Clustering Phenomenon Research Articles

Tattooing is Mainly Cultural: A Representative Twin Study of Tattooing Determinants.

Tattooing has become increasingly common in recent decades, yet little is known regarding factors that influence tattoo behavior. Sources of influence will be important, for instance in aiding studies of long-term health effects. From the population-based Danish Twin Tattoo Cohort established in 2021, the study included 9,173 randomly selected twins born 1920-2004. Among these were 4,790 (52%) responders to a questionnaire on tattooing and lifestyle factors. There were 55% females, 22% were monozygotic twins, and the median age was 51years. Shared influence of tattooing over time was assessed by comparing monozygotic and dizygotic twin pairs. Responders were population representative on sex, age, and lifestyle factors. The cumulative incidence of being tattooed before age 25years increased markedly from 6% (95% CI: 4-7%) for males and 0% (0-1%) for females born in 1925-1960 to 30% (25-35%) for males and 41% (37-46%) for females born in 1981-2004. Tattooing was over twice as common among ever smokers compared to never smokers born in 1981-2004 (average smoking effect at age 25years: 36% (29-43%)). The likelihood of a twin getting tattooed if the co-twin is tattooed, was 2.0 (1.4-2.6) and 1.8 (1.5-2.2) times higher, for monozygotic and dizygotic twins, respectively. The findings indicate that variation in the likelihood of becoming tattooed is primarily explained by shared environmental factors 65% (35-95%), and that genetic influences explained little of this variation. This study demonstrates that strong environmental exposures shared by twin siblings irrespective of degree of genetic relatedness drive the choice for getting tattooed. We conclude that tattooing is a cultural group clustering phenomenon that goes beyond genetically oriented behavioral characteristics.

Impact of Heterogeneous Data on Financial Market Volatility: A Generalized Additive Model Approach

Financial market volatility is driven by a complex interplay of factors, many of which exhibit nonlinear relationships with market behavior. Traditional linear models often struggle to adequately capture these intricate dynamics, particularly when analyzing heterogeneous data sources such as stock prices, interest rates, and macroeconomic indicators. In this paper, a nonlinear regression approach is proposed, specifically using generalized additive models, to better understand the impact of such diverse data on market volatility. By incorporating data from multiple sources, including historical stock prices and interest rates, significant nonlinear relationships between stock returns and interest rates with market volatility are revealed in this paper. The smooth terms for log returns show that volatility increases sharply during periods of negative returns, particularly during market downturns, which is consistent with the widely observed phenomenon of volatility clustering. By accounting for these nonlinear relationships, the model provides valuable insights into how varying economic conditions impact market stability, informing risk management strategies and policymaking.

Glitches and Glitching Clusters in Rotation-powered Pulsars

Abstract The study of pulsar glitch phenomena serves as a valuable probe into the dynamic properties of matter under extreme high-density conditions, offering insights into the physics within neutron stars. Providing theoretical explanations for the diverse manifestations observed in different pulsars has proven to be a formidable challenge. By analyzing the distribution of glitch sizes and waiting times, along with the evolution of cumulative glitch sizes over time, we have uncovered a long-term clustering phenomenon for pulsar glitches. This perspective allows us to approach the distinct glitch representations in various pulsars from a unified standpoint, connecting the same periodicity of observational data to the randomness. Without relying on specific physical models, we utilized the coefficient of variation to numerically determine optimal clustering numbers and clustering periods for sample pulsars. Our analysis involving 27 pulsars has revealed a clear linear relationship between the glitch cluster period and characteristic age. Of interest, the cumulative distribution of functions of cluster sizes and interval times have the same patterns, which can be synchronously fitted by Gaussian processes. These results may indicate novel understandings of glitches and the resulting processes.

