Clustering Phenomenon Research Articles

Production of electrospun hybrid membranes based on polyamide 6 reinforced with hemp fibers dissolved in 1-ethyl-3-methylimidazolium dicyanamide ionic liquid

This paper is focused on the fabrication of novel hybrid materials based on electrospun polyamide fibers reinforced with hemp fibers (HF) dissolved in 1-ethyl-3-methylimidazolium dicyanamide ionic liquid (IL). The physical properties of the hybrid electrospun materials have been evaluated. SEM micrographs showed an interconnected structure between polyamide 6 (PA6) and IL dissolved hemp fibers. X-ray and FTIR analysis were carried out to support the SEM images. DSC and TGA profiles gave information related to the thermal stability of electrospun membrane while barrier properties analysis proved the improvement in terms of water sensitivity and reduction of water clustering phenomena. Moreover, the improvement of mechanical properties was obtained after the introduction of IL dissolved HF leading to an increase in Young’s modulus of about 640 %. The antioxidant properties of reinforced PA6 fibers were even carried out, proving an enhancement of radical scavenging activity of about 68 %. Finally, the computational work and the estimation of surface energy distribution were reported.

Herding behavior in exploring the predictability of price clustering in cryptocurrency market

We propose the K-means approach and the Hidden Markov Model (HMM), to predict the phenomenon of price clustering of the cryptocurrency market. This approach aims to understand the relationship of price clustering with herding behavior, volatility, price, and economic policy uncertainty (EPU). Our results indicate that the (HMM) with four states has the best one-step-ahead forecasting performance. The results gave new insights into the financial analysis of cryptocurrency market about the dynamic relationship between price clustering regimes and different states of the explanatory variable. Our finding proves the efficiency of (HMM) for our sample and provides a good predictability.

Impact of Built Environment on Carbon Emissions from Cross-District Mobility: A Social Network Analysis Based on Private Vehicle Trajectory Big Data

The realization of the “double carbon” goals and the development of green transportation require a focused approach to reducing carbon emissions from private cars. Starting from the perspective of social network analysis, this paper constructs the carbon emission network of private car cross-district mobility based on vehicle trajectory big data in Guangzhou and Foshan and analyzes its spatial network characteristics. Next, the MRQAP model is constructed to examine the impact of built environment factors on carbon emissions from private cars. Furthermore, the paper explores the moderating effect of private car mobility in the central urban area. The results indicate the following: (1) Private vehicle cross-district mobility in the Guangzhou and Foshan region are closely interconnected and exhibit a phenomenon of central clustering. (2) Both population density and the number of road intersections have a positive relationship with private car carbon emissions, and after a series of robustness tests, the results are still valid. (3) Private vehicle mobility in central urban areas contributes to an increase in carbon emissions, and the positive impact is reinforced by population density, while road intersections and private car mobility in central urban areas have a substitutive effect on private car carbon emissions.

Implementation of chiral two-nucleon forces to nuclear many-body methods with Gaussian-wave packets

Abstract Many-body methods that use Gaussian-wave packets to describe nucleon spatial distribution have been widely employed for depicting various phenomena in nuclear systems, in particular clustering. So far, however, the chiral effective field theory, a state-of-the-art theory of nuclear force, has not been applied to such methods. In this paper, we give the formalism to calculate the two-body matrix elements of the chiral two-nucleon forces using the Gaussian-wave packets. We also visualize the matrix elements and investigate the contributions of the central and tensor forces. This work is a foothold towards an ab initio description of various cluster phenomena in view of nucleons, pions, and many-nucleon forces.

Technical Land-Sea Spaces: Impacts of the Port Clusterization Phenomenon on Coasts, Cities and Architectures

Land-sea interactions, extending inland and towards the marine spaces, are affected by major management and design transformations. Globalization processes, port expansion projects and extensive energy transition requests have recently led port institutions to demand more land, engaging deeply with logistics platforms and radically restructuring forms of port governance. In this competitive context, the phenomenon of Port Clusterisation, i.e. the administrative aggregation whereby two or more ports are merged to form port clusters, is heavily impacting the institutional sphere. However, not only does this phenomenon have no control over cities, but its spatial component seems to be neglected by the disciplines of space, such as urbanism and architecture. As a result, port and city institutions lack design tools to tackle urgent challenges as coastal utilization, the need for resilient port-city infrastructures and the regeneration of the port-city architectural heritage. In terms of novelty and contribution to academia, an examination of the spatial footprint of port clusters will allow research to move beyond its state-of-the-art by targeting a phenomenon that, though pivotal, is under-researched, especially within the spatial disciplines.

