## What is/are Cluster C?

Cluster C - The [2Fe-1S] cluster can be isolated in the presence of trimethylphosphine, and the compound with one PMe3 on each iron(II) ion has been crystallographically characterized.^{[1]}To increase the population diversity of cluster centers, the local communication membrane subsystems utilize transport rules between membranes for coevolution of nondominated objects.

^{[2]}A cluster complex with the composition (Bu4N)4[Re6S8(CC6H4-p-NC2)6] having terminal phenoxide ligands is obtained by the reaction of (Bu4N)4[Re6S8Cl6] with silver p-nitrophenolate.

^{[3]}Results and conclusionIn terms of clustering, the proposed method can produce better cluster memberships that affect the cluster compactness and the classifier performances improvement.

^{[4]}tener, it is recovered as sister to the node that cluster C.

^{[5]}Our framework is capable of deployment, configuration, execution, performance trace transformation and aggregation of containerized application frameworks, enabling scripted execution of diverse workloads and cluster configurations.

^{[6]}The DEGs in HG peritoneal metastases compared to non-invasive implants were identified using T-tests performed in the NanoString Diff package, then used to cluster cases using the Eisen cluster 3.

^{[7]}The first phase performs inter-cluster clock synchronization, whereas the second phase accomplishes intra-cluster clock synchronization where each member node synchronizes its clock with the corresponding cluster head.

^{[8]}The effect of cluster Co-X6 (X = S, N, O, and F) doping on the structural, electronic, magnetic, and optical properties of monolayer WS2 were studied by using the first-principles methods, and the results were compared with those in TM-X6 (TM = Mn and Fe, X = S, N, O, and F) doped monolayer ZS2 (Z = Mo and W) alloys from the references.

^{[9]}Different forms of psychotherapy are effective for cluster C personality disorders, but we know less about what in-session processes promote change.

^{[10]}Although the cluster concepts are well-stressed in literature, and in several industries intensities, there are still some contributions that can be addressed to emerging countries.

^{[11]}In this algorithm, the piecewise linear estimation method is adopted to reconstruct feature data in the GIS information database in group, and associated information fusion is performed to the GIS data in the database, and adaptive scheduling is performed to the GIS information feature database through the cascaded distributed scheduling method; according to the spatial distribution of geographic information, vector adjustment is performed to the cluster centre, and the frequent item mining method is adopted to extract features of GIS information, and then sequential processing is adopted to the extracted feature quantity of GIS information; the regularised power density spectrum estimation method is adopted to perform unbiased estimation to GIS information feature data.

^{[12]}Our proposed non-uniform user distribution model is such that a Poisson cluster process with the cluster centers located at SAs in which SAs have a base station offset with their BSs.

^{[13]}Isolated HPIV3 strains were classified within the phylogenetic subcluster C5 but grouped on 2 separate branches of the phylogenetic tree.

^{[14]}55 eV, while coupled-cluster computations significantly overestimate the IE.

^{[15]}Another method makes use of within-cluster crossover information to construct an overall estimator.

^{[16]}The Head Positioning in Acute Stroke Trial (HeadPoST) is a pragmatic, international, cluster crossover randomized trial of 11,093 patients with acute stroke assigned to a lying-flat (0°) or sitting-up (head elevated ≥30°) position.

^{[17]}In this paper, we propose a heuristic algorithm which can solve problems related to only classification, only clustering, or classification with clustering by creating models with the ability to evolve to another class/cluster configuration without a retraining process for new incoming data.

^{[18]}The central cluster is known to harbour other stars more massive than 150M⊙ of similar spectral type and recent astrometric studies on VFTS16 and VFTS72 provide direct evidence that the cluster can eject some of its most massive members, in agreement with theoretical predictions.

^{[19]}Whole-genome sequencing (WGS) differentiated clusters due to active recent transmission, with low single-nucleotide polymorphism–based diversity (cluster C), from clusters involving long-term prevalent strains with higher diversity (clusters A, B).

^{[20]}This value is both consistent and competitive with that derived from cluster catalogues selected in different wavelengths.

^{[21]}The inter-cluster communication is enhanced by dynamic packet forwarding gateway.

^{[22]}The density peaks clustering (DPC) is a clustering method proposed by Rodriguez and Laio (Science, 2014), which sets up a decision graph to identify the cluster centers of data points.

^{[23]}In the first level, we train a number of cluster centres, and use the centres to divide the dataset.

^{[24]}6 × 102 cm−3 s−1 and cluster concentrations under different simulation conditions.

^{[25]}Then, the degradation features are selected as the input of AP clustering to find the cluster centers of different bearing health statuses: “normal”, “slight”, and “severe”.

^{[26]}43%) were assigned to a cluster for which they had the smallest Euclidean distance to the cluster center based on available baseline variables: A1C, BMI, age, age at diagnosis.

^{[27]}The Pearson correlation coefficient was used to assess the dependence between the glucose and insulin levels for each cluster created.

^{[28]}Of these, 35% belonged to cluster C2/1112-15, the most prevalent cluster among adult Danes.

^{[29]}Multitask diffusion strategies are useful to estimate node-specific, or, multiple parameter vectors over a distributed network by exploiting inter-cluster and intra-cluster cooperation.

^{[30]}The first contribution is the introduction of the network-attached accelerator approach, which moves accelerators into a "stand-alone" cluster connected through the Extoll interconnect.

^{[31]}In total, we selected 11 sampling clusters, each cluster containing up to three sampling plots in agricultural fields (field age ranged from 2 to 26 years) and one reference plot in woodland.

^{[32]}It reduces intra-cluster communications by allowing cluster member nodes to send small sized control frames followed by relatively detailed frames from nodes selected by the cluster head node.

^{[33]}They also suggested that opinions are easily polarized when their cluster coefficient were high and the characteristic path length were longer.

^{[34]}We performed a pilot test and a formal test, and evaluated the inter-rater, intra-rater, and internal reliability by calculating the intra-cluster correlation coefficient (ICC), kappa correlation coefficient, and Cronbach's α.

^{[35]}Cluster charges are QM-derived and calculated self-consistently to ensure a polarizable embedding.

^{[36]}Cluster centres chosen in the ascending order of their energy will give the possible near-native structures.

^{[37]}In Euclidean geometry the mean-as used in k-means-is a good estimator for the cluster center, but this does not hold for arbitrary dissimilarities.

