Clustering Solutions Research Articles

Unsupervised Decision Trees for Axis Unimodal Clustering

The use of decision trees for obtaining and representing clustering solutions is advantageous, due to their interpretability property. We propose a method called Decision Trees for Axis Unimodal Clustering (DTAUC), which constructs unsupervised binary decision trees for clustering by exploiting the concept of unimodality. Unimodality is a key property indicating the grouping behavior of data around a single density mode. Our approach is based on the notion of an axis unimodal cluster: a cluster where all features are unimodal, i.e., the set of values of each feature is unimodal as decided by a unimodality test. The proposed method follows the typical top-down splitting paradigm for building axis-aligned decision trees and aims to partition the initial dataset into axis unimodal clusters by applying thresholding on multimodal features. To determine the decision rule at each node, we propose a criterion that combines unimodality and separation. The method automatically terminates when all clusters are axis unimodal. Unlike typical decision tree methods, DTAUC does not require user-defined hyperparameters, such as maximum tree depth or the minimum number of points per leaf, except for the significance level of the unimodality test. Comparative experimental results on various synthetic and real datasets indicate the effectiveness of our method.

Large-scale gene expression data clustering through incremental ensemble approach

Abstract DNA microarray technology monitors gene activity in real-time in living organisms. It creates a large amount of data that helps scientists learn about how genes work. Clustering this data helps understand gene interactions and uncover important biological processes. However, the traditional clustering techniques have difficulties due to the enormous dimensionality of gene expression data and the intricacy of biological networks. Although ensemble clustering is a viable strategy, such high-dimensional data may not lend itself well to traditional approaches. This study introduces a novel technique for gene expression data clustering called incremental ensemble clustering for gene expression data (IECG). There are two steps in the IECG. A technique for grouping gene expression data into windows is presented in the first step, producing a tree of clusters. This procedure is carried out again for succeeding windows that have distinct feature sets. The base clusterings of two consecutive windows are ensembled using a new goal function to form a new clustering solution. By repeating this step-by-step method for further windows, reliable patterns that are beneficial for medical applications can be extracted. The results from both biological and non-biological data demonstrate that the proposed algorithm outperformed the state-of-the-art algorithms. Additionally, the running time of the proposed algorithm has been examined.

Using Existing Clinical Data to Measure Older Adult Inpatients’ Frailty at Admission and Discharge: Hospital Patient Register Study

Background Frailty is a widespread geriatric syndrome among older adults, including hospitalized older inpatients. Some countries use electronic frailty measurement tools to identify frailty at the primary care level, but this method has rarely been investigated during hospitalization in acute care hospitals. An electronic frailty measurement instrument based on population-based hospital electronic health records could effectively detect frailty, frailty-related problems, and complications as well be a clinical alert. Identifying frailty among older adults using existing patient health data would greatly aid the management and support of frailty identification and could provide a valuable public health instrument without additional costs. Objective We aim to explore a data-driven frailty measurement instrument for older adult inpatients using data routinely collected at hospital admission and discharge. Methods A retrospective electronic patient register study included inpatients aged ≥65 years admitted to and discharged from a public hospital between 2015 and 2017. A dataset of 53,690 hospitalizations was used to customize this data-driven frailty measurement instrument inspired by the Edmonton Frailty Scale developed by Rolfson et al. A 2-step hierarchical cluster procedure was applied to compute e-Frail-CH (Switzerland) scores at hospital admission and discharge. Prevalence, central tendency, comparative, and validation statistics were computed. Results Mean patient age at admission was 78.4 (SD 7.9) years, with more women admitted (28,018/53,690, 52.18%) than men (25,672/53,690, 47.81%). Our 2-step hierarchical clustering approach computed 46,743 inputs of hospital admissions and 47,361 for discharges. Clustering solutions scored from 0.5 to 0.8 on a scale from 0 to 1. Patients considered frail comprised 42.02% (n=19,643) of admissions and 48.23% (n=22,845) of discharges. Within e-Frail-CH’s 0-12 range, a score ≥6 indicated frailty. We found a statistically significant mean e-Frail-CH score change between hospital admission (5.3, SD 2.6) and discharge (5.75, SD 2.7; P<.001). Sensitivity and specificity cut point values were 0.82 and 0.88, respectively. The area under the receiver operating characteristic curve was 0.85. Comparing the e-Frail-CH instrument to the existing Functional Independence Measure (FIM) instrument, FIM scores indicating severe dependence equated to e-Frail-CH scores of ≥9, with a sensitivity and specificity of 0.97 and 0.88, respectively. The area under the receiver operating characteristic curve was 0.92. There was a strong negative association between e-Frail-CH scores at hospital discharge and FIM scores (rs=–0.844; P<.001). Conclusions An electronic frailty measurement instrument was constructed and validated using patient data routinely collected during hospitalization, especially at admission and discharge. The mean e-Frail-CH score was higher at discharge than at admission. The routine calculation of e-Frail-CH scores during hospitalization could provide very useful clinical alerts on the health trajectories of older adults and help select interventions for preventing or mitigating frailty.

