Dynamic Mechanism Research Articles

Vibration-Induced Pancake Bouncing of Impacting Droplets on Hydrophobic Surfaces.

Rapid detachment of impacting droplets from solid surfaces is fundamentally interesting and important in many practical applications, including self-cleaning, anti-icing, and energy harvesting. The droplet pancake bouncing is strongly preferred for the reduced contact time and high bouncing velocity. However, the trigger conditions for pancake bouncing are rigorous. Driven by this, this work circumvents the limitations via solid surface vibration. Our results show that the impacting droplet patterns are highly sensitive to the surface vibration parameters (i.e., the vibration amplitude and frequency), and only a reasonable design of vibration parameters enables the impacting droplets to bounce in a pancake shape without the contraction stage. Intriguingly, the pancake bouncing induced by surface vibration exhibits a significant reduction of solid-liquid contact time (up to ≈80%) compared to the traditional bouncing pattern, and the bouncing velocity has been dramatically enhanced. Furthermore, the critical conditions for the pancake bouncing on the hydrophobic surface and the underlying dynamic mechanism are also identified. This work provides an effective method to achieve well-controlled pancake bouncing of impacting droplets for extensive applications.

Characteristics of dynamic mechanics and energy loss in reef limestone concrete during dry-wet carbonation periods

Coral reef limestone is a unique type of rock and soil body characterized by high porosity. Its dynamic mechanical properties under impact loads differ significantly from those of conventional land-sourced aggregate concrete.This study utilizes coral reef limestone as both coarse and fine aggregates to prepare C40 strength concrete. The research investigates the effects of dry-wet carbonation cycles on its dynamic mechanical behavior and energy evolution characteristics using a Split Hopkinson Pressure Bar (SHPB) mechanical testing system.The findings reveal that increasing the number of dry-wet carbonation cycles leads to a significant weakening of the internal structural bonding in coral reef limestone concrete. Notably, the degree of phenolphthalein color change diminishes, while uniaxial compressive strength and tensile strength demonstrate an overall downward trend. The reduction in tensile strength is less pronounced than the decrease in compressive strength. Additionally, the relative dynamic elastic modulus gradually decreases, and a size effect is noted, with a rapid acceleration in mass loss. As the number of dry-wet carbonation cycles increases, dynamic compressive strength declines, and failure modes shift from surface cracking to crush-type failure.The dynamic increase factor (DIF) of the coral reef limestone concrete indicates a high sensitivity to strain rate, with a significant rise in DIF value as the strain rate increases. Various energies generated under impact load exhibit clear strain rate effects. Furthermore, the effects of dry-wet carbonation cycling enhance energy dissipation, especially at 30 cycles, where energy dissipation increases sharply, while a hindering effect on transmitted energy is observed.

Activation of polycystin-1 signaling by binding of stalk-derived peptide agonists.

Polycystin-1 (PC1) is the protein product of the PKD1 gene whose mutation causes autosomal dominant Polycystic Kidney Disease (ADPKD). PC1 is an atypical G protein-coupled receptor (GPCR) with an autocatalytic GAIN domain that cleaves PC1 into extracellular N-terminal and membrane-embedded C-terminal (CTF) fragments. Recently, activation of PC1 CTF signaling was shown to be regulated by a stalk tethered agonist (TA), resembling the mechanism observed for adhesion GPCRs. Here, synthetic peptides of the first 9- (p9), 17- (p17), and 21-residues (p21) of the PC1 stalk TA were shown to re-activate signaling by a stalkless CTF mutant in human cell culture assays. Novel Peptide Gaussian accelerated molecular dynamics (Pep-GaMD) simulations elucidated binding conformations of p9, p17, and p21 and revealed multiple specific binding regions to the stalkless CTF. Peptide agonists binding to the TOP domain of PC1 induced close TOP-putative pore loop interactions, a characteristic feature of stalk TA-mediated PC1 CTF activation. Additional sequence coevolution analyses showed the peptide binding regions were consistent with covarying residue pairs identified between the TOP domain and the stalk TA. These insights into the structural dynamic mechanism of PC1 activation by TA peptide agonists provide an in-depth understanding that will facilitate the development of therapeutics targeting PC1 for ADPKD treatment.

