Cluster Tool Research Articles

Istisna'a Contract as a Financing Tool for Industrial Clusters


 The main objective of this study is to explore how the Istisna’ contract is used as a financing tool for industrial clusters, and how Istisna’ contracts are being used to overcome their problems to increase their effectiveness in the national economy. The study used the descriptive analytical approach to review the scientific literature of industrial clusters and the jurisprudential references to describe the Istisna’ contract and identify its role as a financing tool. As a conclusion, it was being found that Istisna’ contract is a flexible and effective financing tool that could be employed in financing and developing industrial clusters. Also it was recommended that, Islamic Banks adopt financing all components of the industrial clusters through Istisna contract.

Varieties of corona news: a cross-national study on the foundations of online misinformation production during the COVID-19 pandemic.

The online version contains supplementary material available at 10.1007/s42001-022-00193-5.

Single-Cell RNAseq Clustering.

Single-cell RNA sequencing (scRNA-seq) allows the creation of large collections of individual cells transcriptome. Unsupervised clustering is an essential element for the analysis of these data, and it represents the initial step for the identification of different cell types to investigate the cell subpopulation organization of a sample. In this chapter, we describe how to approach the clustering of single-cell RNAseq transcriptomics data using various clustering tools, and we provide some information on the limitations affecting the clustering procedure.

Secuer: Ultrafast, scalable and accurate clustering of single-cell RNA-seq data.

Identifying cell clusters is a critical step for single-cell transcriptomics study. Despite the numerous clustering tools developed recently, the rapid growth of scRNA-seq volumes prompts for a more (computationally) efficient clustering method. Here, we introduce Secuer, a Scalable and Efficient speCtral clUstERing algorithm for scRNA-seq data. By employing an anchor-based bipartite graph representation algorithm, Secuer enjoys reduced runtime and memory usage over one order of magnitude for datasets with more than 1 million cells. Meanwhile, Secuer also achieves better or comparable accuracy than competing methods in small and moderate benchmark datasets. Furthermore, we showcase that Secuer can also serve as a building block for a new consensus clustering method, Secuer-consensus, which again improves the runtime and scalability of state-of-the-art consensus clustering methods while also maintaining the accuracy. Overall, Secuer is a versatile, accurate, and scalable clustering framework suitable for small to ultra-large single-cell clustering tasks.

23 Identifying HPV vaccination initiation hot and cold spots and associated disparities in Texas

Over 90% of HPV-associated cancers are preventable through HPV vaccination. Only 43.5% of Texas adolescents, however, have completed the vaccine series, and the state ranks 47th in vaccination rates across the country. The goal of this study was to examine the geospatial uptake of the HPV vaccine in Texas and the associated disparities. HPV vaccine data from 2017–2021 was obtained from the Texas Department of State Health Services. Zip code-level estimates of individuals 9 years and older who received at least one HPV vaccine since 2017 in the Texas Immunization Information System (ImmTrac2) database were used to create a choropleth map. Through the Environmental Science Research Institute's (ESRI) ArcGIS Pro Version 2.2.0 software, the Cluster and Outlier Analysis Tool was used to determine a “neighborhood” around each zip code. Zip codes were subsequently demarcated as hot or cold spots (or neither) based off Local Moran's I score calculations, meaning they had a statistically significant greater or lesser concentration of vaccinations than would be expected from a random distribution. After identifying hot spots and cold spots of vaccine initiation, we explored the demographic neighborhood characteristics. 346 statistically significant hot and 374 cold spot zip codes of HPV vaccine initiation were identified (Fig. 1). The remainder of the zip codes (1115) were outliers or not part of statistically significant clusters. Hot spot zip codes, or areas of high vaccine initiation, were found along the Gulf coast, in large cities, and in less developed areas near the Texas panhandle. Cold spot zip codes were in the northern Texas panhandle, along the Texas–Louisiana border, and on the periphery of major cities. Compared to cold spot zips, hot spots had significantly higher percentages of children on public insurance, had higher poverty rates, and had higher percentages of African American and Hispanic residents.Download : Download high-res image (217KB)Download : Download full-size image In Texas, areas with higher poverty rates, increased use of public insurance, and higher percentages of minority populations had significantly higher rates of HPV vaccine initiation. This may be due to differences in patient interest in the vaccine, to vaccine access through government insurance, or to outreach efforts that have promoted the vaccine in especially vulnerable populations.

