Clustering Mechanism Research Articles

A method of water resources accounting based on deep clustering and attention mechanism under the background of integration of public health data and environmental economy.

Water resource accounting constitutes a fundamental approach for implementing sophisticated management of basin water resources. The quality of water plays a pivotal role in determining the liabilities associated with these resources. Evaluating the quality of water facilitates the computation of water resource liabilities during the accounting process. Traditional accounting methods rely on manual sorting and data analysis, which necessitate significant human effort. In order to address this issue, we leverage the remarkable feature extraction capabilities of convolutional operations to construct neural networks. Moreover, we introduce the self-attention mechanism module to propose an unsupervised deep clustering method. This method offers assistance in accounting tasks by automatically classifying the debt levels of water resources in distinct regions, thereby facilitating comprehensive water resource accounting. The methodology presented in this article underwent verification using three datasets: the United States Postal Service (USPS), Heterogeneity Human Activity Recognition (HHAR), and Association for Computing Machinery (ACM). The evaluation of Accuracy rate (ACC), Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI) metrics yielded favorable results, surpassing those of K-means clustering, hierarchical clustering, and Density-based constraint extension (DCE). Specifically, the mean values of the evaluation metrics across the three datasets were 0.8474, 0.7582, and 0.7295, respectively.

Multiple mechanisms of self-association of chemokine receptors CXCR4 and CCR5 demonstrated by deep mutagenesis

Chemokine receptors are members of the rhodopsin-like class A GPCRs whose signaling through G proteins drives the directional movement of cells in response to a chemokine gradient. Chemokine receptors CXCR4 and CCR5 have been extensively studied due to their roles in leukocyte development and inflammation and their status as coreceptors for HIV-1 infection, among other roles. Both receptors form dimers or oligomers of unclear function. While CXCR4 has been crystallized in a dimeric arrangement, available atomic resolution structures of CCR5 are monomeric. To investigate their dimerization interfaces, we used a bimolecular fluorescence complementation (BiFC)-based screen and deep mutational scanning to find mutations that change how the receptors self-associate, either via specific oligomer assembly or alternative mechanisms of clustering in close proximity. Many disruptive mutations promoted self-associations nonspecifically, suggesting they aggregated in the membrane. A mutationally intolerant region was found on CXCR4 that matched the crystallographic dimer interface, supporting this dimeric arrangement in living cells. A mutationally intolerant region was also observed on the surface of CCR5 by transmembrane helices 3 and 4. Mutations predicted from the scan to reduce BiFC were validated and were localized in the transmembrane domains as well as the C-terminal cytoplasmic tails where they reduced lipid microdomain localization. A mutation in the dimer interface of CXCR4 had increased binding to the ligand CXCL12 and yet diminished calcium signaling. There was no change in syncytia formation with cells expressing HIV-1 Env. The data highlight that multiple mechanisms are involved in self-association of chemokine receptor chains.

Electrodeposition-Assisted Crystal Growth Regulation of PdBi Clusters on Carbon Cloths for Ethanol Oxidation.

Carbon-supported Pd-based clusters are one of the most promising anodic catalysts for ethanol oxidation reaction (EOR) due to their encouraging activity and practical applications. However, unclear growth mechanism of Pd-based clusters on the carbon-based materials has hindered their extensive applications. Herein, we first introduce multi-void spherical PdBi cluster/carbon cloth (PdBi/CC) composites by an electrodeposition routine. The growth mechanism of PdBi clusters on the CC supports has been systemically investigated by evaluating the selected samples and tuning their compositions, which involve the big difference in standard redox potential between Pd2+/Pd and Bi3+/Bi and easy adsorption of Bi3+ on the surface of Pd-rich seeds. Benefitting from the ensembles of many nanocrystal subunits, multi-void spherical PdBi clusters can present collective properties and novel functionalities. In addition, the outstanding characteristics of CC supports enable PdBi clusters with stable nanostructures. Thanks to the unique structure, Pd20Bi/CC catalysts manifest higher EOR activity and better stability compared to Pd/CC. Systematic characterizations and a series of CO poisoning tests further confirm that the dramatically enhanced EOR activity and stability can be attributed to the incorporation of Bi species and the strong coupling of the structure between PdBi clusters and CC supports.

