Clustering Behavior Research Articles

σ-Electrons rather than π-Electrons Inducing Sensitive Nonlinear Optical Responses in Planar and Quasi-Planar Boron Clusters.

Geometries and electronic structures of planar and quasi-planar boron clusters resemble those of aromatic hydrocarbons, providing opportunities for designing novel nonlinear optical materials. However, the nonlinear optical properties, optical-response mechanisms, and optimal optical-response geometries of boron clusters remain unclear. Accordingly, this study addresses these uncertainties. Boron clusters exhibit good nonlinear optical performance, significantly surpassing that of aromatic hydrocarbons of comparable size. An analysis method based on electron density decomposition is proposed to quantitatively determine the contributions of σ- and π-electrons to the optical responses. The findings reveal that σ-electrons dominate the nonlinear optical responses in planar boron clusters owing to their delocalized characteristics, in marked contrast to aromatic hydrocarbons. Using boron isomers as model systems, boron nanoribbons were confirmed to provide optimal nonlinear optical performances. This study offers valuable insights into the nonlinear optical behaviors of planar and quasi-planar boron clusters, substantially enhancing the rational design of novel nonlinear optical materials.

Structure and stability of copper nanoclusters on monolayer tungsten dichalcogenides.

Layered materials, such as tungsten dichalcogenides (TMDs), are being studied for a wide range of applications, due to their unique and varied properties. Specifically, their use as either a support for low dimensional catalysts or as an ultrathin diffusion barrier in semiconductor devices interconnect structures are particularly relevant. In order to fully realise these possible applications for TMDs, understanding the interaction between metals and the monolayer they are deposited on is of utmost importance. The morphology that arises due to given metal-substrate combinations determines their possible applications and thus is a central characteristic. Previous theoretical studies typically focus on the effects which single metal adatoms, or dopants, have on a TMDs' electronic and optical properties, thereby leaving a knowledge gap in terms of thin film nucleation on TMD monolayers. To address this, we present a density functional theory (DFT) study of the adsorption of small Cu clusters on a range of TMD monolayers, namely WS2, WSe2, and WTe2. We explore how metal-substrate and metal-metal interactions contribute to both the stability of these Cu clusters and their morphology, and investigate the role of the chalcogen in these interactions. We find that single Cu atoms adsorb most strongly to the adsorption site above the W atom, however as nanocluster size increases, Cu tends to be adsorbed atop the chalcogen atoms in the monolayer to facilitate Cu-Cu bond formation. We show that Cu-Cu interactions drive the stability of the adsorbed Cu nanoclusters, with a clear preference for 3D structures on all 3 monolayers studied. Furthermore, significant Cu migration occurs during 0 K relaxation. This, combined with the small activation barriers found for Cu migration suggest facile and dynamic cluster behaviour at finite temperature on all three monolayers. Finally, we find that Cu clusters are generally most stable on WTe2 and least stable on WSe2. This difference however is typically only in the range of 0.1 eV.

Study of Rewetting Behavior in AHWR Fuel Cluster During Loss of Coolant with Water Jet Impingement

This study investigates the rewetting behavior of an Advanced Heavy Water Reactor (AHWR) fuel rod bundle during a loss-of-coolant accident using computational fluid dynamics simulations with ANSYS CFX. The analysis focuses on the cooling effectiveness of radial jet impingement at varying flow rates and its impact on rewetting temperature and wetting delay. Simulations were conducted by maintaining a constant initial wall temperature, with cooling curves and contour profiles extracted from various angular positions along the axial rod surfaces. The results reveal that rewetting is faster near the jet sections due to enhanced coolant interaction, while areas farther from the jets exhibit delayed wetting and elevated wall temperatures, where vapor accumulation hinders heat dissipation. Higher flow rates minimize wetting delays and improve cooling by promoting transition and nucleate boiling. However, irregular coolant splashing and vapor dominance disrupt the uniformity of rewetting across the bundle. The study highlights the limited impact of increased flow rates on achieving consistent rewetting along the entire rod length, with substantial fluctuations observed in cooling performance at different vertical positions. The findings emphasize the need for further research under high-temperature steam conditions to better understand boiling mechanisms and improve the stability of emergency cooling systems in nuclear reactors.