A GIS Approach for Revealing Urban Mobility Hotspots: A Case Study of Amman City in Jordan

Transportation management is an essential aspect of sustainable city development, as transportation networks serve humans in their daily activities. Traffic demand is an important input for transportation planning considering the population growth and travel activities increase, which have produced more traffic congestion throughout the years. Normally, traffic congestions are not uniformly distributed across the city, but rather are concentrated in particular locations. These locations are known as hotspots. We developed a framework for studying urban spatiotemporal characteristics of human mobility behavior within Amman city, where a large amount of trajectory data were processed to extract their pick-up and drop-off points. The available data, which was recorded between May and September 2021, is sorted out in an accumulative way to represent the working days of the week. Travel demand was illustrated through a temporal data representation per day interval as the day was divided into three intervals to further understand mobility behavior throughout the day. Then, the Kernel Density Estimation algorithm (KDE) was applied to derive and categorize different significant hotspots that were visually represented using GIS Software as appropriate spatial clustering tools to produce heatmaps. Finally, the correlation between hotspots and their drop-off points was analyzed to understand the factors influencing the mobility pattern such as day intervals and community structure in terms of residential or commercial. The results enable us to build several strategies and recommendations for transportation planners as well as taxi operators.

How does a combination of numerical modeling, clustering, artificial intelligence, and evolutionary algorithms perform to predict regional groundwater levels?

The prediction of groundwater levels in arid and semi-arid regions is of great importance to tailor the best water management strategies. In this study, we propose a new approach that combines simulation, clustering, and optimization tools for groundwater level prediction. This approach simulates groundwater levels (GWL) using the MODFLOW method, clusters the study aquifer into different clusters using the k-mean method, and predicts regional GWL using the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methods that were optimized by the Harris Hawks Optimization (HHO), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). The efficacy of our approach was evaluated via a case study in northwest Iran. The MODFLOW method simulated the distribution of GWL across the study area with R2 = 0.99, root mean square error (RMSE) = 0.97 m (m) and mean absolute error (MAE) = 0.82 m. The k-means method clustered the aquifer into seven clusters based on the hydraulic conductivity, storage coefficient, groundwater level, groundwater depth, groundwater withdrawal, and aquifer saturation thickness parameters. The prediction of groundwater level for each cluster demonstrated the accurate performance of all optimized models with mean RMSE = 0.6 m and mean absolute percentage error (MAPE) = 0.23 m. The prediction phase identified groundwater level in the previous month (obtained from the MODFLOW method), withdrawal of aquifer, precipitation, temperature, and evaporation as the most influential variables for groundwater levels in different clusters. We recommend the methodology proposed here for the prediction of groundwater levels in different aquifers with heterogeneous characteristics that pose computational burdens and uncertainties.

Scheduling of time-constrained single-arm cluster tools with purge operations in wafer fabrications

Purge operations are widely used to remove residual chemicals and impurities in chambers of wafer fabrication systems to ensure wafer quality. However, such operations lead to more start-up transient processes and wafer delays. In addition, wafer residency time constraints must be met to ensure the wafer quality. Thus, the system scheduling problem with such considerations will become more complicated. This work aims to optimize the start-up transient process of a single-arm cluster tool with purge operations and wafer residency time constraints by using resource-oriented Petri net (ROPN) models. Based on the models, we dynamically depict the behavior of the steady state and start-up transient processes of a single-arm cluster tool with purge operations. Then, a scheduling method that dynamically adjusts the robot waiting time is proposed to find an optimal schedule for such systems with purge operations and wafer residency time constraints. Finally, some examples are tested to illustrate the effectiveness of our method.

Scenarios as collaborative tool empowering the policymaking process. The case of MedTech sector in Europe

In the paper, we analyse trends that impact the transition of the MedTech clusters in Europe to formulate growth scenarios and propose associated policy measures addressing the MedTech ecosystems while on their way to supporting Sustainable Development Goals. The study directly builds on the lessons learned within the S3martMed alliance, grouping the renowned European cluster organisations in MedTech. The research-oriented objectives of the paper are (i) to present the scenario method as a collaborative, knowledge-enabling tool for clusters and cluster policies and (ii) to identify contemporary scenarios for the MedTech sector willing to add value to transition paths of Regional Innovation Systems. Thus, the presented method and the paper itself are not retrospective but future-oriented to envisage new opportunities for SMEs, business support organisations, RTOs, and the users of MedTech products and services. The paper posits that sustainable ecosystems can be fostered by collaborative achievements of MedTech stakeholders on the condition that they keep on foreseeing the external driving forces to help policymakers to converge them into evidence-based policies.