GRIMGEP: Learning Progress for Robust Goal Sampling in Visual Deep Reinforcement Learning

Designing agents, capable of learning autonomously a wide range of skills is critical in order to increase the scope of reinforcement learning. It will both increase the diversity of learned skills and reduce the burden of manually designing reward functions for each skill. Self-supervised agents, setting their own goals, and trying to maximize the diversity of those goals have shown great promise towards this end. However, a currently known limitation of agents trying to maximize the diversity of sampled goals is that they tend to get attracted to noise or more generally to parts of the environments that cannot be controlled (distractors). When agents have access to predefined goal features or expert knowledge, absolute Learning Progress (ALP) provides a way to distinguish between regions that can be controlled and those that cannot. However, those methods often fall short when the agents are only provided with raw sensory inputs such as images. In this work we extend those concepts to unsupervised image-based goal exploration. We propose a framework that allows agents to autonomously identify and ignore noisy distracting regions while searching for novelty in the learnable regions to both improve overall performance and avoid catastrophic forgetting. Our framework can be combined with any state-of-the-art novelty seeking goal exploration approaches. We construct a rich 3D image based environment with distractors. Experiments on this environment show that agents using our framework successfully identify interesting regions of the environment, resulting in drastically improved performances. The source code is available at https://sites.google.com/view/grimgep.

Global trends and knowledge-relationship of symptom clusters in cancer research: a bibliometric analysis over the past 20 years

Abstract Objective To use CiteSpace and VOSviewer to investigate the scientific production in the field of symptom clusters in cancer research. Methods The search was performed using the terms “symptom clusters,” “cancer,” and “oncology” on the Web of Science Core Collection database. The retrieval time was from 2001 to 2021, which covers the last 2 decades. Based on the production theory of scientific knowledge and the data mining of citations, data pertaining to the annual publications, journals, countries, organizations, authors, and keywords that produce symptom clusters in cancer research, as well as their cooperation (collaboration network), were extracted, and then both were clarified by the software tools VOSviewer (version 1.6.16) and CiteSpace (version 6.1.R2). Results A total of 1796 publications were retrieved between 2001 and 2021, and 473 relevant publications were included after screening. The results showed an increasing trend in published articles. The United States had the largest number of publications (261/473, 55.18%), followed by China and Canada. The University of California, San Francisco, was the most productive institution. Current research hotspots included the analysis of symptom clusters and symptom management in patients with breast cancer and lung cancer, as well as any advanced cancer and cancer cachexia; fatigue-related symptom clusters and depression-anxiety symptom cluster; and the impacts of symptom clusters on quality of life. The research frontiers included analysis between health-related quality of life and symptom clusters, data mining in symptom clusters, research on the mental health status of cancer patients, and study of the mechanism and biological pathways of symptom clusters. Conclusions The study provides insight into the global research perspective for the scientific progress on cancer symptom clusters, which suggests a growing scientific interest in this field, and more studies are warranted to guide symptom management.

A high-precision automatic recognition method based on target detection for nanometer scaled precipitates or carbides in different alloys

The recognition and statistical analysis of nanometer scaled second phase (NSP), such as precipitates or carbides in alloys is considered to be very important for optimization of mechanical properties. Statistical analysis of the size, number, and distribution of NSPs is required, and manual statistics are difficult and inefficient. In order to solve the above problems, this paper provides an object detection technology for the automatic identification of the NSPs in different alloys. A dataset is constructed by labeling the NSPs in typical transmission electron micrographs (TEM) photographs using a labelImg software. First of all, based on the YOLOv3 object detection network in deep learning, changing the network input size is compatible with the TEM image size. Secondly, using two improved schemes, a priori box K-means clustering and CBAM attention mechanism, a variety of class NSPs detection network is proposed. The statistical results show that the network developed herein has strong applicability. This network can be used for identification for the NSP in several types of alloys, i.e., Cu–Sc alloy, 4Cr5MoSiV1 and Cu–Ni–Si alloy. The accuracy of this network for the recognition of these NSP in Cu–Sc alloy (Cu4Sc precipitates), 4Cr5MoSiV1 (carbides) and Cu–Ni–Si alloy (Ni2Si precipitates) is 93.55%, 81.82% and 93.13%, respectively.