^{[38]}We introduce clustered millimeter-wave (mmWave) networks with invoking non-orthogonal multiple access (NOMA) techniques, where the NOMA users are modeled as Poisson cluster processes and each cluster contains a base station (BS) located at the center.

^{[39]}In cluster based WSN, the gateway from the cluster collects, aggregates and sends data to the base station.

^{[40]}We show that the myosin phosphatase Pp1 complex, which inhibits non-muscle myosin-II (Myo-II) activity, coordinates border cell shape and cluster cohesion.

^{[41]}Secondly, feature vectors generated from the cluster centers are scattered as far as possible via Principal Component Analysis and sent to the MeanShift model to coarsely locate the candidate area.

^{[42]}The model explains their smaller average black hole masses, their general aversion for cluster compared to isolated environments, the negligible difference in spectral index between primary and secondary jets despite a time difference in their formation, their absence among the most powerful radio quasars and radio galaxies, and their connection to the elusive FRI quasar class, among others.

^{[43]}Bicluster can groups interactions by rows and columns, so we can analyze it easier.

^{[44]}The rationale is that data points close to a cluster center have a high membership value, while data points in between cluster centers share their membership between the different clusters: by optimizing the second criterion we expect an improvement of the quality of the resulting clustering structure.

^{[45]}To reduce the influence of noise objects, objects distant from cluster centers are rejected in some noise rejection approaches including general noise rejection (GNR) and cluster-wise noise rejection (CNR).

^{[46]}A cluster can drive the PEs to optimize either performance or energy.

^{[47]}The X-ray residual image after removing the global emission of the intracluster medium (ICM), however, shows an arc-like positive excess and a negative excess surrounding the central positive excess in the cluster core, which in turn indicates a disturbance of the ICM.

^{[48]}The formation of the cluster core structure is controlled by the bulkiness of the precursor and of the capping ligand.

^{[49]}Cluster complication frequencies were analyzed for their association with main etiological risk factors (smoking and alcohol).

^{[50]}

## k means algorithm

An initialization approach based on the genetic algorithm (GA) was then used to define the initial cluster centroids for subsequent Gaussian mixture model (GMM), self-organizing map (SOM), fuzzy c-mean (FCM), and k-means algorithms.^{[1]}However, the performance of the K-means algorithm depends highly on initial cluster centers and converges to local minima.

^{[2]}However, the results from both of FCM clustering and K-means algorithm, based on the dominant parameters concentrations, determined 3 cluster groups and produced cluster centers (prototypes).

^{[3]}In order to solve this problem, this paper proposes an improved K-means algorithm in the field of intrusion detection for network security, which is based on Intersection over Union in order to optimize initial clustering centers, with the consideration that the more different the data are, the more suitable the data act as the initial cluster centers.

^{[4]}However, the K-means algorithm could not guarantee unique clustering results for the same dataset because its initial cluster centers are select randomly.

^{[5]}This main drawback of k-means algorithm is random selection of initial cluster centers.

^{[6]}With the K-Means algorithm will process the value system and grouping students according to the value closest to the cluster center point.

^{[7]}Aiming at the shortcomings of the K-Means algorithm in the traditional K-Means algorithm, the DBSCAN algorithm is used to divide the order set according to the density, and obtain the batch number K value and the initial cluster center point.

^{[8]}The proposed system applies K-means algorithm to optimise the initial centroids of the improved fuzzy C-means which incorporates the spatial information and also to get a better estimation of their final cluster centres.

^{[9]}Firstly, as the initial cluster center and the value K is difficult to determine in traditional K-means algorithm, an improved K-means algorithm based on density is proposed.

^{[10]}This method is based on the subtraction clustering algorithm and K-means algorithm to determine the cluster center.

^{[11]}First of all, we proposed an inflection point estimation-based density peaks clustering algorithm to replace K-means algorithm used by LEA, which can automatically determine the number of clusters without the influence of the choice of initial cluster centers on the clustering results.

^{[12]}Second, the particle swarm optimization-K-means algorithm is applied to the clustering analysis, and the level of electricity usage is divided by the cluster centers.

^{[13]}The paper improves its algorithm based on compression learning and applies it to the K-means algorithm, which uses the sketch of the original data set to estimate the cluster center.

^{[14]}However, the cluster centers generated by k-means algorithm are irresistibly attracted to the denser regions.

^{[15]}To improve the efficiency and effectiveness of ECD analysis, we proposed an improved K-means algorithm, in which principal component analysis (PCA) was used to reduce the dimensions of smart meter time series data and the initial cluster centers were optimized.

^{[16]}However, the cluster number of k-means algorithm needs to be determined in advance, and the initial cluster center also needs to be randomly selected, so it is easy to fall into local optimal.

^{[17]}In this proposed algorithm, firstly, the template-based K-means algorithm is used to initialize segmentation significantly through the perfect selection of a template, based on gray-level intensity of image; secondly, the updated membership is determined by the distances from cluster centroid to cluster data points using the fuzzy C-means (FCM) algorithm while it contacts its best result, and finally, the improved FCM clustering algorithm is used for detecting tumor position by updating membership function that is obtained based on the different features of tumor image including Contrast, Energy, Dissimilarity, Homogeneity, Entropy, and Correlation.

^{[18]}This paper proposes clustering based K-means algorithm with three closest cluster count K, K + 1, K − 1 and computing radius of each cluster.

^{[19]}Finally, the optimal cluster number and the initial cluster center of the K-means algorithm are determined by the elbow method and the silhouette coefficient method.

^{[20]}However, the results from both of FCM clustering and K-means algorithm, based on the dominant parameters concentrations, determined 3 cluster groups and produced cluster centers (prototypes).

^{[21]}In order to overcome the disadvantages of the conventional k-means algorithm lacking the stability and accuracy, we propose a novel boost k-means algorithm by optimizing the choice of initial cluster centers, and no additional parameters are required.

^{[22]}In the third part, K-means algorithm was used for cluster characterization analysis.

^{[23]}The proposed system replaces the initialization method for cluster centroids in classical k-means algorithms which should solve some of the limitations of the k-means algorithm.

^{[24]}

## k means clustering

Unlike the traditional K-means clustering algorithm, density-distance-based processing procedures were presented to determine the cluster centers.^{[1]}However, due to the influence of mixed pixel, the matching of data points and cluster centers of the traditional k-means clustering algorithm is very difficult.

^{[2]}Despite its significant success, there are still several challenging issues in the k-means clustering research, one of which lies in its high sensitivity to the selection of the initial cluster centers.