Cluster Predictive Model Using Affinity Propagation Algorithm to Group Mushroom 5.8s rRNA Sequences

Background: The main emphasis of the article is biological information on a distinct species of mushroom (Phylum Basidiomycota) data collection of 5.8s rRNA sequences. Macrofungi from the phylum Basidiomycota are predominantly used as therapeutic mushrooms in several countries. During the rainy season, hundreds of macrofungal basidiocarps were discovered in Tamilnadu. The internal transcribed spacer (ITS) and 5.8S rRNA gene sequence markers, which have been collected from NCBI, were used to isolate at least thirty of these strains that fall under the Basidiomycota kingdom (suborders of Polyporales, Hymenochataeles, and Russuales), which have the therapeutic properties of the Basidiomycota kingdom. Objectives: This article’s main objective is to organise the sequences according to similarity utilising multiple sequence alignment and an algorithmic perspective. Methods: In this paper, we use 30  30 pairwise similarity matrix data of these thirty 5.8s rRNA mushroom sequences obtained using the clustal omega tool to develop an affinity propagation approach. As a continuation of earlier work, this will be evaluated against k-means, hierarchical clustering based on the ideal cluster, and time and space complexity. Findings: The affinity propagation algorithm typically discourages providing the initial number of clusters; therefore, the optimal number of cluster values and grouping of clustered results obtained from the affinity propagation algorithm are also the same as the results obtained from the previous existing research work using the kmeans, hierarchical agglomerative clustering algorithm. Novelty: The overall suggested technique involves applying the cluster validation metrics Silhouette score, Calinski-Harabasz Index, and Davies-Bouldin Index methodologies to find the ideal number of clusters. The CD-hit Clustering tool does not offer these metrics, and the Cluster Omega tool does not support this kind of extension work. This follow-up work assists bioinformatics researchers in obtaining favourable results by utilising the existing software prior to working in wet laboratories; rather than wasting a lot of chemical resources, this result will open the door for a targeted approach. Keywords: Affinity propagation; Cluster metrics; Kmeans; mushroom sequences; Bioinformatics; Data science; Silhouette score; Calinski-Harabasz index; Davies-Bouldin index

ScGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data.

Gene expression imputation has been an essential step of the single-cell RNA-Seq data analysis workflow. Among several deep-learning methods, the debut of scGNN gained substantial recognition in 2021 for its superior performance and the ability to produce a cell-cell graph. However, the implementation of scGNN was relatively time-consuming and its performance could still be optimized. The implementation of scGNN 2.0 is significantly faster than scGNN thanks to a simplified close-loop architecture. For all eight datasets, cell clustering performance was increased by 85.02% on average in terms of adjusted rand index, and the imputation Median L1 Error was reduced by 67.94% on average. With the built-in visualizations, users can quickly assess the imputation and cell clustering results, compare against benchmarks and interpret the cell-cell interaction. The expanded input and output formats also pave the way for custom workflows that integrate scGNN 2.0 with other scRNA-Seq toolkits on both Python and R platforms. scGNN 2.0 is implemented in Python (as of version 3.8) with the source code available at https://github.com/OSU-BMBL/scGNN2.0. Supplementary data are available at Bioinformatics online.

HOW AI-SUPPORTED SEARCHES THROUGH OTHER PERSPECTIVES AFFECT IDEATION OUTCOMES

Seeking inspiration from other perspectives is a prominent mechanism to support ideation. AI-based language models can help overcome information processing limits and efficiently structure large solution spaces spanned by prior ideas. However, it remains unclear how the search through a solution space affects the subsequent idea generation. This study explores the influence of different sets of prior idea stimuli pre-structured by an AI-supported clustering on ideation outcomes. The sets varied in quantity and semantic diversity. In a survey experiment, 181 participants generated 447 ideas evaluated according to major idea performance characteristics. Results indicate that seeing an extensive set of ideas from various clusters improves idea novelty and positively and semantic diversity. In a survey experiment, 181 participants generated 447 ideas evaluated according to major idea performance characteristics. Results indicate that seeing an extensive set of ideas from various clusters improves idea novelty and positively interacts with domain-specific knowledge. However, it negatively affects idea feasibility and specificity. These findings encourage innovators seeking particularly novel ideas to complement their current processes with AI-supported clustering tools while taking steps to avoid vagueness.

