Dynamic Clustering Research Articles

STI or DUI modes of innovation: Evidence from comprehensive evaluation of four cities in China

The Science and Technology Innovation Center holds a pivotal position in the national science and technology innovation system, and a scientific evaluation of the “Sci-tech Innovation Center” will guide its construction direction. This study found the advantages and disadvantages of the four cities through comparison; Hence improvement suggestions were proposed for the weaknesses of the four cities. There are two main paths for the government to drive technology innovation: STI (Science and Technology Innovation) mode and DUI (Doing, Using, Interacting) mode. With the aid of the evaluation index system of the Sci-tech Innovation Center, this article uses fuzzy sets, rough sets and fuzzy dynamic clustering methods to comprehensively evaluate the effects of driving technology innovation in the four cities of Beijing, Shanghai, Shenzhen and Guangzhou. The results found that Shenzhen has a significant effect in DUI, and Beijing has a significant effect in STI. The choice of path is related to the abundance of innovation resources.

Fluctuating Chromatin Facilitates Enhancer-Promoter Communication by Regulating Transcriptional Clustering Dynamics.

Enhancers regulate gene expression by forming contacts with distant promoters. Phase-separated condensates or clusters formed by transcription factors (TFs) and cofactors are thought to facilitate these enhancer-promoter (E-P) interactions. Using polymer physics, we developed distinct coarse-grained chromatin models that produce similar ensemble-averaged Hi-C maps but with "stable" and "dynamic" characteristics. Our findings, consistent with recent experiments, reveal a multistep E-P communication process. The dynamic model facilitates E-P proximity by enhancing TF clustering and subsequently promotes direct E-P interactions by destabilizing the TF clusters through chain flexibility. Our study promotes physical understanding of the molecular mechanisms governing E-P communication in transcriptional regulation.

Fault Diagnosis Model of Hydraulic Motor Based on Fuzzy Neural Network

When the hydraulic motor fault occurs,it is not easy to be detected,and the leakage degree will gradually increase.In order toavoid bigger accidents causedby thehydraulic motor fault,the accident isexcluded in theembryonic stage,and the hydraulic motor fault prediction method based onfuzzy neural network isused topredict thehydraulic motor fault.The feature vector is output inthe global meanpooling layer,andthe feature vectormatrix between thehealth state feature vector library and the samples to be measured is constructed.The dynamic cluster graph isobtained by fuzzy clustering,so as to realize the fault diagnosis of thehydraulic motor.The results show that the accuracy of training set,verification set andtest set ishigher than99.8%.The accuracy of diagnosis classification is99.00%,which is better than othercomparison models.In this study,the number of training samples can be appropriately increased ordecreased according to thecurve complexity of the detection target,so as to improve thefeature extraction capability of the convolutional layer andincrease the classification accuracy.

Exact cluster dynamics of indirect reciprocity in complete graphs

Exact cluster dynamics of indirect reciprocity in complete graphs

Dynamic stable Pt13 clusters anchored on isolated ZnOx nanorafts for efficient cycloparaffin dehydrogenation

Methylcyclohexane (MCH) has attracted wide attention as liquid organic hydrogen carriers for safe alternative and high gravimetric hydrogen density. Pt13 cluster with its fascinating structure exhibits excellent performance in cycloalkane dehydrogenation reactions accompanied by balanced C-H cleavage capability. However, the controllable synthesis of stable Pt13 clusters under reaction conditions remains challenging. Herein, we successfully synthesized low-coordinated Pt13 clusters anchored by isolated ZnOx nanorafts on silanol-rich self-pillared zeolite nanosheets (SP-S-1). In situ XAFS spectra combined with theoretical calculations revealed that Pt13 clusters exhibited strong interfacial Pt-Zn bonds and reduced d-band center through orbital hybridization (Zn 3d - Pt 5d), facilitating C-H bond cleavage and toluene desorption. The optimized catalyst exhibited superior MCH dehydrogenation activity and durability, achieving a hydrogen production rate of 3457.9 mmolH2 gPt−1 min−1 at 400 ˚C and maintaining stable for over 100 hours. This study precisely synthesizes dynamic stable Pt13 clusters and provides valuable insights into the dehydrogenation mechanism.