Round offer prices in M&A transactions: costly negotiation and psychological preference

PurposeThe authors of this study aim to investigate possible explanations of the prevalence of price clustering in the final offer prices of mergers and acquisitions (M&A).Design/methodology/approachThe authors use final offer price in M&A deals to investigate the price clustering phenomena. The authors used regressions and logistic regressions to examine potential factors that might affect pricing strategy by looking into one-time acquirers and experienced serial acquirers.FindingsPrice clustering increases with negotiation uncertainties characterized as competitive bidding, number of bidders, challenged deals and duration. Moreover, the authors find persistent price clustering in experienced serial acquirers that are more experienced and better equipped with handling uncertainties, suggesting a preference of using round numbers regardless of levels of uncertainties. The authors' evidence shows that price clustering results from a combination of Harris' (1991) costly negotiation hypothesis where round prices may be used to lower search costs and psychological bias and preference.Originality/valueThe authors appear to be the first to investigate alternative theories that support M&A offer price clustering behavior, finding that both the costly negotiation and psychological bias and preference theories apply to M&A final price formation. Thus, the authors' major contribution, specific to the M&A process, is a clarification of physical and psychological factors associated with bidding and negotiation behavior. The authors are confident that the authors' study impacts conventional knowledge regarding M&A deal negotiation strategies, including bidding behavior, contract negotiation, financial analysis, management practices and risk management.

Spatiotemporal Distributions of Multiple Land Use Functions and Their Coupling Coordination Degree in the Yangtze River Delta Urban Agglomeration, China

In order to gain a comprehensive understanding of land system changes and regional sustainable development, it is crucial to explore the spatiotemporal distributions of multiple land use functions (LUFs). Therefore, herein, considering the Yangtze River Delta urban agglomeration (YRDUA) as the research object, we constructed an evaluation system based on the perspective of production–living–ecological (PLE) land functions. Furthermore, the coupling coordination model, kernel density curve, trend surface analysis, and spatial autocorrelation model were used to examine the spatial and temporal characteristics of LUFs and their coupling coordination and analyze the spatial clustering of the coupling coordination effect in the urban agglomeration from 2005 to 2020. The following results were obtained: The level of PLE functions and its coupling coordination degree in the YRDUA have been improved in the study period, and the distribution of high value areas of different functions is diverse. In terms of the spatial distribution of the coupling coordination degree, the high value areas of production function (PF)–living function (LF) is distributed in “clusters”, the PF–ecological function (EF) demonstrated a single-polarized development structure, and the LF-EF exhibited a multi-core structure. The coupling coordination of the LUFs demonstrated a “high in the east and low in the west” pattern in the east–west direction and an inverted “U” shape in the north–south direction. Moreover, both high-value and low-value areas exhibited a clustering phenomenon, with an evident spatial positive correlation. The results of this study can serve as a reference for the integrated socioeconomic development of the Yangtze River Delta region and the sustainable development of urban land use.

Cluster model of C12 in the density functional theory framework

We employ the constrained density functional theory to investigate cluster phenomena for the $^{12}\mathrm{C}$ nucleus. The proton and neutron densities are generated from the placement of three $^{4}\mathrm{He}$ nuclei ($\ensuremath{\alpha}$ particles) geometrically. These densities are then used in a density constrained Hartree-Fock calculation that produces an antisymmetrized state with the same densities through energy minimization. In the calculations no a priori analytic form for the single-particle states is assumed and the full energy density functional is utilized. The geometrical scan of the energy landscape provides the ground state of $^{12}\mathrm{C}$ as an equilateral triangular configuration of three $\ensuremath{\alpha}\mathrm{s}$ with molecular bond like structures. The use of the nucleon localization function provides further insight to these configurations. One can conclude that these configurations are a hybrid between a pure mean-field and a pure $\ensuremath{\alpha}$ particle condensate. This development could facilitate density functional theory based fusion calculations with a more realistic $^{12}\mathrm{C}$ ground state.