Study on the Spatio-Temporal Patterns of Survival Dynamic Evolution of Specialized Farmers’ Cooperatives and the Influencing Factors of Underdeveloped Areas in China—Taking Yunnan Province as an Example

Analyzing the survival and development environment, internal dynamics, and development direction of specialized farmers’ cooperatives in underdeveloped areas to enhance the vitality of the development of the agricultural industry is a key strategy for the work of the “Three Rural Issues” in China. Based on the data of 3194 specialized farmers’ cooperatives in Yunnan Province from 2000 to 2023, this paper utilizes the spatial measurement method and survival analysis method to study the spatial distribution of their survival and related influencing factors. The study found the following: (1) Cooperatives show a spatial aggregation trend from “high to high” to “low to high”, and the formation of new sub-core areas is accelerating. (2) The establishment stage of cooperatives shows an obvious annual cycle effect, and cooperatives established in the early stage show stronger survival resilience. (3) The factor of “organizational characteristics and technological innovation” significantly prolongs the survival time of cooperatives, while the factor of “establishment stage” has a negative effect. (4) The influence of a cooperative’s asset size and trademark on its operational durability tends to decrease over time, but the influence of relatedness remains relatively stable. (5) Over time, the survival and development patterns of cooperatives at the provincial level show obvious differentiation, and the clustering phenomenon of “low-high” development gradually appears in minority autonomous counties. The results of this study provide a scientific basis for deepening and strengthening the study of the basic rural business system.

Prevalence and associated risk factors of Helicobacter pylori infection in community households in Lanzhou city.

Helicobacter pylori (H. pylori) infection exhibits a familial clustering phenomenon. To investigate the prevalence of H. pylori infection, identify associated factors, and analyze patterns of transmission within families residing in the community. From July 2021 to September 2021, a total of 191 families (519 people) in two randomly chosen community health service centers in the Chengguan District of Lanzhou in Gansu Province, were recruited to fill out questionnaires and tested for H. pylori infection. Individuals were followed up again from April 2023 and June 2023 to test for H. pylori infection. The relationship between variables and H. pylori infection was analyzed using logistic regression and generalized linear mixed models. In 2021, the individual-based H. pylori infection rate was found to be 47.0% (244/519), which decreased to 38.1% (177/464) in 2023. Additionally, the rate of individual-based H. pylori new infection was 22.8% (55/241). The family-based H. pylori infection rate in 2021 was 76.9% (147/191), which decreased to 67.1% (116/173) in 2023, and the rate of family-based H. pylori new infection was 38.6% (17/44). Individual H. pylori infection was positively correlated with age, body mass index (BMI), eating food that was excessively hot, frequent acid reflux, bloating, and halitosis symptoms, and negatively correlated with family size and nut consumption. New individual H. pylori infection was positively correlated with BMI, other types of family structures, drinking purified water, and frequent heartburn symptoms, while negatively correlated with the use of refrigerators and following a regular eating schedule. A larger living area was an independent protective factor for H. pylori infection in households. Frequently consuming excessively hot food and symptoms of halitosis were independent risk factors for H. pylori infection in individuals; frequent consumption of nuts was an independent protective factor for H. pylori infection. Other types of family structure, drinking purified water, and frequent heartburn symptoms were independent risk factors for new individual H. pylori infection; the use of a refrigerator was an independent protective factor for new H. pylori infections. The household H. pylori infection rate in Lanzhou is relatively high and linked to socio-demographic factors and lifestyles. Eradication efforts and control of related risk factors are recommended in the general population.

Acute Promyelocytic Leukemia in the Real World: Understanding Outcome Differences and How We Can Improve Them.

The advent of all-trans retinoic acid (ATRA) and arsenic trioxide (ATO) has revolutionized the treatment of acute promyelocytic leukemia (APL), resulting in excellent rates of remission and long-term survival. However, real-world outcomes often fall short of those observed in clinical trials due to various factors related to patient demographics and clinical practices. This review examines APL treatment outcomes in real-world settings and highlights the phenomenon of APL clusters. Clinical trials frequently exclude older patients and individuals with significant comorbidities, yet these groups represent a substantial portion of patients in clinical practice. Early mortality remains high in real-world settings, compounded by delayed diagnosis and treatment initiation, as well as the inexperience of some community providers and limited resources of their centers in managing APL and its associated complications. High rates of disease and induction-related complications further exacerbate early mortality. Continuous education and collaboration between community healthcare centers and expert institutions are essential, and international partnerships between resource-limited settings and expert centers can improve global APL outcomes. Ongoing monitoring for measurable residual disease (MRD) recurrence and long-term treatment toxicity, coupled with comprehensive patient evaluations, and experienced management, can enhance long-term outcomes. The clustered incidence of APL, while frequently reported, remains poorly understood. Regular reporting of these clusters could provide valuable insights into disease pathology and aid in developing predictive models for APL incidence, which would guide future resource allocation.