Activation of polycystin-1 signaling by binding of stalk-derived peptide agonists

Polycystin-1 (PC1) is the protein product of the PKD1 gene whose mutation causes autosomal dominant Polycystic Kidney Disease (ADPKD). PC1 is an atypical G protein-coupled receptor (GPCR) with an autocatalytic GAIN domain that cleaves PC1 into extracellular N-terminal and membrane-embedded C-terminal (CTF) fragments. Recently, activation of PC1 CTF signaling was shown to be regulated by a stalk tethered agonist (TA), resembling the mechanism observed for adhesion GPCRs. Here, synthetic peptides of the first 9- (p9), 17- (p17), and 21-residues (p21) of the PC1 stalk TA were shown to re-activate signaling by a stalkless CTF mutant in human cell culture assays. Novel Peptide Gaussian accelerated molecular dynamics (Pep-GaMD) simulations elucidated binding conformations of p9, p17, and p21 and revealed multiple specific binding regions to the stalkless CTF. Peptide agonists binding to the TOP domain of PC1 induced close TOP-putative pore loop interactions, a characteristic feature of stalk TA-mediated PC1 CTF activation. Additional sequence coevolution analyses showed the peptide binding regions were consistent with covarying residue pairs identified between the TOP domain and the stalk TA. These insights into the structural dynamic mechanism of PC1 activation by TA peptide agonists provide an in-depth understanding that will facilitate the development of therapeutics targeting PC1 for ADPKD treatment.

Copper‐Catalyzed Enantioselective Hydrophosphorylation of Unactivated Alkynes

P‐stereogenic phosphorus compounds are essential across various fields, yet their synthesis via enantioselective P‐C bond formation remains both challenging and underdeveloped. We report the first copper‐catalyzed enantioselective hydrophosphorylation of alkynes, facilitated by a newly designed chiral 1,2‐diamine ligand. Unlike previous methods that rely on kinetic resolution with less than 50% conversion, our approach employs a distinct dynamic kinetic asymmetric transformation mechanism, achieving complete conversion of racemic starting materials. This reaction is compatible with a broad range of aromatic and aliphatic terminal alkynes, producing products with high yields (up to 95%), exclusive cis selectivity, and exceptional regio‐ and enantioselectivity (> 20:1 r.r. and up to 96% ee). The resulting products were further transformed into a diverse array of enantioenriched P‐stereogenic scaffolds. Preliminary mechanistic studies were conducted to elucidate the reaction details.

Enhancing Small Target Detection in Aerial Imagery with Bi-PAN-FPN and EDMOA-Optimized YOLOv8-s

Across the globe, people are working to build "smart cities" that will employ technology to make people's lives better and safer. Installing cameras at strategic spots across the city to monitor public spaces besides provide real-time footage to law enforcement besides other local authorities is a crucial part of smart city infrastructure, which includes video surveillance. A more effective answer is provided by deep learning algorithms, however research in this area still faces significant problems from changes in target size, form change, occlusion, and illumination circumstances as seen from the drone's perspective. In light of the aforementioned issues, this study presents a highly effective and resilient approach for aerial picture identification. To begin, the concept of Bi-PAN-FPN is presented to enhance the neck component of YOLOv8-s, taking into consideration the prevalent issue of small targets being easily misdetected or ignored in aerial photos. We achieve a more advanced and thorough feature fusion procedure much as feasible by completely considering and reusing multiscale features. To further reduce the amount of parameters in the model and prevent info loss during long-distance feature transfer, the benchmark model's backbone incorporates the GhostblockV2 structure in lieu of a portion of the C2f module. With the help of the Enhanced Dwarf Mongoose Optimization Algorithm (EDMOA), the suggested model's hyper-parameters are optimised. Lastly, a dynamic nonmonotonic focusing mechanism is employed in conjunction with WiseIoU loss as bounding box regression loss. The detector accounts for varying anchor box quality by utilizing "outlier" evaluations, thus improving the complete presentation of the detection task.

Influence of relative distance between fault and tunnel on dynamic response of tunnel: An experimental and numerical investigation

The effect of earthquakes on deep tunnels near faults has always been a significant issue in underground engineering research. The presence of fault fracture zones tends to intensify the seismic response of tunnels. On the basis of the Xianglushan tunnel project, the influence of relative distance between fault and tunnel on the dynamic response of tunnel was investigated utilizing laboratory test, shaking table test, and numerical simulation. The results indicated that as the relative distance between the fault and the tunnel increased, the peak acceleration and peak strain of the tunnel first increased and then decreased. The results of numerical simulations and experimental tests demonstrated that changes in relative distance had a significant influence on the dynamic response of the tunnel. Since the Xianglushan tunnel is located near the fault, the superposition of seismic load and the residual tensile and compressive stress led to the appearance of through-type cracks, which eventually resulted in large deformation or even failure. The left arch springing and left spandrel of the tunnel closer to the fault were prone to failure first. This study provides a reference for the seismic dynamic response and failure mechanism of near-fault tunnels.

