Initial Cluster Research Articles

Coalescing black hole binaries from globular clusters: mass distributions and comparison to gravitational wave data from GWTC-3

ABSTRACTWe use our cluster population model, cBHBd, to explore the mass distribution of merging black hole binaries formed dynamically in globular clusters. We include in our models the effect of mass growth through hierarchical mergers and compare the resulting distributions to those inferred from the third gravitational wave transient catalogue. We find that none of our models can reproduce the peak at m1 ≃ 10 M⊙ in the primary black hole mass distribution that is inferred from the data. This disfavours a scenario where most of the sources are formed in globular clusters. On the other hand, a globular cluster origin can account for the inferred secondary peak at m1 ≃ 35 M⊙, which requires that the most massive clusters form with half-mass densities $\rho _{\rm h,0}\gtrsim 10^4~{\rm M}_{\odot} \, {\rm pc}^{-3}$. Finally, we find that the lack of a high-mass cut-off in the inferred mass distribution can be explained by the repopulation of an initial mass gap through hierarchical mergers. Matching the inferred merger rate above ≃50 M⊙ requires both initial cluster densities $\rho _{\rm h,0}\gtrsim 10^4~{\rm M}_{\odot} \, {\rm pc}^{-3}$, and that black holes form with nearly zero spin. A hierarchical merger scenario makes specific predictions for the appearance and position of multiple peaks in the black hole mass distribution, which can be tested against future data.

Cluster Partitions Optimization Using the Latent Class Analysis Technique

The paper explores the formation of a cluster partition using a system of bundles of vectors. Each object is represented by a vector with coordinates corresponding to its characteristic factors. The study focuses on the principles of changes in the total density of these bundles when objects move from one cluster to another. Based on the findings, an algorithm is proposed to reduce the total intracluster variance of the initial cluster partition.
 The proposed algorithm is based on a modern technique for analyzing latent classes. This technique emphasizes that the forming factors of objects within each cluster should be highly correlated. Since the number of forming factors is arbitrary, the algorithm replaces this requirement with the maximum possible proximity of object factor vectors to the average vector within each cluster. The degree of this closeness is referred to as the tightness of the cluster vector bundle.
 The paper also introduces a new method for quantifying clusters in the improvable clustering constructed using the proposed algorithm. A practical example of the algorithm's application to process medical data is presented. The study discusses the reasons for the dependence of the algorithm's outcome on the choice of initial cluster partition.

Success factors of organizational resilience: a qualitative investigation of four types of sustainable digital entrepreneurs

PurposeThe purpose of this paper is to distinguish different types of sustainable digital entrepreneurs (SDEs) and explore their approaches toward enhancing organizational resilience.Design/methodology/approachInvestigation of entrepreneur characteristics using Grounded Theory methodology; 12 semi-structured telephone interviews with (owner-)managers of digital-resilient small and medium-sized enterprises (SMEs) and start-ups in Germany; adaptation of a sustainability-digitalization-matrix for initial clustering; investigation of reoccurring patterns (within and between clusters) through variable-oriented content analysis; application of the capability-based conceptualization of organizational resilience for synthesis and extension.FindingsFirst, the authors present a new typology of SDEs, including descriptions of the four main types (Process-Oriented System Thinker, Unconventional Strategist, Dynamic Visionary and Success-Oriented Opportunist). Second, the authors propose a conceptual framework with six success factors of organizational resilience. The framework accentuates the influence of SDEs on organizational culture and the macro-environment.Practical implicationsDigital sustainability and resilience are emerging management principles. The insights gained will allow (future) entrepreneurs to perform a self-assessment and replicate approaches toward enhancing SME resilience; for example, governing the co-creation of an organizational culture with a strong integrative view on sustainability and digitalization.Originality/valueSMEs are characterized by high vulnerability and a reactive response to the disruptions caused by sustainability crises and digitalization. Blending sustainable and digital entrepreneurship at a micro-level, the authors identified the success factors underpinning organizational resilience that are associated with the characteristics of four types of SDEs.