Unraveling nonlinear effects of environment features on green view index using multiple data sources and explainable machine learning

Urban greening plays a crucial role in maintaining environmental sustainability and enhancing people’s well-being. However, limited by the shortcomings of traditional methods, studying the heterogeneity and nonlinearity between environmental factors and green view index (GVI) still faces many challenges. To address the concerns of nonlinearity, spatial heterogeneity, and interpretability, an interpretable spatial machine learning framework incorporating the Geographically Weighted Random Forest (GWRF) model and the SHapley Additive exPlanation (Shap) model is proposed in this paper. In this paper, we combine multi-source big data, such as Baidu Street View data and remote sensing images, and utilize semantic segmentation models and geographic data processing techniques to study the global and local interpretation of the Beijing region with GVI as the key indicator. Our research results show that: (1) Within the Sixth Ring Road of Beijing, GVI shows significant spatial clustering phenomenon and positive correlation linkage, and at the same time exhibits significant spatial differences; (2) Among many environmental variables, the increase of green coverage rate has the most significant positive effect on GVI, while the increase of building density shows a strong negative correlation with GVI; (3) The performance of the GWRF model in predicting GVI is excellent and far exceeds that of comparison models.; (4) Whether it is the green coverage rate, urban built environment or socioeconomic factors, their influence on GVI shows non-linear characteristics and a certain threshold effect. With the help of these non-linear influences and explicit threshold effects, quantitative analyses of greening are provided, which can help to assist urban planners in making more scientific and rational decisions when allocating greening resources.

A Nanosized {NiII 18} Cluster with a 'Flying Saucer' Topology Exhibiting Slow Relaxation of Magnetisation Phenomena at Both 15 K and 1.3 K.

A high-nuclearity {Ni18} complex (1) with a unique 'flying saucer' motif has been prepared from the organic chelate, α-methyl-2-pyridine-methanol (mpmH), in conjunction with bridging azido (N3 -) and peroxido (O2 2-) ligands. Magnetic susceptibility measurements revealed the presence of both ferro- and antiferromagnetic exchange interactions between the metal centres in 1, and the stabilization of spin states with appreciable S values at two different temperature regimes. The end-on bridging azido and alkoxido groups are in all likelihood the ferromagnetic mediators, while the η3:η3:μ6-bridging peroxides most likely promote the antiparallel alignment of the metals' spin vectors, yielding an overall non-zero spin ground state for the centrosymmetric compound 1. Furthermore, the {Ni18} nanosized cluster behaves as a single-molecule magnet, exhibiting magnetic hysteresis at low temperatures and two relaxation processes at 15 K and 1.3 K, a very rare phenomenon in polynuclear magnetic 3d-metal clusters.

Quantum versatility in PageRank

Quantum mechanics empowers the emergence of quantum advantages in various fields, including quantum algorithms. Quantum PageRank is a promising tool for a future quantum internet. Recently, arbitrary phase rotations (APRs) have been introduced in the underlying Szegedy's quantum walk of the quantum PageRank algorithm. In this work we thoroughly study the role APR plays in quantum PageRank. We discover the versatility resulting from quantumness. Specifically, we discover the emergence of a cluster phenomenon in rankings considering the rotation phases, i.e., the existence of similar clusters in the distribution of the rankings and their fidelity with the corresponding classical PageRanks, the ranking distribution variance, the coherence and entanglement of PageRank states, and the power-law parameter in the ranking distributions on a scale-free network concerning the two rotation phases. Furthermore, we propose an alternate quantum PageRank with APR which provides an extra tunnel for the analysis of PageRank. We also study the PageRank on the trackback graph of a scale-free graph for the investigation of network information traffic tracking. We demonstrate the rich cluster diversity formed in our alternate quantum PageRank, which offers a novel perspective on the quantum versatility of PageRank. Our results present the quantum-enabled perspective for PageRanking and shed light on the design and application of practical quantum PageRank algorithms. Published by the American Physical Society 2024

