Clustering Solutions Research Articles

Gel‐Derived Amorphous Precursor Enables Homogeneous Pure‐Phase α‐FAPbI3 Films for Efficient and Stable Perovskite Solar Cells

AbstractPure‐phase α‐formamidinium lead iodide (α‐FAPbI3) perovskite solar cells (PSCs) possess potential high efficiency because of suitable bandgap. However, achieving high‐quality α‐FAPbI3 films during the spin‐coating process is challenging without anti‐solvent extraction, due to the spontaneous formation of the more stable δ‐FAPbI3 phase and the complexity of multi‐component perovskite precursor solutions. Here a gel precursor with an amorphous structure to eliminate the need for anti‐solvent extraction in achieving the homogeneous and highly oriented pure‐phase α‐FAPbI3 is devised films. The incorporation of N,N′‐dimethylpropyleneurea, and alkylammonium chloride results in extended iodoplumbate clusters in the perovskite precursor solution. In situ, spectroscopic analysis reveals that the emergence of the stable amorphous gel precursor during the initial solidification stage ensures a uniform solute distribution. This effectively prevents preferentially oriented growth in the crystal phase precursor, leading to a significant enhancement in film homogeneity. As a result, power conversion efficiencies of 24.17% for the PSC, and 19.3% for the module are achieved, along with superior operational stability, retaining 96.1% of their initial efficiency after 850 h at the maximum power point tracking. These demonstrate the best performance for the reported pure‐phase α‐FAPbI3 PSCs without anti‐solvent extraction, showing great promise for scalable manufacture.

Magma Differentiation in Dynamic Mush Domains From the Perspective of Multivariate Statistics: Open‐ Versus Closed‐System Evolution

AbstractOpen‐conduit conditions characterize several of the most hazardous and active volcanic systems of basaltic composition worldwide, persistently refilled by magmatic inputs. Eruptive products with similar bulk compositions, chemically buffered by continual mafic inputs, nevertheless exhibit heterogeneous glass compositions in response to variable magma mixing, crystallization, and differentiation processes within different parts of the plumbing system. Here, we document how multivariate statistics and magma differentiation modeling based on a large data set of glass compositions can be combined to constrain magma differentiation and plumbing system dynamics. Major and trace elements of matrix glasses erupted at Stromboli volcano (Italy) over the last 20 years provide a benchmark against which to test our integrated petrological approach. Principal component analysis, K‐means cluster analysis, and kernel density estimation reveal that trace elements define a multivariate space whose eigenvectors are more readily interpretable in terms of petrological processes than major elements, leading to improved clustering solutions. Comparison between open‐ and closed‐system differentiation models outlines that steady state magma compositions at constantly replenished and erupting magmatic systems approximate simple fractional crystallization trends, due to short magma residence times. Open‐system magma evolution is associated with magma storage crystallinities that are lower than those associated with closed‐system scenarios. Accordingly, open‐system dynamics determine the efficient crystal‐melt separation toward the top of the reservoir, where eruptible melts continuously supply the ordinary activity. Conversely, a mush‐like environment constitutes the bottom of the reservoir, where poorly evolved magmas result from mixing events between mush residual melts and primitive magmas injected from deeper crustal levels.

The least sample size essential for detecting changes in clustering solutions of streaming datasets.

The clustering analysis approach treats multivariate data tuples as objects and groups them into clusters based on their similarities or dissimilarities within the dataset. However, in modern world, a significant volume of data is continuously generated from diverse sources over time. In these dynamic scenarios, the data is not static but continually evolves. Consequently, the interesting patterns and inherent subgroups within the datasets also change and develop over time. The researchers have paid special attention to monitoring changes in cluster solutions of evolving streams. For this matter, several algorithms have been proposed in the literature. However, to date, no study has examined the effect of variability in cluster sizes on the evolution of cluster solutions. Moreover, no guidance is available on determining the impact of cluster sizes on the type of changes they experience in the streams. In the present simulation study using artificial datasets, the evolution of clusters is examined concerning the variability in cluster sizes. The findings are substantial because tracing and monitoring the changes in clustering solutions have a wide range of applications in every field of research. This study determines the minimum sample size required in the clustering of time-stamped datasets.