^{[3]}Since the traditional K-Means clustering algorithm is easy to be sensitive to noise and it is difficult to obtain the optimal initial cluster center position and number, a method based on histogram and K-Means clustering is proposed.

^{[4]}After applying K-means clustering on uncovered customers, we take cluster centers as primary branch candidates and collect Geographic Information System (GIS) information about them.

^{[5]}This method generates the optimal solution by genetic operation of variable length chromosomes, automatically determines the most appropriate number of cluster centers, and effectively solves the existing K-means clustering algorithms’ defects such as highly subject to the constraint of the initial clustering centers and need to specify the specific number of clustering centers.

^{[6]}In each K-means clustering step, both the nearest and the farthest instance to each cluster center are selected into a set.

^{[7]}To cluster countries, k-means clustering method is run in WEKA software for two cluster numbers and four different initial solution approaches.

^{[8]}The procedure to cluster the indoor space characteristics of an urban railway station in this study consists of four steps: data collection, feature vector extraction, K-means clustering, and cluster characteristics analysis.

^{[9]}DFC analysis of ICNs was carried out in GIFT software (Medical Image Analysis Lab, USA) with sliding time-window method and k-means clustering algorithm to categorize DFC by matching the time-windowed connectivity patterns ("cluster centroids") to features in a finite set of connectivity patterns ("states").

^{[10]}K-means clustering method chooses random cluster centres (initial centroid), one for each centroid, and this is the major weakness of K-means.

^{[11]}We used k-means clustering method to cluster condition of traffic flow on road segments.

^{[12]}Based on K-means clustering using randomly selected objects as an initial cluster center, we divided the YRD into different functional areas (optimized-, key-, and restricted-development zones) within the grid to create divisions with consistent biophysical properties, thereby avoiding the impact of urban administrative boundaries on the results.

^{[13]}Estimating cluster center using k-means clustering self-organizing algorithm based on neural network radial basis function.

^{[14]}The DDJKM algorithm can avoid the problem of random selection of initial cluster centers in conventional k-means clustering algorithms, so that isolated nodes will not be selected as initial cluster centers.

^{[15]}We then exploit the k-means clustering algorithm to calculate four cluster centers, which are treated as the final four key points.

^{[16]}Secondly, this dataset is broken down into clusters by using k-means clustering, and the cluster centroids are calculated.

^{[17]}This algorithm called weighted K-means decision cluster classifier (WKDCC) is based on the establishment of decision tree model, but four improvements are proposed: (1) using anchor partition instead of heuristic information such as information gain to search (2) feature weighting; (3) using k-means clustering center instead of centroid as anchor point; (4) asymmetric partition.

^{[18]}To mitigate both shortcomings, we formulate a joint outlier detection and clustering problem, which assigns a prescribed number of datapoints to an auxiliary outlier cluster and performs cardinality-constrained K-means clustering on the residual dataset, treating the cluster cardinalities as a given input.

^{[19]}

## density functional theory

Aiming at identifying the effects of Pd ensemble and cluster size on the selectivity and activity of C2H2 selective hydrogenation over Pd-modified Cu nano-cluster catalysts, the catalytic performance of C2H2 selective hydrogenation over different cluster sizes of Pd-modified Cu catalyst with different Pd ensemble are examined using density functional theory calculations.^{[1]}The stability patterns of single silver, platinum, and palladium atom doped gold cluster cations, MAuN−1 (M = Ag, Pt, Pd; N = 3–6), are investigated by a combination of photofragmentation experiments and density functional theory calculations.

^{[2]}Coupled cluster calculations were performed at high levels to assess MP3, CCSD, CCSD(T), empirical density functional theory dispersion (D3), and the many-body dispersion (MBD) approach.

^{[3]}In this work, we apply density functional theory and high-level coupled cluster calculations to describe the geometry and relative stability of C6H12+˙ radical cations, whose cyclic isomers are prototypes of singly-charged cycloalkanes.

^{[4]}The adsorption and dissociation of NO on the cationic Ta15+ cluster were investigated using the density-functional theory (DFT) calculations, and the Ta-centered bicapped hexagonal antiprism (BHA) structure of cationic Ta15+ cluster can be identified as the global minimum, which reproduces well the infrared multiple photo dissociation (IR-MPD) spectrum.

^{[5]}Density functional theory (DFT), second-order Møller-Plesset perturbation theory (MP2) and CCSD(T) coupled-cluster computations with simple methyl and ethyl substituents indicate that electronic energies of the cis isomers are lowered by roughly 3 to 4 kcal mol−1 when the OH group of these cyclopentanol systems forms an intramolecular contact with the O, S, N or P atom on the adjacent carbon.

^{[6]}The geometries and electronic structures of small M7C (M = Sc, Y, La, Ti, Zr, Hf; C = 0, ±1, ±2) clusters have been calculated at a range of multiplicities at each cluster charge, using density functional theory methods.

^{[7]}In situ spectroscopic characterization captured all the intermediates in the reaction processes, and these data allowed us to calibrate the density-functional-theory cluster calculations, by means of which we were able to show that the charge compensation requirement at the nearest two Al sites arrayed circumferentially in the 10-membered ring of MFI zeolite creates such novel functionalities of cadmium.

^{[8]}The proton affinity, ionization energy, dipole moment, and polarizability values of the neutral molecules were determined from density functional theory and coupled-cluster calculations.

^{[9]}We present 2p core-level spectra of size-selected aluminum and silicon cluster cations from soft X-ray photoionization efficiency curves and density functional theory.

^{[10]}Density functional theory and coupled cluster calculations were performed to support the reaction mechanisms for photoenolization of ketones 1 and 2.

^{[11]}

## explicitly correlated coupled

The spectroscopic parameters, stability, and geometries of the lowest stable isomers of its isoelectronic system [Al, N, S] were characterized using coupled-cluster CCSD(T), explicitly correlated coupled-cluster CCSD(T)-F12, and multireference configuration interaction.^{[1]}Notably, the PES was constructed from explicitly correlated coupled cluster calculations with extrapolation to the complete basis set limit and considered additional energy corrections to account for core-valence electron correlation, higher-order coupled cluster terms beyond perturbative triples, scalar relativistic effects, and the diagonal Born-Oppenheimer correction.

^{[2]}The gas-phase heats of formation of these compounds were determined with the diet-HEAT-F12 protocol, which uses explicitly correlated coupled-cluster calculations along with anharmonic vibrational, scalar relativistic, and diagonal Born-Oppenheimer corrections.

