Dynamic Clustering Research Articles

Islam in the digital infrastructure: the rise of Islamic cyber practices in Northern Nigeria

ABSTRACT This contribution investigates how people in Northern Nigeria appropriated and converted digital technology into the Islamic infrastructure. Using ethnographic and netnographic methods, I examine three ‘infrastructural outcomes’ or ‘infrastructural components’ of Salafi digital practices: the cyber imam figure; new notions of Islamic community (cyber ummah); and the notion of algorithmic religiosity. I propose the term ‘cyber imams’ to describe a set of Salafi scholars who use digital infrastructure to engage in digital da’wah, attract followers, and cultivate religious authority online. The cyber ummah is a dynamic cluster of different types of social media formations that have become a palpable and tangible community of believers through online interactivity and the experience of presence. I define algorithmic religiosity as the ability of social media algorithms to be used to shape certain religious subjectivities, build religious collectivity, and cultivate discrete religious authority. These tropes are enabled and afforded by the emergence of Islamic digital infrastructure.

Risk spillover effects of the Israel–Hamas War on global financial and commodity markets: A time–frequency and network analysis

This paper uses TVP-VAR dynamic spillover method and spectral clustering algorithms to explore the impact of the Israel–Hamas war on the risk contagion in global financial and commodity markets. The war markedly intensified the overall risk spillover, with short-term impacts surpassing long-term effects. The static risk spillover and dynamic spillovers characteristics exhibited marked changes, but the directional risk spillovers in the time domain and the net risk level for each asset reacted differently. There was a significant shift in the global market’s community divisions and net spillover patterns networks, which changed the way risk spreads between the global financial market.

Design and performance assessment of improved evolutionary computing based LEACH protocol for energy efficient and lifetime extension of wireless sensor network

The strengths of a Memetic Algorithm and an enhanced Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol are combined in this research study to propose a revolutionary method for improving the energy efficiency and longevity of Wireless Sensor Networks (WSNs). WSNs are widely used for a variety of applications, including industrial automation, healthcare, and environmental monitoring, where extending network lifetime and energy conservation are crucial. Although successful, conventional clustering algorithms like LEACH feature subpar cluster formation and premature cluster head failures. To get over these restrictions, a memetic algorithm is used to maximize cluster formation and reduce energy usage. This algorithm was influenced by natural evolution and cultural learning processes. The Memetic Algorithm iteratively improves the LEACH protocol in the proposed technique by enhancing dynamic clustering, energy-aware routing, and cluster head selection. These methods work together to increase energy efficiency and lengthen the life of the network. In comparison to conventional LEACH-based WSNs and other cutting-edge algorithms, the paper’s study of the simulation results shows how successful the suggested technique is. In light of network longevity, energy use, and data transmission efficiency, the results obtained demonstrate notable improvements.

Intermolecular interactions probed by rotational dynamics in gas-phase clusters

The rotational dynamics of a molecule is sensitive to neighboring atoms or molecules, which can be used to probe the intermolecular interactions in the gas phase. Here, we real-time track the laser-driven rotational dynamics of a single N2 molecule affected by neighboring Ar atoms using coincident Coulomb explosion imaging. We find that the alignment trace of N-N axis decays fast and only persists for a few picoseconds when an Ar atom is nearby. We show that the decay rate depends on the rotational geometry of whether the Ar atom stays in or out of the rotational plane of the N2 molecule. Additionally, the vibration of the van der Waals bond is found to be excited through coupling with the rotational N-N axis. The observations are well reproduced by solving the time-dependent Schrödinger equation after taking the interaction potential between the N2 and Ar into consideration. Our results demonstrate that environmental effects on a molecular level can be probed by directly visualizing the rotational dynamics.

