Models Of Clusters Research Articles

A Study on the Pricing of Used Sailboats Based on Clustering and Linear Regression

The trading situation of the used sailboat market changes due to factors such as region, market demand and season, and the price fluctuates accordingly. In order to give a reasonable used sailboat pricing model, this paper first clusters the hull characteristics and sailboat models by k-means, and then establishes a linear regression model with the clustered results and the regional economic level. In order to explore whether the regional location has an impact on the pricing, this paper substitutes the data from different regions into the model, compares the coefficients of sailboat models of the same cluster to reflect the differences of regional effects between different regions, and finds that the regional effects of different clusters of ships are different in different regions. Finally, the model is applied to Hong Kong to test the practical application effect of the model. This study has reference value for the pricing of used sailboats.

Multi-Mission Oriented Joint Optimization of Task Assignment and Flight Path Planning for Heterogeneous UAV Cluster

This paper puts forward a joint optimization algorithm of task assignment and flight path planning for a heterogeneous unmanned aerial vehicle (UAV) cluster in a multi-mission scenario (MMS). The basis of the proposed algorithm is to establish constraint and threat models of a heterogeneous UAV cluster to simultaneously minimize range and maximize value gain and survival probability in an MMS under the constraints of task payload, range, and task requirement. On one hand, the objective function for the heterogeneous UAV cluster within an MMS is derived and it is adopted as a metric for assessing the performance of the joint optimization in task assignment and flight path planning. On the other hand, since the formulated joint optimization problem is a multi-objective, non-linear, and non-convex optimization model due to its multiple decision variables and constraints, the roulette wheel selection (RWS) principle and the elite strategy (ES) are introduced in an ant colony optimization (ACO) to solve the complex optimization model. The simulation results indicate that the proposed algorithm is superior and more efficient compared to other approaches.

Short-term prediction of integrated energy load aggregation using a bi-directional simple recurrent unit network with feature-temporal attention mechanism ensemble learning model

In the realm of economic scheduling and optimal operation within integrated energy systems, integrated energy load aggregation plays a pivotal role. Accurate prediction of integrated energy load aggregation enables the refined management of energy resources, thereby enhancing energy utilization efficiency. This paper merges the advantages of clustering algorithms, prediction algorithms and ensemble learning to propose a short-term prediction approach for integrated energy load aggregation. Firstly, the intrinsic distribution of energy consumption data is analyzed through Affinity Propagation (AP) clustering with multi-dimensional similarity. This analysis yields the division of all samples into clusters based on data characteristics. Subsequently, the Transfer entropy is harnessed to conduct correlation analysis within each cluster. For constructing predictive models for each individual cluster, a prediction algorithm based on the Feature-Temporal Attention-enhanced Bi-directional Simple Recurrent Unit (FTA-Bi-SRU) is employed. The Dynamic Weight Average (DWA)-based multi-task loss equilibrium and Multi-Term Adam (MTAdam) loss function are combined to enhance the efficiency of the forecasting model. Finally, a Light Gradient Boosting Machine (LightGBM) model with Bayesian optimization is employed to integrate the predictive outcomes of each individual cluster to generate the overall predictive outcomes for integrated energy load aggregation. The proposed methodology is experimented on real-world energy consumption data to ascertain the efficacy of the prediction method. The experimental results substantiate the superiority of the suggested forecasting approach.

Exploring the low-mass regime of galaxy-scale strong lensing: Insights into the mass structure of cluster galaxies