^{[3]}The vibrational spectra of simple CH3 +-Rg (Rg = He, Ne, Ar, Kr) complexes have been studied by vibrational configuration interaction theory relying on multidimensional potential energy surfaces (PESs) obtained from explicitly correlated coupled cluster calculations, CCSD(T)-F12a.

^{[4]}Using explicitly correlated coupled cluster calculations and after inclusion of the zero point vibrational energy, core-valence and scalar relativistic effects, the AIE is calculated to be AIEcalc = 10.

^{[5]}In this paper, high level ab initio electronic structure calculations using the coupled cluster CCSD(T) and explicitly correlated coupled cluster CCSD(T)-F12 methods with large basis sets extrapolated to the complete basis set limit have been performed on the various [Al,N,C,O] isomers.

^{[6]}

## high level coupled

Further study reveals that two osmapyridiniums containing one or two phosphonium substituents exhibit the character of the triplet ground state, which was supported by the high-level coupled cluster calculations.^{[1]}1 kcal mol-1 ) by using a carbon-boron formal frustrated Lewis pair, which is supported by high-level coupled cluster calculations.

^{[2]}High-level coupled cluster calculations obtained with the Feller-Peterson-Dixon (FPD) approach and new data from the most recent version of the Active Thermochemical Tables (ATcT) are used to reassess the enthalpy of formation of gas-phase C2H2O4 (oxalic acid).

^{[3]}We report on high-level coupled-cluster calculations of electronic states of the neutral endohedral fullerene Li@C20.

^{[4]}High-level coupled cluster calculati.

^{[5]}

## mobility mass spectrometry

Structural assignments of gas-phase magnesium oxide cluster cations, MgnOn+ (n ≦ 24), have been achieved from a comparison of experimental collision cross sections (CCSs) measured using ion mobility mass spectrometry and theoretical CCSs calculated for equilibrium structures optimized by quantum chemical calculations.^{[1]}Geometrical structures of cerium oxide cluster cations, CenOm+ (n = 2–6, m ≤ 2n), were studied by ion mobility mass spectrometry (IMMS).

^{[2]}Geometric structures of gas-phase palladium oxide cluster cations, PdnOm+, were investigated for stable compositions by ion mobility mass spectrometry (IMMS) and quantum chemical calculations.

^{[3]}Structures of stable compositions of sodium oxide cluster cations (Na $_n$ O $_m$ $^+$ , $n$ $\leq$ 11) have been investigated by ion mobility mass spectrometry.

^{[4]}

## time dependent density

The absorption bands in the action spectra were assigned on the basis of time-dependent density functional theory calculations that were benchmarked on equation-of-motion coupled cluster calculations of G•+.^{[1]}Relativistic unrestricted time-dependent density functional theory calculations were performed for obtaining the absorption spectra of gold-cluster complexes Au 4 - S - C n H 2 n - S ′ - Au 4 ′ (n = 2–5).

^{[2]}The symmetry adapted cluster configuration interaction calculation supports this assignment of the S1 electronic state, although the time-dependent density functional theory calculation suggests that the S1 state is 1A2.

^{[3]}

## inter cluster separation

This paper proposes a meta-heuristic optimization based two-stage residential load pattern clustering (LPC) approach to address two main issues that exist in the most current LPC methods: 1) unreasonable typical load pattern (TLP) extraction, 2) a good clustering should achieve reasonable balance between the intra-cluster compactness and inter-cluster separation of the formed clusters.^{[1]}In addition, an improved multi-objective optimization immune algorithm (IMOIA), which simultaneously optimizes the intra-cluster compactness and inter-cluster separation, is proposed to prevent the algorithm from falling into local optimum.

^{[2]}Based on the monotonous feature of BCVI and the linear combination of intra-cluster compactness and inter-cluster separation of clusters, BCVI consumes much lower time cost in finding the optimal clustering number (Kopt) than the commonly used method that utilizes the empirical rule Kmax≤ n to calculate the Kopt.

^{[3]}

## kernel density estimation

Aiming at deficiencies of the clustering by fast search and find of density peaks algorithm (CFSFDP) in which cluster centers cannot be selected automatically and the segmentation of cluster points is unreasonable, this paper proposes an optimized CFSFDP algorithm (O-CFSFDP) based on kernel density estimation and the max-min clustering algorithm.^{[1]}Aiming at deficiencies of clustering by fast search and find of density peaks (CFSFDP) algorithm in the manual selection of the cutoff distance and cluster centers, this paper proposes an adaptive CFSFDP (A-CFSFDP) algorithm, which is based on kernel density estimation and anomaly detection.

^{[2]}For the clustering step, the algorithm uses the kernel density estimation approach to define cluster centers.

^{[3]}

## polyketide synthase gene

The nectriapyrone biosynthetic gene cluster consists of a polyketide synthase gene (NEC1) and an O‐methyltransferase gene (NEC2).^{[1]}In this study, a gene cluster containing the polyketide synthase gene UvPKS1 was analysed via gene replacement and biochemical studies to determine ustilaginoidin biosynthetic pathway in U.

^{[2]}The 15 kb‐spanning gene cluster consists of a polyketide synthase gene, pksI, an O‐methyltransferase, omtI, a FAD‐dependent monooxygenase, moxI, a short chain dehydrogenase, sdrI, a putative extradiol dioxygenase, doxI and a transcription factor gene, aohR.

^{[3]}

## external magnetic field

The entropy properties of the antiferromagnetic spin- 1 ∕ 2 Ising model in the presence of the external magnetic field on the kagome lattice are studied in the framework of various effective-field theory cluster approximations up to the size of the cluster consisting of 12 connected sites, which form typical basic star-like geometrical structure of the kagome lattice.^{[1]}The interaction between magnetic cluster chains formed by the ferroparticles, with the liquid crystal director and the external magnetic field, is explained theoretically by comparing the surface anchoring force and the magnetic moment in the dynamic system.

^{[2]}We investigate the antiferromagnetic spin- 1 ∕ 2 Ising model in the presence of the external magnetic field on the geometrically frustrated kagome lattice using the effective field theory cluster approximation up to the size of the cluster consisting of 12 connected sites which form typical basic geometrical structure of the kagome lattice.

