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Efficient computational algorithms for bootstrapping linear regression models with clustered data are discussed. For ordinary least squares (OLS) regression, a new algorithm is provided for the pairs cluster bootstrap, along with two algorithms for the wild cluster bootstrap. One of these is a new way to express an existing method. For instrumental variables (IV) regression, an efficient algorithm is provided for the wild restricted efficient cluster (WREC) bootstrap. All computations are based on matrices and vectors that contain sums of squares and cross-products for the observations within each cluster, which have to be computed just once before the bootstrap loop begins. Monte Carlo experiments are used to study the finite-sample properties of bootstrap Wald tests for OLS regression and of WREC bootstrap tests for IV regression.

Density-Based clustering are the main clustering algorithms because they can cluster data with different shapes and densities, but some of these algorithms have high time complexity like OPTICS (Ordering Points to Identify Clustering Structure) and DBSCAN (Density-based spatial clustering of applications with noise) where the time complexity up to O( n 2 ). In this paper, we use an approach to reduce this complexity by providing fuzzy clusters to OPTICS, which make the process of finding neighbours within an only narrow region (fuzzy group) instead of searching all the state space making the algorithm faster than the original one with keeping almost the same accuracy of the innovative algorithm. The results show that there is an improvement in execution time using some synthetic and real datasets with high dimensions.

Dockless E-scooter Sharing (DES) has become a popular means of last-mile commute for many smart cities. As e-scooters are getting deployed dynamically and exibly across city regions that expand and/or shrink, accurate prediction of the e-scooter distribution given the recongured regions becomes essential for city planning. We present GCScoot, a novel ow distribution prediction approach for reconguring urban DES systems. Based on real-world datasets with reconguration, we analyze e-scooter distribution features and ow dynamics for the data-driven designs. We propose a novel spatio-temporal graph capsule neural network within GCScoot to predict future dockless e-scooter ows given the recongured regions. GCScoot pre-processes historical spatial e-scooter distributions into ow graph structures, where discretized city regions are considered as nodes and inter-region ows as edges. To facilitate initial training, we cluster the regions and generate virtual data for new deployment regions based on their peers in the same cluster. Given above designs, the region-to-region correlations embedded within the temporal ow graphs are captured via the multi-graph capsule convolutional neural network which accurately predicts the DES ows. Extensive studies upon four e-scooter datasets (total >3.4 million rides) in four populous US cities have corroborated accuracy and effectiveness of GCScoot in predicting the e-scooter distributions.

Developing and exploring organic–inorganic hybrid multi-lanthanide (Ln) implanted heteropolyoxometalates (HPOMs) has bloomed into an emerging research field. In this article, two neoteric D-gluconic acid (H6GA) concatenating multi-LnIII implanted heteropolytungstates K14H10[Ln4(H2O)4W6(H2GA)4O12(B-α-TeW9O33)4]∙60H2O [Ln = La3+ (1), Pr3+ (2)] were obtained in acidic aqueous system. Attractively, in the polyanion structure of 1 and 2, six WVI and four LnIII centers are connected by four flexible H2GA4− ligands via carboxyl and hydroxyl groups, resulting to the heterometallic [Ln4(H2O)4W6(H2GA)4O12]8+ cluster and then the heterometallic cluster is surrounded by four [B-α-TeW9O33]8– segments. Electrochemical measurements for the 1@CFMCN/GCE sensor (CFMCN = carboxyl-functionalized multiwalled carbon nanotube; GCE = glass carbon electrode) demonstrate that 1@CFMCN/GCE shows benign recognition response to detecting noradrenaline (NDA). This research expands the structural diversity of Ln-implanted HPOMs and presents an electrochemical platform of recognizing NDA in the field of biosensors.

So-called spatial web queries retrieve web content representing points of interest, such that the points of interest have descriptions that are relevant to query keywords and are located close to a query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, the top-k spatial textual cluster retrieval ( $k$ -STC) query that returns the top-k clusters that (i) are located close to a query location, (ii) contain objects that are relevant with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a DBSCAN-based approach and an OPTICS-based approach that rely on on-line density-based clustering and that exploit early stop conditions. Empirical studies on real data sets offer evidence that the paper's proposals can find good quality clusters and are capable of excellent performance.

Distributed machine learning (DML) is an increasingly important workload. In a DML job, each communication stage can comprise a coflow, and there are dependencies among its coflows. Thus, efficient coflow scheduling becomes critical for DML jobs. However, the majority of existing solutions focus on scheduling single-stage coflows with no dependencies. While there are a few studies schedule dependent coflows of multi-stage jobs, they suffer from either practical or theoretical issues. In this paper, we study how to schedule dependent coflows of multiple DML jobs to minimize the total job completion time (JCT) in a shared cluster. To solve this problem without any prior knowledge of job information, we present an online coflow-aware optimization framework called Parrot. The core idea in Parrot is to infer the job with the shortest remaining processing time (SRPT) each time and dynamically control the inferred job's bandwidth based on how confident it is an SRPT job while being mindful of not starving any other job. We have proved that Parrot algorithm has an approximation ratio of $O(M)$ , where M is the number of jobs. The results from large-scale trace-driven simulations further demonstrate that our Parrot can reduce the total JCT by up to 58.4%, compared to the state-of-the-art solution Aalo.