Uncovering the coupling effect with energy-related carbon emissions and human development variety in Chinese provinces

Lessening energy-related carbon emissions has become a crucial measure to achieve Chinese carbon neutrality. This study is the first to construct a Difference in Carbon pressures-adjusted Human Development Index (DCHDI) model for the purpose of exploring the coupling effect between carbon emissions and human development variety from 2000 to 2019 in Chinese provinces. We demonstrate the following. (1) The total energy-related carbon footprint of 30 provinces in China reached 10.2 billion tons in 2019, with an average annual growth rate of 6.93% over the past two decades; and the provinces with the highest carbon emissions per capita are InnerMongolia, Ningxia, and Shanxi. (2) At the provincial level, we observed that the Human Development Index (HDI), which includes life expectancy, education, and income, has been rising, while Beijing, Shanghai, and Tianjin entered the super-high HDI level before 2008. (3) The entire coupling effect of 30 Chinese provinces has been broadly fortified in the last 20 years, but the growth rate of DCHDI values in 2011–2019 has slowed down compared with that in 2000–2010; the clustering phenomenon demonstrated that this discovery is associated with historical peaks in total carbon emissions. (4) The coordination degree of carbon emissions per capita and HDI was verified, and 96% of the data points were found in the range of super high coupling coordination degree. Overall, this study provides the government with worthwhile guidance for decision-making and carbon reduction strategies for other countries struggling to advance human sustainable development.

APOL1 genotype in African American patients with kidney neoplasm

Context: African Americans (AA) may carry high risk Apolipoprotein L1 ( APOL1) genetic variants that predispose them to significantly higher incidence of chronic kidney disease (CKD) and renal cell carcinoma (RCC). Objective: To study the role of APOL1 risk alleles in patients of African descent with kidney neoplasms. Design: AA patients with nephrectomy procedure for kidney neoplasms were retrospectively studied for APOL1 genotype. Non-AA patients with kidney neoplasm served as control group. Results: Thirty-six AA patients with kidney neoplasms were included. Sixteen (44%) patients carried no APOL1 risk alleles; 13 (36%) patients carried one APOL1 risk allele and 7 (19%) patients carried two APOL1 risk alleles. Increased risk of end stage kidney disease (ESKD) was observed in patients with two APOL1 risk alleles (OR = 3.45). We also observed a higher papillary carcinoma (pRCC) percentage in patients with two risk alleles. Five (71.4%) of the seven patients with two risk-alleles had pRCC. In patients with zero or one APOL1 risk allele ( n = 29), 8 (28%) had pRCC. However, in AA patients with ESKD ( n = 6), only 2 (33%) had PRCC. Among the non-AA patients ( n = 319), 7 (2.2%) patients presented with ESKD, of which 2 (33%) had pRCC. Conclusions: We observed a cluster phenomenon in APOL1 high-risk genotype, ESKD, and kidney neoplasm. We observed significantly more frequent papillary RCC prevalence in AA patients with high-risk APOL1 genotype.

Designing TOGAXSI: Telescope for inverse-kinematics cluster-knockout reactions

We have launched a new research project named “ONOKORO” to elucidate the formation mechanism of clusters in atomic nuclei. The project comprehensively investigates clustering phenomena in the medium-to-heavy mass region with inverse-kinematics cluster-knockout reactions at the intermediate energy facilities. The new detector array named “TOGAXSI” is under construction for this purpose. It measures scattering angles and the energies of deuterons, tritons, 3He, and α particles emitted at forward angles from the cluster-knockout reactions. It also measures the recoil protons emitted from the scattering events at large angles. High-angular and high-energy resolutions with wide solid angles are required to achieve a good cluster separation energy resolution and a reasonably high yield. The designed telescope consists of silicon trackers and GAGG:Ce calorimeters. This paper shows the required conditions and design concepts of TOGAXSI.

Hydrodynamic interactions between squirmers near walls: far-field dynamics and near-field cluster stability

Confinement increases contacts between microswimmers in dilute suspensions and affects their interactions. In particular, boundaries have been shown experimentally to lead to the formation of clusters that would not occur in bulk fluids. To what extent does hydrodynamics govern these boundary-driven encounters between microswimmers? We consider theoretically the symmetric boundary-mediated encounters of model microswimmers under gravity through far-field interaction of a pair of weak squirmers, as well as the lubrication interactions occurring after contact between two or more squirmers. In the far field, the orientation of microswimmers is controlled by the wall and the squirming parameter. The presence of a second swimmer influences the orientation of the original squirmer, but for weak squirmers, most of the interaction occurs after contact. We thus analyse next the near-field reorientation of circular groups of squirmers. We show that a large number of swimmers and the presence of gravity can stabilize clusters of pullers, while the opposite is true for pushers; to be stable, clusters of pushers thus need to be governed by other interactions (e.g. phoretic). This simplified approach to the phenomenon of active clustering enables us to highlight the hydrodynamic contribution, which can be hard to isolate in experimental realizations.

