Remote Clusters Research Articles

How Does Tropical Cyclone‐Induced Remote Moisture Transport Affect Precipitation Over East Asia

AbstractAnalyzing tropical cyclone‐induced remote moisture transport clusters (TRCs) and their effects on precipitation is crucial for understanding precipitation formation and enhancing forecast precision. Prior research, primarily case‐based, did not fully grasp the nature of TRCs. Utilizing an objective TRC identification method, we categorized 65 TRC tracks in East Asia into five types and examined their traits and precipitation links. The findings indicate that the moisture transport height of TRCs varies due to multiple factors, with higher transport linked to the warm conveyor belt and lower transport attributed to Taiwan’s mountain range influencing low‐level moisture. The precipitation peak altitudes of TRCs at higher latitudes are greater, and their precipitation intensity is positively correlated with terrain type, especially coastlines. This study underscores the diversity in TRC characteristics and related precipitation, suggesting that future research should consider the directionality of TRC tracks.

TOLEDO: enhancing Maestro GUI for non-expert users to perform massive MD simulations

Classical Molecular Dynamics (MD) simulates the dynamical evolution of biological systems at the atomic level. Using MD in conjunction with high-performance computing (HPC) architectures, we can evaluate the possible interactions between a ligand library against one protein target to find a drug that can influence a protein target to cure a disease. Simultaneously, we can also obtain information about their dynamic evolution. One of the primary software packages for MD simulations is Desmond, which employs Maestro for the setup, execution, and analysis of MD through a graphical user interface (GUI), which is suitable even for non-expert users. However, using the GUI, users can typically run only one short (less than 1000 ns) MD each time. Our work aims to create a method/protocol to run several MD simulations simultaneously on a remote HPC cluster within Maestro-Desmond. In this work, we provide TOLEDO (Throughput Optimization of Ligand-Protein Systems Exploration through Dynamics simulation in Optimized HPC systems) to overcome such limitations and run several MD simulations simultaneously. The best feature of TOLEDO is its independence from the usual time constraints of many clusters, with storage space being the only limitation. To run TOLEDO, we prepare/set up the protein-ligand complex before running MD via Maestro GUI. Next, we run the main TOLEDO script for several MD simulations on a supercomputer. When TOLEDO finishes, users obtain reports and graphics. The obtained results are easily interpretable. In essence, TOLEDO significantly enhances MD throughput beyond the capabilities of the Maestro GUI.

Remote sensing for site selection in vegetation survey along a successional gradient in post-industrial vegetation.

Vegetation characteristics are an important proxy to measure the outcome of ecological restoration and monitor vegetation changes. Similarly, the classification of remotely sensed images is a prerequisite for many field ecological studies. We have a limited understanding of how the remote sensing approach can be utilized to classify spontaneous vegetation in post-industrial spoil heaps that dominate urban areas. We aimed to assess whether an objective a priori classification of vegetation using remotely sensed data allows for ecologically interpretable division. We hypothesized that remote sensing-based vegetation clusters will differ in alpha diversity, species, and functional composition; thereby providing ecologically interpretable division of study sites for further analyses. We acquired remote-sensing data from Sentinel 2A for each studied heap from July to September 2020. We recorded vascular plant species and their abundance across 400 plots on a post-coal mine in Upper Silesia, Poland. We assessed differences in alpha diversity indices and community-weighted means (CWMs) among remote sensing-based vegetation units. Analysis of remotely sensed characteristics revealed five clusters that reflected transition in vegetation across successional gradients. Analysis of species composition showed that the 1st (early-succession), 3rd (late-succession), and 5th (mid-succession) clusters had 13, 10, and 12 exclusive indicator species, respectively, however, the 2nd and 4th clusters had only one species. While the 1st, 2nd, and 4th can be combined into a single cluster (early-succession), we found the lowest species richness in the 3rd cluster (late-succession) and the highest in the 5th cluster (mid-succession). Shannon's diversity index revealed a similar trend. In contrast, the 3rd cluster (late-succession) had significantly higher phylogenetic diversity. The 3rd cluster (late-succession) had the lowest functional richness and the highest functional dispersion. Our approach underscored the significance of a priori classification of vegetation using remote sensing for vegetation surveys. It also highlighted differences between vegetation types along a successional gradient in post-mining spoil heaps.

