Cascade Clustering Research Articles

Synergistic Co-Assembly of Natural Oxidase and Peptide-Heme Nanozyme for In Situ H2O2 Removal with Enhanced Catalytic Efficiency and Stability.

Hydrogen peroxide, a byproduct of the oxidase catalytic process, significantly compromises the enzymatic activity and stability of natural oxidases, thereby restricting their industrial applications. To address this challenge, we developed a coassembly system combining a natural oxidase with a peptide-heme (PepHeme) nanozyme possessing catalase-like activity on a ferritin platform. This system, demonstrated by using natural choline oxidase and galactose oxidase as models, forms an oxidase-catalase cascade reaction. Leveraging the self-assembly properties of ferritin and the directional association enabled by sortase ligation, we successfully constructed clusters of oxidases and PepHeme nanozymes. This design facilitated the directional immobilization of natural enzymes and nanozymes and increased the local concentration of these cascade clusters, thus significantly enhancing both the catalytic efficiency and stability of the enzyme, especially the hydrogen peroxide tolerance and long-term stability. This cascade system offers a promising solution to the hydrogen peroxide byproduct issue in the industrial applications of oxidases.

A Study of Occluded Person Re-Identification for Shared Feature Fusion with Pose-Guided and Unsupervised Semantic Segmentation

The human body is often occluded by a variety of obstacles in the monitoring system, so occluded person re-identification is still a long-standing challenge. Recent methods based on pose guidance or external semantic clues have improved the representation and related performance of features; there are still problems, such as weak model representation and unreliable semantic clues. To solve the above problems, we proposed a feature extraction network, named shared feature fusion with pose-guided and unsupervised semantic segmentation (SFPUS). This network will extract more discriminative features and reduce the occlusion noise on pedestrian matching. Firstly, the multibranch joint feature extraction module (MFE) is used to extract feature sets containing pose information and high-order semantic information. This module not only provides robust extraction capabilities but can also precisely segment occlusion and the body. Secondly, in order to obtain multiscale discriminant features, the multiscale correlation feature matching fusion module (MCF) is used to match the two feature sets, and the Pose–Semantic Fusion Loss is designed to calculate the similarity of the feature sets between different modes and fuse them into a feature set. Thirdly, to solve the problem of image occlusion, we use unsupervised cascade clustering to better prevent occlusion interference. Finally, performances of the proposed method and various existing methods are compared on the Occluded-Duke, Occluded-ReID, Market-1501 and Duke-MTMC datasets. The accuracy of Rank-1 reached 65.7%, 80.8%, 94.8% and 89.6%, respectively, and the mAP accuracy reached 58.8%, 72.5%, 91.8% and 80.1%. The experiment results demonstrate that our proposed SFPUS holds promising prospects and performs admirably compared with state-of-the-art methods.

Dual-attentive cascade clustering learning for visible-infrared person re-identification

Dual-attentive cascade clustering learning for visible-infrared person re-identification

The surface defects of HOPG induced by low-energy Ar+ ion irradiation

Low-energy heavy ion irradiation is a promising surface modification technique due to its ability of precise controlling and tuning the density and location of defects in materials. In this work, 1-keV Ar+ ions with irradiation doses from 9.6 × 1012 ions/cm2 to 3.5 × 1015 ions/cm2 has been used to bombard the pristine highly oriented pyrolytic graphite (HOPG) at elevated temperature. The irradiated HOPG surfaces have been analyzed by multi-methods. Huge and sub-nm hillock-like surface defects are mainly concentrated on the irradiated HOPG surface, and are well consistent with the results from the Monte Carlo simulation. In particular, the surface defects exhibit a high thermal stability during the annealing procedure. The number of the surface defects increases as the irradiation dose increases and reaches the saturation point at a high dose, which is explained by the formation of vacancy clusters in collision cascades. The enhancement of light absorption in the ultraviolet-visible range for the irradiated surface is observed and mainly ascribed to the synergistic effect of the huge and sub-nm hillocks-like surface defect and defect energy levels in the forbidden bandgaps.