Oxidation mechanism of sulfur-containing compounds and antioxidant depletion dynamics: Insights into interactions

Sulfur-containing compounds (SCCs) are the most abundant heteroatomic organic species (HOS) in petroleum fuels. This study investigates the dynamics of SCCs in surrogate fuel systems, focusing on their oxidation stability, reactivity, and interactions. An analysis of eleven selected SCCs, spanning both reactive (thiols, sulfides, and disulfides) and nonreactive (thiophenes and benzothiazole) species, was conducted at 140 °C. The investigation delves into reaction and degradation mechanisms, exploring resulting products, binary mixtures of reactive SCCs for potential interactions, and multicomponent SCC systems to understand nuanced interaction. Findings reveal unique reaction pathways in single-component SCCs, synergetic and antagonistic effects in binary mixtures, and substantial decreases in reaction rates in all multicomponent systems. Moreover, SCCs expedited antioxidant consumption, highlighting their pivotal role in fuel stability and degradation. The study contributes valuable insights into SCC dynamics, advocating further exploration of HOS’s interactions for enhanced fuel formulation and stability testing strategies.

Event-based nonfragile state estimation for memristive neural networks with multiple time-delays and sensor saturations

This article investigates the issue of nonfragile state estimation (SE) for memristor-based neural networks (MNNs) with leakage delay and proportional delay. In actual engineering, a multitude of usefulness data are transmitted to the estimator through the networks, which stress the burden on communication bandwidth. A dynamic event-triggered mechanism (DETM) that relies on incomplete measurements is utilised to select valuable data. A novel delay-dependent criterion for the existence of the event-based state estimator is derived in terms of a convex optimisation problem by means of the Lyapunov theory and some integer inequalities technique. In the end, two numerical simulations are shown to illustrate the validity of the proposed theoretical methods.

Enhanced knowledge graph recommendation algorithm based on multi-level contrastive learning

Integrating the Knowledge Graphs (KGs) into recommendation systems enhances personalization and accuracy. However, the long-tail distribution of knowledge graphs often leads to data sparsity, which limits the effectiveness in practical applications. To address this challenge, this study proposes a knowledge-aware recommendation algorithm framework that incorporates multi-level contrastive learning. This framework enhances the Collaborative Knowledge Graph (CKG) through a random edge dropout method, which constructs feature representations at three levels: user-user interactions, item-item interactions and user-item interactions. A dynamic attention mechanism is employed in the Graph Attention Networks (GAT) for modeling the KG. Combined with the nonlinear transformation and Momentum Contrast (Moco) strategy for contrastive learning, it can effectively extract high-quality feature information. Additionally, multi-level contrastive learning, as an auxiliary self-supervised task, is jointly trained with the primary supervised task, which further enhances recommendation performance. Experimental results on the MovieLens and Amazon-books datasets demonstrate that this framework effectively improves the performance of knowledge graph-based recommendations, addresses the issue of data sparsity, and outperforms other baseline models across multiple evaluation metrics.

A two-layer economic resilience model for distribution network restoration after natural disasters

Improving the resilience of energy networks and minimizing outages is crucial, making the development of self-healing strategies essential for ensuring a continuous power supply and boosting resilience. As decentralized prosumers become more integrated into smart grids, innovative coordination methods are required to fully leverage their potential and improve the system's self-healing capabilities. Hence, in this paper, we present a decentralized two-layer model for active distribution networks (ADNs) incorporating commercial energy hubs, Parking lots (PLs), hydrogen refueling stations, and mobile units (MUs). This model aims to maximize the flexible capacities of flexible prosumers under emergencies and includes a dynamic mechanism for transporting hydrogen to refueling stations via trucks. In the first layer of the model, the AND operator provides the required services to flexible prosumers, including commercial energy hubs, PLs and hydrogen refueling stations. During this layer, the movement paths of mobile units, such as battery-based mobile storage units (MSUs) and mobile hydrogen units (MHUs), are determined. In the second layer, prosumers independently plan and decide the extent of their service contributions. Subsequently, with the actual service capacities established, the AND operator re-optimizes the initial planning to finalize the system schedule and precise movement paths of the mobile units. The proposed model was tested on a modified 83-bus distribution network using the CPLEX solver in GAMS. Simulation results demonstrate a 54.9 % reduction in forced load shedding (FLS) due to the dispatch of mobile units and a further 84.4 %% reduction in FLS from leveraging the flexible capacities of commercial energy hubs and PLs.

