Cluster Tool Research Articles

Analysis of global research on vegetable seedlings and transplants and their impacts on product quality.

Previous research has established that using high-quality planting material during the early phase of vegetable production significantly impacts success and efficiency, leading to improved crop performance, faster time to harvest and better profitability. In the present study, we conducted a global analysis of vegetable seedlings and transplants, providing a comprehensive overview of research trends in seedling and transplant production to enhance the nutritional quality of vegetables. The analysis involved reviewing and quantitatively analysing 762 articles and 5248 keywords from the Scopus database from 1971 to 2022. We used statistical, mathematical and clustering tools to analyse bibliometrics and visualise the most relevant research topics. A visualisation map was generated to identify the evolution of keywords used in the articles, resulting in five clusters for further analysis. Our study highlights the importance of the size of seed trays for the type of crop, the mechanical seeder used and the greenhouse facilities to produce desirable transplants. We identified grafting and light-emitting diode (LED) lighting technology as rapidly expanding technologies in vegetable seedlings and transplant production used to promote plant qualitative profile. There is a need for sustainable growing media to optimise resources and reduce input use. Thus, applying grafting, LED artificial lighting, biostimulants, biofortification and plant growth-promoting microorganisms in seedling production can enhance efficiency and promote sustainable vegetable nutritional quality by accumulating biocompounds. Further research is needed to explore the working mechanisms and devise novel strategies to enhance the product quality of vegetables, commencing from the early stages of food production. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Scheduling a Single-Arm Two-Cluster Tool With a Process Module Failure Subject to Wafer Residency Time Constraints

Multi-cluster tools are widely adopted in semiconductor manufacturing. When a process module (PM) at a step fails, a multi-cluster tool cannot complete the process recipe of work-in-process wafers and must be forced to enter a closedown process to be empty. To increase the throughput of a wafer fab, it is economically significant to shorten the failure-closedown process. However, due to the wafer residency time constraints, it is highly challenging to respond to a PM failure and find a corresponding optimal schedule. By assuming no parallel PM at a step, this work is the first to study this important issue for scheduling multi-cluster tools. We analyze the task sequences and synchronization conditions for robot activities to avoid deadlock in a shared buffer module. Upon these analyses, for process-dominant multi-cluster tools whose optimal steady-state schedule is known, algorithms are proposed to synthesize the proper sequences for robots in case of PM failures, then, a nonlinear program model is proposed to find an optimal schedule for the corresponding closedown process or decide no feasible solutions. The proposed model can uniformly deal with different scenarios of PM failures. Examples are given to illustrate the application of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In a wafer fab, there are hundreds, even thousands, of cluster tools. It is common that a failure of a processing module happens in a cluster tool. How to intelligently respond to such a random failure in a multi-cluster tool is an important issue in real-world production. This paper studies the scheduling problem of a multi-cluster tool in case of a failure at a PM. For a multi-cluster tool in case of a failure module, the proposed method can significantly reduce the loss of work-in-process wafers and shorten the closedown process.

Leveraging diverse cell-death patterns to predict the clinical outcome of immune checkpoint therapy in lung adenocarcinoma: Based on muti-omics analysis and vitro assay.

