Cluster Boundaries Research Articles

Fractal analyses of AlxGa1−xN thin film surfaces on AlN at different annealing temperatures

The determination of heteromorphological structures formed on the surface during annealing of AlxGa1−xN thin film grown on sapphire substrate using the metal organic chemical vapor deposition technique at different temperatures was investigated by fractal analysis method. The images of the surfaces of the thin films were taken by atomic force microscopy (AFM) at temperatures of 900, 1000, 1050 and 1200 °C respectively. AFM images were digitised in bitmap format according to the annealing temperatures. It was determined that the fractal dimensions obtained a linear correlation with the annealing temperatures. The results confirm the hypothesis that surface relaxation by the thermal action can produce fractal-like structures at particle or cluster boundaries. It is found that the observed cluster formation of superficial particles decreases as increasing temperature. The increase in temperature reduces the rate of superficial particle coating. To determine the surface roughness of the AlxGa1−xN thin film according to the annealing temperature, the AFM images were digitized in tagged image file format and the statistical root mean square, mean value, mean roughness, skewness and kurtosis values of the films were calculated. The roughness is a result of the thin film surface heteromorphology formed due to the specific annealing process. It is proved that the fractal dimensions of the AlxGa1−xN thin film increase as the annealing temperature rises. The particles coalesce on the surface and cluster in different types and sizes at each different annealing temperature, forming islets of different sizes. The skewness and kurtosis values show a different and irregular change.

Density clustering method based on k-nearest neighbor propagation

Abstract Density clustering, an effective data analysis tool, performs well on arbitrary shapes and non-convex datasets. However, it still has some limitations in identifying the cluster structures of datasets with irregular shapes and uneven density distribution. Aiming at the above problem, this paper proposes a density clustering method based on k-nearest neighbor propagation. Firstly, based on the theory of k-nearest neighbor, the nearest hub points are defined to replace the sample points within their k-nearest neighbors, and the cluster boundary is ascertained relying on the propagation of the nearest hub points with density gradient trend so as to divide the nearest hub points into multiple groups; secondly, a group merging method of the nearest hub points based on the set similarity is given to obtain the set of nearest hub points; and finally, the remaining sample points are assigned to the set of their nearest hub points to obtain the final clusters. The correctness and effectiveness of the algorithm are verified by experiments on synthetic and UCI datasets, and the results show that the algorithm can better cope with the clustering needs of multiple complex data distributions compared with other comparative algorithms.

Galaxy groups in the presence of cosmological constant: Increasing the masses of groups

The boundaries of galaxy groups and clusters are defined by the interplay between the Newtonian attractive force and the decoupling from the local expansion of the Universe. This work extends the definition of a zero radial acceleration surface (ZRAS) and the turnaround surface (TS) for a general distribution of the masses in an expanding background, governed by the cosmological constant. We apply these definitions to different galaxy groups in the local Universe, mapping these groups up to ten megaparsec distances. We discuss the dipole and the quadrupole rate for the Local Group of Galaxies and the implementations on the Hubble diagram correction and galaxy groups virialization. With these definitions, we present the surfaces showing the interplay between the local expansion vs the local Newtonian attraction for galaxy groups in the local Universe. Further, we estimate the masses of different galaxy groups and show that the inclusion of the Cosmological Constant in the analysis predicts these masses to be higher by 5-10%. For instance the Local Group of galaxies is estimated to be (2.47±0.08)⋅1012M⊙. For the groups with enough tracers close to the TS, the contribution of the Cosmological Constant makes the masses to be even higher. The results show the importance of including the local cosmic expansion in analyzing the Cosmic Flow of the local Universe.

Can the splashback radius be an observable boundary of galaxy clusters?

The splashback radius was proposed as a physically motivated boundary of clusters as it sets the limit between the infalling and the orbitally dominated regions. However, galaxy clusters are complex objects connected to filaments of the cosmic web from which they accrete matter that disturbs them and modifies their morphology. In this context, estimating the splashback radius and the cluster boundary becomes challenging. In this work, we use a constrained hydrodynamical simulation replicating the Virgo cluster embedded in its large-scale structure to investigate the impact of its local environment on the splashback radius estimate. We identify the splashback radius from 3D radial profiles of dark matter density, gas density, and pressure in three regions representative of different dynamical states: accretion from spherical collapse, filaments, and matter outflow. We also identify the splashback radius from 2D-projected radial profiles of observation-like quantities: mass surface density, emission measure, and Compton-y. We show that the splashback radius mainly depends on the dynamics in each region and the physical processes traced by the different probes. We find multiple values for the splashback radius ranging from 3.3 ± 0.2 to 5.5 ± 0.3 Mpc. In particular, in the regions of collapsing and outflowing materials, the splashback radii estimated from gas density and pressure radial profiles overestimate that of the dark matter density profiles, which is considered the reference value given that the splashback radius was originally defined from dark matter simulations in pioneering works. Consequently, caution is required when using the splashback radius as a boundary of clusters, particularly in the case of highly disturbed clusters like Virgo. We conclude with a discussion of the detection of the splashback radius from pressure radial profiles, which could be more related to an accretion shock, and its detection from stacked radial profiles.