Balancing exploration and exploitation: Unleashing the adaptive power of automatic cuckoo search for meta-heuristic optimization

Meta-heuristic optimization algorithms are versatile and efficient techniques for solving complex optimization problems. When applied to clustering algorithms, these algorithms offer numerous advantages over traditional optimization methods, including global search capabilities, iterative refinement processes, robustness to initial conditions, and flexibility in handling diverse clustering objectives and constraints. Employing meta-heuristic optimization in clustering algorithms leads to improved accuracy, scalability, robustness, and flexibility in finding optimal or near-optimal clustering solutions. These algorithms generate new individuals iteratively using nature-inspired operations to obtain high-quality results. However, they often suffer from slower convergence and lack guarantees of finding the best solution for every problem, posing ongoing challenges in algorithm development. This study focuses on addressing the issue of premature convergence in metaheuristic algorithms by introducing an automatic cuckoo search (AuCS) algorithm. The AuCS algorithm aims to strike a balance between exploration and exploitation by dynamically updating the step size in each generation, thereby avoiding premature convergence. To evaluate the effectiveness of the proposed algorithm, experiments were conducted on 13 standard benchmark functions and 14 CEC 2005 benchmark functions. In overall performance, AuCS has the best optimum value in 72.22% of cases. This demonstrates the efficacy of the proposed algorithm in achieving improved clustering accuracy and minimizing intra-cluster distance. The proposed AuCS algorithm was applied to data clustering and compared with four swarm optimization algorithms. Here, AuCS outperforms these well-known algorithms in 5 out of 7 datasets. The experimental evaluations in both benchmark functions and clustering problems confirm the promising results of the proposed algorithm, suggesting that AuCS could be considered as a potential improvement over the cuckoo search algorithm.

Complex fuzzy-probabilistic analysis of information on drilling mud losses

In recent years, classification and clustering have been widely used for processing and analyzing information for the purpose of structuring, ordering, summarizing, and sorting. Classification and clustering are used when working with information processes both in enterprises (large and medium-sized) and in various fields of scientific activity, which is especially important in the context of the constant growth of processed information. At the same time, during cluster analysis, an important task is to assess its quality. In this work, cluster analysis was used to identify loss circulation zones when drilling wells and classify them by severity (intensity). To determine the quality of the cluster analysis, the entropy value was calculated, which should tend to a minimum. In our case, it was 0.23, which allows us to judge the fairly high quality of the cluster solution.

Improving cryo-EM grids for amyloid fibrils using interface-active solutions and spectator proteins

Preparation of cryoelectron microscopy (cryo-EM) grids for imaging of amyloid fibrils is notoriously challenging. The human islet amyloid polypeptide (hIAPP) serves as a notable example, as the majority of reported structures have relied on the use of nonphysiological pH buffers, N-terminal tags, and seeding. This highlights the need for more efficient, reproducible methodologies that can elucidate amyloid fibril structures formed under diverse conditions. In this work, we demonstrate that the distribution of fibrils on cryo-EM grids is predominantly determined by the solution composition, which is critical for the stability of thin vitreous ice films. We discover that, among physiological pH buffers, HEPES uniquely enhances the distribution of fibrils on cryo-EM grids and improves the stability of ice layers. This improvement is attributed to direct interactions between HEPES molecules and hIAPP, effectively minimizing the tendency of hIAPP to form dense clusters in solutions and preventing ice nucleation. Furthermore, we provide additional support for the idea that denatured protein monolayers forming at the interface are also capable of eliciting a surfactant-like effect, leading to improved particle coverage. This phenomenon is illustrated by the addition of nonamyloidogenic rat IAPP (rIAPP) to a solution of preaggregated hIAPP just before the freezing process. The resultant grids, supplemented with this “spectator protein”, exhibit notably enhanced coverage and improved ice quality. Unlike conventional surfactants, rIAPP is additionally capable of disentangling the dense clusters formed by hIAPP. By applying the proposed strategies, we have resolved the structure of the dominant hIAPP polymorph, formed in vitro at pH 7.4, to a final resolution of 4 Å. The advances in grid preparation presented in this work hold significant promise for enabling structural determination of amyloid proteins which are particularly resistant to conventional grid preparation techniques.