Robust Parameter Optimisation of Noise-Tolerant Clustering for DENCLUE Using Differential Evolution

Clustering samples based on similarity remains a significant challenge, especially when the goal is to accurately capture the underlying data clusters of complex arbitrary shapes. Existing density-based clustering techniques are known to be best suited for capturing arbitrarily shaped clusters. However, a key limitation of these methods is the difficulty in automatically finding the optimal set of parameters adapted to dataset characteristics, which becomes even more challenging when the data contain inherent noise. In our recent work, we proposed a Differential Evolution-based DENsity CLUstEring (DE-DENCLUE) to optimise DENCLUE parameters. This study evaluates DE-DENCLUE for its robustness in finding accurate clusters in the presence of noise in the data. DE-DENCLUE performance is compared against three other density-based clustering algorithms—DPC based on weighted local density sequence and nearest neighbour assignment (DPCSA), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Variable Kernel Density Estimation–based DENCLUE (VDENCLUE)—across several datasets (i.e., synthetic and real). The study has consistently shown superior results for DE-DENCLUE compared to other models for most datasets with different noise levels. Clustering quality metrics such as the Silhouette Index (SI), Davies–Bouldin Index (DBI), Adjusted Rand Index (ARI), and Adjusted Mutual Information (AMI) consistently show superior SI, ARI, and AMI values across most datasets at different noise levels. However, in some cases regarding DBI, the DPCSA performed better. In conclusion, the proposed method offers a reliable and noise-resilient clustering solution for complex datasets.

Mechanism Insight into Direct Amidation Catalyzed by Zr Salts: Evidence of Zr Oxo Clusters as Active Species.

The capricious reactivity and speciation of earth-abundant metals (EAM) hinder the mechanistic understanding essential to boost their efficiency and versatility in catalysis. Moreover, metal's solution chemistry and reactivity are conventionally controlled using organic ligands, while their fundamental chemistry in operando conditions is often overlooked. However, in this study, we showcase how a better understanding of in operando conditions may result in improved catalytic reactions. By assessing the composition and structure of active species for Zr-catalyzed direct amide bond formations under operating conditions, we discovered zirconium oxo clusters form quickly and are likely active species in the reactions. Formation of these clusters dismisses the use of additional organic ligands, inert atmosphere, anhydrous solvents, or even water scavenging to provide amides in good to excellent yields. More specifically, dodeca- and hexazirconium oxo clusters (Zr12 and Zr6, respectively) rapidly form from commercial, readily available Zr salts under reaction conditions known to afford amides directly from nonactivated carboxylic acid and amine substrates. Extended X-ray absorption fine structure (EXAFS) experiments confirm the presence of oxo clusters in solution throughout the reaction, while their key role in the mechanism is supported by an in-depth computational study employing density functional theory (DFT) and molecular dynamics (MD) methods. These results underline the value of (earth-abundant) metals' intrinsic solution chemistry to transformative mechanistic understanding and to enhance the sustainability of organic transformations.

