THE POSSIBILITIES OF THE “CLUSTER CARTOGRAPHY” TOOL FOR THE STUDY OF THE INNER STRUCTURES OF GALAXY CLUSTERS

We describe the possibilities of the “Cluster Cartography” tool which was created for detailed study of the 2D distribution of galaxies in the clusters. The main tasks of the “Cluster Cartography” tool were the detailed study of the morphologyof galaxy clusters using the statistically significant numerical criteria as well as to detect their regular peculiarities. The tool allows to create the 2D map with positions of galaxies in the cluster field and show for each cluster member its shape and orientation as a best-fit ellipse using input catalogue data. The size of symbols for galaxies correspond to input data.It may reflect the galaxy image in arcseconds from catalogue in the map 4000×4000arcsec. Another way connects the size of the symbol with the magnitude of the galaxy. Tool is able to build the map in four modes: the symbols are dots; the symbols are circles with diameters reflected the magnitudes of galaxies; the symbols are ellipses with size reflected the magnitudesand both ellipticities and orientation from the input catalogue; the symbols illustrate the shape of galaxies in projection to the celestial sphere. The “Cluster Cartography” algorithms allow to detect the standard cases in galaxy distribution, suchas the degree of concentration to the cluster center and/or to some line on a statistically significant level using the numerical criteria. Also “Cluster Cartography” allows to detect other features, such as crosses, semi-crosses, complex crosses and short compact chains, as well as to export the list of galaxies forming the peculiarities for the futurestudy. The final version of the “Cluster Cartography” allows to realize the modern scheme for detailed morphological classification of galaxy clusters. The “Cluster Cartography” is powerful and perspective tool for study of features of galaxy clusters.

Penerapan Antarmuka Adaptif Berbasis Perilaku Pemain pada E-Learning Bidang Pemrograman

Pada masa pandemi seperti sekarang ini, bidang pendidikan mengalami dampak yang signifikan dalam pelaksanaannya. Proses belajar mengajar yang sebelumnya dilakukan secara tatap muka, saat ini harus dilaksanakan secara daring. Pembelajaran daring dengan memanfaatkan e-learning sangat bermanfaat bagi pembelajar karena dapat diakses secara daring kapan pun dan dari mana pun. E-learning diharapkan tidak hanya menjadi tempat berbagi file materi sebagai pendukung pembelajaran, tetapi perannya dituntut dapat menggantikan peran guru atau dosen di kelas. Dalam proses belajar mengajar, kondisi dan perilaku siswa merupakan hal yang penting diketahui. Pengetahuan tentang kondisi dan keadaan siswa ketika belajar dapat digunakan oleh pengajar sebagai bahan untuk meningkatkan kualitas maupun proses pembelajaran bagi siswa. Kebanyakan e-learning yang ada sekarang belum dilengkapi dengan fitur untuk mendeteksi keadaan siswa selama menggunakan sistem. Pada makalah ini dilakukan penerapan antarmuka yang adaptif sebagai cerminan e-learning yang mampu menyesuaikan dengan karakteristik perilaku pengguna. Pengujian kinerja menunjukkan hasil bahwa dengan jumlah data log mencapai 950 pengguna aktif, dibutuhkan waktu 23,35 ms. Sementara itu, pada uji fungsionalitas, sistem berhasil menampilkan antarmuka sesuai dengan cluster member.

Quiescent galaxies in a virialized cluster at redshift 2: Evidence for accelerated size-growth

We present an analysis of the galaxy population in XLSSC 122, an X-ray selected, virialized cluster at redshift z = 1.98. We utilize HST WFC3 photometry to characterize the activity and morphology of spectroscopically confirmed cluster members. The quiescent fraction is found to be $88^{+4}_{-20}$ per cent within 0.5r500, significantly enhanced over the field value of $20^{+2}_{-2}$ per cent at z ∼ 2. We find an excess of “bulge-like” quiescent cluster members with Sersic index n > 2 relative to the field. These galaxies are found to be larger than their field counterparts at 99.6 per cent confidence, being on average $63^{+31}_{-24}$ per cent larger at a fixed mass of M⋆ = 5 × 1010 M⊙. This suggests that these cluster member galaxies have experienced an accelerated size evolution relative to the field at z > 2. We discuss minor mergers as a possible mechanism underlying this disproportionate size growth.

Analisis Dan Penerapan Algoritma K-Means Dalam Strategi Promosi Kampus Akademi Maritim Suaka Bahari

The process of accepting new cadet candidates at the Maritime Academy of Marine Sanctuary every year, produces a lot of data in the form of profiles of prospective cadets. The activity caused a large accumulation of data, it became difficult to identify prospective cadets. This research discusses the application of data mining to generate profiles that have similar attributes. One of the data mining techniques used to identify a group of objects that have the same characteristics is Cluster Analysis. The data clustering method is divided into one or more clusters that have the same characteristics called K-means. The method that the author uses is knowledge discovery in databases (KDD) consisting of Data, Data Cleaning, Data transformation, Data mining, Pattern evolution, knowledge. Implementation of K-means Clustering process using Rapid Miner. Attributes used by NIT, Level, Name, Student Status, Type of Registration, Gender, Place of Birth, Date of Birth, Religion, School Origin, School Origin Department, Religion, GPA, Subdistrict, District/ City, Province. Returns the number of clusters 30 (k=30). From the research results based on davies bouldin test on K-means algorithm resulted in the closest value of 0 is k = 29 with Davies bouldin: 0.070, with the most cluster member distribution in cluster 16 containing cluster members 115 items.

