Cluster Environment Research Articles

Evaluation of transport processes over North China Plain and Yangtze River Delta using MAX-DOAS observations

Abstract. Pollutant transport has a substantial impact on the atmospheric environment in megacity clusters. However, owing to the lack of knowledge of vertical pollutant structure, quantification of transport processes and understanding of their impacts on the environment remain inadequate. In this study, we retrieved the vertical profiles of aerosols, nitrogen dioxide (NO2), and formaldehyde (HCHO) using multi-axis differential optical absorption spectroscopy (MAX-DOAS) and analyzed three typical transport phenomena over the North China Plain (NCP) and Yangtze River Delta (YRD). We found the following: (1) the main transport layers (MTL) of aerosols, NO2, and HCHO along the southwest–northeast transport pathway in the Jing-Jin-Ji region were approximately 400–800, 0–400, and 400–1200 m, respectively. The maximum transport flux of HCHO appeared in Wangdu (WD), and aerosol and NO2 transport fluxes were assumed to be high in Shijiazhuang (SJZ), both urban areas being significant sources feeding regional pollutant transport pathways. (2) The NCP was affected by severe dust transport on 15 March 2021. The airborne dust suppressed dissipation and boosted pollutant accumulation, decreasing the height of high-altitude pollutant peaks. Furthermore, the dust enhanced aerosol production and accumulation, weakening light intensity. For the NO2 levels, dust and aerosols had different effects. At the SJZ and Dongying (DY) stations, the decreased light intensity prevented NO2 photolysis and favored NO2 concentration increase. In contrast, dust and aerosols provided surfaces for heterogeneous reactions, resulting in reduced NO2 levels at the Nancheng (NC) and Xianghe (XH) stations. The reduced solar radiation favored local HCHO accumulation in SJZ owing to the dominant contribution of the primary HCHO. (3) Back-and-forth transboundary transport between the NCP and YRD was found. The YRD-to-NCP and NCP-to-YRD transport processes mainly occurred in the 500–1500 and 0–1000 m layers, respectively. This transport, accompanied by the dome effect of aerosols, produced a large-scale increase in PM2.5, further validating the haze-amplifying mechanism.

New high-precision strong lensing modeling of Abell 2744

We present a new strong lensing (SL) model of the Hubble Frontier Fields (HFF) galaxy cluster Abell 2744, at z = 0.3072, by exploiting archival Hubble Space Telescope (HST) multiband imaging and Multi Unit Spectroscopic Explorer (MUSE) follow-up spectroscopy. The lens model considers 90 spectroscopically confirmed multiple images (from 30 background sources), representing the largest secure sample for this cluster field prior to the recently acquired James Webb Space Telescope (JWST) observations. The inclusion of the substructures within several extended sources as model constraints allowed us to accurately characterize the inner total mass distribution of the cluster and the position of the cluster critical lines. We included the lensing contribution of 225 cluster members, 202 of which are spectroscopically confirmed. We complemented this sample with 23 photometric member galaxies that are identified with a convolution neural network methodology with a high degree of purity. We also measured the internal velocity dispersion of 85 cluster galaxies, down to mF160W = 22, to independently estimate the role of the subhalo mass component in the lens model. We investigated the effect of the cluster environment on the total mass reconstruction of the cluster core with two different mass parameterizations. We considered the mass contribution from three external clumps, either based on previous weak lensing studies, or extended HST imaging of luminous members around the cluster core. In the latter case, the observed positions of the multiple images were better reproduced, with a remarkable accuracy of 0.​​″37, a factor of ∼2 smaller than previous lens models, which exploited the same HST and MUSE data sets. As part of this work, we developed and made publicly available a Strong Lensing Online Tool (SLOT) to exploit the predictive power and the full statistical information of this and future models, through a simple graphical interface. We plan to apply our new high-precision SL model to the first analysis of the Grism Lens-Amplified Survey from Space-JWST-Early Release Science (GLASS-JWST-ERS) program, specifically to measure the intrinsic physical properties of high-z galaxies from robust magnification maps.

Benchmarking a novel ensemble docking method.

Docking is an important tool in the kit of researchers and engineers working on molecular design and drug development problems. It is computationally efficient enough that it can be used on thousands or tens of thousands of structures with reasonable access to a cluster environment, and has the capacity to find binding poses and even estimate affinities. It has many well-known weaknesses, however. One significant weakness is that it does not properly account for conformational heterogeneity in receptors. This general weakness can produce incredibly poor results in cases where this heterogeneity changes the conformation of the binding site, since the receptor's ensemble can be mostly populated with states where the pocket of interest is closed (sterically occluded), as is the case with cryptic pockets.

A SqueeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques analyze a study area using a set of SAR image data composed of time series, reaching millimeter surface subsidence accuracy. To effectively acquire the subsidence information in low-coherence areas without obvious features in non-urban areas, an MT-InSAR technique, called SqueeSAR, is proposed to improve the density of the subsidence points in the study area by fusing the distributed scatterers (DS). However, SqueeSAR filters the DS points individually during spatial adaptive filtering, which requires significant computer memory, which leads to low processing efficiency, and faces great challenges in large-area InSAR processing. We propose a spatially adaptive filtering parallelization strategy based on the Spark distributed computing engine in a Hadoop distributed cluster environment, which splits the different DS pixel point data into different computing nodes for parallel processing and effectively improves the filtering algorithm’s performance. To evaluate the effectiveness and accuracy of the proposed method, we conducted a performance evaluation and accuracy verification in and around the main city of Kunming with the original Sentinel-1A SLC data provided by ESA. Additionally, parallel calculation was performed in a YARN cluster comprising three computing nodes, which improved the performance of the filtering algorithm by a factor of 2.15, without affecting the filtering accuracy.

Integrated governance of the Yangtze River Delta port cluster using niche theory: A case study of Shanghai Port and Ningbo-Zhoushan Port

The integrated governance of port clusters is an important way for economic powers to improve port efficiency and marine economic benefits. As the Yangtze River Delta port cluster is the most densely distributed port cluster with the largest throughput in China's coastal region, further strengthening its integrated governance and enhancing its global influence is a key element of China's strategy to promote the integrated development of the Yangtze River Delta region. We investigate the niches of Shanghai Port and Ningbo-Zhoushan Port, both of which are critical ports in the Yangtze River Delta port cluster. The results show that Shanghai Port is superior to Ningbo-Zhoushan Port in terms of economic contribution, competitiveness, resource utilization, and sustainable development capability. Meanwhile, Ningbo-Zhoushan Port has advantages in natural hardware and resources and has huge development potential. To enhance the economic radiation and international competitiveness of the Yangtze River Delta port cluster, it is necessary to strengthen integrated governance, form a reasonable division of labor among ports, and achieve win‒win cooperation among ports. In addition, the government should strengthen the overall planning of coastal management, improve the functional positioning of each port, strengthen the integrated management of waterways, and promote the innovation of cooperation models between ports. Finally, the integrated management of the ecological environment of the Yangtze River Delta port cluster should be strengthened, especially the integrated cooperation in marine ecological protection.

Achieving new SQL query performance levels through parallel execution in SQL Server

This article provides an in-depth look at implementing parallel SQL query processing using the Microsoft SQL Server database management system. It examines how parallelism can significantly accelerate query execution by leveraging multi-core processors and clustered environments. The article explores SQL Server's sophisticated parallel processing capabilities including automatic query parallelization, intra-query parallelism techniques like parallel joins and parallel data aggregation, as well as inter-query parallelism for concurrent query execution. It covers key considerations around effective parallelization such as managing concurrency and locks, handling data skew, resource governance, and monitoring. Challenges like debugging parallel plans and potential bottlenecks from excessive parallelism are also discussed along with mitigation strategies. Real-world examples demonstrate how judicious application of parallel processing helps optimize complex analytics workloads involving massive datasets. The insights presented provide guidance to database developers and administrators looking to enable parallel SQL query execution in SQL Server environments for substantial performance gains and scalability.

Automated Log Classification Using Deep Learning

System logs are a rich source of information. The complexity and amount of data contained in log files is increasing rapidly, especially with the development of cloud computing in clustered environments. Log analysis is effective to find the root cause of an issue. Because of the huge volume of unstructured logs, manual inspection becomes difficult and time consuming. Hence, there is a need to automate the process of log analysis. Current state-of-the-art deep learning-based techniques are trained on generic datasets and do not perform well on specific system logs. The proposed invention is focused to predict errors from large system specific logs by using manual vectorization technique called “LogWord2Vec” and combining it with data-cleaning, augmentation, embedding, and deep learning method together. Unlike other solutions, our method does not require a large dataset, and works well on medium to small sized datasets. The proposals described in this disclosure help in improving both the accuracy and the efficiency of log parsing systems. We have used combination of Convolution neural network (CNN) layer and long short-term memory (LSTM) layer to build the model and obtained accuracy of 99.19% with 0.81% of misclassification.