^{[3]}

## c means algorithm

Furthermore, the Taylor theorem is used to approximate the functions for calculating the weight value of each object and updating the membership matrix and the cluster centers as the polynomial functions which only include addition and multiplication operations such that the weighed possibilistic c-means algorithm can be securely and correctly performed on the encrypted data in cloud.^{[1]}The main idea of fuzzy c-means energy is to quickly compute the two types of cluster center functions for all points in image domain by fuzzy c-means algorithm locally with a proper preprocessing procedure before the curve starts to evolve.

^{[2]}The fuzzy c-means algorithm (FCM) is aimed at computing the membership degree of each data point to its corresponding cluster center.

^{[3]}

## open source software

Current work is based on an on-premise cluster computing approach for fast and efficient photogrammetry process using open source software such as OpenDroneMap combined with light-weight containerization techniques such as Docker (version 17.^{[1]}We describe the establishment of an affordable high-performance computing bioinformatics cluster consisting of 3 nodes, constructed using ordinary desktop computers and open-source software including Linux Fedora, SLURM Workload Manager, and the Conda package manager.

^{[2]}The Digital Repository at Scale that Invites Computation (DRAS-TIC) Fedora research project, funded by a two-year National Digital Platform grant from the Institute for Museum and Library Services (IMLS), is producing open-source software, tested cluster configurations, documentation, and best-practice guides that enable institutions to manage linked data repositories with petabyte-scale collections reliably.

^{[3]}

## star formation rate

Moreover, the star formation rate and the specific star formation rate are strongly dependent on stellar mass even when the distance from the cluster core is used as a proxy for the environment, rather than the halo mass.^{[1]}The inferred relation between accretion rate and star formation rate does not appear to depend on environment, as no difference is seen in the cluster/proto-cluster compared to the field.

^{[2]}Exploiting constraints derived from Hubble Space Telescope images and Keck longslit spectroscopy, our finding of apparent debris trails and dramatically enhanced star formation rates in an additional seven RPS candidates support the hypothesis that RPS, and hence rapid galaxy evolution in high-density environments, is intricately linked to cluster collisions.

^{[3]}

## density peak clustering

This paper used the density peak clustering to determine the cluster centers of various categories of images, and took it as the target spectrum, and took the clustering results as the ground data.^{[1]}Its main feature is to use the density peak clustering algorithm to perform initial clustering to obtain the number of clusters and the cluster center of each cluster.

^{[2]}This method solves the problem of automatically determining cluster centers by using the improved density peak clustering algorithm combined with a greedy algorithm.

^{[3]}

## logistic regression model

These procedures are the Logistic Regression model (ignoring the DRG effect), the Generalized Estimating Equation (GEE) that takes into account the within DRG clustering effect (but the within cluster correlation is treated as nuisance parameter), and the Generalized Linear Mixed Model (GLIMMIX).^{[1]}Using logistic regression model, the cluster category of the highest grade of inflammation showed to be associated with worse cognitive performance in women only.

^{[2]}Although K-means is simple and can be used for a wide variety of data types, it is quite sensitive to initial positions of cluster centers which determine the final cluster result, which either provides a sufficient and efficiently clustered dataset for the logistic regression model, or gives a lesser amount of data as a result of incorrect clustering of the original dataset, thereby limiting the performance of the logistic regression model.

^{[3]}

## support vector regression

The online stage has two steps: (1) a coarse positioning algorithm is adopted to find the most similar sub database by matching the cluster center with the fingerprint of the node tested, which will narrow the search space and consequently save time; (2) in the sub database area, a support vector regression (SVR) algorithm with its parameters being optimized by particle swarm optimization (PSO) is used for fine positioning, thus improving the online positioning accuracy.^{[1]}In this study, a three-level mixed-effects least squares support vector regression (MLS-SVR) model is proposed to extend the standard least squares support vector regression (LS-SVR) model for handling cluster correlated data.

^{[2]}

## complete basis set

In this work we have performed benchmark coupled cluster calculations at the complete basis set limit for a large number of different isomers of representative clusters of third row elements.^{[1]}

## means clustering algorithm

Various techniques have been developed to enhance the performance of UK-means clustering algorithm but they are all centered on two major factors: choosing initial cluster centers and determining the number of clusters.^{[1]}

## cascade hydro photovoltaic

Taking the cascade hydro-photovoltaic-pumped storage combines power generation technology as the research object, this paper summarizes its research status in recent years, and discusses the prospects of three key technologies of multi-time-scale planning of hydro-photovoltaic-pumped storage power generation which adapts to complex dynamic and static operating conditions, dynamic safe operation interval analysis and cluster control of cascade hydro-photovoltaic hybrid system and intelligent dispatching and optimal control of cascade hydro-photovoltaic-pumped storage hybrid power station with consideration taken on stochastic behaviors.^{[1]}

## Gene Cluster C

Despite evidence for high diversity in gene cluster content among closely related strains, the microevolutionary processes driving gene cluster gain, loss, and neofunctionalization are largely unknown.^{[1]}INTRODUCTION The common genetic variant (rs1051730) in the 15q24 nicotinic acetylcholine receptor gene cluster CHRNA5-CHRNA3-CHRNB4 was associated with smoking quantity and has been reported to be associated also with reduced ability to quit smoking in pregnant women but results were inconsistent in nonpregnant women.

^{[2]}The team, led by Eric Oswald and Jean-Philippe Nougayrede of Inserm, a French medical research institute, reported that strains of Escherichia coli containing a particular gene cluster could break double-stranded DNA in mammalian cells (Science 2006, DOI: 10.

^{[3]}The SM biosynthetic gene cluster comprises of genes encoding for NRPS/PKS, a transcription factor, and other accessory genes essential for assembly and maturation of SM.

^{[4]}pseudoalcaligenes CECT5344 cynFABDS gene cluster codes for the putative transcriptional regulator CynF, the ABC-type cyanate transporter CynABD, and the cyanase CynS.

^{[5]}Sequence analyses identified the presence of a resistance gene cluster comprising the genes sul2-ΔstrA-dfrA14-ΔstrA-ΔstrB.

^{[6]}One family of sperm-expressed genes (Zp3r, C4bpa) in the mammalian gene cluster called the regulator of complement activation (RCA) encodes proteins that bind eggs and mediate reproductive success, and are therefore expected to show high relative rates of nonsynonymous nucleotide substitution in response to sexual selection in comparison to other genes not involved in gamete binding at fertilization.

^{[7]}We found that overexpression or deletion of the let-7adf gene cluster causes altered IL-6 induction both in tissue culture cells induced by LPS treatment in vitro as well as in a Salmonella infection mouse model in vivo.

