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Journal articles on the topic 'Density'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

Youssef, M. M. A. "Relationship between plant density and population density of Meloidogyne incognita on eggplant." Pakistan Journal of Nematology 37, no. 1 (January 10, 2019): 21–24. http://dx.doi.org/10.18681/pjn.v37.i01.p21-24.

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2

De Koning, J. R. A., E. J. Bakker, and P. C. Rem. "Sorting of vegetable seeds by magnetic density separation in comparison with liquid density separation." Seed Science and Technology 39, no. 3 (October 1, 2011): 593–603. http://dx.doi.org/10.15258/sst.2011.39.3.06.

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3

Shabat, Hafedh Ali, Khamael Raqim Raheem, and Wafaa Mohammed Ridha Shakir. "Blind Steganalysis Method Using Image Spectral Density and Differential Histogram Correlative Power Spectral Density." Journal of Image and Graphics 12, no. 1 (2024): 10–15. http://dx.doi.org/10.18178/joig.12.1.10-15.

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Recent research has demonstrated the success of employing neural networks for the purpose of detecting image tampering. Nevertheless, the utilization of reference-free steganalysis has become increasingly popular as a result of the challenges associated with obtaining an annotated dataset. This dataset is crucial for the classification process using neural networks, which aims to detect and identify instances of tampering. This paper introduces a robust approach to blind steganalysis, utilizing image spectral density and differential histogram correlative power spectral density. The proposed method employed two distinct forms of image data, namely a gray-scale image and true-color image data. The results indicate that the proposed methodology successfully achieved the anticipated outcomes in identifying manipulated images as evidenced by its successful application on the two distinct datasets. In the experiment results, the proposed technique succeeded quite well in terms of accuracy at low embedding ratios. Also, it successfully recognized sequential and random least significant bit steganography.

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4

Li, Jie, and Si Ming Tu. "Density-equicontinuity and Density-sensitivity." Acta Mathematica Sinica, English Series 37, no. 2 (February 2021): 345–61. http://dx.doi.org/10.1007/s10114-021-0211-2.

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5

Karhausen, L. R. "Incidence Density is no Density." Scandinavian Journal of Social Medicine 21, no. 1 (March 1993): 1–2. http://dx.doi.org/10.1177/140349489302100101.

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6

McWeeny, Roy. "Density functions and density functionals." Philosophical Magazine B 69, no. 5 (May 1994): 727–35. http://dx.doi.org/10.1080/01418639408240141.

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7

Ghosh, Malay, and Partha Sarker. "Density divergence and density convergence." Journal of Statistical Research 56, no. 1 (February 1, 2023): 1–10. http://dx.doi.org/10.3329/jsr.v56i1.63943.

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Divergence between two distributions has been of statistical interest for more than a century, beginning with Karl Pearson with his famous chisquare test. The paper revisits some of the well-known density divergence measures, and studies their interrelationship. In addition, it is demonstrated how Scheffe’s pointwise density convergence implies convergence of distributions, based on different divergence measures. Journal of Statistical Research 2022, Vol. 56, No. 1, pp. 1-10

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8

Kalmenov, Tynysbek, Aidana Les, and Ulzada Iskakova. "DETERMINATION OF DENSITY OF ELLIPTIC POTENTIAL." Eurasian Mathematical Journal 12, no. 4 (2021): 43–52. http://dx.doi.org/10.32523/2077-9879-2021-12-4-43-52.

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9

Melnyk, Anatoliy, and Volodymyr Saviak. "High Density Highperformance Computing Systems Cooling." Advances in Cyber-Physical Systems 3, no. 2 (November 10, 2018): 112–24. http://dx.doi.org/10.23939/acps2018.02.112.

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10

Gaikwad, Anand, Shreya Shreya, and Shivani Patil. "Vehicle Density Based Traffic Control System." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 511–14. http://dx.doi.org/10.31142/ijtsrd10938.

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11

Rahman, M. M., M. G. Mwakangwale, J. G. Hampton, and M. J. Hill. "Plant density affects soybean seed quality." Seed Science and Technology 33, no. 2 (July 1, 2005): 521–25. http://dx.doi.org/10.15258/sst.2005.33.2.27.

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12

Norwood, O’Tar T. "Density." International Society of Hair Restoration Surgery 3, no. 3 (March 1993): 8.2–8. http://dx.doi.org/10.33589/3.3.0008.

