Clustering Behavior Research Articles

Decomposition of Zero-Dimensional Persistence Modules via Rooted Subsets

AbstractWe study the decomposition of zero-dimensional persistence modules, viewed as functors valued in the category of vector spaces factorizing through sets. Instead of working directly at the level of vector spaces, we take a step back and first study the decomposition problem at the level of sets. This approach allows us to define the combinatorial notion of rooted subsets. In the case of a filtered metric space M, rooted subsets relate the clustering behavior of the points of M with the decomposition of the associated persistence module. In particular, we can identify intervals in such a decomposition quickly. In addition, rooted subsets can be understood as a generalization of the elder rule, and are also related to the notion of constant conqueror of Cai, Kim, Mémoli and Wang. As an application, we give a lower bound on the number of intervals that we can expect in the decomposition of zero-dimensional persistence modules of a density-Rips filtration in Euclidean space: in the limit, and under very general circumstances, we can expect that at least 25% of the indecomposable summands are interval modules.

Artefact design and societal worldview.

Technological artefacts are created in accordance with the values and worldviews of their designers. In operation, they act as a medium, facilitating and constraining human interaction with, and perception of, the world. When used on a large scale, they may lastingly affect societal ethos. If institutional structures of domination allocate the resources necessary for artefact design and development to some population groups over others, the direction and extent of such an effect may lead to increased disparity and inequity. While the direct influence of technology on opinion is well-studied, the evaluation of non-epistemic values, assumptions and presuppositions is a hurdle in the way of a deeper understanding of the large-scale effects of asymmetries in worldviews embodied by artefacts. Here, we show that artefacts have a strong potential to bias societal worldviews when they are distributed unevenly across the value spectrum. They can affect the clustering behaviour of agents with regard to worldview, both aiding and hindering intra- and inter-cluster diversity, depending on their distribution and frequency. Our findings underline the distributional sensitivity of worldview dynamics to institutional structures of domination. We highlight the importance of procedural interventions such as participatory design, which explicitly acknowledges existing asymmetries and redistributes power accordingly.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

Phase Behavior and Atomic Dynamics in RbxNa1-x: Insights from Machine Learning Interatomic Potentials based on Ab Initio Molecular Dynamics.

Liquid alkali metal alloys have garnered significant attention because of their potential applications in coolant systems and batteries, driven by the need for environmental conservation and technological development. However, research on these complex systems is limited, necessitating a deeper understanding to ensure their safe and effective utilization. This study presents a comprehensive investigation of the factors that determine the phase diagram of RbxNa1-x. By reproducing the experimental results using the thermodynamic integration method and machine learning interatomic potentials based on ab initio molecular dynamics simulations, we uncovered the delicate balance between the energy and entropy contributions that influence the phase stability of these liquid metal alloys. Further analysis of the liquid phase revealed the crucial roles of volume and atomic mass. Additionally, the coordination numbers of the atoms revealed distinct clustering behaviors, where Na atoms tended to avoid proximity to other Na atoms, whereas Rb atoms exhibited a strong tendency to cluster together. Moreover, the diffusion dynamics further illustrated the asymmetry in the behavior of Rb and Na, with Rb showing increased diffusion at higher concentrations and Na exhibiting higher diffusion at lower concentrations. These findings offer significant insights into the phase stability and the dynamic and structural properties of these complex liquid metal alloys.

Understanding melting behavior of aluminum clusters using machine learned deep neural network potential energy surfaces.