Advanced LUAD shows limited response to treatment including immune therapy. With the development of sequencing omics, it is urgent to combine high-throughput multi-omics data to identify new immune checkpoint therapeutic response markers. Using GSE72094 (n = 386) and GSE31210 (n = 226) gene expression profile data in the GEO database, we identified genes associated with lung adenocarcinoma (LUAD) death using tools such as "edgeR" and "maftools" and visualized the characteristics of these genes using the "circlize" R package. We constructed a prognostic model based on death-related genes and optimized the model using LASSO-Cox regression methods. By calculating the cell death index (CDI) of each individual, we divided LUAD patients into high and low CDI groups and examined the relationship between CDI and overall survival time by principal component analysis (PCA) and Kaplan-Meier analysis. We also used the "ConsensusClusterPlus" tool for unsupervised clustering of LUAD subtypes based on model genes. In addition, we collected data on the expression of immunomodulatory genes and model genes for each cohort and performed tumor microenvironment analyses. We also used the TIDE algorithm to predict immunotherapy responses in the CDI cohort. Finally, we studied the effect of PRKCD on the proliferation and migration of LUAD cells through cell culture experiments. The study utilized the TCGA-LUAD cohort (n = 493) and identified 2,901 genes that are differentially expressed in patients with LUAD. Through KEGG and GO enrichment analysis, these genes were found to be involved in a wide range of biological pathways. The study also used univariate Cox regression models and LASSO regression analyses to identify 17 candidate genes that were best associated with mortality prognostic risk scores. By comparing the overall survival (OS) outcomes of patients with different CDI values, it was found that increased CDI levels were significantly associated with lower OS rates. In addition, the study used unsupervised cluster analysis to divide 115 LUAD patients into two distinct clusters with significant differences in OS timing. Finally, a prognostic indicator called CDI was established and its feasibility as an independent prognostic indicator was evaluated by Cox proportional risk regression analysis. The immunotherapy efficacy was more sensitive in the group with high expression of programmed cell death models. Relationship between programmed cell death (PCD) signature models and drug reactivity. After evaluating the median inhibitory concentration (IC50) of various drugs in LUAD samples, statistically significant differences in IC50 values were found in cohorts with high and low CDI status. Specifically, Gefitinib and Lapatinib had higher IC50 values in the high-CDI cohort, while Olaparib, Oxaliplatin, SB216763, and Axitinib had lower values. These results suggest that individuals with high CDI levels are sensitive to tyrosine kinase inhibitors and may be resistant to conventional chemotherapy. Therefore, this study constructed a gene model that can evaluate patient immunotherapy by using programmed cell death-related genes based on muti-omics. The CDI index composed of these programmed cell death-related genes reveals the heterogeneity of lung adenocarcinoma tumors and serves as a prognostic indicator for patients.

Virtual Cell-Based Scheduling Approach to Single-Robotic-Arm Cluster Tools Subject to Wafer Residency Time Constraints

Virtual Cell-Based Scheduling Approach to Single-Robotic-Arm Cluster Tools Subject to Wafer Residency Time Constraints

Scheduling Cluster Tools for Concurrent Processing: Deep Reinforcement Learning With Adaptive Search

Scheduling Cluster Tools for Concurrent Processing: Deep Reinforcement Learning With Adaptive Search

Scheduling of Single-Arm Cluster Tools Handling Multiple Wafer Types Based on Double-Layer Configuration of Processing Modules

Scheduling of Single-Arm Cluster Tools Handling Multiple Wafer Types Based on Double-Layer Configuration of Processing Modules

A Novel Mixed Integer Programming Model With Precedence Relation-Based Decision Variables for Non-Cyclic Scheduling of Cluster Tools

A Novel Mixed Integer Programming Model With Precedence Relation-Based Decision Variables for Non-Cyclic Scheduling of Cluster Tools

Digital Twin-Based Optimization of Operational Parameters for Cluster Tools in Semiconductor Manufacturing

Digital Twin-Based Optimization of Operational Parameters for Cluster Tools in Semiconductor Manufacturing

Generalized Optimal Scheduling of Cluster Tools With Reentrance and Residency Time Constraints

Generalized Optimal Scheduling of Cluster Tools With Reentrance and Residency Time Constraints

Efficient Scheduling of Residency-Time-Constrained Dual-Armed Cluster Tools With Condition-Based Chamber Cleaning Operations

Efficient Scheduling of Residency-Time-Constrained Dual-Armed Cluster Tools With Condition-Based Chamber Cleaning Operations

Morphological and Electrical Characterization of AlGaN/GaN Heterostructures Modified By Atomic Layer Etching

