Interaction Topology Research Articles

Structural, vibrational and thermal study of Bis(4-Carboxyanilinium) sulphate a new organo-sulphate adduct of 4-amino benzoic acid

Bis(4-carboxyanilinium) sulphate (B4-CASO4), a new organo-sulphate adduct of 4-amino benzoic acid, was synthesized and characterized by single-crystal X-ray diffraction, DSC and FTIR. B4-CASO4 crystallises in the monoclinic space group, P21/c, Z = 4, with two 4-CA cations and a sulphate anion in an asymmetric unit. These cations, 4-CA was found to exist as a cyclic acid dimer. In B4-CASO4 crystal the 4-CA cations and sulphate anions are connected through a three-dimensional network of strong charge-assisted N–H…O and C–OH…O hydrogen bonds. The infrared vibrational frequencies of B4-CASO4 were assigned and comparison of the spectral features of 4-ABA and B4-CASO4 suggest presence of a stronger NH…O and C–OH…O hydrogen bond interactions in B4-CASO4. Using the computational tool of energy framework analysis, the intermolecular interaction topology has been quantitatively analysed. A very high values of interaction energies are found for the hydrogen bonded anion-cation pairs in the crystal structure of B4-CASO4. The DSC, IR and computational results corroborate well with the structural data of B4-CASO4.

Fully Distributed Adaptive Finite-Time Consensus for Uncertain Nonlinear Multiagent Systems.

In this article, the adaptive finite-time consensus problem is discussed for uncertain nonlinear multiagent systems. In contrast with the correlative literature, the systems permit multiple uncertainties (not only in control coefficients but also in inherent nonlinearities), and typically the consensus protocol is pursued in a fully distributed fashion (independent from any global information of network topology). This essentially challenges the realization of the finite-time consensus. To overcome the challenge, a new continuous fully distributed protocol is proposed by combining adaptive techniques such that the finite-time consensus of the systems under investigation is achieved. Remarkably, a dynamic high gain, rather than multiple ones in the related literature, is adequate to resist two kinds of uncertainties and to guarantee the fully distributed fashion of the consensus protocol. Moreover, the adaptive finite-time consensus protocol is specified on the scenario of leader-following multiagent systems. Simulation results of three interaction topologies are acquired to illustrate the validity and the wider applicability of the proposed protocol.

Mechanism of Huo-Xiang-Zheng-Qi in Preventing and Treating COVID-19: A Study Based on Network Pharmacology and Molecular Docking Techniques

Objective: The Chinese herbal formula Huo-Xiang-Zheng-Qi (HXZQ) is effective in preventing and treating coronavirus disease 19 (COVID-19) infection; however, its mechanism remains unclear. This study used network pharmacology and molecular docking techniques to investigate the mechanism of action of HXZQ in preventing and treating COVID-19. Methods: The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to search for the active ingredients and targets of the 10 traditional Chinese medicines (TCMs) of HXZQ prescription (HXZQP). GeneCards, Online Mendelian Inheritance in Man (OMIM), Pharmacogenomics Knowledge Base (PharmGKB), Therapeutic Target Database (TTD), and DrugBank databases were used to screen COVID-19-related genes and intersect them with the targets of HXZQP to obtain the drug efficacy targets. Cytoscape 3.8 software was used to construct the drug-active ingredient–target interaction network of HXZQP and perform protein–protein interaction (PPI) network construction and topology analysis. R software was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, AutoDock Vina was utilized for molecular docking of the active ingredients of TCM and drug target proteins. Results: A total of 151 active ingredients and 250 HXZQP targets were identified. Among these, 136 active ingredients and 67 targets of HXZQP were found to be involved in the prevention and treatment of COVID-19. The core proteins identified in the PPI network were MAPK1, MAPK3, MAPK8, MAPK14, STAT3, and PTGS2. Using GO and KEGG pathway enrichment analysis, HXZQP was found to primarily participate in biological processes such as defense response to a virus, cellular response to biotic stimulus, response to lipopolysaccharide, PI3K-Akt signaling pathway, Th17 cell differentiation, HIF-1 signaling pathway, and other signaling pathways closely related to COVID-19. Molecular docking results reflected that the active ingredients of HXZQP have a reliable affinity toward EGFR, MAPK1, MAPK3, MAPK8, and STAT3 proteins. Conclusion: Our study elucidated the main targets and pathways of HXZQP in the prevention and treatment of COVID-19. The study findings provide a basis for further investigation of the pharmacological effects of HXZQP.