Rapid iodine oxoacid nucleation enhanced by dimethylamine in broad marine regions

Abstract. Recent experiments have revealed a vital nucleation process of iodic acid (HIO3) and iodous acid (HIO2) under marine boundary layer conditions. However, HIO3–HIO2 nucleation may not effectively drive the observed rapid new particle formation (NPF) in certain coastal regions influenced by urban air masses. Dimethylamine (DMA) is a promising basic precursor to enhance nucleation considering its strong ability to stabilize acidic clusters and the wide distribution in marine atmosphere, while its role in HIO3–HIO2 nucleation remains unrevealed. Hence, a method combining quantum chemical calculations and Atmospheric Cluster Dynamics Code (ACDC) simulations was utilized to study the HIO3–HIO2–DMA nucleation process. We found that DMA can preferentially accept the proton from HIO3 as a basic precursor in the most stable configurations of HIO3–HIO2–DMA clusters. Kinetically, the participation of DMA in the cluster formation pathways of the iodine oxoacid system could be significant at the 10−1 to 1 pptv level of [DMA]. Furthermore, DMA can enhance the cluster formation rates of the HIO3–HIO2 system in marine and polar regions near DMA sources more than 103-fold. Compared to the classical nucleation mechanism, the HIO3–HIO2–DMA mechanism exhibits strong nucleation ability, worthy of consideration as a promising mechanism in marine and polar regions rich in amine sources. The newly proposed HIO3–HIO2–DMA ternary mechanism might provide an explanation for some missing fluxes of atmospheric iodine particles.

YS-SLAM: YOLACT++ based semantic visual SLAM for autonomous adaptation to dynamic environments of mobile robots

Aiming at the problem of poor autonomous adaptability of mobile robots to dynamic environments, this paper propose a YOLACT++ based semantic visual SLAM for autonomous adaptation to dynamic environments of mobile robots. First, a light-weight YOLACT++ is utilized to detect and segment potential dynamic objects, and Mahalanobis distance is combined to remove feature points on active dynamic objects, also, epipolar constraint and clustering are employed to eliminate feature points on passive dynamic objects. Then, in terms of the semantic labels of dynamic and static components, the global semantic map is divided into three parts for construction. The semantic overlap and uniform motion model are chose to track moving objects and the dynamic components are added to the background map. Finally, a 3D semantic octree map is constructed that is consistent with the real environment and updated in real time. A series of simulations and experiments demonstrated the feasibility and effectiveness of the proposed approach.

Drivers for Clustering and Inter-Project Collaboration—A Case of Horizon Europe Projects

This paper investigates the drivers and dynamics of clustering and inter-project collaboration within the framework of the Horizon Europe and Horizon 2020 projects. Leveraging a survey-based approach, we examine key themes surrounding the perception of clustering, the willingness to share information under legal confidentiality, and motivations for engaging with partners from different projects. The survey instrument, implemented via Microsoft Forms, was distributed among the consortia of eight EU projects participating in the SOLID4B cluster. Notably, the questionnaire was meticulously crafted based on an in-depth analysis of the SOLID4B case and comprehensive discussions with project coordinators and communication and dissemination managers from all participating projects. These discussions aimed to establish a clear roadmap for the cluster, ensuring the questionnaire’s relevance and usefulness for all participants. Data analysis was conducted within the same platform, facilitating efficient data processing and visualization. Our findings reveal that a significant majority of respondents (48 out of 55) perceive clustering as a valuable asset, indicative of a positive shift in perspectives. Challenges related to confidentiality were addressed through nuanced insights, with respondents demonstrating a willingness to share routine best practices, significant breakthroughs, and deliverables within a legally protected framework. Furthermore, a robust majority (40 out of 55) expressed a keen interest in collaborative endeavors, underscoring a collective drive to extend activities beyond individual project boundaries. The study highlights the importance of clustering with other projects in maximizing the impact of the Horizon program, extending stakeholder networks, and sharing knowledge and achievements in research and innovation. These insights contribute to a deeper understanding of the motivations and challenges surrounding clustering and collaboration within the Horizon Europe and Horizon 2020 projects. Ultimately, the findings pave the way for informed strategies aimed at fostering a dynamic and interconnected research community.

Topologically Defective Lattice Potential‐Based Gain‐Dissipative Ising Annealer with Large Noise Margin

AbstractGain‐dissipative Ising machines (GIMs) are annealers inspired by physical systems such as Ising spin glasses to solve combinatorial optimization problems. Compared to traditional quantum annealers, GIM is relatively easier to scale and can save on additional power consumption caused by low‐temperature cooling. However, traditional GIMs have a limited noise margin. Specifically, their normal operation requires ensuring that the noise intensity is lower than their saturation fixed point amplitude, which may result in increased power consumption to suppress noise‐induced spin state switching. To enhance the noise robustness of GIM, in this study a GIM based on a topologically defective lattice potential (TDLP) is proposed. Numerical simulations demonstrate that the TDLP‐based GIM can accurately simulate the bifurcation spin evolution in the Ising model. Furthermore, through the MAXCUT benchmark based on G‐set graphs, the optimal performance of TDLP‐based GIM is shown to surpass that of traditional GIMs. Additionally, the proposed TDLP‐based GIM successfully solves the MAXCUT benchmark and domain clustering dynamics benchmark based on G‐set graphs when the noise intensity exceeds its saturation fixed‐point amplitude. This indicates that the proposed system provides a promising architecture for breaking the small noise constraints required by traditional GIMs.