Modern microelectronic components for high-power and high-frequency applications based on III-V compound semiconductors offer high break down voltage (GaN: 5 MV/cm and AlN: 15 MV/cm) at low specific on-resistance. However, it requires reliability and reproducibility of the production processes. Going to higher frequencies (5 GHz and beyond) and more integration in consumer electronics, smaller structure sizes are needed. This is going along with increasing demands on the manufacturing processes like dry etching. The main goal is to reach smaller and high controllable etching rates, going down to single atomic layers, low damage, and minimized surface roughness. Atomic layer etching (ALE) represents a key technology in order to achieve such requirements.An ALE process consists of two independent steps forming a repeatable cycle with a specific etching amount per cycle (EPC). First a surface modification is done by a chemical precursor i.e., adsorbing or oxidizing the surface. The second step is the removal of the modified layer, either physically by low energy non-reactive ion bombardment (Plasma Enhanced ALE [PEALE]) or chemically by a reactive species (Thermal ALE). The two steps are separated by an inert gas purge. This purging grants the removal of the chemistry after the first step, preventing further chemical reaction and transporting the chemical products out of the chamber after the second step.In this study, a PEALE conducted on different tools is used, for gate and contact recess study in a AlGaN/GaN heterostructure to form high electron mobility transistors (HEMT) with reduced ohmic contact resistance and threshold voltages. The results show a reduction of ohmic contact resistance at reduced forming temperatures and a linear dependence of the threshold voltage on gate recess depth. In order to determine the etching rate and roughness, atomic force microscopy (AFM) measurements were performed.The PEALE module is either integrated into a cluster tool consisting of atomic layer deposition (ALD) and chemical vapor deposition (CVD) or done in a combined ALD/ALE chamber. This opens the possibility of etching and depositing in-situ, reducing contamination and degradation of the substrate material.By this means, metal insulator semiconductor (MIS)-HEMTS with a recessed gate are studied. The monitoring of the individual processes is achieved by test structures like transfer length method structures (TLM) and metal oxide semiconductor (MOS), which showed the improved electric performance of these devices and may indicate unwanted interface issues, respectively. Additional, pre- and post-treatments could improve the interfaces further. The results will be presented and discussed in terms of ALE process development and optimization.This work was financially supported within the ALEStar project by the government of the Free-state of Saxony and the European Regional Development Fund under grant no. SAB 100402929.

Classification of dendritic spiking neurons’ shape through clustering and machine learning techniques

A synapse is a specialized region between two adjacent neurons that allows information to be transmitted from one cell to another. The formation of these connections and transfer of signals via electrical impulses is a critical characteristic of neuronal cells. The synapse is formed by the axonal bouton on the axon side and the dendritic spine, a specialized outgrowth of the dendritic membrane, on the dendrite side. Dendritic spines exhibit diverse shapes and sizes, differing significantly across brain regions, cell categories, and animal species. Changes in dendritic spine morphology occur in neurodevelopmental and neurodegenerative ailments while also responding to external stimuli. Although deemed to facilitate synaptic plasticity, further investigation is essential for establishing the correlation between spine construction and its function. To address the issue in modern neurobiology of characterizing synapse morphology on 3D neuron images, the development of effective analytical methods is necessary. Our team has produced an open-source software solution for precise dendritic spine segmentation using 3D dendrite images. This software calculates the 10 most widely used 3D-adapted morphological features [1, 2] and enables classification and clustering of dendritic spine data sets to determine their shape. In addition to numerical features for describing the shape of a dendritic spine, researchers proposed using a histogram of chord lengths, known as the chord length distribution histogram (CLDH). This involves generating a set of random chords within the dendritic spine’s volume, connecting its outer boundaries and forming a histogram. By setting n=30 000, the probabilistic fluctuations of the histogram become insignificant. The derived metrics were then used for clustering and classifying the dataset. Classification based on predetermined morphological groups is a frequently used approach for analyzing the morphology of dendritic spines. This method involves categorizing spines into established groups such as thin, mushroom, and stubby. Experimenters generally perform classification in a semi-automated manner, leading to considerable error. We have created a spine categorization tool using a machine learning algorithm. The tool classifies spines based on a consensus reached by eight experts who manually labeled the training dataset. The accuracy of this method surpasses 77% when using classical morphological features and is comparable to expert labeling. The implementation of this approach reduces classification bias and complexity. Recent studies, including those using live microscopy in vitro and in vivo, indicate that dendritic spine shapes exhibit a continuum rather than distinct categories [3]. Therefore, it is essential to establish a dependable methodology for evaluating and examining the morphology of dendritic spines. We have created a clustering tool that establishes both the number of groups and their content based on data, rather than the experimenter’s discretion. This tool leverages the k-means and DBSCAN algorithms for its representation. Three clustering methods are presented to determine the number of clusters: the silhouette method, the elbow method, and a novel method, developed by the authors, based on the max class divergence criteria. The authors assume that cluster quality improves as clusters differ significantly in the number of mushroom/thin/stubby spine classes, as marked by experts. The benefit of this approach is that it considers the particular data type for clustering. Without this knowledge, assessing the quality of clustering is challenging. The use of the CLDH metric for clustering yielded a consistent and stable number of clusters (n=5) across all three methods described. These clusters contain dendritic spines that share similar shapes and have been validated by experts. In contrast, using classical metrics resulted in a variable cluster count ranging from n=4 to n=14. These findings suggest that the CLDH metric, with its complexity, provides enough information about synapse shape to enable precise clustering.