The evolution and dissolution mechanism of θ-Al2Cu phase and the analysis of precipitation of a novel AlCu phase during elevated aging

The evolution and dissolution mechanism of the θ-Al2Cu precipitates were investigated in this study by transmission electron microscopy (TEM) during the re-aging at high temperatures for a long time. The results advance the current knowledge of dissolution and its associated mechanisms of the θ-Al2Cu phase in AlCu alloys. In this study, the coarsening and dissolution of θ-Al2Cu phase in T6 heat-treated Al4Cu alloy during various times (0−1000h) of re-aging temperature 420 °C were studied experimentally and theoretically. The morphological characterization revealed that the θ-Al2Cu phases gradually increase in size and decrease in volume density during the aging process, which can be attributed to coarsening and dissolution phenomena occurring during re-aging. Additionally, a body-centered tetragonal AlCu phase is precipitated within the θ-Al2Cu phase during the high temperature re-aging process, which is a characteristic of this phase prior to dissolution. Moreover, the primary mechanisms responsible for the high temperature melting of the θ-Al2Cu phase are the formation of the AlCu phase and the initial melting of the Cu-poor region in the θ-Al2Cu phase. The Cu atoms in θ-Al2Cu phase dissolve and diffuse into the Al matrix, forming L12 ordered AlCu solute clusters through long-range diffusion.

School-to-Work Transition in the Youth Labor Market in Central and Eastern Europe: A Cluster Analysis Approach

Abstract Background This study analyzes education, training, and the youth labor market in central and eastern Europe. Objectives This study aims to evaluate similarities and differences in youth labor markets among eleven central and eastern European countries from 2008 to 2021. It specifically examines three aspects: wage ratios, early departure from education or training, and the share of the population not in employment, education, or training. Methods/Approach This study applies hierarchical clustering and multidimensional scaling to panel data. The complete-link method organizes countries into clusters. This study combines three-dimensional Cartesian projections and two-dimensional projections based on multidimensional scaling with dendrograms and heatmaps, to graphically illustrate the "school-to-work" transition across this region. Results Clustering highlights the Visegrád countries, the Baltics, and the Balkans as zones with internally homogeneous yet externally heterogeneous challenges for the youth generation. As the outliers in each of these regions, Poland, Estonia, and Bulgaria support clustering solutions that deviate from conventional understandings of central and eastern Europe. Conclusions Historical and geographical ties continue to define this region’s youth labor markets across political and economic dimensions. Clustering analysis identifies triumphs and struggles in policymaking in some of the poorest and most politically challenging member-states of the European Union.

Generation-fusion strategy in metal-earth ionosome investigation using single-entity electrochemistry at the micro-water/trifluorotoluene interface

Ionosomes are water clusters/droplets formed in an organic phase via the spontaneous assembly of ionic bilayers by hydrated cations or anions. The ionosomes formed by monovalent ions, i.e., Li+, Na+, K+, Cl−, etc., have been thoroughly investigated. Herein, single nanowater clusters (viz. ionosomes) in organic solutions, which are formed by the transfer of metal earth cations (i.e., Mg2+, Ca2+, Ba2+) into organic lipophilic electrolytes, were discovered using single-entity electrochemistry (SEE). The generation-fusion strategy involving two-step potentiostatic chronoamperometry was applied to investigate single Mg2+-ionosomes at the water/α,α,α-trifluorotoluene (w/TFT) interface. After an exciting potential forced the hydrophilic ion into the organic phase, a train of current spikes with information on the integrated charges, frequency, and duration were recorded to gain insight into the ionosomes. Variates were thoroughly investigated, including the exciting potential, exciting time, recording potential, size of the applied w/TFT interface, ion species, and concentration. These results suggest that: (1) the size of a single Mg2+-ionosome was revealed; (2) the fleshly formed ionosomes are mainly located on the organic side of the w/TFT interfacial surface instead of diffusing into the organic bulk; (3) when the interface decreases, fewer ionosomes form, and the size of the ionosomes decreases; (4) the mean charge carried by a single ionosome is positively related to the charge density of a single cation; (5) by alternating the recording potential, the negative zeta potential of Mg2+-ionosomes was revealed; (6) Mg2+-ionosomes can form when the aqueous solution contains only 2 μmol·L−1 Mg2+. This work thoroughly investigated hydrated metal earth ion clusters in the organic phase via SEE and provides insights into the interface electric double layer from the electrochemistry perspective. In addition to promoting the understanding of ionosomes, this strategy can provide potential applications to identify high-purity water.