Context. Several recent studies have highlighted a discrepancy between the strong lensing (SL) properties of observed cluster galaxies and the predictions of Λ cold dark matter (CDM) cosmological hydrodynamical simulations. This discrepancy can be interpreted as the result of observed cluster members being more compact than their simulated counterparts. Aims. In this work, we aim at a direct measurement of the compactness of a few selected galaxy-scale lenses in massive clusters, testing the accuracy of the scaling laws adopted to describe the members in SL models of galaxy clusters. Methods. We selected the multiply imaged sources MACS J0416.1−2403 ID14 (z = 3.221), MACS J0416.1−2403 ID16 (z = 2.095), and MACS J1206.2−0847 ID14 (z = 3.753). Eight multiple images were observed for the first SL system, and six for the latter two. We focused on the main deflector of each galaxy-scale SL system (identified as members 8971, 8785, and 3910, respectively), and modelled its total mass distribution with a truncated isothermal sphere. To account for the lensing effects of the remaining components of the cluster, we took the most accurate SL model of its mass distribution available. To include the uncertainty and the systematics affecting the cluster-scale mass models, we explored the posterior probability distribution of its parameters and extracted 100 cluster mass distributions. For each of them, we optimised the mass parameters of the galaxy-scale lens: the bootstrapping procedure allowed us to obtain a realistic estimate of the uncertainty on their values. Results. We measured a truncation radius value of 6.1−1.1+2.3 kpc, 4.0−0.4+0.6 kpc, and 5.2−1.1+1.3 kpc for members 8971, 8785, and 3910, corresponding to total mass values of M = 1.2−0.1+0.3 × 1011 M⊙, M = 1.0−0.1+0.2 × 1010 M⊙, and M = 6.3−1.1+1.0 × 1010 M⊙, respectively. Alternative non-truncated models with a higher number of free parameters do not lead to an improved description of the SL system and show some parametric degeneracies. We measured the stellar-to-total mass fraction within the effective radius for the three cluster members, finding 0.51 ± 0.21, 1.0 ± 0.4, and 0.39 ± 0.16, respectively. Conclusions. We find that a parameterisation of the physical properties of cluster galaxies in SL models based on power-law scaling relations with respect to the observed total luminosity cannot accurately describe the compactness of the members over their full total mass range. Our results, instead, agree with recent modelling of the cluster members based on the Fundamental Plane relation. Finally, we report good agreement between our predicted values of the stellar-to-total mass fraction within the effective radius and those of early-type galaxies from the Sloan Lens ACS Survey. Our work significantly extends the regimes of the current samples of lens galaxies, towards the mass range that will be probed by the Euclid, Rubin, and James Webb Telescopes.

Industrial Clusters in Slovakia-Urban Development

For almost three decades, Slovakia has been undergoing reform processes related to restructuring industry and in expectation of the sustainability of the industrial structure with an important link to the landscape. The aim of this article is the presentation of the development models of the industrial clusters in synergy with the economic dimension, landscape, settlement structure, and sustainability. The background of the article is an evaluation of industry and its impact on the landscape between the 19th and 21st centuries. The development of industry influenced the territorial and spatial development of towns and landscapes, forcing a new identity on them. Development mirrored the rate of innovation in the domain of technologies and construction. The issues examined herein required the application of a combination of quantitative and qualitative research methods, including a creative method called SCAMPER. Our scheme and models of industrial parks and cooperation clusters are a result of theoretical and field research and reflect variable mobility and sustainability. The conclusions of the research indicate a turnover in the original order of importance in the industrial activity: environment–man–production. This is the contribution of evolution and the building of eco-friendly industrial structures.

One Ligand to Bind them All: S~C~S2- Carbon- and Sulfur-Based Gem-Dianion as Structuring Ligand for Iron Polymetallic Assemblies.

Numerous synthetic models of the FeMo-co cluster of nitrogenases have been proposed to find the simplest structure with relevant reactivity. Indeed, such structures are able to perform multi-electrons reduction processes, such as the conversion of N2 to ammonia, and of CO2 into methane and alkenes. The most challenging parameter to imitate is indeed the central carbide ligand, which is believed to maintain the integrity of iron sulfide assembly during the course of catalytic cycles. The study proposes the use of bis(diphenylthiophosphinoyl)methanediide (SCS)2- as an ideal platform for the synthesis of bi- and tetra-metallic iron complexes, in which the iron-carbon interaction is maintained upon structural diversification and redox state changes.