Prism rather than tetrahedron: low-energy structures for gaseous gold clusters Au10(O2)n + by density functional calculations

The purpose of this study is to reveal the adsorption mechanism of oxygen molecules on gold clusters. density functional theory is employed to investigate the low-energy structure of multiple O2 adsorption on the Au10 + cluster and findings are compared with IR spectra. The nature of bonding of the O2 molecule and Au10 + cluster has been characterised through several metrics like binding energy, dissociation energy, bond length, and vibrational frequencies. The result shows that the lowest-energy structures are prism-shaped rather than tetrahedron-shaped, where only one O2 is chemically adsorbed while others are physically adsorbed. Chemically adsorbed η2-O2 behaves like free O2 –, forming a single electron π bond with Au10 +, while physically adsorbed η1-O2 does not result in an effective chemical bond and behaves like free O2. This study provides insights into the mechanism of oxygen molecule adsorption on gold clusters and can contribute to further research on the catalytic mechanism of gold clusters. Highlights Lowest energy structures are prism-shaped rather than tetrahedron-shaped, which is supported by IR spectrum and chemical hardness. Chemically adsorbed O2 is activated and displays structural and spectral features of free O2 –, while physically adsorbed O2 is close to free O2 in vibrational frequency and bond length. Chemical adsorption is equivalent to a single electron π bond while physical adsorption forms no effective chemical bond due to the lack of electronic pairing between Au10 + and O2.

Polyhedral Colloidal Clusters Assembled from Amphiphilic Nanoparticles in Deformable Droplets.

Polyhedral colloidal clusters assembled from functional inorganic nanoparticles have attracted great interest in both scientific research and applications. However, the spontaneous assembly of colloidal nanoparticles into polyhedral clusters with regular shape and tunable structures remains a grand challenges. Here, we successfully construct Mackay icosahedral and regular tetrahedral colloidal clusters assembled from gold nanoparticles grafted with a mixture of polystyrene (PS) and poly(2-vinylpyridine) (P2VP) homopolymers by precisely tuning the interfacial interaction between the nanoparticles and the oil/water interface. By increasing the proportion of hydrophilic P2VP ligands on the surface of gold nanoparticles, the Mackay icosahedral clusters can transform into regular tetrahedral clusters in order to maximize the surface area of the polyhedral assembly. Furthermore, we reveal the formation mechanism of these regular polyhedral colloidal clusters. The formation of polyhedral colloidal clusters is not only dependent on the entropy but also determined by the interfacial free energy. This finding demonstrates an effective approach to organize nanoparticles into polyhedral colloidal clusters with potential applications in various fields.

Impact of Developmental Changes of GABAA Receptors on Interneuron-NG2 Glia Transmission in the Hippocampus.

NG2 glia receive synaptic input from neurons, but the functional impact of this glial innervation is not well understood. In the developing cerebellum and somatosensory cortex the GABAergic input might regulate NG2 glia differentiation and myelination, and a switch from synaptic to extrasynaptic neuron-glia signaling was reported in the latter region. Myelination in the hippocampus is sparse, and most NG2 glia retain their phenotype throughout adulthood, raising the question of the properties and function of neuron-NG2 glia synapses in that brain region. Here, we compared spontaneous and evoked GABAA receptor-mediated currents of NG2 glia in juvenile and adult hippocampi of mice of either sex and assessed the mode of interneuron-glial signaling changes during development. With patch-clamp and pharmacological analyses, we found a decrease in innervation of hippocampal NG2 glia between postnatal days 10 and 60. At the adult stage, enhanced activation of extrasynaptic receptors occurred, indicating a spillover of GABA. This switch from synaptic to extrasynaptic receptor activation was accompanied by downregulation of γ2 and upregulation of the α5 subunit. Molecular analyses and high-resolution expansion microscopy revealed mechanisms of glial GABAA receptor trafficking and clustering. We found that gephyrin and radixin are organized in separate clusters along glial processes. Surprisingly, the developmental loss of γ2 and postsynaptic receptors were not accompanied by altered glial expression of scaffolding proteins, auxiliary receptor subunits or postsynaptic interaction proteins. The GABAergic input to NG2 glia might contribute to the release of neurotrophic factors from these cells and influence neuronal synaptic plasticity.

