Clustering Effect Research Articles

Luminescence Modulation in Boron‐Cluster‐Based Luminogens via Boron Isotope Effects

Recent advances in luminescent materials highlight the significant impact of hydrogen isotope effects on improving optoelectronic properties. However, the research on the influence of the boron isotope effects on photophysical properties remains underdeveloped. This study focused on exploring the boron isotope effects in boron‐cluster‐based luminogens. In doing so, we designed and synthesized carborane‐based luminogens containing 98% 10B and 95% 11B, respectively, and observed distinct photophysical behaviors. Compared to the 10B‐enriched luminogens, the 11B‐enriched counterparts can significantly enhance luminescence efficiency, prolong emission lifetime, and reduce full‐width at half‐maximum. Additionally, increased thermal stability, redshifted B–H vibrations, and a fourfold enhanced electrochemiluminescence intensity have also been observed. On the other hand, the biological assessments of a 10B‐enriched luminogen reveals low cytotoxicity, high boron uptake, and excellent fluorescence imaging capability, indicating the potential application in boron neutron capture therapy (BNCT). This work presents the first comprehensive exploration on the boron isotope effects in boron clusters, and provides valuable insights into the rational design of organic luminogens for advanced optoelectronic and biomedical applications.

On Latent Structure Examination of Behavioral Measuring Instruments in Complex Empirical Settings

A multiple-step procedure is outlined that can be used for examining the latent structure of behavior measurement instruments in complex empirical settings. The method permits one to study their latent structure after assessing the need to account for clustering effects and the necessity of its examination within individual levels of fixed factors, such as gender or group membership of substantive relevance. The approach is readily applicable with binary or binary-scored items using popular and widely available software. The described procedure is illustrated with empirical data from a student behavior screening instrument.

Sub‐Nanometer‐Scale Cu9S5 Enables Efficiently Electrochemical Nitrate Reduction to Ammonia

AbstractThe sub‐nanometer is a key feature size in materials science. Unlike single‐atom and nanomaterials, size effects and inter‐component cooperative actions in sub‐nanomaterials will effective on its performance is more significant. Here, 0.95 nm ordered arrangement Cu9S5 sub‐nanowires (Cu9S5 SNWs) are synthesized through the co‐assembly effect of inorganic nuclei (Cu9S5) and clusters (phosphotungstic acid‐PTA), achieving a significant increase in the specific surface area of the sample and ≈100% atomic exposure rate, which is the key to its high catalytic activity. PTA clusters not only act as a “charge transfer station” to accelerate the inter‐component electron transfer process, but also facilitate the dissociation of water and provide more hydrogen protons, thus dramatically facilitating the electrocatalytic process. The experimental results show that the Cu9S5 SNWs exhibited excellent nitrate reduction reaction (NO3−RR) properties. The Faraday efficiency (FE) of NO3−RR is 90.4% at the optimum potential −0.3 VRHE (reversible hydrogen electrode) and the ammonia production is as high as 0.37 mmol h−1 cm−2, which is superior to most reported electrocatalysts. In addition, the Zn‐NO3− liquid‐flow battery devices assembled using Cu9S5 SNWs as electrode materials show excellent application results. This work provides a reference for the design of highly efficient sub‐nanoscale NO3−RR electrocatalysts.

Intuitive-K-prototypes: A mixed data clustering algorithm with intuitionistic distribution centroid

Data sets are usually mixed with numerical and categorical attributes in the real world. Data mining of mixed data makes a lot of sense. This paper proposes an Intuitive-K-prototypes clustering algorithm with improved prototype representation and attribute weights. The proposed algorithm defines intuitionistic distribution centroid for categorical attributes. In our approach, a heuristic search for initial prototypes is performed. Then, we combine the mean of numerical attributes and intuitionistic distribution centroid to represent the cluster prototype. In addition, intra-cluster complexity and inter-cluster similarity are used to adjust attribute weights, with higher priority given to those with lower complexity and similarity. The membership and non-membership distance are calculated using the intuitionistic distribution centroid. These distances are then combined parametrically to obtain the composite distance. The algorithm is judged for its clustering effectiveness on the real UCI data set, and the results show that the proposed algorithm outperforms the traditional clustering algorithm in most cases.