Spatiotemporal Changes and Influencing Factors of the Coupled Production–Living–Ecological Functions in the Yellow River Basin, China

The imbalance in the “production–living–ecology” function (PLEF) has become a major issue for global cities due to the rapid advancement of urbanization and industrialization worldwide. The realization of PLEF coupling and coordination is crucial for a region’s sustainable development. Existing research has defined the concept of PLEF from the perspective of land function and measured its coupling coordination level using relevant models. However, there is still room for improvement in the indicator system, research methods, and other aspects. This work builds a PLEF coupling coordination evaluation-index system based on the perspective of human habitat using multi-source data in order to examine the spatial differences in PLEF coupling coordination level and the influencing factors in the Yellow River Basin (YRB). Using the modified coupling coordination model, the Moran index, spatial Markov chain model, and geographically weighted random forest model were introduced to analyze its spatial and temporal differentiation and influencing factors. The results found that (a) the level of PLEF coupling coordination in the YRB from 2010 to 2022 has been improving, and the number of severely imbalanced cities has been reduced from 23 to 15, but the level of downstream cities’ coupling coordination is significantly higher than that of upstream cities. The probability of cities maintaining their own level is greater than 50%, and there is basically no cross-level transfer. (b) The Moran index of the PLEF coupling coordination level has risen from 0.137 to 0.229, which shows a significant positive clustering phenomenon and is continually strengthening. The intercity polarization effect is being continually enhanced as seen in the LISA clustering diagram. (c) There is significant heterogeneity between the influencing factors in time and space. In terms of importance level, the series is per capita disposable income (0.416) > nighttime lighting index (0.370) > local general public budget expenditure (0.332) > number of beds per 1000 people (0.191) > NO2 content in the air (0.110). This study systematically investigates the dynamic evolution of the coupled coordination level of PLEF in the YRB and its influencing mechanism, which is of great practical use.

The Influence of the Flow of Scientific and Technological Factors on the High-Quality Development of Regional Economy

In the new stage of China’s economic transformation from high-speed growth to high-quality development, scientific and technological factors have become the new driving force for the high-quality development of China’s economy. Therefore, how the flow of scientific and technological factors affects the high-quality development of regional economy is worthy of in-depth discussion. Based on the panel data of 30 provinces (municipalities and districts) in China from 2006 to 2019, this paper calculates the high-quality economic development index by constructing a comprehensive index system, and empirically examines the direct effect, spatial effect and threshold effect of the flow of scientific and technological factors (including scientific and technological talent factors and scientific and technological capital factors) on high-quality regional economic development. The results show the following: (1) The flow of science and technology talents and science and technology capital is the main driving factor for the high-quality development of regional economy, and the flow of science and technology talents and science and technology capital can play a complementary role. (2) High-quality regional economic development in China presents a certain clustering phenomenon in geographical space. The flow of scientific and technological talent elements and scientific and technological capital elements can stimulate the improvement of high-quality economic development in surrounding areas through the spatial spillover effect of high-quality economic development. (3) Only when the flow of science and technology factors is matched with a higher degree of financial structure and industrial structure can the flow of science and technology factors more effectively promote the high-quality development of regional economy. (4) The higher the level of industrialization and economic development, the more obvious the impact of the flow of scientific and technological talent factors and scientific and technological capital factors on the high-quality economic development. Meanwhile, the lower the degree of marketization, the more obvious the impact of the flow of scientific and technological capital factors on the high-quality economic development.