Fault Location Measurement of Sensor Nodes based on Fuzzy Control Algorithm

Aiming at the problems of the traditional fault location measurement method for sensor nodes, such as more energy consumption and longer measurement time, a fault location measurement method for sensor nodes based on fuzzy control algorithm is designed and proposed. First of all, the fuzzy control algorithm is analyzed; then the clustering based on cluster head diagnosis is carried out for the network, that is, the nodes that meet the cluster head conditions and are set as normal cluster heads are selected as cluster heads. Finally, combined with the fuzzy control algorithm, the fault location of each cluster member node is measured directly by cluster head nodes. The simulation results show that the proposed method has good performance.

LENG8 is a differentially expressed gene in brain metastatic human breast cancer.

Metastasis to the brain is a clinical problem in patients with breast cancer (1-3). We mined published microarray data (4, 5) to compare primary and metastatic tumor transcriptomes for the discovery of genes associated with brain metastasis in humans with metastatic breast cancer. We found that the leukocyte receptor cluster member 8, encoded by LENG8, was among the genes whose expression was most different in the brain metastases of patients with metastatic breast cancer as compared to primary tumors of the breast. LENG8 mRNA was present at increased quantities in brain metastatic tissues as compared to primary tumors of the breast. Importantly, expression of LENG8 in primary tumors was significantly correlated with patient overall survival. Modulation of LENG8 expression may be relevant to the biology by which tumor cells metastasize from the breast to the brain in humans with metastatic breast cancer.

Rekomendasi Wisata Umbul dengan K-Means Clustering

District Klaten has many springs that are used by people for many things; one of them used for tourist attractions is umbul tourism. It is difficult to use K-Means Clustering Method for determine umbul tourism according to classification and spread in district Klaten. K-Means Clustering is a method of grouping data by taking parameters of a number clusters, and partitioning data into clusters, based on similarities between data in one cluster and dissimilarities between different clusters, the center of the cluster is the average of the cluster member values it called as centroid. The results of this study are grouping the umbul truism which are divided into three clusters namely Enough, Good and The Best. The result of the data, there are 4 umbul tourisms in the first cluster is Beautiful category, namely Tirtomoyo, Buto, Pancuran, and Besuki. In the third cluster of umbul tourism has good category, namely Tirto Mulyani umbul, Gedaren, Sumber Nila, Manten, Sigedang, and Kajen. In the best category in the second cluster has 8 umbul tourisms, namely Nila umbul, Tirto Mulyono, Ingas, Lumban Tirto, Ponggok, Tirto Raharjo, Jolotundo, and Tirtomulyono.Keywords: K-Means Clustering, Umbul, Umbul Category, Tourist Destination

Evidence of an interaction between jets and intra-cluster magnetic layer

Galaxy clusters are known to harbor magnetic field, however, the extent of the influence of the intra-cluster magnetic field on the cluster member galaxies remains an unresolved question. Intra-cluster magnetic field can be observed as density contact discontinuity formed by cool and dense plasma running into hot ambient plasma, and this exist in the central region of a merging galaxy cluster Abell 3376. Here we report on unambiguous evidence of an interaction between the relativistic electrons and intra-cluster magnetic fields from MeerKAT observations of a radio galaxy MRC0600-399, having bent jets. Contrary to typical bent jets, the jet shows a 90 degrees bend at the contact discontinuity and the collimated jet further extends over 100 kpc from the bend point. The spectral index flattens downstream of the bend point, indicating cosmic-ray re-acceleration. High-resolution numerical simulations reveal that the ordered magnetic field along the discontinuity, at which the intra-cluster magnetic field can be compressed and amplified, plays a significant role to the change in the direction of the jet propagation. The overall morphology of the bent jet bears remarkable similarities with the simulations, which greatly strengthens our understanding of the interaction between relativistic electrons and intra-cluster magnetic field.

μ⋆ masses: weak-lensing calibration of the Dark Energy Survey Year 1 redMaPPer clusters using stellar masses

ABSTRACT We present the weak-lensing mass calibration of the stellar-mass-based μ⋆ mass proxy for redMaPPer galaxy clusters in the Dark Energy Survey Year 1. For the first time, we are able to perform a calibration of μ⋆ at high redshifts, z > 0.33. In a blinded analysis, we use ∼6000 clusters split into 12 subsets spanning the ranges 0.1 ≤ z < 0.65 and μ⋆ up to ${\sim} 5.5 \times 10^{13} \, \mathrm{M}_{\odot }$, and infer the average masses of these subsets through modelling of their stacked weak-lensing signal. In our model, we account for the following sources of systematic uncertainty: shear measurement and photometric redshift errors, miscentring, cluster-member contamination of the source sample, deviations from the Navarro–Frenk–White halo profile, halo triaxiality, and projection effects. We use the inferred masses to estimate the joint mass–μ⋆–z scaling relation given by $\langle M_{200c} | \mu _{\star },z \rangle = M_0 (\mu _{\star }/5.16\times 10^{12} \, \mathrm{M_{\odot }})^{F_{\mu _{\star }}} ((1+z)/1.35)^{G_z}$. We find $M_0= (1.14 \pm 0.07) \times 10^{14} \, \mathrm{M_{\odot }}$ with $F_{\mu _{\star }}= 0.76 \pm 0.06$ and Gz = −1.14 ± 0.37. We discuss the use of μ⋆ as a complementary mass proxy to the well-studied richness λ for: (i) exploring the regimes of low z, λ < 20 and high λ, z ∼ 1; and (ii) testing systematics such as projection effects for applications in cluster cosmology.