Constructing a Galaxy Cluster Catalog in IllustrisTNG300 Using the Mulguisin Algorithm

We present a new simulated galaxy cluster catalog based on the IllustrisTNG simulation. We use the Mulguisin (MGS) algorithm to identify galaxy overdensities. Our cluster identification differs from the previous friends-of-friends (FoF) cluster identification in two aspects: (1) we identify cluster halos based on the galaxy subhalos instead of unobservable dark matter particles, and (2) we use the MGS algorithm, which separates galaxy overdensities hosted by massive galaxies. Our approach provides a cluster catalog constructed in a way similar to the construction of observed cluster catalogs using spectroscopic surveys. The MGS cluster catalog lists 303 halos with M 200 > 1014 M ⊙, including ∼10% more than the FoF catalog. The MGS catalog includes more systems because we separate some independent massive MGS cluster halos that are bundled into a single FoF halo. These independent MGS halos are apparently distinguishable in the galaxy spatial distribution and the phase-space diagram. Because we construct a refined cluster catalog that identifies local galaxy overdensities, we evaluate the effect of MGS clusters on the evolution of galaxies better than when using the FoF cluster catalog. The MGS halo identification also enables effective identifications of merging clusters by selecting systems with neighboring galaxy overdensities. We thus highlight the fact that the MGS cluster catalog is a useful tool for studying clusters in cosmological simulations and for comparing with observed cluster samples.

Gravitational wave luminosity distance-weighted anisotropies

Measurements of the luminosity distance of propagating gravitational waves can provide invaluable information on the geometry and content of our Universe. Due to the clustering of cosmic structures, in realistic situations we need to average the luminosity distance of events coming from patches inside a volume. In this work we evaluate, in a gauge-invariant and fully-relativistic treatment, the impact of cosmological perturbations on such averaging process. We find that clustering, lensing and peculiar velocity effects impact estimates for future detectors such as Einstein Telescope, Cosmic Explorer, the Big Bang Observer and DECIGO. The signal-to-noise ratio of the angular power spectrum of the average luminosity distance over all the redshift bins is 17 in the case of binary black holes detected by Einstein Telescope and Cosmic Explorer. We also provide fitting formulas for the corrections to the average luminosity distance due to general relativistic effects.

A heterogeneous continuum traffic flow model considering mixed connected and autonomous vehicles

Under the background of mixed scenarios of Connected and Autonomous Vehicles and Connected and Autonomous Electric Vehicles, a novel mixed car-following model considering the information of multiple vehicles and the difference of safety spacing in different driving scenarios is proposed. Then, the corresponding heterogeneous continuum traffic flow model is derived. Secondly, through the linear and nonlinear analysis approaches, the linear stability condition and the KdV-Burgers equation are yielded to describe the characteristics of traffic flow evolution. Finally, numerical simulations are performed to explore the density evolution of traffic flow under different initial density and permeability conditions. The conclusions can be summarized as follows: (1) The derived model can well simulate spatiotemporal phase changes and traffic flow phenomena such as stop-and-go waves and local cluster effects. (2) With the increase of the permeability of electric vehicles and speed difference sensitivity coefficient, the road density decreases from 0.15veh/m to 0.06veh/m, the traffic congestion area decreases, and the traffic congestion is effectively suppressed, which also verifies the rationality of the above theory.

Efficient Discrete Clustering With Anchor Graph.

Spectral clustering (SC) has been applied to analyze varieties of data structures over the past few decades owing to its outstanding breakthrough in graph learning. However, the time-consuming eigenvalue decomposition (EVD) and information loss during relaxation and discretization impact the efficiency and accuracy especially for large-scale data. To address above issues, this brief proposes a simple and fast method named efficient discrete clustering with anchor graph (EDCAG) to circumvent postprocessing by binary label optimization. First of all, sparse anchors are adopted to accelerate graph construction and obtain a parameter-free anchor similarity matrix. Subsequently, inspired by intraclass similarity maximization in SC, we design an intraclass similarity maximization model between anchor-sample layer to cope with anchor graph cut problem and exploit more explicit data structures. Meanwhile, a fast coordinate rising (CR) algorithm is employed to alternatively optimize discrete labels of samples and anchors in designed model. Experimental results show excellent rapidity and competitive clustering effect of EDCAG.

