Cluster Tool Research Articles

Enhanced drift self-calibration of low-cost sensor networks based on cluster and advanced statistical tools

The escalating use of low-cost sensors in environmental monitoring demands effective management of sensor drift and errors. To tackle this challenge, lightweight calibration techniques are essential for accurate sensor readings. This paper presents a novel calibration method for low-cost sensors, prioritizing the detection and correction of sensor drifts. The approach incorporates clustering for efficient task distribution and leverages Inverse Distance Weighting (IDW) for calibration precision. Also, advanced statistical tools, including the Two-Sample Kolmogorov–Smirnov test (TSKS test) and Exponential Moving Average (EMA), assess and enhance sensor data stability based on historical measurements. Additionally, the Root Update Estimator (RUE) is utilized to adjust predicted values and improve the model’s adaptability to changes in the underlying data distribution. Extensive simulation experiments using an Intel Berkeley Research Laboratory (IBRL) dataset affirm the method’s effectiveness in swiftly addressing sensor drifts. These findings advance low-cost sensor calibration, boosting reliability and precision in environmental monitoring.

Cycle time analysis for cluster tools with parallel process chambers, processing time variation, and chamber cleaning operations

Cluster tools are the key equipment in semiconductor manufacturing. For high-end chip manufacturing processes, to ensure the wafer quality, periodical cleaning operations for process chambers in such tools are required for eliminating the residual gas and chemicals. In operating a tool, the wafer processing time in a process chamber may vary within a range. It is meaningful to predict the cycle time of cluster tools with chamber cleaning operations and processing time variation. By doing so, automation material handling system can be told when a lot is completed by a cluster tool such that it can assign an overhead hoist transporter to transport this lot at the right time. This plays an important role in improving the productivity of a whole semiconductor manufacturing system. This work conducts the cycle time analysis of cluster tools with chamber cleaning operations and processing time variation. Specifically, it proposes a novel method to calculate the average cycle time of a single-arm cluster tool under which an optimal schedule can be obtained. Then, for a dual-arm cluster tool, an efficient algorithm is developed to approximate the average system cycle time under which an optimal schedule can be obtained. Experiments show that the gap between the average system cycle time obtained by simulations and the average system cycle time obtained by the proposed method is no more than 0.1% which demonstrate the effectiveness of the proposed method.

An innovative two-stage machine learning-based adaptive robust unit commitment strategy for addressing uncertainty in renewable energy systems

Confronting the challenge of intermittent renewables, current unit commitment practices falter, urging the development of novel short-term generation scheduling techniques for enhanced microgrid stability. This study presents an adaptive robust unit commitment approach using machine learning techniques for renewable power systems, computing the Calinski-Harabasz index to identify prediction inaccuracies related to intermittent sources. The uncertainties are subsequently grouped together using the spatial clustering tool, and the average density of the K-means distribution is calculated. The clustering of points in space, considering noise, discrete uncertainty in renewable energy sources, and outliers within the comprehensive uncertainty set, is addressed via a nonparametric algorithm. The implementation of established methodologies and frameworks, in conjunction with density-based spatial clustering of applications with noise, introduces an innovative method for vulnerability clustering. This methodology guarantees that every cluster aligns with data pertaining to vulnerabilities of renewable energy sources. The performance of the suggested method is showcased by conducting experiments on modified IEEE 39-bus and 118-bus test systems that use intermittentwindpower. The results demonstrate that the proposed framework may lower the cost of robustness by 8–48% compared to traditional robust optimization techniques. The results of stochastic programming showed that the optimized system with a stable economic organization would have 75 % faster calculations.

Genetic Diversity of Rhipicephalus (Boophilus) microplus for a Global Scenario: A Comprehensive Review.