Some properties of time-varying bearing formation

This paper explores the properties of Bearing Persistently Exciting (BPE) formations defined in d(d≥2)-dimensional space, a class of time-varying bearing formations whose configuration can be uniquely determined up to a translational Euclidean vector using only bearing and velocity measurements. By analyzing the bearing Laplacian matrix in relation to PE conditions on the bearings, we provide results on necessary conditions, and necessary & sufficient conditions to guarantee that a formation is BPE. We also explore the concept of Relaxed Bearing Rigidity (RBR), a particular case of BPE formation, which relaxes the classical conditions on interaction topologies required by Bearing Rigidity to maintain a rigid shape. Distributed estimators are also proposed to estimate the configuration of the BPE formation using bearing and velocity measurements. Simulation results are provided to validate the proposed estimators.

A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multi-vehicle systems (MVSs) in complex obstacle-laden environments. The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles, connected via a directed interaction topology, subject to simultaneous unknown heterogeneous nonlinearities and external disturbances. The central aim is to achieve effective and collision-free formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering, while not demanding global information of the interaction topology. Toward this goal, a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance. Furthermore, a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed. It is proved that, with the proposed protocol, the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed. Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.

Safety control using barrier certificates for multiagent systems with input saturation and formation constraints

Maintaining the given operational area is critical in guaranteeing the safety of nonlinear second-order multiple autonomous agents. The properties of multiagent systems and several physical constraints, including bounded modeling error and actuator saturation, dramatically affect the maneuverability of multiagent systems inside the specified operational area. Moreover, the existing safety control algorithms heavily rely on the boundaries of the operational area. To overcome this issue, by constructing a novel scalable control technique, the safety area for multiagent systems can be transformed into input-constrained control barriers along each coordinate of motion for agents. It is shown that the safety of each agent and the global asymptotic stability are guaranteed under the proposed distributed control algorithm. The asymmetrical closed-form scheme for the agent's safety rule is built by applying the adjustable low and high bounds of the control signals associated with the actual control inputs, which are repeatedly computed by using new local measurements as the agents move, and the saturated control inputs with asymmetrical constraints are ensured. The absolute values of the modeling errors and external disturbances can be tracked by the proposed safety controller. Super-twisting control (STC) is employed to address the formation constraint problem of multiagent systems, where the effect that arises from uncertain nonlinear complexity of the agents and external disturbances is eliminated. Moreover, finite-time convergence, a desirable robust behavior of multiagent systems, and the formation constraint are simultaneously achieved. Furthermore, the stability of the proposed integrated control strategy for multiagent systems is analyzed, which reveals that the proposed distributed safety control can seamlessly integrate with the robust control protocol with minimum modification under the directed information interaction topology. Safety formation control calibration and tuning are carried out, and comparative simulation results are provided to illustrate the effective performance of the obtained theoretical results.

Copper(II) Schiff base complex derived from salen ligand: structural investigation, Hirshfeld surface analysis, anticancer and anti-SARS-CoV-2

This work deals with the synthesis and characterization of copper(II) complex [Cu(salen)(H2O)](1) of salen-type Schiff base ligand derived from the condensation of 5-bromo-2-hydroxy-3-methoxybenzaldehyde and ethylenediamine in EtOH. This complex was characterized by different spectroscopic and physicochemical methods. Single crystal X-ray crystallography study revealed that Cu(II) in complex (1) is five-coordinate and adopts a distorted square pyramidal geometry. A DFT calculation was employed to evaluate the optimized electronic structure, HOMO-LUMO, energy gap, and global parameters. A detailed structural and non-covalent interaction on the complex is investigated by single crystal structure analysis and computational approaches. The strength of the interaction and 3D topology of the crystal packing are visualized through an energy framework. Hirshfeld surface and 2D fingerprint plots have been explored in the crystal structure of the complex. The anticancer properties of copper(II) complex was studied against the selected cancerous cell lines of breast cancer, cervical cancer, colon cancer and hepatocellular carcinoma. Additionally, molecular docking and MD simulations was performed on the complex to predict the binding mode and interactions between the ligand and the main protease of the SARS-CoV-2 (PDB ID: 7CBT and 7D1M). The molecular docking calculations of the complex (1) with SARS-CoV-2 virus revealed the binding energy of –8.1 kcal/mol and –7.5 kcal/mol with an inhibition constant of 3.245 µM and 2.318 µM at inhibition binding site of receptor towards 7CBT and 7D1M main protease (Mpro), respectively. Besides this, molecular docking results (–7.6 kcal/mol, 3.196 µM) towards Escherichia coli PBP2 targets (PDB ID: 6G9S) was also studied. Communicated by Ramaswamy H. Sarma