A category incremental continuous learning model for imbalance arrhythmia detection

Abstract The development of efficient arrhythmia detection systems is crucial for physiological measurements and computer-aided diagnosis. Existing systems rely mainly on offline learning methods and lack the ability to assimilate new data or recognize emerging categories. To address these challenges, this study introduces an arrhythmia detection model that is resistant to data imbalance and has continuous learning capabilities, specifically for incremental learning of new ECG data categories. The system incorporates constraints derived from the new class data and implements a dynamic mechanism for updating connection weights, facilitating the incremental continual learning of classes. Confronted with the problem of models forgetting the original data and overfitting with the added data in continuous learning, we introduce a data balancing method by regularization to balance the model's memory and learning of the two types of data. Facing the data imbalance problem in continuous learning, we introduce a posteriori probability weighting strategy. This strategy assigns greater importance to high-value samples based on the model's posterior residual kernel density estimates. Comprehensive testing of the model using various datasets from the MIT-BIH database indicates superior performance in incremental learning tasks. The results reveal that the model not only excels in class incremental learning but also ensures effective balancing across different data classes.

An environmental mitigation behavior model predicting waste reduction among young coastal tourists in Bangladesh

PurposeThe main purpose of this study is to develop an environmental mitigation behavior model (EMBM) for predicting waste reduction behaviors among young coastal tourists in Bangladesh by modifying and extending the theory of planned behavior (TPB).Design/methodology/approachThe research has been administered by applying mixed method (both qualitative and quantitative), where study-1(qualitative) identifies factors, study-2 (quantitative) verifies factors by employing exploratory factor analysis (EFA) approach, and study-3 confirms factors influencing waste reduction behaviors among young coastal tourists by employing confirmatory factor analysis (CFA). Study-2 collects 385 valid responses and analyzes by applying the principal component analysis (PCA) technique with the Varimax rotation method by using SPSS-v25, and Study-3 collects 501 valid responses and analyzes by partial least structural equation modeling (PLS-SEM) technique, using Smart PLS3.3.3.FindingsThe study findings have revealed that waste reduction intention and perceived ecological balance significantly influence waste reduction behaviors among young coastal tourists in Bangladesh. Further, waste reduction intention is significantly predicted by mitigation attitude, resource conservation norms and environmental perceptions. Furthermore, environmental ethics significantly affects mitigation attitudes and resource conservation norms.Practical implicationsThe study offers several insightful implications (e.g. incentives, charging fines, environmental awareness-related knowledge-based campaign, etc.) for policymakers and industry operators, which may be a dynamic cost-effective mechanism for reducing waste at coastal tourism destinations in Bangladesh as well as in the world.Originality/valueThis study addresses the need for developing a model that can explain waste reduction behaviors among young coastal tourists in Bangladesh, and thus, the study uniquely postulates the theory of environmental mitigation behavior for predicting waste reduction behaviors by modifying and extending the TPB.

Mechanosensitive fluorescence lifetime probes for investigating the dynamic mechanism of ferroptosis

Deciphering the dynamic mechanism of ferroptosis can provide insights into pathogenesis, which is valuable for disease diagnosis and treatment. However, due to the lack of suitable time-resolved mechanosensitive tools, researchers have been unable to determine the membrane tension and morphology of the plasma membrane and the nuclear envelope during ferroptosis. With this research, we propose a rational strategy to develop robust mechanosensitive fluorescence lifetime probes which can facilitate simultaneous fluorescence lifetime imaging of the plasma membrane and nuclear envelope. Fluorescence lifetime imaging microscopy using the unique mechanosensitive probes reveal a dynamic mechanism for ferroptosis: The membrane tension of both the plasma membrane and the nuclear envelope decreases during ferroptosis, and the nuclear envelope exhibits budding during the advanced stage of ferroptosis. Significantly, the membrane tension of the plasma membrane is always larger than that of the nuclear envelope, and the membrane tension of the nuclear envelope is slightly larger than that of the nuclear membrane bubble. Meanwhile, the membrane lesions are repaired in the low-tension regions through exocytosis.