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13

Jooris, Roland, Marco Antonio Campos, Stefaan van den Bremt, and John Irons. "Density." Sirena: poesia, arte y critica 2007, no. 1 (2007): 150–51. http://dx.doi.org/10.1353/sir.2007.0066.

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14

KWELA, ADAM, and PAOLO LEONETTI. "DENSITY-LIKE AND GENERALIZED DENSITY IDEALS." Journal of Symbolic Logic 87, no. 1 (November 23, 2021): 228–51. http://dx.doi.org/10.1017/jsl.2021.95.

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AbstractWe show that there exist uncountably many (tall and nontall) pairwise nonisomorphic density-like ideals on $\omega $ which are not generalized density ideals. In addition, they are nonpathological. This answers a question posed by Borodulin-Nadzieja et al. in [this Journal, vol. 80 (2015), pp. 1268–1289]. Lastly, we provide sufficient conditions for a density-like ideal to be necessarily a generalized density ideal.

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15

Ciesielski. "Density and I-density continuous homeomorphisms." Real Analysis Exchange 18, no. 2 (1992): 367. http://dx.doi.org/10.2307/44152281.

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16

Bader, Richard F. W. "The density in density functional theory." Journal of Molecular Structure: THEOCHEM 943, no. 1-3 (March 2010): 2–18. http://dx.doi.org/10.1016/j.theochem.2009.10.022.

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17

崔, 蕴华. "Integer Density Distribution and Prime Density." Pure Mathematics 08, no. 03 (2018): 193–202. http://dx.doi.org/10.12677/pm.2018.83024.

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18

Slyper, Arnold H. "Low-Density Lipoprotein Density and Atherosclerosis." JAMA 272, no. 4 (July 27, 1994): 305. http://dx.doi.org/10.1001/jama.1994.03520040067042.

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19

Soirat, A., M. Flocco, and L. Massa. "ApproximatelyN-representable density functional density matrices." International Journal of Quantum Chemistry 49, no. 3 (January 20, 1994): 291–98. http://dx.doi.org/10.1002/qua.560490317.

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20

March, N. H. "Density functional theory via density matrices." International Journal of Quantum Chemistry 56, S29 (February 25, 1995): 137–44. http://dx.doi.org/10.1002/qua.560560814.

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21

Ayers, Paul W. "Density bifunctional theory using the mass density and the charge density." Theoretical Chemistry Accounts 115, no. 4 (December 16, 2005): 253–56. http://dx.doi.org/10.1007/s00214-005-0019-7.

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22

Tomášková, I., R. Pokorný, and V. Marek M. "Influence of stand density, thinning and elevated CO₂ on stem wood density of spruce." Journal of Forest Science 53, No. 9 (January 7, 2008): 400–405. http://dx.doi.org/10.17221/2142-jfs.

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Stem wood density (SWD) of young Norway spruce trees (<i>Picea abie</I>s [L.] Karst.) growing at ambient (A variant, 350 µmol(CO<sub>2</sub>)/mol) and elevated (E variant, A + 350 µmol(CO<sub>2</sub>)/mol) atmospheric CO<sub>2</sub> concentration inside of the glass domes with adjustable windows was estimated after six and eight years of the cultivation. Stand density of two subvariants (s – sparse with ca 5,000 trees/ha and d – dense with ca 10,000 trees/ha) and thinning impact (intensity of 27%) on SWD and its variation along the stem vertical profile were investigated. After six years of CO<sub>2</sub> fumigation, stems of sparse subvariant had about 10% lower values of SWD comparing to dense ones, although the difference was not statistically significant. In 2004 (two years after thinning), the SWD values were higher in all subvariants along the whole stem vertical profile. This increase was more obvious in E variant (about 6% in d subvariant and only 3% in s subvariant). The highest increase of SWD values was found in Ed subvariant, particularly in the middle stem part (about 8%, statistically significant increase).

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23

Lin, Jun-Lin. "Generalizing Local Density for Density-Based Clustering." Symmetry 13, no. 2 (January 24, 2021): 185. http://dx.doi.org/10.3390/sym13020185.

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Discovering densely-populated regions in a dataset of data points is an essential task for density-based clustering. To do so, it is often necessary to calculate each data point’s local density in the dataset. Various definitions for the local density have been proposed in the literature. These definitions can be divided into two categories: Radius-based and k Nearest Neighbors-based. In this study, we find the commonality between these two types of definitions and propose a canonical form for the local density. With the canonical form, the pros and cons of the existing definitions can be better explored, and new definitions for the local density can be derived and investigated.