Wind speed modeling for cascade clusters of wind turbines Part 2: Wind speed reduction and aggregation superposition

Wind speed calculation is not only the core problem of the wind energy conversion, but also the key to improve the WT conversion efficiency. Wind speed is affected by many factors, so it is difficult to model. The influencing factors of the wind speed and relationship between wind speed are contained in big data of the WTs’ historical monitoring. In this paper, the “coupling element” (CE) and “aggregation element” (AE) of cascade clusters of wind turbines (CCWT) at different wind speeds are taken as the research objects to study the characteristics of the wind speed reduction (WSR) and aggregation superposition. We analyze and calculate the numerical relations in the historical data of the WTs, and obtain the WSR model of CE including the WTs distribution and state parameters. This paper further analyzes the distribution characteristics of the AE, studies the aggregation distribution and convergence form of multiple incoming streams, and puts forward the wind speed aggregation superposition (WSAS) model of AE which considers the friction energy dissipation between the different incoming streams. The WSR and aggregation model of the WTs in CCWT proposed in this paper has been verified by the examples with high accuracy and robustness.

Wind speed modeling for cascade clusters of wind turbines part 1: The cascade clusters of wind turbines

Abstract Wind energy conversion efficiency has always been an important issue for wind farms. And wind speed calculation is the basic task and key work of wind energy conversion optimization. The cascade clusters of wind turbines are directly related to wind speed, and affected by the terrain, wake disturbance, location distribution and other factors. So it is very difficult to adopt parameter modeling. The cascade characteristics among cluster wind turbines (WTs) are embodied in historical operation data of the WTs. Taking the input wind direction as the initial parameter, we construct the WTs location correlation matrix of the neighborhood distribution relationship of WTs location; we then obtain the correlation relationship of the WTs production wind speed and power by combining the WTs production monitoring data. At the same time, “coupling element” and “aggregation element” WTs can be obtained from the cascade clusters. By verifying the data of a large wind farm, the model proposed in this paper clarifies the relationship between the wind speed and the cascade clusters; using this model, we can calculate the cluster distribution under different wind conditions. It is highly practical and can be applied to other wind farms to support formulation of the efficiency optimization strategies.

SAR Image Change Detection Based on Nonlocal Low-Rank Model and Two-Level Clustering

Change detection (CD) has found a wide range of applications in many fields. In this article, we propose a novel nonlocal low-rank (NLR) based method for multitemporal synthetic aperture radar image CD. This method jointly exploits the powerful NLR-based despeckling and the effective cascade clustering. First, the NLR model is used to generate the difference image (DI), which consists of a patch grouping process and a low-rank minimizing process. Especially, the NLR minimization model contains a data fidelity term, which is based on the statistical distribution of speckle noise, and a regularization term, which uses the weighted nuclear norm. Then, the alternating direction methods of multipliers is introduced to solve this minimization problem. Second, after DI is generated, the principal component analysis is employed to extract the feature and a two-level clustering method is used to generate the final change map, which separates the intermediate class by using the neighbor information with Gaussian weighted distance. Experiment results demonstrate the effectiveness of the proposed method by comparing with some state-of-the-art methods.

Classification of clusters in collision cascades

The structure of defect clusters formed in a displacement cascade plays a significant role in the micro-structural evolution during irradiation. Molecular dynamics simulations have been widely used to study collision cascades and subsequent clustering of defects. We present a novel method to pattern match and classify defect clusters. A cluster is characterized by the geometrical and topological histograms of its angles and distances which can then be used as similarity metrics. The technique is demonstrated by matching similar clusters for different cluster shapes like ring, crowdions etc. in a database of cascade damage configurations in Fe and W at different energies. We further use graph based dimensionality reduction techniques and unsupervised machine learning on the features of all the clusters present in the database to find classes of clusters. The classification successfully separates out many already known categories of clusters such as crowdions, planar crowdion pairs, rings and perpendicular crowdions. The dimensionality and size of different classes provides a broad categorization of classes. The distribution of different classes of shapes among cascades of different elements and energies shows the exclusivity of shapes to elements and energies. We discuss the key points and computational efficiency of the algorithms along with the various prominent results of their application. We discuss the motivation for using machine learning and statistics for the problems and compare different techniques. The algorithms along with the supporting analysis and visualizations give an unsupervised approach for classification and study of defect clusters in cascades. The distribution of cluster shapes and structures along with the shape properties like diffusivity, stability, etc. can be used as input to higher scale models in a multi-scale radiation damage study.