Rhipicephalus microplus poses a substantial threat to livestock health and agricultural economies worldwide. Its remarkable adaptability to diverse environments and hosts is a testament to its extensive genetic diversity. This review delves into the genetic diversity of R. microplus, employing three pivotal genetic markers: the cytochrome c oxidase I (COX1) gene, ribosomal genes, and microsatellites. The COX1 gene, a crucial tool for genetic characterization and phylogenetic clustering, provides insights into the adaptability of ticks. Ribosomal genes, such as internal transcribed spacer regions (ITS-1 and2) as well as 18S and 28S, are routinely utilized for species differentiation. However, their use is limited due to indels (insertions and deletions). Microsatellites and minisatellites, known for their high polymorphism, have been successfully employed to study populations and genetic diversity across various tick species. Despite their effectiveness, challenges such as null alleles and marker variations warrant careful consideration. Bm86, a well-studied vaccine candidate, exhibits substantial genetic diversity. This diversity directly influences vaccine efficacy, posing challenges for developing a universally effective Bm86-based vaccine. Moreover, the review emphasizes the prevalence of genes associated with synthetic pyrethroid resistance. Identifying single nucleotide polymorphisms in the acaricide-resistant genes of R. microplus has facilitated the development of molecular markers for detecting and monitoring resistance against synthetic pyrethroids. However, mutations in sodium channels, the target site for synthetic pyrethroid, correlate well with the resistance status of R. microplus, which is not the case with other acaricide target genes. This study underscores the importance of understanding genetic diversity in developing effective tick management strategies. The choice of genetic marker should be tailored based on the level of taxonomic resolution and the group of ticks under investigation. A holistic approach combining multiple markers and integrating additional molecular and morphological data may offer a more comprehensive understanding of tick diversity and relationships. This research has far-reaching implications in formulating breeding programs and the development of vaccine against ticks and tick-borne diseases (TTBDs) as well as strategies for the management of resistant ticks.

Wafer Delay Minimization in Scheduling Single-Arm Cluster Tools with Two-Space Process Modules

Wafer Delay Minimization in Scheduling Single-Arm Cluster Tools with Two-Space Process Modules

Clustering and visualization of single-cell RNA-seq data using path metrics.

Recent advances in single-cell technologies have enabled high-resolution characterization of tissue and cancer compositions. Although numerous tools for dimension reduction and clustering are available for single-cell data analyses, these methods often fail to simultaneously preserve local cluster structure and global data geometry. To address these challenges, we developed a novel analyses framework, Single-Cell Path Metrics Profiling (scPMP), using power-weighted path metrics, which measure distances between cells in a data-driven way. Unlike Euclidean distance and other commonly used distance metrics, path metrics are density sensitive and respect the underlying data geometry. By combining path metrics with multidimensional scaling, a low dimensional embedding of the data is obtained which preserves both the global data geometry and cluster structure. We evaluate the method both for clustering quality and geometric fidelity, and it outperforms current scRNAseq clustering algorithms on a wide range of benchmarking data sets.

Graph-Aligned Random Partition Model (GARP)

Bayesian nonparametric mixtures and random partition models are powerful tools for probabilistic clustering. However, standard independent mixture models can be restrictive in some applications such as inference on cell lineage due to the biological relations of the clusters. The increasing availability of large genomic data requires new statistical tools to perform model-based clustering and infer the relationship between homogeneous subgroups of units. Motivated by single-cell RNA data we develop a novel dependent mixture model to jointly perform cluster analysis and align the clusters on a graph. Our flexible graph-aligned random partition model (GARP) exploits Gibbs-type priors as building blocks, allowing us to derive analytical results for the probability mass function (pmf) on the graph-aligned random partition. We derive a generalization of the Chinese restaurant process from the pmf and a related efficient and neat MCMC algorithm to implement Bayesian inference. We illustrate posterior inference under the GARP using single-cell RNA-seq data from mice stem cells. We further investigate the performance of the model in recovering the underlying clustering structure as well as the underlying graph by means of simulation studies. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Next-generation IEDB tools: a platform for epitope prediction and analysis.