Deep neural network-based deep potentials (DP), developed by Tuo et al., have been used to compute the thermodynamic properties of free aluminum clusters with accuracy close to that of density functional theory. Although Jarrold and collaborators have reported extensive experimental measurements on the melting temperatures and heat capacities of free aluminum clusters, no reports exist for finite-temperature abinitio simulations on larger clusters (N > 55 atoms). We report the heat capacities and melting temperatures for 32 clusters in the size range of 48-342 atoms, computed using the multiple histogram technique. Extensive molecular dynamics (MD) simulations at twenty four temperatures have been performed for all the clusters. Our results are in very good agreement with the experimental melting temperatures for 19 clusters. Except for a few sizes, the interesting features in the heat capacities have been reproduced. To gain insight into the striking features reported in the experiments, we used structural and dynamical descriptors such as temperature-dependent mean squared displacements and the Lindemann index. Bimodal features observed in Al116 and the weak shoulder seen in Al52 are attributed to solid-solid structural transitions. In confirmation of the earlier reports, we observe that the behavior of the heat capacities is significantly influenced by the nature of the ground state geometries. Our findings show that the sharp drop in the melting temperature of the 56-atom cluster is a consequence of the change in the geometry of Al55. Mulliken population analysis of Al55 reveals that the charge-induced local electric field is responsible for the strong bonding between core and surface atoms, leading to the higher melting temperature. Our calculations do not support the lower melting temperature observed in experimental studies of Al69. Our results indicate that Al48 is in a liquid state above 600K and does not support the high melting temperature reported in the experiment. It turns out that the accuracy of the DP model by Tuo et al. is not reliable for MD simulations beyond 750K. We also report low-lying equilibrium geometries and thermodynamics of 11 larger clusters (N = 147-342) that have not been previously reported, and the melting temperatures of these clusters are in good agreement with the experimental ones.

Accelerated Simulations Reveal Physicochemical Factors Governing Stability and Composition of RNA Clusters.

Under certain conditions, RNA repeat sequences phase separate, yielding protein-free biomolecular condensates. Importantly, RNA repeat sequences have also been implicated in neurological disorders, such as Huntington's disease. Thus, mapping repeat sequences to their phase behavior, functions, and dysfunctions is an active area of research. However, despite several advances, it remains challenging to characterize the RNA phase behavior at a submolecular resolution. Here, we have implemented a residue-resolution coarse-grained model in LAMMPS─that incorporates both the RNA sequence and structure─to study the clustering propensities of protein-free RNA systems. Importantly, we achieve a multifold speedup in the simulation time compared to previous work. Leveraging this efficiency, we study the clustering propensity of all 20 nonredundant trinucleotide repeat sequences. Our results align with findings from experiments, emphasizing that canonical base-pairing and G-U wobble pairs play dominant roles in regulating cluster formation of RNA repeat sequences. Strikingly, we find strong entropic contributions to the stability and composition of RNA clusters, which is demonstrated for single-component RNA systems as well as binary mixtures of trinucleotide repeats. Additionally, we investigate the clustering behaviors of trinucleotide (odd) repeats and their quadranucleotide (even) counterparts. We observe that odd repeats exhibit stronger clustering tendencies, attributed to the presence of consecutive base pairs in their sequences that are disrupted in even repeat sequences. Altogether, our work extends the set of computational tools for probing RNA cluster formation at submolecular resolution and uncovers physicochemical principles that govern the stability and composition of the resulting clusters.

Investigation of the adsorption behaviors of novel Au3 cluster decorated arsenene nanosheets towards thiophene and methanethiol detection: A DFT study

First principles calculations were carried out to describe the interactions between Aun-arsenene nanosheets and methanethiol/thiophene molecules. Both Au-arsenene and Au3-arsenene nanosheets exhibit smaller band gap than the pure arsenene system due to the emergence of new energy bands near the Fermi level. Thus, the conductivity and sensor capabilities are significantly enhanced for the Au functionalized arsenene nanosheets. Calculated formation energies of −2.38 eV and −5.37 eV for Au-arsenene and Au3-arsenene nanosheets indicate their structural stability. The adsorption of methanethiol and thiophene molecules on the Au-arsenene substrates spontaneously occurs due to the negative calculated adsorption energies. Besides, thiophene molecule exhibits the highest adsorption energy (−0.52 eV) on the Au-arsenene substrate through its S atom strongly connected to the Au atom. The moderate adsorption energies indicate the shorter recovery times for sensing applications. Our work sheds light on the exploration of Au-arsenene as an effective candidate for sensing methanethiol and thiophene molecules.