MSCFS-RP: A Colored-Petri-Net-Based Analysis Model for Master–Slave Cloud File Systems with Replication Pipelining

As a typical information system, a cloud file system enables the storage, retrieval, and management of data on remote servers or server clusters. The reliable design of such systems is critical to ensure the security of data and availability of services. However, designing correct-by-construction systems is challenging due to the complexity of and concurrency inherent in cloud file systems. Further, existing works on cloud file system analysis often focus on specific systems or lack formal modeling and verification, leading to potential design flaws and security vulnerabilities. To address these issues, we propose MSCFS-RP, which is a formal analysis model based on colored Petri nets. Leveraging the strengths of colored Petri nets in representing diverse information types with colored tokens and defining explicit rules for concurrent interactions, our model captures the writing and reading processes of clients, meta servers, and clusters. With strong formalism and support for verification using CPN Tools, we rigorously evaluate key properties such as replication consistency under various scenarios. The results demonstrate that MSCFS-RP satisfies these properties, validating its effectiveness and trustworthiness in managing information within cloud storage systems.

Remote response modes on the paced excitable C. elegans network

In paced excitable systems, a traditional viewpoint on signal transmission is that an excitable pacing signal should propagate from the source to its excited neighbors in a sequential rule, which is known as the normal (SRM). However, in our present paper, by extensively exploring the response modes emerged on the , we found that, besides the normal SRM, a response mode that breaks the sequence rule and contains isolated excited clusters or even isolated excited nodes on the propagation path of the response structure can be detected. Such remote response nodes and clusters are excited discontinuously from the source node, forming the phenomenon of the (RRM). An (EDAA) is proposed to theoretically study the mechanism of the RRM, based on which key role played by the excited nodes possessing nonexcited upstream driving neighbors is confirmed in the formation of the RRM. We further verify the applicability and the universality of the EDAA in identifying the RRM on general paced excitable networks. These contributions are expected to shed light on the study of complex response modes in paced excitable systems and to have great impact in related topics. Published by the American Physical Society 2024

High-risk human papillomaviruses l1 gene isolates identified in Western Kazakhstan

Kazakhstani researchers reported a significant prevalence of highly carcinogenic human papillomavirus types in the country.<br /> The article <b>aimed</b> to present HPV L1 gene sequencing developments in women affected with cervical cancer throughout the western part of Kazakhstan with provided findings on the geographic pathways of obtained isolates.<br /> <b>Methods. </b>The HPV L1 gene was amplified using the consensus primers MY09HPV 5’-CGTCCMARRGGAWACTGATC-3’ and MY11HPV 5’ – GCMCAGGGWCATAAYAATGG-3’.  The purified DNA was used as the target for direct nucleotide sequencing. Phylogenetic analyses were conducted using the MegAlign program from the LASERGENE software package (version 6.0; DNA star, Madison, WI) and with MEGA version 5.0 software. A multiple alignment was created through Clustal W software, and the neighbor-joining method was used to construct the phylogenetic tree.<br /> <b>Results.</b> Of 70 HPV samples transported to the Astana shared laboratory for gene L1 sequencing, only ten appeared fit to obtain isolates (14.3%). The viral load of the samples ranged from 3.3 to 8.2, and the range of DNA concentration was from 8.16 to 69.6 ng/uL. HPV16 unique Kazakhstani isolate from Aktobe, having its own branch, and not yet registered in the world genebank, was revealed. An isolate of potentially carcinogenic HPV53 forming a remote cluster with KF436822/1, KU951264.1 - Southwest China, and 97% identity with EU056643.1 - Ireland, and acted as a single agent for invasive cervical cancer was identified.<br /> In general, the sequencing findings indicate the variety of ways for HPV pervasion into the western region of Kazakhstan: North and South America, Europe, and Asia.<br /> <i><i>The study was recorded in the ISRCTN registry, No. 7154910, 02/01/2018.</i></i>

An edge architecture for enabling autonomous aerial navigation with embedded collision avoidance through remote nonlinear model predictive control

In this article, we present an edge-based architecture for enhancing the autonomous capabilities of resource-constrained aerial robots by enabling a remote nonlinear model predictive control scheme, which can be computationally heavy to run on the aerial robots' onboard processors. The nonlinear model predictive control is used to control the trajectory of an unmanned aerial vehicle while detecting, and preventing potential collisions. The proposed edge architecture enables trajectory recalculation for resource-constrained unmanned aerial vehicles in relatively real-time, which will allow them to have fully autonomous behaviors. The architecture is implemented with a remote Kubernetes cluster on the edge side, and it is evaluated on an unmanned aerial vehicle as our controllable robot, while the robotic operating system is used for managing the source codes, and overall communication. With the utilization of edge computing and the architecture presented in this work, we can overcome computational limitations, that resource-constrained robots have, and provide or improve features that are essential for autonomous missions. At the same time, we can minimize the relative travel time delays for time-critical missions over the edge, in comparison to the cloud. We investigate the validity of this hypothesis by evaluating the system's behavior through a series of experiments by utilizing either the unmanned aerial vehicle or the edge resources for the collision avoidance mission.