The Next-Generation (NG) IEDB Tools website (https://nextgen-tools.iedb.org) provides users with a redesigned interface to many of the algorithms for epitope prediction and analysis that were originally released on the legacy IEDB Tools website. The initial release focuses on consolidation of all tools related to HLA class I epitopes (MHC binding, elution, immunogenicity, and processing), making all of these predictions accessible from a single application and allowing for their simultaneous execution with minimal user inputs. Additionally, the PEPMatch tool for identifying highly similar epitopes in a set of curated proteomes, as well as a tool for epitope clustering, are available on the site. The NG Tools site allows users to build data pipelines by sending the output of one tool as input for the next. Over the next several years, all pre-existing IEDB Tools, and any newly developed tools, will be integrated into this new site. Here we describe the philosophy behind the redesign and demonstrate the utility and productivity enhancements that are enabled by the new interface.

Designing meaningful continuous representations of T cell receptor sequences with deep generative models

T Cell Receptor (TCR) antigen binding underlies a key mechanism of the adaptive immune response yet the vast diversity of TCRs and the complexity of protein interactions limits our ability to build useful low dimensional representations of TCRs. To address the current limitations in TCR analysis we develop a capacity-controlled disentangling variational autoencoder trained using a dataset of approximately 100 million TCR sequences, that we name TCR-VALID. We design TCR-VALID such that the model representations are low-dimensional, continuous, disentangled, and sufficiently informative to provide high-quality TCR sequence de novo generation. We thoroughly quantify these properties of the representations, providing a framework for future protein representation learning in low dimensions. The continuity of TCR-VALID representations allows fast and accurate TCR clustering and is benchmarked against other state-of-the-art TCR clustering tools and pre-trained language models.

Clustering source code from automated assessment of programming assignments

AbstractClustering of source code is a technique that can help improve feedback in automated program assessment. Grouping code submissions that contain similar mistakes can, for instance, facilitate the identification of students’ difficulties to provide targeted feedback. Moreover, solutions with similar functionality but possibly different coding styles or progress levels can allow personalized feedback to students stuck at some point based on a more developed source code or even detect potential cases of plagiarism. However, existing clustering approaches for source code are mostly inadequate for automated feedback generation or assessment systems in programming education. They either give too much emphasis to syntactical program features, rely on expensive computations over pairs of programs, or require previously collected data. This paper introduces an online approach and implemented tool—AsanasCluster—to cluster source code submissions to programming assignments. The proposed approach relies on program attributes extracted from semantic graph representations of source code, including control and data flow features. The obtained feature vector values are fed into an incremental k-means model. Such a model aims to determine the closest cluster of solutions, as they enter the system, timely, considering clustering is an intermediate step for feedback generation in automated assessment. We have conducted a twofold evaluation of the tool to assess (1) its runtime performance and (2) its precision in separating different algorithmic strategies. To this end, we have applied our clustering approach on a public dataset of real submissions from undergraduate students to programming assignments, measuring the runtimes for the distinct tasks involved: building a model, identifying the closest cluster to a new observation, and recalculating partitions. As for the precision, we partition two groups of programs collected from GitHub. One group contains implementations of two searching algorithms, while the other has implementations of several sorting algorithms. AsanasCluster matches and, in some cases, improves the state-of-the-art clustering tools in terms of runtime performance and precision in identifying different algorithmic strategies. It does so without requiring the execution of the code. Moreover, it is able to start the clustering process from a dataset with only two submissions and continuously partition the observations as they enter the system.

The Evolving Landscape of Flowcytometric Minimal Residual Disease Monitoring in B-Cell Precursor Acute Lymphoblastic Leukemia.