Multiscale clustering of heavy and light small particles in turbulent channel flow at high Reynolds numbers

To understand the fundamental mechanism of the clustering of heavy and light small particles in high-Reynolds-number wall turbulence, we conduct direct numerical simulations of particle dispersions in turbulent channel flow at the friction Reynolds number Reτ=2000. Heavy and light particles form clusters with different shapes, and the cluster size is larger for particles with longer relaxation time. To reveal the origin of these multiscale clusters of particles, we investigate particle distributions around multiscale vortices. Vortices tend to sweep out heavy particles and tend to attract light ones with a relaxation time comparable with their turnover time. These clustering phenomena due to coherent vortices can occur irrespective of their size, existing height and the friction Reynolds number. We can therefore explain the multiscale nature of particle clustering in terms of the time scale matching between particles and vortices.

APPLYING DEEP LEARNING FOR HEALTHCARE IN SMART CITY VIA INTERNET OF THINGS

In the traditional city for healthcare in IoT, it has been proposed to replace traditional yield models with mathematical models that do not require the assumption of defect density functions. The selection of input parameters in these models is very important, and all the variation factors on the wafer must be included as far as possible. The factors of clustering are usually described by clustering indicators, but some specific clustering patterns will cause the clustering indicators to misjudge the clustering degree, resulting in the yield estimation error becoming larger. In view of this, the proposed study has classified the defect patterns on the wafer into four types: random distribution, regional concentrated distribution, linear distribution and circular distribution, by means of three pattern characteristics analysis. A comparison is made only using cluster indicators to describe cluster phenomena and a model that uses cluster indicators and cluster graphs to describe cluster phenomena. The research results show that when constructing the yield model, the clustering pattern and the clustering index are used to describe the clustering phenomenon in smart city via Internet of things, which is preferred to solely considering the clustering index, as the consequent degree of accuracy far exceeds the improvement of changing the “number of effective grains” in relation to the clustering index. Therefore, the yield rate can be estimated more accurately by using clustering graphs with clustering indicators; the estimated yield in the yield model, with the clustering pattern parameter, is indeed closer to the actual yield than the yield model without the clustering pattern parameter.

Development of multiscale analysis method for predicting thermo-mechanical properties of polymeric nanocomposites containing clustered nanoparticles

Thermo-mechanical properties of polymeric nanocomposites containing clustered nanoparticles are investigated using molecular dynamics (MD) simulation. Comparing between the dispersion and cluster models, it is revealed that the thermo-mechanical properties are decreased due to the clustering phenomenon. For effectively predicting the thermo-mechanical properties of polymeric nanocomposites, a multiscale analysis method is developed by linking the MD simulation and finite element homogenization analysis. Using the multiscale analysis, the elastic and shear moduli, and coefficient of thermal expansion (CTE) of the interphase can be obtained, and it is revealed that the reinforcement effect of the interphase is decreased due to the cluster phenomenon of nanoparticles. In addition, it is showed that this method can be used to accurately predict the elastic and shear moduli, and CTE of polymeric nanocomposites because of the clustering phenomenon.

Imprints of clustering in multiplicity fluctuations

In this paper, we investigate the multiplicity fluctuations of charged particles observed in high-energy nuclear collisions and relate them to the size of hadronizing systems which happen during such processes. We use the average multiplicities langle Nrangle and variances Varleft( Nright) of multiplicity distributions of charged particles produced in centrality selected collisions of relativistic heavy-ion nuclei to evaluate the dynamic variance varOmega and study its dependence on the size of colliding systems. We connect the observed system-size dependence of multiplicity fluctuations with the clustering phenomena and the finiteness of the hadronizing sources and the thermal bath.

A Comprehensive Evaluation Framework of Water-Energy-Food System Coupling Coordination in the Yellow River Basin, China.