High Availability’s Implementation on the Fortigate Firewall Using SD-WAN Zone and HA Cluster Active-Passive

Networks that contain sensitive data or have high-security requirements require reliable and dependable network security solutions. FortiGate Firewall, as one of the popular network security solutions, provides High Availability (HA) features that ensure network availability and reliability. This study aims to analyze the implementation of HA on FortiGate Firewall in specific scenarios, such as networks with sensitive data or networks with high-security requirements. A real-world case study is applied to evaluate the effectiveness of HA implementation on FortiGate Firewall in enhancing network reliability and security. In this research, the implementation of HA on FortiGate Firewall at PT ABC was successfully built using the PPDIOO method and deploying SD-WAN load balancing and failover configurations, along with the utilization of a high-availability cluster mechanism. The HA implementation on FortiGate proves to be an effective solution, as demonstrated in the SD-WAN Zone testing of the Source-destination SD-WAN load balancing mode, which showed effectiveness in evenly and optimally distributing traffic among multiple available links, resulting in a 93.3% reduction in downtime. Furthermore, the testing of HA cluster mode in active-passive configuration achieved a 91.8% reduction in downtime compared to the pre-HA implementation state.

CLUSTER MECHANISM FOR MANAGING INVESTMENT ATTRACTION IN THE FIELD OF AGROTOURISM

This article discusses the mechanism of investment process management, their main elements. The importance of attracting investments in tourism as the leading sector of the economy of Uzbekistan, in particular in the field of agrotourism, was also highlighted. The mechanisms of management of attracting investments in the field of agrotourism are analyzed. It is noted that in conditions of fierce market competition, mutual integration is of great importance for improving the efficiency and competitiveness of organizations and ensuring investment attractiveness. It is noted that the cluster mechanism for managing the attraction of investments in the field of agrotourism is one of the most effective mechanisms for achieving a synergistic effect. The structure of the regional tourism cluster has been developed.

Microstructure evolution mechanisms and a physically-based constitutive model for an Al–Zn–Mg–Cu–Zr aluminum alloy during hot deformation

High-temperature flow features of the Al−Zn−Mg−Cu−Zr aluminum alloy was revealed by hot compression tests. The evolution mechanisms of dislocation clusters, subgrain, and dynamic recrystallization (DRX) grains, are thoroughly explored by EBSD and TEM analysis. Experimental results suggest that the high strain rate can exacerbate dislocation clusters formation, as well as subgrain nucleation/accumulation, inducing the increasing of flow stress. Nevertheless, the noticeable annihilation of substructures, as well as the growth of DRX grains, emerge at the higher temperature, causing the descending of flow stress. Three types of DRX nucleating mechanisms, i.e., discontinuous DRX (DDRX), geometric DRX (GDRX) and continuous DRX (CDRX) are activated in the Al−Zn−Mg−Cu−Zr aluminum alloy during hot compression. Simultaneously, the GDRX often appears at a high compressed temperature or a low strain rate. A physically-based (PB) model is proposed to collaboratively reconstruct true stresses and microstructure evolution features. The estimated values of true stress, DRX fractions and average grain size preferably fit the experimental data, indicating the proposed PB model can precisely catch the thermal compression behaviors and microstructure evolution characteristics of the Al−Zn−Mg−Cu−Zr aluminum alloy.

Insights into radiation resistance of titanium alloys from displacement cascade simulations

Radiation damage in beam window materials limits the use of high power proton beams in high energy physics research. The alloy Ti-6Al-4V is presently used as a beam window material but a prospective alternative, Ti-15V-3Cr-3Sn-3Al has been proposed. Since both these alloys contain dual phases at room temperature, we compare the radiation damage in the α and β-phases of these two materials via primary knock-on atom (PKA) cascade simulations in the 10–40 keV energy range. At PKA energies 30 and 40 keV, the number of Frenkel pairs in the ballistic stage is higher in the β-phase of Ti-6Al-4V than that in the β-phase of Ti-15V-3Cr-3Sn-3Al almost by a factor of 2. The α-phase, of both these alloys, by far outperforms the β-phases of the two alloys, both in terms of damage during the ballistic stage and in terms of the surviving defects. The average displacement threshold energy (Ed) in the α-phase of both alloys was found to be 66 eV while that in the β-phase of Ti-15-3-3 was 55 eV and in the β-phase of Ti-6-4 was 46. While the number of surviving defects is almost equal in both alloys, the vacancy and interstitial clustering mechanisms differ notably, which can impact the degree of radiation hardening and loss of ductility. Our simulations show larger vacancy and interstitial clusters form in Ti-6Al-4V as compared to that in Ti-15-3-3-3 alloy. These results indicate that Ti-15-3-3-3 alloys may be a promising candidate for next generation beam window material with a higher radiation tolerance than the existing Ti-6-4 alloy.