Members of an array of zinc finger proteins specify distinct Hox chromatin boundaries.

Partitioning of repressive from actively transcribed chromatin in mammalian cells fosters cell-type specific gene expression patterns. While this partitioning is reconstructed during differentiation, the chromatin occupancy of the key insulator, CTCF, is unchanged at the developmentally important Hox clusters. Thus, dynamic changes in chromatin boundaries must entail other activities. Given its requirement for chromatin loop formation, we examined cohesin-based chromatin occupancy without known insulators, CTCF and MAZ, and identified a family of zinc finger proteins (ZNFs), some of which exhibit tissue-specific expression. Two such ZNFs foster chromatin boundaries at the Hox clusters that are distinct from each other and from MAZ. PATZ1 was critical to the thoracolumbar boundary in differentiating motor neurons and mouse skeleton, while ZNF263 contributed to cervicothoracic boundaries. We propose that these insulating activities act with cohesin, alone or combinatorially, with or without CTCF, to implement precise positional identity and cell fate during development.

Research on a Train Safety Driving Method Based on Fusion of an Incremental Clustering Algorithm and Lightweight Shared Convolution.

This paper addresses the challenge of detecting unknown or unforeseen obstacles in railway track transportation, proposing an innovative detection strategy that integrates an incremental clustering algorithm with lightweight segmentation techniques. In the detection phase, the paper innovatively employs the incremental clustering algorithm as a core method, combined with dilation and erosion theories, to expand the boundaries of point cloud clusters, merging adjacent point cloud elements into unified clusters. This method effectively identifies and connects spatially adjacent point cloud clusters while efficiently eliminating noise from target object point clouds, thereby achieving more precise recognition of unknown obstacles on the track. Furthermore, the effective integration of this algorithm with lightweight shared convolutional semantic segmentation algorithms enables accurate localization of obstacles. Experimental results using two combined public datasets demonstrate that the obstacle detection average recall rate of the proposed method reaches 90.3%, significantly enhancing system reliability. These findings indicate that the proposed detection strategy effectively improves the accuracy and real-time performance of obstacle recognition, thereby presenting important practical application value for ensuring the safe operation of railway tracks.

Clustering of high-dimensional observations

We present a novel clustering method for high-dimensional, low sample size (HDLSS) data. The method is distance-based, takes advantage of the distance concentration phenomenon and the limiting values of the dissimilarity indices to construct clusters. We describe an algorithm that orders each row of the dissimilarity matrix to estimate the change points, which define cluster boundaries. We construct an agreement matrix of the Rand indices of the row clusters. The minimum of the row sum of the agreement matrix provides us with the best clusters. We prove that the new method achieves perfect clustering as the number of features diverges for a fixed sample size. Several examples are presented to illustrate the proposed method. We compare the new method with four other clustering techniques, including high-dimensional k-means, minimal spanning tree and Hierarchical Scan. The clustering methods are applied to the Lymphoma data set.

A Model to Analyze Industrial Clusters to Measure Land Use Efficiency in China

An understanding of how land use efficiency and industrial clusters interact helps one to make informed decisions that balance economic benefits with sustainable urban development. The emergence of industrial clusters is a result of market behavior, while the determination of administrative boundaries is a result of government behavior. When these two are not consistent, it can lead to distortions in the allocation of land resources. However, current research on industrial development and land use efficiency is based on agglomeration within administrative regions rather than on industrial clusters. This study addresses this gap by identifying industrial clusters based on the spatial distribution of enterprises and analyzing their impact on land use efficiency. This study uses the density-based spatial clustering of applications with noise (DBSCAN) algorithm to identify industrial clusters, the convex hull algorithm to study their morphology, and spatial econometrics to measure the relationship between land use efficiency and the scale of industrial clusters. The results indicate the following: (1) the density of manufacturing industry (MI) clusters is significantly higher than that of information technology industry (ITI) clusters, and larger industrial clusters tend to be more circular in shape; (2) there is a positive correlation between the scale of industrial clusters and land use efficiency, and industrial clusters with varying levels of land use efficiency are interspersed throughout; (3) significant differences exist between the boundaries of industrial clusters and administrative regions, which could lead to biases when analyzing land use efficiency based on administrative regions. This study provides theoretical support for government policies on improving land use efficiency in China.