Optimizing Energy Efficiency in Opportunistic Networks: A Heuristic Approach to Adaptive Cluster-Based Routing Protocol

Opportunistic Networks (OppNets) are characterized by intermittently connected nodes with fluctuating performance. Their dynamic topology, caused by node movement, activation, and deactivation, often relies on controlled flooding for routing, leading to significant resource consumption and network congestion. To address this challenge, we propose the Adaptive Clustering-based Routing Protocol (ACRP). This ACRP protocol uses the common member-based adaptive dynamic clustering approach to produce optimal clusters, and the OppNet is converted into a TCP/IP network. This protocol adaptively creates dynamic clusters in order to facilitate the routing by converting the network from a disjointed to a connected network. This strategy creates a persistent connection between nodes, resulting in more effective routing and enhanced network performance. It should be noted that ACRP is scalable and applicable to a variety of applications and scenarios, including smart cities, disaster management, military networks, and distant places with inadequate infrastructure. Simulation findings demonstrate that the ACRP protocol outperforms alternative clustering approaches such as kRop, QoS-OLSR, LBC, and CBVRP. The analysis of the ACRP approach reveals that it can boost packet delivery by 28% and improve average end-to-end, throughput, hop count, and reachability metrics by 42%, 45%, 44%, and 80%, respectively.

Correction to: Dynamic clustering of spatial–temporal rainfall and temperature data over multi‑sites in Yemen using multivariate functional approach

Correction to: Dynamic clustering of spatial–temporal rainfall and temperature data over multi‑sites in Yemen using multivariate functional approach

Disparity-driven heterogeneous nucleation in finite-size adaptive networks.

Phase transitions are crucial in shaping the collective dynamics of a broad spectrum of natural systems across disciplines. Here, we report two distinct heterogeneous nucleation facilitating single step and multistep phase transitions to global synchronization in a finite-size adaptive network due to the trade off between time scale adaptation and coupling strength disparities. Specifically, small intracluster nucleations coalesce either at the population interface or within the populations resulting in the two distinct phase transitions depending on the degree of the disparities. We find that the coupling strength disparity largely controls the nature of phase transition in the phase diagram irrespective of the adaptation disparity. We provide a mesoscopic description for the cluster dynamics using the collective coordinates approach that brilliantly captures the multicluster dynamics among the populations leading to distinct phase transitions. Further, we also deduce the upper bound for the coupling strength for the existence of two intraclusters explicitly in terms of adaptation and coupling strength disparities. These insights may have implications across domains ranging from neurological disorders to segregation dynamics in social networks.

A Novel Classification Algorithm Based on the Synergy Between Dynamic Clustering with Adaptive Distances and K-Nearest Neighbors

This paper introduces a novel supervised classification method based on dynamic clustering (DC) and K-nearest neighbor (KNN) learning algorithms, denoted DC-KNN. The aim is to improve the accuracy of a classifier by using a DC method to discover the hidden patterns of the apriori groups of the training set. It provides a partitioning of each group into a predetermined number of subgroups. A new objective function is designed for the DC variant, based on a trade-off between the compactness and separation of all subgroups in the original groups. Moreover, the proposed DC method uses adaptive distances which assign a set of weights to the variables of each cluster, which depend on both their intra-cluster and inter-cluster structure. DC-KNN performs the minimization of a suitable objective function. Next, the KNN algorithm takes into account objects by assigning them to the label of subgroups. Furthermore, the classification step is performed according to two KNN competing algorithms. The proposed strategies have been evaluated using both synthetic data and widely used real datasets from public repositories. The achieved results have confirmed the effectiveness and robustness of the strategy in improving classification accuracy in comparison to alternative approaches.