APPLICATION OF ARTIFICIAL NEURAL NETWORKS AND ACHIEVEMENTS OF TECHNOLOGICAL RE-ENGINEERING OF MODERN INDUSTRIAL PRODUCTION

The article deals with the issue of the justification mechanism for the choice of directions of technological reengineering. It was determined that the effectiveness of the transformations primarily depends on the methods, tools for choosing directions for the radical transformation of the technological basis of the production enterprise and, in general, on the technological policy. The justification of the directions of technological training engineering is connected with a large volume of interdependent variables of innovative transformations and requires a fundamental analysis of their behaviour and impact on production process. Today, neural networks are one of the most famous and effective tools for intelligent data analysis, which is developed thanks to achievements in the field of artificial intelligence theory and computer science. Intelligent systems based on artificial neural networks and fuzzy logic allow solving the tasks of forecasting, optimization, pattern recognition and management. To train the network, it is necessary to have a set of values of input values (factors) and corresponding to each separate set of values of the desired output value. Such an approach completely coincides with the tasks of choosing the directions of technological reengineering at the enterprises of the innovation cluster. For this purpose, significant external and internal factors influencing the effectiveness of the cluster policy have been determined. In contrast to internal, external to the greatest extent allow solving the tasks of forecasting, optimization, pattern recognition and management. In the conditions of an imperfect regional innovation system and taking into account this factor, for the efficiency of solving the tasks of technological reengineering of industrial production, a methodology is proposed that combines foresight forecasting of the prospects of technological reengineering of enterprises with the theory of artificial neural networks. The mechanism of interaction of artificial neural networks and the foresight method in determining directions of technological reengineering based on regional cluster policy are proposed.

Cluster algebras generated by projective cluster variables

We introduce the notion of a lower bound cluster algebra generated by projective cluster variables as a polynomial ring over the initial cluster variables and the so-called projective cluster variables. We show that under an acyclicity assumption, the cluster algebra and the lower bound cluster algebra generated by projective cluster variables coincide. In this case we use our results to construct a basis for the cluster algebra. We also show that any coefficient-free cluster algebra of types An or A˜n is equal to the corresponding lower bound cluster algebra generated by projective cluster variables.

MWCRSF: Mobility-based weighted cluster routing scheme for FANETs

Flying Ad-Hoc Network (FANET) formed by a swarm of Unmanned Aerial Vehicles (UAVs) is currently a trending research topic because of its capability in performing complex tasks more rapidly and efficiently. However, the high-speed mobility of UAVs, limited energy, and dynamically changing topology have led to critical challenges for the reliability of communication between UAVs, especially when designing routing schemes in FANET. In this paper, we introduce a Mobility-based Weighted Cluster Routing Scheme (MWCRSF) for FANET performance enhancement within the constraints of network resources. MWCRSF utilizes the high-level search efficiency of the Sparrow Search Algorithm (SSA) and a new clustering algorithm based on SSA is first proposed to set up the initial UAV clusters. A weighted sum-based algorithm is then proposed for Cluster Head (CH) selection, where the residual energy, node correlation degree, relative mobility, distance to the Base Station (BS), and average distance are adopted as the selection criteria. To keep the UAV clusters updated, an efficient cluster maintenance algorithm is proposed as well. In order to provide timely and reliable data transmission to the BS, the optimal next-hop route selection is performed considering the link lifetime, residual energy, and distance between nodes and BS. We exploit the power of skyline operators in multi-criteria decision-making to reduce the search space and improve the efficiency of CH selection and next-hop routing. The simulation results reveal that the proposed MWCRSF scheme outperforms the existing methods under different metrics including cluster lifetime, energy efficiency, throughput, and network lifetime.

Features of the clusterization method application in marketing research of the pharmaceutical market of Ukraine