In hazardous gas leakage accidents in chemical clusters, quickly and accurately locating the diffusion source via mobile sensors (searcher) can effectively speed up emergency response. While state-of-the-art researches designed kinds of autonomous sensing and searching techniques, e.g. cognitive-based strategies to complete the localization of the leakage source; the current studies cannot meet chemical cluster’s requirements with complex road network constraints and spatial scales. To address this problem, we firstly combine a cognitive search strategy with great theoretical performance (Entrotaxis algorithm) with an intermittent search strategy and propose a fresh searching algorithm, namely Entrotaxis-Turn (ET). The hybrid algorithm can make use of the triggered turn motion to bypass obstacles to avoid falling into a loop, thereby improving the success rate and saving the searching time. Secondly, we extend the ET algorithm to the multi-robot collaboration pattern (Multi-ET algorithm), in which each mobile sensor shares the information collected along its route with each other. To reveal the performance of the proposed algorithms, a simulation scene is built in a typical chemical cluster scene and diffused gas is generated based on the advection-diffusion equation. Through Monte Carlo simulations, the optimal parameter combinations in the ET and Multi-ET algorithm are obtained. Finally, a verification experiment is carried out based on the simulation scene generated by the computational fluid dynamics (CFD) model. Results show that compared with the Entrotaxis algorithm, the efficiency and success rate of the ET algorithm are dramatically improved. Moreover, the Multi-ET algorithm obtains the optimal source searching performance.

In order to solve the problem of hot spot caused by uneven energy consumption of nodes in Wireless Sensor Networks (WSNs) and reduce the network energy consumption, a novel cluster routing algorithm called CRPL for ring based wireless sensor networks using Particle Swarm Optimization (PSO) and Lion Swarm Optimization (LSO) is proposed in this paper. In CRPL, the optimal cluster head (CH) of each ring are selected by using LSO whose fitness function is composed of energy,number of neighbor nodes, number of cluster heads and distance. Moreover, PSO with a multi-objective fitness function considering distance, energy and cluster size is used to find the next hop relay node in the process of data transmission, and the optimal routing paths are obtained, so as to alleviate the hot spot problem as well as decrease the energy consumption in the routing process. The simulation results show that, compared with some existing optimization algorithms, CRPL has better effects in balancing the energy consumption of the network and prolonging the life cycle of the network.

Distributed in-memory computing frameworks usually have lots of parameters (e.g., the buffer size of shuffle) to form a configuration for each execution. A well-tuned configuration can bring large improvements of performance. However, to improve resource utilization, jobs are often share the same cluster, which causes dynamic cluster load conditions. According to our observation, the variation of cluster load reduces effectiveness of configuration tuning. Besides, as a common problem of cluster computing jobs, overestimation of resources also occurs during configuration tuning. It is challenging to efficiently find the optimal configuration in a shared cluster with the consideration of memory-sparing. In this paper, we introduce MespaConfig, a job-level configuration optimizer for distributed in-memory computing jobs. Advancements of MespaConfig over previous work are features including memory-sparing and load-sensitive. We evaluate MespaConfig by 6 typical Spark programs under different load conditions. The evaluation results show that MespaConfig improves the performance of six typical programs by up to 12 compared with default configurations. MespaConfig also achieves at most 41% reduction of configuration memory usage and reduces the optimization time overhead by 10.8 compared with the state-of-the-art approach.

In this paper, we describe our efforts to reproduce the results reported in the SC19 paper by Hidayetoglu et al., titledMemXCT: Memory-Centric X-ray CT Reconstruction withMassive Parallelization. MemXCTs single-device performance, parallelized via OpenMP and MPI using AMD Zen2 CPU cores (second-generation EPYC processor) and NVIDIA V100 GPU devices. Both compute families are available as Microsoft Azurecloud HPC offerings. With these resources, we were are able to reproduce most of the results and exceed the performance of larger inputs on an AMD EPYC HBv2 cluster. We are also able to reproduce the trends of Strong Scaling (SS) for Optimized CPU and GPU versions. Differences in performance of the CPU version were observed due to differences in the underlying hardware, input size, and number of available nodes.

In this paper, we investigate temporal clusters of extremes defined as subsequent exceedances of high thresholds in a stationary time series. Two meaningful features of these clusters are the probability distribution of the cluster size and the ordinal patterns giving the relative positions of the data points within a cluster. Since these patterns take only the ordinal structure of consecutive data points into account, the method is robust under monotone transformations and measurement errors. We verify the existence of the corresponding limit distributions in the framework of regularly varying time series, develop non-parametric estimators and show their asymptotic normality under appropriate mixing conditions. The performance of the estimators is demonstrated in a simulated example and a real data application to discharge data of the river Rhine., Deutsche Forschungsgemeinschaft, Projekt DEAL

Clustering taxi data is commonly used to understand spatial patterns of urban mobility. In this paper, we propose a new clustering model called Origin-Destination-means (OD-means). OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs. In the first layer of the hierarchy, the clusters are separated automatically based on the variation of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy corresponds to the sub clustering process of small clusters based on the distance between the origin and destination of each cluster. The algorithm is tested on a large data set of taxi GPS data from Santiago, Chile, and compared to other clustering algorithms. In contrast to them, our proposed model is capable of detecting general and local travel patterns in the city due to its hierarchical structure.

