pore structure enhances the hydrophobicity of the wall and makes the droplets more easily broken during spreading. When the droplet initial energy is certain and the wall structure conditions change, there is a strong competitive relationship between its spreading and penetration. When the droplet initial energy is increased, its spreading and penetration strengths are significantly increased. The research results can provide a reference for the erosion process of condensed-phase droplets impinging on the char layer and provide theoretical basis and data support for the design and optimization of SRMs.

Selective-Hop Graph Neural Networks for semi-supervised node classification

Message-passing Graph Neural Networks (GNNs) are remarkably successful in graph representation learning. Most of them propagate and aggregate information within fixed-size receptive-fields (RFs) for all nodes. In this paper, our empirical studies discover that different-sized RFs are often desired for distinct graphs and target nodes. To this end, we propose a dynamic selection mechanism by allowing each node to adaptively adjust the size of its RFs according to the graph properties. In particular, we design an architectural unit called Selective-Hop (SH) block, in which information from neighborhoods within different hops are merged using dual attention mechanisms: (i) node-wise attention on each network branch to capture mutual information between the subgraphs and the entire graph; and (ii) channel-wise attention on different network branches to detect the significance of different hops at the node-level granularity. As such, the SH block determines neighbors flexibly without the constraints of hops. By stacking several SH blocks in GNN style, we instantiate two new GNN variants, SHGCN and SHSGC. Empirical results on eight benchmark datasets for semi-supervised node classification reveal that our new models achieve a considerable improvement over the state-of-the-art methods under low-homophily or heterophily and maintain competitive performance under homophily.

Generation characteristics and molecular reaction dynamics mechanism of PAHs during oxygen-lean combustion of jet coal

Severe coalfield fires occur in China, mainly in the northwest provinces of Xinjiang, Inner Mongolia, and Ningxia. Among the organic pollutants produced by oxygen-lean combustion, PAHs are considered one of the most dangerous pollutants due to their environmental contamination and “carcinogenic, teratogenic, and mutagenic” effects on human beings. In this paper, to address the problems of PAHs generation and reaction mechanism in coalfield fire, the jet coal from Xinjiang Zhundong Coalfield was selected to conduct the experiments, and the combustion products were detected using GC–MS and FTIR. The molecular dynamics method based on the reaction force field was also used to calculate the reaction process of 17 single molecules (5083 atoms) of jet coal combusted from 300 K to 4000 K at 0.5 oxygen equivalent ratio. The experimental results show that the generation of PAHs increased roughly from 573 K to 773 K at the three oxygen concentrations, and an increase in oxygen content can enhance PAHs generation to some degree. The structural evolutions and product changes in the system have been traced, and it is also clarified that the reaction history of oxygen-lean combustion mainly experiences two stages: thermal decomposition of coal structure and transformation of aromatic structure. The generation mechanism of PAHs is proposed from the perspective of molecular reaction dynamics. On the one hand, it is due to the breakage of active sites such as C-S, C-O, and O-H during the decomposition of coal structure. On the other hand, it originates from the polycondensation reaction triggered by the dehydrogenation of aromatic structure that enlarges the aromatic dimension The research results reveal the generation characteristics and reaction pathways of PAHs in the oxygen-lean combustion of low-rank coal in the fire zone and provide certain references for coal fire management and pollutant control.

Nonlinear combined resonance of magneto-electro-elastic plates

The existing articles on the nonlinear resonances of magneto-electro-elastic (MEE) structures are mainly devoted to the parametric resonance and primary resonance, neglecting the coupling effect between parametric and forced excitations. To reveal this issue, the coupled longitudinal and transverse excitations are considered to investigate the combined resonance of MEE plates, in which the simply supported ends is considered. The expressions of magnetic-, electric- and displacement- fields are determined using the Maxwell's equation and first order shear deformation theory (FSDT). Applying the Galerkin method, the nonlinear ordinary differential equations are derived. The combined resonance problem of MEE plates with simply supported ends is solved by developing the method of varying amplitude (MVA). And the resonance trajectory is depicted by amplitude-frequency diagram in the follow-up analysis, in which the complicated dynamical phenomena with jump, hysteresis and multi-stable solution can be observed. To reveal the dynamic mechanism, comprehensive numerical analyses including electric potential, magnetic potential, excitations, temperature variation and other factors are conducted, the bifurcation and chaotic dynamic behaviors are also analyzed. The results elucidate that these parameters under discussion play significant roles.

Energy harvesting from wake-induced vibration of flexible flapper behind a bluff body

Piezoelectric energy harvesting from ambient vibrations offers a promising small-scale energy generation strategy, with wake-induced vibration of flexible structures being an ideal candidate. This study examines a bluff body followed by a fully flexible piezoelectric flapper in a viscous free-stream flow using an in-house discrete forcing immersed boundary method-based fluid-structure-electric energy solver for parametric investigation. Different vortex shedding regimes are identified based on vortex formation around the flexible flapper. The complex and interdependent spatiotemporal dynamics of the wake and flexible body dictated by parameters such as bending rigidity and the gap space between the flapper and bluff body result in various deformation profiles, influencing the strain rate and output power. The study also investigates the independent variation of flapper length and its impact on vortical arrangements and flexibility, introducing different oscillation modes. The present study takes a nuanced view of the overall dynamics and their mutual effect on the power output, unlike most existing studies where enhancing the amplitude and frequency of oscillations for an optimal output was the main concern. Factors such as flapper curvature, its asymmetry, and periodicity have been especially highlighted in the context of the output and the corresponding parametric spaces investigated. Interestingly, the increase in piezo-flapper length has seen a reduction in output, though it was instrumental in bringing symmetry back. The study offers comprehensive insights into ideal harvesting regimes and the underlying dynamical mechanisms and can contribute toward the design of future energy harvesting devices.