^{[8]}One challenge is that the tomato NRC4 gene cluster consists of three paralogues and the related NRC5 gene.

^{[9]}Genome-wide transcriptomic and chromatin immunoprecipitation analyses demonstrate that the protein binds to only four genomic sites, acting as a repressor of a 30-kb gene cluster comprising 23 open reading frames encoding lipases and β-oxidation enzymes.

^{[10]}The nectriapyrone biosynthetic gene cluster consists of a polyketide synthase gene (NEC1) and an O‐methyltransferase gene (NEC2).

^{[11]}We identified a gene cluster comprising eight genes that was up-regulated during growth with arabitol as a sole carbon source.

^{[12]}Several hexonates, including d‐altronate, d‐idonate and l‐gluconate, which are also substrates of C785_RS13685, also significantly up‐regulated the gene cluster containing C785_RS13685, suggesting a possibility that pyruvate and d‐ or l‐glycerate were ultimately produced (novel Route III).

^{[13]}In contrast, the tox-argK gene cluster coding for phaseolotoxin was detected in K3 and in the biovar 1 strains (K3, Kw30, and Psa92), and produced a false-positive amplicon for the hopAM1-like gene in this study.

^{[14]}The fogacin biosynthetic type II polyketide synthase (PKS) gene cluster contains a hydroxymethylglutaryl‐coenzyme A synthase (HCS) cassette, which is usually responsible for β alkylation in the type I PKS system.

^{[15]}Genome and proteome analyses revealed that a gene cluster containing a flavin-dependent monooxygenase and a flavin reductase was highly up-regulated in response to sulfonamides.

^{[16]}Despite evidence for high diversity in gene cluster content among closely related strains, the microevolutionary processes driving gene cluster gain, loss and neofunctionalization are largely unknown.

^{[17]}The other gene cluster confers resistance to a limited range of antibiotics and is found only in Chromobacterium subtsugae, a subset of this genus that is entirely nonpathogenic.

^{[18]}Here, we report the identification of the elsinochrome and phleichrome BGCs of Elsinoë fawcettii and Cladosporium phlei, respectively, based on gene cluster conservation with the CTB and hypocrellin BGCs.

^{[19]}Genome comparison revealed that a 21499-bp DNA fragment containing a putative angular dioxygenase gene cluster consisting of the dioxygenase-, ferredoxin reductase- and ferredoxin-encoding genes (pzcA1A2, pzcC and pzcD) is missed in the PCA degradation-deficient mutant WH99M.

^{[20]}This galactan degradation cascade includes a gene cluster containing galactan degradation genes (ganA and ganB), two transporter component genes (ganQ and ganP), and the sugar-binding lipoprotein-encoding gene ganS [12].

^{[21]}In primates, the ATAD3 gene cluster contains ATAD3A, ATAD3B and ATAD3C.

^{[22]}A recent genome-wide association study identified a locus in the fatty acid desaturase (FADS) gene cluster conferring susceptibility to BD.

^{[23]}Rapid and exact cloning of polysaccharide gene cluster can be achieved by this method.

^{[24]}We also found that spatial reorganization of a histone gene cluster could control gene transcription.

^{[25]}Background: rs9357347 located at the triggering receptor expressed on myeloid cells (TREM) gene cluster could increase TREM2 and TREM-like transcript 1 (TREML1) brain gene expression, which is considered to play a protective role against Alzheimer’s disease (AD).

^{[26]}A gene cluster composed of five genes is required for MEL biosynthesis.

^{[27]}The CFA-Ile biosynthetic gene cluster contains a regulatory gene, cfaR, which directly controls the expression of the phytotoxin structural genes.

^{[28]}One class of molecules, colibactins, are produced from the gene cluster called the polyketide synthase (pks) island.

^{[29]}vesiculosa HM13 revealed that this bacterium has a gene cluster coding for a non-canonical type II protein secretion system (T2SS) homolog in addition to a gene cluster coding for canonical T2SS.

^{[30]}pinensis as a putative azasugar producer, via observation of a three-gene cluster coding for putative aminotransferase, alcohol dehydrogenase, and sugar phosphatase enzymes, similar to the previously reported azasugar biosynthetic signature identified in Bacillus amyloliquefaciens FZB42.

^{[31]}The dhurrin-producing yeast strain was used as a chassis to investigate previously uncharacterized enzymes identified close to the biosynthetic gene cluster containing the dhurrin pathway enzymes.

^{[32]}UIC 10036 was sequenced, and bioinformatic analysis revealed a cyanobactin‐like biosynthetic gene cluster consistent with the structure of 1.

^{[33]}The results show that the mcy gene cluster can be engineered in E.

^{[34]}Microbial assemblages in mat samples with the anatoxin-a gene cluster consistently had a lower abundance of Burkholderiales (Betaproteobacteria) species than did mats without the anatoxin-producing genes.

^{[35]}BYK-11038 revealed that the candidate biosynthetic gene cluster contains 21 open reading frames (ORFs) including three modular polyketide synthases (PKSs; SprA, SprB and SprC), which were composed of 4 modules with one loading module and 18 additional ORFs (SprD to SprU) spanning a distance of 55 kbp.

^{[36]}The tdh genes and the T3SS2 gene cluster constitute an 80 kb pathogenicity island known as Vp-PAI located on the chromosome II.

^{[37]}Genomic sequence analysis has revealed that a compact nif (nitrogen fixation) gene cluster comprising 9–10 genes nifBHDKENX(orf1)hesAnifV is conserved in diazotrophic Paenibacillus species.

^{[38]}The putative ascochitine biosynthesis gene cluster comprises 11 genes that have undergone rearrangement and gain-and-loss events relative to the citrinin biosynthesis gene cluster in Monascus ruber.

^{[39]}RESULTS A 2764-bp gene cluster containing the type II dihydropteroate synthase gene sul2 and the aminoglycoside phosphotransferase genes strA and strB was detected in the 1200-1400-year-old Antarctic ice core (DF-63.

^{[40]}The diazotrophic Paenibacillus polymyxa WLY78 possesses a minimal nitrogen fixation gene cluster consisting of nine genes (nifB nifH nifD nifK nifE nifN nifX hesA and nifV).

^{[41]}lilacinum that are synthesized, modified, and regulated by a gene cluster consisting of 20 genes.

^{[42]}caccae possesses a single gene cluster consisting of four genes, including a gene encoding the putative FOS degradation enzyme sucrose-6-phosphate hydrolase (S6PH).