Irregular Travel Groups Detection Based on Cascade Clustering in Urban Subway

Travel smart cards record passengers’ travel histories, which makes it possible to study personal traveling behaviors and passengers’ mobility patterns. The existing researches on smart card data pay less attention to those who beg, steal or busk during traveling, and they are called irregular passengers in this paper. Moreover, the group consisted of irregular passengers is called irregular travel group. In this paper, we propose an approach to recognize irregular travel groups based on cascade clustering. Firstly, passengers’ travel sequences of active state in hours are extracted as representations of travel patterns according to the records of smart cards in a continuous time period. The sequences are clustered with the K-means algorithm to detect irregular passengers. Then, travel similarities between the derived irregular passengers are measured and irregular travel groups are recognized based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. The proposed method is applied to real Beijing subway smart card data, and the results are validated with the data derived from SINA Micro-blogs. Experimental results show that the detected irregular passengers’ movement areas are consistent with the ground truth.

A Many-Objective Evolutionary Algorithm With Two Interacting Processes: Cascade Clustering and Reference Point Incremental Learning

Researches have shown difficulties in obtaining proximity while maintaining diversity for many-objective optimization problems. Complexities of the true Pareto front pose challenges for the reference vector-based algorithms for their insufficient adaptability to the diverse characteristics with no priori. This paper proposes a many-objective optimization algorithm with two interacting processes: cascade clustering and reference point incremental learning (CLIA). In the population selection process based on cascade clustering (CC), using the reference vectors provided by the process based on incremental learning, the nondominated and the dominated individuals are clustered and sorted with different manners in a cascade style and are selected by round-robin for better proximity and diversity. In the reference vector adaptation process based on reference point incremental learning, using the feedbacks from the process based on CC, proper distribution of reference points is gradually obtained by incremental learning. Experimental studies on several benchmark problems show that CLIA is competitive compared with the state-of-the-art algorithms and has impressive efficiency and versatility using only the interactions between the two processes without incurring extra evaluations.

Predicting neutron damage using TEM with in situ ion irradiation and computer modeling

We have constructed a computer model of irradiation defect production closely coordinated with TEM and in situ ion irradiation of Molybdenum at 80 °C over a range of dose, dose rate and foil thickness. We have reexamined our previous ion irradiation data to assign appropriate error and uncertainty based on more recent work. The spatially dependent cascade cluster dynamics model is updated with recent Molecular Dynamics results for cascades in Mo. After a careful assignment of both ion and neutron irradiation dose values in dpa, TEM data are compared for both ion and neutron irradiated Mo from the same source material. Using the computer model of defect formation and evolution based on the in situ ion irradiation of thin foils, the defect microstructure, consisting of densities and sizes of dislocation loops, is predicted for neutron irradiation of bulk material at 80 °C and compared with experiment. Reasonable agreement between model prediction and experimental data demonstrates a promising direction in understanding and predicting neutron damage using a closely coordinated program of in situ ion irradiation experiment and computer simulation.

Cascade Subspace Clustering

In this paper, we recast the subspace clustering as a verification problem. Our idea comes from an assumption that the distribution between a given sample x and cluster centers Omega is invariant to different distance metrics on the manifold, where each distribution is defined as a probability map (i.e. soft-assignment) between x and Omega. To verify this so-called invariance of distribution, we propose a deep learning based subspace clustering method which simultaneously learns a compact representation using a neural network and a clustering assignment by minimizing the discrepancy between pair-wise sample-centers distributions. To the best of our knowledge, this is the first work to reformulate clustering as a verification problem. Moreover, the proposed method is also one of the first several cascade clustering models which jointly learn representation and clustering in end-to-end manner. Extensive experimental results show the effectiveness of our algorithm comparing with 11 state-of-the-art clustering approaches on four data sets regarding to four evaluation metrics.