Galactic properties that favour star cluster formation: a statistical view

ABSTRACT The presence or absence of star clusters in galaxies, and the properties of star cluster populations compared to their host galaxy properties, are important observables for validating models of cluster formation, galaxy formation, and galaxy assembly. In this work, we apply a Bayesian approach to fit two models to data from surveys of young clusters in star-forming galaxies. The first model is a logistic regression, which allows us to include galaxies which do not have any young clusters. The second model is a hurdle model, which includes galaxies with zero clusters and also incorporates information about the total mass in the cluster system. We investigate two predictors (star formation rate and total stellar mass in the galaxy) and look at clusters younger than 10 or 100 Myr. We find that in all cases, star formation rate is the better predictor for both the probability of hosting clusters and the total mass in the cluster system. We compare our results to similar models for old globular clusters, and conclude that star cluster formation was more abundant and more efficient at higher redshifts, likely because of the high gas content of galaxies at that time.

Hematological, clinical, immunophenotypic characterization, and treatment outcomes of prognostically significant genetic subtypes of B-lineage acute lymphoblastic leukemia: A report of 1021 patients from India.

The published literature on hematological, clinical, flowcytometric-immunophenotyping, and minimal residual disease outcomes of the prognostically important genetic subtypes of acute lymphoblastic leukemia (ALL) is scarce from low-income countries. For newer classifications such as BCR::ABL1-like ALLs, the scarcity of patient-level data is even more pronounced. The authors performed comprehensive detection of recurrent gene fusions and BCR::ABL1-like ALL cases followed by immunophenotypic profiling and obtained clinical outcome parameters for a large cohort (n=1021) of patients from India. This cohort included a significant number of patients with BCR::ABL1-like ALL subtype and other genetic subtypes of ALL. Patients with BCR::ABL1-positive and BCR::ABL1-like ALL were significantly older, had male preponderance, and expressed a higher white blood cell count than BCR::ABL1-negative cases (p<.05). Logistic regression modeling of B-lineage-ALL (B-ALL) subtypes revealed that cluster of differentiation (CD)36 is a strong statistically significant predictive marker of BCR::ABL1-like ALL (p<.05). Furthermore, patients with BCR::ABL1-like ALLs show a significantly higher frequency of CD36 expression compared to BCR::ABL1-negative ALLs (p<.05). In terms of clinical symptoms, lymphadenopathy is a strong statistically significant predictive marker in BCR::ABL1-like ALLs compared to BCR::ABL1-negative ALL cases (p<.05). In terms of treatment outcomes, minimal residual disease (MRD) positivity in BCR::ABL1-positive ALL cases were statistically significant (p<.05), and BCR::ABL1-like ALL cases had high MRD-positivity as compared to BCR::ABL1-negative ALL cases but did not show statistical significance. The findings evince the use of novel therapies and personalized treatment regimens to improve the overall survival of the newer incorporated entities in B-ALLs. This is the first report characterizing the hematological, clinical, flowcytometric-immunophenotyping, and minimal residual disease outcomes of the prognostically significant subtypes of ALLs in patients from India. Characterizing the hematological, clinical, flowcytometric-immunophenotyping, and minimal residual disease outcomes of the prognostically significant subtypes (n=1021) of acute lymphoblastic leukemia (ALLs) in patients from India. We have made two independent logistic regression models of cluster of differentiation (CD) markers and clinical symptoms to differentiate prognostically significant subtypes of ALLs. Logistic regression analysis of CD markers revealed CD36 as a strong predictor in BCR::ABL1-like ALL cases compared to BCR::ABL1-negative ALL cases. Logistic regression analysis of clinical symptoms revealed lymphadenopathy significantly predicts BCR::ABL1-like ALLs (p<.05). In terms of treatment outcomes, BCR::ABL1-positive ALL had statistically significant minimal residual disease (MRD) (p<.05), and BCR::ABL1-like ALL cases had high MRD-positivity but did not show statistical significance as compared to BCR::ABL1-negative ALLs.