Industry clusters and firm performance: Evidence from the leather product industry in Addis Ababa

Industrial clustering is the co-location of firms in a geographic area, which can lead to numerous economic benefits such as joint resources and knowledge pooling, R&D collaboration, technology sharing, and joint marketing. This study aims to examine the influence of industrial clustering on the performance of firms in the leather and leather products industry in Addis Ababa. Moreover, this study aims to investigates whether innovation capacity and collaboration networks indirectly contribute to the relationship between clustering factors and firms' performance. In this study, both primary and secondary data were collected for analysis. Partial Least Squares Structural Equation Modeling was employed to explore the impact of clustering on performance in terms of innovation, job creation, attracting investment, export intensity, and productivity growth. This study found that a one-unit increase in cluster factors is associated with a 0.43-unit increase in firm performance. The mediation analysis revealed that collaboration networks mediate the relationship between cluster factors and firm performance by 0.21 and innovation capacity mediates the relationship between cluster factors and firm performance by 0.15 units. The findings indicate that industrial clusters strongly influence firm performance within the leather industry in Addis Ababa. Therefore, the results suggest that innovation capacity and collaboration networks are mechanisms through which industrial clusters affect firm performance. Future researchers should explore the cluster effect in other industrial sectors and regions to validate the research findings.

Moderate-to-vigorous physical activity modulates the association between serum 25-hydroxyvitamin D and bone stiffness in European children and adolescents

It remains unclear how serum 25-hydroxyvitamin D (25(OH)D) concentrations relate to childhood bone health. We hypothesized that 25(OH)D was inversely associated with bone turnover biomarkers and positively associated with bone stiffness. Cross-sectional analyses were performed using data from participants (2-15-year-old, 51% boys) from the IDEFICS/I.Family cohort, comprising 3,638 serum 25(OH)D measurements collected in 2007/2008 and 2012/2013 across eight European countries. A biomarker of bone formation (serum osteocalcin), a biomarker of bone resorption (serum C-terminal telopeptides of type I collagen [CTx]), and stiffness index measured using calcaneal quantitative ultrasound were considered outcomes. Linear mixed-effects models were used to adjust for confounders (i.e., age, sex, parental education, time spent in sports club, dairy products consumption, sedentary behavior, height and weight z-scores), the cluster effect of country and repeated measurements. Interactions of calcium intake, moderate-to-vigorous physical activity (MVPA) and weight status with 25(OH)D on outcomes were tested. Only 1 in 3 participants reached the sufficient 25(OH) D concentration of 20 ng/ml. Sufficient 25(OH)D was associated with higher stiffness index if participants had MVPA ≥ 60 min/day (β = 12.14, p < 0.05). Moreover, 25(OH)D was inversely associated with CTx (β = -7.09, p < 0.05); this association was positive but not statistically significant among primary school children living with overweight/obesity. No interaction was observed for calcium intake. In conclusion, serum 25(OH)D and CTx were inversely associated. MVPA interacted the positive association between 25(OH)D and bone stiffness, highlighting the importance of promoting MVPA guidelines in future vitamin D and bone health interventions.

Двоетапний метод розв’язання задачі комівояжера на основі генетичного алгоритму

One of the main tasks in logistics is to find the most efficient route in the traveling salesman problem on a given transportation network, allowing for the servicing of the maximum number of customers while considering certain criteria. In the typical traveling salesman problem, the objective function is most often the route length or duration. However, such a formulation does not account for the subjectivity in evaluating the travel durations at different stages, which may be influenced by various objective and subjective factors. The problem of the traveling salesman with fuzzily defined travel durations between network nodes is considered. The approach to solving the problem is based on a genetic algorithm with enhanced mutation procedures and population diversity generation. Trapezoidal fuzzy numbers are used to formalize fuzzy values, presented in the generalized case based on the application of Gaussian distribution and corresponding characteristics. A two-stage scheme for solving the optimization problem of the traveling salesman on a given transportation network is proposed, combining the genetic algorithm and Ward's clustering method. In the first stage, a clustering procedure is conducted using Ward's method. In the second stage, based on the obtained set of clusters and the identified optimal inter-cluster distances, route duration optimization is performed within the clusters. To test the efficiency of the developed method, two types of models were generated: one with a limited number of available paths and one with a fully connected topology. Computational experiments were conducted to apply the developed method to solving the traveling salesman problem with travel durations between cities represented as fuzzy numbers. A comparison of numerical calculation results was performed. The obtained results demonstrated the effectiveness of preliminary clustering when using the genetic algorithm to find the best local optima under the same input parameters.