Enantioselective control in chiral crystallization of ethylenediamine sulfate using optical trapping with circularly polarized laser beams.

In this study, we conducted successful experiments on ethylenediamine sulfate (EDS), an organic compound, to investigate its enantioselectivity in chiral crystallization. We employed optical trapping with circularly polarized laser beams, using a continuous wave laser at 1064nm. By focusing the laser at the air-solution interface of a heavy water-saturated EDS solution, the formation of sub-micrometer-sized chiral EDS crystals was verified. Two generated enantiomorphs (d-crystal and l-crystal) were identified by the rotating analyzer method. The enantioselectivity in the chiral crystallization of EDS was assessed through 30 to 60 times experiments conducted under various conditions of laser powers and polarization modes, utilizing the count of generated crystals for each enantiomorph in the evaluation. Circularly polarized lasers at a specific power created an imbalance in the generation probability of the enantiomorphs, resulting in crystal enantiomeric excess values of 23% and -30%. The enantioselectivity mechanism was explored from two perspectives: refractive index differences of two enantiomorphs and 3D helical optical forces. Study of the thermodynamic mechanism was insufficient to explain the outcomes. Conversely, the 3D helical optical force mechanism revealed that the forces acting on EDS clusters in solution induced helical fluid motion, driving EDS nucleation, with the helicity of fluid motion determining the crystal's chirality. This approach will present new insights into chirality in industrial and research fields, with potential applications in regard to improving optical resolution and addressing the origin of homochirality.

Measuring the similarity between shapes of buildings using graph edit distance

ABSTRACT Building data play a crucial role in geographic information science, and building shape similarity can be used for spatial inquiry, cartographic generalization, map updating, data quality assessment, and other spatial data mining applications. However, conventional shape representation and measurement methods use geometric and statistical measures and may not effectively capture the subtle differences among building shapes. Geospatial artificial intelligence methods often overlook nuanced differences in building contours, posing a challenge when precise fine-grained measurements are required. Furthermore, they have low interpretability. This study proposes a graph edit distance-based method for measuring the similarity between building shapes. It uses geometrics characteristics to construct a graph, matches and associates similar regional features between two building contours, and defines edit costs based on geometric and positional differences that align with the Gestalt principle of visual similarity. It converts the optimal edit path problem to the maximum weighted cluster solution problem and utilizes a heuristic approach to achieve the exact optimal solution within a moderately suitable timeframe. The experimental results demonstrated that the proposed method identifies detailed differences between building contours, providing accurate similarity measurement with high interpretability.

Cluster dynamics of proton irradiated RPV steel at room temperature

The life of reactor pressure vessels in nuclear power plants will be extended to 60 years, while the irradiation data of materials from commercial and experimental reactors are rare and expensive. There is an urgent need to solve how to accurately predict the changes in microstructure and mechanical properties of high-dose irradiated materials of RPV. The cluster dynamics method can be used to establish the relationship between nanostructure and macro properties and to study its evolution over a long period of years. Based on the average rate field theory, a cluster dynamics model was developed for the evolution of irradiated microstructure of RPV steel, which considers both homogeneous and heterogeneous nucleation of solute clusters. The model was successfully used to simulate the influence of the proton irradiation-induced cluster size and number density of RPV model steel with a low copper content at room temperature.

Elastic distributed dataset RDD (Resilient Distributed Datasets)

In recent years, the amount of data to be processed has become larger and larger, and it is difficult for a single processor to handle data, so we need to work hard to develop the expansion mode of cluster mode. Mapreduce, Storm, etc. are a solution for clusters to process data. However, we still have parallel processing, user resource sharing and other issues to be addressed. RDD data model is the key to solve these problems. In this paper, RDD is applied to Spark for detailed expansion, and project experiments are carried out to study the RDD data model. The programming language used in this article is python. The advantage of studying distributed iterative computing is that during the calculation process, the task calculation fails and can be recovered. The default is to allow four failures recoverable. If the stage fails, one can recover, and if the partition fails, one can also recover. Multiple partitions can improve parallelism and efficiency. After a shuffle, a partition failed because the many-to-one relationship needs to be recalculated.