A fuzzy q-closest alignment model

The paper examines the issue of well-posedness of the Cucker-Smale model with communication restricted to the q-closest neighbors, known also as the Cucker-Dong model. With agents oscillating on the boundary of different clusters, the system becomes difficult to precisely define, which leads to further problems with kinetic limits as the number of agents tends to infinity. We introduce the fuzzy q-closest system, which circumvents the issues with well-posedness. For such a system we prove a stability estimate for measure-valued solutions and perform the kinetic mean-field limit.

Quantifying surface urban heat island variations and patterns: Comparison of two cities in three-stage dynamic rural–urban transition

Urban heat island (UHI) not only reflects the environmental thermal comfort and energy consumption, but also affects the urban meso‑scale climate. There are many researches related with UHI mainly focusing on urban and rural area, while neglecting dynamic rural–urban transition especially in a rapid urbanization in China. Beijing and Zhengzhou are studied by using city clustering algorithm (CCA) and boundary generation algorithm (BGA) to delineate the urban, peri‑urban and rural boundaries from 2000 to 2023 within three stages. Fourier transform model was used to identify the UHI patterns. Results show: 1) Two cities have undergone obvious expansions in 20 years, with a consistent mean LST decrease from urban to peri‑urban and rural areas in three stages. 2) The distribution of UHII was more consistent in Beijing, while it varied more in Zhengzhou across seasons. 3) The UHI patterns notably differ, with Zhengzhou experiencing variable patterns and Beijing consistently showing oblate patterns. 4) The profiles of UHII and NDVI in two cities varied seasonally and reflected urban expansions in terms of longitude and latitude. Understanding the long-term changes and patterns of urban heat islands in different cities will provide information for formulating adaptive policies for urban sustainability.

A new soft clustering method for traffic prediction in bike-sharing systems

For the efficient management of bike-sharing systems (BSSs), accurate demand predictions are crucial to address the uneven distribution of bikes at various stations. Recent studies have explored a hierarchical prediction framework using cluster-level models to more accurately estimate demand at the station level. However, in frameworks based on hard clustering, where each station is exclusively assigned to one of several clusters, prediction accuracy tends to be lower for stations at the cluster boundaries. To improve accuracy for such stations, this study proposes a novel soft clustering algorithm for BSSs. The key idea is to allow stations to belong to multiple clusters, calculating the membership degree for each station based on transitions between stations and clusters obtained through hard clustering. This study also investigated the impact of restricting clusters to which individual stations belong based on distance or usage history. Two approaches, distance- and usage-based, were employed to determine the clusters to which each station belongs. Experimental results using Seoul Bike data demonstrate the effectiveness of the proposed method in enhancing traffic prediction accuracy within the hierarchical prediction framework. Notably, excluding clusters with minimal usage for each station using the usage-based approach yielded the best performance.

Reanalysis of cluster randomised trial data to account for exposure misclassification using a per-protocol and complier-restricted approach.

The intention-to-treat (ITT) analysis of the Applying Wolbachia to Eliminate Dengue (AWED) trial estimated a protective efficacy of 77.1% for participants resident in areas randomised to receive releases of wMel-infected Aedes aegypti mosquitoes, an emerging dengue preventive intervention. The limiting assumptions of ITT analyses in cluster randomised trials and the mobility of mosquitoes and humans across cluster boundaries indicate the primary analysis is likely to underestimate the full public health benefit. Using spatiotemporally-resolved data on the distribution of Wolbachia mosquitoes and on the mobility of AWED participants (n = 6306), we perform complier-restricted and per-protocol re-examinations of the efficacy of the Wolbachia intervention. Increased intervention efficacy was estimated in all analyses by the refined exposure measures. The complier-restricted analysis returned an estimated efficacy of 80.7% (95% CI 65.9, 89.0) and the per-protocol analysis estimated 82.7% (71.7, 88.4) efficacy when comparing participants with an estimated wMel exposure of 80% compared to those with <20%. These reanalyses demonstrate how human and mosquito movement can lead to underestimation of intervention effects in trials of vector interventions and indicate that the protective efficacy of Wolbachia is even higher than reported in the primary trial results.