Transcriptomic and metabolomic analysis of poplar response to feeding by Hyphantria cunea

Populus cathayana × canadansis ‘Xinlin 1’ (‘P.‘xin lin 1’) with the characteristics of rapid growth and high yield, is frequently attacked by herbivorous insects. However, little is known about how it defenses against Hyphantria cunea (H. cunea) at molecular and biochemical levels. Differences in the transcriptome and metabolome were analyzed after ‘P. ‘xin lin 1’ leaves were fed to H. cunea for 0h, 2h, 4h, 8h, 16h and 24h. In the five comparison groups including 2h vs. CK, 4h vs. CK, 8h vs. CK, 16h vs. CK, and 24h vs. CK, a total of 8925 genes and 842 metabolites were differentially expressed. A total of 825 transcription factors (TFs) were identified, which encoded 56 TF families. The results showed that the top four families with the highest number of TFs were AP2/ERF, MYB, C2C2, bHLH. Analyses of leaves which were fed to H. cunea showed that the differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) were significantly enriched in plant hormone signal transduction pathway, MAPK signaling pathway, flavonoid, flavone and flavonol and anthocyanin biosynthesis pathway. Additionally, there were a number of genes significantly up-regulated in MAPK signaling pathway. Some compounds involved in plant hormone signal transduction and flavonoid/flavone and flavonol/ anthocyanin pathways such as jasmonic acid (JA), jasmonoyl-L-Isoleucine (JA-Ile), kaempferol and cyanidin-3-O-glucoside were induced in infested ‘P.‘xin lin 1’. This study provides a new understanding for exploring the dynamic response mechanism of poplar to the infestation of H. cunea.

Complexity and response of bio-inspired energy harvesters based on wing-beat pattern

Abstract This paper aims to investigate the dynamical mechanism of bio-inspired energy harvesters based on wing-beat pattern under harmonic excitation. Due to the existence of the gravity force in the established model, the harmonic balance method is utilized to calculate the theoretical results, which has the advantage to keep the influence of gravity force. Multiple solutions are found in the high frequency region, and they are very close in the amplitude of displacement and voltage due to the special structure of the bio-inspired energy harvester. Direct time-domain analysis verifies the effectiveness of theoretical results. The influence mechanism of the equivalent stiffness is also explored, which leads to the appearance of different states. Then, the root mean square (RMS) voltage and average power are analyzed. It is observed that a smaller damping coefficient and equivalent capacitance enhance the average electrical output and achieve greater output power. Subsequently, the bifurcation and complexity properties of the harvester are discussed. Complex phenomena are observed under different external excitations and equivalent damping, including double periodic bifurcation, multiple periodic bifurcation, and chaos phenomena. The complexity analyses confirm the effectiveness of the bifurcation results. The distribution of complexity also exhibits significant fluctuations, closely correlated with the trend of the bifurcation diagram.

Response of Nitrifiers to Gradually Increasing pH Conditions in a Membrane Nitrification Bioreactor: Microbial Dynamics and Alkali-Resistant Mechanism

Nitrification and nitrifiers are pH-sensitive especially under the alkaline environment in the activated sludge system. However, it is unclear how nitrifiers and nitrification respond to long-term alkaline environment. This study employed a continuous flow membrane nitrification bioreactor to investigate the dynamics of nitrification efficiency and microbial community adaptation under a 320-day alkaline operation. Results showed that activated sludge adapted remarkably to a progressive increase in pH from 7.5 to 10.0, achieving robust nitrification with average ammonia removal efficiencies of 96.6 ± 2.2%. Subsequently, an integrated alkali-resistant mechanism of nitrifiers was proposed. Specifically, under the long-term operation of pH 10.0, certain bacteria secreted enhanced extracellular acidic polysaccharides (i.e., up to 10.95 ± 0.27 mg·g−1 MLVSS in soluble extracellular polymeric substances (EPS)) and acidic organic compounds (e.g., humic acids increased by 1.47-fold in tightly bounded EPS) to neutralize external alkalinity. Moreover, significant enrichments in both the ammonia oxidizing bacteria Nitrosomonas (by 1.3%) and the nitrite oxidizing bacteria Nitrospira (by 5.4%) were observed in at a 170-day operation of pH 10.0 condition. Meanwhile, norank_f__JG30-KF-CM45 (2.0%) and Rhodobacter (0.9%) also contributed to ammonia removal at pH 10.0. On the cellular-level, bacteria enabled to maintain intracellular pH stabilization primarily through cation/proton antiporters, evidenced by significant increases of NhaA, TrkA and KefB activities by 98.0%, 151.7% and 115.2%, respectively. A 43.1% increase in carbonic anhydrase activity also facilitated consumption of aqueous OH− ions through biomineralization, leading to CaCO3 deposition on microbial surface. These findings further enhanced understandings of physiological adaptation of nitrifiers in the long-term alkaline activated sludge system.