Study of drought tolerance of winter oat varieties

This research was conducted during the period 2019-2021 at the Institute of Agriculture in Karnobat, Bulgaria. In order to establish suitable parents for a selection of drought resistant varieties, twelve accessions of introduced winter oats were studied. The genetic similarity and distance of the accessions was determined by means of cluster analysis. The highest drought resistance was exhibited by varieties Lustre, Image, Westfinnischer Schwarz, Powys, Lee and Fulghum . A good combination of drought resistance, grain weight per spike and 1000-grain weight was found for Lustre, Image and the Heros variety was from the accessions group with medium drought resistance. The cluster analysis revealed that the promising combinations can be obtained by crossing varieties from the most remote clusters, such as Lustre (first cluster) with varieties Panema, Roanoke, and Winter Turf from the third cluster. Keywords: winter oats, accessions, drought resistance, productivity, cluster analysis

Objective identification of tropical cyclone‐induced remote moisture transport using digraphs

AbstractTropical cyclone (TC)‐induced remote moisture transport is the fundamental cause of TC‐induced remote precipitation. However, despite increasing attention having been paid to TC‐induced remote moisture transport over the past few decades, a method for the objective identification of TC remote moisture transport remains lacking, which is crucial to understanding the complex rainfall mechanisms associated with TC‐induced remote moisture transport over recent decades. We set out to solve this issue in the present study by using a series of newly developed processing algorithms. Firstly, we identified vertically integrated water vapour transport (IVT) pathways using spatially smoothed moving window quantiles, and then used the maximum gradient method to segment IVT clusters from pathways. Relationship digraphs were constructed for IVT clusters to flexibly interpret the spatio‐temporal merging and splitting processes among them. Finally, TC clusters (TCCs) and TC remote clusters (TRCs) were identified in succession based on the TC tracks and diagraphs of IVT clusters. Applications of these processing algorithms showed that the TCCs and TRCs at the same time step can be identified successfully by applying our method. The generality of the objective identification method was validated using data covering four decades. Our algorithms revealed discontinuous and uneven moisture transport, especially those associated with TCs, which benefits studies of remote rainfall associated with TCs. Furthermore, it facilitates the construction of IVT pathway and cluster datasets covering the past several decades, which can be used for analysing related characteristics and thereby revealing possible physical mechanisms underlying the nature of TRCs.

Kubernetes-based Deep Learning with Blockchain for Cancer Image Prediction

The ability of modern artificial intelligence techniques to analyze complex data, including medical images, with greater precision and depth has significantly increased the amount of real-world data that can be collected for scientific research. This has led to the development of a global library and elevated computing choices to meet the demand for training model calculation. The development of medical image biomarkers involves additional processes that don't necessitate spending so much money on expensive computer resources. To collect and manage cancer data, this study presents a Blockchain-enabled deep learning framework for the Kubernetes cluster which replicates the training set of parameters of DL techniques across remote cluster nodes. Healthcare providers' data will be synchronized thanks to the distributed storage system known as the blockchain. Data are gathered for this suggested framework utilizing the Kaggle Dataset from several hospitals. Data preprocessing shows pixel correction and data augmentation. Following data preprocessing, U2-Net segmentation is used to segment up the images. This article uses blockchain to decentralize data, security, and privacy. A system called Kubernetes cluster is used to manage diverse containerized applications, Workload Distribution, Scalability, and Storage Management. The Kubernetes cluster is revealed in the study when investigating the cancer prediction capabilities of the hybrid learning algorithms VGG19 and BiLSTM. This proposed paper forecasts the article and explains the various applications for breast, kidney, and oral cancer predicated on the Blockchain-Enabled deep Learning model on Kubernetes and providing authentication for the use of the majority of the platform's features using a provided case.