Dipole-Modulated Carrier Dynamics and Charge Transport Behavior of Ligand-Protected Metal Chalcogenide Nanoclusters.

Atomically precise nanoclusters, distinguished by their unique nuclearity- and structure-dependent properties, hold great promise for applications of energy conversion and electronic transport. However, the relationship between ligands and their properties remains a mystery yet to be unrevealed. Here, the influence of ligands on the electronic structures, optical properties, excited-state dynamics, and transport behavior of Re12S16 dimer clusters with different ligands is explored using density functional theory combined with time-domain nonadiabatic molecular dynamic simulations. The correlation between ligands and the excited-state dynamics of nanoclusters is elucidated. The ligand replacement introduces a built-in electric field at the dimer interface, inhibiting the recombination of excited carriers and increasing the voltage threshold. This study paints a physical picture of the ligand effect on nanoclusters in terms of geometric configuration, electronic structure, optical properties, carrier dynamics, and transport behavior, paving a pathway toward their applications in optoelectronic materials.

Establishing Machine Learning Operations for Continual Learning in Computing Clusters: A Framework for Monitoring and Optimizing Cluster Behavior

Establishing Machine Learning Operations for Continual Learning in Computing Clusters: A Framework for Monitoring and Optimizing Cluster Behavior

Effect of Cu on precipitation hardening and clustering behavior of Al-Zn-Mg alloys in the early stage of aging

Effect of Cu on precipitation hardening and clustering behavior of Al-Zn-Mg alloys in the early stage of aging

CFD-DEM simulation of clustering behavior in a riser - effect of the collision model

CFD-DEM simulation of clustering behavior in a riser - effect of the collision model

Inhibition of EphA2 by syndecan-4 in wounded skin regulates clustering of fibroblasts

Abstract Upon injury, fibroblasts in the surrounding tissue become activated, migrating into the wound in a controlled manner. Once they arrive, they contract the wound and remodel the stroma. While certain cell surface receptors promote fibroblast migration, others cause repulsion between fibroblasts upon contact, seemingly opposing their clustering within the wound bed. Eph receptor–ephrin interactions on colliding cells trigger this repulsion, but how fibroblasts transition to clustering behaviour during healing remains unclear. Syndecan-4 modulates transmembrane receptors involved in wound healing, including receptors for the extracellular matrix and growth factors. As a result, Sdc4–/– mice experience delayed healing due to impaired fibroblast recruitment. In this study, we report that syndecan-4 also regulates fibroblast repulsion during wound healing. We discover that syndecan-4 inhibits the expression and signalling of EphA2 by activating PKCα. Changes in syndecan-4 expression, such as those observed during wound healing, alter fibroblast behaviour from repulsion to adhesion upon cell collision by modulating EphA2 levels. Moreover, we find that EphA2 expression is suppressed in wound bed fibroblasts in a syndecan-4-dependent manner, explaining how fibroblast clustering is achieved during wound healing.

Single-Molecule Magnet Behavior and Spin Structure of an FeIII7 Cartwheel Cluster Revealed by Sub-Kelvin Magnetometry and Mössbauer Spectroscopy: The Final Pieces of the Puzzle.

The spin frustration and other magnetic properties of the "cartwheel" heptanuclear cluster [FeIII7O3(O2CtBu)9(Me-dea)3(H2O)3] (Me-deaH2 = N-methyldiethanolamine) have been previously investigated; we present here a Mössbauer spectroscopic study and sub-Kelvin magnetization and ac susceptibility measurements which enable a complete magnetic picture of this frustrated cluster. 57Fe Mössbauer spectra above 150 K showed three doublets in a 1:3:3 ratio, which could be assigned by their respective quadrupole splittings to the central Fe(1) and the peripheral Fe(2) and Fe(3). The field dependence of the corresponding magnetic sextets at 3 K showed that the spins on the central Fe(1) and the three peripheral Fe(2) sites with O5N coordination are oriented mutually coparallel, while these are antiparallel to the spins on the peripheral Fe(3) sites with O6 coordination, resulting in an overall S = 5/2 ground state. This provides experimental confirmation of the previously proposed spin ground state structure. Upon cooling to sub-Kelvin temperatures, a crossover to spin blocking with TB ≈ 0.21 K could be observed. This single-molecule magnet behavior had been expected but had not been observable with a conventional SQUID. The anisotropy barrier, of 3-fold symmetry, can be described in terms of the parameter D/kB = -0.47 K and a fourth-order perturbation; the latter enables thermally activated quantum tunneling through the excited sublevel mz = ± 3/2, with an activation barrier of U/kB = 1.9 K.

