Emulation of baryonic effects on the matter power spectrum and constraints from galaxy cluster data

Baryonic feedback effects consist of a major systematic for upcoming weak-lensing and galaxy-clustering surveys. In this paper, we present an emulator for the baryonic suppression of the matter power spectrum. The emulator is based on the baryonification model, containing seven free parameters that are connected to the gas profiles and stellar abundances in haloes. We show that with the baryonic emulator, we can not only recover the power spectra of hydro-dynamical simulations at sub-percent precision, but also establish a connection between the baryonic suppression of the power spectrum and the gas and stellar fractions in haloes. This connection allows us to predict the expected deviation from a dark-matter-only power spectrum using measured X-ray gas fractions of galaxy groups and clusters. With these measurements, we constrain the suppression to exceed the percent-level at k=0.1-0.4 h/Mpc and to reach a maximum of 20-28 percent at around k∼ 7 h/Mpc (68 percent confidence level). As a further step, we also perform a detailed parameter study and we present a minimum set of four baryonic parameters that are required to recover the scale and redshift dependence observed in hydro-dynamical simulations. The baryonic emulator can be found at https://github.com/sambit-giri/BCemu.

The Goals of “Green” Clusters in Tourism

Tourist clusters are widely used in domestic practice as tools for the development of tourist infrastructure and improving the competitiveness of tourist products. The strategic effectiveness of clusters is determined by the extent to which the goals of their formation and functioning take into account the trends in the development of national and global tourism, as well as the interests of global, national and local stakeholders. Environmental challenges, transforming the system of socio-ecological and economic interests of stakeholders, require a corresponding adjustment of goals and goal-setting procedures at all stages of the cluster life cycle. The article substantiates the need for greening the goals system of cluster initiatives as a key condition and the first step in the system of measures for the “green” reorientation of existing and emerging clusters; offers a definition of a “green” tourism cluster as an organizational mechanism for greening the tourism industry, as well as a set of joint goals of its participants, which allows us to reach a compromise between the mission of greening tourism activities and the commercial success of the cluster. Data from the Russian and European Cluster Observatories, the European Cluster Collaboration Platform, and the TCI Network, a global network on clusters, innovation, and competitiveness for the period from 2003 to the present, are the information base for the study of the goals of cluster initiatives.

Measuring muon tracks in Baikal-GVD using a fast reconstruction algorithm

The Baikal Gigaton Volume Detector (Baikal-GVD) is a km$$^3$$ 3 -scale neutrino detector currently under construction in Lake Baikal, Russia. The detector consists of several thousand optical sensors arranged on vertical strings, with 36 sensors per string. The strings are grouped into clusters of 8 strings each. Each cluster can operate as a stand-alone neutrino detector. The detector layout is optimized for the measurement of astrophysical neutrinos with energies of $$\sim $$ ∼ 100 TeV and above. Events resulting from charged current interactions of muon (anti-)neutrinos will have a track-like topology in Baikal-GVD. A fast $$\chi ^2$$ χ 2 -based reconstruction algorithm has been developed to reconstruct such track-like events. The algorithm has been applied to data collected in 2019 from the first five operational clusters of Baikal-GVD, resulting in observations of both downgoing atmospheric muons and upgoing atmospheric neutrinos. This serves as an important milestone towards experimental validation of the Baikal-GVD design. The analysis is limited to single-cluster data, favoring nearly-vertical tracks.

Cluster Data Analysis with a Fuzzy Equivalence Relation to Substantiate a Medical Diagnosis

This study aims to develop a methodology for the justification of medical diagnostic decisions based on the clustering of large volumes of statistical information stored in decision support systems. This aim is relevant since the analyzed medical data are often incomplete and inaccurate, negatively affecting the correctness of medical diagnosis and the subsequent choice of the most effective treatment actions. Clustering is an effective mathematical tool for selecting useful information under conditions of initial data uncertainty. The analysis showed that the most appropriate algorithm to solve the problem is based on fuzzy clustering and fuzzy equivalence relation. The methods of the present study are based on the use of this algorithm forming the technique of analyzing large volumes of medical data due to prepare a rationale for making medical diagnostic decisions. The proposed methodology involves the sequential implementation of the following procedures: preliminary data preparation, selecting the purpose of cluster data analysis, determining the form of results presentation, data normalization, selection of criteria for assessing the quality of the solution, application of fuzzy data clustering, evaluation of the sample, results and their use in further work. Fuzzy clustering quality evaluation criteria include partition coefficient, entropy separation criterion, separation efficiency ratio, and cluster power criterion. The novelty of the results of this article is related to the fact that the proposed methodology makes it possible to work with clusters of arbitrary shape and missing centers, which is impossible when using universal algorithms. Doi: 10.28991/esj-2021-01305 Full Text: PDF

Determinants of Stunting in Children Aged 6-59 Months in Glagah Sub-District, Indonesia