^{[43]}This gene cluster consists of eight genes, among which six are conserved in the helvolic acid gene cluster except fusC1 and fusB1.

^{[44]}SNJ042 revealed the OMS biosynthetic gene cluster consisting of a nonribosomal peptide synthetase (NRPS) gene and three genes for amino acid modification.

^{[45]}A hypoxia-typic morphotype is generated through the expression of a subtelomeric gene cluster containing genes that alter the hyphal surface and perturb interhyphal interactions to disrupt in vivo biofilm and infection site morphologies.

^{[46]}In this study, a gene cluster containing the polyketide synthase gene UvPKS1 was analysed via gene replacement and biochemical studies to determine ustilaginoidin biosynthetic pathway in U.

^{[47]}The sch gene cluster contains several putative regulatory genes.

^{[48]}We analyzed the biosynthetic gene cluster content of the three species to correlate their biosynthetic capacities, by matching them with the specialized metabolites detected in the current study.

^{[49]}Then, the biosynthetic gene cluster ctc of Streptomyces aureofaciens ATCC 10762 was integrated into the chromosome of SR0.

^{[50]}

## Initial Cluster C

An initialization approach based on the genetic algorithm (GA) was then used to define the initial cluster centroids for subsequent Gaussian mixture model (GMM), self-organizing map (SOM), fuzzy c-mean (FCM), and k-means algorithms.^{[1]}However, the performance of the K-means algorithm depends highly on initial cluster centers and converges to local minima.

^{[2]}The selection of the initial cluster center is done in order to lower the intensity of distant points.

^{[3]}In addition to this, the proposed EQIE-FCM approach also finds the optimal number of clusters and more accurate location of initial cluster centers for these benchmark datasets.

^{[4]}However, it's documented that the K-Means algorithmic program could get suboptimal solutions, looking on the selection of the initial cluster centers.

^{[5]}Next, the Canopy algorithm was introduced to cluster the weight data, and identify the initial cluster centers for the KMC.

^{[6]}In order to solve this problem, this paper proposes an improved K-means algorithm in the field of intrusion detection for network security, which is based on Intersection over Union in order to optimize initial clustering centers, with the consideration that the more different the data are, the more suitable the data act as the initial cluster centers.

^{[7]}However, the K-means algorithm could not guarantee unique clustering results for the same dataset because its initial cluster centers are select randomly.

^{[8]}Despite its significant success, there are still several challenging issues in the k-means clustering research, one of which lies in its high sensitivity to the selection of the initial cluster centers.

^{[9]}Since the traditional K-Means clustering algorithm is easy to be sensitive to noise and it is difficult to obtain the optimal initial cluster center position and number, a method based on histogram and K-Means clustering is proposed.

^{[10]}Firstly, regarding the clustering center set as the population particle, and the global search ability of GSA is used to optimize the initial cluster centers.

^{[11]}This main drawback of k-means algorithm is random selection of initial cluster centers.

^{[12]}If initial cluster centers (membership degree initialization in FCM) are not selected appropriately, it may yield poor results.

^{[13]}Aiming at the shortcomings of the K-Means algorithm in the traditional K-Means algorithm, the DBSCAN algorithm is used to divide the order set according to the density, and obtain the batch number K value and the initial cluster center point.

^{[14]}AP is firstly utilized to determine the number of CHs and to search for the optimal initial cluster centers for K-medoids.

^{[15]}Firstly, as the initial cluster center and the value K is difficult to determine in traditional K-means algorithm, an improved K-means algorithm based on density is proposed.

^{[16]}In addition to the concentration of exogenous ligands, the rate of particle growth and final product distribution were dependent on temperature and initial cluster concentration.

^{[17]}Fuzzy C-means (FCM) clustering method has been widely used in image segmentation that plays an important role in a variety of applications in image processing and computer vision systems, but the performance of FCM heavily relies on the initial cluster centers which are difficult to determine.

^{[18]}First of all, we proposed an inflection point estimation-based density peaks clustering algorithm to replace K-means algorithm used by LEA, which can automatically determine the number of clusters without the influence of the choice of initial cluster centers on the clustering results.

^{[19]}Although fuzzy k‐prototypes algorithms are well known for their efficiency in clustering numerical and categorical data, they are sensitive to the selection of initial cluster centers.

^{[20]}However, FCM is significantly sensitive to the initial cluster center and easily trapped in a local optimum.

^{[21]}The orthogonal initialization aims to improve the uniformity of the initial cluster centers in the objective space instead of the decision space, which can enhance the convergence performance.

^{[22]}Various techniques have been developed to enhance the performance of UK-means clustering algorithm but they are all centered on two major factors: choosing initial cluster centers and determining the number of clusters.

^{[23]}It also overcomes two drawback of clustering methods such as being sensitive to the initial cluster centers and need for specifying the number of clusters.

^{[24]}Furthermore, we improve the max-min distance method to optimize the initial cluster centers.

^{[25]}In addition, they need to randomly determine the initial cluster centers at the clustering stage and the clustering performance is not stable.

^{[26]}The proposed method can add information of predefined scene categories from different images, which is beneficial for the selection of initial cluster centers, and, to a certain extent, alleviates the problem that the amount of samples to be clustered in a single course of clustering can be too large.

^{[27]}In this paper, we use Markov chain Monte Carlo sampling to approximate the seeding step of k-means++, and get the initial cluster centers of k-means.

^{[28]}Traditional clustering algorithms are susceptible to initial cluster centers, which leads to the degradation of clustering quality.

^{[29]}To improve the efficiency and effectiveness of ECD analysis, we proposed an improved K-means algorithm, in which principal component analysis (PCA) was used to reduce the dimensions of smart meter time series data and the initial cluster centers were optimized.

^{[30]}The point set of high density number is extracted from the original data set as a new training set, and the point in the point set of the high density number is selected as the initial cluster center point.

^{[31]}However, the cluster number of k-means algorithm needs to be determined in advance, and the initial cluster center also needs to be randomly selected, so it is easy to fall into local optimal.

^{[32]}Therefore, a new approach to initialize centroids for k-means is proposed in this paper on the basis of the concept to choose the well separated data-objects as initial cluster centroids instead of pure arbitrary selection.

^{[33]}Based on K-means clustering using randomly selected objects as an initial cluster center, we divided the YRD into different functional areas (optimized-, key-, and restricted-development zones) within the grid to create divisions with consistent biophysical properties, thereby avoiding the impact of urban administrative boundaries on the results.