Collision-free formation-containment control for a group of UAVs with unknown disturbances

This paper focuses on the formation-containment control problem for a group of fixed-wing unmanned aerial vehicles (UAVs) subjected to unknown disturbances. To solve this problem, a two-layer control framework is developed by decoupling the problem into a trajectory generation problem at the group level and a trajectory tracking problem at the individual level. Firstly, based on the directed interaction topology and the required configuration of the UAVs, two types of finite-time estimators are constructed to generate the desired trajectories of the leading UAVs and the following UAVs, respectively. Meanwhile, to guarantee the safety of each UAV, the smooth collision avoidance potential function and obstacle avoidance potential function are designed. After that, using the dynamic surface control (DSC) technique and combined with the established potential functions, a distributed control law with the ability of collision avoidance is presented for each UAV to track the generated trajectory without any collision, where the homogeneous extended state observer (HESO) is developed to compensate for the mismatched and matched disturbances. The stability of the whole closed-loop system is provided using the Lyapunov theorem. Finally, simulation results demonstrate the effectiveness of the proposed control algorithm.

Network subgraph-based approach for analyzing and comparing molecular networks.

Molecular networks are built up from genetic elements that exhibit feedback interactions. Here, we studied the problem of measuring the similarity of directed networks by proposing a novel alignment-free approach: the network subgraph-based approach. Our approach does not make use of randomized networks to determine modular patterns embedded in a network, and this method differs from the network motif and graphlet methods. Network similarity was quantified by gauging the difference between the subgraph frequency distributions of two networks using Jensen–Shannon entropy. We applied the subgraph approach to study three types of molecular networks, i.e., cancer networks, signal transduction networks, and cellular process networks, which exhibit diverse molecular functions. We compared the performance of our subgraph detection algorithm with other algorithms, and the results were consistent, but other algorithms could not address the issue of subgraphs/motifs embedded within a subgraph/motif. To evaluate the effectiveness of the subgraph-based method, we applied the method along with the Jensen–Shannon entropy to classify six network models, and it achieves a 100% accuracy of classification. The proposed information-theoretic approach allows us to determine the structural similarity of two networks regardless of node identity and network size. We demonstrated the effectiveness of the subgraph approach to cluster molecular networks that exhibit similar regulatory interaction topologies. As an illustration, our method can identify (i) common subgraph-mediated signal transduction and/or cellular processes in AML and pancreatic cancer, and (ii) scaffold proteins in gastric cancer and hepatocellular carcinoma; thus, the results suggested that there are common regulation modules for cancer formation. We also found that the underlying substructures of the molecular networks are dominated by irreducible subgraphs; this feature is valid for the three classes of molecular networks we studied. The subgraph-based approach provides a systematic scenario for analyzing, compare and classifying molecular networks with diverse functionalities.