Background: According to basic health research in Indonesia from 2018, the national prevalence of stunting among children under five is 30.80%. Half of the ten highest-priority villages for national stunting interventions in the Lamongan District are located in Glagah Sub-district. Purpose: This study aimed to identify the determinants of stunting in children aged 6 to 59 months in the Muslim population in the Glagah Sub-district, Lamongan District, 2019. Methods: The design of this study was an analytic observational case-control. The population was mothers with children aged 6 to 59 months in Glagah Sub-district. The samples comprised 44 cases and 88 controls. They were paired with matching variables, including gender and clean water sources. Cluster sampling techniques and probability proportional to the size sampling method were utilized to calculate the sample size for each cluster. Data collection was carried out using a modified research questionnaire. Research was carried out in June–July 2019. Bivariate analysis was performed with chi-square and independent t-tests at the significance level α= 0.05. Results: The factors found to be related to stunting were bodyweight at birth (p-value 0.01; eta2 0.09), protein intake (p-value 0.01; eta2 0.12), energy intake (p-value 0.01; eta2 0.19), maternal height (p-value 0.01; eta2 0,08), and parenting pattern (p-value 0.03; ORpermissive 3.33, ORmoderate 1.69). Conclusion: Determinants associated with stunting were bodyweight at birth, protein and energy intake, maternal height, and parenting pattern. Integrated Service Post officers can provide education and workshops on good parenting patterns to parents of toddlers.

Comprehensive Analysis of Bacterial Community Structure and Diversity in Sichuan Dark Tea (Camellia sinensis)

Bacteria and fungi present during pile-fermentation of Sichuan dark tea play a key role in the development of its aesthetic properties, such as color, taste, and fragrance. In our previous study, high-throughput sequencing of dark tea during fermentation revealed Aspergillus was abundant, but scarce knowledge is available about bacterial communities during pile-fermentation. In this study, we rigorously explored bacterial diversity in Sichuan dark tea at each specific stage of piling. Analysis of cluster data revealed 2,948 operational taxonomic units, which were divided into 42 phyla, 98 classes, 247 orders, 461 families, 1,052 genera, and 1,888 species. Certain members of the family Enterobacteriaceae were dominant at early stages of fermentation YC, W1, and W2; Pseudomonas at middle stage W3; and the highest bacterial diversity was observed at the final quality-determining stage W4. Noticeably, probiotics, such as Bacillus, Lactobacillus, Bifidobacterium, and Saccharopolyspora were also significantly higher at the quality-determining stage W4. Our findings might help in precise bacterial inoculation for probiotic food production by increasing the health benefits of Sichuan dark tea. This research also falls under the umbrella of the “Establish Good Health and Well-Being” Sustainable Development Goals of the United Nations Organization.

CLG clustering for dropout prediction using log-data clustering method

<span lang="EN-US">Implementation of data mining, machine learning, and statistical data from educational department commonly known as educational data mining. Most of school systems require a teacher to teach a number of students at one time. Exam are regularly being use as a method to measure student’s achievement, which is difficult to understand because examination cannot be done easily. The other hand, programming classes makes source code editing and UNIX commands able to easily detect and store automatically as log-data. Hence, rather that estimating the performance of those student based on this log-data, this study being more focused on detecting them who experienced a difficulty or unable to take programming classes. We propose CLG clustering methods that can predict a risk of being dropped out from school using cluster data for outlier detection.</span>

Analisis Algoritma K-Medoids pada Sistem Klasterisasi Produksi Perikanan Tangkap Kabupaten Aceh Utara

Belum adanya sistem pengelolaan data produksi perikanan tangkap di Kabupaten Aceh Utara menyebabkan pemerintah Kabupaten Aceh Utara kesulitan dalam mengklasterisasi data produksi perikanan tangkap. Penelitian ini bertujuan untuk menerapkan algoritma k-medoids dalam sistem klasterisasi data produksi perikanan tangkap berbasis web menjadi tiga klaster. Penelitian ini menggunakan data produksi perikanan tangkap di Kabupaten Aceh Utara tahun 2019-2020 yang diperoleh dari Dinas Kelautan dan Perikanan Kabupaten Aceh Utara. Hasil penelitian dengan 10 kali pengujian menunjukkan bahwa nilai rata-rata iterasi k-medoids sebesar 2,5 dengan jumlah iterasi terbanyak 4 iterasi dan iterasi terkecil senilai 2 iterasi. Hasil cluster data produksi perikanan tangkap dengan jenis tangkapan ikan Albakora masuk kedalam potensi produksi tangkapan klaster sedang. Jenis tangkapan ikan Alu-alu, Tongkol Krai, Tuna Mata Besar, Tuna Sirip Biru Selatan masuk kedalam cluster rendah. Jenis tangkapan ikan Banyar, Bawal Hitam dan Bawal Putih masuk kedalam klaster tinggi. Adapun hasil klaster yang terbentuk dapat membantu pemerintah Kabupaten Aceh Utara dalam mengambil kebijakan untuk menambah nilai produksi tangkapan ikan di Kabupaten Aceh Utara.