Similarity joins and clustering for SPARQL

The SPARQL standard provides operators to retrieve exact matches on data, such as graph patterns, filters and grouping. This work proposes and evaluates two new algebraic operators for SPARQL 1.1 that return similarity-based results instead of exact results. First, a similarity join operator is presented, which brings together similar mappings from two sets of solution mappings. Second, a clustering solution modifier is introduced, which instead of grouping solution mappings according to exact values, brings them together by using similarity criteria. For both cases, a variety of algorithms are proposed and analysed, and use-case queries that showcase the relevance and usefulness of the novel operators are presented. For similarity joins, experimental results are provided by comparing different physical operators over a set of real world queries, as well as comparing our implementation to the closest work found in the literature, DBSimJoin, a PostgreSQL extension that supports similarity joins. For clustering, synthetic queries are designed in order to measure the performance of the different algorithms implemented.

Efficient security interface for high-performance Ceph storage systems

Ceph portrays a resilient clustered storage solution with supporting object, block, and file storage capabilities with no single point of failure. Despite these qualifications, data confidentiality defines a concern in the system, as authentication and access control are the only data protection security services in Ceph. CephArmor was proposed as a third-party security interface to protect data confidentiality by adding an extra protection layer to data at rest. Despite the added layer, the initial design of the API needed to be more efficient in addressing security and performance simultaneously. In this study, we propose a new architectural design to address the associated issues with the preliminary prototype. Comprehensive performance and security analysis verify the improvement of the proposed method compared to the initial approach. The benchmark result has indicated a 37% improvement on average in IOPS, elapsed time, and bandwidth for the write benchmark compared to the initial model.

Normalising sequence lengths using the relative duration of episodes: an application to doctoral trajectories in Germany.

To address significant variation of sequence lengths of doctoral trajectories, we propose sequence normalisation using the relative duration of episodes. We employ episode data from a panel study of doctorate holders in Germany where doctoral trajectories are measured in single months and differ in length up to several years. Utilising normalised sequences instead of absolute sequences, we are better able to identify typical trajectories. The graphical presentation of the cluster solutions more accurately depicts the underlying processes. Furthermore, it offers the possibility to define reference sequences without a fixed length. Normalising sequences instead of distances thus proves an easily implementable method to compare sequences of different lengths when the identification of patterns is a priority.

Silicate-lunar regolith composite: A vacuum self-hardened and comprehensively durable extraterrestrial construction material

Extraterrestrial exploration, represented by Lunar base construction, has been promoted worldwide as a multi-disciplinary, cutting-edge and strategic issue. Herein we proposed a novel extraterrestrial construction material composed of Lunar/Martian regolith simulants and only 4 wt% of silicate adhesive. This silicate-regolith composite can be directly self-hardened in vacuum within 4.5 h without any curing to achieve 29 MPa of hardening strength. Meanwhile, it shows comprehensive durability with stable microstructures and >70 % of residual strength against long-term vacuum exposure, huge temperature difference and intense γ-ray and proton radiations. Its in-situ solidification and durability originate from vacuum dehydration, where silica clusters in solution spontaneously cohere into a solid network with strong connections of silica tetrahedra and bound water. A moderate silicate modulus around 2.5 and appropriate modifications can disperse finer silica clusters to increase oligomer silica units in solution and enhance hardening strength. Compared with lunar geopolymer and other existing extraterrestrial construction materials, this vacuum self-hardening composite allows a cryogenic preparation even below −40 ℃ using diverse regolith with a wide range of compositions and sizes, proposing a facile, durable and versatile solution towards in-situ extraterrestrial constructions.