Detection of minimal residual disease (MRD) is a major independent prognostic marker in the clinical management of pediatric and adult B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL), and risk stratification nowadays heavily relies on MRD diagnostics. MRD can be detected using flow cytometry based on aberrant expression of markers (antigens) during malignant B-cell maturation. Recent advances highlight the significance of novel markers (e.g., CD58, CD81, CD304, CD73, CD66c, and CD123), improving MRD identification. Second and next-generation flow cytometry, such as the EuroFlow consortium's eight-color protocol, can achieve sensitivities down to 10-5 (comparable with the PCR-based method) if sufficient cells are acquired. The introduction of targeted therapies (especially those targeting CD19, such as blinatumomab or CAR-T19) introduces several challenges for flow cytometric MRD analysis, such as the occurrence of CD19-negative relapses. Therefore, innovative flow cytometry panels, including alternative B-cell markers (e.g., CD22 and CD24), have been designed. (Semi-)automated MRD assessment, employing machine learning algorithms and clustering tools, shows promise but does not yet allow robust and sensitive automated analysis of MRD. Future directions involve integrating artificial intelligence, further automation, and exploring multicolor spectral flow cytometry to standardize MRD assessment and enhance diagnostic and prognostic robustness of MRD diagnostics in BCP-ALL.

P155 Gene expression in hypermobility and the role of inflammation

Abstract Background/Aims Hypermobile conditions, including Ehlers-Danlos syndrome and joint hypermobility syndrome, encompass a broad spectrum of heterogenous clinical entities characterised by excessive range of joint movements and variable extra-articular features. The etiological basis of these conditions is thought to relate to abnormal collagen assembly, which impairs the structural integrity of connective tissue, although precise mechanisms remain both debated and incompletely understood. Despite the established strong heritability of hypermobility, a definitive genetic cause remains unidentifiable in most hypermobile patients, highlighting the need to further characterize the genetic landscape of hypermobile conditions. In this study, we therefore employ transcriptomic analysis as an alternate means of elucidating the genetic basis of hypermobility. Methods We tested for the effects of hypermobility on gene expression, at baseline and following an inflammatory challenge, by re-analysing existing transcriptomic data gathered from healthy subjects and patients with fibromyalgia and chronic fatigue using total RNA sequencing of blood samples (Illumina NextSeq 500). From each participant, two blood samples were taken on separate occasions: one after a saline injection and the other after an inflammatory challenge, the typhoid vaccination, in a cross-over design. Participants were assigned to hypermobile or non-hypermobile conditions in accordance with criterion 1: generalized joint hypermobility of the Diagnostic Criteria for Hypermobile Ehlers-Danlos Syndrome (hEDS) produced by the International Consortium on Ehlers-Danlos Syndromes & Related disorders. Differential gene expression analysis comparing hypermobile and non-hypermobile participants was performed using EdgeR software in R studio. Baseline differences were determined by comparing post-saline samples between groups. Differential responses to the inflammatory challenge were detected by comparison of post-typhoid samples between groups. Gene ontology on differentially expressed genes was performed using DAVID’s Functional Annotation Clustering tool. Results We found 288 differentially expressed genes post-saline (i.e at baseline) and 1113 post-typhoid at p < 0.05 although none passed false discovery rate. At baseline, differentially expressed genes were significantly enriched for nuclear, endoplasmic reticulum, and microtubular genes (enrichment score = 2.96, 2.02, 1.3 respectively); and post-typhoid, for 24 clusters, the three most significant being synaptic, ion transport, and plasma membrane genes (enrichment score = 11.34, 8.71, and 7.3 respectively). Conclusion These data demonstrate distinct patterns of gene expression, both at baseline and during inflammation, which implicate potential biological pathways in the pathophysiology of hypermobility. These results also contribute to a growing understanding of the genetic basis of hypermobility and may inform future higher-powered studies to validate our findings. Disclosure J. Porter: None. M. Amato: None. K. Themelis: None. H. Critchley: None. S. Newbury: None. K. Davies: None. N. Harrison: None. B. Towler: None. J. Eccles: Grants/research support; J.E. has received funding from Versus Arthritis, National Institute for Health Research and the Fibroduck Foundation.