Video tracking of single cells to identify clustering behavior

Cancer cell clustering is a critical factor in metastasis, with cells often believed to migrate in groups as they establish themselves in new environments. This study presents preliminary findings from an in vitro experiment, suggesting that co-culturing cells provides an effective method for observing this phenomenon, even though the cells are grown as monolayers. We introduce a novel single-cell tracking approach based on graph theory to identify clusters in PC3 cells cultivated in both monoculture and co-culture with PC12 cells, using 66-h time-lapse recordings. The initial step consists of defining “linked” pairs of PC3 cells, laying the foundation for the application of graph theory. We propose two alternative definitions for cell pairings. The first method, Method 1, defines cells as “linked” at a given time t if they are close together within a defined time period before and after t. A second potential alternative method, Method 2, pairs cells if there is an overlap between the convex hulls of their respective tracks during this time period. Pairing cells enables the application of graph theory for subsequent analysis. This framework represents a cell as a vertex (node) and a relation between two cells as an edge. An interconnected set of high-degree nodes (nodes with many connections or edges) forms a subgraph, or backbone, that defines a patch (cluster) of cells. All nodes connected to this backbone are part of the subgraph. The backbone of high-degree nodes functions as a partition (or cut) of the initial graph. Two consecutive clusters in the video are considered to share the same identity if the following cluster contains at least p = 75 % of the cells from the preceding cluster, and the mean positions of their cells are within △r = 75μm. PC3 cells grown in co-culture appear to form persistent clusters exceeding 10 cells after 40–50 h incubation following seeding. In contrast, PC3 cells cultured alone (mono-culture) did not exhibit this behavior. This approach is experimental and requires further validation with a broader dataset.

Measuring the intracluster medium velocity structure within the A3266 galaxy cluster

We present a detailed analysis of the velocity structure of the hot intracluster medium (ICM) within the A3266 galaxy cluster, including new observations taken between June and November 2023. Firstly, morphological structures within the galaxy cluster were examined using a Gaussian gradient magnitude (GGM) and an adaptively smoothed GGM filter applied to the EPIC-pn X-ray image. Then, we applied a novel XMM-Newton EPIC-pn energy scale calibration, which uses instrumental Cu Kα as a reference for the line emission, to measure the line-of-sight velocities of hot gas within the system. This approach enabled us to create two-dimensional projected maps for velocity, temperature, and metallicity, showing that the hot gas displays a redshifted systemic velocity relative to the cluster redshift across all fields of view. Further analysis of the velocity distribution through non-overlapping circular regions demonstrates consistent redshifted velocities extending up to 1125 kpc from the cluster core. Additionally, the velocity distribution was assessed along regions following surface brightness discontinuities, where we observed redshifted velocities in all regions, with the largest velocities reaching 768 ± 284 km/s. Moreover, we computed the velocity probability density function (PDF) from the velocity map. We applied a normality test, finding that the PDF adheres to an unimodal normal distribution consistent with theoretical predictions. Lastly, we computed a velocity structure function for this system using the measured line-of-sight velocities. These insights advance our understanding of the dynamic processes within the A3266 galaxy cluster and contribute to our broader knowledge of ICM behavior in merging galaxy clusters.

Effect of the clustering behavior of Co and Ta on the stability of nickel-based Superalloys

Effect of the clustering behavior of Co and Ta on the stability of nickel-based Superalloys

A probabilistic approach driven credit card anomaly detection with CBLOF and isolation forest models

