Implementasi Algoritma K-Nearest Neighbour Untuk Menentukan Nomor Klasifikasi Buku Studi Kasus: Perpustakaan Universitas Katolik Musi Charitas)

Classification of library books is important to allow visitors in search of a book. The classification system in the library of the Catholic University of Charity Musi using guide books dewey decimal classification (DDC). The problem in this research is the difficulty in determining the classification number of new books. By utilizing the methods of Information Retrieval (IR) or retrieval of information, so in this study will build an application program for classification of library books. The method will be used to classify the book library is a method of k-nearest neighbor (k-NN). The application program classification of library books is built with training data from library books Musi-Caritas Catholic University and the test data is a new book. Applications are made capable of classifying new library book

When is more (not) better? On the relationships between the number of information ties and newcomer assimilation and learning

Social capital plays a critical role in newcomer adjustment. However, research is lacking regarding the effective mobilization of social capital, in terms of how different information network characteristics jointly influence newcomer adjustment. Drawing on the literature on social networks and newcomer adjustment, we distinguish two crucial processes of newcomer adjustment, namely assimilation and learning, and propose that the extent to which newcomers' number of information ties influences the assimilation and learning processes depends on the frequency of social interactions (i.e., tie strength) and the status of network contacts (i.e., network status). To test our hypotheses, four waves of data were collected from a sample of 178 organizational newcomers. The results suggest that when network status is low, mobilizing a large information network reduces newcomers' organizational identification (an assimilation indicator), which in turn reduces their job satisfaction. Conversely, mobilizing a large information network with weak ties enhances newcomers' role clarity (a learning indicator) and in turn boosts their task performance. Overall, this study highlights the importance of considering tie strength and network status together with the number of information ties in efforts to facilitate newcomer adjustment.</p

When is more (not) better? On the relationships between the number of information ties and newcomer assimilation and learning

Social capital plays a critical role in newcomer adjustment. However, research is lacking regarding the effective mobilization of social capital, in terms of how different information network characteristics jointly influence newcomer adjustment. Drawing on the literature on social networks and newcomer adjustment, we distinguish two crucial processes of newcomer adjustment, namely assimilation and learning, and propose that the extent to which newcomers' number of information ties influences the assimilation and learning processes depends on the frequency of social interactions (i.e., tie strength) and the status of network contacts (i.e., network status). To test our hypotheses, four waves of data were collected from a sample of 178 organizational newcomers. The results suggest that when network status is low, mobilizing a large information network reduces newcomers' organizational identification (an assimilation indicator), which in turn reduces their job satisfaction. Conversely, mobilizing a large information network with weak ties enhances newcomers' role clarity (a learning indicator) and in turn boosts their task performance. Overall, this study highlights the importance of considering tie strength and network status together with the number of information ties in efforts to facilitate newcomer adjustment.</p

Specificity testing of the IPMA.

<p>Panels A–C and G–H show the results of wells reacted with reference sera against PPRV, GPV, FMDV, BTV and <i>Brucella</i>. GFP expression is shown in panels D–F and J and K. Panel I shows the result of mock-infected BHK-SLAM cells. The results show that only wells reacted with PPRV sera were stained reddish-brown (panel A).</p

MOESM4 of Identification of genes and pathways potentially related to PHF20 by gene expression profile analysis of glioblastoma U87 cell line

Additional file 4. Significant pathway analysis of differentially expressed genes (DEGs) related to PHF20

Additional file 9 of Beneficial effect of the short-chain fatty acid propionate on vascular calcification through intestinal microbiota remodelling

Additional file 9: Supplementary Figure 5. Amelioration of intestinal microbiota imbalance in rats by rectal propionate administration. (a) Principal coordinate analysis (PCoA) diagram showing the β-diversity of the intestinal microbiota among the three groups. (b) and (c) α-diversity of the intestinal microbiota. (d) Relative abundance of intestinal microbiota constituents at the phylum level. (e) Analysis of the differences in the intestinal microbiota by LEfSe. (f) Spearman’s correlation analysis of the relationship of the intestinal microbiota with LPS and SCFAs. Negative and positive correlations are denoted in blue and red, respectively. (g), (h) and (i) Acetate, propionate and butyrate levels in plasma, respectively. (j), (k) and (l) Acetate, propionate and butyrate concentrations in faeces, respectively. (m) Plasma LPS levels. Data are presented as the mean ± standard deviation (SD). Statistical significance was determined using one-way ANOVA or the Kruskal–Wallis test (Tukey post hoc test). NS for P > 0.05, #P < 0.25, ##P < 0.1, *P< 0.05, ***P< 0.001, ****P< 0.0001

Additional file 7 of Beneficial effect of the short-chain fatty acid propionate on vascular calcification through intestinal microbiota remodelling

Additional file 7: Supplementary Figure 3. Attenuation of macrophage infiltration and expression of TNF-α in calcified vessel walls by oral propionate administration. (a) Immunofluorescence staining for macrophages and TNF-α in calcified vessel walls (original magnification ×100). (b) Quantitative analysis of the CD68-positive area. (c) Quantitative analysis of the TNF-α-positive area. Data are presented as the mean ± standard deviation (SD). Statistical significance was determined using one-way ANOVA (Tukey’s post hoc test). ***P< 0.001, ****P< 0.0001

Additional file 6 of Beneficial effect of the short-chain fatty acid propionate on vascular calcification through intestinal microbiota remodelling

Additional file 6: Supplementary Figure 2. Oral propionate administration attenuated VDN-induced microbial dysbiosis in rats. (a) Relative abundance of intestinal microbiota constituents at the genus level. (b) Plasma creatinine levels. (c) Ratio between the relative abundance of Firmicutes and Bacteroidetes. (d-r) Relative abundance of identified differentially abundant bacterial groups at different taxonomic levels. Data are presented as the mean ± standard deviation (SD). Statistical significance was determined using one-way ANOVA or the Kruskal–Wallis test (Tukey or Dunnett post hoc test). NS for P > 0.05, *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001

Additional file 14 of Beneficial effect of the short-chain fatty acid propionate on vascular calcification through intestinal microbiota remodelling

Additional file 14: Supplementary Figure 8. Attenuation of macrophage infiltration and TNF-α expression in calcified vessel walls by the propionate-modulated intestinal microbiota. (a) Immunofluorescence staining for macrophages and TNF-α in calcified vessel walls (original magnification×100). (b) Quantitative analysis of the CD68-positive area. (c) Quantitative analysis of the TNF-α-positive area. Data are presented as the mean ± standard deviation (SD). Statistical significance was determined using one-way ANOVA (Tukey post hoc test). NS for P > 0.05, ***P< 0.001, ****P< 0.0001

Additional file 21 of Beneficial effect of the short-chain fatty acid propionate on vascular calcification through intestinal microbiota remodelling

Additional file 21: Supplementary Table 10. The average sequencing depth and standard deviation for each dataset in this study