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24

Kim, Somin. "Mathematical Connection and Teaching Methods of Frequency Density." Korean School Mathematics Society 23, no. 4 (December 31, 2020): 509–21. http://dx.doi.org/10.30807/ksms.2020.23.4.005.

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This study began with the discovery of the concept of frequency density in Singapore textbooks and in a set of subject contents of the UK's General Certificate of Secondary Education. To understand the mathematical meaning of frequency density, the mathematical connection of frequency density was considered in terms of mathematics internal connections and mathematics external connections. In addition, the teaching method of frequency density was introduced. In terms of mathematical internal connections, the connections among the probability density function, relative frequency density, and frequency density in high school statistics were examined. Regarding mathematical external connections, the connection with the density concept in middle school science was analyzed. Based on the mathematical connection, the study suggested the need to introduce the frequency density concept. For the teaching method of frequency density, the Singapore secondary mathematics textbook was introduced. The Singapore textbook introduces frequency density to correctly represent and accurately interpret data in histograms with unequal class intervals. Therefore, by introducing frequency density, Korea can consistently teach probability density function, relative frequency density, and frequency density, emphasizing the mathematical internal connections among them and considering the external connections with the science subject. Furthermore, as a teaching method of frequency density, we can consider the method provided in the Singapore textbook.

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25

Zhao, Yanan, Yufang Huang, Shuai Li, Xu Chu, and Youliang Ye. "Improving the growth, lodging and yield of different density-resistance maize by optimising planting density and nitrogen fertilisation." Plant, Soil and Environment 66, No. 9 (September 14, 2020): 453–60. http://dx.doi.org/10.17221/178/2020-pse.

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Matching the planting density, fertilisation, and genotype is crucial to improve the maize yield. Here, two-year field trials, including 4 densities and 3 nitrogen (N) rates for 2 maize cultivars, were conducted to study the effects of planting density and N rate on maize growth, lodging, spike characters, and yield. Compared with 360 kg/ha, N application of 180 kg/ha decreased the plant, ear height, and stem circumference of WeiKe 702 (WK702), while increased the plant height and stem circumference, but decreased ear height of ZhongDan 909 (ZD909). Meanwhile, the N application of 180 kg/ha greatly reduced the lodging rates of maize under the high density. The maize yield increased and reached the maximum yield at 7.5 × 10<sup>4</sup> plant/ha, and then decreased with increasing density. The N application of 180 kg/ha increased yield by 0.49, 0.73, 5.38, 7.81% from low to high planting densities, and reduced the bald tip length by 18.86%. WK702 was more sensitive to the planting density and N application, with greater variation of yield and spike traits than ZD909 under the densification. Therefore, the N application of 180 kg/ha and a density of 7.5 × 10<sup>4</sup> plant/ha could improve maize growth and lodging, and therefore increase maize yield.

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26

Sakuramoto, Kazumi. "Density-Dependent Effect Occurs Regardless of Density." OALib 03, no. 11 (2016): 1–11. http://dx.doi.org/10.4236/oalib.1103112.

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27

Guo, Weixing. "Density Slopes in Variable Density Flow Modeling." Water 13, no. 22 (November 20, 2021): 3292. http://dx.doi.org/10.3390/w13223292.

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Variable density flow (VDF) modeling is a valuable tool for assessing the potential impacts of global climate change and sea level rise on coastal aquifers. When using any of these modeling tools, a quantitative relationship is needed to compute the fluid density from salt concentration. A full understanding of the relationship between fluid density and solute concentration and the correct implementation of the equation of state are critical for variable density modeling. The works of Baxter and his colleagues in the early 20th century showed that fluid density could be linearly correlated to salt concentrations. A constant density slope of 0.7 is often assumed and applied. The assumption is reasonable when the salinity is less than 100‰. The density slope can also be defined from chloride concentration data with the assumption of a constant ratio (55%) between chloride and total dissolved solids (TDS). Field data from central Florida indicate that the chloride/TDS ratio can be as low as 5%. Therefore, TDS is the preferred water quality data for fluid density determination in variable density modeling. Other issues with density slope are also discussed, and some commonly used values of density slope are provided in this technical note.

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28

Zhang, Zhiyong, Qingsheng Zhu, Fan Zhu, Junnan Li, Dongdong Cheng, Yi Liu, and Jiangmei Luo. "Density decay graph-based density peak clustering." Knowledge-Based Systems 224 (July 2021): 107075. http://dx.doi.org/10.1016/j.knosys.2021.107075.

