ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.

Bayesian serial revision method for RLLC cluster systems failure prediction

Failure prediction plays an important role for many tasks such as optimal resource management in large-scale system. However, accurately failure number prediction of repairable large-scale long-running computing （RLLC） is a challenge because of the reparability and large-scale. To address the challenge, a general Bayesian serial revision prediction method based on Bootstrap approach and moving average approach is put forward, which can make an accurately prediction for the failure number. To demonstrate the performance gains of our method, extensive experiments on the data of Los Alamos National Laboratory （LANL） cluster is implemented, which is a typical RLLC system. And experimental results show that the prediction accuracy of our method is 80.2 %, and it is a greatly improvement with 4 % compared with some typical methods. Finally, the managerial implications of the models are discussed.

New Chiral Metal Cluster Systems for Catalytic AsymmetricSyntheses of Chiral Alcohols

The efficient chiral Ru 3(CO) 12 systems were prepared in situ from Ru 3(CO) 12 and various chiral diimino-or diamino-diphosphine tetradentate ligands. The systems have been used for the asymmetric transfer hydrogenation of propiophenone in 2-propanol, leading to 1-phenyl-1-propanol in a 98% yield and 96% e.e. The IR study suggests that the carbonyl hydride anion [HRu 3(CO) 11]- most probably exists as a principal species under the reaction conditions. The high chiral efficiency may be due to the synergetic effect produced by the neighboring ruthenium atoms and a special chiral micro-environment involving the polydentate ligand and the Ru 3 framework.

Self-Organization of the Nearest Neighborhoods of Cluster System Elements:Computer Simulation

The percolation fields constructed around the elements of a cluster system in the phase spaces of properties are studied.It is shown that such neighborhoods significantly increase the number of structure parameters of the system under study,expanding the possibilities of analytical description.To study the structure and properties of such systems in the proposed model,a three-dimensional continuum percolation problem with interacting elements is solved.The dependences of the structure and properties of clusters on the parameters of the generation processes of the cluster system are studied,and analytical dependences are obtained.

Analysis and Simulation of Molecular Dynamics of Lysozyme in Water Cluster System

The influence of water on protein conformation was investigated by simulating the molecular dynamics of a model protein lysozyme in different water systems.The lysozyme-water system with TIP3P water model and lysozyme-water cluster system with six-ring water model were evaluated.In addition,the radial distribution function of solvent around lysozyme was calculated.It is found that the distribution of water molecules around lysozyme is similar to that of water clusters.The analyses of dihedral angles and disulfide bonds of lysozyme show that the conformation of lysozyme is severely damaged in the lysozyme-water cluster system compared with that in the lysozyme-water system.This difference can be attributed to the formation of larger number of intermolecular hydrogen bonds between lysozyme and water cluster.It is in agreement with the analysis that water clusters can change the degree of denaturation in the process of heat denaturation of lysozyme.

Reliability analysis of web server cluster systems based on proportional hazards model

An approach for web server cluster(WSC)reliability and degradation process analysis is proposed.The reliability process is modeled as a non-homogeneous Markov process(NHMH)composed of several non-homogeneous Poisson processes(NHPPs).The arrival rate of each NHPP corresponds to the system software failure rate which is expressed using Cox s proportional hazards model(PHM)in terms of the cumulative and instantaneous load of the software.The cumulative load refers to software cumulative execution time,and the instantaneous load denotes the rate that the users requests arrive at a server.The result of reliability analysis is a time-varying reliability and degradation process over the WSC lifetime.Finally,the evaluation experiment shows the effectiveness of the proposed approach.

Metal abundance and kinematical properties of the M81 globular cluster system

We present metal abundance properties of 144 globular clusters associated with M81. These globulars represent the largest globular cluster sample in M81 till now. Our main results are： the distribution of metallicities is bimodal, with metallicity peaks at [Fe/H] -1.51 and -0.58, and the metal-poor globular clusters tend to be less spatially concentrated than the metal-rich ones; the metal-rich globular clusters in M81 do not demonstrate a centrally concentrated spatial distribution like the metalrich ones in M31 do; like our Galaxy and M31, the globular clusters in M81 have a small radial metallicity gradient. These results are consistent with those obtained from a small sample of M81 globular clusters. In addition, this paper shows that there is evidence that a strong rotation of the M81 globular cluster system around the minor axis exists, and that rotation is present in the metal-rich globular cluster subsample, but the metal-poor globular cluster subsample shows no evidence of rotation. The most significant difference between the rotation of the metal-rich and metal-poor globular clusters occurs at intermediate projected galactocentric radii. Our results confirm the conclusion of Schroder et al. that M81＇s metal-rich globular clusters at intermediate projected radii are associated with a thick disk of M81.