VIZPLAN: A VISUAL ANALYTICS PLATFORM FOR THE ASSESSMENT OF MULTIDIMENSIONAL INDICATORS OVER TIME

Abstract. In this paper, we introduce VizPlan, a new platform to support the assessment of multidimensional indicators over time. VizPlan includes a visualisation scheme based on a radial visual structure that allows the direct comparison of indicator values over time, a search tool to support the identification of entities whose indicators are similar to each other, and a clustering tool to group entities according to their indicator scores. VizPlan was designed and implemented to be flexible; it can be easily tailored to the visualization and analysis of any multidimensional temporal data. In this paper, the use of VizPlan is illustrated in the context of three case studies concerning the analysis of sustainability indicators to support urban planning: key performance indicators related to the sustainable development goals, walkability analysis, and bus service availability assessment. All case studies refer to real data related to Norwegian cities, especially Ålesund. VizPlan is available as an open source software at https://github.com/Rylern/VizPlan – As of May 2022.

Cadastre Typology as a Baseline for Incremental Improvement of Spatial Cadastre in Jakarta: Towards a Complete Cadastre

Improving the quality of spatial cadastre remains a challenge in Indonesia. The lack of data quality impacts the legal uncertainty of land rights and the inequality of control, ownership, use, and utilization of land. This study discusses the efforts that can be made to achieve an accurate, assured, and authoritative spatial cadastre by referring to cadastral regulations in Indonesia, especially in urban areas. This research focuses on three spatial elements: boundary determination survey, measurement methods, and base map accuracy in order to assess systematic and sporadic registration activities previously conducted in two sub-districts in North Jakarta. The areas are located in a compacted urban area that consists of 19,173 land parcels as a research sample. A multivariate clustering tool is used to analyze the grouping of land parcels into a cadastral typology (comply/not comply). This study indicates that the level of compliance of the land parcel maps against three spatial elements are the following: (1) the compliance to boundary determination survey by 100%; (2) the compliance to measurement method by 17.36%; and (3) the compliance to base map accuracy by 0%. This paper explains how the cadastre typology can be used as an indicator of compliance as well as a baseline to improve the quality of spatial data.

A model-based constrained deep learning clustering approach for spatially resolved single-cell data.

Spatially resolved scRNA-seq (sp-scRNA-seq) technologies provide the potential to comprehensively profile gene expression patterns in tissue context. However, the development of computational methods lags behind the advances in these technologies, which limits the fulfillment of their potential. In this study, we develop a deep learning approach for clustering sp-scRNA-seq data, named Deep Spatially constrained Single-cell Clustering (DSSC). In this model, we integrate the spatial information of cells into the clustering process in two steps: (1) the spatial information is encoded by using a graphical neural network model, and (2) cell-to-cell constraints are built based on the spatial expression pattern of the marker genes and added in the model to guide the clustering process. Then, a deep embedding clustering is performed on the bottleneck layer of autoencoder by Kullback-Leibler (KL) divergence along with the learning of feature representation. DSSC is the first model that can use information from both spatial coordinates and marker genes to guide cell/spot clustering. Extensive experiments on both simulated and real data sets show that DSSC boosts clustering performance significantly compared with the state-of-the-art methods. It has robust performance across different data sets with various cell type/tissue organization and/or cell type/tissue spatial dependency. We conclude that DSSC is a promising tool for clustering sp-scRNA-seq data.

Zero-phase angle asteroid taxonomy classification using unsupervised machine learning algorithms