CuO Nanoparticles and Microaggregates: An Experimental and Computational Study of Structure and Electronic Properties.

The link between morphology and properties is well-established in the nanoparticle literature. In this report, we show that different approaches in the synthesis of copper oxide can lead to nanoparticles (NPs) of different size and morphology. The structure and properties of the synthesized NPs are investigated with powder X-ray diffraction, scanning electron microscopy (SEM), and diffuse reflectance spectroscopy (DRS). Through detailed SEM analyses, we were able to correlate the synthetic pathways with the particles' shape and aggregation, pointing out that bare hydrothermal pathways yield mainly spheroidal dandelion-like aggregates, whereas, if surfactants are added, the growth of the nanostructures along a preferential direction is promoted. The effect of the morphology on the electronic properties was evaluated through DRS, which allowed us to obtain the electron bandgap in every system synthesized, and to find that the rearrangement of threaded particles into more compact structures leads to a reduction in the energy difference. The latter result was compared with Density Functional Theory (DFT) computational models of small centrosymmetric CuO clusters, cut from the tenorite crystal structure. The computed UV-Vis absorption spectra obtained from the clusters are in good agreement with experimental findings.

Economic assessment of the present state of cluster formation in Azerbaijani oil and gas companies

Clusters attach great importance to supporting the activities of business entities in various sectors of the economy, as well as accelerating their development, economic and social development of regions throughout the country, modernization of the infrastructure base of the economy. Observation of the processes of economic development taking place in the countries of the world shows that in order to ensure high economic development, competitive sectors of the economy, a system of enterprises producing large volumes of exportoriented products, comprehensive economic development is of paramount importance. In general, at present the role of optimal models of economic development in the rise of developed countries to this level is great, and it is in these processes that the institution of the cluster is of paramount importance. Keywords: clustering; innovative models of clusters; innovative industrial cluster; fuel and energy complex; infrastructure of the economy.

Updates to the DScribe library: New descriptors and derivatives.

We present an update of the DScribe package, a Python library for atomistic descriptors. The update extends DScribe's descriptor selection with the Valle-Oganov materials fingerprint and provides descriptor derivatives to enable more advanced machine learning tasks, such as force prediction and structure optimization. For all descriptors, numeric derivatives are now available in DScribe. For the many-body tensor representation (MBTR) and the Smooth Overlap of Atomic Positions (SOAP), we have also implemented analytic derivatives. We demonstrate the effectiveness of the descriptor derivatives for machine learning models of Cu clusters and perovskite alloys.

Model Fitness and Predictive Accuracy in Linear Mixed-Effects Models with Latent Clusters

In clustered data, observations within a cluster show similarity between themselves because they share common features different from observations in the other clusters. In a given population, different clustering may surface because correlation may occur across more than one dimension. The existing multilevel analysis techniques of the primal linear mixed-effect models are limited to natural clusters which are often not realistic to capture in real-life situations. Therefore, this paper proposes dual linear mixed models (DLMMs) for modeling unobserved latent clusters when such are present in data sets to yield appreciable gains in model fitness and predictive accuracy. The methodology explored the development and analysis of the dual linear mixed models (DLMMs) based on the derived latent clusters from the natural clusters using multivariate cluster analysis. A published data set on political analysis was used to demonstrate the efficiency of the proposed models. The proposed DLMMs have yielded minimum values of the models' assessment criteria (Akaike information criterion, Bayesian information criterion, and root mean squared error), and hence, outperformed the classical PLMMs in terms of model fitness and predictive accuracy.