One of the main capabilities of atom probe tomography (APT) is the ability to not only identify but also characterize early stages of precipitation at length scales that are not achievable by other techniques. One of the most popular methods to identify nanoscale clustering in APT data, based on the density-based spatial clustering of applications with noise (DBSCAN), is used extensively in many branches of research. However, it is common that not all of the steps leading to the selection of certain parameters used in the analysis are reported. Without knowing the rationale behind parameter selection, it may be difficult to compare cluster parameters obtained by different researchers. In this work, a simple open-source tool, PosgenPy, is used to justify cluster search parameter selection via providing a systematic sweep through parameter values with multiple randomizations to minimize a false-positive cluster ratio. The tool is applied to several different microstructures: a simulated material system and two experimental datasets from a low-alloy steel . The analyses show how values for the various parameters can be selected to ensure that the calculated cluster number density and cluster composition are accurate.

Previous research on the genesis of industrial clusters has focused on macrolevel (e.g., agglomeration economies and institutions) or mesolevel explanatory factors (e.g., serial entrepreneurship, spin-offs). Less studied are the microfoundations of cluster genesis, intended as the individual- and group-level processes underlying such macrolevel outcomes. Yet, microfoundations are key to understanding the “primordial soup” of cluster genesis—that is, the processes unfolding in the early moments of cluster formation, before the first emergence of commercial activity. Through a historical case study of the British Motorsport Valley (1911–1970s), we trace back the primordial origins of this cluster to the casual leisure activities of groups of amateur motorsport enthusiasts who then prompted the professionalization of motorsport racing and its transformation into the business at the core of the industrial cluster. We theorize that clusters emerge through the layering of different domains (casual leisure, serious leisure, and business), each made of three elements (actors, activities, and artifacts), which interact via two microlevel mechanisms: (1) localizing passion, a shared emotional energy by which people become affectively attached to the spaces where they carry out activities that they enjoy; (2) domain repurposing, the shift of a configuration of actors, activities, and artifacts toward a new purpose, originating a new domain. Whereas domain repurposing induces the transformation of activities from leisure to business (thus originating the industry at the core of a cluster), localizing passion anchors the activities to the same geographical area (clustering the industry). Our key contribution is to explore the emotional microfoundations of cluster genesis.

In this article, we investigate the distributed Nash equilibrium (NE) seeking problem for the multi-cluster game under switching communication topologies. Specifically, the communication topology switches between a group of jointly connected digraphs. First, a new distributed NE seeking algorithm for the multi-cluster games is designed by the consensus protocol and gradient play rule under the switching communication topologies. Furthermore, in order to make the algorithm still applicable when the agent only knows part of the decision information, the leader-following consensus protocol is used to generate the estimates for all agents action in the cluster under the assumption that the switching topology between clusters is directed and strongly connected. A more general NE seeking algorithm for the multi-cluster games is designed. For these two algorithms, the results of local convergence and non-local convergence are given, respectively. Two examples verify the validity of the theoretical results.

For the first time, the present work shows a combined theoretical and experimental analysis of the preferred adsorption sites for both methanol and methoxy species over Cu, Pt, and bimetallic Cu-Pt clusters - up to 5 atoms in size - supported on CeO2(111) as model systems. DFT+U calculations compared to methanol adsorption experiments followed by DRIFT spectroscopy over Cu, Pt, and bimetallic Cu-Pt nanoparticles supported over CeO2 octahedra, exposing only {111} crystal planes. From our DFT+U calculations, we found that methanol adsorption over the CuPt/CeO2(111) system is slightly favored, compared to the pristine CeO2(111) surface. Similarly, methoxy species formed through methanol dehydrogenation (CH3O- + H+) adsorb preferably over the metallic clusters, compared to the pristine oxide surface. This promotion is explained by the higher coordination of the methoxy species over the cluster, especially for those containing a higher amount of Cu atoms. This results highlights the oxidized Cu sites' role in the catalytic process, followed by the surface cerium atoms reduction of the support. The interaction of methanol with the substrate oxygen weakens the O-H bond, which results in a reduction in the energy barrier when the reaction occurs on the surface and interface, compared when this occurs exclusively on the metal cluster. However, after this step, the potential energy surface's depth suggests that methoxy species prefer to be adsorbed over the metal clusters. Experimentally, methanol was adsorbed over Cu, Pt, and bimetallic Cu-Pt nanoparticles supported over CeO2 {111}-octahedral particles. Through in-situ DRIFTS, bands related to adsorbed methanol and methoxy species were identified. A blue shift in all the bands was observed, related to metals’ influence in adsorbed species' vibrational mode. In the experimental spectra, new contributions in Cu-containing catalysts appeared, which were identified as adsorbed species over the metallic cluster and the interface, rationalized by calculated spectra.