Analytical properties and solutions for the synchronous pulsating bubble clusters distributed on a spherical surface

The present work concerns with the nonlinear dynamics for the synchronous pulsating bubble clusters uniformly distributed on a spherical surface. First, the governing equation for such clusters with 4/6/8/12/20 coupled bubbles are established. Second, the maximum and minimum radii for the gas-filled bubble clusters are analyzed according to the first integral. Third, by introducing suitable nonlocal transformations, two novel equivalent parametric analytical solutions in the form of Weierstrass elliptic function are constructed for the gas-filled bubble clusters for a specific polytropic exponent κ=3/2 without considering the surface tension, and based on which we immediately derive the parametric analytical solution for the corresponding vapor bubble clusters. Further, to consider the case of arbitrary polytropic exponent and surface tension, we develop a direct approach to construct the parametric analytical solution using Jacobi elliptic function for gas-filled bubble clusters. It is shown that, the behaviors and results for the bubble clusters will degenerate to the corresponding ones for single bubbles as the radius of the bubble cluster approaches infinity. In addition, on the basis of the analytical results, dynamic properties and motion laws of the bubble clusters are also discussed.

Mass modeling and kinematics of galaxy clusters in modified gravity

The chameleon screening mechanism has been constrained many a time using dynamic and kinematic galaxy cluster observables. Current constraints are, however, insensitive to different mass components within galaxy clusters and have been mainly focused on a single mass density profile, the Navarro-Frenk-White mass density model. In this work, we extend the study of the Chameleon screening mechanism in galaxy clusters by considering a series of mass density models, namely: generalized-Navarro-Frenk-While, b-Navarro-Frenk-While, Burket, Isothermal and Einasto. The coupling strength (β) and asymptotic value of the chameleon field (ϕ ∞) are constrained by using kinematics analyses of simulated galaxy clusters, generated both assuming General Relativity and a strong chameleon scenario. By implementing a Bayesian analysis we comprehensively show that the biases introduced due to an incorrect assumption of the mass model are minimal. Similarly, we also demonstrate that a spurious detection of evidence for modifications to gravity is highly unlikely when utilizing the kinematics of galaxy clusters.

A reduced cluster dynamics modeling of radiation damage in tungsten

A reduced cluster dynamics modeling of radiation damage in tungsten

Multiscale-informed irradiation growth model of Zr-Sn-Nb alloys

A systematic multiscale-informed model is developed to predict the irradiation growth behavior of Zr-Sn-Nb alloys, which considers the anisotropy and temperature dependence of both plasticity and irradiation, as well as the alloying effect of Zr alloys. This model consists of a cluster dynamics submodel to consider the kinetics of irradiation defect, an alloying effect submodel informed by atomic simulations and experiments, a microstructure transition submodel derived from discrete dislocation dynamics, and a continuous irradiation growth submodel based on crystal plasticity. It effectively captures the irradiation-induced coevolution of multiple microstructures, including point defects, mobile clusters, dislocation lines and irradiation loops on the prismatic and basal plane, as well as Nb-induced precipitates. It is suitable for high-dose irradiation conditions as it reasonably considers the transition from high-density irradiation loops to tangled dislocation network. The predicted irradiation growth strain, as well as the density and size of irradiation loops, are in good agreement with almost all the available experiments for pure Zr, Zr-Sn, Zr- Nb, and Zr-Sn-Nb alloys at different irradiation doses in the temperature range of 473 - 673 K. This work is hoped to provide a powerful tool for developing irradiation resistance cladding materials.