The aim of the article. The purpose of this study is to group companies operating on the pharmaceutical market of Ukraine, using clustering methods based on sets of statistical indicators of their activities for the period 2008-2022, with the aim of the isolated groups further evaluating in terms of perspectives for the development of their marketing activities in this market. Analysis results. The article is devoted to the features of the cluster analysis using with the aim to distinguish groups of pharmaceutical enterprises operating on the pharmaceutical market of Ukraine based on the use of a unified system of performance indicators. Key features of the clusterization methods application are given, which will allow to form groups from individual elements of the studied population based on scientifically proven approaches and to develop productive managerial solutions in the field of their marketing activities in accordance with the obtained results. The current state and key trends in the development of the pharmaceutical market of Ukraine are analyzed. Cluster analysis was implemented using the Sklearn library in the jupyter notebook environment. The Silhouette method was used to determine the number of clusters. At the final stage of clusterization it is necessary to profile the selected clusters by describing their key parameters and characteristics. As a result, three clusters were obtained, which are conventionally named as "Leaders", "Average" and "Outsiders" in accordance with the revealed characteristic features and indicators of market activities performance of the studied enterprises.In this way, the method cluster analysis makes it possible to consolidate a large number of competing enterprises fairly quickly by a set of indicators important for researchers, which have different natures, measurements, sources of their generation, as well as different frequencies of relevant calculations. In turn, the visualization of the results of this method application allows to visualize the promising directions of a more in-depth marketing analysis of the features of the market activity of competing pharmaceutical enterprises. Conclusions and directions for further research. Clustering of enterprises operating on the pharmaceutical market of Ukraine allows to visualize their current market positions on the basis of the consolidation of both the observation units themselves and the key indicators of their activities. It also allows to identify the main features and characteristics of their marketing activities, which is a valuable resource for further competitive benchmarking by those pharmaceutical companies that claim to strengthen their own market positions based on the development and practical application of effective marketing strategies and tactics of their activities in conditions of increased competition on the market and a number of negative trends currently taking place on it.

The growth of intermediate mass black holes through tidal captures and tidal disruption events

ABSTRACTWe present N-body simulations, including post-Newtonian dynamics, of dense clusters of low-mass stars harbouring central black holes (BHs) with initial masses of 50, 300, and 2000 M⊙. The models are evolved with the N-body code bifrost to investigate the possible formation and growth of massive BHs by the tidal capture of stars and tidal disruption events (TDEs). We model star–BH tidal interactions using a velocity-dependent drag force, which causes orbital energy and angular momentum loss near the BH. About ∼20–30 per cent of the stars within the spheres of influence of the black holes form Bahcall–Wolf cusps and prevent the systems from core collapse. Within the first 40 Myr of evolution, the systems experience 500–1300 TDEs, depending on the initial cluster structure. Most (>95 per cent) of the TDEs originate from stars in the Bahcall–Wolf cusp. We derive an analytical formula for the TDE rate as a function of the central BH mass, density, and velocity dispersion of the clusters ($\dot{N}_{\mathrm{TDE}} \propto M\mathrm{_{BH}}\rho \sigma ^{-3}$). We find that TDEs can lead a 300 M⊙ BH to reach $\sim 7000 \, \mathrm{{M}_{\odot }}$ within a Gyr. This indicates that TDEs can drive the formation and growth of massive BHs in sufficiently dense environments, which might be present in the central regions of nuclear star clusters.

A semiparametric clustering method for the screening of retired Li-ion batteries from electric vehicles

This study proposes a semiparametric clustering method (SPCM) to screen the batteries retired from electric vehicles (EVs) for echelon utilization. To quickly capture the static and dynamic battery characteristics, a hybrid test combining the low-current hybrid pulse power characterization (L-HPPC) test and China light-duty vehicle test cycle (CLTC) is designed. The pseudo-two-dimensional (P2D) model is combined with genetic algorithm (GA), and model parameters are identified by the hybrid test. Then, a clustering matrix containing sensitive model parameters is established and processed by principal component analysis (PCA). In SPCM, the Euclidean-distance-based clustering matrix is divided into high-density and low-density parts through a predetermined mixing proportion. For high-density data, hierarchical clustering (HC) is used to determine the initial cluster centers, and fuzzy C-means (FCM) method is utilized to determine the core clusters, while the low-density data is assigned to the nearest neighbours. Finally, 18 open-access datasets are employed to validate the effectiveness of the proposed SPCM. These results indicate that the proposed SPCM is superior to K-mean, K-medoid, and FCM in terms of normalized mutual information (NMI). Furthermore, the pulse testing and capacity testing of the re-grouped retired batteries further demonstrate the effectiveness of the SPCM in the screening of retired batteries.

Maintenance of genome integrity by the late-acting cytoplasmic iron-sulfur assembly (CIA) complex.