Formation of Defects in Displacement Cascades in Molybdenum: Simulation of Molecular Dynamics

In this work, the process of the formation of defects and their clusters in collision cascades has been investigated in molybdenum by the molecular dynamics method. We have obtained results on the number of arising defects, the fraction of defects in clusters, and cluster-size distribution. Their dependence on the energy of primary knocked-on atom and temperature of the material has been studied. A comparison with calculations using the SRIM program and NRT model has been made. The effect of annealing defects after the development of cascades on the number of arising defects has been examined.

Gabor Feature Based Unsupervised Change Detection of Multitemporal SAR Images Based on Two-Level Clustering

In this letter, we propose a simple yet effective unsupervised change detection approach for multitemporal synthetic aperture radar images from the perspective of clustering. This approach jointly exploits the robust Gabor wavelet representation and the advanced cascade clustering. First, a log-ratio image is generated from the multitemporal images. Then, to integrate contextual information in the feature extraction process, Gabor wavelets are employed to yield the representation of the log-ratio image at multiple scales and orientations, whose maximum magnitude over all orientations in each scale is concatenated to form the Gabor feature vector. Next, a cascade clustering algorithm is designed in this discriminative feature space by successively combining the first-level fuzzy c-means clustering with the second-level nearest neighbor rule. Finally, the two-level combination of the changed and unchanged results generates the final change map. Experimental results are presented to demonstrate the effectiveness of the proposed approach.

TMREC: A Database of Transcription Factor and MiRNA Regulatory Cascades in Human Diseases.

Over the past decades, studies have reported that the combinatorial regulation of transcription factors (TFs) and microRNAs (miRNAs) is essential for the appropriate execution of biological events and developmental processes. Dysregulations of these regulators often cause diseases. However, there are no available resources on the regulatory cascades of TFs and miRNAs in the context of human diseases. To fulfill this vacancy, we established the TMREC database in this study. First, we integrated curated transcriptional and post-transcriptional regulations to construct the TF and miRNA regulatory network. Next, we identified all linear paths using the Breadth First Search traversal method. Finally, we used known disease-related genes and miRNAs to measure the strength of association between cascades and diseases. Currently, TMREC consists of 74,248 cascades and 25,194 cascade clusters, involving in 412 TFs, 266 miRNAs and 545 diseases. With the expanding of experimental support regulation data, we will regularly update the database. TMREC aims to help experimental biologists to comprehensively analyse gene expression regulation, to understand the aetiology and to predict novel therapeutic targets.TMREC is freely available at http://bioinfo.hrbmu.edu.cn/TMREC/.

ICONE23-1859 RATE THEORY ANALYSIS OF IRRADIATION DAMAGE IN METAL : NEUTRON ENERGY DEPENDENCE

An effort has been made to investigate defect accumulation processes in the reactor structure steel during irradiation. Simultaneous reaction rate equations were constructed on a basis of the reaction rate theory, and solved numerically on a computer to understand such microstructural changes as the formation of voids and dislocation loops in the steel. The effect of cascade cluster formation on defect accumulation processes is especially focused. When the production of cascade clusters is considered, voids and dislocation loops created during irradiation become small in size and their number densities become large, which results in an increase in total volume swelling. The dependence of defect accumulation processes during irradiation on incident neutron energy is discussed, which may lead to an establishment of the irradiation correlation rule.

On the lower energy limit of electronic stopping in simulated collision cascades in Ni, Pd and Pt

We have investigated the effect of the choice of kinetic energy threshold Tc for electronic stopping (Se) in molecular dynamics (MD) simulations of collision cascades. We find that while the choice of Tc has only a negligible effect in low energy cascades, it has a pronounced effect on the fraction of defects found in clusters in high energy cascades. In order to gain insight into the level of energy losses expected in cascades, we have simulated a large number of cascades in Ni, Pd and Pt, with primary knock-on atom (PKA) energies between 30eV and 200keV. Since the energy losses affect the total atomic displacements especially in high energy cascades, we use this measure to determine a reasonable value of Tc. By directly comparing the simulated mixing efficiency to ion beam mixing experiments, we conclude that a threshold of Tc=1eV is too low for simulations of high energy cascades in all materials considered. A value of Tc=10eV is more realistic, although for Pd and Pt, this results in a slight over-prediction of the mixing efficiency.