Machine Learning Algorithms Based on Time Series Pre-Clustering for Nocturnal Glucose Prediction in People with Type 1 Diabetes

Background: Machine learning offers new options for glucose prediction and real-time glucose management. The aim of this study was to develop a machine learning-based algorithm that takes into account glucose dynamics patterns for predicting nocturnal glucose in individuals with type 1 diabetes. Methods: To identify glucose patterns, we applied a hierarchical clustering algorithm to real-time continuous glucose monitoring data obtained from 570 adult patients. Machine learning algorithms with or without pre-clustering were used for modeling. Results: Eight clusters without nocturnal hypoglycemia and six clusters with at least one low-glucose episode were identified by the cluster analysis. When forecasting time series without hypoglycemia with a prediction horizon (PH) of 15 or 30 min, gradient boosting trees (GBTs) with pre-clustering and random forest (RF) with pre-clustering outperformed algorithms based on medoids of time series clusters, the Holt model, and GBTs without pre-clustering. When forecasting time series with low-glucose episodes, a model based on the pre-clustering and GBTs provided the highest predictive accuracy at PH = 15 min, and a model based on RF with pre-clustering was the best at PH = 30 min. Conclusions: The results indicate that the clustering of glucose dynamics can enhance the efficacy of machine learning algorithms used for glucose prediction.

Strategic Formation of Agricultural Market Clusters in Ukraine: Emerging as a Global Player

This study investigates the cluster approach to optimize strategies for agricultural enterprises in Ukraine, emphasizing geographical proximity as a key factor in cluster formation. The research applies Kohonen Self-Organizing Maps (SOMs) and Ward’s hierarchical clustering to classify enterprises based on storage capabilities, transport logistics, crop yields, and military risk exposure. By analyzing these factors, this study identifies distinct patterns of innovation adoption, strategic management, and economic resilience among the clusters. The findings highlight variations in competitiveness and resource efficiency, providing a detailed understanding of regional economic performance. Unlike previous research, this study offers a novel integration of conflict-related risks into the clustering methodology, revealing new insights into how military factors influence cluster dynamics. Comprehensive maps and diagrams illustrate the spatial and economic distribution of clusters, aiding in visual interpretation. The results propose strategic measures tailored to enhance agricultural productivity and competitiveness, particularly in Ukraine’s current military context. This approach offers a more adaptive framework for managing agricultural enterprises, promoting resilience and long-term sustainability in global markets.

Improving QoS in cloud resources scheduling using dynamic clustering algorithm and SM‐CDC scheduling model

SummaryQuality of Service (QoS) regulates and controls network resources by setting priorities for specific data types. Many clustering algorithms are used to cluster cloud workloads, most of which are static. However, the lack of dynamic algorithms is seen in the face of huge databases that are real‐time and according to the existing clustering conditions. Additionally, fair allocation of tasks on servers and efficient resource utilization pose challenges. In this research, two solutions are proposed to improve the quality of service: the first solution uses the chameleon dynamic algorithm, a method to improve service quality. The chameleon algorithm has been able to show significant performance due to its high accuracy in detecting the smallest distance between clusters. This dynamic algorithm outperforms static algorithms with classification accuracy and response speed, which are the most important parameters of service quality. The second part of the proposed solution is to use the Scheduling Model using Cloud Data Centers (SM‐CDC) system to select the best service provider based on the clustering done in the previous step. A SM‐CDC technique is developed to handle cloud storage center tasks that are stored in electronic devices. According to the comparison with existing scheduling policies, SM‐CDC offered 36% decrease on response time, 50% reduction on cost of resources, and 40% improvement on QoS Satisfaction.

Dynamics of paramagnetic permanent chains and self-assembled clusters under a rapidly rotating magnetic field.

We study experimentally and theoretically the dynamics of permanent paramagnetic chains and mixed clusters formed by permanent paramagnetic chains and paramagnetic particles under the influence of a time-varying magnetic field. First, we examine the dynamics of permanent chains at high frequencies (∼50 to 1000Hz). These permanent chains exhibit continuous rotational motion with a frequency several orders of magnitude lower than that of the magnetic field. We develop a theoretical model that accurately describes the dependence of the rotational dynamics of chains on their length, as well as the amplitude and frequency of the external magnetic field in this high frequency regime. Next, we examine how cluster dynamics are affected by the presence of permanent chains. We show that the rotation of clusters composed of a high proportion of permanent chains is slowed down but remains qualitatively well described by the theoretical model we developed for homogeneous clusters of isotropic particles. We propose that the decrease in angular velocity for mixed clusters is due to the hardening of the cluster's 2D elastic modulus caused by the increase of the steric interaction parameter stemming from the presence of chemical links between particles in the chains.