Back-to-back high category atmospheric river landfalls occur more often on the west coast of the United States

The catastrophic December 2022-January 2023 nine atmospheric rivers in California underscore the urgent need to better understand such high-risk weather extremes. Here we applied a machine learning clustering tool to understand the activity of atmospheric river clusters. Reanalysis results show that clusters with high density, that is the time fraction under atmospheric river conditions within a cluster, exhibit more frequent high-category atmospheric rivers, alongside an increased likelihood for extreme precipitation and severe land surface responses. The key circulation patterns of atmospheric river clusters are primarily attributed to subseasonal variability. Furthermore, the occurrence and density of atmospheric river clusters are modulated by the daily variability of the geopotential height field. Climate model projections suggest that atmospheric river clusters with higher density and higher categories will be more frequent as warming level increases. Our findings emphasize the important role of atmospheric river clusters in the development of climate adaptation and resilience strategies.

Improving high throughput manufacture of laser-inscribed graphene electrodes via hierarchical clustering

Laser-inscribed graphene (LIG), initially developed for graphene supercapacitors, has found widespread use in sensor research and development, particularly as a platform for low-cost electrochemical sensing. However, batch-to-batch variation in LIG fabrication introduces uncertainty that cannot be adequately tracked during manufacturing process, limiting scalability. Therefore, there is an urgent need for robust quality control (QC) methodologies to identify and select similar and functional LIG electrodes for sensor fabrication. For the first time, we have developed a statistical workflow and an open-source hierarchical clustering tool for QC analysis in LIG electrode fabrication. The QC process was challenged with multi-operator cyclic voltammetry (CV) data for bare and metalized LIG. As a proof of concept, we employed the developed QC process for laboratory-scale manufacturing of LIG-based biosensors. The study demonstrates that our QC process can rapidly identify similar LIG electrodes from large batches (n ≥ 36) of electrodes, leading to a reduction in biosensor measurement variation by approximately 13% compared to the control group without QC. The statistical workflow and open-source code presented here provide a versatile toolkit for clustering analysis, opening a pathway toward scalable manufacturing of LIG electrodes in sensing. In addition, we establish a data repository for further study of LIG variation.

Spatial and Temporal Analysis: A GIS-Based Application Tool for Crime Monitoring and Clustering in Malawi

For the purposes of monitoring, evaluating, and conducting a geographical analysis of crime-related data, the study used geospatial technology to collect crime data based on spatial location and the Malawi Police Data Digest of 2019 and 2020. In a more generic sense, knowing the geographic patterns of crime in Malawi using GIS technology can help determine how to make and implement important decisions to reduce crimes in Malawi. The Malawi Police Service has established a number of database management systems to help with crime monitoring. Notwithstanding, it has not yet fully integrated Geographic Information Systems across all jurisdictions. Maps showing crime locations and crime hotspot zones are therefore not included in the crime data and statistics report provided by the Malawi Police Service. In this light, a lot of people have become victims of various forms of crimes in areas where those crimes are also prevalent. To collect, track, and analyze crime data in Malawi for this study, Geographical Information System (GIS) particularly network analysis techniques were used. Network Analysis was used to identify crime hotspots by analyzing crime data as a network of interconnected events and locations. The rationale behind this was to treat each crime event as a node in the network and the spatial relationships between the crimes as edges. By analyzing this network, patterns and relationships between crime events were revealed, allowing for the identification of crime hotspots. The study found that Lilongwe in the central region and the capital city registered the highest number of crimes seconded by Blantyre in the southern region and followed by Mangochi. Mzimba recorded high crimes in the northern region. In Malawi, the traditional systems of intelligence and criminal record keeping have failed to satisfy the demands of today's crime situation. Manual methods neither give accurate, dependable, or complete data 24 hours a day nor do they help in trend forecasting and decision assistance. It also leads to poorer productivity and inefficient workforce use. The appropriate application of information technology is the solution to this ever-increasing challenge.