The critical problem in managing a bike sharing system (BSS) is to solve the imbalance in the number of available bikes by stations and times, which negatively affects the users' riding experience. To address this issue, many BSS operators rearrange bikes using a fleet of trucks. Moreover, the effectiveness and efficiency of rebalancing operations heavily rely on accurate traffic prediction in BSS. Hence, many researchers have developed methods to enhance the prediction performance. One approach to increase the accuracy is to hierarchically predict demand from higher level (cluster or system) to station level. In the hierarchical prediction frameworks, the cluster-level to predict the demand of the total usage of groups of stations is introduced and it is crucial to partition stations into clusters that can increase the stability of actual usage and prediction. Therefore, the present work proposes a new spatial contiguity-constrained hierarchical clustering algorithm for BSS. The proposed algorithm is based on hierarchical clustering, which is deterministic, and uses the hourly proportions of check-outs and check-ins to define the temporal usage pattern of each station that does not require cluster assignment. Thus, different from existing methods, our proposed algorithm is deterministic and fast. In addition, the proposed algorithm is proven to be superior to other clustering methods in terms of traffic prediction at both the cluster and station levels.

In order to explore novel high-energy and green oxidants, oxygen-rich clusters O4−/0 and O6−/0 were investigated by laser-vaporization source, mass spectrometry and theoretical calculations. Most of the low-lying isomers of On−/0 clusters are polyline structures consisting of O2 and O3 units. With one exception, O6, which has a chair-form cyclic structure with D3d symmetry, just like cyclohexane. It has the largest HOMO-LUMO energy gap of the On−/0 (n = 4, 6) clusters. The energy performances of O6 cluster were also estimated, which the density is 2.03 g/cm3, the solid enthalpy of formation is 114.75 kcal/mol, and the theoretical specific impulse (Isp) of H2(L)/O6 cluster is calculated to be 507.78 s.

C2H2 semi-hydrogenation has been widely applied in industry to eliminate trace C2H2 from C2H4 feed. C2H2 semi-hydrogenation to C2H4 on a series of the newly designed catalysts, graphdiyne (GDY) as a new carbon allotrope supported different sizes of PdxMy clusters (PdxMy/GDY, M = Cu, Ag, Au, Ni; x+y = 1–3), were studied using DFT calculations. The results found that C2H2 semi-hydrogenation to C2H4 on PdxMy/GDY catalysts exhibits that both the activity and selectivity greatly depend on the composition and size of PdxMy/GDY catalysts. Surprisingly, our results for the first time discovered the Pd1/GDY catalyst with GDY supported the single atom Pd that presents the best selectivity and activity toward C2H4 formation compared to the previously reported catalysts so far in C2H2 semi-hydrogenation. This study would provide a theoretical clue for designing and screening out the potential catalysts with GDY supported small sizes of PdxMy and other metal clusters in C2H2 hydrogenation.

Hidayetolu et al. (2019) proposed a novel memory-centric computation system, MemXCT. As a challenge at SC20, we reproduce the computational efficiency of MemXCT on our Azure cloud cluster. Our experiments evaluate the overall performance and the strong scalability with real datasets and verify part of the conclusions in the original paper. Digital artifacts from these experiments are available at: 10.5281/zenodo.5598108.

In this paper, we consider the problem of federated learning (FL) with training data that are not independent and identically distributed (non-IID) across the clients. To cope with data heterogeneity, Iterative Federated Clustering Algorithm (IFCA) has been proposed. IFCA partitions the clients into a number of clusters and lets the clients in the same cluster optimize a shared model. However, in IFCA, the clusters are non-overlapping, which leads to an inefficient utilization of the local information since the knowledge of a client is used by only one cluster during each round. To capture the complex nature of real-world data, soft clustering methods with overlapping clusters have been proposed that attain superior performance over the hard ones. Motivated by this, we propose a new algorithm named Federated Learning with Soft Clustering (FLSC) by combining the strengths of soft clustering and IFCA, where the clients are partitioned into overlapping clusters and the information of each participating client is used by multiple clusters simultaneously during each round. The experimental results show that FLSC achieves better learning performance on the classification tasks on the MNIST and Fashion-MNIST datasets, compared with the state-of-the-art baseline methods, i.e., the global model method and IFCA.

In wireless sensor networks (WSNs), the imbalanced energy consumption in data transmission may cause energy holes around the sink, which dramatically shorten the network lifespan. Current solutions have two limitations: one is the inevitable traffic gathered around the sink, and the other is the overly ideal network model, e.g., the square region or circular area. In this paper, we focus on strip-shaped networks, and propose a novel data transmission scheme, which employs a few super nodes near the sink to take traffic load. Due to the high energy capacities and communication abilities, super nodes transfer a part of data of the network and send it to the sink directly. The entire network is partitioned into multiple clusters, and super nodes are placed in a specific cluster. We discover that, the greatest effect on the network lifetime is the energy consumption of two clusters: the cluster nearest to the sink and the upstream cluster nearest to the super nodes. To improve the system longevity, we make the two clusters have equal energy dissipation, and thus obtain the optimal location of super nodes. Some simulations are carried out to verify the reasonability of the results and exhibit the advantage of our scheme in terms of network lifetime.