Localization of Cacna1s to ON Bipolar Dendritic Tips Requires mGluR6-Related Cascade Elements

L-type voltage gated calcium channels in retina localize primarily at the presynaptic active zones of photoreceptors and bipolar cells where they modulate glutamate release. However, the pore forming subunit Cacna1s of certain L-type channels is also expressed postsynaptically at the tips of ON bipolar cell dendrites where it colocalizes with mGluR6, but has an unknown function. At these dendritic tips, the components of the mGluR6 signaling cascade cluster together in a macromolecular complex, and each one's localization often depends on that of the others. Thus, we explored if Cacna1s is part of the mGluR6 complex. We determined Cacna1s expression by PCR using an ON bipolar library, by Western blotting, and by standard immunohistochemistry. The PCR amplification confirmed expression of the transcript in ON bipolar cells, and Western blotting showed the expected bands. Immunostaining for Cacna1s was stronger in the dendritic tips of rod bipolar cells than in those of ON cone bipolar cells. This staining severely decreased in mice missing various mGluR6 cascade elements (Grm6(-/-), Gnao1(-/-), Gnb3(-/-), Gng13(-/-), and Trpm1(-/-)). During development, the ratio of the number of Cacna1s puncta to the number of presynaptic ribbons followed a sigmoidal pattern, rising rapidly from P13 to P17. The mGluR6 expression preceded that of Cacna1s and RGS11. Our results show that the localization and stability of Cacna1s depend on the expression of mGluR6 and its cascade components, and they suggest that Cacna1s is part of the mGluR6 complex. We hypothesize that Cacna1s contributes to light adaptation by permeating calcium.

Rate theory cluster dynamics simulations including spatial correlations within displacement cascades

Mean-field cluster dynamics (CD) using rate theory is a convenient method to simulate the nucleation, growth, and coarsening of clusters under continuous irradiation or ion implantation up to high doses. When displacement cascades are produced, clusters are directly nucleated in cascades, so that creation rates of clusters must be introduced in CD. However, these rates are difficult to determine since CD cannot intrinsically account for spatial correlations between clusters in cascades. In this article a space homogenization method is proposed to determine an effective irradiation source term for CD, based on the knowledge of the primary damage and on a modified kinetic Monte Carlo algorithm. The effective irradiation term is shown to be dynamic, in the sense that it depends on the cluster density in CD simulations. The interest of the method is demonstrated in iron in the case of 20-keV primary knock-on atoms and 60-keV helium implantation.

Influence of impurities on the evolution of vacancy-type defects in neutron-irradiated nickel

In order to investigate the effect of impurities on vacancy defect evolution in nickel, specimens with high (5N) and technical (3N) purity were neutron-irradiated at ∼330K in the IVV-2M reactor (Russia) to fluencies in the range of 1×1021–1×1023n/m2 (E>0.1MeV) corresponding to displacement dose levels in the range of about 0.0001–0.01dpa and subsequently stepwise annealed to about 900K. The specimens of Ni with different purities were characterized both in as-irradiated state as well as after post-irradiation annealing by positron annihilation spectroscopy. The formation of three-dimensional vacancy clusters (3D-VCs) in cascades was observed under neutron irradiation. The density and size of 3D-VCs depended not only on dose level, but also on purity. The population of 3D-VCs in the technical Ni is lower than that in the high-purity Ni. 3D-VCs collapse into secondary-type clusters (stacking fault tetrahedra (SFTs) and vacancy loops) during stepwise annealing at 350–450K (stage III in Ni). The suppression of secondary cluster formation in 3N Ni is attributed to an effective vacancy interaction with impurity carbon atoms, which based on a relatively large vacancy–carbon atom binding energy (0.32–0.35eV). The trapping of vacancies released at the collapse of 3D-VCs by the interstitial impurity atoms dominates at low irradiation dose level (10−4dpa). Thus, we found that carbon impurity atoms have strong effects both on the primary vacancy-type defect aggregation and on the secondary vacancy clusters and complexes formation.