Role of inclusion clusters on fatigue crack initiation in powder metallurgy nickel-based FGH96 superalloy

Inclusion clusters with a size of several tens of micrometers were found to be inclined to early crack initiation, resulting in an abnormally reduced fatigue life. Fatigue tests were conducted to elucidate crack initiation behaviors of inclusion clusters in nickel-based powder metallurgy (P/M) FGH96 superalloy. The inclusion clusters with varying sizes and shapes located at grain boundaries are composed of Al2O3, MgO and ZrO2 particles. The inclusion cluster with large size within 25-35 μm, small average particle distance within 0-4 μm and large orientation relative to loading direction within 35-66° will be more likely to cause crack initiation. In particular, the inclusion clusters with large size and high aggregation degree experience extruding during processing and subsequent heat-treatments due to the difference in thermal expansion coefficient with the matrix behave multiple cracking before fatigue loading, thereby facilitating crack initiation in the surrounding matrix grains. Moreover, as the main cracking part of inclusion clusters, the Al2O3 particles exhibit self-cracking and interface debonding, owing to higher hardness and poorer deformation coordination degree with matrix compared to other component inclusion particles.

Three-phase CFD-DEM study on the hydrodynamics of a riser system with liquid injection

Riser reactors are frequently applied in the process industry for highly important catalytic processes. In this work, a computational fluid dynamics- discrete element method (CFD-DEM) for a riser with liquid injection was developed. This model treats the gas as a continuous phase whereas the liquid droplets and catalyst particles are treated as discrete elements. The presence of droplets causes particles to be wet, which is taken into account via coverage models of the particles. In addition, the liquid on the particles will change the collision behavior, which is included using an energy balance approach. This CFD-DEM study is conducted simulating a lab-scale pseudo-2D riser. Based on the comparison, the assumption of fully covered particles results in a significant increase in the solids holdup and clustering behavior of the riser which, especially at the low liquid flow rate, results in an over-prediction. The partial coverage simulations show that only half of the particles are covered, which has a major effect on the collisions.

Atomic scale in-situ observation of gas-solid interaction regulating the pre-nucleation process of Pd atomic clusters

Advancements in nanotechnology have propelled the understanding of atomic-scale nucleation processes, essential for the evolution of atomic manufacturing. The process of amorphous precursors nucleation showcases a complex transition influenced by various factors. Utilizing aberration-corrected ETEM and theoretical calculation, we explore the nucleation of amorphous Pd atomic clusters. This study examines the nucleation dynamics and gas-solid interaction regulating of Pd clusters on ultrathin carbon films, prepared via electron beam evaporation. HRTEM observation and FFT analysis reveal that, in the early stage of nucleation, Pd cluster growth predominantly follows an Ostwald ripening-like mechanism. Atoms from smaller clusters migrate and attach to larger ones, facilitating their progression to critical nucleation size. Environmental conditions significantly influence this process; hydrogen atmospheres lower the surface energy of Pd clusters, reducing the critical nucleation size, while argon atmospheres impede growth of Pd clusters by occupying migration sites on the carbon surface. These insights into atomic cluster behavior and environmental interactions are crucial for understanding the early stages of nucleation from amorphous to crystalline and can help opens new avenues for the controlled fabrication of materials with optimized functionalities.

The Cumulation of Debris Clouds around a Fast-rotating Asteroid

Abstract The rotational mass loss has been realized to be a prevalent mechanism to produce low-speed debris near the asteroid, and the size composition of the asteroid’s surface regolith has been closely measured by in situ explorations. However, the full-scale evolution of the shedding debris has not been examined using the observed particle sizes, which may hold vital clues to the initial growth of an asteroid moonlet and help us to understand the general mechanisms that dominate the formation of asteroid systems. This paper presents our study on the cumulative evolution of the debris cloud formed by a rotationally unstable asteroid. A semianalytical model is developed to characterize the spatiotemporal evolution of the debris cloud posterior to a shedding event. Large-scale discrete element method (DEM) simulations are performed to quantify the clustering behavior of the debris particles in the mechanical environment near the asteroid. As a result, we found that the cumulation of a steady debris cloud is dominated by large pieces of debris, and the shedding particles follow a common migration trend, which fundamentally determines the mass distribution of the debris cloud. For the accretion analysis, we sketched the life cycle of a debris cluster and showed its dependency on particle size. The DEM simulations adopt physical parameters estimated from observations and asteroid missions. The results confirm that porous fluffy cluster structures can form shortly after a shedding event with magnitudes the same as the observed shedding activities. Measurements of these structures show that they themselves possess a certain strength and have the capacity to collisions to absorb dissociative particles that collide with them.