Contrastive encoder pre-training-based clustered federated learning for heterogeneous data

Federated learning (FL) is a promising approach that enables distributed clients to collaboratively train a global model while preserving their data privacy. However, FL often suffers from data heterogeneity problems, which can significantly affect its performance. To address this, clustered federated learning (CFL) has been proposed to construct personalized models for different client clusters. One effective client clustering strategy is to allow clients to choose their own local models from a model pool based on their performance. However, without pre-trained model parameters, such a strategy is prone to clustering failure, in which all clients choose the same model. Unfortunately, collecting a large amount of labeled data for pre-training can be costly and impractical in distributed environments. To overcome this challenge, we leverage self-supervised contrastive learning to exploit unlabeled data for the pre-training of FL systems. Together, self-supervised pre-training and client clustering can be crucial components for tackling the data heterogeneity issues of FL. Leveraging these two crucial strategies, we propose contrastive pre-training-based clustered federated learning (CP-CFL) to improve the model convergence and overall performance of FL systems. In this work, we demonstrate the effectiveness of CP-CFL through extensive experiments in heterogeneous FL settings, and present various interesting observations.

Constraints on the Hubble constant from supernova Refsdal's reappearance.

The gravitationally lensed supernova Refsdal appeared in multiple images produced through gravitational lensing by a massive foreground galaxy cluster. After the supernova appeared in 2014, lens models of the galaxy cluster predicted that an additional image of the supernova would appear in 2015, which was subsequently observed. We use the time delays between the images to perform a blinded measurement of the expansion rate of the Universe, quantified by the Hubble constant (H0). Using eight cluster lens models, we infer [Formula: see text]. Using the two models most consistent with the observations, we find [Formula: see text]. The observations are best reproduced by models that assign dark-matter halos to individual galaxies and the overall cluster.

Computational Insights into the Mechanism of Nitric Oxide Generation from S-Nitrosoglutathione Catalyzed by a Copper Metal-Organic Framework.

The controlled generation of nitric oxide (NO) from endogenous sources, such as S-nitrosoglutathione (GSNO), has significant implications for biomedical implants due to the vasodilatory and other beneficial properties of NO. The water-stable metal-organic framework (MOF) Cu-1,3,5-tris[1H-1,2,3-triazol-5-yl]benzene has been shown to catalyze the production of NO and glutathione disulfide (GSSG) from GSNO in aqueous solution as well as in blood. Previous experimental work provided kinetic data for the catalysis of the 2GSNO → 2NO + GSSG reaction, leading to various proposed mechanisms. Herein, this catalytic process is examined using density functional theory. Minimal functional models of the Cu-MOF cluster and glutathione moieties are established, and three distinct catalytic mechanisms are explored. The most thermodynamically favorable mechanism studied is consistent with prior experimental findings. This mechanism involves coordination of GSNO to copper via sulfur rather than nitrogen and requires a reductive elimination that produces a Cu(I) intermediate, implicating a redox-active copper site. The experimentally observed inhibition of reactivity at high pH values is explained in terms of deprotonation of a triazole linker, which decreases the structural stability of the Cu(I) intermediate. These fundamental mechanistic insights may be generally applicable to other MOF catalysts for NO generation.

Models for cluster randomized designs using ranked set sampling.

Cluster randomized designs (CRD) provide a rigorous development for randomization principles for studies where treatments are allocated to cluster units rather than the individual subjects within clusters. It is known that CRDs are less efficient than completely randomized designs since the randomization of treatment allocation is applied to the cluster units. To mitigate this problem, we embed a ranked set sampling design from survey sampling studies into CRD for the selection of both cluster and subsampling units. We show that ranking groups in ranked set sampling act like a covariate, reduce the expected mean squared cluster error, and increase the precision of the sampling design. We provide an optimality result to determine the sample sizes at cluster and sub-sample level. We apply the proposed sampling design to a dental study on human tooth size, and to a longitudinal study from an education intervention program.

Road network and deforestation of indigenous lands in the Brazilian Amazon

This research evaluates the relationship between the official and unofficial road network and deforestation in the Brazilian Amazon within and around 332 indigenous lands from 2008 to 2021. The study uses infrastructural and environmental data. Two analyses were performed: clustering by the Gaussian Mixture Model to find group similarities and regressions by weighted least squares (WLS) and the generalized linear models (GLM) to extract factors related to deforestation considering the complete set and the clusters. The results revealed six clusters. Some suffer direct impacts from roads, while others are better preserved. Regression models revealed active fires, illegal mining, and that unofficial roads within ILs to be the drivers for deforestation. The general results indicated that for every 1 km of an additional unofficial route, deforestation increases by 0.036 km2. However, when analyzing the regression models for clusters, we concluded that only 45% of the lands are currently affected by road infrastructure.