Сolor in the architecture of art clusters

The article considers the capabilities of color in the organization of the architectural environment of art clusters, highlights the features of art clusters, emphasizes the relationship of the properties of an art cluster with the formation of its color environment.

An energy-efficient hierarchical algorithm of dynamic obstacle avoidance for unmanned surface vehicle

Most of the existing studies developed and improved local path planning algorithms independently of global planning, i.e., ignoring the global optimal constrains. To meet the requirements of practical applications, this paper presented an energy-efficient hierarchical collision avoidance algorithm for unmanned surface vehicle operating in clustered dynamic environments. For the global level, genetic algorithm was modified by strategies of greedy-inspired population initialization, penalty-based multi-objective fitness function, and joint crossover. For the local level, velocity obstacle was combined with dynamic window approach to provide the kinematic constraints of the vehicle to its admissible velocities and simplified collision avoidance rules to guide the evasive maneuvers. Simulations showed that the proposed global algorithm was superior to three other algorithms in terms of path length, path smoothness, and convergence speed regardless of the environment size. The performance of the local algorithm was also verified for various encounter scenarios and speed ratios. In addition, the combination of the global and local planning can effectively solve the path optimization and dynamic obstacle avoidance in a designed offshore environment of fish cage culture.

Educational cluster environment as a criteria for professional competence development of students

Educational cluster environment as a criteria for professional competence development of students

Qualitative Clustering of Software Repositories Based on Software Metrics

Software repositories contain a wealth of information about the aspects related to software development process. For this reason, many studies analyze software repositories using methods of data analytics with a focus on clustering. Software repository clustering has been applied in studying software ecosystems such as GitHub, defect and technical debt prediction, software remodularization. Although some interesting insights have been reported, the considered studies exhibited some limitations. The limitations are associated with the use of individual clustering methods and manifesting in the shortcomings of the obtained results. In this study, to alleviate the existing limitations we engage multiple cluster validity indices applied to multiple clustering methods and carry out consensus clustering. To our knowledge, this study is the first to apply the consensus clustering approach to analyze software repositories and one of the few to apply the consensus clustering to software metrics. Intensive experimental studies are reported for software repository metrics data consisting of a number of software repositories each described by software metrics.We revealed seven clusters of software repositories and relate them to developers’ activity. It is advocated that the proposed clustering environment could be useful for facilitating the decision making process for business investors and open-source community with the help of the Gartner’s hype cycle.

Map-Reduce based Distance Weighted k-Nearest Neighbor Machine Learning Algorithm for Big Data Applications

With the evolution of Internet standards and advancements in various Internet and mobile technologies, especially since web 4.0, more and more web and mobile applications emerge such as e-commerce, social networks, online gaming applications and Internet of Things based applications. Due to the deployment and concurrent access of these applications on the Internet and mobile devices, the amount of data and the kind of data generated increases exponentially and the new era of Big Data has come into existence. Presently available data structures and data analyzing algorithms are not capable to handle such Big Data. Hence, there is a need for scalable, flexible, parallel and intelligent data analyzing algorithms to handle and analyze the complex massive data. In this article, we have proposed a novel distributed supervised machine learning algorithm based on the MapReduce programming model and Distance Weighted k-Nearest Neighbor algorithm called MR-DWkNN to process and analyze the Big Data in the Hadoop cluster environment. The proposed distributed algorithm is based on supervised learning performs both regression tasks as well as classification tasks on large-volume of Big Data applications. Three performance metrics, such as Root Mean Squared Error (RMSE), Determination coefficient (R2) for regression task, and Accuracy for classification tasks are utilized for the performance measure of the proposed MR-DWkNN algorithm. The extensive experimental results shows that there is an average increase of 3% to 4.5% prediction and classification performances as compared to standard distributed k-NN algorithm and a considerable decrease of Root Mean Squared Error (RMSE) with good parallelism characteristics of scalability and speedup thus, proves its effectiveness in Big Data predictive and classification applications.