STAD: Ship trajectory anomaly detection in ocean with dynamic pattern clustering

With the continuous increase of maritime traffic, developing an efficient and accurate model for ship trajectory anomaly detection has become crucial for ensuring maritime transportation safety. The high complexity and variability of the marine environment lead to diverse ship trajectory patterns, making it challenging to learn effective trajectory representations for accurately identifying anomalies. We thus proposed an unsupervised deep learning model called STAD for ship trajectory anomaly detection in ocean to address this challenge. Concretely, STAD leverages offset reconstruction-based representation learning and a deep Gaussian Mixture Model (GMM) estimation network to learn the underlying complex clustering patterns of ship trajectories and utilize the learned patterns to enhance trajectory anomaly detection. Extensive experiments on multiple AIS datasets indicate that our model significantly outperforms existing methods in detecting multiple representative types of ship trajectory anomalies, including shift deviation, abnormal heading, and abnormal speeding. This study could help closely monitor the status of ship movement and detect abnormal behaviors in advance, thus benefiting maritime safety.

Dynamic cluster field modeling of collective chemotaxis

Collective migration of eukaryotic cells is often guided by chemotaxis, and is critical in several biological processes, such as cancer metastasis, wound healing, and embryogenesis. Understanding collective chemotaxis has challenged experimental, theoretical and computational scientists because cells can sense very small chemoattractant gradients that are tightly controlled by cell-cell interactions and the regulation of the chemoattractant distribution by the cells. Computational models of collective cell migration that offer a high-fidelity resolution of the cell motion and chemoattractant dynamics in the extracellular space have been limited to a small number of cells. Here, we present Dynamic cluster field modeling (DCF), a novel computational method that enables simulations of collective chemotaxis of cellular systems with O(1000)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}}(1000)$$\\end{document} cells and high-resolution transport dynamics of the chemoattractant in the time-evolving extracellular space. We illustrate the efficiency and predictive capabilities of our approach by comparing our numerical simulations with experiments in multiple scenarios that involve chemoattractant secretion and uptake by the migrating cells, cell migration in confined spaces, regulation of the attractant distribution by cell motion, and interactions of the chemoattractant with an enzyme. The proposed algorithm opens new opportunities to address outstanding problems that involve collective cell migration in the central nervous system, immune response and cancer metastasis.

Transport in cellular aggregates described by fluctuating hydrodynamics

Biological functionality of cellular aggregates is largely influenced by the activity and displacements of individual constituent cells. From a theoretical perspective this activity can be characterized by hydrodynamic transport coefficients of diffusivity and conductivity. Motivated by the clustering dynamics of bacterial microcolonies we propose a model of active multicellular aggregates and use recently developed macroscopic fluctuation theory to derive a fluctuating hydrodynamics for this model system. Both semianalytic theory and microscopic simulations show that the hydrodynamic transport coefficients are affected by nonequilibrium microscopic parameters and significantly decrease inside of the clusters. We further find that the Einstein relation connecting the transport coefficients and fluctuations breaks down in the parameter regime where the detailed balance is not satisfied. This study offers valuable tools for experimental investigation of hydrodynamic transport in other systems of cellular aggregates such as tumor spheroids and organoids. Published by the American Physical Society 2024

Tscluster: A python package for the optimal temporal clustering framework

Temporal clustering extends the conventional task of data clustering by grouping time series data according to shared temporal trends across sociospatial units, with diverse applications in the social sciences, especially urban science. The two dominant methods are as follows: Time Series Clustering (TSC), with dynamic cluster centres but static labels for each entity, and Sequence Label Analysis (SLA), with static cluster centres but dynamic labels. To implement the universe of models spanning the design space between TSC and SLA, we present tscluster, an open-source Python framework. tscluster offers: (1) several innovative techniques, such as Bounded Dynamic Clustering (BDC), that are not available in existing libraries, allowing users to set an upper bound on the number of label changes and identify the most dynamically evolving time series; (2) a user-friendly interface for applying and comparing these methods; (3) globally optimal solutions for the clustering objective by employing a mixed-integer linear programming formulation, enhancing the reproducibility and robustness of the results in contrast to existing methods based on initialization-sensitive local optimization; and (4) a suite of visualization tools for interpretability and comparison of clustering results. We present our framework using a case study of neighbourhood change in Toronto, comparing two methods available in tscluster. Supplemental materials provide an additional case study of local business development in Chicago and a detailed mathematical exposition of our framework. tscluster can be installed via PyPI (pypi.org/project/tscluster), and the source code is accessible on Github (github.com/tscluster-project/tscluster). Documentation is available online at the tscluster website (tscluster.readthedocs.io).