For mankind's survival and development, water, energy, and food (WEF) are essential material guarantees. In China, however, the spatial distribution of WEF is seriously unbalanced and mismatched. Here, a collaborative governance mechanism that aims at nexus security needs to be urgently established. In this paper, the Yellow River Basin in China with a representative WEF system, was selected as a case. Firstly, a comprehensive framework for WEF coupling coordination was constructed, and the relationship and mechanism between them were analyzed theoretically. Then, we investigated the spatiotemporal characteristics and driving mechanisms of the coupling coordination degree (CCD) with a composite evaluation method, coupling coordination degree model, spatial statistical analysis, and multiscale geographic weighted regression. Finally, policy implications were discussed to promote the coordinated development of the WEF system. The results showed that: 1) WEF subsystems showed a significant imbalance of spatial pattern and diversity in temporal changes; 2) the CCD for the WEF system varied little and remained at moderate coordination. Areas with moderate coordination have increased, while areas with superior coordination and mild disorder have decreased. In addition, the spatial clustering phenomenon of the CCD was significant and showed obvious characteristics of polarization; and 3) the action of each factor is self-differentiated and regionally variable. For different factors, GDP per capita was of particular importance, which contributed most to the regional development's coupling coordination. For different regions, GDP per capita, average yearly precipitation, population density, and urbanization rate exhibited differences in geographical gradients in an east-west direction. The conclusion can provide references for regional resource allocation and sustainable development by enhancing WEF system utilization efficiency.

Critical behaviors of cascading dynamics on multiplex two-dimensional lattices

We study the critical phenomena of viable clusters in multiplex two-dimensional lattices using numerical simulations. We identify viable sites on multiplex lattices using two cascading algorithms: the cascade of activations ( $${\textrm{CA}}$$ ) and deactivations ( $${\textrm{CD}}$$ ). We found that the giant viable clusters identified by $${\textrm{CA}}$$ and $${\textrm{CD}}$$ processes exhibit different critical behaviors. Specifically, the critical phenomena of $${\textrm{CA}}$$ processes are consistent with the ordinary bond percolation on a single layer but $${\textrm{CD}}$$ processes exhibit the critical behaviors consistent with mutual percolation on multiplex lattices. In addition, we computed the susceptibility of cascading dynamics using the concept of ghost field. Our results suggest that the $${\textrm{CA}}$$ and $${\textrm{CD}}$$ processes generate viable clusters in different ways.

Spatio-temporal intention learning for recommendation of next point-of-interest

ABSTRACT Next point-of-interest (POI) recommendation has been applied by many internet companies to enhance the user travel experience. Recent research advocates deep-learning methods to model long-term check-in sequences and mine mobility patterns of people to improve recommendation performance. Existing approaches model general user preferences based on historical check-ins and can be termed as preference pattern models. The preference pattern is different from the intention pattern, in that it does not emphasize the user mobility pattern of revisiting POIs, which is a common behavior and kind of intention for users. An effective module is needed to predict when and where users will repeat visits. In this paper, we propose a Spatio-Temporal Intention Learning Self-Attention Network (STILSAN) for next POI recommendation. STILSAN employs a preference-intention module to capture the user’s long-term preference and recognizes the user’s intention to revisit some specific POIs at a specific time. Meanwhile, we design a spatial encoder module as a pretrained model for learning POI spatial feature by simulating the spatial clustering phenomenon and the spatial proximity of the POIs. Experiments are conducted on two real-world check-in datasets. The experimental results demonstrate that all the proposed modules can effectively improve recommendation accuracy and STILSAN yields outstanding improvements over the state-of-the-art models.

Vibration reduction mechanism of Van der Pol oscillator under low-frequency forced excitation by means of nonlinear energy sink

Van der Pol (VDP) oscillator under forced excitation has complicated vibration modes, and its harm to the system cannot be ignored. The vibration reduction mechanism of VDP oscillator by means of nonlinear energy sink (NES) is explored under low-frequency forced excitation. Firstly, a mechanical model of VDP oscillator coupled NES is established by utilizing Newton’s law. Then the time history diagrams under different excitation amplitudes are compared to measure the vibration reduction effect of the coupled system. Finally, the stability and Hopf bifurcation behavior of the corresponding autonomous system are discussed, and the mechanism of cluster phenomenon and the mechanism of vibration reduction are revealed. It is interesting that, the vibration reduction effect is optimal when cluster phenomenon occurs, which is a typical vibration model in slow–fast system. The real part of eigenvalues and hysteresis time are critical factors affecting vibration reduction. These researches provide a theoretical basis for the vibration reduction of VDP oscillator coupled NES.