^{[34]}Several shortcomings associated with these techniques have been identified and resolved such as initial cluster center selection, number of clusters, slow convergence rate, local optima etc.

^{[35]}To reduce the computing time of algorithms, this paper introduces a method for finding reasonable initial cluster centers.

^{[36]}The DDJKM algorithm can avoid the problem of random selection of initial cluster centers in conventional k-means clustering algorithms, so that isolated nodes will not be selected as initial cluster centers.

^{[37]}After that, the GMM Tree algorithm is used to identify the number of clusters and initial clusters in each subspace dataset and passing these initial cluster centers to k-means to generate base subspace clustering results.

^{[38]}Here FACO algorithm situates optimal initial cluster centroid for the MKFCM, thus improve all applications affiliated fuzzy clustering such as foreground segmentation in image processing.

^{[39]}Specifically, we firstly present a Hadoop-based hybrid feature selection algorithm to find the most effective feature sets and propose an improved density-based initial cluster centers selection algorithm to solve the problem of outliers and local optimal.

^{[40]}In the premise part identification, the cluster number and initial cluster centers are obtained at first by using entropy-based clustering method.

^{[41]}DB-Kmeans uses a new selection method of initial cluster center in K-means and set the neighborhood radius in DBSCAN to dynamic.

^{[42]}In the proposed protocol, the Mamdani fuzzy inference system (FIS) is used twice to select the initial cluster center and the final CH.

^{[43]}The proposed algorithm for selecting initial cluster centers on the basis of minimal spanning tree (MST) is presented.

^{[44]}Whale Optimization Algorithm can provide optimal solutions and more stable clustering results because there is no dependence on initial cluster center initialization.

^{[45]}Specifically, we firstly present a Hadoop-based hybrid feature selection algorithm to find the most effective feature sets and propose an improved density-based initial cluster centers selection algorithm to solve the problem of outliers and local optimal.

^{[46]}We, finally, proposed a self-learning algorithm, which can start from any arbitrary initial cluster configuration, and, finally, find the corresponding balanced solution of AGC, where all nodes and clusters are satisfied with the final cluster configuration.

^{[47]}Finally, the optimal cluster number and the initial cluster center of the K-means algorithm are determined by the elbow method and the silhouette coefficient method.

^{[48]}The method is addressed to improve k-Means by defining an initial cluster center through the mean result of the hierarchical clustering method.

^{[49]}However, K-Means performance is highly influenced by the choice of initial cluster centers, which may lead to suboptimal solutions.

^{[50]}

## Coupled Cluster C

The absorption bands in the action spectra were assigned on the basis of time-dependent density functional theory calculations that were benchmarked on equation-of-motion coupled cluster calculations of G•+.^{[1]}In this work we have performed benchmark coupled cluster calculations at the complete basis set limit for a large number of different isomers of representative clusters of third row elements.

^{[2]}The nuclear-electronic orbital (NEO) framework enables computationally practical coupled cluster calculations of multicomponent molecular systems, in which all electrons and specified nuclei, typically protons, are treated quantum mechanically.

^{[3]}Finally, a linearized coupled cluster correction on top of pCCD proved to be most reliable for the majority of investigated systems, featuring smaller nonparallelity errors compared to perturbation-theory-based approaches.

^{[4]}DLPNO-CCSD(T) allows for routine quadruple-ζ basis set quality coupled cluster calculations for the species comprised of ∼30 non-H atoms.

^{[5]}Notably, the PES was constructed from explicitly correlated coupled cluster calculations with extrapolation to the complete basis set limit and considered additional energy corrections to account for core-valence electron correlation, higher-order coupled cluster terms beyond perturbative triples, scalar relativistic effects, and the diagonal Born-Oppenheimer correction.

^{[6]}The vibrational spectra of simple CH3 +-Rg (Rg = He, Ne, Ar, Kr) complexes have been studied by vibrational configuration interaction theory relying on multidimensional potential energy surfaces (PESs) obtained from explicitly correlated coupled cluster calculations, CCSD(T)-F12a.

^{[7]}Coupled cluster calculations were performed at high levels to assess MP3, CCSD, CCSD(T), empirical density functional theory dispersion (D3), and the many-body dispersion (MBD) approach.

^{[8]}Further study reveals that two osmapyridiniums containing one or two phosphonium substituents exhibit the character of the triplet ground state, which was supported by the high-level coupled cluster calculations.

^{[9]}Three different models have been tested and are compared to each other and to the results of ab initio calculations at the coupled cluster CC2/cc-pVTZ and SCS-CC2/cc-pVTZ level of theory.

^{[10]}Among the WFT methods, a high accuracy of the single-reference coupled cluster CCSD(T) method is confirmed by a number of examples studied, including systems with noticeable nondynamic correlation effects.

^{[11]}Using explicitly correlated coupled cluster calculations and after inclusion of the zero point vibrational energy, core-valence and scalar relativistic effects, the AIE is calculated to be AIEcalc = 10.

^{[12]}In this work, we apply density functional theory and high-level coupled cluster calculations to describe the geometry and relative stability of C6H12+˙ radical cations, whose cyclic isomers are prototypes of singly-charged cycloalkanes.

^{[13]}Coupled cluster calculations predict that a simple halogenation in the norephedrine/phenylalanine residues shifts the isomer equilibrium almost completely toward the active trans-conformation, due to enhanced intramolecular interactions specific to the active isomer.

^{[14]}The potential energy surface describing the interaction of the SH+ ion in its ground X3Σ- electronic state with molecular hydrogen has been computed through restricted coupled cluster calculations including single, double, and (perturbative) triple excitations [RCCSD(T)], with the assumption of fixed bond lengths.

^{[15]}1 kcal mol-1 ) by using a carbon-boron formal frustrated Lewis pair, which is supported by high-level coupled cluster calculations.

^{[16]}High-level coupled cluster calculations obtained with the Feller-Peterson-Dixon (FPD) approach and new data from the most recent version of the Active Thermochemical Tables (ATcT) are used to reassess the enthalpy of formation of gas-phase C2H2O4 (oxalic acid).

^{[17]}Sampling from the π-electron distribution with these orbitals yields a quadrupole moment comparable to coupled cluster CCSD(T) calculations.

^{[18]}Coupled cluster calculations which include both singles, doubles and selected triple excitations (CCSD(T)), give very close agreement with the spectral data.

^{[19]}High-level coupled cluster