How precisely are solute clusters in RPV steels characterized by atom probe experiments?

Atom probe tomography (APT) is a powerful microscopy technique to characterize nano-sized clusters of the alloying elements in the bulk of reactor pressure vessel (RPV) steels. These clusters are known to dominantly determine the evolution of mechanical properties under irradiation. The results are conventionally summarized as the overall number density N and the average diameter D of the solute clusters identified in the material. Here, we illustrate that these descriptors are intrinsically imprecise because they are steered by the parameters involved in the measurement and data processing, some of which are directly under the control of the operators, but some others not. Consequently, a direct comparison between data derived at different laboratories is compromised, and key trends such as the evolution with dose, are masked. This study relies on a state-of-the-art physical model for neutron irradiation in steels to make reliable estimates of the microstructure before the measurement is performed, which allows the prediction of the population of solute clusters that are not seen by APT. We mimic APT measurements from simulated microstructures, performing a detailed study of the effects of the parameters of the analysis. We show that the values of N and D reported in the scientific literature can be matched by the predictions of our theoretical model only if specific sets of parameters are used for each laboratory that issued the measurements. We also show that if, on the contrary, all studied cases are analyzed in a consistent way, the scatter of N and D values is reduced. Specifically, we find that the average diameter D is nearly a constant value with dose, independently of the material's chemical compositions, while N increases with dose, but is also influenced by other variables. The approach we developed and used proves to have added value as complement to APT experiments in reactor pressure vessel steels.

Uncovering the Pathway of Serine Octamer Magic Number Cluster Formation during Electrospray Ionization: Experiments and Simulations.

Electrospray ionization (ESI) of serine (Ser) solution generates Ser8H+ as an abundant magic number cluster. ESI clustering of most other solutes yields nonspecific stoichiometries. It is unclear why Ser8H+ dominates in the case of Ser, and how Ser8H+ forms during ESI. Even the location of Ser8H+ formation is contentious (in solution, in ESI droplets, or elsewhere). Here we unravel key aspects of the l-Ser8H+ formation pathway. Harsh ion sampling conditions promote the collision-induced dissociation (CID) of regular ESI analytes. Unexpectedly, Ser8H+ was seemingly resistant against CID during ion sampling, despite its extremely low tandem mass spectrometry (MS/MS) stability. This unusual behavior reveals that Ser8H+ forms during ion sampling. We propose the following pathway: (1) Nonspecific Ser clusters are released when ESI droplets evaporate to dryness. These initial clusters cover a wide size range, from a few Ser to hundreds or thousands of monomers. (2) The clusters undergo dissociation during ion sampling, mostly via successive loss of neutral monomers. For any source activation voltage, there is a subpopulation of clusters for which this CID cascade tends to terminate at the octamer level, culminating in Ser8H+-dominated product distributions. Mobile proton molecular dynamics simulations were used to model the entire pathway. Ser8H+ structures formed in these simulations were consistent with ion mobility experiments. The most compact structures resembled the model of [Scutelnic, V. J. Am. Chem. Soc. 2018, 140, 7554-7560], with numerous intermolecular salt bridges and H-bonds. Our findings illustrate how the interplay of association and dissociation reactions across phase boundaries can culminate in magic number clusters.