An equivalent model‐based asynchronous dispatch method for clusters of flexible distributed energy resources

AbstractWith the increasing number of flexible distributed energy resources, it is difficult for the distribution network to control various devices directly. Therefore, managing and dispatching the resources in a cluster way is required. This article proposes an equivalent model‐based asynchronous dispatch framework, which is composed of three layers, the distribution network, clusters, and distributed energy resources. With this framework, economic dispatch is carried out with the equivalent models of clusters, simplifying the dispatch problem of the distribution network. The numerical test results show that the proposed method can significantly reduce the calculation time and maintain high accuracy in the economic dispatch problem.

Made-to-measure modelling of globular clusters

ABSTRACT We present the first application of the made-to-measure method for modelling dynamical systems to globular clusters. Through the made-to-measure algorithm, the masses of individual particles within a model cluster are adjusted while the system evolves forward in time via a gravitational N-body code until the model cluster is able to reproduce select properties of an observed cluster. The method is first applied to observations of mock isotropic and anisotropic clusters while fitting against the cluster’s 3D or projected density profile, density weighted mean-squared velocity profile, or its density profile with individual mean-squared velocity profiles assuming $10 {{\ \rm per\ cent}}$ uncertainty in each of these observables. We find that a cluster’s 3D density profile can easily be reproduced by the made-to-measure method, with minor discrepancies in the outer regions if fitting against a cluster’s projected surface density or projected kinematic properties. If an observed cluster is anisotropic, only fitting against the cluster’s density profile and individual mean-squared velocity profiles will fully recover the full degree of anisotropy. Partial anisotropy can be recovered as long as two kinematic properties are included in the fit. We further apply the method to observations of the Galactic globular cluster M4 and generate a complete 6D representation of the cluster that reproduces observations of its surface density profile, mean-squared proper motion velocity profile, and mean-squared line of sight velocity profile. The M2M method predicts M4 is primarily isotropic with a mass of $9.2\pm 0.4\times 10^4\, \mathrm{ M}_{\odot }$ and a half-mass radius of 3.7 ± 0.1 pc.

Pair distribution function analysis of discrete nanomaterials in PDFgui

Pair distribution functions (PDFs) are a leading tool for atomic structure analysis of nanomaterials. However, the most widely used programs for refining atomic structure against PDF data are based on extended crystallographic models, which cannot be applied to discrete, whole nanoparticles. This work describes a straightforward approach to simulate and refine atomistic models of discrete clusters and nanoparticles employing widely used PDF modelling programs such as PDFgui [Farrow et al. (2007). J. Phys. Condens. Matter, 19, 335219] that utilize extended crystallographic models. In this approach, the whole particle to be modelled is contained within an expanded, and otherwise empty, unit cell that is sufficiently large to avoid correlations between atoms in neighbouring unit cells over the r range analysed. The PDF of the particle is simulated as a composite using two conventional `phases': one that calculates the atom–atom correlations and one that approximates the local number density. This approach is first validated for large nanoparticles that are well modelled by a conventional shape factor model, and then applied to simulate the PDF of discrete particles and low-dimensional materials (graphene and MXene) and to model the experimental PDF data for single-layer FeS nanosheets. A comparison of this approach with the DiffPy-CMI program [Juhás et al. (2015). Acta Cryst. A71, 562–568], which calculates the PDF of discrete species, shows that the composite modelling approach is equally or more accurate. Example input files for implementing this approach within PDFgui and TOPAS [Coelho (2018). J. Appl. Cryst. 51, 210–218], and recommendations for selecting model parameters for reliable application of this refinement strategy, are provided.