Cluster Mechanism of the Explosive Processes Initiation in the Matter

The relationship between substance characteristic temperatures: autoignition, melting, flash, boiling is demonstrated and analyzed. Based on the oscillatory and step changes presence, a conclusion was made about the supramolecular structures presence and periodicity in the n-alkanes homologous series. A method for modeling equivalent lengths of peroxide supramolecular structures for predicting the explosion and fire hazard parameters of n-alkanes is proposed. An approximation dependence was developed for predicting autoignition temperatures tai of n-alkanes. It is shown that stoichiometric concentrations of the various supramolecular peroxide structures formation accord to different flammability and explosion limits. A correlation between tai and Anti-Knock Index (AKI) was established. An approximation dependence was developed for predicting n-alkanes AKI. The detonation propensity index КD was introduced based on cluster supramolecular structures modeling and melting temperatures. It is shown that КD indicator correlates with the n-alkanes AKI and with the explosives detonation velocity. The possibility of taking into account during calculations the supramolecular structures presence at the combustion stage confirms their existence.

Be13 cluster adsorbs water molecules splitting to produce H2 based on density functional theory

Research on the production of hydrogen from water by catalytic decomposition using metal clusters is of great significance in addressing the global energy crisis. Due to the quantum size effect, tunability, and ultra-high surface area, metal clusters exhibit many unique properties, including catalytic performance and optical properties. In this work, the lowest energy structure of Be13 cluster and intermediate states, as well as the hydrogen production mechanism of Be13 cluster and intermediate states + single water molecule were studied by DFT calculations at the PBE0-D3/DEF2TZVP level. The results show that both Be13 cluster and intermediate states release energy during water molecules adsorption and both Be13 cluster and its intermediate state + single water molecule reactions are exothermic. The electronic structures of the original and adsorbed structures were analyzed by ESP method, and their DOS plots were drawn. We investigated the chemical bonding and interactions of the initial structure, reactants and products using IRI analysis. Our results help to provide a basis for developing a more effective method for generating hydrogen from water molecules.

A cluster-based trusted routing method using fire hawk optimizer (FHO) in wireless sensor networks (WSNs)

Today, wireless sensor networks (WSNs) are growing rapidly and provide a lot of comfort to human life. Due to the use of WSNs in various areas, like health care and battlefield, security is an important concern in the data transfer procedure to prevent data manipulation. Trust management is an affective scheme to solve these problems by building trust relationships between sensor nodes. In this paper, a cluster-based trusted routing technique using fire hawk optimizer called CTRF is presented to improve network security by considering the limited energy of nodes in WSNs. It includes a weighted trust mechanism (WTM) designed based on interactive behavior between sensor nodes. The main feature of this trust mechanism is to consider the exponential coefficients for the trust parameters, namely weighted reception rate, weighted redundancy rate, and energy state so that the trust level of sensor nodes is exponentially reduced or increased based on their hostile or friendly behaviors. Moreover, the proposed approach creates a fire hawk optimizer-based clustering mechanism to select cluster heads from a candidate set, which includes sensor nodes whose remaining energy and trust levels are greater than the average remaining energy and the average trust level of all network nodes, respectively. In this clustering method, a new cost function is proposed based on four objectives, including cluster head location, cluster head energy, distance from the cluster head to the base station, and cluster size. Finally, CTRF decides on inter-cluster routing paths through a trusted routing algorithm and uses these routes to transmit data from cluster heads to the base station. In the route construction process, CTRF regards various parameters such as energy of the route, quality of the route, reliability of the route, and number of hops. CTRF runs on the network simulator version 2 (NS2), and its performance is compared with other secure routing approaches with regard to energy, throughput, packet loss rate, latency, detection ratio, and accuracy. This evaluation proves the superior and successful performance of CTRF compared to other methods.

The Adsorption Behaviors of CO and H2 to FeO onto CaO Surfaces: A Density Functional Theory Study.

The adsorption behaviors of CO and H2 to FeO onto CaO surfaces have been studied using the density functional theory (DFT) to determine the reactions of FeO by CO and H2. The adsorption mechanisms of FeO clusters on the CaO(100) and CaO(110) surfaces were calculated first. The structure of the Ca(110) surface renders it highly chemically reactive compared with the Ca(100) surface because of low coordination. After gas adsorption, CO bonds to the O atom of FeO, forming CO2 compounds in both configurations through the C atom. H2 favors the O atom of FeO, forming H2O compounds and breaking the Fe-O bond. Comparing the adsorption behavior of two reducing gases to FeO on the Ca surface, the reaction of the CO molecule being adsorbed to generate CO2 compounds is exothermic. The reaction of H2 molecule adsorption to generate H2O compounds is endothermic. This property is essential for the inertial-collision stage of the reduction. However, the dissociation of the CO2 compound from the reaction interface will overcome a high energy barrier and slow down the reduction. The H2O compound dissociates from the surface more easily, which can accelerate the reduction.