Context. We are in an era of large catalogs and, thus, statistical analysis tools for large data sets, such as machine learning, play a fundamental role. One example of such a survey is the Sloan Moving Object Catalog (MOC), which lists the astrometric and photometric information of all moving objects captured by the Sloan field of view. One great advantage of this telescope is represented by its set of five filters, allowing for taxonomic analysis of asteroids by studying their colors. However, until now, the color variation produced by the change of phase angle of the object has not been taken into account. Aims. In this paper, we address this issue by using absolute magnitudes for classification. We aim to produce a new taxonomic classification of asteroids based on their magnitudes that is unaffected by variations caused by the change in phase angle. Methods. We selected 9481 asteroids with absolute magnitudes of Hg, Hi and Hz, computed from the Sloan Moving Objects Catalog using the HG*12 system. We calculated the absolute colors with them. To perform the taxonomic classification, we applied a unsupervised machine learning algorithm known as fuzzy C-means. This is a useful soft clustering tool for working with data sets where the different groups are not completely separated and there are regions of overlap between them. We have chosen to work with the four main taxonomic complexes, C, S, X, and V, as they comprise most of the known spectral characteristics. Results. We classified a total of 6329 asteroids with more than 60% probability of belonging to the assigned taxonomic class, with 162 of these objects having been characterized by an ambiguous classification in the past. By analyzing the sample obtained in the plane Semimajor axis versus inclination, we identified 15 new V-type asteroid candidates outside the Vesta family region.

Feature Clustering of Noisy Data and Application in the Currency Market

With the increase in high-dimensional data, researchers pay more attention to dimensionality reduction techniques because there are many noisy, redundant and irrelevant features in high-dimensional data. The existence of noisy features leads to decrease performance when analyzing high-dimensional data. Also, unsupervised dimensionality reduction techniques are widely used due to the lack of available labels. Feature clustering is an unsupervised dimensionality reduction technique to partition features into clusters in which features are strongly related. In addition, the Pearson correlation coefficient is widely used as a similarity tool for feature clustering. However, the Pearson correlation coefficient is easily influenced by outliers and noises, thus leading to misleading results. This paper focuses on the influence of dissimilarity measures on the clustering of noisy features. Heavy-tailed distributions are used for modeling data with outliers and noises. Therefore, we introduce a new dissimilarity measure based on a new dependence coefficient of heavy-tailed distributions. The performance of feature clustering using the proposed dissimilarity is evaluated using ARI and internal criteria on artificial and real currency market datasets. Experiment results have demonstrated the effectiveness of the proposed feature clustering method.

Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model.

Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments.

Variational inference and sparsity in high-dimensional deep Gaussian mixture models

Gaussian mixture models are a popular tool for model-based clustering, and mixtures of factor analyzers are Gaussian mixture models having parsimonious factor covariance structure for mixture components. There are several recent extensions of mixture of factor analyzers to deep mixtures, where the Gaussian model for the latent factors is replaced by a mixture of factor analyzers. This construction can be iterated to obtain a model with many layers. These deep models are challenging to fit, and we consider Bayesian inference using sparsity priors to further regularize the estimation. A scalable natural gradient variational inference algorithm is developed for fitting the model, and we suggest computationally efficient approaches to the architecture choice using overfitted mixtures where unnecessary components drop out in the estimation. In a number of simulated and two real examples, we demonstrate the versatility of our approach for high-dimensional problems, and demonstrate that the use of sparsity inducing priors can be helpful for obtaining improved clustering results.

Constrained Fourier estimation of short-term time-series gene expression data reduces noise and improves clustering and gene regulatory network predictions

BackgroundBiological data suffers from noise that is inherent in the measurements. This is particularly true for time-series gene expression measurements. Nevertheless, in order to to explore cellular dynamics, scientists employ such noisy measurements in predictive and clustering tools. However, noisy data can not only obscure the genes temporal patterns, but applying predictive and clustering tools on noisy data may yield inconsistent, and potentially incorrect, results.ResultsTo reduce the noise of short-term (< 48 h) time-series expression data, we relied on the three basic temporal patterns of gene expression: waves, impulses and sustained responses. We constrained the estimation of the true signals to these patterns by estimating the parameters of first and second-order Fourier functions and using the nonlinear least-squares trust-region optimization technique. Our approach lowered the noise in at least 85% of synthetic time-series expression data, significantly more than the spline method (p<10^{-6}). When the data contained a higher signal-to-noise ratio, our method allowed downstream network component analyses to calculate consistent and accurate predictions, particularly when the noise variance was high. Conversely, these tools led to erroneous results from untreated noisy data. Our results suggest that at least 5–7 time points are required to efficiently de-noise logarithmic scaled time-series expression data. Investing in sampling additional time points provides little benefit to clustering and prediction accuracy.ConclusionsOur constrained Fourier de-noising method helps to cluster noisy gene expression and interpret dynamic gene networks more accurately. The benefit of noise reduction is large and can constitute the difference between a successful application and a failing one.