On the iron content of Mn-Ni-Si-rich clusters that form in reactor pressure vessel steels during exposure to neutron irradiation

Solute cluster formation during service is the primary cause of embrittlement in reactor pressure vessel (RPV) steels. Atom probe tomography (APT) has consistently shown that the solute clusters are enriched in Cu, P, Mn, Ni, and Si compared to the matrix. However, there is pronounced disagreement within the literature as to the Fe content of the solute clusters in low-Cu RPV steels; some authors report Fe contents in excess of 80 at.% whilst others attribute all measured Fe in the clusters to aberrations from APT and report Fe-free features. This discrepancy has profound implications for determining whether cluster formation is radiation-induced or radiation-enhanced.In this article, we perform a systematic analysis of the published literature to demonstrate the variance present in the measured Fe content of solute clusters characterised by APT. We investigate potential explanatory variables for this variation and discuss the difficulties in performing statistical analyses on these data. To determine the impact of APT aberrations on the measured Fe content of solute clusters, we combine results from: simulations that account for the limited spatial precision in APT; models that predict ion trajectories in the presence of non-uniform electrostatic fields; and experimental data.Our results show that APT aberrations can introduce large amounts of artificial Fe into solute clusters, especially at the small sizes (radius <1.5 nm) typically observed in RPV steels. We also show, however, that some of the variance in Fe content in the literature may be explained by real differences in the clusters’ Fe contents.

Quantitative evaluation of internal cluster validation indices using binary data sets

AbstractAimsDifferent clustering methods often classify the same data set differently. Selecting the “best” clustering solution from alternatives is possible with cluster validation indices. Because of the large variety of cluster validation indices (CVIs), choosing the most suitable index concerning the data set and clustering algorithms is challenging. We aim to assess different internal clustering validation indices.MethodsArtificial binary data sets with equal‐ and unequal‐sized well‐separated a priori clusters were simulated and three levels of noise were then added. Twenty replications of each of the six types of data sets (two group sizes × three levels of noise) were created and analyzed by three clustering algorithms with Jaccard dissimilarity. Twenty‐seven clustering validation indices are evaluated including both geometric and non‐geometric indices.ResultsAlthough, in theory, all CVIs could differentiate between good and wrong classifications, only a few perform as expected with noisy data. Tau and silhouette widths proved to be the best geometric CVIs both for equal and unequal cluster sizes. Among non‐geometric indices, crispness and OptimClass performed best.ConclusionWe recommend using these best‐performing CVIs. We suggest plotting the CVI value against the number of clusters because the lack of a sharp peak means that the position of the maximum is uncertain.

Efficient optimization of robust project scheduling for industry 4.0: A hybrid approach based on machine learning and meta-heuristic algorithms

This research contributes significantly to the domain of Industry 4.0 by offering a nuanced approach to the multi-objective optimization of the resource-constrained project scheduling problem (RCPSP) under uncertainty. Focused on the context of smart product platforming, this study introduces a novel methodology that not only considers traditional factors like time and cost but also incorporates quality and risk aspects, crucial for personalized product fulfillment. In this regard, a comprehensive four-objective mathematical model is proposed to minimize project completion time, total project costs, and project risks while simultaneously enhancing overall project quality. Real-world uncertainty is acknowledged through the incorporation of uncertain parameters for the time, risk, and quality associated with each project activity. To address this uncertainty, a robust optimization method is applied based on Bertsimas and Sim's approach. Moreover, to optimize the proposed model, the Hybrid Red Deer and Genetic Algorithm (HRDGA) is proposed, which is leveraging a machine learning approach for clustering solutions. The numerical results demonstrate that increasing the project budget by 30% leads to an upward trend in total project costs and a reduction in the minimum acceptable quality by 10%–30% results in a decreasing trend in the total project cost. This research emphasizes the adoption of Industry 4.0 enabling technology within the project scheduling platform, particularly highlighting its significance for personalized product fulfillment.