Dynamic event-triggered consensus for stochastic delay multi-agent systems under directed topology

This paper addresses the mean-square exponential consensus (MSEC) challenge in stochastic delay multi-agent systems (MASs) under directed topology. The main contributions include avoiding infinitely fast sampling and conducting a random stability analysis. First, a dynamic event-triggered mechanism (DETM) with a fixed positive lower bound for the inter-execution time is designed to prohibit Zeno behavior. The proposed control strategy does not involve the expectations of system signals. Then, sampling errors are estimated using Itô isometry, which ensures that the fixed control at each work interval is maintained with adequate feedback capacity to mitigate the effects of these errors. Furthermore, with the help of a Halanay inequality in integral form and Lyapunov theorem framework, some conditions are proposed to guarantee the consensus of stochastic delay MASs under directed topology. Finally, an example is presented to prove the effectiveness of the DETM method.

Mechanical behavior of water ingress process for the locally damaged submerged floating tunnel and its interpretation: A full-length experimental study

The submerged floating tunnel (SFT) is an innovative transportation solution for traversing deep-water regions. However, local damage and subsequent water ingress pose significant threats to the structural integrity and safety of SFTs. This critical issue has been unexplored in the field. This study addresses this gap by presenting a comprehensive experimental investigation using a full-length physical model of the SFT. The dynamic response and failure mechanisms caused by water ingress within the SFT tube, influenced by various factors such as immersion depth, damaged opening angle, damaged opening area, and external connectivity, are systematically investigated. The results indicate that the failure of the anchor cable and tube structure of the SFT progresses through three stages after water ingress occurs in the damaged SFT. It is observed that damage closer to the bottom of the tube reduces the risk of failure in the mooring system. Notably, air pressure is a critical factor influencing the water ingress into the tube body. The attenuation of cable tension and tube deformation is significantly slower when the internal air is isolated from the external environment. The proposed novel structural sealing segmental disaster prevention design scheme substantially mitigates the risk of structural damage under compromised conditions and water ingress. This study provides valuable insights into enhancing disaster prevention strategies for SFTs and advances the understanding of their mechanical behavior in response to damage and water ingress.

Dynamic event-triggered neuroadaptive fault-tolerant control of quadrotor UAV with a novel cosine kernel

The fault-tolerant control (FTC) for trajectory tracking of a quadrotor Unmanned aerial vehicle (UAV) has attracted researchers. The non-linear model of the UAV, coupled with model uncertainties, external disturbances, and actuator failures, requires the function approximation of the lumped non-linearity for controller design. One of the most efficient ways to approximate non-linearity is using Radial basis function neural networks (RBFNNs). To date, RBFNNs have been formulated, trained, and used directly for function approximation, which requires considerable computation to derive the control laws. The proposed control and parameter estimation laws approximate the non-linearity by using RBFNNs indirectly. The proposed laws with virtual parameter estimation do not require the actual formulation of RBFNNs and their weights, thus saving on computational resources. For kernel optimization, the Gaussian kernels with exponential terms in RBFNNs are replaced by cosine kernels with algebraic terms, which shows faster convergence as per simulation results. To save on communication bandwidth, static event-triggering communication mechanisms (SECM) and dynamic event-triggering mechanisms (DECM) have been proposed. As DECM works on dynamically changing variables, it saves more communication bandwidth, as tested in simulation. Lyapunov stability analysis proves that errors are uniformly ultimately bounded (UUB). The performance of the proposed algorithm has been tested through numerical simulations, which show superior performance when compared with similar studies. The proposed algorithm has been validated in a real-time Gazebo simulator.