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29

Ciesielski. "Density-to-deep-I-density continuous functions." Real Analysis Exchange 17, no. 1 (1991): 171. http://dx.doi.org/10.2307/44152202.

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30

Mejía-Rodríguez, Daniel, and Aurélien de la Lande. "Multicomponent density functional theory with density fitting." Journal of Chemical Physics 150, no. 17 (May 7, 2019): 174115. http://dx.doi.org/10.1063/1.5078596.

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31

Hou, Jian, and Aihua Zhang. "Enhancing Density Peak Clustering via Density Normalization." IEEE Transactions on Industrial Informatics 16, no. 4 (April 2020): 2477–85. http://dx.doi.org/10.1109/tii.2019.2929743.

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32

Gasparini, Mauro. "Bayesian density estimation via dirichlet density processes." Journal of Nonparametric Statistics 6, no. 4 (January 1996): 355–66. http://dx.doi.org/10.1080/10485259608832681.

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33

Nagy, Á., and E. Bene. "Total electron density from thes-electron density." Physical Review A 57, no. 5 (May 1, 1998): 3458–61. http://dx.doi.org/10.1103/physreva.57.3458.

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34

Papachristodoulos, Ch, and N. Papanastassiou. "q-Density points and q-density topologies." Topology and its Applications 159, no. 7 (April 2012): 1799–805. http://dx.doi.org/10.1016/j.topol.2011.04.021.

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35

Koga, Toshikatsu, Kotomi Sano, and Tetsuya Morita. "Marginal electron density and density-difference functions." Theoretica Chimica Acta 81, no. 1-2 (1991): 21–30. http://dx.doi.org/10.1007/bf01113375.

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36

Kim, Inkang, and Pierre Pansu. "Density of Zariski density for surface groups." Duke Mathematical Journal 163, no. 9 (June 2014): 1737–94. http://dx.doi.org/10.1215/00127094-2690696.

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37

Capelle, K., and L. N. Oliveira. "Density-functional approach to spin-density waves." Europhysics Letters (EPL) 49, no. 3 (February 1, 2000): 376–82. http://dx.doi.org/10.1209/epl/i2000-00159-8.

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38

Pan, Xiao-Yin, and Viraht Sahni. "Density and physical current density functional theory." International Journal of Quantum Chemistry 110, no. 15 (August 17, 2010): 2833–43. http://dx.doi.org/10.1002/qua.22862.

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39

Sham, L. J. "Density functionals beyond the local density approximation." International Journal of Quantum Chemistry 28, S19 (June 19, 2009): 491–95. http://dx.doi.org/10.1002/qua.560280845.

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40

Huang, Xiao-Zhou, and Kazumi Maki. "Density of states of quasi-one-dimensional charge-density and spin-density waves." Physical Review B 40, no. 4 (August 1, 1989): 2575–77. http://dx.doi.org/10.1103/physrevb.40.2575.

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41

Motta, M., D. E. Galli, S. Moroni, and E. Vitali. "Imaginary time density-density correlations for two-dimensional electron gases at high density." Journal of Chemical Physics 143, no. 16 (October 28, 2015): 164108. http://dx.doi.org/10.1063/1.4934666.

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42

Mudryy, Ihor. "Evaluation of compressibility indicators for housing density." Theory and Building Practice 2023, no. 1 (June 20, 2023): 15–20. http://dx.doi.org/10.23939/jtbp2023.01.015.

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Construction in an existing building usually increases the time required to organize and perform process operations. The paper analyzes the existing methods for evaluating compressibility for various regulatory documents and their relationship with the conditions of the construction site and surrounding buildings, shows the need to systematize approaches to compressibility evaluation and develop appropriate design recommendations. Based on theoretical research, the author concludes that there is a need to formulate the terms "compact construction conditions" or "conditions of dense housing"; to compile a list of compressibility conditions and their classification, grouping in order to identify the impact of each group on the cost and duration of construction and installation works; to develop a general procedure for assessing the compressibility of work conditions (indicator) and the necessary measures depending on its quantitative value.

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43

Sianipar, Yulika, and Muflihah Isnawati. "PENGARUH PEMBERIAN JUS LIDAH BUAYA (ALOE VERA) TERHADAP KADAR KOLESTEROL LOW DENSITY LIPOPROTEIN (LDL) DAN HIGH DENSITY LIPOPROTEIN (HDL)." Journal of Nutrition College 1, no. 1 (October 4, 2012): 241–48. http://dx.doi.org/10.14710/jnc.v1i1.740.