Indian summer monsoon history during the last glacial cycle revealed by a loess sequence from the Tibetan Plateau

The Indian Summer Monsoon (ISM) has a profound influence on the environment and people of East Asia. However, despite its importance, the variability and dynamic mechanisms of the ISM remain inadequately understood. This study investigates the evolution of the Indian Summer Monsoon (ISM) since the Last Interglacial by analyzing the well-preserved Cuoweng (CW) loess–paleosol sequence in the southeastern Tibetan Plateau. A robust chronological framework is established using quartz optically stimulated luminescence (OSL) dating and K-feldspar post-infrared infrared stimulated luminescence (pIRIR) dating. The history of the ISM over the past ~135 ka is reconstructed based on multiple environmental proxies. The results indicate that the ISM experienced significant glacial–interglacial fluctuations during the last glacial cycle, exhibiting a strengthened monsoon during warm periods and a weakened one during cold periods. Additionally, suborbital-scale cyclic variations in the ISM during the marine isotope stage (MIS) 5 are revealed, with enhanced monsoon conditions during MIS 5a, 5c, and 5e and weakened conditions during MIS 5b and 5d. These findings suggest that the ISM variations are primarily influenced by a combination of high- and low-latitude forcing. This study provides new insights into the complex responses of the ISM to climate change, enhancing our understanding of its potential future changes.

Unveiling the Dynamic Mechanisms of Generative AI in English Language Learning: A Hybrid Study Based on fsQCA and System Dynamics

The burgeoning development of generative artificial intelligence (GenAI) has unleashed transformative potential in reshaping English language education. However, the complex interplay of learner, technology, pedagogy, and contextual factors that shape the effectiveness of GenAI-assisted language learning remains underexplored. This study employed a novel mixed-methods approach, integrating qualitative comparative analysis (QCA) and system dynamics (SD) modeling, to unravel the multi-dimensional, dynamic mechanisms underlying the impact of GenAI on English learning outcomes in higher education. Leveraging a sample of 33 English classes at the Harbin Institute of Technology, the QCA results revealed four distinct configurational paths to high and low learning effectiveness, highlighting the necessary and sufficient conditions for optimal GenAI integration. The SD simulation further captured the emergent, nonlinear feedback processes among learner attributes, human–computer interaction, pedagogical practices, and ethical considerations, shedding light on the temporal evolution of the GenAI-empowered language-learning ecosystem. The findings contribute to the theoretical advancement of intelligent language education by constructing an integrative framework encompassing learner, technology, pedagogy, and context dimensions. Practical implications are generated to guide the responsible design, implementation, and optimization of GenAI in English language education, paving the way for learner-centric, adaptive learning experiences in the intelligence era.

How cognitive skills affect strategic behavior: Cognitive ability, fluid intelligence and judgment

We explore the influence of cognitive ability and judgment on strategic behavior in the beauty contest game (where the Nash equilibrium action is zero). Using the level-k model of bounded rationality, cognitive ability and judgment both predict higher level strategic thinking. However, individuals with better judgment choose zero less frequently, and we uncover a novel dynamic mechanism that sheds light on this pattern. Taken together, our results indicate that fluid (i.e., analytical) intelligence is a primary driver of strategic level-k thinking, while facets of judgment that are distinct from fluid intelligence drive the lower inclination of high judgment individuals to choose zero.

Advancing mechanistic understanding of cohesive sediment transport: Integrating flume experiments, field measurements, and modelling approaches in a gravel-bed river

Gravel-bed rivers draining mountainous forested headwater regions are critically important for drinking water supply and ecological integrity. These rivers, however, have been increasingly impacted by intensifying anthropogenic and natural (especially climate change exacerbated) landscape disturbances that commonly increase hillslope/channel connectivity and the delivery of cohesive sediment (<63 μm) and associated pollutants. Despite the known deleterious threats of excess cohesive sediments, there is still limited understanding of their transport and intra-gravel storage due to the complexities of such processes. Accordingly, the objectives of this study were to: i) calibrate and validate a semi-empirical cohesive sediment transport model (RIVFLOC) using the observations from flume experiments; ii) estimate the intra-gravel storage capacity for cohesive sediment with the calibrated model based on the field dataset (collected in two field campaigns between 2019 and 2021), and; iii) investigate mechanisms of cohesive sediment transport dynamics in this gravel-bed river, identifying knowledge gaps and areas for future research. Our results showed that despite the increased floc settling velocity, deposition was hindered by turbulent flow fields. The model predicted that ~60 % of upstream cohesive sediment would ingress within the 10 km study reach due to the flow interaction with the gravel-bed. Despite the agreement between flume and field observations on ingress rates and preferential ingress of coarser (~100 μm) flocs, notable differences were observed between modelled and field datasets, highlighting unknowns regarding cohesive sediment exfiltration without framework mobilization. This study uniquely integrates field measurements, flume experiments, and modelling strategies to evaluate the transport and fate of cohesive sediment in a gravel-bed river. Accordingly, our findings advance current knowledge on the mechanistic understanding of cohesive sediment transport and highlight future research directions.