Drug expiration study using Raman spectroscopy and super paramagnetic clustering

Currently, there is a belief that drugs used after the expiration date no longer work, or even these can cause some damage. In the present work, several types of drugs on the market with different expiration dates are analyzed to find out if these, with the expired expiration date, present spectroscopic differences from those drugs whose expiration date is still valid. This study used the Raman spectroscopy technique to determine the chemical composition of drugs. To measure the drug Raman spectra, Horiba equipment, LabRam HR800, was used with an Olympus confocal microscope focusing a laser of 830 nm and 17 mW on the drugs through a 50 X Leica long-range objective. The Super Paramagnetic Clustering (SPC) method was applied to classify the Raman spectra. In the SPC method, the clustering process is based on a phenomenon of clustering observed in nature at the atomic level and perfectly described by a statistical physics model known as the Potts model, which describes the interacting spins on a crystalline lattice. This clustering method allows for identifying hierarchical structures in the spectra data banks. Fourteen drugs were analyzed, including 2 capsules, 5 tablets, 2 liquid samples, 4 ointments, and one spray with 1 to 3 expired expiration dates. Comparing drugs with expiration dates that are still valid and expired, the SPC results applied to the Raman spectra showed that, although some drugs indicated chemical differences, others indicated no chemical differences, even among those with up to two expired expiration dates. The results showed that Raman spectroscopy and SPC are excellent tools for discriminating between expired and non-expired drugs. The Principal Components Analysis (PCA) was also applied as a cross-checking method of the SPC result, obtaining consistent results. To our knowledge, it is the first preliminary result evaluating the usefulness of Raman spectroscopy and SPC in identifying expired drugs distributed on the market.

Characterization of droplet clusters in a turbulent multiphase gas cloud using a model cough simulator

This study aims to investigate the transport of droplets ejected from an artificial cough simulator, which releases a turbulent puff of droplets into the surrounding air, closely resembling the human coughing process. The focus is on understanding droplet clustering within the multiphase gas cloud across various operating conditions that emulate the wide variation in the spray characteristics in actual human subjects owing to infection severity, age, and gender. Time-resolved particle image velocimetry (PIV) technique was employed to measure the velocity of both droplet and gas phases. It also facilitates the identification and characterization of droplet clusters through Voronoi analysis of the PIV images. The area and length scale of individual droplet clusters were measured, and the degree of droplet clustering was quantified using the clustering index and relative droplet number density within the clusters. Additionally, the interferometric laser imaging for droplet sizing technique was utilized for planar measurement of individual droplet sizes. The range of Stokes number indicated partial to poor response of the droplets to the turbulent eddies. The results reported, for the first time, the presence of droplet clusters in the simulated coughing process. The wide spectrum of cluster size and self-similar evolution of droplet clusters unveil a multi-scale clustering phenomenon, shedding light on the intricate dynamics of the respiratory droplet dispersion process. The study comprehensively investigates the role of injection pressure on droplet clustering and the spatial development of the clusters, revealing some interesting findings, which are discussed.

Analysis of Behaviour Pattern of Bicycle-sharing Users Based on Complex Network: a Case Study of Beijing Downtown Area

Abstract. The introduction of the Bicycle-sharing System (BSS) has provided great convenience for residents for short travel in city. However, the parking lot planning and bicycle logistics distribution remain suboptimal, adversely affecting traffic order. Studying the spatial and temporal usage patterns can be helpful for better management of BSS. In this paper, we propose an analytical framework for detecting the bicycle-sharing pattern based on the community detection algorithms from complex networks to study the spatial-temporal patterns of usage.Firstly we identify the potential demand for bicycle among urban residents with a dataset from the BSS in Beijing downtown area. Then we construct a linkage network for modelling the BSS by applying community detection algorithms to identify regions of both high and low connectivity. Following this, based on the division of sub-areas, three indicators (graph density, cross-area travel demand, and confidence ellipses) are established for characterizing these sub-areas. Our findings include: (1) The usage pattern of the BSS within the downtown area consistently exhibits relatively stable clustering phenomena over time. (2) The usage pattern is related to the urban spatial structure, with significant differences between weekdays and weekends. (3) Residents tend to complete their cycling within the current sub-areas. (4) Generally, smaller sub-areas tend to have denser bicycle travel behaviour. These insights are vital for improving BSS parking lot planning and logistics distribution.