Dynamic edge clustering and task scheduling for edge assisted metaverse system in the field of remote work and collaboration

SummaryThe metaverse, a future digital world for living, working, learning, and interacting, is rapidly gaining significance, particularly in the domain of remote work and collaboration. This emerging digital landscape demands high‐performance, low‐latency, and scalable services to provide an immersive user experience. The current technology used in metaverse systems has some limitations, which emphasize the importance of adopting emerging technologies like Edge Computing (EC). However, as the number of users and data volume increases, it can impact both system performance and scalability of the edge‐assisted metaverse system. Additionally, the uneven distribution of edge servers can cause inconsistencies and result in high latency. To overcome these challenges, this paper proposes a dynamic edge clustering and task scheduling approach for edge‐assisted metaverse systems (DTAM) in the field of remote work and collaboration. The proposed approach addresses the challenges of high user volume and uneven resource distribution by incorporating dynamic clustering and edge server assistance to improve clustering performance. Furthermore, a Prioritized Experience Replay‐based Deep Q‐learning algorithm with state augmentation (PERDQSA) for task scheduling is introduced to improve sample efficiency and performance. The performance of the proposed DTAM is evaluated against existing techniques, and experimental results demonstrate its significant superiority in terms of specific metrics such as bandwidth, task response time, energy efficiency, and latency. The experiments demonstrated that DTAM outperforms Transformation‐based Edge Computing Deep Q‐Learning (TransEC‐DQL) in several key metrics. Specifically, DTAM achieves 28.5% reduction in latency, 13.7% reduction in response time, and 6.4% improvement in bandwidth compared to TransEC‐DQL. These results signify that DTAM can deliver a significantly enhanced user experience in the metaverse, particularly in the context of remote work and collaboration.

On Inter‐bubble Hydrodynamics and Cluster Formation in Non‐Newtonian Bubbly Columns: A Review

AbstractA comprehensive understanding of the inter‐bubble dynamics and the cluster formations in non‐Newtonian fluids is pivotal for chemical and biomedical engineering applications. In this review, we summarize current research efforts to provide a fundamental understanding and engineering principles of the interactions between bubbles and the dynamics of bubble clusters in non‐Newtonian fluids. Although the majority of research is still predominantly conducted through experimentation, the significance of computational fluid dynamics in elucidating interaction mechanisms has become increasingly prominent in recent years. Moreover, the gas‐liquid systems reviewed in this paper are driven by gravity, which is closer to the actual industrial processes. We indicate the effects of non‐Newtonian fluid special rheological properties (especially viscoelasticity and shear‐dependent viscosity) on the interaction between multiple bubbles and the formation of clusters. We summarize the main empirical correlations in related research, which are useful for the optimization of industry. Ultimately, we address the current trends, limitations, and future directions in this research field.

MolDStruct: Modeling the dynamics and structure of matter exposed to ultrafast x-ray lasers with hybrid collisional-radiative/molecular dynamics.

We describe a method to compute photon-matter interaction and atomic dynamics with x-ray lasers using a hybrid code based on classical molecular dynamics and collisional-radiative calculations. The forces between the atoms are dynamically determined based on changes to their electronic occupations and the formation of a free electron cloud created from the irradiation of photons in the x-ray spectrum. The rapid transition from neutral solid matter to dense plasma phase allows the use of screened potentials, reducing the number of non-bonded interactions. In combination with parallelization through domain decomposition, the hybrid code handles large-scale molecular dynamics and ionization. This method is applicable for large enough samples (solids, liquids, proteins, viruses, atomic clusters, and crystals) that, when exposed to an x-ray laser pulse, turn into a plasma in the first few femtoseconds of the interaction. We present four examples demonstrating the applicability of the method. We investigate the non-thermal heating and scattering of bulk water and damage-induced dynamics of a protein crystal using an x-ray pump-probe scheme. In both cases, we compare to the experimental data. For single particle imaging, we simulate the ultrafast dynamics of a methane cluster exposed to a femtosecond x-ray laser. In the context of coherent diffractive imaging, we study the fragmentation as given by an x-ray pump-probe setup to understand the evolution of radiation damage in the time range of hundreds of femtoseconds.