Asymptotics of the Spectrum of a Threedimensional Hartree Type Operator Near Upper Boundaries of Spectral Clusters

Asymptotics of the Spectrum of a Threedimensional Hartree Type Operator Near Upper Boundaries of Spectral Clusters

An Intuitionistic Fuzzy C-Means and Local Information-Based DCT Filtering for Fast Brain MRI Segmentation.

Structural and photometric anomalies in the brain magnetic resonance images (MRIs) affect the segmentation performance. Moreover, a sudden change in intensity between two boundaries of the brain tissues makes it prone to data uncertainty, resulting in the misclassification of the pixels lying near the cluster boundaries. The discrete cosine transform (DCT) domain-based filtering is an effective way to deal with structural and photometric anomalies, while the intuitionistic fuzzy C-means (IFCM) clustering can handle the uncertainty using the intuitionistic fuzzy set (IFS) theory. In this background, we propose two novel approaches, namely, the DCT-based intuitionistic fuzzy C-means (DCT-IFCM) and the DCT-based local information IFCM (DCT-LIFCM), which effectively deal with the Rician and Gaussian noises and also handle the data uncertainty problem to provide high segmentation accuracy. The DCT-IFCM approach performs the histogram-based segmentation, while the DCT-LIFCM uses the pixel-wise computation to include the spatial information. Although the DCT-LIFCM delivers slightly better performance than the DCT-IFCM, the latter is very fast in providing equally high segmentation accuracy. An exhaustive performance analysis is provided to demonstrate the superior performance of the proposed algorithms compared with the state-of-the-art algorithms, including those based on the DCT-based filtering approach and the IFS theory.

Tailored clustering method to identify quasi-regional sites

Classical clustering (CC) normally divides a database into a predetermined number of fixed clusters before assigning each target site into one of these clusters to build a quasi-local model. Because the range of target sites is much larger than the range of clusters, it is possible that the identified cluster contains database sites that are weakly correlated to some assigned target sites, especially when these target sites are located near the cluster boundaries. The existence of weakly correlated sites in the cluster may make the quasi-local model less effective in predicting soil properties at the target site. This paper proposes a Tailored Clustering Enabled Regionalization (TCER) framework that can minimize the inference uncertainty at a target site. It mainly consists of two parts: (1) a site similarity measure to quantify the similarity between database sites and the target site and (2) a novel tailored clustering (TC) approach to identify the optimal cluster (called quasi-regional cluster) of database sites with the highest site similarity measures. In comparison to the traditional TC, the novel TC does not require users to specify a site similarity threshold that divides database sites into those within the cluster and those outside the cluster. Finally, a hierarchical Bayesian model is applied to the quasi-regional cluster (rather than the entire database) to infer geotechnical/geological properties at the target site. The capability of TCER is verified using synthetic and real examples. It is shown that the proposed TCER is computationally efficient and can reduce inference uncertainty.

Conserved patterns and locomotor-related evolutionary constraints in the hominoid vertebral column

The evolution of the hominoid lineage is characterized by pervasive homoplasy, notably in regions such as the vertebral column, which plays a central role in body support and locomotion. Few isolated and fewer associated vertebrae are known for most fossil hominoid taxa, but identified specimens indicate potentially high levels of convergence in terms of both form and number. Homoplasy thus complicates attempts to identify the anatomy of the last common ancestor of hominins and other taxa and stymies reconstructions of evolutionary scenarios. One way to clarify the role of homoplasy is by investigating constraints via phenotypic integration, which assesses covariation among traits, shapes evolutionary pathways, and itself evolves in response to selection. We assessed phenotypic integration and evolvability across the subaxial (cervical, thoracic, lumbar, sacral) vertebral column of macaques (n = 96), gibbons (n = 77), chimpanzees (n = 92), and modern humans (n = 151). We found a mid-cervical cluster that may have shifted cranially in hominoids, a persistent thoracic cluster that is most marked in chimpanzees, and an expanded lumbosacral cluster in hominoids that is most expanded in gibbons. Our results highlight the highly conserved nature of the vertebral column. Taxa appear to exploit existing patterns of integration and ontogenetic processes to shift, expand, or reduce cluster boundaries. Gibbons appear to be the most highly derived taxon in our sample, possibly in response to their highly specialized locomotion.