Prediction of Floor Failure Depth Based on Dividing Deep and Shallow Mining for Risk Assessment of Mine Water Inrush

Understanding and predicting floor failure depth is crucial for both mitigating mine water inrush hazards and safeguarding groundwater resources. Mining activities can significantly disturb the geological strata, leading to shifts and damage that may result in floor cracks. These disruptions can extend to confined aquifers, thereby increasing the risk of water inrushes. Such events not only pose a threat to the safety of mining operations but also jeopardize the sustainability of surrounding groundwater systems. Therefore, accurately predicting floor failure depth to take effective coal seam floor management measures is the key to reducing the impact of coal seam mining on water resources. Seventy-eight sets of data on coal seam floor failure depth in China were collected, and the main controlling factors were considered: mining depth (D1), working face inclination length (D2), coal seam inclination (D3), and mining thickness (D4). Firstly, the distance evaluation function based on Euclidean distance was constructed as the clustering effectiveness index, and the optimal cluster number K = 3 was determined. The collected data were clustered into three categories using the K-means clustering algorithm. It was found that the clustering results were positively correlated with the size of D1, indicating that D1 played a dominant role in the clustering. The D1 dividing points of the three types of samples were between 407.7~414.9 m and 750~900 m. On this basis, the grey correlation analysis method was used to analyze the order of the influence weights of the main controlling factors of coal seam floor failure depth. For the first group, the order was D2 &gt; D1 &gt; D3 &gt; D4, while, in the other two, it was D1 &gt; D2 &gt; D3 &gt; D4. D1 emerged as the most influential factor, surpassing D2. Therefore, D1 between 407.7 and 414.9 m could be used as the boundary, the first group could be classified as shallow mining, and the second and third groups could be classified as deep mining. Based on this boundary, CatBoost prediction models for the depth of coal seam floor failure in deep and shallow parts were constructed and the prediction results of the model test set were compared with the calculation results of the empirical formula. These models exhibited superior accuracy with a lower mean squared error (MSE) and mean absolute error (MAE) and a higher R-squared (R2) compared to the empirical formula. This study helps to enhance the understanding of coal seam floor behavior, guide floor management, and protect groundwater resources by defining deep and shallow mining to accurately predict floor failure depth.

A two-stage strategy for brain-inspired unsupervised learning in spiking neural networks

Spiking Neural Networks (SNNs) are emerging as a promising model in the field of neuromorphic computing, offering possibilities for efficient bio-inspired computing. Unsupervised learning in SNNs plays a crucial role in exploring neural network dynamics and achieving efficient artificial intelligence systems. However, current unsupervised learning in SNNs mostly relies on single-stage learning strategies, which limits the possibility of combining diverse learning mechanisms and network structures, resulting in networks that lack flexibility and adaptability for low-power task processing. Inspired by the multi-stage and multi-strategy learning in the human brain, this paper proposes a two-stage learning strategy from coarse to fine to enhance the efficiency of unsupervised learning in SNNs. To quickly extract structural features from spike data in the first stage, we propose the Spiking-SOM algorithm, which effectively learns patterns in spike data and achieves clustering effects. Additionally, we integrate Rate encoding and Temporal encoding through a visual attention mechanism, where this hybrid encoding helps meet the need for rapid information processing in the first stage, while also aiding in detailed feature representation in the second stage. Compared to the traditional single-stage learning framework, this work maintains high accuracy in unsupervised classification while significantly reducing computational costs and speeding up training. This exploration offers a new direction for future learning frameworks in SNNs, helping to further improve the energy efficiency of the network.