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Latar Belakang: Dislipidemia merupakan salah satu faktor risiko terjadinya penyakit jantung koroner. Lidah buaya (Aloe vera) mengandung beberapa bahan aktif dapat menurunkan kolesterol dalam darah. Penelitian ini bertujuan untuk mengetahui pengaruh pemberian jus lidah buaya dengan dosis bertingkat terhadap kolesterol LDL dan kolesterol HDL pada wanita dislipidemia. Metode : Penelitian ini merupakan true experiment dengan pre test - post test with control group design. Subjek penelitian adalah karyawati di Dinas Koperasi dan UKM Provinsi Jawa Tengah serta Balai Latihan Koperasi dan UKM Provinsi Jawa Tengah yang diambil secara consecutive sampling, besar sampel adalah 43 orang yang dibagi secara acak dalam tiga kelompok. Kelompok kontrol tidak diberi lidah buaya, kelompok perlakuan 1 dan kelompok perlakuan 2 diberikan lidah buaya sebanyak 100 mg/hari dan 200 mg/hari yang diberikan dalam bentuk jus selama 14 hari. Kadar kolesterol LDL dan kadar kolesterol LDL diukur sebelum dan sesudah intervensi. Analisis kolesterol LDL dan kolestrol HDL menggunakan metode enzimatik. Data yang diperoleh dianalisis menggunakan uji paired t-test dan anova pada derajat kemaknaan 5%. Hasil : Pada pemberian jus Aloe vera 200 mg menyebabkan penurunan kadar kolesterol LDL dan meningkatkan kadar kolesterol HDL secara bermakna (p<0,05). Kadar kolesterol LDL menurun sebesar 20,36% dan kadar kolesterol HDL meningkat sebesar 18,87% setelah diberikan jus lidah buaya selama 14 hari. Pemberian jus Aloe vera 100 mg dapat menurunkan kadar kolesterol LDL tetapi juga mengalami penurunan kadar kelosterol HDL tetapi tidak bermakan (P>0,05) . Simpulan: Pemberian jus lidah buaya 200 mg/hari dapat menurunkan kadar kolesterol LDL dan meningkatkan kolesterol HDL secara bermakna.

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44

BHASKAR, SIDDHARTH. "THICKET DENSITY." Journal of Symbolic Logic 86, no. 1 (February 15, 2021): 110–27. http://dx.doi.org/10.1017/jsl.2020.55.

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AbstractWe define a new type of “shatter function” for set systems that satisfies a Sauer–Shelah type dichotomy, but whose polynomial-growth case is governed by Shelah’s two-rank instead of VC dimension. We identify the least exponent bounding the rate of growth of the shatter function, the quantity analogous to VC density, with Shelah’s $\omega $ -rank.

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45

Beaumont, Chris, and B. W. Silverman. "Density Estimation." Journal of the Operational Research Society 37, no. 11 (November 1986): 1102. http://dx.doi.org/10.2307/2582699.

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46

Hernández Moreno, Silverio. "Housing Density." Revista INVI 30, no. 84 (August 2015): 199–201. http://dx.doi.org/10.4067/s0718-83582015000200008.

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47

Alaniz Uribe, Francisco, and Bram Van der Heijden. "Virtual Density." Canadian Planning and Policy / Aménagement et politique au Canada 2022 (August 31, 2022): 49–70. http://dx.doi.org/10.24908/cppapc.v2022i1.15440.

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The process of densification in existing communities is complex and often encounters resistance. Public engagement is a crucial component to this process and requires appropriate visual and spatial communication tools. This pilot project explored the use of Virtual Reality (VR) as a spatial communication tool that uses CAD graphics with new visualization technology to provide the public with an immersive experience in a virtual environment. Using a pair of VR goggles and a digital 3D model, different scenarios were presented to members of the public to test their perception of various density models. Three density scenarios were presented to the public, both in the form of traditional posters and using a headset and VR computer model. We found that the public were able to better understand the scenarios and were more accepting of densification when while visualizing the proposed density scenarios via the VR interface.

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48

Miller, Scott, and R. Cowen. "Relative Density." Science News 153, no. 11 (March 14, 1998): 163. http://dx.doi.org/10.2307/4010121.

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49

Osgood, Richard A. "Shoreline Density." Lake and Reservoir Management 21, no. 1 (March 2005): 125–26. http://dx.doi.org/10.1080/07438140509354420.

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50

Sacks, N. P. "Mammographic density." Menopause International 9, Supplement 2 (December 22, 2003): 16. http://dx.doi.org/10.1258/136218003323023571.

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