Identification Method of High Incidence Areas of Bad Driving Behaviors Based on DBSCAN-Graham Combination

&lt;p&gt;This study utilizes data obtained from vehicle positioning, speed, and engine status through satellite positioning system monitoring terminals. The data is processed using spherical interpolation of latitude and longitude to handle missing values. The study establishes indicators for identifying bad driving behaviors, including speeding, rapid acceleration and deceleration, prolonged vehicle idling, and fatigue driving. The traditional DBSCAN algorithm is improved by incorporating both temporal and spatial search ranges into the algorithm, enabling cluster analysis of bad driving behaviors from both temporal and geographical perspectives. Finally, the Graham algorithm is utilized to calculate cluster boundaries, determining the specific locations and boundaries of high incidence areas of bad driving behaviors. This research aids regulatory authorities in more precisely supervising risk areas during transportation processes in terms of time and location.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Design, integration, and field evaluation of a robotic blossom thinning system for tree fruit crops

AbstractThe United States (US) apple industry relies heavily on semi‐skilled manual labor force for essential field operations such as training, pruning, blossom and green fruitlet thinning, and harvesting. Blossom thinning is one of the crucial crop‐load management practices to achieve desired crop load, fruit quality, and return bloom. While several techniques such as chemical and mechanical thinning are available for large‐scale blossom thinning, such approaches often yield unpredictable thinning results and may damage the canopy, spurs, and leaf tissue. Hence, growers still depend on laborious, labor‐intensive, and expensive manual hand blossom thinning for desired thinning outcomes. This research presents a robotic solution for precision blossom thinning in apple orchards using a deep learning‐based computer vision system, a six‐degrees‐of‐freedom UR5e robotic manipulator, and an electrically actuated miniature end‐effector. The integrated robotic system was evaluated in a commercial apple orchard which showed promising results for targeted and selective blossom thinning. Two thinning approaches, center and boundary thinning, were investigated to evaluate the system's ability to remove varying proportions of flowers from apple flower clusters. During boundary thinning, the end‐effector was actuated around the cluster boundary, while center thinning involved end‐effector actuation only at the cluster centroid for a fixed duration of 2 s. Field evaluation results showed that the boundary thinning approach thinned 67.2% of flowers from the targeted clusters with a cycle time of 9.0 s per cluster, whereas the center thinning approach thinned 59.4% of flowers with a cycle time of 7.2 s per cluster. Upon further improvement for commercial adoption, the proposed system could help address problems faced by apple growers with current hand, chemical, and mechanical blossom thinning approaches.

Enhancement of the Classification Performance of Fuzzy C-Means through Uncertainty Reduction with Cloud Model Interpolation

As an information granulation technology, clustering plays a pivotal role in unsupervised learning, serving as a fundamental cornerstone for various data mining techniques. The effective and accurate classification of data is a central focus for numerous researchers. For a dataset, we assert that the classification performance of a clustering method is significantly influenced by uncertain data, particularly those situated at the cluster boundaries. It is evident that uncertain data encapsulate richer information compared with others. Generally, the greater the uncertainty, the more information the data holds. Therefore, conducting a comprehensive analysis of this particular subset of data carries substantial significance. This study presents an approach to characterize data distribution properties using fuzzy clustering and defines the boundary and non-boundary characteristics (certainty and uncertainty) of the data. To improve the classification performance, the strategy focuses on reducing the uncertainty associated with boundary data. The proposed scheme involves inserting data points with the cloud computing technology based on the distribution characteristics of the membership functions to diminish the uncertainty of uncertain data. Building upon this, the contribution of boundary data is reassigned to the prototype in order to diminish the proportion of uncertain data. Subsequently, the classifier is optimized through data label (classification error) supervision. Ultimately, the objective is to leverage clustering algorithms for classification, thereby enhancing overall classification accuracy. Experimental results substantiate the effectiveness of the proposed scheme.

Fascin-induced bundling protects actin filaments from disassembly by cofilin.

Actin filament turnover plays a central role in shaping actin networks, yet the feedback mechanism between network architecture and filament assembly dynamics remains unclear. The activity of ADF/cofilin, the main protein family responsible for filament disassembly, has been mainly studied at the single filament level. This study unveils that fascin, by crosslinking filaments into bundles, strongly slows down filament disassembly by cofilin. We show that this is due to a markedly slower initiation of the first cofilin clusters, which occurs up to 100-fold slower on large bundles compared with single filaments. In contrast, severing at cofilin cluster boundaries is unaffected by fascin bundling. After the formation of an initial cofilin cluster on a filament within a bundle, we observed the local removal of fascin. Notably, the formation of cofilin clusters on adjacent filaments is highly enhanced, locally. We propose that this interfilament cooperativity arises from the local propagation of the cofilin-induced change in helicity from one filament to the other filaments of the bundle. Overall, taking into account all the above reactions, we reveal that fascin crosslinking slows down the disassembly of actin filaments by cofilin. These findings highlight the important role played by crosslinkers in tuning actin network turnover by modulating the activity of other regulatory proteins.