Kinematics of the Open Cluster System in the Galaxy

Absolute proper motions and radial velocities of 202 open clusters in the solar neighborhood, which can be used as tracers of the Galactic disk, are used to investigate the kinematics of the Galaxy in the solar vicinity, including the mean heliocentric velocity components （u1, u2, u3） of the open cluster system, the characteristic velocity dispersions （σ1,σ2,σ3）, Oort constants （A, B） and the large-scale radial motion parameters （C, D） of the Galaxy. The results derived from the observational data of proper motions and radial velocities of a subgroup of 117 thin disk young open clusters by means of a maximum likelihood algorithm are： （u1,u2,u3） = （-16.1 ± 1.0,-7.9 ±1.4,-10.4±1.5） km·s^-1, （σ1,σ2,σ3） = （17.0±0.7, 12.2±0.9, 8.0±1.3） km·S^-1, （A, B） = （14.8±1.0, - 13.0±2.7） km·s^-1 kpc^-1, and （C, D） = （1.5 ± 0.7, -1.2 ±1.5） km·s^-1 kpc^-1. A discussion on the results and comparisons with what was obtained by other authors is given.

Parallel recovery method in shared-nothing spatial database cluster system

Shared nothing spatial database cluster system provides high availability since a replicated node can continue service even if any node in cluster system was crashed. However if the failed node wouldn’t be recovered quickly, whole system performance will decrease since the other nodes must process the queries which the failed node may be processed. Therefore the recovery of cluster system is very important to provide the stable service. In most previous proposed techniques, external logs should be recorded in all nodes even if the failed node does not exist. So update transactions are processed slowly. Also recovery time of the failed node increases since a single storage for all database is used to record external logs in each node. Therefore we propose a parallel recovery method for recovering the failed node quickly.

Fixed-Time Cluster Optimization for Multi-Agent Systems Based on Piecewise Power-Law Design

Dear Editor,This letter focuses on the fixed-time(FXT)cluster optimization problem of first-order multi-agent systems(FOMASs)in an undirected network,in which the optimization objective is the sum of the objective functions of all clusters.A novel piecewise power-law control protocol with cooperative-competition relations is proposed.Furthermore,a sufficient condition is obtained to ensure that the FOMASs achieve the cluster consensus within an FXT.

System error iterative identification for underwater positioning based on spectral clustering

The observation error model of the underwater acous-tic positioning system is an important factor to influence the positioning accuracy of the underwater target.For the position inconsistency error caused by considering the underwater tar-get as a mass point,as well as the observation system error,the traditional error model best estimation trajectory(EMBET)with little observed data and too many parameters can lead to the ill-condition of the parameter model.In this paper,a multi-station fusion system error model based on the optimal polynomial con-straint is constructed,and the corresponding observation sys-tem error identification based on improved spectral clustering is designed.Firstly,the reduced parameter unified modeling for the underwater target position parameters and the system error is achieved through the polynomial optimization.Then a multi-sta-tion non-oriented graph network is established,which can address the problem of the inaccurate identification for the sys-tem errors.Moreover,the similarity matrix of the spectral cluster-ing is improved,and the iterative identification for the system errors based on the improved spectral clustering is proposed.Finally,the comprehensive measured data of long baseline lake test and sea test show that the proposed method can accu-rately identify the system errors,and moreover can improve the positioning accuracy for the underwater target positioning.

A Hybrid Small-Cell and Clustered Cell-Free Massive MIMO System under Mobility Scenarios

Recently,cell-free(CF)massive multipleinput multiple-output(MIMO)becomes a promising architecture for the next generation wireless communication system,where a large number of distributed access points(APs)are deployed to simultaneously serve multiple user equipments(UEs)for improved performance.Meanwhile,a clustered CF system is considered to tackle the backhaul overhead issue in the huge connection network.In this paper,taking into account the more realistic mobility scenarios,we propose a hybrid small-cell(SC)and clustered CF massive MIMO system through classifications of the UEs and APs,and constructing the corresponding pairs to run in SC or CF mode.A joint initial AP selection of this paradigm for all the UEs is firstly proposed,which is based on the statistics of estimated channel.Then,closed-form expressions of the downlink achievable rates for both the static and moving UEs are provided under Ricean fading channel and Doppler shift effect.We also develop a semi-heuristic search algorithm to deal with the AP selection for the moving UEs by maximizing the weight average achievable rate.Numerical results demonstrate the performance gains and effective rates balancing of the proposed system.

A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

Multi-Step Clustering of Smart Meters Time Series:Application to Demand Flexibility Characterization of SME Customers

Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.