3-D probability density imaging of Euler solutions using gravity data: a case study of Mount Milligan, Canada

Euler deconvolution is a widely used automatic or semi-automatic method for potential field data. However, it yields many spurious solutions that complicate interpretation and must be reduced, eliminated, recognized, or ignored during interpretation. This study proposes a post-processing algorithm that converts Euler solutions produced by tensor Euler deconvolution of gravity data with an unprescribed structural index into probability values (p values) using the B-spline series density estimation (BSS) method. The p values of the Euler solution set form a probability density distribution on the estimation grid. The BSS method relies on the fact that while spurious solutions are sparse and ubiquitous, Euler deconvolution yields many similar or duplicate solutions, which may tightly cluster near real sources. The p values of the Euler solution clusters form multi-layered isosurfaces that can be used to discriminate neighboring target sources because the p values of spurious solutions are vanishingly small, making it simple to remove their interference from the probability density distribution. In all synthetic cases, the geometric outlines of anomaly sources are estimated from probability density isosurfaces approximating synthetic model parameters. The BSS method was then applied to airborne gravity data from Mount Milligan, British Columbia, Canada. Subsequently, results from synthetic models and field data show that the proposed method can successfully localize meaningful geological targets.

A process capability function approach to multiple response surface optimization based on a posterior procedure

Multiple response surface optimization (MRSO) aims to determine a setting of input variables that simultaneously optimizes multiple responses. The process capability function (PCF) approach is an attractive alternative to MRSO because it considers both the mean and variance of the responses systematically through the process capability index. In MRSO, the responses are often in conflict. Thus, the preference information of a decision maker (DM) on the tradeoffs among the responses should be considered. However, the existing PCF methods are based on an unrealistic assumption that all DM preference information is given in advance. In this article, a posterior preference articulation procedure based on the PCF approach, called P-PCF, is proposed. P-PCF first generates nondominated solutions using the ε-constraint method and then allows the DM to select his/her preferred cluster or final solution using the K-means clustering method. The proposed method is illustrated using a well-known MRSO problem.

P455 Deploying machine learning algorithms to stratify patients with inflammatory bowel disease using routinely collected electronic health records

Abstract Background Personalised medicine approaches are eagerly awaited to facilitate individualisation of medical care for patients with inflammatory bowel disease (IBD). Multiple approaches have already been explored in attempts to stratify patients into different prognostic trajectories. In this study we aimed to use machine learning algorithms to cluster patients based on their routinely collected electronic health records in an unsupervised approach. Methods We interrogated the rich electronic health records (EHR) of 619 patients with a diagnosis of IBD requiring an advanced therapy and attending the IBD service at King’s College Hospital (KCH), London, UK. Data for a seven year period (June 2016-May 2023) were included with variables that captured diagnosis, treatments, service utilisation and investigations. Ethical approval was provided following application to the KERRI committee (KCH committee for accessing anonymised HER). In a preliminary analysis, we employed a multi-step approach to analyse patient data, identify significant features through k-prototypes clustering and a combination of parametric and non-parametric statistical tests. We examined different cluster solutions up to six clusters. Results We included 38 quantitative and 32 qualitative variables for 350 patients with a diagnosis of CD and 269 with a diagnosis of UC. Missing data were addressed by imputation by k-NN, mode value and zero. In the context of the unsupervised learning approach, when k=3, key differentiating features included faecal calprotectin levels, body mass index, mean platelet volume, marital status, taking advanced therapy for less than 3 years, and service access (telephone or clinic appointments). Conclusion This first pass analysis suggests that patients who are relatively early in their journey with at least moderately active IBD take up more recourses of the IBD service. This associates also with their social circumstances. Further work will explore these interactions in more detail.