Metabolic shifts of oceans: Summoning bacterial interactions

Metabolic scaling can pinpoint monitoring priorities for anomalous communities and inform local eco-engineering restoration enhancing ecosystem function. We discovered an entropic Kleiber’s Law between bacterioplankton phylum total directed interactions and population and community abundane, with an average exponent Φ≃23 in striking accordance with theoretical expectations. The taxa-independent allometric relationship largely varies by habitat (shifting from 2/3 to 3/4 as power-exponent) and is better explained by the community abundance spectrum and its organization than by the envirome defined by environmental interactions. Yet, greater envirome disorganization meant higher environmental impacts causing larger bacteriome disorganization toward random interaction topologies and higher Kleiber’s Law exponents. Habitat-related bacteriome differences revealed that keystone phyla were highly interacting and rare (like Acidobacteria, SBR1093, and ZB3) maintaining weak but salient interactions with both rare (like Spirochaetes and Firmicutes) and dominant phyla. Consistent with their small divergence from main Kleiber’s law and abundance spectrum patterns cosmopolitan phyla, like Bacteroidetes and Proteobacteria, were dominant but weakly interacting (in magnitude) emphasizing the importance of weak ties in collective dynamics. The most salient interactions were always between phyla with high and low node centrality defined by their total directed interactions and abundance. Envirome network centrality shows temperature and nutrient concentrations (total and dissolved organic phosphorus) as central in estuarine habitats impacted by both global ocean factors and near-shore river effluxes; salinity and suspended particle matter were central in more off-shore habitats. Our results emphasize the importance of a probabilistic assessment of community interactions over individual bacterial abundance analyses when searching for collective patterns informative of ecosystem state and their environmental determinants.

Consensus of nonlinear multiagent systems with uncertain disturbance under stochastic switching topologies

In this paper, we investigate the mean square consensus problem of leader‐following nonlinear multiagent systems which suffer from white noise disturbance under randomly switching interaction topologies. The control protocol of each agent is designed based on its local information from its neighbors and the leader. Using matrix inequality theory and stochastic stability theory, a sufficient condition is presented to guarantee the mean square consensus. Moreover, two special cases: balanced topologies switching randomly and random connection failures are considered. Finally, two corresponding numerical simulations are provided to demonstrate the theoretical results.

Distributed adaptive fault-tolerant formation control for heterogeneous multiagent systems under switching directed topologies

This paper studies the cooperative fault-tolerant formation control problem of tracking a dynamic leader for heterogeneous multiagent systems consisting of multipile unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults under switching directed interaction topologies. Based on local neighborhood formation information, the distributed fault-tolerant formation controllers are constructed to ensure that all follower UAVs and UGVs can accomplish the demanding formation configuration in the state space and track the dynamic leader’s trajectory. By incorporating the sliding mode control and adaptive control technique, the actuator faults and unknown parameters of follower agents can be compensated. Through the theoretical analysis, it is proved that the cooperatively semiglobally uniformly ultimately boundedness of the closed-loop system is guaranteed, and the formation tracking errors converge to a small adjustable neighborhood of the origin. A simulation example is introduced to show the validity of the proposed distributed fault-tolerant formation control algorithm.

Oscillation suppression and chimera states in time-varying networks.

Complex network theory has offered a powerful platform for the study of several natural dynamic scenarios, based on the synergy between the interaction topology and the dynamics of its constituents. With research in network theory being developed so fast, it has become extremely necessary to move from simple network topologies to more sophisticated and realistic descriptions of the connectivity patterns. In this context, there is a significant amount of recent works that have emerged with enormous evidence establishing the time-varying nature of the connections among the constituents in a large number of physical, biological, and social systems. The recent review article by Ghosh et al. [Phys. Rep. 949, 1-63 (2022)] demonstrates the significance of the analysis of collective dynamics arising in temporal networks. Specifically, the authors put forward a detailed excerpt of results on the origin and stability of synchronization in time-varying networked systems. However, among the complex collective dynamical behaviors, the study of the phenomenon of oscillation suppression and that of other diverse aspects of synchronization are also considered to be central to our perception of the dynamical processes over networks. Through this review, we discuss the principal findings from the research studies dedicated to the exploration of the two collective states, namely, oscillation suppression and chimera on top of time-varying networks of both static and mobile nodes. We delineate how temporality in interactions can suppress oscillation and induce chimeric patterns in networked dynamical systems, from effective analytical approaches to computational aspects, which is described while addressing these two phenomena. We further sketch promising directions for future research on these emerging collective behaviors in time-varying networks.

Distributed Prescribed-Time Consensus Observer for High-Order Integrator Multi-Agent Systems on Directed Graphs

This brief deals with the distributed consensus observer design problem for high-order integrator multi-agent systems on directed graphs, which intends to estimate the leader states accurately in a prescribed time interval. A new kind of distributed prescribed-time observers (DPTO) on directed graphs is first formulated for the followers, which is implemented in a cascading manner. Then, the prescribed-time zero-error estimation performance of the above DPTO is guaranteed for both time-invariant and time-varying directed interaction topologies, based on strictly Lyapunov stability analysis and mathematical induction method. Finally, the practicability and validity of this new distributed observer are illustrated via a numerical simulation example.