Ion clustering, aggregation and diffusion in amide based deep eutectic solvents: A microstructural investigation using various NMR spectroscopic techniques and molecular dynamics simulation

A thorough understanding of the structure and dynamics of deep eutectic solvents (DESs) is necessary to optimize the uses of this new class of solvents for many potential applications. Herein, we have studied ion clustering, aggregation and diffusion mechanism in DESs composed of acetamide and sodium/potassium thiocyanate using 1D NMR, pulsed field gradient stimulated echo (PFGSE) NMR and 2D nuclear Overhauser effect spectroscopy (2D NOESY) techniques, and molecular dynamics simulation. We have varied the fraction of salts to investigate the composition dependent microscopic structural insight. A great enhancement in diffusion coefficient (D) of acetamide with increasing the fraction of KSCN (fKSCN) has been observed in PFGSE measurements. The experimental diffusion activation energy (Ea) decreases significantly with increasing fKSCN, which is in excellent agreement with the Ea values obtained from simulated mean squared displacements (MSD). The calculated radial distribution functions (RDF) indicate that the Na+ ions strongly coordinate with acetamide molecules and form ion-clusters and aggregated structures in the medium. However, as Na+ ions are gradually replaced by K+ ions, ion-clustering and aggregation decrease due to relatively weak coordination ability of larger cation, which supports the enhancement of experimental D with increasing fKSCN. In the 2D NOESY study, the intensity of the cross peaks between methyl and amide protons of acetamide decreases relative to the diagonal peak intensity as the fKSCN increases in the medium. This study indicates that the extent of intermolecular NOE reduces as the structural flexibility increases due to less efficient ion-clustering with increasing K+ ion concentration, thus strongly corroborates the results from PFGSE experiment and simulation. Furthermore, 1D and 2D NMR data exhibit an intramolecular chemical exchange between two amide protons in acetamide, the extent of which increases with increasing temperature as well as fKSCN in the DES system. The present study provides a multifaceted picture of composition-dependent distinct microstructures in DES.

A Design of Fuzzy Rule-Based Models With Data Privacy

One of the currently visible requirement of data analysis is to cope with data privacy requirements. The quest for privacy triggers design challenges in the construction of Machine Learning models. In this study, we propose a novel and systematic design methodology of fuzzy rule-based models completed in the following privacy-driven environment. Multidimensional data are available for model design in two different formats: those attributes that are privacy sensitive are provided at the higher non-numeric level of abstraction represented in the form of information granules while the remaining ones are available directly. It is shown that the granular manifestation of data becomes beneficial in the formation of the conditions of the rules whereas numeric attributes are available for the construction of the conclusions of the rules. The generic development scheme is discussed. Some generalization is introduced to cope with a number of data islands associated with corresponding data owners. It is shown that a formation of the data structure calls for a mechanism of collaborative clustering. A way of handling information granules at different levels of information granularity is discussed. Some illustrative examples are presented.

Different mechanisms of synapsin-induced vesicle clustering at inhibitory and excitatory synapses

Synapsins cluster synaptic vesicles (SVs) to provide a reserve pool (RP) of SVs that maintains synaptic transmission during sustained activity. However, it is unclear how synapsins cluster SVs. Here we show that either liquid-liquid phase separation (LLPS) or tetramerization-dependent cross-linking can cluster SVs, depending on whether a synapse is excitatory or inhibitory. Cell-free reconstitution reveals that both mechanisms can cluster SVs, with tetramerization being more effective. At inhibitory synapses, perturbing synapsin-dependent LLPS impairs SV clustering and synchronization of gamma-aminobutyric acid (GABA) release, while preventing synapsin tetramerization does not. At glutamatergic synapses, the opposite is true: synapsin tetramerization enhances clustering of glutamatergic SVs and mobilization of these SVs from the RP, while synapsin LLPS does not. Comparison of inhibitory and excitatory transmission during prolonged synaptic activity reveals that synapsin LLPS serves as a brake to limit GABA release, while synapsin tetramerization enables rapid mobilization of SVs from the RP to sustain glutamate release.