Iron-sulfur (Fe-S) clusters are unique, redox-active co-factors ubiquitous throughout cellular metabolism. Fe-S cluster synthesis, trafficking, and coordination result from highly coordinated, evolutionarily conserved biosynthetic processes. The initial Fe-S cluster synthesis occurs within the mitochondria; however, the maturation of Fe-S clusters culminating in their ultimate insertion into appropriate cytosolic/nuclear proteins is coordinated by a late-acting cytosolic iron-sulfur assembly (CIA) complex in the cytosol. Several nuclear proteins involved in DNA replication and repair interact with the CIA complex and contain Fe-S clusters necessary for proper enzymatic activity. Moreover, it is currently hypothesized that the late-acting CIA complex regulates the maintenance of genome integrity and is an integral feature of DNA metabolism. This review describes the late-acting CIA complex and several [4Fe-4S] DNA metabolic enzymes associated with maintaining genome stability.

Acoustic characteristics of English/n, l, t, k/ produced by native speakers of American English and Japanese at and near the word boundary

The acoustic characteristics of English /n, l, t, k/ produced by native speakers of American English and Japanese at and near the word boundary are compared. The phonemes are produced in two-word sequences in sentence contexts, /n/ or /l/ appears at the end of the first word (e.g., an earth, you'll earn) or at the beginning of the second word (e.g., a nurse, you learn), while /t/ or /k/ appears at the beginning of the second word (e.g., stops talking, ice cream) or as the second segment of the initial cluster /sk/ of the second word (e.g., stop stalking, I screamed). Acoustic analysis revealed that English speakers' /n/ in coda position is shorter and creakier, while Japanese speakers' /n/ is as long as in onset. In addition, English speakers' vowels in /Vl#/ context are more velarized than vowels in /V#l/ context, whereas Japanese speakers' vowels in /Vl#/ context are less /l/-colored. English speakers produced initial /t/ and /k/ with much longer VOT than in /st/, /sk/ conditions, but because Japanese voiceless stops are weakly aspirated, Japanese speakers /t/ and /k/ not significantly different in VOT between the two contexts, leading potentially to syllable misparsing by English listeners

A method for clustering rock discontinuities with multiple properties based on an improved netting algorithm

Clustering analysis is fundamental for determining dominant discontinuity properties in rock engineering. Orientation has commonly been considered the only factor when performing clustering, which ignores the contributions of other discontinuity properties to the deformations and strengths of rock masses. This study proposes an improved netting algorithm to identify discontinuity sets based on multiple discontinuity properties. The method takes ten discontinuity properties as the clustering factors: dip direction, dip, trace length, spacing, aperture, infilling material, infilling percentage, roughness, water permeability, and rock strength. Meanwhile, a novel weighting method is used to weigh each property and combines the advantages of subjective and objective weighting methods. The validity of the proposed method is tested using artificial data based on the Monte Carlo method and in situ data from the relevant literature. The results indicate that the method can effectively filter the noise data, and the rejection rate is approximately 26%. The initial number of sets and initial clustering centers are not necessary, which facilitates achieving global optimization. Finally, an open-pit mine slope in Xinjiang Province, China, is used as a case study to illustrate the utility of the method. The new method is considered a potentially useful tool to rapidly obtain dominant discontinuity sets in rock engineering.

ИНТЕРНАЦИОНАЛИЗАЦИЯ КЛАСТЕРНЫХ ПРЕДПРИЯТИЙ МАЛОГО И СРЕДНЕГО БИЗНЕСА

The article considers foreign scientific literature devoted to exogenous factors (regarding the interaction of clusters with global innovation systems) that limit the interaction of firms with global markets and value chains. The following research methods were applied in the article: analysis of modern scientific literature by foreign authors, focused on the aspect of internationalization of cluster small and medium-sized businesses. The purpose of this study is to consider the importance of the internationalization of clusters in a theoretical aspect. Analyzed 52 scientific articles in the field of internationalization of cluster enterprises of small and medium-sized businesses. The study characterizes the positive and negative impact of internationalization on clusters. The positive aspects of inter-cluster cooperation in the international space are highlighted. The factors due to which the internationalization of the cluster can be achieved are determined. The conclusion is made about the potential positive and negative impact of internationalization on clusters. The positive impacts include access to knowledge through cluster networks/relationships, observation/imitation, inter-firm labor mobility, technology diffusion, higher likelihood of being "found" by international counterparties, etc.); to a negative impact – local congestion, intense local competition, distrust between cluster members, excessive dependence on specific resources of the region, "poaching" of workers by other cluster firms, loss of technological advantage due to imitation/"theft" of technologies, etc.