Spatially-resolved cluster dynamics modeling of irradiation growth

We develop here a spatially resolved, three-dimensional continuum model coupling cluster dynamics (SR-CD) and crystal plasticity to investigate irradiation growth in zirconium. The model uses scale separation to divide the population of the irradiation cluster into mobile and immobile families. Small interstitial and vacancy clusters are modeled using anisotropic reaction–diffusion equations. Among the immobile clusters, an atomistically-informed vacancy cluster to vacancy loop transition is taken into account. The coupling between the evolution equation of CD and the plastic deformation of the material is two-fold, with stress-informed bias factors and local inelastic strains computed from the evolution of the evolving cluster population. The numerical implementation of the model utilizes the finite element method to analyze both single-crystal and polycrystalline samples. The growth strains that are computed align well with the experimental data provided by Carpenter for single-crystal Zr. Furthermore, the transformation of a vacancy cluster into a complete vacancy loop, occurring at a size of 14 nm, is in agreement with experimental observations and atomistic simulations. The density, size, and growth rate of the dislocation loops, denoted as 〈c〉 and 〈a〉, also exhibit good agreement with transmission electron microscopy (TEM) analysis of irradiated Zr and its alloys. Our findings demonstrate that there is a spatial correlation between the growth of these dislocation loops and growth strains, significantly influenced by the crystal size. To explain the expansion of the 〈a〉 axis and the contraction of the 〈c〉 axis in irradiated Zr, it is necessary to consider the diffusion anisotropy difference (DAD) of mobile interstitial species. We show that the PWR Kearns parameters, specifically fr = 0.63, ft = 0.32, fa = 0.05, confer enhanced irradiation resistance to Zr along the principal directions when compared to single crystals. Additionally, reducing the grain size to nanograins further enhances the resistance to irradiation-induced growth, particularly along the direction with the highest volume fraction of basal poles [0001].

Unsupervised Detection of Behavioural Drifts With Dynamic Clustering and Trajectory Analysis

Unsupervised Detection of Behavioural Drifts With Dynamic Clustering and Trajectory Analysis

The spin, expansion, and contraction of open star clusters

Empirical constraints on the internal dynamics of open clusters are important for understanding their evolution and evaporation. High-precision astrometry from Gaia DR3 is thus useful to observe aspects of the cluster dynamics. This work aims to identify dynamically peculiar clusters such as spinning and expanding clusters. We also quantify the spin frequency and expansion rate and compare them with $N$-body models to identify the origins of the peculiarities. We used the latest Gaia DR3 and archival spectroscopic surveys to analyse the radial velocities and proper motions of the cluster members in 1379 open clusters. A systematic analysis of synthetic clusters was performed to demonstrate the observability of the cluster spin along with effects of observational uncertainties. $N$-body simulations were used to understand the evolution of cluster spin and expansion for initially non-rotating clusters. We identified spin signatures in ten clusters (and 16 candidates). Additionally, we detected expansion in 18 clusters and contraction in three clusters. The expansion rate is compatible with previous theoretical estimates based on the expulsion of residual gas. The orientation of the spin axis is independent of the orbital angular momentum. The spin frequencies are much larger than what was expected from simulated, initially non-rotating clusters. This indicates that $>1$<!PCT!> of the clusters are born rotating and/or they have undergone strong interactions. Higher precision observations are required to increase the sample of such dynamically peculiar clusters and to characterise them.

PopDCN: Popularity-Aware Dynamic Clustering Scheme for Distributed Caching in ICN

PopDCN: Popularity-Aware Dynamic Clustering Scheme for Distributed Caching in ICN

Examining the dynamics of European renewable energy clusters: insights into technological collaboration

ABSTRACT With an emphasis on the European Renewable Energy Clusters (RECs), the study examines the impact of local and global collaborations on technological competitiveness within RECs. Drawing on illustrative examples from wind and solar energy clusters in Europe, the research challenges the prevailing notion of the need for extensive global collaborations for innovation. Instead, it highlights the pivotal role of local collaborations and technologically homophile community ties in sustaining technological competitiveness within green high-tech clusters. The article contributes to the green-tech innovation and clusters literature by providing valuable insights into how companies in clusters engage in patent collaborations, particularly in the context of green energy technologies. The study spans the period from 1998 to 2018, offering a comprehensive analysis of collaborations in two mature renewable energies: wind and solar. It particularly investigates the Wind cluster in Navarra, Spain and the Munich Solar cluster in Germany.