In vitro assessment of assembly and sex pheromones in the control of larval stages of brown dog ticks

The increasing incidence of tick-borne diseases and the growing resistance to conventional acaricides underscore the urgent need for innovative and sustainable tick management strategies. This study investigates the potential of assembly and sex pheromones for the control of larval stages of Rhipicephalus sanguineus, the brown dog tick, which poses significant health risks to dogs. The ticks were collected from various canine hosts in Thrissur, Kerala and subjected to Petri-dish bioassays using vapour patches impregnated with synthetic assembly pheromone (AP) and sex pheromone (SP), both individually and in combination. The efficacy of these semiochemicals was assessed based on the attraction and clustering behaviour of unfed larvae and partially fed adult ticks over a 24-hour period. Results demonstrated a significantly higher attraction to AP-impregnated patches, with notable efficacy observed in causing tick aggregation, particularly beyond 30 min of exposure. While SP also showed potential in attracting ticks, the combination of SP and AP did not result in a synergistic effect. The study highlights the potential of using semiochemicals as an environmentally sustainable alternative to traditional acaricides, highlighting their role in integrated pest management strategies Keywords: Rhipicephalus sanguineus, adenine, guanine, xanthine, 2,6-DCP, Petri-dish bioassay

Influence of temperature gradients on helium diffusion and clustering in tungsten: A molecular dynamics study

Tungsten and its alloys, used as plasma-facing materials in fusion reactors, endure high-flux, low-energy helium ion irradiation and temperature gradients induced by thermal load and shocks. This study utilizes molecular dynamics (MD) simulations to explore the diffusion and clustering behavior of helium (He) in tungsten (W) under temperature gradients. The migration behavior of isolated helium atoms, small helium clusters, and helium self-interstitial atom (He-SIA) clusters within tungsten is analyzed. It is revealed that both helium and He-SIA clusters tend to migrate towards higher temperature regions, exhibiting negative thermophoresis. The nucleation of helium clusters, the formation of Frenkel pairs, and the interactions between self-interstitial clusters and helium clusters are also investigated. The results indicate that helium concentration significantly impacts He nucleation behavior. Directional diffusion may lead to areas of high concentration over time, potentially promoting the formation of He clusters and bubbles. These insights enhance the understanding of tungsten's performance and durability as a plasma-facing component in fusion reactors.

Clustering of RNA Polymerase II C-Terminal Domain Models upon Phosphorylation.

RNA polymerase II (Pol II) C-terminal domain (CTD) is known to have crucial roles in regulating transcription. CTD has also been highly recognized for undergoing phase separation, which is further associated with its regulatory functions. However, the molecular interactions that the CTD forms to induce clustering to drive phase separations and how the phosphorylation of the CTD affects clustering are not entirely known. In this work, we studied the concentrated solutions of two heptapeptide repeat (2CTD) models at different phosphorylation patterns and protein and ion concentrations using all-atom molecular dynamics simulations to investigate clustering behavior and molecular interactions driving the cluster formation. Our results show that salt concentration and phosphorylation patterns play an important role in determining the clustering pattern, specifically at low protein concentrations. The balance between inter- and intrapeptide interactions and counterion coordination together impact the clustering behavior upon phosphorylation.