Three imidazole-modified Ag-polyoxovanadates frameworks (APFs) with a controllable molar ratio of Ag+ to polyoxovanadates (POVs) [Ag(IM)2]2V4O12∙2Ag(IM)2 (APF-1), [Ag2(1-eIM)4]2[Ag(1-eIM)2]3∙2Ag(1-eIM)2∙3(1-HeIM)[V10O28]2 (APF-2) and [Ag(1-pIM)2]3[HV10O28]∙2Ag(1-pIM)2∙2H2O (APF-3) (IM = imidazole; 1-eIM = 1-ethylimidazole and 1-pIM = 1-propylimidazole) have been successfully achieved by self-assembly of POVs, Ag+ cations, and three different imidazole derivatives. Interestingly, the molar ratios of Ag+ to POVs vary from 4:1, 4.5:1 to 5:1 by changing the vanadium resources and imidazole derivatives. Notably, the coordination environment of Ag+ cations and the structure of POVs in the APFs are also different. Specifically, for APF-1, the four Ag atoms adopt three-coordinated and four-coordinated geometries, respectively, and Ag-imidazole complexes and [V4O12]4− cluster form the one-dimensional polymeric chains. While Ag atoms in APF-2 and APF-3 exhibit two-, four- and five-coordinated geometries for APF-2, four-, five- and six-coordinated geometries for APF-3, respectively. These Ag+ cations and decavanadate clusters are assembled into the 2D supramolecular structure through the Ag-O bonds and Ag…Ag argentophilic interaction. Remarkably, thus-obtained APF-2 can serve as powerful efficient heterogeneous catalyst for construction of C-N bond and detoxification of simulant sulfur mustard (yields up to 99%), which enable successful recycling for three cycles with remained catalytic activities and structure stability.

The intrinsic origins and formation of atomic-scale structure in multicomponent alloys remain largely unknown owing to limited simulations and inaccessible experiments. Herein, we report the formation of three-dimensional periodicity from a disordered atomic-scale structure to an imperfect/perfect ordered cluster and finally to long-range translational and rotational symmetry coupled with Nb heterogeneity. Significant atomic-scale structural clustering and atomic arrangements involving solvent or solute atoms simultaneously occurred during isothermal annealing. A close relationship between atomic-scale structural evolution and composition variation has important implications in depicting the chemical and topological packing during the early crystallization stage in metallic glasses. This work can provide a comprehensive understanding of how short-range orders evolve into long-range periodicity and will further shed light on the origins and nature of metallic glasses.

The person re-identification (ReID) method in a single-domain achieves appealing performance, but its reliance on label information greatly limits its extensibility. Therefore, the unsupervised cross-domain ReID method has received extensive attention. Its purpose is to optimize the model by using the labelled source domain and the unlabelled target domain and finally make the model well generalized in the target domain. We propose an unsupervised cross-domain ReID method based on median stable clustering (MSC) and global distance classification (GDC). Specifically, the measurement method used by MSC comprehensively considers the similarity between clusters, the number of samples in a cluster, and the combined similarity within a cluster. Different from the method based on triple loss, GDC can separate the distance distribution of positive and negative sample pairs in a global scope. In addition, considering that model performance is very sensitive to probability parameters when source domain memory is reconsolidated, we designed a dynamic memory reconsolidation (DMR) method to reduce the influence of parameters on performance. Extensive experiments on large-scale datasets (Market-1501, DukeMTMC-reID and MSMT17) demonstrate the superior performance of MSC-GDC over the state-of-the-art methods.

Energy efficiency is considered as the most potential issue in Wireless Sensor Networks (WSNs), since they incorporate limited sized batteries that could not be recharged or replaced. The energy possessed by the sensor nodes needs to be optimally utilized in order to extend the lifetime expectancy with guaranteed QoS in the network. In this paper, a Hybrid Artificial Bee Colony and Monarchy Butterfly Optimization Algorithm (HABC-MBOA)-based Cluster Head Selection Scheme is proposed for the predominant selection of cluster heads under clustering process. This proposed HABC-MBOA replaces the employee bee phase of ABC with mutated butterfly adjusting operator of MBOA for preventing earlier trapping of solutions into a local optimal point and delayed convergence by maintaining the tradeoff between exploitation and exploration. This proposed HABC-MBOA plays an anchor role in eliminating inadequacy of ABC algorithm towards global search potential. This proposed HABC-MBOA also eliminates the possibility of cluster heads being overloaded with maximum number of sensor nodes, that results in rapid death of the sensor nodes during the deployment of impotent cluster head selection process. The simulation results confirmed that the number of alive nodes in the network is determined to be 18.92% superior to the benchmarked cluster head selection approaches.

[EN] Porous Fe carboxylates are amongst the most promising MOF-based materials due to their low price, low toxicity, metal environments (including open metal sites) and remarkable (meso)porosity variety. Fe-MOFs based on the cluster [FeO(X)(solvent)], that is, MIL-101(Fe), MIL-100(Fe) and semiamorphous Fe-BTC, are of particular interest. These three materials are quite related each other: (i) MIL-100(Fe) and MIL-101(Fe) have the same zeolitic topology MTN and two types of mesocavities, whereas (ii) Fe-BTC and MIL-100(Fe) form an unprecedented semiamorphous / fully-crystallized pair, having in common the metal cluster, the composition, one mesocavity, etc but without becoming a nano- / micro-crystalline pair. This work describes the room-temperature synthesis, characterization and catalytic performance in the aerobic cyclohexene oxidation of the semiamorphous Fe-BDC, which together with MIL-101(Fe) would form the second semiamorphous / crystallized pair in MOFs. Unfortunately, Fe-BDC could not be prepared in water as solvent, but in either ethanol or in N,N-dimethylformamide. It possesses relatively high textural properties (above 500 mg) and key common features with MIL-101(Fe): XRD reflections at the same 2θ positions, similar thermal stability, almost equal linker conformations, etc. Fe-BDC became quite active in the solvent-free aerobic oxidation of cyclohexene under mild conditions, surpassing the activity performance of the well-known commercial Fe-BTC catalyst in the same reaction under the same mild conditions., This work has been partially financed by CSIC through the programs: (i) CVCSIC-AEPP-Ayudas Extraordinarias para preparación de proyectos 2019 (2019AEP076) and (ii) i-COOP-2018 (COOPA20271). This work has been also financed by the CONACyT project A1–5-30646. J.G.F. acknowledges a Ph.D. CONACyT grant (687839). R.D.G. thanks the JAE-Intro CSIC grant (JAEINT18_EX_0043).