Optimal sensor placement for the routine monitoring of urban drainage systems: A re-clustering method

The construction of an efficient monitoring network is critical for the effective and safe management of urban drainage systems. This study developed a re-clustering methodology that incorporates additional perspectives beyond node similarity to improve the traditional clustering process for optimal sensor placement. Instead of targeting event-specific water quality or hydraulic monitoring, the method integrates the water hydraulic and quality characteristics of nodes in response to the demand for routine monitoring. The implementation of this method first applies model simulation to generate the attribute datasets required for clustering analysis, and then re-clusters the initial clustering result according to the constructed re-clustering potential indices. And the information theory-based evaluation metrics were introduced to quantitatively assess the sensor deployment scheme obtained by amalgamating the two clustering results. Two networks with different drainage systems and sizes were chosen as case studies to illustrate the application of the framework. The results demonstrate that the clustering process enables to expand the information contained in the monitoring network, and that the re-clustering strategy can generate more comprehensive and practical solutions upon this basis.

Adaptive Reflection Detection and Control Strategy of Pointer Meters Based on YOLOv5s

Reflective phenomena often occur in the detecting process of pointer meters by inspection robots in complex environments, which can cause the failure of pointer meter readings. In this paper, an improved k-means clustering method for adaptive detection of pointer meter reflective areas and a robot pose control strategy to remove reflective areas are proposed based on deep learning. It mainly includes three steps: (1) YOLOv5s (You Only Look Once v5-small) deep learning network is used for real-time detection of pointer meters. The detected reflective pointer meters are preprocessed by using a perspective transformation. Then, the detection results and deep learning algorithm are combined with the perspective transformation. (2) Based on YUV (luminance-bandwidth-chrominance) color spatial information of collected pointer meter images, the fitting curve of the brightness component histogram and its peak and valley information is obtained. Then, the k-means algorithm is improved based on this information to adaptively determine its optimal clustering number and its initial clustering center. In addition, the reflection detection of pointer meter images is carried out based on the improved k-means clustering algorithm. (3) The robot pose control strategy, including its moving direction and distance, can be determined to eliminate the reflective areas. Finally, an inspection robot detection platform is built for experimental study on the performance of the proposed detection method. Experimental results show that the proposed method not only has good detection accuracy that achieves 0.809 but also has the shortest detection time, which is only 0.6392 s compared with other methods available in the literature. The main contribution of this paper is to provide a theoretical and technical reference to avoid circumferential reflection for inspection robots. It can adaptively and accurately detect reflective areas of pointer meters and can quickly remove them by controlling the movement of inspection robots. The proposed detection method has the potential application to realize real-time reflection detection and recognition of pointer meters for inspection robots in complex environments.

Batch alignment of single-cell transcriptomics data using deep metric learning

scRNA-seq has uncovered previously unappreciated levels of heterogeneity. With the increasing scale of scRNA-seq studies, the major challenge is correcting batch effect and accurately detecting the number of cell types, which is inevitable in human studies. The majority of scRNA-seq algorithms have been specifically designed to remove batch effect firstly and then conduct clustering, which may miss some rare cell types. Here we develop scDML, a deep metric learning model to remove batch effect in scRNA-seq data, guided by the initial clusters and the nearest neighbor information intra and inter batches. Comprehensive evaluations spanning different species and tissues demonstrated that scDML can remove batch effect, improve clustering performance, accurately recover true cell types and consistently outperform popular methods such as Seurat 3, scVI, Scanorama, BBKNN, Harmony et al. Most importantly, scDML preserves subtle cell types in raw data and enables discovery of new cell subtypes that are hard to extract by analyzing each batch individually. We also show that scDML is scalable to large datasets with lower peak memory usage, and we believe that scDML offers a valuable tool to study complex cellular heterogeneity.