Identifying overcontrol and undercontrol personality types among young people using the five factor model, and the relationship with disordered eating behaviour, anxiety and depression

BackgroundOvercontrol and undercontrol personality types have been associated with an increase in eating pathology, depression and anxiety. The aim of the research was to explore whether latent overcontrol and undercontrol personality types could be identified using cluster analysis of the facets of the five factor model (FFM). We further aimed to understand how these personality types were associated with eating pathology, depressed mood and anxiety.MethodsA total of 561 participants (394 women and 167 men), aged 16–30 years in Australia completed a survey designed to assess disordered eating, FFM personality traits, anxiety, depression and stress. A systematic four-step process using hierarchical, k-means, and random forest cluster analyses were used to identify a meaningful 3-cluster solution.ResultsThe results revealed a cluster solution that represented overcontrol, undercontrol and resilient personality types, and highlighted facets of the FFM that were associated with each type. Both overcontrol and undercontrol personality types were associated with increased clinical symptoms compared to the resilient types.ConclusionsIt was concluded that FFM facets may potentially be more meaningful than broad domains in identifying personality types, and that both overcontrol and undercontrol personality types are likely associated with increased clinical symptoms.

Normalization of direct citations for clustering in publication-level networks: evaluation of six approaches

AbstractClustering of publication networks is an efficient way to obtain classifications of large collections of research publications. Such classifications can be used to, e.g., detect research topics, normalize citation relations, or explore the publication output of a unit. Citation networks can be created using a variety of approaches. Best practices to obtain classifications using clustering have been investigated, in particular the performance of different publication-publication relatedness measures. However, evaluation of different approaches to normalization of citation relations have not been explored to the same extent. In this paper, we evaluate five approaches to normalization of direct citation relations with respect to clustering solution quality in four data sets. A sixth approach is evaluated using no normalization. To assess the quality of clustering solutions, we use three measures. (1) We compare the clustering solution to the reference lists of a set of publications using the Adjusted Rand Index. (2) Using the Silhouette width measure, we quantity to which extent the publications have relations to other clusters than the one they have been assigned to. (3) We propose a measure that captures publications that have probably been inaccurately assigned. The results clearly show that normalization is preferred over unnormalized direct citation relations. Furthermore, the results indicate that the fractional normalization approach, which can be considered the standard approach, causes specific inaccurate assignments, which we intend to address in this paper. The geometric normalization approach has a similar performance as the fractional approach regarding Adjusted Rand Index and Silhouette width but leads to fewer inaccurate assignments at moderate to high granularity levels. We therefore believe that the geometric approach may be preferred over the fractional approach.

Exploring solute behavior and texture selection in magnesium alloys at the atomistic level

This study advances our understanding of how chemical binding and solute distribution impact grain boundary segregation behavior and subsequent annealing texture modification in lean Mg-X-Zn alloys (X = RE or Ca). Notably, differences in Ca and Gd solute behavior at grain boundaries were revealed, where Ca exhibited stronger binding to vacancy sites than Gd, resulting in elevated Ca segregation and an RD-TD-type texture. The introduction of Zn showed significant synergistic effects on solute clustering, with Gd-Zn pairs forming more favorably than Ca-Zn pairs, leading to a strong synergy between Zn and Gd. This promoted their co-segregation and high concentration at the grain boundary, generating a unique TD-spread texture. In contrast, weaker binding in Ca-Zn pairs did not affect Ca segregation but influenced Zn segregation, which underscores the importance of solute binding behavior in alloy design concepts. Additionally, the combined atomic-scale experiments and ab initio predictions provide strong evidence that selective texture development in Mg alloys is tied to heterogeneous solute-boundary interactions, where the sensitivity of the binding energy to volumetric strain affects solute segregation at grain boundaries, resulting in varying grain boundary mobilities and specific texture component growth. It also emphasizes that solute behavior in clustering and segregation is influenced not only by atomic size but also by chemical binding strength with vacancies or co-added Zn.