A Comparative Analysis Of Collaborative Filtering Models For Game Recommendation Using Cosine Similarity, SVD, K-Means Clustering And Real-Time Game Insights

This paper presents a study on GameRS, a game recommendation system that employs collaborative filtering techniques, including Cosine Similarity, Singular Value Decomposition (SVD), and K-means. Clustering, in conjunction with real-time game insights facilitated by Groq AI. The system integrates data from the RAWG API to provide game recommendations and dynamically retrieves game details, including genres, platforms, reviews, ratings, release dates, trailers, and gameplay mechanics. Furthermore, it presents a novel User Satisfaction Index for Games (USIG), a metric designed to assess anticipated enjoyment by considering factors such as rating, genre similarity, and platform similarity. Users may pose specific inquiries related to games, to which Groq AI provides succinct and accurate answers. GameRS, developed with Streamlit, manages both the front-end interface and back-end logic, allowing users to access recommendations, game details, trailers, and additional features. Evaluation results indicate that Cosine Similarity surpasses alternative methods regarding recall and hit rate, whereas SVD and K-means provide insights into latent user preferences and clustering behaviors.

Super-resolution microscopy unveils the nanoscale organization and self-limiting clustering of CD47 in human erythrocytes.

The transmembrane protein CD47, an innate immune checkpoint protein, plays a pivotal role in preventing healthy erythrocytes from immune clearance. Our study utilized stochastic optical-reconstruction microscopy (STORM) and single-molecule analysis to investigate the distribution of CD47 on the human erythrocyte membrane. Contrary to previous findings in mouse erythrocytes, we discovered that CD47 exists in randomly distributed monomers rather than in clusters across the human erythrocyte membrane. Using 2nd antibody-induced crosslinking, we found that CD47 aggregates into stable clusters within minutes. By comparing these STORM results with those of the fully mobile protein CD59 and the cytoskeleton-bound membrane protein glycophorin C under similar conditions, as well as devising two-color STORM co-labeling and co-clustering experiments, we further quantitatively revealed an intermediate, self-limiting clustering behavior of CD47, elucidating its fractional (∼14%) attachment to the cytoskeleton. Moreover, we report reductions in both the amount of CD47 and its clustering capability in aged erythrocytes, providing new insight into erythrocyte senescence. Together, the combination of STORM and 2nd antibody-based crosslinking unveils the unique self-limiting clustering behavior of CD47 due to its fractional cytoskeleton attachment.

Binary colloidal clusters with quantum dots for nanoscopic device applications

Colloidal clusters have garnered significant interest for their ability to elucidate nanoscopic phenomena in atomic arrangements and to enable the fabrication of complex nanostructures for advanced photonic and electronic applications. This study introduces a novel approach using binary colloidal clusters composed of semiconducting quantum dots and conductive gold-coated latex microspheres, fabricated via evaporation-driven self-assembly within emulsion droplets. This configuration not only demonstrates the binary colloidal clusters' capability for self-organization but also highlights the distinct regions formed by the smaller quantum dots, crucial for the electrical functionality of these nanoscopic devices. We have observed diode-like rectification behavior in these binary colloidal clusters, demonstrating their potential as active nanoscopic devices. Our findings present groundbreaking methodologies for integrating semiconductive and conductive materials in a colloidal form, offering a scalable and versatile platform for the development of next-generation devices in photonics and nanoelectronics.

IoT Sensor Network Electricity Consumption Behaviour Using ClusterAnalysis Algorithm for Network Environment

The Internet of Things (IoT) sensor network plays a crucial role in monitoring electricity consumption behavior by collecting real-time data from various connected devices. Utilizing cluster analysis algorithms, researchers can effectively segment and analyze consumption patterns within a network environment. By grouping similar usage behaviors, these algorithms reveal insights into energy efficiency, peak usage times, and anomalies in consumption. The paper presents a novel methodology for analyzing and classifying users' electricity consumption behavior, utilizing the Clustering Behavior Analysis Weighted Classification (CBAWC) algorithm. By leveraging cluster analysis techniques and weighted classification, the proposed approach allows for the segmentation of consumers into distinct clusters based on their consumption patterns. Through the application of CBAWC, the study provides insights into the diverse behaviors exhibited by consumers, ranging from moderate to high consumption levels, varied peak hours, and peak days. The classification results demonstrate the effectiveness of the algorithm in accurately assigning users to their respective clusters, enabling stakeholders to better understand consumption trends and tailor energy management strategies accordingly. Through the application of CBAWC, consumers are segmented into distinct clusters based on their consumption patterns. For example, clusters exhibit varying average daily consumption levels, with Cluster 1 consuming around 300 kWh, Cluster 2 consuming approximately 450 kWh, and Cluster 3 consuming about 280 kWh on average. Additionally, clusters display different peak hours per day and peak days per month. The classification results demonstrate the algorithm's effectiveness, with high accuracy scores ranging from 0.86 to 1.00 across different user groups.