An organic-inorganic hybrid FeIII–PrIII-included 2-germano-20-tungstate [Pr(H2O)8]2H2[Fe4(H2O)4 (pca)4Ge2W20O72]•34H2O (Hpca = 2-pyridinecarboxylic acid) (1) was hydrothermally prepared. Its polyoxoanion comprises one tetra-FeIII incorporated [Fe4(H2O)4(pca)4Ge2W20O72]8- hybrid entity and two [Pr(H2O)8]3+ ornamental cations. The [Fe4(H2O)4(pca)4Ge2W20O72]8- 2-germano-20-tungstate entity can be regarded as an infrequent S-type [Ge2W20O72]16- cluster pocketed by four [Fe(H2O)(pca)]2+ cations. The S-type [Ge2W20O72]16- cluster could be imagined as condensation of two divacant Keggin [α-GeW10O37]10- segments by sharing two atoms. It is of interest is that carboxyl O and pyridine N atoms on pca ligands concurrently bind with Fe3+ cations in a five-membered heterocyclic fashion to increase the stability of the whole structure. Furthermore, the electrochemical biosensing properties of 1 as the modified electrode material have been investigated for detecting norepinephrine (NPP), showing a low detection limit of 3.25 μmol/L. This work not only enriches structures of heterometallic germanotungstates (GTs), but also expands applications of polyoxometalates (POMs) in the electrochemical biosensing field.

In view of the fact that the types of tasks display an upward trend as the growth of service demands, and the different types of tasks arrive at the system without regularity. Moreover, the resources deployed in cloud are insufficiency to be flexibly provisioned in the face of obvious workload fluctuations. In this paper, we present a method of elastic resource provisioning using date clustering in cloud service platform. The framework of method consists of three core components. In workload classification, we propose a clustering ensemble method, which utilizes a novel distance decision-making method to obtain the final results. Our method can effectively partition the arriving tasks into several clusters based on similarity among tasks. For each cluster, we forecast the amount of tasks arriving at next moment by prediction model based on time-series to provide reference for the follow-up resource provisioning. Afterwards, an energy-saving resource provisioning method is designed to dynamically provide resources for tasks in each cluster. We implement the experiments and the results show that our method achieves 92.3%, 91.2%, and 3679.2 kWh respectively in terms of guarantee ratio, resource utilization and total energy consumption, which demonstrates the effectiveness of proposed method for dynamic resource provisioning.

We report the first disubstituted hetero-ten-vertex closo cluster [(CrGe9)Cr2(CO)13]4− with three adjacent Cr(CO)n units adopting both η5 and η1 coordination modes, which was synthesized through the reaction of “KGe1.67” with (MeCN)3Cr(CO)3 and Cr(CO)6 in ethylenediamine (en) solution. In contrast to the η1-Cr atoms forming localized two-center two-elelctron (2c-2e) Cr-Ge bonds, the hetero atom η5-Cr exhibits versatile bonding mechanisms including three 5c-2e and five 8c-2e delocalized bonds which account for Huckel aromaticity. Intricate multi-center bonding patterns delineate the multiple local σ-aromatic characters of the title cluster displaying explicit spherical aromaticity.

This technical note investigates the cluster consensus control for general linear multi-agent systems. The focus of this technical note is especially to solve the following problem: in what kinds of connection scenarios can agents achieve cluster consensus regardless of the coupling strength (CCRCS). Different from existing literature, this technical note extends the case to general partition graphs, including both acyclic and cyclic partition cases. New analysis methods are put forward. It is proved that, under some basic assumptions, the multi-agent systems can achieve CCRCS if and only if the interaction topology satisfies the non-vicious circle condition, which is proposed for the first time. An algorithm is presented which can check whether a given interaction topology satisfies the non-vicious circle condition. The result can also be extended to heterogeneous cases that agents from different clusters possess non-identical system dynamics.

A cluster-based drag model is proposed for the gas–solid circulating fluidized bed (CFB) riser by including the cluster information collected from image processing and wavelet analysis into the calculation of system drag. The performance of the proposed drag model is compared with some commonly used drag models. A good agreement with the experimental data is achieved by the proposed cluster-based drag model. Error analysis of the proposed cluster-based drag model based on the local distributions of solids holdup and particle velocity is conducted. The clustering phenomenon in the low-density and high-density CFB risers and the effect of the cluster size on the simulation accuracy are also numerically studied by the proposed drag model.