Reconstructing orbits of galaxies in extreme regions (ROGER) III: Galaxy evolution patterns in projected phase space around massive X-ray clusters

ABSTRACT We use the roger code by de los Rios et al. to classify galaxies around a sample of X-ray clusters into five classes according to their positions in the projected phase space diagram: cluster galaxies, backsplash galaxies, recent infallers, infalling galaxies, and interlopers. To understand the effects of the cluster environment to the evolution of galaxies, we compare across the five classes: stellar mass, specific star formation rate, size, and morphology. Following the guidelines of Coenda et al., a separate analysis is carried out for red and blue galaxies. For red galaxies, cluster galaxies differ from the other classes, having a suppressed specific star formation rate, smaller sizes, and are more likely to be classified as ellipticals. Differences are smaller between the other classes, however backsplash galaxies have significantly lower specific star formation rates than early or recent infalling galaxies. For blue galaxies, we find evidence that recent infallers are smaller than infalling galaxies and interlopers, while the latter two are comparable in size. Our results provide evidence that, after a single passage, the cluster environment can diminish a galaxy’s star formation, modify its morphology, and can also reduce in size blue galaxies. We find evidence that quenching occurs faster than morphological transformation from spirals to ellipticals for all classes. While quenching is evidently enhanced as soon as galaxies get into clusters, significant morphological transformations require galaxies to experience the action of the physical mechanisms of the cluster for longer time-scales.

Breakdown of planetary systems in embedded clusters

ABSTRACT We report the first simulations of planetary system dynamics as affected by an embedded cluster environment. Such environments are generally believed to be relevant for the large majority of newborn stars of solar type. Moreover, our cluster model is more realistic than in previous work. We focus on a giant planet system with five members, which represents a likely precursor of our solar system. Our main result is that the perturbing effects of close encounters with cluster stars trigger dynamical chaos leading to breakdown of the system with a significant probability, especially if the natal gas discs are short-lived and the clusters are highly concentrated. When breakdown occurs, all planets except Jupiter suffer a large risk of being ejected from the system or extracted into distant orbits with semimajor axes of hundreds or thousands of astronomical units. This is consistent with recent estimates of a large abundance of low-mass, free-floating planets. We demonstrate a possibility for Jupiter and Saturn to evolve into hot Jupiter orbits by tidal circularization during the chaotic evolution. Even so, the low occurrence rate of this outcome indicates that the real hot Jupiters in general have an origin unrelated to dynamical evolution in birth clusters.

Detection of anisotropic satellite quenching in galaxy clusters up to z ∼ 1

ABSTRACT Satellite galaxies in the cluster environment are more likely to be quenched than galaxies in the general field. Recently, it has been reported that satellite galaxy quenching depends on the orientation relative to their central galaxies: satellites along the major axis of centrals are more likely to be quenched than those along the minor axis. In this paper, we report a detection of such anisotropic quenching up to z ∼ 1 based on a large optically selected cluster catalogue constructed from the Hyper Suprime-Cam Subaru Strategic Program. We calculate the quiescent satellite galaxy fraction as a function of orientation angle measured from the major axis of central galaxies and find that the quiescent fractions at 0.25 < z < 1 are reasonably fitted by sinusoidal functions with amplitudes of a few per cent. Anisotropy is clearer in inner regions (<r200m) of clusters and not significant in cluster outskirts (>r200m). We also confirm that the observed anisotropy cannot be explained by differences in local galaxy density or stellar mass distribution along the two axes. Quiescent fraction excesses between the two axes suggest that the quenching efficiency contributing to the anisotropy is almost independent of stellar mass, at least down to our stellar mass limit of $M_{*}=1\times 10^{10}\, {\rm M}_{\odot }$. Finally, we argue that the physical origins of the observed anisotropy should have shorter quenching time-scales than $\sim 1\, \mathrm{Gyr}$, like ram-pressure stripping, because, for anisotropic quenching to be observed, satellites must be quenched before their initial orientation angles are significantly changed.

Gas phase C6H6 - anion: Electronic stabilization by opening of the benzene ring.

It is well established that an isolated benzene radical anion is not electronically stable. In the present study, we experimentally show that electron attachment to benzene clusters leads to weak albeit unequivocal occurrence of a C6H6 - moiety. We propose here-based on electronic structure calculation-that this moiety actually corresponds to linear structures formed by the opening of the benzene ring via electron attachment. The cluster environment is essential in this process since it quenches the internal energy released upon ring opening, which in the gas phase leads to further dissociation of this anion.