Coupling Effect of Mn Addition and Deformation on Mechanical and Electrical Properties of Al-Zr Alloys

In order to increase the strength of Al-Zr alloys, which are promisingly used for heat-resistant conductors, the coupling effect of Mn addition (0.16 wt.% and 0.88 wt.%) and deformation on the precipitation, mechanical, and electrical properties of an Al-0.18wt.% Zr alloy was studied using transmission electron microscopy (TEM), atom probe tomography (APT), hardness testing, and electrical conductivity measurement, respectively. Results showed that the Mn addition fully suppresses the Al3Zr precipitation in both hot-deformed and undeformed cases, which is mainly due to a strong Mn-vacancy bonding, in which Mn atoms seize vacancies and hence reduce the available vacancies for Al3Zr nucleation. Minor 0.16 wt.% Mn addition causes a simultaneous decrease in hardness and electrical conductivity, regardless of whether there is deformation. The higher 0.88 wt.% Mn addition, however, significantly increases the hardness by over 40%, especially in combination with deformation. Possible influencing factors such as grain size, dislocations, intergranular/intragranular precipitation, and solute clusters are comparatively discussed in terms of microstructural features and mechanical/electrical properties that are tuned by Mn addition and/or deformation. It is found that the Mn addition can make remarkable contributions to the hardness and thermal stability of the Al-Zr alloys when coupled with deformation.

Soliton cluster solutions of nonlinear Schrödinger equations with variable coefficients in Bessel lattice

The goal of this article is to obtain soliton cluster solutions of (2 + 1)-dimensional nonlinear variable coefficient Schrödinger equations through computerized symbolic computation. By applying the self-similarity method, one soliton solution is constructed for the NLS equations with variable coefficient, which provide a model of the propagation of the soliton waves. Consequently, the solitonary cluster solution is achieved in different structures. Additionally, the propagation of the obtaining solitonary cluster solutions is analyzed and discussed. The results are useful to explain the soliton phenomena in nonlinear optics.

Evolution of waterborne diseases: A case study of Khyber Pakhtunkhwa, Pakistan.

In Pakistan, the degradation of drinking water quality is exacerbated by the increasing population size and rapid industrialization. Contaminated water serves as the predominant source of numerous diseases, including diarrhea, gastroenteritis, and typhoid. This article explores the evolution of waterborne diseases across 21 districts of the Khyber Pakhtunkhwa province in Pakistan by monitoring changes in the clustering solutions. The data employed in this study were sourced from 21 districts of KP by the Director-General Health Services. Cluster analysis was utilized to uncover patterns in waterborne disease incidence, while principal component analysis was employed to reveal underlying patterns and reduce dimensionality. Additionally, the MONItoring Clusters (MONIC) framework was applied for change detection, facilitating the identification of significant shifts in disease patterns over time and aiding in the understanding of temporal dynamics. Our analysis indicates that two clusters survived consistently over time, while other clusters exhibited inconsistency. Profiling of the surviving clusters (C12 → C24 → C32 → C43) suggests a gradual increase in cases of bloody diarrhea in the Swat Valley, Hangu, Karak, and Lakki Marwat regions. Similarly, profiling of the surviving clusters (⊙→ C22 → C34 → C44) suggests an increase in the acute watery diarrhea (non-cholera) and typhoid fever in the regions of Peshawar, Nowshera, and Swabi. The findings of this study hold significant importance as they pinpoint the most vulnerable regions for various waterborne diseases. These insights offer valuable guidance to policymakers and health officials, empowering them to implement effective measures for controlling waterborne diseases in the respective regions of Khyber Pakhtunkhwa, Pakistan.

Discretize Relaxed Solution of Spectral Clustering via a Nonheuristic Algorithm.

Spectral clustering and its extensions usually consist of two steps: 1) constructing a graph and computing the relaxed solution and 2) discretizing relaxed solutions. Although the former has been extensively investigated, the discretization techniques are mainly heuristic methods, e.g., k -means (KM), spectral rotation (SR). Unfortunately, the goal of the existing methods is not to find a discrete solution that minimizes the original objective. In other words, the primary drawback is the neglect of the original objective when computing the discrete solution. Inspired by the first-order optimization algorithms, we propose to develop a first-order term to bridge the original problem and discretization algorithm, which is the first nonheuristic to the best of our knowledge. Since the nonheuristic method is aware of the original graph cut problem, the final discrete solution is more reliable and achieves the preferable loss value. We also theoretically show that the continuous optimum is beneficial to discretization algorithms though simply finding its closest discrete solution is an existing heuristic algorithm which is also unreliable. Sufficient experiments significantly show the superiority of our method.