Clustering phenomena in low dimensions for a boundary Yamabe problem

Clustering phenomena in low dimensions for a boundary Yamabe problem

A comprehensive index for assessing the sustainable blue economy: A Japanese application

The sustainable blue economy is crucial for coastal regions. It not only fosters economic development along the coastlines but also reduces their impact on marine environments and ecosystems. This will be a significant economic growth point for coastal areas in future decades. However, comprehensive research on assessing the blue economy is currently limited; existing evaluation methods are few and may not be applicable to all contexts. This study introduces the Blue Economy Index (BEI), a novel framework for evaluating the sustainable blue economy, and applies it in Japan's 39 coastal prefectures. Grounded in multi-dimensional analysis, the BEI includes four sectors: Ocean Resources and Marine Industry, Environmental and Ecological Sustainability, Socio-Economic Stability, and Risk Management. Utilizing Japan's prefectural data and addressing regional disparities, the index applies four weighting scenarios—Equalitarianism, Preservationist, Sustainable Use, and Extractive Use—to reflect different developmental strategies. The result indicates a significant imbalance in blue economy development across prefectures, with leading regions like Hokkaido, Tokyo, and Okinawa vastly outpacing others such as Ibaraki and Tottori. Additionally, the development status in 39 coastal prefectures in Japan demonstrates the cluster phenomenon and exhibits a '2 high, 3 middle, 1 low' development pattern, which comprises two extraordinary clusters and four distinctive clusters. We propose to implement zonal management for Japan's Exclusive Economic Zone (EEZ) and establish a National Ocean Agency. Promoting committee-based regional management, provide corresponding subsidies to enhance capacity building. Additionally, and drive the digital transformation in the marine sector through quantifiable indicators as BEI. The study underscores the necessity for improving current policy and regulation, as well as promoting inter-prefectural collaboration to boost the development of the blue economy. The BEI's comprehensive approach offers an example of global sustainable blue economy assessments. Policymakers and stakeholders in other countries can flexibly use and adjust the BEI method to conduct appropriate regional assessments and develop quantifiable strategies to improve the potential of marine sectors.

Fractal information dissemination and clustering evolution on social hypernetwork

The complexity of systems stems from the richness of the group interactions among their units. Classical networks exhibit identified limits in the study of complex systems, where links connect pairs of nodes, inability to comprehensively describe higher-order interactions in networks. Higher-order networks can enhance modeling capacities of group interaction networks and help understand and predict network dynamical behavior. This paper constructs a social hypernetwork with a group structure by analyzing a community overlapping structure and a network iterative relationship, and the overlapping relationship between communities is logically separated. Considering the different group behavior pattern and attention focus, we defined the group cognitive disparity, group credibility, group cohesion index, hyperedge strength to study the relationship between information dissemination and network evolution. This study shows that groups can alter the connected network through information propagation, and users in social networks tend to form highly connected groups or communities in information dissemination. Propagation networks with high clustering coefficients promote the fractal information dissemination, which in itself drives the fractal evolution of groups within the network. This study emphasizes the significant role of “key groups” with overlapping structures among communities in group network propagation. Real cases provide evidence for the clustering phenomenon and fractal evolution of networks.

Development of the full Lagrangian approach for modeling dilute dispersed media flows (a review)