Survey for Distant Stellar Aggregates in the Galactic Disk: Detecting 2000 Star Clusters and Candidates, along with the Dwarf Galaxy IC 10

Despite having data for over 109 stars from Gaia, less than 104 star clusters and candidates have been discovered. In particular, distant star clusters are rarely identified, due to the challenges posed by heavy extinction and great distance. However, Gaia data has continued to improve, enabling even fainter cluster members to be distinguished from field stars. In this work, we introduce a star-cluster search method based on the DBSCAN algorithm; we have made improvements to make it better suited for identifying clusters on dimmer and more distant stars. After having removed member stars of known Gaia-based clusters, we identified 2086 objects with ∣b∣ < 10°, of which 1488 are highly reliable open star clusters, along with 569 candidates, 28 globular cluster candidates, and one irregular galaxy (IC 10) at low Galactic latitudes. We found that the proper motion of IC 10 is similar to, yet slightly different from, the water maser observations, which is an important result for the comparison with Gaia and the Very Long Baseline Array. When compared with the star clusters appearing in Gaia Data Release (DR) 2/EDR3, we found nearly 3 times as many new objects above a distance of 5 kpc, including hundreds of them above A v > 5 mag. This has enabled us to detect a higher number of old clusters, over a billion years old, that are difficult to detect due to observational limitations. Our findings significantly expand the remote cluster sample and enhance our understanding of the limits of Gaia DR3 data in stellar aggregates research.

Parametric Optimization on HPC Clusters with Geneva

Many challenges of today’s science are parametric optimization problems that are extremely complex and computationally intensive to calculate. At the same time, the hardware for high-performance computing is becoming increasingly powerful. Geneva is a framework for parallel optimization of large-scale problems with highly nonlinear quality surfaces in grid and cloud environments. To harness the immense computing power of high-performance computing clusters, we have developed a new networking component for Geneva—the so-called MPI Consumer—which makes Geneva suitable for HPC. Geneva is most prominent for its evolutionary algorithm, which requires repeatedly evaluating a user-defined cost function. The MPI Consumer parallelizes the computation of the candidate solutions’ cost functions by sending them to remote cluster nodes. By using an advanced multithreading mechanism on the master node and by using asynchronous requests on the worker nodes, the MPI Consumer is highly scalable. Additionally, it provides fault tolerance, which is usually not the case for MPI programs but becomes increasingly important for HPC. Moreover, the MPI Consumer provides a framework for the intuitive implementation of fine-grained parallelization of the cost function. Since the MPI Consumer conforms to the standard paradigm of HPC programs, it vastly improves Geneva’s user-friendliness on HPC clusters. This article gives insight into Geneva’s general system architecture and the system design of the MPI Consumer as well as the underlying concepts. Geneva—including the novel MPI Consumer—is publicly available as an open source project on GitHub (https://github.com/gemfony/geneva) and is currently used for fundamental physics research at GSI in Darmstadt, Germany.

Microbial Composition on Abandoned and Reclaimed Mining Sites in the Komi Republic (North Russia)

Restoration of anthropogenically disturbed soils is an urgent problem in modern ecology and soil biology. Restoration processes in northern environments are especially important, due to the small amounts of fertile land and low levels of natural succession. We analyzed the soil microbiota, which is one of the indicators of the succession process is the soil. Samples were obtained from three disturbed soils (self-overgrown and reclaimed quarries), and two undisturbed soils (primary and secondary forests). Primary Forest soil had a well-developed soil profile, and a low pH and TOC (total organic carbon) amount. The microbial community of this soil had low richness, formed a clear remote cluster in the beta-diversity analysis, and showed an overrepresentation of Geobacter (Desulfobacteriota). Soil formation in clay and limestone abandoned quarries was at the initial stage, and was caused by both a low rate of mineral profile formation and severe climatic conditions in the region. Microbial communities of these soils did not have specific abundant taxa, and included a high amount of sparse taxa. Differences in taxa composition were correlated with abiotic factors (ammonium concentration), which, in turn, can be explained by the parent rock properties. Limestone quarry reclaimed by topsoil coverage resulted in an adaptation of the top soil microbiota to a novel parent rock. According to the CCA analysis, the microbial composition of samples was connected with pH, TOC and ammonium nitrogen concentration. Changes in pH and TOC were connected with ASVs from Chloroflexota, Gemmatimonadota and Patescibacteria. ASVs from Gemmatimonadota also were correlated with a high ammonium concentration.