Comparison of Matrix Product State and Multiconfiguration Time-Dependent Hartree Methods for Nonadiabatic Dynamics of Exciton Dissociation.

The excited-state dynamics of organic molecules, molecular aggregates, and donor-acceptor clusters is typically governed by the interplay of electronic excitations and, due to their flexibility and soft bonding, by the interaction with their vibrations. This interaction in these systems can be characterized by a few relevant electronic states that are coupled to numerous vibrational normal modes, encompassing a vast configurational space of the molecules. The full quantum simulation of these type of systems has been long dominated by the multiconfiguration time-dependent Hartree (MCTDH) approach and its multilayer variants, which are considered the gold standard in the presence of electron-vibration coupling with a large number of modes. Recently, also the matrix product state ansatz (MPS) with appropriate time-evolution schemes has been applied to these types of Hamiltonians. In this article, we provide a numerical comparison of excited-state dynamics between the MCTDH and MPS approaches for two electron-vibration coupled systems. Notably, we consider two models for exciton dissociation at a P3HT:PCBM heterojunction, comprising two electronic states and 100 vibrational modes, and 26 electronic states and 113 vibrational modes, respectively. While both methods agree very well for the first model, more pronounced deviations are found for the second model. We trace back the divergence between the methods to the different way entanglement is treated.

Changes in Language Learners’ Affect: A Complex Dynamic Systems Theory Perspective

AbstractThis study investigated changes in motivation, self‐efficacy beliefs, and a range of emotions, including enjoyment, hope, pride, curiosity, anxiety, boredom, apathy, confusion, and shame, from a complex dynamic systems theory (CDST) perspective over a 2‐year period in the Hungarian English as a Foreign Language (EFL) context. Using the same questionnaire, we collected data four times throughout 4 semesters from 101 participants studying English in two Hungarian high schools. For data analysis, we used latent growth curve modeling (LGCM) to detect the group‐level changes in learners’ motivation, self‐efficacy, and emotions. We also employed dynamic cluster analysis to identify trends in learners’ trajectories regarding these variables. In our panel data, linear models described the data well concerning the ought‐to second language (L2) self, language learning experience, boredom, apathy, and confusion, and for enjoyment, curiosity, anxiety, and shame, nonlinear models had the best fit. We could also identify trajectories depicting attractor states and learner paths that featured influences of perturbations.

Dynamical clustering and wetting phenomena in inertial active matter

Dynamical clustering is a key feature of active matter systems composed of self-propelled agents that convert environmental energy into mechanical motion. At the micron scale, where overdamped dynamics dominate, particles with opposite motility can obstruct each other’s movement, leading to transient dynamical arrest. This arrest can promote cluster formation and motility-induced phase separation. However, in macroscopic agents, where inertia plays a significant role, clustering is heavily influenced by bounce-back effects during collisions, which can impede cluster growth. Here we present an experiment based on active granular particles, in which inertia can be systematically tuned by changing the shaker frequency. As a result, a set of phenomena driven and controlled by inertia emerges. Before the suppression of clustering, inertia induces a transition in the cluster’s inner structure. For small inertia, clusters are characterized by the crystalline order typical of overdamped particles, while for large inertia clusters with liquid-like order are observed. In addition, in contrast to microswimmers, where active particles wet the boundary by primarily forming clusters attached to the container walls, in an underdamped inertial active system, walls do not favor cluster formation and effectively annihilate motility-induced wetting phenomena. As a consequence, inertia suppresses cluster nucleation at the system boundaries.