Recent ground thermo-hydrological changes in a southern Tibetan endorheic catchment and implications for lake level changes

Abstract. Climate change modifies the water and energy fluxes between the atmosphere and the surface in mountainous regions such as the Qinghai–Tibet Plateau (QTP), which has shown substantial hydrological changes over the last decades, including rapid lake level variations. The ground across the QTP hosts either permafrost or is seasonally frozen, and, in this environment, the ground thermal regime influences liquid water availability, evaporation and runoff. Consequently, climate-induced changes in the ground thermal regime may contribute to variations in lake levels, but the validity of this hypothesis has yet to be established. This study focuses on the cryo-hydrology of the catchment of Lake Paiku (southern Tibet) for the 1980–2019 period. We process ERA5 data with downscaling and clustering tools (TopoSCALE, TopoSUB) to account for the spatial variability of the climate in our forcing data (Fiddes and Gruber, 2012, 2014). We use a distributed setup of the CryoGrid community model (version 1.0) to quantify thermo-hydrological changes in the ground during this period. Forcing data and simulation outputs are validated with data from a weather station, surface temperature loggers and observations of lake level variations. Our lake budget reconstruction shows that the main water input to the lake is direct precipitation (310 mm yr−1), followed by glacier runoff (280 mm yr−1) and land runoff (180 mm yr−1). However, altogether these components do not offset evaporation (860 mm yr−1). Our results show that both seasonal frozen ground and permafrost have warmed (0.17 ∘C per decade 2 m deep), increasing the availability of liquid water in the ground and the duration of seasonal thaw. Correlations with annual values suggest that both phenomena promote evaporation and runoff. Yet, ground warming drives a strong increase in subsurface runoff so that the runoff/(evaporation + runoff) ratio increases over time. This increase likely contributed to stabilizing the lake level decrease after 2010. Summer evaporation is an important energy sink, and we find active-layer deepening only where evaporation is limited. The presence of permafrost is found to promote evaporation at the expense of runoff, consistently with recent studies suggesting that a shallow active layer maintains higher water contents close to the surface. However, this relationship seems to be climate dependent, and we show that a colder and wetter climate produces the opposite effect. Although the present study was performed at the catchment scale, we suggest that this ambivalent influence of permafrost may help to understand the contrasting lake level variations observed between the south and north of the QTP, opening new perspectives for future investigations.

Combination of Multiple Operando and In-Situ Characterization Techniques in a Single Cluster System for Atomic Layer Deposition: Unraveling the Early Stages of Growth of Ultrathin Al2O3 Films on Metallic Ti Substrates

This work presents a new ultra-high vacuum cluster tool to perform systematic studies of the early growth stages of atomic layer deposited (ALD) ultrathin films following a surface science approach. By combining operando (spectroscopic ellipsometry and quadrupole mass spectrometry) and in situ (X-ray photoelectron spectroscopy) characterization techniques, the cluster allows us to follow the evolution of substrate, film, and reaction intermediates as a function of the total number of ALD cycles, as well as perform a constant diagnosis and evaluation of the ALD process, detecting possible malfunctions that could affect the growth, reproducibility, and conclusions derived from data analysis. The homemade ALD reactor allows the use of multiple precursors and oxidants and its operation under pump and flow-type modes. To illustrate our experimental approach, we revisit the well-known thermal ALD growth of Al2O3 using trimethylaluminum and water. We deeply discuss the role of the metallic Ti thin film substrate at room temperature and 200 °C, highlighting the differences between the heterodeposition (&lt;10 cycles) and the homodeposition (&gt;10 cycles) growth regimes at both conditions. This surface science approach will benefit our understanding of the ALD process, paving the way toward more efficient and controllable manufacturing processes.

Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks

BackgroundAs an important task in bioinformatics, clustering analysis plays a critical role in understanding the functional mechanisms of many complex biological systems, which can be modeled as biological networks. The purpose of clustering analysis in biological networks is to identify functional modules of interest, but there is a lack of online clustering tools that visualize biological networks and provide in-depth biological analysis for discovered clusters.ResultsHere we present BioCAIV, a novel webserver dedicated to maximize its accessibility and applicability on the clustering analysis of biological networks. This, together with its user-friendly interface, assists biological researchers to perform an accurate clustering analysis for biological networks and identify functionally significant modules for further assessment.ConclusionsBioCAIV is an efficient clustering analysis webserver designed for a variety of biological networks. BioCAIV is freely available without registration requirements at http://bioinformatics.tianshanzw.cn:8888/BioCAIV/.

Consensus clustering with missing labels (ccml): a consensus clustering tool for multi-omics integrative prediction in cohorts with unequal sample coverage.

Multi-omics data integration is a complex and challenging task in biomedical research. Consensus clustering, also known as meta-clustering or cluster ensembles, has become an increasingly popular downstream tool for phenotyping and endotyping using multiple omics and clinical data. However, current consensus clustering methods typically rely on ensembling clustering outputs with similar sample coverages (mathematical replicates), which may not reflect real-world data with varying sample coverages (biological replicates). To address this issue, we propose a new consensus clustering with missing labels (ccml) strategy termed ccml, an R protocol for two-step consensus clustering that can handle unequal missing labels (i.e. multiple predictive labels with different sample coverages). Initially, the regular consensus weights are adjusted (normalized) by sample coverage, then a regular consensus clustering is performed to predict the optimal final cluster. We applied the ccml method to predict molecularly distinct groups based on 9-omics integration in the Karolinska COSMIC cohort, which investigates chronic obstructive pulmonary disease, and 24-omics handprint integrative subgrouping of adult asthma patients of the U-BIOPRED cohort. We propose ccml as a downstream toolkit for multi-omics integration analysis algorithms such as Similarity Network Fusion and robust clustering of clinical data to overcome the limitations posed by missing data, which is inevitable in human cohorts consisting of multiple data modalities. The ccml tool is available in the R language (https://CRAN.R-project.org/package=ccml, https://github.com/pulmonomics-lab/ccml, or https://github.com/ZhoulabCPH/ccml).

Distribution characteristics of the free-roaming dog population of the western area of Tongatapu, Kingdom of Tonga: Developing a GIS for biosecurity and animal welfare projects

The free-roaming dog population of Tongatapu, Kingdom of Tonga, is a ubiquitous, well-known and persistent feature of the island. Information on the quantity, condition and spatial distribution characteristics of these dogs is needed for projects aimed at improving biosecurity conditions in different impacted sectors of society, while addressing the management of an animal associated with humans. Health and safety of the human population, agriculture and biodiversity are directly affected by the presence of free-roaming dogs in Tongan communities. A survey conducted over western Tongatapu recorded locations of free-roaming dogs sighted when driving along the roads of 12 towns and villages. Locations were recorded using an iPad and the mobile application Collector for ArcGIS. A total of 1152 dogs was recorded, mostly within villages and residential roads. Patterns of spatial distribution of dog locations were described using kernel density, hotspot analysis, and cluster and outlier analysis tools available in ArcGIS Pro. Using the species distribution modelling algorithms Maxent and random forest (RF), the suitability of the entire island for free-roaming dogs was determined using as input the dog locations and environmental variables consisting of population, land use and road types. Results of both algorithms show that roads and residential areas consistently show the highest probability of free-roaming dog presence. The RF algorithm also produced maps that show predictions of the values of other variables collected during the survey, including body-condition scores, age group and sex types over the entire island. Overall, the maps produced and the database created contribute a baseline biosecurity knowledge-base supporting rational decision-making and governance for the island.