A Network Pharmacology-Based Study on Vital Pharmacological Pathways and Targets of Eucommiae Cortex Acting on Osteoporosis

Background Eucommiae Cortex is a Chinese herbal medicine with bone protective effects and treats osteoporosis. This study aimed to explore the pharmacological mechanisms of this complex mixture. Methods The active compounds and disease targets involved in the study were obtained from publicly available websites and databases. Core target genes were identified by protein-protein interaction (PPI) network and topology analysis and mapped to critical components by a “component-target” regulatory network. Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway analysis and Gene Ontology (GO)analysis were performed to analyze the biological processes of target genes. Moreover, we carried out molecular docking, cell experiments, and quantitative real-time PCR to propel the research forward. Results Eucommiae Cortex contained 28 active ingredients and 85 potential targets for antiosteoporosis. PPI networks and topology analysis screened 17 core target genes. The therapeutic mechanism involves a series of biological reactions, including host-virus response, chemical stress, oxidative stress, and cell cycle control. The KEGG enrichment illustrated that the MAPK signaling pathway might play a significant role. The final experiment detected ten genes (EGF, AKT1, JUN, MAPK8, MAPK1, CASP3, FOS, VEGFA, EGFR, and MYC) and three compounds (quercetin, kaempferol, and beta- carotene) in the MAPK pathway. Firstly, the CCK-8 and ALP activity test results showed that three compounds could enhance the proliferation and differentiation of osteoblast-like MC3T3-E1 cells. Secondly, molecular docking confirmed the favorable binding potential. Subsequently, we observed that adding 1∗10−6 mol/L quercetin, 1∗10−5 mol/L kaempferol, and 1∗10−5 mol/L beta-carotene activated the ERK/JNK cascades and the heterodimer complex AP-1(Fos/Jun) in the MAPK pathway. Conclusion MAPK pathway might provide an essential mechanism for the antiosteoporosis effect of Eucommiae Cortex. Eucommiae Cortex and its active ingredients have the potential to treat osteoporosis.

Insights on novel type V deep eutectic solvents based on levulinic acid.

Type V natural deep eutectic solvents considering menthol, thymol, and levulinic acids are studied considering a combined experimental and theoretical approach to develop a multiscale characterization of these fluids with particular attention to intermolecular forces (hydrogen bonding) and their relationships with macroscopic behavior. Density, viscosity, refraction index, and thermal conductivity were measured as a function of temperature, providing a thermophysical characterization of the fluids. Quantum chemistry was applied to characterize hydrogen bonding in minimal molecular clusters, allowing us to quantify interaction strength, topology (according to atoms in a molecule theory), and electronic properties. Classical molecular dynamics simulations were also performed, allowing us to characterize bulk liquid phases at the nanoscopic level, analyzing the fluid's structuring, void distribution, and dynamics. The reported results allowed us to infer nano-macro relationships, which are required for the proper design of these green solvents and their application for different technologies.

Synchronization of Finite-Field Networks With Time Delays

In this paper, finite-field networks (FFNs) with time delays are investigated. We analyze the dynamics of FFNs with time delays in view of linear recursion theory. It is shown that delayed FFNs behave in a pattern similar to non-delayed FFNs in the sense that any state sequence finally reaches a periodic behavior within finite steps. In addition, a criterion assuring purely periodic behaviors is given. For delayed FFNs, an algebra-theoretic criterion for synchronization is proposed for the first time which imposes constraints on the network matrix tuple and the interaction weights. As an application, we study the synchronization problem of delayed FFNs with a tree-structured interaction topology and accordingly provide a criterion of a simple form. Inspired by the existing results, for the tree case, we prove that a given delayed FFN solves a consensus problem if and only if the same does the corresponding non-delayed FFN. As a comparison, examples are shown to illustrate similar conclusion is invalid for the general synchronization problems since the consensus is included in the synchronization. In the end, we identify the specific periodic behavior of synchronized FFNs satisfying the proposed sufficient condition.

Pattern Discovery in Multilayer Networks.