Abstract 6454: Genomic characterization of vulvar squamous cell carcinoma

Abstract Introduction: Understanding the molecular underpinnings of vulvar squamous cell carcinoma (vSCC) has strong potential for clinical impact, given the unmet need for this rare cancer. vSCCs have two main etiologies, including human papillomavirus (HPV) infection or skin conditions such as lichen sclerosis. Clinically, few treatment options with limited efficacy are available for recurrent vSCCs and large whole transcriptome disease subtyping studies are limited due to the rarity of this cancer type. Here, we present a whole transcriptome subtyping study of 244 vSCC clinical samples with DNA genomic profiling. Furthermore, we compared vSCCs to other SCC disease subtypes (pan-SCC), including lung, esophageal, skin, urothelial, anal, and head and neck (HN). Our goal was to gain novel insights into the molecular landscape of vSCCs to inform new routes of therapy. Methods: Deidentified records of SCC patients were selected from the Tempus clinico-genomic database. The vSCC cohort was predominantly composed of, but not exclusively, naive or pre-treatment primary tissue. The pan-SCC cohort was limited to randomly sampled primary pre-treatment female samples with paired RNA and DNA sequencing. To perform vSCC subtyping, we used bulk RNA-seq and clustered the samples using FastPG-CC, an internally developed consensus-clustering extension of FastPG, a graph-based parallelized clustering tool. We next performed differential expression, pathway analysis, and cell type abundance estimation. Results: Unbiased gene expression clustering analysis identified three vSCCs clusters; an HPV+ cluster (HPV+, 32% of vSCC samples), HPV- cluster (HPV-, 33%), and an intermediate cluster which contained both HPV+ and HPV- samples (Mix, 35%). The HPV- cluster was enriched for TP53 and TERT promoter mutations, EMT and tumor microenvironment gene expression-derived pathways, and was most similar to HPV- oral HN tumors. The HPV+ cluster was enriched with PIK3CA mutations, cell cycle gene expression and was most similar to other HPV+ SCC samples, including cervical, anal, and HN. The Mix cluster was enriched for neutrophils, fatty acid metabolism and was most similar to skin SCC. Conclusions: We performed a multi-modal molecular subtyping analysis on a large cohort of vSCCs and identified three molecular subtypes, which enabled us to identify heterogeneity within vSCC, and observe that a subset of vSCCs share features with SCC tumor types for which there are emerging therapy options. Thus, this approach identifies a subset vSCCs that may be responsive to existing SCC therapeutics. Citation Format: Sara R. Selitsky, Tasha Thong, Kimberley Roche, Justin Guinney, Praveen Sethupathy, Catherine Igartua. Genomic characterization of vulvar squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6454.

Longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types.

Accurate clustering of mixed data, encompassing binary, categorical, and continuous variables, is vital for effective patient stratification in clinical questionnaire analysis. To address this need, we present longmixr, a comprehensive R package providing a robust framework for clustering mixed longitudinal data using finite mixture modeling techniques. By incorporating consensus clustering, longmixr ensures reliable and stable clustering results. Moreover, the package includes a detailed vignette that facilitates cluster exploration and visualization. The R package is freely available at https://cran.r-project.org/package=longmixr with detailed documentation, including a case vignette, at https://cellmapslab.github.io/longmixr/.

Simple approaches for evaluation of OTU quality based on dissimilarity arrays

An accurate and complete taxonomic description of the diversity present in an environmental sample is out of reach at this time. Instead, metabarcoding is used today and it is expected that OTUs represent a category relevant for biodiversity inventories on a molecular basis. However, artefacts in the production of OTUs can occur at different stages and may impact ecological conclusions. We propose to evaluate the quality of OTUs in a sample by characterising the deviation of each OTU’s dissimilarity array from that of an ideal OTU where all sequences are at distances smaller than the barcoding gap. We consider two deviations: the creation of composed OTUs, corresponding to the artificial merging of several OTUs and the creation of noisy OTUs that contain some sequences that are loosely associated with the core sequence of the OTUs and that do not form a compact subgroup. We propose a simple and automatic 2-step method that successively categorises the OTUs of a sample as composed or single and then identifies OTUs with noise amongst the single ones. The associated code is available at https://forgemia.inra.fr/alain.franc/otu_shape. We applied the method on 32 samples of diatoms from Arcachon Bay (France) that represent contrasted environmental conditions and we obtained good agreement with expert categorisation of OTUs. We suggest that single OTUs without noise can be used as such for further ecological studies. Composed OTUs should be post-treated with classical clustering or community detection tools. The quality of single OTUs with noise remains to be further tested via supplementary studies on a diversity of organisms.