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology

Since genes do not function individually, the gene module is considered an important tool for interpreting gene expression profiles. In order to consider both functional similarity and expression similarity in module identification, GMIGAGO, a functional Gene Module Identification algorithm based on Genetic Algorithm and Gene Ontology, was proposed in this work. GMIGAGO is an overlapping gene module identification algorithm, which mainly includes two stages: In the first stage (initial identification of gene modules), Improved Partitioning Around Medoids Based on Genetic Algorithm (PAM-GA) is used for the initial clustering on gene expression profiling, and traditional gene co-expression modules can be obtained. Only similarity of expression levels is considered at this stage. In the second stage (optimization of functional similarity within gene modules), Genetic Algorithm for Functional Similarity Optimization (FSO-GA) is used to optimize gene modules based on gene ontology, and functional similarity within gene modules can be improved. Without loss of generality, we compared GMIGAGO with state-of-the-art gene module identification methods on six gene expression datasets, and GMIGAGO identified the gene modules with the highest functional similarity (much higher than state-of-the-art algorithms). GMIGAGO was applied in BRCA, THCA, HNSC, COVID-19, Stem, and Radiation datasets, and it identified some interesting modules which performed important biological functions. The hub genes in these modules could be used as potential targets for diseases or radiation protection. In summary, GMIGAGO has excellent performance in mining molecular mechanisms, and it can also identify potential biomarkers for individual precision therapy.

A new interpoint distance-based clustering algorithm using kernel density estimation

A novel nonparametric clustering algorithm is proposed using the interpoint distances between the members of the data to reveal the inherent clustering structure existing in the given set of data, where we apply the classical nonparametric univariate kernel density estimation method to the interpoint distances to estimate the density around a data member. Our clustering algorithm is simple in its formation and easy to apply resulting in well-defined clusters. The algorithm starts with objective selection of the initial cluster representative and always converges independently of this choice. The method finds the number of clusters itself and can be used irrespective of the nature of underlying data by using an appropriate interpoint distance measure. The cluster analysis can be carried out in any dimensional space with viability to high-dimensional use. The distributions of the data or their interpoint distances are not required to be known due to the design of our procedure, except the assumption that the interpoint distances possess a density function. Data study shows its effectiveness and superiority over the widely used clustering algorithms.

Supramolecular Host–Guest Assemblies of [M6Cl14]2–, M = Mo, W, Clusters with γ-Cyclodextrin for the Development of CLUSPOMs

Host–guest assemblies open up opportunities for developing novel functional CLUSPOM multicomponent systems based on transition metal clusters (CLUS), polyoxometalates (POMs) and macrocyclic organic ligands. In water–ethanol solution γ-cyclodextrin (γ-CD) interacts with halide metal clusters [M6Cl14]2– (M = Mo, W) to form sandwich-type structures. The supramolecular association between the clusters and CDs, however, remains weak in solution, and the interactions are not strong enough to prevent the hydrolysis of the inorganic guest. Although analysis of the resulting crystal structures reveals inclusion complexation, 1H NMR experiments in solution show no specific affinity between the two components. The luminescent properties of the host–guest compounds in comparison with the initial cluster complexes are also studied to evaluate the influence of CD.

Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis.

Identification of clinical phenotypes in critically ill COVID-19 patients could improve understanding of the disease heterogeneity and enable prognostic and predictive enrichment. However, previous attempts did not take into account temporal dynamics with high granularity. By including the dimension of time, we aim to gain further insights into the heterogeneity of COVID-19. We used granular data from 3202 adult COVID patients in the Dutch Data Warehouse that were admitted to one of 25 Dutch ICUs between February 2020 and March 2021. Parameters including demographics, clinical observations, medications, laboratory values, vital signs, and data from life support devices were selected. Twenty-one datasets were created that each covered 24 h of ICU data for each day of ICU treatment. Clinical phenotypes in each dataset were identified by performing cluster analyses. Both evolution of the clinical phenotypes over time and patient allocation to these clusters over time were tracked. The final patient cohort consisted of 2438 COVID-19 patients with a ICU mortality outcome. Forty-one parameters were chosen for cluster analysis. On admission, both a mild and a severe clinical phenotype were found. After day 4, the severe phenotype split into an intermediate and a severe phenotype for 11 consecutive days. Heterogeneity between phenotypes appears to be driven by inflammation and dead space ventilation. During the 21-day period, only 8.2% and 4.6% of patients in the initial mild and severe clusters remained assigned to the same phenotype respectively. The clinical phenotype half-life was between 5 and 6 days for the mild and severe phenotypes, and about 3 days for the medium severe phenotype. Patients typically do not remain in the same cluster throughout intensive care treatment. This may have important implications for prognostic or predictive enrichment. Prominent dissimilarities between clinical phenotypes are predominantly driven by inflammation and dead space ventilation.