Reliable Wide-Area Data Transfers for Streaming Workflows

Many large science projects rely on remote clusters for (near) real-time data processing, thus they demand reliable wide-area data transfer performance for smooth end-to-end workflow executions. However, data transfers are often exposed to performance variations due to the changing network (e.g., background traffic) and dataset (e.g., average file size) conditions, necessitating adaptive solutions to meet stringent performance requirements of delay-sensitive streaming workflows. In this article, we propose <i>FStream++</i> to provide reliable transfer performance for large streaming science applications by dynamically adjusting transfer settings to adapt to changing transfer conditions. <i>FStream++</i> combines three optimization methods as <i>dynamic tuning</i> , <i>online profiling</i> , and <i>historical analysis</i> to swiftly and accurately discover optimal transfer settings that can meet workflow requirements. Dynamic tuning uses a heuristic model to predict the values of transfer parameters based on dataset characteristics and network settings. Since heuristic models fall short to incorporate many important factors such as I/O throughput and resource interference, we complement it with online profiling to execute a real-time search for a subset of transfer settings. Finally, historical analysis takes advantage of the long-running nature of streaming workflows by storing and analyzing previous performance observations to shorten the execution time of online profiling. We evaluate the performance of <i>FStream++</i> by transferring several synthetic and real-world workloads in high-performance production networks and show that it offers up to <inline-formula><tex-math notation="LaTeX">$3.6x$</tex-math></inline-formula> performance improvement over legacy transfer applications and up to 24% over our previous work <i>FStream</i> .

A Study on routing optimization of Wireless Sensor Networks using an Enhanced LEACH protocols

The LEACH routing protocol equalizes network power consumption by randomly selecting cluster headnodes in a loop, which will cause network malfunctions. Therefore, to solve this problem, it is necessary to reduce the data transmission energy consumption in the routing protocol and increase the network life cycle. However, there is also the problem that cluster headers have a wide range, and forwarding data from cluster headers consumes a lot of power in LEACH, which has not yet been resolved. In this article, we present an approach to optimize the routing protocol. First, the optimal number of cluster heads is calculated according to the total energy consumption per round to reduce the probability of excessive distribution of cluster heads. The cluster head is then used as the nucleus to construct the Voronoi diagram. Nodes in the same Voronoi diagram become a cluster, so the energy consumption of intra-cluster communication would be lower. Finally, to optimize the multi-hop routing protocol, an ant colony algorithm is added using a cluster header close to the BS to receive and forward it from a remote cluster header. Based on MATLAB simulation data, the protocol can significantly extend the lifetime of WSNs compared to the LEACH protocol and increase the energy efficiency per node unit per round. The energy consumption of the proposed approach is unique. The approach improved first node kill time (FND) by 127%, 22.2%, and 14.5% relative to LEACH, LEACH-C, and SEP, respectively.

Разработка комплекса параллельных программ решения обратных задач гравиметрии и магнитометрии для сеток большой размерности

The most important problems in studying of the structure of the earth’s crust are the inverse problems of gravimetry and the problems of magnetometry. The problem is in finding interfaces between layers with different constant densities using known gravitational data. The methods for solving these problems are based on the ideas of iterative regularization. After discretization, these are reduced to systems of nonlinear functions of large dimensions. The need to improve the accuracy of the results of solving problems entails an increase in the computation time. The paper describes a remote computing system and an integrated program complex for graphics accelerators that implement the fastest and most memory-efficient algorithms developed earlier and based on gradient methods. The remote computing system considered in the work is a web portal that is a universal solution for launching tasks on remote clusters. The most important advantage of the portal is the simplicity of its use: when connecting to a cluster to carry out calculations, you no longer need to install additional software on the cluster itself, and you do not need a privileged account to work with the cluster. All that is required is a valid account on the remote cluster, the rest of the work on communication with the data processing center (DPC) is taken over by the portal. The portal can easily scale with the growth of the number of users who can download the necessary algorithms and perform calculations using the data center.

Bacillus cytotoxicus Genomics: Chromosomal Diversity and Plasmidome Versatility.