CDSKNNXMBD: a novel clustering framework for large-scale single-cell data based on a stable graph structure

BackgroundAccurate and efficient cell grouping is essential for analyzing single-cell transcriptome sequencing (scRNA-seq) data. However, the existing clustering techniques often struggle to provide timely and accurate cell type groupings when dealing with datasets with large-scale or imbalanced cell types. Therefore, there is a need for improved methods that can handle the increasing size of scRNA-seq datasets while maintaining high accuracy and efficiency.MethodsWe propose CDSKNNXMBD (Community Detection based on a Stable K-Nearest Neighbor Graph Structure), a novel single-cell clustering framework integrating partition clustering algorithm and community detection algorithm, which achieves accurate and fast cell type grouping by finding a stable graph structure.ResultsWe evaluated the effectiveness of our approach by analyzing 15 tissues from the human fetal atlas. Compared to existing methods, CDSKNN effectively counteracts the high imbalance in single-cell data, enabling effective clustering. Furthermore, we conducted comparisons across multiple single-cell datasets from different studies and sequencing techniques. CDSKNN is of high applicability and robustness, and capable of balancing the complexities of across diverse types of data. Most importantly, CDSKNN exhibits higher operational efficiency on datasets at the million-cell scale, requiring an average of only 6.33 min for clustering 1.46 million single cells, saving 33.3% to 99% of running time compared to those of existing methods.ConclusionsThe CDSKNN is a flexible, resilient, and promising clustering tool that is particularly suitable for clustering imbalanced data and demonstrates high efficiency on large-scale scRNA-seq datasets.

Study on the Development Efficiency of Leisure Agriculture in Suburban Towns: An Empirical Analysis Based on the Case of Xinkou Town, Tianjin City

The leisure agriculture in suburban towns provides important space for citizens to carry out leisure activities. This paper takes Xinkou town in Tianjin city as an example to analyze the development efficiency of leisure agriculture in suburban towns. On the premise of establishing the indicators for evaluating the development efficiency of leisure agriculture in Xinkou town, this paper first uses the data envelopment analysis (DEA) to measure the input and output indicators of leisure agriculture in 18 natural villages in Xinkou town, and calculates the comprehensive technical efficiency, target values, redundancy and returns to scale of leisure agriculture, and then evaluates the development efficiency of leisure agriculture in Xinkou town. Furthermore, by using the high/low clustering tool under the ArcGIS spatial statistics module, this paper conducts a high/low clustering analysis of the development efficiency of leisure agriculture in 18 natural villages of Xinkou town, and figures out the reasons for the high/low clustering of natural villages in Xinkou town spatially. After the research and analysis, the following conclusions are drawn: From the input–output perspective, except for Zhengzhuangzi village, Diliubu village, Yuejiakai village, and Dangcheng village, the development efficiency of leisure agriculture in other villages is relatively low due to their poor resource foundations, or deficiencies in management experience and transportation location. The development of leisure agriculture in Xinkou town is unbalanced, showing a pattern of polarized clusters: Diliubu village and Shuigaozhuang village form the most obvious high-value cluster, while Xiaoshawo village, Bijia village and Xuanjiayuan village form the most obvious low-value cluster, where the southeast corner of Xinkou town registers the lowest development efficiency value of leisure agriculture because it is far away from the town center and the main transportation arteries, and falls short of high-quality pastoral scenery. For the future development of leisure agriculture in Xinkou town, it is supposed to optimize the land use policy, provide active support to the competitive industrial and commercial capital, non-governmental capital, rural collective economic organizations, farmers’ cooperatives, and family farms, strengthen the in-depth integration of industries, improve the support system for new-type leisure agriculture, and build leisure agriculture brands with local features.