Unlocking high photosensitivity direct laser writing and observing atomic clustering in glass

The direct laser writing (DLW) of photoluminescent metal clusters is inspiring intensive research in functional glasses. However, understanding the influence of the host structure on cluster formation and visualizing DLW-induced clusters at the atomic scale remains challenging. In this work, we develop a highly photosensitive fluorophosphate glass through fluorine incorporation. The addition of fluorine establishes a conducive environment for Ag+ ions before DLW and enhances the availability of reducing agents and diffusion pathways during DLW. These advantages facilitate the formation of Ag clusters under low-energy single-pulsed DLW. Increasing laser energy results in a combination of Ag clusters and glasses defect, forming a dot + ring photoluminescent pattern. Atom probe tomography (APT), a technique capable of mapping the elemental spatial distribution and identifying clustering, is employed to gain more information on laser-induced clusters. Comparison of APT results between samples without and with DLW reveals the formation of Ag clusters after laser writing. The design concept and characterization enrich the understanding of Ag cluster behavior in glasses. This knowledge opens the possibility of rational design of clusters confined in glasses and inspires their synthesis for various applications.

Environmental DNA reveals the spatiotemporal distribution and migration characteristics of the Yangtze finless porpoise, the sole aquatic mammal in the Yangtze River

The Yangtze Finless Porpoise (YFP) is one of the 13 global flagship species identified by the World Wildlife Fund and is classified as “Critically Endangered.” It is also the only extant aquatic mammal in the Yangtze River. In this study, 44 sampling points were deployed across the middle and lower reaches of the Yangtze River, with vertical sampling sections established in four key areas. Using environmental DNA (eDNA) and species distribution model(SDM), we explored the spatiotemporal distribution of YFPs and predicted their potential suitable habitats. The results indicate that the YFP has a relatively wide distribution during the flood season but exhibits clustering behavior during the dry season, showing a patchy distribution and a migratory trend from the midstream to downstream of the main channel. Predictions using the MAXENT model reveal varying trends in suitable habitat under different scenarios. Overall, YFP's potential habitat is expected to expand by 2050, but due to rising temperatures, it will contract by 2070. Elevation (dem, 65.4%), human footprint index (hfp, 8.8%), and isothermality (bio3, 8%) are key factors influencing habitat suitability. These findings demonstrate that eDNA is an effective tool for monitoring large aquatic organisms and provide scientific evidence for the conservation of the YFP.

Unveiling Carbon Cluster Coating in Graphene CVD on MgO: Combining Machine Learning Force field and DFT Modeling.

In this study, we investigate the behavior of carbon clusters (Cn, where n ranges from 16 to 26) supported on the surface of MgO. We consider the impact of doping with common impurities (such as Si, Mn, Ca, Fe, and Al) that are typically found in ores. Our approach combines density functional theory calculations with machine learning force field molecular dynamics simulations. It is found that the C21 cluster, featuring a core-shell structure composed of three pentagons isolated by three hexagons, demonstrates exceptional stability on the MgO surface and behaves as an "enhanced binding agent" on MgO-doped surfaces. The molecular dynamics trajectories reveal that the stable C21 coating on the MgO surface exhibits less mobility compared to other sizes Cn clusters and the flexible graphene layer on MgO. Furthermore, this stability persists even at temperatures up to 1100K. The analysis of the electron localization function and potential function of Cn on MgO reveals the high localization electron density between the central carbon of the C21 ring and the MgO surface. This work proposes that the C21 island serves as a superstable and less mobile precursor coating on MgO surfaces. This explanation sheds light on the experimental defects observed in graphene products, which can be attributed to the reduced mobility of carbon islands on a substrate that remains frozen and unchanged.