Businesses throughout the world are starting to accept more and more electronic payment alternatives for in-person and online purchases. Unusual behaviour such as internet fraud and payment defaults, which may lead to significant monetary losses, have become increasingly widespread as credit card use in online buying has expanded. To find a solution to this problem, researchers looked into a number of different machine learning classifiers that may identify irregularities in the data pertaining to credit card transactions. Overlapping class samples and an uneven distribution of classes make it hard to find outliers in this data. General learning algorithms may favour samples from the majority class, and the detection rate of anomalies in samples from minority classes is thus low. Our proposed Credit Card Outlier Detection (CCOD) model incorporates many machine learning algorithms to enhance the detection rates of credit card anomalies. Utilising the stratified sampling technique and the k-fold cross-validation process, we address data imbalance and overfitting. Working with optimized selected features rather than all features reduces the danger of overfitting and improves model performance. It has been noted that the findings of Cluster-Based Local Outlier Factor (CBLOF) classifier on European Credit Card Dataset and Isolation Forest classifier on German credit card dataset were superior to those of the other classifiers with Mathew correlation coefficient value of 0.95 and 0.97 respectively. The CBLOF, an advanced outlier detection method that evaluates the local density of data points within clusters. By comparing the density of a point to the densities of its surrounding clusters, CBLOF identifies outliers based on their deviation from normal cluster behaviour. In general, this CCOD model shows that it is better at dealing with issues like uneven class distribution, overfitting, and classes that overlap.

Exploration of helium behavior in the irradiation-induced γ phase of tungsten compared to the original α phase

Tungsten holds immense promise as a plasma-facing material in fusion reactors. However, helium bubble formation and the resulting structural changes raise concerns about its long-term performance. To address these issues, it is crucial to have a detailed understanding of the changes that occur within tungsten during the fusion process. A significant change is the helium pressure-induced phase transformation in tungsten from its body-centered cubic α phase to the face-centered cubic γ phase. This study investigates helium clustering behavior within both the α and γ phases of tungsten using density functional theory. Our calculations suggest that helium clustering is less favorable in the transformed γ phase compared to the α phase, indicating that the γ phase is more resistant to helium-induced degradation.

Do wild-caught fly larvae cooperatively forage?

Animals often form organized cooperative foraging groups, where individual members must adhere to specific rules to maintain cohesiveness. These groups face the challenge of managing potential intruders, who may or may not assist in foraging. In semi-liquid food environments, Drosophila larvae learn to synchronize their movements into clusters, which are thought to make feeding more efficient. Individuals who do not synchronize with the group are excluded from the cluster. Whether clustering behavior occurs in wild-caught larvae, and if so, the extent of their selectivity in group membership, remains unknown. Here, we show that clustering occurs across a number of fly species, and the capacity to join different clusters varies both between and within species. We collected and observed a larval cluster from rotting fruit in the field, yielding seven fly species. Subsequent tests for clustering on five lines from this collection and 20 other inbred wild-caught lines revealed that all species, except D. suzukii, exhibit clustering behavior. Each line demonstrates varying capacities to become members of different clusters. This study also indicates that there is high genetic variance in how individual lines cluster with each other that is not explained by cross species features. Additionally, combinations of wild species with lab benchmark strains give varied outcomes in resultant adult fitness. The ability to co-cluster varies between and within species boundaries. However, fly lines that cluster with another tend to impart fitness both to themselves and their host. Our findings demonstrate that multiple species of fly larvae can co-cluster. This behavior tends to confer mutual benefits to cluster members, suggesting significant ecological implications in Drosophila communities.

Spatiotemporal Cell Control via High-Precision Electronic Regulation of Microenvironmental pH.

Accurate regulation of extracellular pH is crucial for controlling cell behaviors and functions. However, typical methods, which primarily rely on replacing cell culture media or using ionic diffusion, are slow, nondirectional, and lack spatiotemporal resolution. Here, we develop a microfabricated device that regulates microenvironmental pH within specific localized zones with high precision (uncertainty <0.1 pH units) and temporal resolution. The device uses a synchronization strategy that coordinates two processes: pulsatile modulation of pH through microelectrolysis and ultrasensitive graphene-electronic pH sensing, which operates in antiphase to the modulation. Using this device, we show real-time control of the dynamic behaviors of microscale clusters of bacteria (motility) and cardiomyocytes (calcium signaling and necrotic injury) in response to precisely regulated extracellular pH variations. Our device addresses the limitations of typical pH-altering techniques and holds significant potential to advance cell biology, physiology, tissue engineering, and regenerative medicine.

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 onab initiomolecular 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.

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'.

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.