In bioinformatics, complex cellular modeling and behavior simulation to identify significant molecular interactions is considered a relevant problem. Traditional methods model such complex systems using single and binary network. However, this model is inadequate to represent biological networks as different sets of interactions can simultaneously take place for different interaction constraints (such as transcription regulation and protein interaction). Furthermore, biological systems may exhibit varying interaction topologies even for the same interaction type under different developmental stages or stress conditions. Therefore, models which consider biological systems as solitary interactions are inaccurate as they fail to capture the complex behavior of cellular interactions within organisms. Identification and counting of recurrent motifs within a network is one of the fundamental problems in biological network analysis. Existing methods for motif counting on single network topologies are inadequate to capture patterns of molecular interactions that have significant changes in biological expression when identified across different organisms that are similar, or even time-varying networks within the same organism. That is, they fail to identify recurrent interactions as they consider a single snapshot of a network among a set of multiple networks. Therefore, we need methods geared towards studying multiple network topologies and the pattern conservation among them. Contributions: In this paper, we consider the problem of counting the number of instances of a user supplied motif topology in a given multilayer network. We model interactions among a set of entities (e.g., genes)describing various conditions or temporal variation as multilayer networks. Thus a separate network as each layer shows the connectivity of the nodes under a unique network state. Existing motif counting and identification methods are limited to single network topologies, and thus cannot be directly applied on multilayer networks. We apply our model and algorithm to study frequent patterns in cellular networks that are common in varying cellular states under different stress conditions, where the cellular network topology under each stress condition describes a unique network layer. We develop a methodology and corresponding algorithm based on the proposed model for motif counting in multilayer networks. We performed experiments on both real and synthetic datasets. We modeled the synthetic datasets under a wide spectrum of parameters, such as network size, density, motif frequency. Results on synthetic datasets demonstrate that our algorithm finds motif embeddings with very high accuracy compared to existing state-of-the-art methods such as G-tries, ESU (FANMODE)and mfinder. Furthermore, we observe that our method runs from several times to several orders of magnitude faster than existing methods. For experiments on real dataset, we consider Escherichia coli (E. coli)transcription regulatory network under different experimental conditions. We observe that the genes selected by our method conserves functional characteristics under various stress conditions with very low false discovery rates. Moreover, the method is scalable to real networks in terms of both network size and number of layers.

Event-triggered leader-following consensus of multi-agent systems under semi-Markov switching topology with partially unknown rates

An event-triggered leader-following consensus problem for multi-agent systems with nonlinear dynamics was investigated in this study. The interaction topologies among the agents that we considered are randomly switched ones, governed by a semi-Markov process with partially unknown rates. By building the state error model between the leader and followers, the consensus problem is first converted into a stability problem. Moreover, an event-triggered transmission scheme based on sampling data was proposed to reduce communication redundancy. The consensus controller and event-triggered parameters can be designed effectively. By constructing a Lyapunov–Krasovskii functional (LKF) with a triple integral, the sufficient conditions required to guarantee the event-triggered consensus can be reached with respect to the linear matrix inequalities (LMIs). Ultimately, the validity of the theoretical results is demonstrated by a numerical example.

Path-dependent dynamics induced by rewiring networks of inertial oscillators.

In networks of coupled oscillators, it is of interest to understand how interaction topology affects synchronization. Many studies have gained key insights into this question by studying the classic Kuramoto oscillator model on static networks. However, new questions arise when the network structure is time varying or when the oscillator system is multistable, the latter of which can occur when an inertial term is added to the Kuramoto model. While the consequences of evolving topology and multistability on collective behavior have been examined separately, real-world systems such as gene regulatory networks and the brain may exhibit these properties simultaneously. It is thus relevant to ask how time-varying network connectivity impacts synchronization in systems that can exhibit multistability. To address this question, we study how the dynamics of coupled Kuramoto oscillators with inertia are affected when the topology of the underlying network changes in time. We show that hysteretic synchronization behavior in networks of coupled inertial oscillators can be driven by changes in connection topology alone. Moreover, we find that certain fixed-density rewiring schemes induce significant changes to the level of global synchrony that remain even after the network returns to its initial configuration, and we show that these changes are robust to a wide range of network perturbations. Our findings highlight that the specific progression of network topology over time, in addition to its initial or final static structure, can play a considerable role in modulating the collective behavior of systems evolving on complex networks.