A Green Economy Approach to Solid Waste Management in Minna, Nigeria

This study is based on solid waste recycling activities in Minna, Nigeria. The objectives of the study are to take inventory of recycling outlets, to examine the recycling inputs and the products produced by the recyclers, to assess the operating environment of the recycling cluster and to investigate the livelihood opportunities that recycling generates. The result shows that the recycling cluster occupies an area of 0.64 hectares that accommodates 141 recycling outlets with about 40% of the outlets occupying below the standard shop size of 7.2m2. The recycling outlets use 18 types of recovered materials to reproduce 27 varieties of products. Each recycling outlet engages about 3.4 persons and offers training to an average of 6 persons each. In general, the sector contributes to creation of employment, income generation, skill development, asset building and social capital formation.

Large-Scale Distributed System and Design Methodology for Real-Time Cluster Services and Environments

The demand for a large-scale distributed system, such as a smart grid, which includes real-time interconnection, is rapidly increasing. To provide a seamless connected environment, real-time communication and optimal resource allocation of cluster microgrid platforms (CMPs) are essential. In this paper, we propose two techniques for real-time interconnection and optimal resource allocation for a large-scale distributed system. In particular, to configure a CMP, we analyze the data transfer rate and utilization rate from the intelligent electronic device (IED), collecting the power production data to the individual controller. The details provided in this paper are used to design a sample value, i.e., raw data transfer, on the basis of the IEC 61850 protocol for mapping. The choice of sampled values is to attain the critical time requirement, data transmission of current transformers, voltage transformers, and protective relaying of less than 1 s without complicating the real-time implementation. Furthermore, in this paper, a way to determine the optimal number of physical resources (i.e., CPU, memory, and network) for a given system is discussed. CPU ranged from 0.9 to 0.98 while each cluster increased from 10 to 1000. With the same condition, memory utilized almost 100% utilization from 0.98 to 1. Lastly, the network utilization rate was 0.96 and peaked at 1 at most. Based on the results, we confirm that a large-scale distributed system can provide a seamless monitoring service to distribute messages for each IED, and this can provide a configuration for CMP without exceeding 100% utilization.

Barbell-shaped giant radio galaxy with ∼100 kpc kink in the jet

We present for the first time a study of peculiar giant radio galaxy (GRG) J223301+131502 using deep multi-frequency radio observations from GMRT (323, 612, and 1300 MHz) and LOFAR (144 MHz) along with optical spectroscopic observations with the WHT 4.2m optical telescope. Our observations have firmly established its redshift of 0.09956 and unveiled its exceptional jet structure extending more than ∼200 kpc leading to a peculiar kink structure of ∼100 kpc. We measure the overall size of this GRG to be ∼1.83 Mpc; it exhibits lobes without any prominent hotspots and closely resembles a barbell. Our deep low-frequency radio maps clearly reveal the steep-spectrum diffuse emission from the lobes of the GRG. The magnetic field strength of ∼5 μG and spectral ages between about 110 to 200 mega years for the radio lobes were estimated using radio data from LOFAR 144 MHz observations and GMRT 323 and 612 MHz observations. We discuss the possible causes leading to the formation of the observed kink feature for the GRG, which include precession of the jet axis, development of instabilities and magnetic reconnection. Despite its enormous size, the Barbell GRG is found to be residing in a low-mass (M200 ∼ 1014 M⊙) galaxy cluster. This GRG with two-sided large-scale jets with a kink and diffuse outer lobes residing in a cluster environment, provides an opportunity to explore the structure and growth of GRGs in different environments.

Measuring cavity powers of active galactic nuclei in clusters using a hybrid X-ray–radio method

Measurements of the quantity of radio-mode feedback injected by an active galactic nucleus into the cluster environment have mostly relied on X-ray observations, which reveal cavities in the intracluster medium excavated by the radio lobes. However, the sensitivity required to accurately constrain the dimensions of these cavities has proven to be a major limiting factor and it is the main bottleneck of high-redshift measurements. We describe a hybrid method based on a combination of X-ray and radio observations, which aims to enhance our ability to study radio-mode feedback. In this paper, we present one of the first samples of galaxy clusters observed with the International LOFAR Telescope (ILT) at 144 MHz and use this sample to test the hybrid method at lower frequencies than before. By comparing our measurements with results found in literature based on the traditional method using only X-ray observations, we find that the hybrid method provides consistent results to the traditional method. In addition, we find that the correlation between the traditional method and the hybrid method improves as the X-ray cavities are more clearly defined. This suggests that using radio lobes as proxies for cavities may help to circumvent systematic uncertainties in the cavity volume measurements. Encouraged by the high volume of unique ILT observations which have been successfully processed, this hybrid method enables radio-mode feedback to be studied at high redshifts for the first time even for large samples of clusters.