Clustering categorical data: Soft rounding k-modes

Over the last three decades, researchers have intensively explored various clustering tools for categorical data analysis. Despite the proposal of various clustering algorithms, the classical k-modes algorithm remains a popular choice for unsupervised learning of categorical data. Surprisingly, our first insight is that in a natural generative block model, the k-modes algorithm performs poorly for a large range of parameters. We remedy this issue by proposing a soft rounding variant of the k-modes algorithm (SoftModes) and theoretically prove that our variant addresses the drawbacks of the k-modes algorithm in the generative model. Finally, we empirically verify that SoftModes performs well on both synthetic and real-world datasets.

Effects of repeated binge intake of the pyrovalerone cathinone derivative 3,4-methylenedioxypyrovalerone on prefrontal cytokine levels in rats - a preliminary study.

Drugs of abuse activate neuroimmune signaling in addiction-related regions of the brain, including the prefrontal cortex (PFC) which mediates executive control, attention, and behavioral inhibition. Traditional psychostimulants including methamphetamine and cocaine are known to induce PFC inflammation, yet the effects of synthetic cathinone derivatives are largely unexplored. In this study, we examined the ability of repeated binge-like intake of the pyrovalerone cathinone derivative 3,4-methylenedioxypyrovalerone (MDPV) to alter cytokine profiles in the PFC. Male and female rats were allowed to intravenously self-administer MDPV (0.05 mg/kg/infusion) or saline as a control under conditions of prolonged binge-like access, consisting of three 96 h periods of drug access interspersed with 72 h of forced abstinence. Three weeks following cessation of drug availability, PFC cytokine levels were assessed using antibody arrays. Employing the unsupervised clustering and regression analysis tool CytoMod, a single module of co-signaling cytokines associated with MDPV intake regardless of sex was identified. With regards to specific cytokines, MDPV intake was positively associated with PFC levels of VCAM-1/CD106 and negatively associated with levels of Flt-3 ligand. These findings indicate that prolonged MDPV intake causes changes in PFC cytokine levels that persist into abstinence; however, the functional ramifications of these changes remain to be fully elucidated.

Abstract 16360: Myocardial Injury and Rapid Unsupervised Topological Clustering of Electronic Health Record Features Predict Death and Length of Stay During a COVID-19 Hospitalization

Background: Elevated troponin levels, a biomarker of myocardial injury (MI), has emerged as a predictor of acute COVID-19 outcomes. However, a lack of routine use in most patients makes it difficult to evaluate as a stand-alone predictor. We aimed to combine troponin levels and a new rapid, unbiased topological clustering pipeline to test for improvements in clinical risk stratification during COVID hospitalizations. Methods: SlicerDicer (SD) is an intuitive, anonymous clinical electronic health record (EHR) data exploration tool that helps characterize clinical outcomes in hospital cohorts. EHR data and outcomes were extracted with SD and clustered with Mapper Plus (MP), a clustering tool that identifies clinically relevant disjoint clusters. Initial clusters were further stratified by troponin level. Measured outcomes were in-hospital mortality rates (MR) and in-hospital length of stay (LOS). Results: 2692 patient records (2020-2021) with an overall MR of 6.3% and 97 clinical predictors were analyzed. MP identified 3 clusters with significant differences in MR (C1- 25.8%, C2- 4.2%, C3- 0.23%, P&lt;0.001) and LOS in hours (C1- 290, C2- 132, C3- 8.3, P&lt;0.001). Further stratification based on the troponin level reclassified the 3 MP clusters into 4 sub-clusters with distinctive MR (SC1- 32.6%, SC2- 17.3%, SC3- 2.3%, SC4-0.09%, P&lt;0.001) based on MI presence. Conclusions: Using SD-based anonymous EHR data and unsupervised topological clustering with MP during a COVID hospitalization provides a low-effort, robust pipeline that predicts in-hospital MR and LOS. When MP clusters were re-stratified by the presence of MI, new sub-clusters (4) showed reclassification of both high- and low-risk patients. This risk classification schema provides a new pragmatic and computational framework for selecting hospitalized patients with COVID that can benefit from further in-hospital therapies and mechanistic studies on the role of myocardial injury in COVID-19 prognosis.