OPTimising MEDicine information handover after Discharge (OPTMED-D): protocol for development of a multifaceted intervention and stepped wedge cluster randomised controlled trial

BackgroundGeneral practitioners (GP) and community pharmacists need information about hospital discharge patients’ medicines to continue their management in the community. This necessitates effective communication, collaboration, and reliable information-sharing. However, such handover is inconsistent, and whilst digital systems are in place to transfer information at transitions of care, these systems are passive and clinicians are not prompted about patients’ transitions. There are also gaps in communication between community pharmacists and GPs. These issues impact patient safety, leading to hospital readmissions and increased healthcare costs.MethodsA three-phased, multi-method study design is planned to trial a multifaceted intervention to reduce 30-day hospital readmissions. Phase 1 is the co-design of the intervention with stakeholders and end-users; phase 2 is the development of the intervention; phase 3 is a stepped wedge cluster randomised controlled trial with 20 clusters (community pharmacies). Expected intervention components will be a hospital pharmacist navigator, primary care medication management review services, and a digital solution for information sharing. Phase 3 will recruit 10 patients per pharmacy cluster/month to achieve a sample size of 2200 patients powered to detect a 5% absolute reduction in unplanned readmissions from 10% in the control group to 5% in the intervention at 30 days. The randomisation and intervention will occur at the level of the patient’s nominated community pharmacy. Primary analysis will be a comparison of 30-day medication-related hospital readmissions between intervention and control clusters using a mixed effects Poisson regression model with a random effect for cluster (pharmacy) and a fixed effect for each step to account for secular trends.Trial registrationThis trial is registered with the Australian New Zealand Clinical Trials Registry: ACTRN12624000480583p, registered 19 April 2024.

Research on the Impact and Spillover Effects of Urbanization Process on Grain Production

Food security is an important strategic issue for the country. With the advancement of urbanization, food security needs to be re examined. Based on panel data from 31 provinces (regions, cities) in China from 2011 to 2022, this study analyzed the cluster effect of grain production using Moran index, and explored the impact of urbanization process on grain production and its spatial spillover effects through spatial Durbin model. Finally, the convergence of grain production was studied based on alpha and beta convergence models. The research results show that: (1) there is a significant cluster effect in grain production in various provinces, manifested as high-value clustering and low value clustering; (2) The urbanization rate promotes grain production in the province while suppressing grain production in neighboring provinces, while economic activity suppresses grain production in the province but promotes grain production in neighboring provinces. Overall, the urbanization rate significantly promotes grain production, while economic activities have a significant inhibitory effect on grain production; (3) There is no "catch-up effect" in grain production among provinces and grain functional zones, and the gap is showing a widening trend. This study aims to provide a reference basis for formulating food security policies.

Optimal dividend policy with self-exciting claims in the Gamma–Omega model

In this paper, we consider the optimal dividend policy for an insurance company under a contagious insurance market, where the occurrence of a claim can trigger sequent claims. This clustering effect is modelled by a self-exciting Hawkes process where the intensity of claims depends on its historical path. In addition, we include the concept of bankruptcy to allow the insurance company to operate with a temporary negative surplus. The objective of the management is to obtain the optimal dividend strategy that maximises the expected discounted dividend payments until bankruptcy. The Hamilton–Jacobi–Bellman variational inequalities (HJBVIs) are derived rigorously. When claim sizes follow exponential distributions and the bankruptcy rate is a positive constant, the value function can be obtained based on the Gerber–Shiu penalty function and the optimal dividend barrier can be solved numerically. Finally, numerical examples are demonstrated to show the impact of key parameters on the optimal dividend strategy.

Multi-view clustering with adaptive anchor and bipartite graph learning

Multi-view subspace clustering optimizes and integrates the graph structure information of each view. At present, the subspace clustering methods based on the abstract graph have better performance and improve the clustering results. Although the existing clustering algorithms have achieved excellent results, there are still four deficiencies: 1) Expensive time overhead, with most algorithms having a high time complexity; 2) Using fixed anchor points and the separation of anchor graph learning from subsequent graph construction, resulting in inadequate use of underlying graph structure between views; 3) Multi-view data have inevitable noise and in the original high-dimensional space data fusion may cause the loss of important information; 4) Most algorithms require additional post-processing to obtain clustering labels. To solve the above problems, we innovate a novel anchor-adaptive multi-view clustering algorithm based on a bipartite graph . Specifically, anchor graph learning and subspace graph construction are adaptively combined into a unified optimization framework. The projection matrix, consensus anchor matrix, and similarity matrix optimize each other to promote the clustering effect and structure a constrained bipartite graph to describe the relationship between representative points and sample points. At the same time, connectivity constraints are used to ensure that the connected components represent clusters directly. Our method has linear time complexity about sample size. Comparison experiments with the state-of-the-art algorithms demonstrate the effectiveness of the proposed method on various benchmark datasets. Moreover, our method is robust to noisy data and suitable for large-scale datasets.