Continuum models of media with zero pressure are widely used in various branches of physics and mechanics, including studies of a dilute dispersed phase in multiphase flows. In zero-pressure media, the particle trajectories may intersect, “folds” and “puckers” of the phase volume may arise, and “caustics” (the envelopes of particle trajectories) may appear, near which the density of the medium sharply increases. In recent decades, the phenomena of clustering and aerodynamic focusing of inertial admixture in gas and liquid flows have attracted increasing attention of researchers. This is due to the importance of taking into account the inhomogeneities in the impurity concentration when describing the transport of aerosol pollutants in the environment, the mechanisms of droplet growth in rain clouds, scattering of radiation by dispersed inclusions, initiation of detonation in two-phase mixtures, as well as when solving problems of two-phase aerodynamics, interpretation of measurements obtained by LDV or PIV methods, and in many other applications. These problems gave an impetus to a significant increase in the number of publications devoted to the processes of accumulation and clustering of inertial particles in gas and liquid flows. Within the framework of classical two-fluid models and standard Eulerian approaches assuming single-valuedness of continuum parameters of the media, it turns out impossible to describe zones of multi-valued velocity fields and density singularities in flows with crossing particle trajectories. One of the alternatives is the full Lagrangian approach proposed by the author earlier. In recent years, this approach has been further developed in combination with averaged Eulerian and Lagrangian (vortex-blob method) methods for describing the dynamics of the carrier phase. Such combined approaches made it possible to study the structure of local zones of accumulation of inertial particles in vortex, transient, and turbulent flows. This article describes the basic ideas of the full Lagrangian approach, provides examples of the most significant results which illustrate the unique capabilities of the method, and gives an overview of the main directions of further development of the method as applied to transient, vortex, and turbulent flows of “gas-particle” media. Some of the ideas discussed and the results presented below are of a more general interest, since they are also applicable to other models of zero-pressure media.

Exploring mesophase formation: Structural characterization approaches in a soft sphere model

This study employs Molecular Dynamics simulations to enhance the structural characterization of sphere mixture systems, focusing on local order parameters to identify order-disorder transitions within lamellar and hexagonal mesophases. The structural characterization included the use of neural networks and the search for clusters with topological order. We have identified the order-disorder transition at a characteristic temperature TOD, effectively captured by the structural parameters analyzed. Below TOD, the system organizes into an ordered mesophase, while above this temperature, it transitions into an isotropic liquid state. Moreover, within the isotropic region, we observe that the liquid begins to form clusters. This clustering phenomenon becomes evident below a specific temperature, denoted as T⁎. At T⁎, there are noticeable changes in the volume versus temperature curves, indicating a process of micellization or clustering in the system. The structural parameters used in our study also register this behavioral change at T⁎, further supporting the occurrence of this phenomenon. Additionally, our analysis of the overlap in detecting ordered particles between the two structural parameters using the Sørensen-Dice similarity coefficient reveals a significant spatial correlation in the isotropic phase, with a correlation higher than what would be expected in a random case. This indicates that both neural networks and topological cluster classification detect statistically similar ordered regions in the isotropic phase, highlighting the clustering. Our findings reveal that the structural parameters effectively capture both the order-disorder transition at TOD and the clustering process at T⁎. These results provide valuable insights into the self-assembly mechanisms and phase behavior in this soft-sphere model, advancing our understanding of self-assembly processes in material science, particularly in exploring phase behavior in mesogenic nanoparticle systems.

Ab initio framework for nuclear scattering and reactions induced by light projectiles

A quantitative and predictive microscopic theoretical framework that can describe reactions induced by α particles (4He nuclei) and heavier projectiles is currently lacking. Such a framework would contribute to reducing uncertainty in the modeling of stellar evolution and nucleosynthesis and provide the basis for achieving a comprehensive understanding of the phenomenon of nuclear clustering (the organization of protons and neutrons into distinct substructures within a nucleus). We have developed an efficient and general configuration-interaction framework for the description of low-energy reactions and clustering in light nuclei. The new formalism takes full advantage of powerful second-quantization techniques, enabling the description of α-α scattering and an exploration of clustering in the exotic 12Be nucleus. We find that the 4He(α,α)4He differential cross section computed with non-locally regulated chiral interactions is in good agreement with experimental data. Our results for 12Be indicate the presence of strongly mixed helium-cluster states consistent with a molecular-like picture surviving far above the 6He+6He threshold, and reveal the strong influence of neutron decay in both the 12Be spectrum and in the 6He(6He,α)8He cross section. We expect that this approach will enable the description of helium burning cross sections and provide insight on how three-nucleon forces influence the emergence of clustering in nuclei.