Battery energy storage system (BESS) plays an important role in the grid-scale application due to its fast response and flexible adjustment. Energy loss and inconsistency of the battery will degrade the operating efficiency of BESS in the process of power allocation. BESS usually consists of many energy storage units, which are made up of parallel battery clusters with a cell-pack-cluster hierarchical structure. This paper presents a power allocation strategy based on cluster switching to relieve the stated problem in two levels. Cluster switching is identified as a new control approach to eliminating the imbalanced state-of-charge (SOC) in the cluster level. In the unit level, an optimization model is constructed for power allocation, where the objective function consists of two aspects: minimizing battery energy loss and maximizing SOC consistency. Finally, the effectiveness of the power allocation strategy for BESS in peak shaving and frequency regulation is validated by simulation and scaled-down experiment. The results prove that the power allocation strategy can reduce the battery energy loss and prevent from overcharging/overdischarging to extend the battery lifetime.

In this brief, we investigate the cluster synchronization of multi-weighted complex networks with pinning control. We assume that the network contains multiple coupling matrices, and they can be all asymmetric (including the symmetric case), which is more practical in the real world. For multiple asymmetric coupling matrices, the Lyapunov function is hard to design. We firstly define a candidate region for every matrix. Then if these regions are not too far, we can find (at least) a common vector lying in each region. This method has geometrical meaning, so it is different from previous LMI method. That is to say, we can design a new Lyapunov function with a vector combining all normalized left eigenvectors (NLEVec) corresponding to the zero eigenvalue of coupling matrices. With some sufficient conditions presented accordingly, the realization of the cluster synchronization is guaranteed. Especially, we carefully discuss the case with three coupling matrices. The adaptive rule for cluster synchronization is also designed. Finally, numerical simulations are given to demonstrate our results’ effectiveness.

This paper studies the security cluster synchronization of complex networks under denial-of-service (DoS) attacks. First, an adaptive strategy is designed to update coupling weights and gains for ensuring cluster synchronization, even if the control channels of complex networks suffer from DoS attacks. This security strategy is developed in a distributed framework, in which only local information is utilized instead of any global information, such as network topology and the number of nodes. Then, the security cluster synchronization analysis against DoS attacks is extended to the case where only partial coupling weights on edges are updated. By constructing the suitable Lyapunov functions with exponential terms, the corresponding security cluster synchronization conditions are presented, and the DoS attack duration ratio that system can tolerate is also obtained. At last, a network composed of Chuas circuits is employed to validate the efficiency of the derived methods.

This paper establishes asymptotic results for the maximum likelihood and restricted maximum likelihood (REML) estimators of the parameters in the nested error regression model for clustered data when both of the number of independent clusters and the cluster sizes (the number of observations in each cluster) go to infinity. Under very mild conditions, the estimators are shown to be asymptotically normal with an elegantly structured covariance matrix. There are no restrictions on the rate at which the cluster size tends to infinity but it turns out that we need to treat within cluster parameters (i.e. coefficients of unit-level covariates that vary within clusters and the within cluster variance) differently from between cluster parameters (i.e. coefficients of cluster-level covariates that are constant within clusters and the between cluster variance) because they require different normalisations and are asymptotically independent.

Wind farm cluster penetration and its long‐distance delivery have become the important form of wind power accommodation of power network in China. For the adaptability of power network planning to the output volatility of clustering wind farms, three steps are studied as follows. Initially, the output uncertainty model of clustering wind farms based on Auto Regression Integrated Moving Average (ARIMA)‐Generalized Autoregression Conditional Heteroscedasticity (GARCH)‐Pair Copula is put forward to analyze the intermittency, fluctuation and clustering wind power correlation. Secondly, Full Cost (FC) theory is introduced to account social expense brought by grid‐connected clustering wind farms from four aspects of static cost, dynamic cost, environmental cost and inter‐generational cost. Meanwhile, available wind power transmission capacity is defined to reflect wind power accommodation based on transmission margin. Finally, the adaptive efficiency index is proposed by the ratio of available wind power transmission capacity to FC. The transmission adaptability planning model considering cluster correlation of wind farms is built with the objective function of the adaptive efficiency index. Taking the actual regional power grid as an example, the results show that the planning scheme considering the optimal adaptive efficiency index of clustering wind farms can be better adapted to the future power network environment.

Partitioning data into internally homogeneous parts is an important problem when mining in situ engineering data. In this paper, a polynomial regression-based fuzzy c-means (PR-FCM) clustering algorithm that utilizes the functional relationships among the attributes of the input dataset is proposed. In this algorithm, a polynomial regression equation is taken as the center of each cluster instead of the cluster prototype used in conventional FCM, and the difference between a sample and a cluster prototype is defined as the distance between the actual value of one attribute and the corresponding predicted value provided by its own polynomial regression equation. An alternating optimization method is designed to optimize the new clustering objective function of the proposed algorithm. A series of experiments on synthetic and real-world datasets are conducted to evaluate the performance of the PR-FCM algorithm, which exhibits higher effectiveness and possesses more advantages than the original FCM algorithm. The PR-FCM algorithm is applied to tunnel boring machine (TBM) operation data from a TBM project in China. The experimental results show that the proposed algorithm can effectively cluster TBM operation data.