Development of high-quality and water-resistant PBG through H-PDMS modification: From experiments to molecular dynamics simulation

The effective treatment and high-value application of phosphogypsum (PG) are of critical importance for the sustainable development of this construction material. However, its poor water-resistance property has limited its application in the construction field. In order to expand the application potential of phosphorous-building gypsum (PBG) as a durable material, potassium hydroxide (KOH) and hydroxyl‑terminated polydimethylsiloxane (H-PDMS) were used as the activating agent, and the water-resistant modifying agent in this study, respectively, to develop a high-quality and water-resistant PBG product. In addition, molecular dynamics was used to reveal the modification mechanism of H-PDMS and the water-resistance mechanism of KOH activating PBG. The study results show that H-PDMS can significantly improve the water-resistance performance of PBG test blocks. KOH can increase the surface activity of PBG by introducing -OH radicals, promote the reaction between H-PDMS and PBG, and form a dense hydrophobic layer on PBG, thus effectively improving its water-resistance performance. The number of surface active -OH radicals in PBG, the clustering effect of H-PDMS, and impurities in PBG can all result in different experimental and simulation outcomes. Meanwhile, the electrostatic force is an important factor influencing the adsorption of water droplets on the surface of PBG, and the reaction of H-PDMS with PBG normally occurs between Si-O and S-O. This study has systematically interpreted the working mechanism of water-resistant PBG, presenting an important significance in the utilization of PG resources.

Orbital magnetism through inverse Faraday effect in metal clusters

Abstract In view of the recent increased interest in light-induced manipulation of magnetism in nanometric length scales this work presents metal clusters as promising elementary units for generating all-optical ultrafast magnetization. We perform a theoretical study of the opto-magnetic properties of metal clusters through ab-initio real-time (RT) simulations in real-space using time-dependent density functional theory (TDDFT). Through ab-initio calculations of plasmon excitation with circularly polarized laser pulse in atomically precise clusters of simple and noble metals, we discuss the generation of orbital magnetic moments due to the transfer of angular momentum from light field through optical absorption at resonance energies. Notably, in the near-field analysis we observe self-sustained circular motion of the induced electron density corroborating the presence of nanometric current loops which give rise to orbital magnetic moments due to the inverse Faraday effect (IFE) in the clusters. The results provide valuable insights into the quantum many-body effects that influence the IFE-mediated light-induced orbital magnetism in metal clusters depending on its geometry and chemical composition. At the same time, they explicitly demonstrate the possibility for harnessing magnetization in metal clusters, offering potential applications in the field of all-optical manipulation of magnetism.

Trivariate Joint Modeling for Family Data with Longitudinal Counts, Recurrent Events and a Terminal Event with Application to Lynch Syndrome.

Trivariate joint modeling for longitudinal count data, recurrent events, and a terminal event for family data has increased interest in medical studies. For example, families with Lynch syndrome (LS) are at high risk of developing colorectal cancer (CRC), where the number of polyps and the frequency of colonoscopy screening visits are highly associated with the risk of CRC among individuals and families. To assess how screening visits influence polyp detection, which in turn influences time to CRC, we propose a clustered trivariate joint model. The proposed model facilitates longitudinal count data that are zero-inflated and over-dispersed and invokes individual-specific and family-specific random effects to account for dependence among individuals and families. We formulate our proposed model as a latent Gaussian model to use the Bayesian estimation approach with the integrated nested Laplace approximation algorithm and evaluate its performance using simulation studies. Our trivariate joint model is applied to a series of 18 families from Newfoundland, with the occurrence of CRC taken as the terminal event, the colonoscopy screening visits as recurrent events, and the number of polyps detected at each visit as zero-inflated count data with overdispersion. We showed that our trivariate model fits better than alternative bivariate models and that the cluster effects should not be ignored when analyzing family data. Finally, the proposed model enables us to quantify heterogeneity across families and individuals in polyp detection and CRC risk, thus helping to identify individuals and families who would benefit from more intensive screening visits.