Identifying a competency improvement strategy for infection prevention and control professionals:A rapid systematic review and cluster analysis

Remarkable progress has been made in infection prevention and control(IPC)in many countries,but some gaps emerged in the context of the coronavirus disease 2019(COVID-19)pandemic.Core capabilities such as standard clinical precautions and tracing the source of infection were the focus of IPC in medical institutions during the pandemic.Therefore,the core competences of IPC professionals during the pandemic,and how these contributed to successful prevention and control of the epidemic,should be studied.To investigate,using a systematic review and cluster analysis,fundamental improvements in the competences of infection control and prevention professionals that may be emphasized in light of the COVID-19 pandemic.We searched the PubMed,Embase,Cochrane Library,Web of Science,CNKI,WanFang Data,and CBM databases for original articles exploring core competencies of IPC professionals during the COVID-19 pandemic(from January 1,2020 to February 7,2023).Weiciyun software was used for data extraction and the Donohue formula was followed to distinguish high-frequency technical terms.Cluster analysis was performed using the within-group linkage method and squared Euclidean distance as the metric to determine the priority competencies for development.We identified 46 studies with 29 high-frequency technical terms.The most common term was“infection prevention and control training”(184 times,17.3%),followed by“hand hygiene”(172 times,16.2%).“Infection prevention and control in clinical practice”was the most-reported core competency(367 times,34.5%),followed by“microbiology and surveillance”(292 times,27.5%).Cluster analysis showed two key areas of competence:Category 1(program management and leadership,patient safety and occupational health,education and microbiology and surveillance)and Category 2(IPC in clinical practice).During the COVID-19 pandemic,IPC program management and leadership,microbiology and surveillance,education,patient safety,and occupational health were the most important focus of development and should be given due consideration by IPC professionals.

Consolidated cluster systems for data centers in the cloud age： a survey and analysis

In the cloud age, heterogeneous application modes on large-scale infrastructures bring about the chal- lenges on resource utilization and manageability to data cen- ters. Many resource and runtime management systems are developed or evolved to address these challenges and rele- vant problems from different perspectives. This paper tries to identify the main motivations, key concerns, common fea- tures, and representative solutions of such systems through a survey and analysis. A typical kind of these systems is gener- alized as the consolidated cluster system, whose design goal is identified as reducing the overall costs under the quality of service premise. A survey on this kind of systems is given, and the critical issues concerned by such systems are sum- marized as resource consolidation and runtime coordination. These two issues are analyzed and classified according to the design styles and external characteristics abstracted from the surveyed work. Five representative consolidated cluster systems from both academia and industry are illustrated and compared in detail based on the analysis and classifications. We hope this survey and analysis to be conducive to both de- sign implementation and technology selection of this kind of systems, in response to the constantly emerging challenges on infrastructure and application management in data centers.

Fast time-division color electroholography using a multiple-graphics processing unit cluster system with a single spatial light modulator

We demonstrate fast time-division color etectroholography using a multiple-graphics-processing-unit （GPU） cluster system with a spatial light modulator and a controller to switch the color of the reconstructing light. The controller comprises a universal serial bus module to drive the liquid crystal optical shutters. By using the controller, the computer-generated hologram （CGH） display node of the multiple-GPU cluster system synchronizes the display of the CGH with the color switching of the reconstructing light. Fast time-division color electroholography at 20 fps is realized for a three-dimensional object comprising 21,000 points per color when 13 GPUs are used in a multiple-GPU cluster system.

Effect of Impurity on Critical Conditions of Colloidal Cluster Nucleation in Colloidal System

Due to depletion interactions, a few of colloidal spheres will be packed into cluster or clusters, even a phase transition may take place if the volume fraction of system is large enough. In a binary colloidal system, if the mole fraction of one component is very small, then it can be taken as the impurity of the other component. In this work, the effect of impurity on critical conditions of colloidal cluster nucleation was studied by Carnahan-Starling state equation and the principle of entropy maximum. The results show that, even the mole fraction of small-spheres is very small, the critical volume fraction is obvious smaller than that of one component system, so the influence on critical volume fraction from impurity is very huge and cannot be ignored. In addition, it is also found that, the larger the volume fraction of the system is, the larger cluster density can be packed, however, the critical size of nucleating cluster is almost independent of the density of the cluster.

Cluster consensus of second-order multi-agent systems via pinning control

This paper investigates the cluster consensus problem for second-order multi-agent systems by applying the pinning control method to a small collection of the agents. Consensus is attained independently for different agent clusters according to the community structure generated by the group partition of the underlying graph and sufficient conditions for both cluster and general consensus are obtained by using results from algebraic graph theory and the LaSalle Invariance Principle. Finally, some simple simulations are presented to illustrate the technique.

Power interconnected system clustering with advanced fuzzy C-mean algorithm

An advanced fuzzy C-mean （FCM） algorithm was proposed for the efficient regional clustering of multi-nodes interconnected systems. Due to various locational prices and regional coherencies for each node and point, modified similarity measure was considered to gather nodes having similar characteristics. The similarity measure was needed to contain locafi0nal prices as well as regional coherency. In order to consider the two properties simultaneously, distance measure of fuzzy C-mean algorithm had to be modified. Regional clustering algorithm for interconnected power systems was designed based on the modified fuzzy C-mean algorithm. The proposed algorithm produces proper classification for the interconnected power system and the results are demonstrated in the example of IEEE 39-bus interconnected electricity system.