Bacillus cytotoxicus is the thermotolerant representative of the Bacillus cereus group. This group, also known as B. cereus sensu lato, comprises both beneficial and pathogenic members and includes psychrotolerant and thermotolerant species. Bacillus cytotoxicus was originally recovered from a fatal outbreak in France in 1998. This species forms a remote cluster from the B. cereus group members and reliably contains the cytk-1 gene, coding for a cytotoxic variant of cytotoxin K. Although this species was originally thought to be homogenous, intra-species diversity has been recently described with four clades, six random amplified polymorphic DNA (RAPD) patterns, and 11 plasmids profiles. This study aimed to get new insights into the genomic diversity of B. cytotoxicus and to decipher the underlying chromosomal and plasmidial variations among six representative isolates through whole genome sequencing (WGS). Among the six sequenced strains, four fitted the previously described genomic clades A and D, while the remaining two constituted new distinct branches. As for the plasmid content of these strains, three large plasmids were putatively conjugative and three small ones potentially mobilizable, harboring coding genes for putative leaderless bacteriocins. Mobile genetic elements, such as prophages, Insertion Sequences (IS), and Bacillus cereus repeats (bcr) greatly contributed to the B. cytotoxicus diversity. As for IS elements and bcr, IS3 and bcr1 were the most abundant elements and, along with the group II intron B.c.I8, were found in all analyzed B. cytotoxicus strains. When compared to other B. cytotoxicus strains, the type-strain NVH 391-98 displayed a relatively low number of IS. Our results shed new light on the contribution of mobile genetic elements to the genome plasticity of B. cytotoxicus and their potential role in horizontal gene transfer.

A techno-economic analysis of a hybrid energy system for the electrification of a remote cluster in western Saudi Arabia

Remote areas electrification is usually achieved through costly and eco-unfriendly diesel generator (DG) systems with additional cost-obstacles arising through diesel–fuel transportation. Hybridization of renewable energy systems (RESs) with DG units offers a dynamic approach of electrification for the remote areas. A techno-economic study is required to assess the viability of RES in such locations. This research work examines the case of Al-Shumaisi cluster, located in the west of Makkah Province, Kingdom of Saudi Arabia (KSA). This is where the Makkah transport company (MTC) is located. A techno-economic investigation is performed for a commercial load profile of MTC with a hybrid wind/DG/fuel cell/battery/electrolyzer system. In this study, two hybrid configurations are proposed: 1) wind/DG/FC/battery system; 2) wind/FC/battery system. Moreover, another configuration that uses only DG units is also investigated. A comparative study is achieved for the most optimal combination, depending on the gross present cost (GPC) and levelized cost of energy (LCOE) as the essential parameters. Furthermore, other financial parameters like operation and maintenance (O&M) costs, capital costs, and replacement costs are investigated. The well-recognized simulation tool, HOMER (Hybrid Optimization of Multiple Energy Resources) developed by the National Renewable Energy Laboratory, is used to fulfill this study. The sensitivity investigation is carried out by optimally scaling the configuration capacities as well as by monitoring the future changes like weather, load demand, and fuel price uncertainties. The results of the sensitivity investigation and HOMER simulation confirm that the wind/DG/FC/battery hybrid off-grid configuration is the most economic and eco-friendly solution with a total GPC of 7.045 million USD, COE of 0.271 USD/kWh, and CO2 emissions of 566 tons. This signifies the 13.84% reduction of net present cost compared to DG-based units, with a 64.2% decrease of pollutants and zero unmet load.

Optimal Task Assignment for Integrated Cloud and Edge Networks With Tree Topology

The rapid growth of mobile applications and the Internet of Things generates a huge amount of data that can hardly be processed by end devices. Recently, Integrated Cloud and Edge (ICE) networks have been proposed to support smart applications and services by leveraging both remote clouds and edge clusters. However, task assignment over ICE networks has been proved NP-hard, which makes it challenging in practice. In this work, we discover that a polynomial-time optimal solution does exist for the task assignment problem when the ICE network is organized with tree topology. Specifically, the studied problem for the ICE network with tree topology is transformed into an equivalent graph optimization problem. Theoretical analysis is then provided to find the polynomial-time optimal solution. Evaluation results demonstrate the correctness and efficiency of the discovered solution.

YMCA-1: A New Remote Star Cluster of the Milky Way?*

Abstract We report the possible discovery of a new stellar system (YMCA-1), identified during a search for small scale overdensities in the photometric data of the YMCA survey. The object’s projected position lies on the periphery of the Large Magellanic Cloud about 13° apart from its center. The most likely interpretation of its color–magnitude diagram, as well as of its integrated properties, is that YMCA-1 may be an old and remote star cluster of the Milky Way at a distance of 100 kpc from the Galactic center. If this scenario could be confirmed, then the cluster would be significantly fainter and more compact than most of the known star clusters residing in the extreme outskirts of the Galactic halo, but quite similar to Laevens 3. However, much deeper photometry is needed to firmly establish the actual nature of the cluster, and the distance to the system.