Perovskite and organic solar cells usually require electron-transport interlayers to efficiently transport electrons from the photoactive layer to the metal electrode. In general, pure organic or i...

Atmospheric organic acids (OAs) are expected to enhance methanesulfonic acid (MSA)-driven new particle formation (NPF). However, the exact role of OAs in MSA-driven NPF remains unclear. Here, we employed a two-step strategy to probe the role of OAs in MSA–methylamine (MA) NPF. Initially, we evaluated the enhancing potential of 12 commonly detected OAs in ternary MA–MSA–OA cluster formation by considering the formation free energies of the (MSA)1(MA)1(OA)1 clusters and the atmospheric concentrations of the OAs. It was found that formic acid (ForA) has the highest potential to stabilize the MA–MSA clusters. The high enhancing potential of ForA results from its acidity, structural factors such as no intramolecular H bonds, and high atmospheric abundance. The second step is to extend the MSA–MA–ForA system to larger cluster sizes. The results indicate that ForA can indeed enhance MSA–MA NPF at atmospheric conditions (the upper limited temperature is 258.15 K), indicating that ForA might have an important role in MSA-driven NPF. The enhancing effect of ForA is mainly caused by an increased formation of the (MSA)2(MA)1 cluster, which is involved in the pathway of binary MSA–MA nucleation. Hence, our results indicate that OAs might be required to facilitate MSA-driven NPF in the atmosphere.

One often observes small but measurable differences in the diffraction data measured from different crystals of a single protein. These differences might reflect structural differences in the protein and may reveal the natural dynamism of the molecule in solution. Partitioning these mixed-state data into single-state clusters is a critical step that could extract information about the dynamic behavior of proteins from hundreds or thousands of single-crystal data sets. Mixed-state data can be obtained deliberately (through intentional perturbation) or inadvertently (while attempting to measure highly redundant single-crystal data). To the extent that different states adopt different molecular structures, one expects to observe differences in the crystals; each of the polystates will create a polymorph of the crystals. After mixed-state diffraction data have been measured, deliberately or inadvertently, the challenge is to sort the data into clusters that may represent relevant biological polystates. Here, this problem is addressed using a simple multi-factor clustering approach that classifies each data set using independent observables, thereby assigning each data set to the correct location in conformational space. This procedure is illustrated using two independent observables, unit-cell parameters and intensities, to cluster mixed-state data from chymotrypsinogen (ChTg) crystals. It is observed that the data populate an arc of the reaction trajectory as ChTg is converted into chymotrypsin.

Subsequence clustering is an important issue in time series data mining. Observing that most time series consist of various patterns with different unknown lengths, we propose an optimization framework to adaptively estimate the lengths and representations for different patterns. Our framework minimizes the inner subsequence cluster errors with respect to subsequence clusters and segmentation under time series cover constraint where the subsequence cluster lengths can be variable. To optimize our framework, we first generate abundant initial subsequence clusters with different lengths. Then, three cluster operations, i.e., cluster splitting, combination and removing, are used to iteratively refine the cluster lengths and representations by respectively splitting clusters consisting of different patterns, joining neighboring clusters belonging to the same pattern and removing clusters to the predefined cluster number. During each cluster refinement, we employ an efficient algorithm to alternatively optimize subsequence clusters and segmentation based on dynamic programming. Our method can automatically and efficiently extract the unknown variable-length subsequence clusters in the time series. Comparative results with the state-of-the-art are conducted on various synthetic and real time series, and quantitative and qualitative performances demonstrate the effectiveness of our method.

In this paper, a novel work pattern for large-scale workpiece machining, that is, multi-mobile robot cluster machining, is proposed. The key idea is to use distributed coordination control to link multiple robots as a cluster system. The coordination control law acts as a virtual constraint, and multiple robots move synchronously under a common leader. Two topological structures are introduced to describe the communication networks of the cluster system, namely, the N-chain and close-loop topologies. The convergence rate and resistance index of different topological structures are analyzed, and the applications of different topologies in varied machining scenarios are discussed. Two experiments are conducted on the multi-mobile robot system. The cluster system with a close-loop topology is used for the collaborative handling task, where the relative distance of two mobile robots can be limited within 6 cm. The cluster system with an N-chain topology is used for large-scale wind turbine blade machining. The cluster machining system requires little manual intervention and guarantees the synchronization performance of the machining system.

Recently, many real-world applications exploit multi-view data, which is collected from diverse domains or obtained from various feature extractors and reflect different properties or distributions of the data. In this work, a novel unsupervised multi-view framework is proposed to cluster such data. The proposed method, called Multi-View clustering with Adaptive Sparse Memberships and Weight Allocation (MVASM), pays more attention to constructing a common membership matrix with proper sparseness over different views and learns the centroid matrix and its corresponding weight of each view. Concretely, MVASM method attempts to learn a common and flexible sparse membership matrix to indicate the clustering, which explores the underlying consensus information of multiple views, and solves the multiple centroid matrices and weights to utilize the view-specific information and further modifies the above-mentioned membership matrix. In addition, the theoretical analysis, including the determination of the power exponent parameter, convergence analysis, and complexity analysis are also presented. Compared to the state-of-the-art methods, the proposed method improves the performance of clustering on different public datasets and demonstrates its reasonability and superiority.