Hybrid raven roosting intelligence framework for enhancing efficiency in data clustering

The field of data exploration relies heavily on clustering techniques to organize vast datasets into meaningful subgroups, offering valuable insights across various domains. Traditional clustering algorithms face limitations in terms of performance, often getting stuck in local minima and struggling with complex datasets of varying shapes and densities. They also require prior knowledge of the number of clusters, which can be a drawback in real-world scenarios. In response to these challenges, we propose the "hybrid raven roosting intelligence framework" (HRIF) algorithm. HRIF draws inspiration from the dynamic behaviors of roosting ravens and computational intelligence. What distinguishes HRIF is its effective capacity to adeptly navigate the clustering landscape, evading local optima and converging toward optimal solutions. An essential enhancement in HRIF is the incorporation of the Gaussian mutation operator, which adds stochasticity to improve exploration and mitigate the risk of local minima. This research presents the development and evaluation of HRIF, showcasing its unique fusion of nature-inspired optimization techniques and computational intelligence. Extensive experiments with diverse benchmark datasets demonstrate HRIF's competitive performance, particularly its capability to handle complex data and avoid local minima, resulting in accurate clustering outcomes. HRIF's adaptability to challenging datasets and its potential to enhance clustering efficiency and solution quality position it as a promising solution in the world of data exploration.

Testing Outlier Detection Algorithms for Identifying Early Stage Solute Clusters in Atom Probe Tomography.

Atom probe tomography (APT) is commonly used to study solute clustering and precipitation in materials. However, standard techniques used to identify and characterize clusters within atom probe data, such as the density-based spatial clustering applications with noise (DBSCAN), often underperform with respect to small clusters. This is a limitation of density-based cluster identification algorithms, due to their dependence on the parameter Nmin, an arbitrary lower limit placed on detectable cluster sizes. Therefore, this article attempts to consider the characterization of clustering in atom probe data as an outlier detection problem of which k-nearest neighbors local outlier factor and learnable unified neighborhood-based anomaly ranking algorithms were tested against a simulated dataset and compared to the standard method. The decision score output of the algorithms was then auto thresholded by the Karcher mean to remove human bias. Each of the major models tested outperforms DBSCAN for cluster sizes of <25 atoms but underperforms for sizes >30 atoms using simulated data. However, the new combined k-nearest neighbors (k-NN) and DBSCAN method presented was able to perform well at all cluster sizes. The combined k-NN and seven methods are presented as a new approach to identifying clusters in APT.

Optimizing the mechanical and corrosion properties of an ultrafine-grained Al-Cu-Mg alloy through cyclic deformation: Clusters and lattice defects

To optimize its corrosion and mechanical properties, an Al-Cu-Mg alloy is cyclically processed by high-pressure torsion (HPT), involving a 1/2 turn clockwise and a 1/2 turn counter-clockwise per cycle. The dislocation density increases to 3.04×1014 m−2 on 1 cycle HPT and decreases gradually to 2.23×1014 m−2 for higher cyclic rotations (i.e. 5 cycle HPT). Due to the rapid elimination of the geometrically necessary dislocations, the refinement of grain size in cyclic HPT is more efficient than the monotonic HPT (i.e. it decreases from 2.48 μm to 112 nm on 1 cycle HPT). The HPT samples are electrochemically corroded in a simulated seawater environment. 1-cycle HPT process causes a strong increase in hardness (to 232 HV) and an increase in corrosion potential (to −0.517 V). The trade-off between hardness/strength and corrosion resistance is broken on cyclic HPT processing. Aided by excess vacancies induced by HPT, Cu-Mg clusters form on and near grain boundaries through a ‘vacancy-pump’ mechanism, causing solute depleted zones (SDZ) adjacent to GBs. The combined effects of ultrafine grains, solute cluster segregation and SDZs enhance the strength and corrosion resistance of the Al-Cu-Mg alloys. This study provides a potential strategy to design the microstructures of an Al alloy, and to achieve a higher hardness and a better corrosion performance of that material.